The effect of a diet containing high extrinsic sugars on …epubs.surrey.ac.uk/809577/1/Thesis...

The effect of dietary sugars on triacylglycerol metabolism in subjects at

increased risk of metabolic syndrome

By

Andrea Marino

Faculty of Health and Medical Science

Diabetes and Metabolic Medicine

University of Surrey

Thesis submitted for the degree of

Doctor of Philosophy

2015

Abstract

Background: High sugar diet may increase plasma triacylglycerol (TG) levels and

cause dyslipidaemia, resulting in a higher cardiometabolic risk. High sugar intake may

also promote the accumulation of ectopic fat in the liver.

Objectives: To determine the effect of two isocaloric diets, low and high in extrinsic

sugars (6% or 26% total energy respectively corresponding to the lower and upper 2.5th

percentile of the intake in men aged 40-65 in the UK) but with the same total

carbohydrate content, on fasting plasma TG, liver fat, lipoprotein concentration, and

very low density lipoprotein (VLDL) TG kinetics and sources of fatty acids for VLDL-TG

synthesis (by stable isotope techniques), in men at increased risk of metabolic

syndrome.

Study design: Participants were randomised in a two-way cross-over design with two

12-week dietary phases, low or high in extrinsic sugar. Dietary exchange of sugar for

starch was achieved using a range of supermarket foods low or high in total sugar

(≤10% or ≥40% respectively) consumed in the homes of participants. Participants were

divided in two groups, low liver fat (n=14) and high liver fat (n=11) (liver fat < or >5% by

magnetic resonance spectroscopy), in order to investigate the impact of liver fat on the

lipid response to dietary sugar.

Results: Liver fat was higher in both groups after the high sugar diet, although the

magnitude of this effect was greater in men with high liver fat (median [IQR] as % liver

fat volume: 15.3 [11.8-45.7] vs 11.4 [8.2-25.6]; P=0.018) than in men with low liver fat

(1.7 [1.0-6.6] vs 1.4 [0.7-1.9]; P=0.025). VLDL1-TG production was significantly higher

after the high sugar diet than the low sugar diet only in men with low liver fat (mean ±

1

SEM: 16603±1406 mg/day vs 12358±1154 mg/day, P=0.001), due mainly to higher

contribution of fatty acids from splanchnic sources (6923±1102 mg/day vs 4286±604

mg/day, P=0.008) and from hepatic de novo lipogenesis (1269±402 mg/day vs 526±137

mg/day, P=0.032). On the other hand, VLDL2-TG production was significantly higher

after the high sugar diet than the low sugar diet in the high liver fat group (4902±693

mg/day vs 3704±429 mg/day, P=0.019), but not in the low liver fat group, and this was

mainly due to a higher contribution of splanchnic sources of fatty acids (3054±459

mg/day vs 1981±277 mg/day, P=0.002). No significant differences in VLDL-TG

catabolism were observed.

Conclusion: This study showed clear differences in the response of lipid metabolism to

sugar intake in the two liver fat groups, especially with regard to liver fat accumulation

and VLDL-TG metabolism. Unexpectedly, a major role in these changes was played by

the splanchnic sources of fatty acids rather than by systemically derived fatty acids or

hepatic de novo lipogenesis. A low sugar intake close to the latest guidelines for sugar

consumption in the general population (5% total energy intake according to the World

Health Organisation and UK’s Scientific Advisory Committee on Nutrition), may be

beneficial in both lipoprotein metabolism and liver fat, thus improving the

cardiometabolic health in these individuals, particularly in men with high liver fat.

2

Declaration

This thesis and the work to which it refers are the results of my own efforts. Any ideas,

data, images or text resulting from the work of others (whether published or

unpublished) are fully identified as such within the work and attributed to their originator

in the text, bibliography or in footnotes. This thesis has not been submitted in whole or

in part for any other academic degree or professional qualification. I agree that the

University has the right to submit my work to the plagiarism detection service

TurnitinUK for originality checks. Whether or not drafts have been so-assessed, the

University reserves the right to require an electronic version of the final document (as

submitted) for assessment as above. The thesis is available for Library use on the

understanding that it is copyright material and that no quotation from the thesis may be

published without proper acknowledgement or consent.

Signed: …………………………… Date: …………………………..

3

Acknowledgement

I would like to express my sincere gratitude to:

Prof. Margot Umpleby, Head of Diabetes and Metabolic Medicine Department, my

main supervisor and Dr. Barbara Fielding, Lecturer in Nutritional Sciences, my co-

supervisor, for giving me the opportunity to pursue this PhD and for their valuable

advice throughout. In particular, I greatly appreciate the insightful and inspiring

discussions we had, which helped me to maintain motivation. I thank them for their

ongoing patience and guidance, understanding and encouragement throughout this

experience, which has been both an enjoyable and challenging learning curve. Words

cannot adequately express my gratitude for their support, especially in difficult

moments.

Dr. Fariba Shojaee Moradie, Senior Research Fellow, for her excellent training and

supervision during the clinical trials.

Dr. Xuefei Li, former Postdoctoral Research Fellow, for her excellent training and

supervision in the laboratory, as well as for her great support and advice in the

beginning of my PhD experience. Her friendship and guidance was much appreciated.

Dr. Nicola Jackson, Diabetes Research Support Project Manager, for her huge

support in the laboratory and for introducing me to mass spectrometry.

All the other people involved in the CHOT study and all the participants.

4

Special thanks and gratitude to:

Dr. Samantha Searle, my wife, for her valuable support and encouragement

throughout and for helping me to persevere in order to complete the Ph D.

My friends, Father Elio Alberti, Umberto Reviezzo and Dr. Massimo Pancione, for

their continual morale boosting over the last few years.

My family, who are always in my heart and have provided ongoing support. In

particular, I would like to dedicate this work to my dad, who has been a role model for

me with his honesty and values, and my mum, who would have been proud to see me

fulfil this important achievement in my life.

My colleagues, with who I have shared some memorable experiences and good laughs,

making this journey more enjoyable.

All my friends, both here in the UK and in Italy (or elsewhere in the world) who are part

of my life; there is always a special place in my heart for each and every one of them.

5

Statement of Contributions

Personnel ContributionsProf. Margot Umpleby Principal supervisor

Dr. Barbara Fielding (Lecturer in Nutritional Sciences)

Co-supervisor. Laboratory assistance, plasma water measurements, determination of palmitate kinetics and GC-MS measurement

Prof. Bruce Griffin (Professor of Nutritional Metabolism)

Supervisor of the study

Cheryl Isherwood (Study coordinator & nutritionist)

Development of Dietary Exchange Model, participant recruitment, study and home visits

Dr. Fariba Shojaee-Moradie (Senior researcher)

Stable isotope study day and determination of plasma insulin

Dr. Nicola Jackson (Diabetes Research Support Project Manager)

Research facilities and assistance, plasma water measurements, GC-MS assistance

Dr. Xuefei Li (Post-doctoral researcher)

Stable isotope study day and laboratory measurements

Dr. Aryaty Ahmed Recruitment, dietary intervention and follow up, stable isotope study day and plasma metabolites measurements

Dr.Najlaa Alsini Determination of palmitate kinetics

Jo Batt (Lab technician) Lab work – determination of plasma lipids and glucose

Dr. John Wright (Medical consultant at CEDAR centre)

Medical consultation, assisting during stable isotope study day

Julie Fitzpatrick and Louise Thomas (MRI Unit, Hammersmith Hospital)

1H-MRS for liver fat scan

Research nurses and doctors (CEDAR Centre)

Assisting in blood sampling and medical help

6

Table of Contents

Abstract............................................................................................................................0

Declaration.......................................................................................................................3

Acknowledgement............................................................................................................4

Statement of Contributions...............................................................................................6

List of Figures.................................................................................................................13

List of Tables..................................................................................................................16

List of abbreviations.......................................................................................................18

Chapter 1: Introduction...................................................................................................22

1.1 Cardiometabolic risk and sugar consumption: a brief overview...........................22

1.2 Lipoprotein metabolism........................................................................................23

1.2.1 Lipoprotein structure......................................................................................23

1.2.2 Major lipoprotein classes...............................................................................24

1.2.3 Major apolipoproteins involved in lipoprotein metabolism.............................26

1.2.4 Important enzymes involved in lipoprotein metabolism.................................29

1.2.5 The exogenous pathway: CM metabolism....................................................30

1.2.6 The endogenous pathway: VLDL Metabolism...............................................33

1.2.7 LDL metabolism.............................................................................................35

1.2.8 Reverse cholesterol transport and HDL metabolism.....................................37

1.3 Assembly and secretion of VLDL.........................................................................41

1.4 Regulation of VLDL-TG by degradation of apo-B100...........................................44

1.5 Sources of fatty acids for TG synthesis in the liver..............................................47

1.6 The role of insulin in VLDL-TG Metabolism..........................................................50

7

1.6.1 The role of insulin on VLDL-TG secretion.....................................................50

1.6.2 The role of insulin on VLDL-TG catabolism...................................................51

1.7 The metabolic syndrome......................................................................................52

1.7.1 Introduction....................................................................................................52

1.7.2 Prevalence.....................................................................................................53

1.7.3 Pathogenesis.................................................................................................56

1.8 Atherogenic lipoprotein phenotype (ALP) and plasma TG...................................60

1.9 Liver accumulation of TG and non-alcoholic fatty liver disease (NAFLD)............62

1.9.1 Introduction....................................................................................................62

1.9.2 Diagnosis.......................................................................................................63

1.9.3 Pathogenesis.................................................................................................64

1.9.4 Dietary sugar and NAFLD.............................................................................65

1.10 Carbohydrate induced hypertriglyceridemia (HPTG).........................................67

1.11 Stable isotopes tracer techniques......................................................................72

1.11.1 General tracer theory...................................................................................72

1.11.2 Measurement of VLDL-TG kinetics.............................................................75

1.11.3 Measurement of the sources of fatty acids for TG synthesis in the liver.....78

1.12 Proposed work....................................................................................................80

1.13 Hypothesis..........................................................................................................82

1.14 Study aims..........................................................................................................82

Chapter 2: Subjects and methods..................................................................................84

2.1 Study participants.................................................................................................84

2.2 Study design.........................................................................................................86

2.3 Study procedures.................................................................................................89

2.3.1 Anthropometrics............................................................................................89

2.3.2 ApoE genotype..............................................................................................89

8

2.3.3 Collection of blood samples...........................................................................89

2.3.4 Magnetic resonance imaging (MRI) and spectroscopy (MRS)......................90

2.4 Study power..........................................................................................................91

2.5 Study protocol.......................................................................................................92

2.5.1 DNL...............................................................................................................93

2.5.2 Palmitate Ra and contribution of systemic palmitate to VLDL-TG and other

metabolites.............................................................................................................94

2.5.3 VLDL-TG kinetics..........................................................................................94

2.6 Laboratory methods..............................................................................................95

2.6.1 Separation of VLDL1 and VLDL2 fractions by ultracentrifugation....................96

2.6.2 Lipid extraction..............................................................................................97

2.6.3 Thin layer chromatography (TLC) and hydrolysis of TG...............................98

2.6.4 Ion exchange chromatography for glycerol purification...............................100

2.6.5 Derivatisation of glycerol.............................................................................100

2.6.6 Measurement of glycerol enrichment by GC-MS........................................101

2.6.7 Glycerol standard preparation for VLDL-TG fractions.................................103

2.6.8 Preparation of plasma glycerol samples......................................................104

2.6.9 Glycerol standard preparation for plasma glycerol......................................106

2.6.10 Measurement of palmitate enrichment from VLDL-TG fractions by GC-MS

..............................................................................................................................107

2.6.11 Palmitate standard preparation for VLDL-TG fractions.............................109

2.6.12 Preparation of plasma palmitate samples.................................................110

2.6.13 Palmitate standard preparation for plasma palmitate................................112

2.6.14 Measurement of DNL................................................................................115

2.6.15 Palmitate standard preparation for DNL....................................................116

2.6.16 Measurement of plasma water enrichment...............................................117

9

2.6.17 Measurement of metabolite concentration in plasma and fraction............118

2.7 Data analysis......................................................................................................121

2.7.1 Multi-compartmental model to determine VLDL-TG kinetics.......................121

2.7.2 Calculations.................................................................................................123

2.8 Statistical methods.............................................................................................127

2.8.1 Parametric tests...........................................................................................127

2.8.2 Non-parametric tests...................................................................................128

Chapter 3: Dietary intake, liver fat, plasma TG and lipoprotein concentrations...........130

3.1 Introduction.........................................................................................................130

3.2 Aims....................................................................................................................131

3.3 Methods..............................................................................................................131

3.4 Results................................................................................................................132

3.4.1 Subjects characteristics...............................................................................132

3.4.2 Achieved composition of the two diets........................................................136

3.4.3 The effect of extrinsic sugar on liver fat.......................................................137

3.4.4 The effect of extrinsic sugars on plasma TG levels.....................................140

3.4.5 The effect of extrinsic sugar on plasma cholesterol and total apoB............142

3.4.6 The effect of extrinsic sugar on VLDL1 and VLDL2 composition..................143

3.4.7 Effect of extrinsic sugar on VLDL particle size............................................147

3.4.8 The effect of extrinsic sugars on VLDL-TG levels.......................................151

3.5 Discussion..........................................................................................................155

3.5.1 Diet..............................................................................................................155

3.5.2 Liver fat........................................................................................................156

3.5.3 Plasma TG...................................................................................................161

3.5.4 Other outcomes...........................................................................................164

3.6 Conclusion..........................................................................................................165

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Chapter 4: VLDL-TG kinetics.......................................................................................167

4.1 Introduction.........................................................................................................167

4.2 Aims....................................................................................................................167

4.3 Methods..............................................................................................................168

4.4 Results: the effect of extrinsic sugar on VLDL-TG kinetics................................169

4.4.1 VLDL-TG kinetics by modelling glycerol enrichment data...........................169

4.4.2 VLDL-TG kinetics by modelling palmitate enrichment data.........................178

4.4.3 Comparing VLDL-TG kinetics from glycerol and palmitate modelling.........182

4.4.4 Overview of VLDL-TG production and liver fat changes.............................185

4.5 Discussion..........................................................................................................186

4.5.1 Comparing VLDL-TG kinetics from glycerol and palmitate enrichment data

..............................................................................................................................186

4.5.2 VLDL-TG kinetics and liver fat.....................................................................188

4.5.2 The effect of dietary sugar on VLDL-TG kinetics........................................191

4.6 Conclusion..........................................................................................................193

Chapter 5: Different sources of fatty acids for VLDL-TG...........................................194

5.1 Introduction.........................................................................................................194

5.2 Aims....................................................................................................................195

5.3 Methods..............................................................................................................195

5.4 Results................................................................................................................196

5.4.1 Contribution of systemic NEFA to VLDL-TG synthesis...............................196

5.4.2 Contribution of hepatic DNL derived fatty acids to VLDL-TG synthesis......200

5.4.3 Contribution of other splanchnic sources of fatty acid to VLDL-TG synthesis

..............................................................................................................................204

5.4.4 Summary.....................................................................................................210

5.4.5 Adipose tissue lipolysis and fat oxidation in the liver...................................212

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5.5 Discussion..........................................................................................................214

5.5.1 Systemic NEFA...........................................................................................214

5.5.2 Hepatic DNL................................................................................................216

5.5.3 Other splanchnic sources............................................................................218

5.5.4 Adipose tissue lipolysis and fat oxidation in the liver...................................220

5.5 Conclusion..........................................................................................................221

Chapter 6: General discussion.....................................................................................222

6.1 Response to low and high sugar diets...............................................................222

6.2 Limitations..........................................................................................................228

6.3 Future work.........................................................................................................229

6.4 Conclusion..........................................................................................................231

List of references..........................................................................................................232

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List of Figures

Figure 1.1: General structure of a lipoprotein particle showing all its components…….24

Figure 1.2: The density and size-distribution of the major classes of lipoprotein particles

………………………………………………………………………..…………………………25

Figure 1.3: The exogenous pathway……………………………………………………..…32

Figure 1.4: The endogenous pathway…………………………………………………..….34

Figure 1.5: Forward and reverse cholesterol transport………………………………..….38

Figure 1.6: HDL metabolism………………………………………………………………...41

Figure 1.7: VLDL assembly, secretion and regulation by apoB100 degradation……….46

Figure 1.8: Sources of fatty acids for hepatic and VLDL-TG……………………..………49

Figure 1.9: Pathophysiology of the metabolic syndrome…………………………….…...56

Figure 1.10: Formation of sdLDL……………………………………………………….……61

Figure 1.11: Utilization of fructose and glucose in the liver……………………………….70

Figure 2.1: Schematic of the study design………………………………………………….87

Figure 2.2: Schematic of the clinical study………………………………………………….92

Figure 2.3: Tracers used in the study protocol and their metabolic fate………………...93

Figure 2.4: Preparation and processing of glycerol and palmitate samples from VLDL

fractions and plasma………………………………………………………………………….95

Figure 2.5: Separation pattern of different classes of lipids after TLC…………………..99

Figure 2.6: Derivatisation and fragmentation of glycerol………………………………..102

Figure 2.7: Selective ion monitoring for ion fragment of triacetyl-glycerol in a typical

VLDL-TG sample……………………………………………………………………………103

Figure 2.8: Glycerol standard curve for VLDL-TG fractions…………………..…………104

Figure 2.9: Selective ion monitoring for ion fragment of triacetyl-glycerol in a typical

plasma glycerol sample……………………………………………………………………..105

Figure 2.10: Glycerol standard curve for plasma glycerol……………………………….106

Figure 2.11: Selective ion monitoring ion fragment of PAME in a typical VLDL-TG

sample………………………………………………………………………………………...108

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Figure 2.12: Palmitate standard curve for VLDL-TG fractions……………………….....109

Figure 2.13: Selective ion monitoring ion fragment of PAME in a typical plasma

palmitate sample……………………………………………………………………………..111

Figure 2.14: Palmitate standard curve for VLDL-TG fractions…………………….……112

Figure 2.15: Palmitate standard curve for plasma palmitate concentration…………...114

Figure 2.16: Total ion chromatogram for palmitate concentration……………………...115

Figure 2.17: Palmitate standard curve for DNL samples………………………………..117

Figure 2.18: Compartmental model for the kinetics of VLDL1 and VLDL2-TG by using

stable isotopically labelled glycerol………………………………………………………...122

Figure 3.1: Flow diagram of participants…………………………………………………..133

Figure 3.2: IHCL level expressed as % of liver volume after the two dietary interventions

LSP and HSP…………………………………………………………………………………138

Figure 3.3: Relation between the liver fat content (IHCL) and total visceral fat at the end

of each dietary intervention…………………………………………………………………139

Figure 3.4: Effect of HSP and LSP on plasma TG concentrations……………………..141

Figure 3.5: Effect of HSP and LSP on the average number of TG molecules per ApoB in

VLDL1 particles……………………………………………………………………………….148

Figure 3.6: Effect of HSP and LSP on the average number of TG molecules per ApoB in

VLDL2 particles……………………………………………………………………………….150

Figure 3.7: Effect of HSP and LSP on VLDL1-TG concentrations………………………152

Figure 3.8: Effect of HSP and LSP on VLDL2-TG concentrations………………………154

Figure 4.1: Plasma glycerol and VLDL-TG glycerol enrichment curves after the two

dietary interventions in the whole cohort…………………………………………………..170

Figure 4.2: Overview of VLDL-TG kinetics after the two dietary intervention in the whole

cohort………………………………………………………………………………………....171

Figure 4.3: Relation between VLDL-TG PR and IHCL at the end the two dietary

interventions…………………………………………………………………………..……...172

Figure 4.4: Overview of VLDL-TG kinetics after the two dietary intervention in the HLF

group…………………………………………………………………………………….…….174

Figure 4.5: Overview of VLDL-TG kinetics after the two dietary intervention in the LLF

group………………………………………………………………………………………….176

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Figure 4.6: Plasma and VLDL-TG palmitate enrichment curves at the end of the two

dietary interventions in the whole cohort…………………………………………..……...178

Figure 4.7: Bland–Altman analysis of the difference of VLDL-TG PR obtained from

glycerol and palmitate modelling…………………………………………………………..184

Figure 4.8: Effect of diet on liver fat and total VLDL-TG production………………...….186

Figure 5.1: Systemic NEFA contribution to VLDL1-TG…………………………..………197

Figure 5.2: Systemic NEFA contribution to VLDL2-TG…………………………..………199

Figure 5.3: Hepatic DNL contribution to VLDL1-TG……………………………….……..201

Figure 5.4: Hepatic DNL contribution to VLDL2-TG………………………………………203

Figure 5.5: Other splanchnic sources contribution to VLDL1-TG……………………….205

Figure 5.6: Correlation found for non-DNL splanchnic sources contribution to VLDL1-

TG………………………………………………………………………………………….….206

Figure 5.7: Other splanchnic sources contribution to VLDL2-TG………………….……209

Figure 5.8: Contribution of different sources of fatty acids to VLDL-TG……………….211

Figure 6.1: Effect of diet on VLDL-TG metabolism………………………………………226

15

List of Tables

Table 1.1: Compositions of the major classes of lipoproteins…………………………….26

Table 1.2: Characteristics of the major apolipoproteins involved in lipid

metabolism……………………………………………………………………………………..27

Table 1.3: Definitions of the metabolic syndrome by different institutions………...…….55

Table 2.1: Inclusion and exclusion criteria for the study……………………..……………84

Table 2.2: The cardio-metabolic risk score…………………………………………..……..85

Table 2.3: Mean energy and macronutrient intakes of men (NDNS)……………………88

Table 2.4: Target percentages of food energy intakes on the two diets………...………89

Table 2.5: Preparation of standard for VLDL-TG glycerol……………………………….104

Table 2.6: Preparation of standard for plasma glycerol………………………………….106

Table 2.7: Preparation of standard for VLDL-TG palmitate samples…………………..109

Table 2.8: Preparation of standards for plasma palmitate………………………………112

Table 2.9: Preparation of standard for measuring plasma palmitate

concentrations………………………………………………………………………………..113

Table 2.10: Palmitate standards for DNL samples……………………………………….116

Table 3.1: Baseline characteristics of participants at screening visit………………..…134

Table 3.2: Characteristics of participants after each dietary intervention………….…..135

Table 3.3: Intake of energy and macronutrients………………………………………….136

Table 3.4: Total, LDL and HDL cholesterol and total apoB levels after each dietary

intervention…………………………………………………………………………………..142

Table 3.5: VLDL1 and VLDL2 composition after each dietary intervention…………….144

Table 3.6: VLDL1 and VLDL2 TG-apoB and TG-cholesterol molar ratios after each

dietary intervention…………………………………………………………………………146

Table 4.1: VLDL-TG kinetics measured using glycerol tracer, comparing LSP vs HSP in

the whole cohort…………………………………………………………………………….171

16


the HLF group………………………………………………………………………………..174


the LLF group………………………………………………………………………………..176

Table 4.4: VLDL-TG kinetics measured using palmitate tracer, comparing LSP vs HSP

in the whole cohort…………………………………………………………………………..179


in the HLF group……………………………………………………………………………..180


in the LLF group……………………………………………………………………………..181

Table 5.1: Palmitate kinetics and concentration of palmitate, plasma NEFA and plasma

3-OHB after LSP and after HSP in whole cohort, HLF and LLF groups………………213

17

List of abbreviations

ABCA1 ATP-binding cassette protein A1

ALP Atherogenic lipoprotein phenotype

ALT Alanine aminotransferase

APE Atom percent excess

Apo Apolipoprotein

AST Aspartate aminotransferase

ATGL Adipose TG lipase

ATP III National Cholesterol Education Program-Adult Treatment Therapy III

BMI Body max index

CE Cholesteryl ester

CEDAR Centre for Endocrinology and Diabetic Research

CETP Cholesteryl ester transfer protein

CI Chemical ionization

ChREBP Carbohydrate response element-binding protein

CM Chylomicron

COP-II Coat protein complex II

CRP C-reactive protein

CVD Cardiovascular disease

CT Computed tomography

DAG Diacylglycerols

DEXA Dual-energy x-ray absorptiometry

DNL De novo lipogenesis

EGIR European Group for the Study of Insulin Resistance

EI Electron impact ionization

ELISA Enzyme-linked immunosorbent assay

ER Endoplasmic reticulum

18

FA Fatty acid

FAME Fatty acid methyl ester

FC Free cholesterol

FCR Fractional catabolic rate

GC-MS Gas chromatography mass spectrometry

HDL High density lipoprotein

HL Hepatic lipase

HLF High liver fat

HSL Hormone sensitive lipase

HSP High sugar phase

IDF International Diabetes Federation

IDL Intermediate density lipoprotein

IHCL Intra-hepatocelluar lipid

INSIG 1 Insulin-induced gene 1

IL Interleukin

IQR Interquartile range

LCAT Lecithin-cholesterol acyltransferase

LDL Low density lipoprotein

LLF Low liver fat

LPL Lipoprotein lipase

LSP Low sugar phase

LXR Liver-X-receptor

MAG Monoacylglycerols

MCR Metabolic clearance rate

MRI Magnetic resonance imaging

MRS Magnetic resonance spectroscopy

MTP Microsomal triglyceride transfer protein

MUFA Monounsaturated fatty acid

NAFLD Non-alcoholic fatty liver disease

19

NASH Non-alcoholic steatohepatitis

NDNS National Diet and Nutrition Survey

NEFA Non-esterified fatty acids

NMES Non-milk extrinsic sugars

OFN Oxygen free nitrogen

PAI-1 Plasminogen activator inhibitor 1

PAME Palmitate methyl ester

PCI Positive chemical ionisation

PERPP Post-ER pre-secretory proteolytic process

PI3K Phosphatidylinositol 3-kinase

PL Phospholipid

PLTP PL transfer protein

PR Production rate

PUFA Polyunsaturated fatty acid

QC Quality control

Ra Rate of appearance

Rd Rate of disappearance

RISCK Reading, Imperial, Surrey, Cambridge and Kings

SACN Scientific Advisory Committee on Nutrition

SAT Subcutaneous adipose tissue

SR-BI Scavenger receptor class BI

SCAP SREBP cleavage activating protein

SCD1 Stearoyl-CoA desaturase 1

SD Standard deviation

sdLDL Small dense LDL

SEM Standard error of the means

SNARE Soluble N-ethylmaleimide–sensitive factor attachment receptor

SPE Solid phase extraction

SREBP Sterol regulatory element-binding protein

20

T2DM Type 2 diabetes mellitus

TG Triacylglycerol

TLC Thin layer chromatography

TNF Tumour necrosis factor

tPA Tissue plasminogen activator

TRL TG-rich lipoprotein

TTR Tracer to tracee ratio

VAT Visceral adipose tissue

VLDL Very low density lipoprotein

WHO World Health Organization

GT Gamma-glutamyl transferase

1H-MRS Proton magnetic resonance spectroscopy

3-OHB 3-hydroxybutirate

21

Chapter 1: Introduction

1.1 Cardiometabolic risk and sugar consumption: a brief overviewThe metabolic syndrome consists of a collection of risk factors for cardiovascular

disease (CVD), namely abdominal obesity, glucose intolerance, hypertension and

atherogenic lipoprotein phenotype (ALP), and is characterised by insulin resistance in

liver, adipose tissue and skeletal muscle (Kaur 2014). Furthermore, this condition can

also be accompanied by the accumulation of ectopic fat in the liver, a condition known

as non-alcoholic fatty liver disease (NAFLD) (Moore 2010). High levels of plasma

triacylglycerol (TG) both in the postprandial and in the postabsorptive states, is an

essential condition for the development of ALP which occur through the remodelling of

LDL particles into the more atherogenic small and dense particles LDL (sdLDL) (Austin

et al. 1996). High levels of plasma TG may result from increased production of very low

density lipoprotein (VLDL) by the liver and/or impaired clearance of these particles by

the action of lipoprotein lipase (LPL) within the peripheral tissues (Taskinen et al. 2011).

A low fat, high carbohydrate diet as recommended as an alternative to a high fat diet in

order to reduce risk factors such as plasma LDL cholesterol concentrations, can

somehow paradoxically lead to increased levels of plasma TG, therefore, not resulting

in a decrease of CVD risk. The lipid response to dietary carbohydrates is extremely

variable and depends on several factors such as the total amount of carbohydrate, the

content in fibre and fat, and the proportion of extrinsic sugars (also known as free

sugars). An excessive intake of extrinsic sugars (chiefly fructose and sucrose) may

22

increase plasma TG levels (Stanhope et al. 2008), which in turn will aggravate the

cardiometabolic risk. Sugar can trigger this effect in two ways, either directly by altering

TG metabolism and/or indirectly by causing weight gain when consumed in excess.

Interestingly, fructose and sucrose appear to stimulate de novo lipogenesis (DNL) in the

liver (Elliott et al. 2002). However, the majority of the studies that controlled for these

dietary components were extreme with respect to the energy contribution from fat and

carbohydrate, or based on liquid formula meals. Therefore, their outcome, while useful

for elucidating the mechanism driving this effect, has not been translated into dietary

guidelines because the diets were not related to what people actually eat.

1.2 Lipoprotein metabolism

1.2.1 Lipoprotein structureThe lipids circulating in the blood, mainly non-esterified fatty acids (NEFA), TG and

cholesterol, are not water soluble and require specialized transport mechanisms. Fatty

acids in plasma circulate bound to albumin, whereas TG and cholesterol circulate in

macromolecular complexes called lipoproteins. Each lipoprotein particle has a

hydrophobic lipid core, containing TG and cholesteryl esters (CE), and a surface

monolayer of amphipathic phospholipids (PL) and unesterified ‘free’ cholesterol (FC)

associated with one or more specific proteins called apolipoproteins (Figure 1.1).

23

Figure 1.1: General structure of a lipoprotein particle showing all its components. Each lipoprotein particle has a hydrophobic lipid core, containing TG and CE, and a surface monolayer of amphipathic PL and FC associated with one or more apolipoproteins. TG, triacylglycerol; CE, cholesteryl ester; PL, phospholipid; FC, FC

1.2.2 Major lipoprotein classes The lipoproteins are a heterogeneous group with different lipid and protein composition,

and different sizes and functions. Lipoproteins are classified in different classes

according to their density (based on their floatation during ultracentrifugation) and size.

Density and size are inversely related as shown in Figure 1.2, showing the different

classes of lipoproteins.

24

Figure 1.2: The density and size-distribution of the major classes of lipoprotein particles. Lipoproteins are classified by density and size, which are inversely related. CM, chylomicron; HDL, high-density lipoprotein; IDL, intermediate-density lipoprotein; LDL, low-density lipoprotein; VLDL, very low density lipoprotein. Based on “Harrison's Principles of Internal Medicine”, 18 th

Edition (Longo et al. 2012)

Different classes of lipoprotein have different metabolic functions. CM and VLDL

particles are referred to together as the triacylglycerol-rich lipoproteins (TRL) since they

are relatively rich in TG compared to other lipoproteins. Their main role is to deliver TG

to tissues. On the other hand, LDL and high density lipoprotein (HDL), which are

smaller and denser, are more involved with transport of cholesterol to and from tissues.

25

Table 1.1 show the characteristics (with the focus on the composition) of the major

lipoproteins, although not taking into account their different sub-fractions.

Table 1.1: Compositions of the major classes of lipoproteins

Class Origin Major apolipoproteins

Composition (% dry weight)

Protein TG FC CE PL

CM Intestine B48, A-I, A-IV, C, E 2 86 2 3 7

VLDL Liver B100, C, E 8 55 7 12 18

IDL Liver, from VLDL B100, C, E 15 31 7 23 22

LDL Liver, from VLDL B100, E 22 6 8 42 22

HDL Intestine and liver A-I, A-II, C, E 40-55 4 4 12-20 25-30

CM, chylomicron; HDL, high density lipoprotein; IDL, intermediate density lipoprotein; LDL, low density lipoprotein; VLDL, very low density lipoprotein. Based on “Biochemistry of Lipids, Lipoproteins and Membranes”, 4th Edition (Vance et al. 2002)

1.2.3 Major apolipoproteins involved in lipoprotein metabolismThe characteristics of the major apolipoproteins involved in lipoprotein metabolism and

their metabolic function and associated lipoproteins are listed in Table 1.2.

26

Table 1.2: Characteristics of the major apolipoproteins involved in lipid metabolismApolipoprotein (origin)

Lipoprotein Molecular mass (kDa)

Metabolic functions

A-I (intestine,

liver)

HDL, CM 28 Activator of LCAT (Sorci-Thomas et

al. 2009). Ligand for HDL receptor.

A-II (intestine,

liver)

HDL, CM 17 May inhibit LCAT (Labeur et al.

1998) and stimulate HL activity

(Mowri et al. 1996)

A-IV (intestine) CM, HDL 46 Associated with the formation of

TRL; suggested role in appetite

regulation (Tso et al. 1999)

B100 (liver) LDL, VLDL, IDL 513 Necessary for the assembly and

secretion of VLDL; binds LDL-

receptor

B48 (intestine) CM 241 Necessary for the assembly and

secretion of CM (van Greevenbroek

et al. 1998)

C-I VLDL, HDL, CM 6.6 Inhibitor of CETP (Gautier et al.

2000); inhibitor of LPL (Berbee et

al. 2005); Possible activator of

LCAT (Albers et al. 1979)

C-II (liver) VLDL, HDL, CM 8.9 Activator of LPL (Goldberg et al.

1990)

C-III (liver) VLDL, HDL, CM 8.8 Inhibitor of hepatic uptake of TRL

particles; inhibitor of LPL

(Ginsberg et al. 1986)

D (brain, adipose

tissue)

HDL 20 Activates LCAT (Steyrer et al.

1988)

E (liver) VLDL, HDL, CM 34 Binds LDL-receptor (Willnow 1997)

CETP, cholesterol ester transfer protein; CM, chylomicron; HDL, high density lipoprotein; HL, hepatic lipase; IDL, intermediate density lipoprotein; LDL, low density lipoprotein; LPL, lipoprotein lipase; LCAT, lecithin-cholesterol acyl transferase; TRL, TG-rich lipoprotein; VLDL, very low density lipoprotein. Based on “Lipid biochemistry-an introduction”, 5 th Edition (Gurr et al. 2002)

27

Apolipoproteins combine with lipids in order to form different classes of lipoprotein

particles. Different combinations of lipid and protein produce lipoprotein particles of

different density and size. ApoA-I, apoA-II, apoA-IV, apoC-I, apoC-II, apoC-III, and

apoE3 are known as exchangeable apolipoproteins because of their ability to move and

exchange between lipoprotein particles. Importantly, apoC-II and apoC-III show

opposite functions, the former acting as an activator of LPL (Havel et al. 1970), the

latter acting as an inhibitor of TG clearance from TRL by inhibiting LPL (Ginsberg et al.

1986). Thus, it is the ratio CII/CIII in a particle that determines the susceptibility to the

action of LPL (Jong et al. 2000).

ApoB is a large protein that can be found in two forms: apoB100 (MW 513 kDa) and

apoB48 (MW 241 kDa). The latter is the truncated form of apoB100 and represents

about 48% of apoB100 sequence (Olofsson et al. 1987). They are both coded by the

same gene. ApoB100, the full length protein, is produced by the liver and incorporated

in VLDL particles, whereas apoB48 is produced in the intestine, by editing of the

messenger RNA in order to introduce a stop codon at residue 2153 of 4536. ApoB48 is

incorporated into CM particles (Fujino et al. 1999). There is only one molecule of apoB

per particle. As a result of the RNA editing, apoB48 and apoB100 share a common N-

terminal sequence, but apoB48 lacks apoB100’s C-terminal LDL-receptor binding

region.

ApoE is also a ligand for the LDL-receptor and is found in TRL (Willnow 1997). There

are three genetic variants, E2, E3 and E4 (Utermann 1987), showing a different affinity

for the receptor, thus contributing to the variation in lipoprotein concentration. E3 is the

most common allele in the general population (Eichner et al. 2002). ApoE from VLDL,

CM, and CM remnants binds to specific receptor cells in the liver. E2 isoform is less

28

efficient at binding the receptor. By contrast, E3 and E4 alleles are much more efficient

in these processes. The difference in uptake of postprandial lipoprotein particles will

affect the regulation of LDL-receptor in the liver, contributing to genotypic differences in

total and LDL cholesterol levels. In general, E2 lowers total cholesterol levels whereas

E4 raises them (Eichner et al. 2002).

1.2.4 Important enzymes involved in lipoprotein metabolismLipoprotein lipase (LPL) is responsible for the hydrolysis of TG contained in lipoprotein

particles, and the subsequent release of NEFA, which can be taken up by tissues for re-

esterification or oxidation, or released into the systemic circulation. It can only act on

particles containing apoC-II (Cryer 1981). This enzyme is found in several extra-

hepatic tissues, and in particular in adipose tissue, skeletal muscle, and heart muscle. It

is produced within the cells and then exported to the luminal surface of the capillaries

where it binds non-covalently to negatively charged glycosaminoglycan, such as

heparan sulphate (Enerback et al. 1993). Homodimerization is required before LPL can

be secreted from cells (Braun et al. 1992). LPL regulation is tissue specific and its

actions are modulated both transcriptionally and post-transcriptionally (Goldberg et al.

2009). The latter might involve actions of the glycosylphosphatidylinositol HDL binding

protein (Beigneux et al. 2007), angiopoietin-like proteins, which reduce LPL dimer

formation (Sukonina et al. 2006). Adipose tissue LPL activity is high after feeding and

low during fasting, whereas the opposite situation seems to occur in the heart and

skeletal muscle (Goldberg et al. 2009). Insulin has been shown to stimulate LPL in

adipose tissue by increasing the level of LPL mRNA and regulating LPL activity through

both post-transcriptional and post-translational mechanisms (Goldberg et al. 2009). By

29

contrast, in muscle, insulin has been shown to have a slightly inhibitory effect, whereas

exercise increased LPL activity (Kiens et al. 1989).

Hepatic lipase (HL) is structurally related to LPL and along with the latter and with

pancreatic lipase is a member of the same lipase family (Perret et al. 2002). HL is found

mainly in the liver. It is produced within the hepatocyte and then exported to the luminal

surface of the capillaries where it binds non-covalently to negatively charged

glycosaminoglycan. HL can hydrolyze TG and PL in all lipoproteins, but is predominant

in the conversion of IDL to LDL and the conversion of post-prandial TG-rich HDL into

the postabsorptive TG-poor HDL (Connelly et al. 1998).

Lecithin-cholesterol acyl transferase (LCAT) is an enzyme synthesized by the liver and

is found associated with lipoproteins containing apoA-I (mainly HDL but also LDL),

which is also its activator. By transferring a fatty acid from position 2 of the

phosphatidylcholine to the 3-hydroxy group of cholesterol the enzymatic reaction yields

CE and lysophosphatidylcholine (Jonas 1991). Since CE is more hydrophobic than FC,

it translocates into the core of the lipoprotein particle contributing to the conversion of

discoidal HDL into spherical, cholesterol-rich HDL2 particles (Shih et al. 2009).

1.2.5 The exogenous pathway: CM metabolismThe exogenous pathway of lipoprotein metabolism is responsible for the transport of

dietary lipids within the body. Dietary TG molecules are hydrolysed by lipases within the

intestinal lumen and emulsified with bile acids to form micelles (Frayn 2010). Dietary

cholesterol, fatty acids, and fat-soluble vitamins are absorbed in the proximal small

intestine. Cholesterol is esterified in the enterocyte to form CE. Longer-chain fatty acids

(>12 carbons) are incorporated into TG and packaged with apoB48, CE, PL and FC to

form CM. Nascent CM particles are secreted into the intestinal lymph and delivered via

30

the thoracic duct directly to the systemic circulation, where they are extensively

processed by peripheral tissues before reaching the liver. In the circulation CM particles

undergo hydrolysis by LPL on the capillary endothelial surfaces of adipose tissue, heart

and skeletal muscle releasing fatty acids for uptake by adjacent myocytes or

adipocytes. Some of the released free fatty acids bind albumin before entering the cells

and are transported to other tissues, especially to the liver (Fielding 2011). The CM

particles progressively shrink in size as the hydrophobic core is hydrolysed and the

hydrophilic lipids (FC and PL) and apolipoproteins on the particle surface are

transferred to HDL. This process eventually leads to the conversion of CM into CM

remnants. CM remnants are rapidly removed from the circulation by the liver through a

process mediated by apoE interaction with LDL-receptor and LDL-receptor-related

protein (LRP) (Willnow 1997). As a consequence, most of CM and their remnants in the

blood will be cleared after a 12 hour fast. However, there are some pathological

conditions affecting CM metabolism, such as type I and III hyperlipidemias, in which

these particles accumulate because they are not efficiently removed (Beaumont et al.

1970). The exogenous pathway of lipoprotein metabolism is summarized in Figure 1.3.

31

Figure 1.3: The exogenous pathway. Nascent CM are secreted into the intestinal lymph and delivered via the thoracic duct directly to the systemic circulation, where they undergo hydrolysis by LPL (located on the capillary endothelial surfaces of adipose tissue, heart and skeletal muscle), and releasing FAs for uptake by these tissues. The CM particles progressively shrink in size as the hydrophobic core is hydrolysed and the hydrophilic lipids (FC and PL) and apolipoproteins on the particle surface are transferred to HDL, becoming CM remnants. CM remnants are then removed by the liver. Apo, apolipoprotein; CM, chylomicron; FA, fatty acids; FC, FC; HDL, high density lipoprotein; LPL, lipoprotein lipase; LRP, LDL-receptor-related protein; PL, phospholipids; TG, triacylglycerol. Based on “Metabolic regulation: a human perspective”, 3rd Edition (Frayn 2010)

32

1.2.6 The endogenous pathway: VLDL MetabolismThe endogenous pathway refers to the distribution of TG from the liver to other tissues,

in VLDL. VLDL particles resemble CM particle in protein composition but contain

apoB100 rather than apoB48 and have a higher ratio of cholesterol to TG. The TG

molecules contained in VLDL are derived predominantly from the esterification of long-

chain fatty acids in the liver. The packaging of hepatic TG with the other major

components of the nascent VLDL particle (apoB100, CE, PL, and vitamin E) requires

the action of the enzyme microsomal triglyceride transfer protein (MTP) (Rustaeus et al.

1998). VLDL particles can be divided in VLDL1 and VLDL2 according to their density and

lipid composition. Indeed, VLDL particles can be secreted as TG-poor denser particles

(VLDL2) or TG-rich less dense particles (VLDL1) by the liver (Stillemark-Billton et al.

2005). In order to convert TG-poor into TG-rich VLDL, further addition of TG to the

VLDL particle is necessary. The assembly and the secretion of VLDL particles will be

covered and discussed in more detail in section 1.3. Importantly, VLDL2 can be also

derived from the delipidation of VLDL1 through the action of LPL as discussed above.

After secretion into the plasma, VLDL acquires multiple copies of apoE and apoC by

transfer from HDL (Frayn 2010). As with CM, the TG from VLDL particles will be

hydrolysed by the action of LPL, especially in muscle and adipose tissue. As this

happens, TG-rich VLDL1 particles are converted first into TG-poor VLDL2 particles and

then, after further action of LPL, into IDL which contain roughly similar amounts of

cholesterol and TG. The liver removes approximately 40–60% of IDL by LDL-receptor–

mediated endocytosis via binding to apoE. The remainder of IDL is remodelled by HL to

form LDL. During this process, most of the TG in the particle is hydrolysed, and all

apolipoproteins except apoB100 are transferred to other lipoproteins. The endogenous

pathway is outlined in Figure 1.4.

33

Figure 1.4: The endogenous pathway. VLDL particles can be secreted as TG-poor denser particles (VLDL2) or TG-rich less dense particles (VLDL1) by the liver (see section 1.3). VLDL2

can be also derived from the delipidation of VLDL1 through the action of LPL. The VLDL particles progressively shrink in size as the hydrophobic core is hydrolysed and the hydrophilic lipids (FC and PL) and apolipoproteins on the particle surface are transferred to HDL. TG-rich VLDL1

particles are converted first into TG-poor VLDL2 particles and then, after further action of LPL, into IDL which contain roughly similar amounts of cholesterol and TG. The liver removes approximately 40–60% of IDL by LDL-receptor–mediated endocytosis via binding to apoE. The remainder of IDL is remodelled by HL to form LDL. Apo, apolipoprotein; FA, fatty acids; FC, FC; HDL, high density lipoprotein; HL, hepatic lipase; IDL, intermediate density lipoprotein; LDL, low density lipoprotein; LPL, lipoprotein lipase; PL, phospholipids; TG, triacylglycerol; VLDL, very low density lipoprotein.

34

1.2.7 LDL metabolismLDL particles are the main carriers of cholesterol in the circulation and play an

important role in cholesterol transport and metabolism. Most LDL particles originate

from the metabolism of TRL particles. In the VLDL1-VLDL2-IDL-LDL cascade, the

particle is depleted of TG by the action of LPL and HL. In addition, the particle loses

most of the associated apolipoproteins, except the essential apoB100, with HL playing

an important role in the conversion of IDL into LDL (Beisiegel 1998). LDL particles are

responsible for the regulation of cell cholesterol content by delivering cholesterol to

those cells that require additional cholesterol beyond that produced internally, a process

known as forward cholesterol transport. The cholesterol cellular uptake increases when

LDL-receptor expression increases as a result of depleted levels of cholesterol in the

cell. LDL particles can pass through the junctions between capillary endothelial cells

and bind to the LDL-receptor on the cell membranes that recognize apoB100. The LDL-

receptor is expressed in most nucleated cells, although LDL uptake is particularly active

in the liver and in those tissues that depend on cholesterol for particular purposes, such

as the adrenal glands and the ovaries where cholesterol is used for steroid hormone

synthesis (Brown et al. 1983). LDL uptake into the cells by endocytosis is followed by

lysosomal degradation leading to release of FC from the hydrolysis of CE into the

cytosol and recycling of the LDL-receptor to the cell membrane (Beisiegel 1998).

Defects in the LDL-receptor and its function cause familial hypercholesterolemia, a

genetic disorder in which the LDL-receptor activity is reduced either because of a

reduced number of LDL-receptors, or formation of structurally altered LDL-receptors

(Brown et al. 1975).

The cholesterol homeostasis in the cell is controlled through a feedback regulatory

system mediated by a family of transcription factors known as sterol regulatory element-

35

binding proteins (SREBPs) (Brown et al. 1997). SREBP-2 is associated with SREBP

cleavage activating protein (SCAP) on the endoplasmic reticulum (ER) membrane.

When the level of cholesterol in the ER membrane is low, the SCAP/SREBP complex is

incorporated into coat protein complex-II (COP-II)-coated vesicles and transported to

the Golgi (Sun et al. 2005). Once in the Golgi, SREBP-2 is cleaved. The N-terminal

domain translocates into the nucleus and activates LDL-receptor gene and other genes

involved in cholesterol synthesis, including the major regulatory enzyme 3-hydroxy-3-

methylglutaryl-CoA reductase (Miserez et al. 2002). High levels of cholesterol in the ER

membrane will induce a conformational change in SCAP (following direct interaction

between cholesterol and SCAP) (Radhakrishnan et al. 2004), resulting in an increased

affinity for insulin-induced gene 1 (INSIG-1) protein (Sun et al. 2005), thus preventing

the incorporation of the SCAP/SREBP complex into COP-II-coated vesicles.

In addition to the classical LDL-receptor, macrophages derived from circulating

monocytes can take up LDL via scavenger receptors. Scavenger receptors recognize

chemically and biologically modified lipoproteins due to oxidative damage to the lipids

and apoB100 contained in the particle (Steinberg et al. 1989). Although the uptake of

these modified LDL particles from the artery wall via scavenger receptors would appear

beneficial, when this pathway is overwhelmed, the accumulation of cholesterol-laden

macrophages (foam cells) may result (Moore et al. 2006). Unlike the LDL-receptor,

these receptors are not subject to down-regulation by the level of cellular cholesterol

(Brown et al. 1979). This process may lead to the formation of fatty steaks, which are

the earliest lesion of atherosclerosis, and consist mainly of these lipid-laden

macrophages (Gerrity et al. 1980).

LDL is not a homogeneous group of lipoproteins but consists of subspecies with distinct

properties (Krauss et al. 1982), such as size, density and composition. Two well

36

established techniques allow the separation of LDL subclasses with a good accuracy:

gradient gel electrophoresis (Krauss et al. 1982), which separates LDL particles on the

basis of their differing size, and analytic ultracentrifugation (Griffin et al. 1990), which

separates LDL particles on the basis of their density. The distribution of these different

subspecies is important from a clinical point of view, particularly in relation to the

cardiovascular risk (Superko et al. 2008). A predominance of smaller and denser LDL

(sdLDL) particles has been associated with an increased CVD risk. Raised levels of

sdLDL particles represent one of the features of the atherogenic lipoprotein phenotype

(ALP), which will be further discussed in section 1.8.

1.2.8 Reverse cholesterol transport and HDL metabolismHDL particles are involved in removing cholesterol from tissues and transporting it to

the liver for excretion. This process is known as the reverse cholesterol transport

pathway. LDL particles, on the other hand, deliver cholesterol to those tissues that

require additional cholesterol beyond that produced internally (forward cholesterol

transport), as mentioned in section 1.2.7. Both pathways are outlined in Figure 1.5.

37

Figure 1.5: Forward and reverse cholesterol transport. Forward cholesterol transport: cholesterol is secreted by the liver in VLDL particles, which are converted into LDL particles by LPL and HL, and then taken up by other tissues via the LDL-receptor; some of these particles will be taken up again by the liver via LDL-receptor. Reverse cholesterol transport: cholesterol is removed from peripheral tissues by HDL particles via interaction with the receptor ABC-A1; this cholesterol is transferred to the liver by interaction with the receptor SR-BI and may be excreted in the bile. Alternatively, CE in HDL is transferred via the action of CETP to TRL particles, which may be then taken up by the liver. ABC-A1, ATP-binding cassette protein A1; CE, cholesteryl ester; CETP, cholesteryl ester transfer protein; FC, FC; HDL, high density lipoprotein; LDL, low density lipoprotein; SR-BI, scavenger receptor class BI; VLDL, very low density lipoprotein; TRL, triacylglycerol rich lipoprotein. Based on “Metabolic regulation: a human perspective”, 3 rd Edition (Frayn 2010)

All nucleated cells synthesize cholesterol, but only hepatocytes and enterocytes can

effectively excrete cholesterol from the body, into either the bile or the gut lumen (Lewis

2006). In the liver, cholesterol is excreted into the bile, either directly or after conversion

to bile acids (Schwartz et al. 2004).

Nascent HDL particles, known as pre-β HDL, are produced by the intestine and the

liver. Newly secreted apoA-I rapidly acquires PL and FC from its site of synthesis via

efflux promoted by the membrane protein ATP-binding cassette protein A1 (ABC-A1),

38

which transfers FC from the cell membrane to the HDL particle (Rader 2006). This

process results in the formation of discoidal HDL particles that interact with cells and

collect the excess cellular cholesterol. HDL particles also acquire the excess surface

material (FC, PL and apolipoproteins) released during the lipolysis of TRL particles

mediated by LPL (Lewis et al. 2005). Within the HDL particle, the FC is esterified by

LCAT, which is activated by apoA-I (see section 1.2.3) (Sorci-Thomas et al. 2009), and

the more hydrophobic CE moves to the core of the HDL particle. Thus, these particles

acquire more CE and become mature, spherical, cholesterol-rich particles (Kardassis et

al. 2014). These cholesterol-rich particles can be separated by ultracentrifugation into

the larger HDL2 and the smaller HDL3. HDL cholesterol is transported to liver either

directly by the interaction with specific receptors or indirectly by transferring CE to the

TRLs and from these particles to the liver. The direct pathway involves the interaction

of the HDL particle with scavenger receptor class BI (SR-BI) expressed on the surface

of the hepatocytes (Rhainds et al. 2004). Studies conducted on hepatocytes suggest

that SR-BI mediates the internalization of the whole HDL particle, with subsequent off-

loading of cholesterol and secretion of a smaller cholesterol-depleted HDL particle

(Silver et al. 2001).

An alternative pathway by which HDL cholesterol is metabolized and ultimately

transported to the liver, mediated by the CE transfer protein (CETP), has been

described in humans but not in rodents (which lack CETP) (Rader 2006). In this

pathway CETP catalyses the exchange of hydrophobic lipids between lipoprotein

particles simply by facilitated diffusion down a concentration gradient (McPherson et al.

1991). When the concentration of plasma TG is high (especially when there is a large

number of VLDL particles or postprandially when CM concentration is high), CETP

promotes the transfer of CE from HDL to TRL, while TG moves in the opposite

39

direction. After CETP-mediated lipid exchange, the TG-enriched HDL becomes a much

better substrate for HL, which hydrolyses TG and PL (see section 1.2.4) to generate

smaller CE-depleted HDL3 particles, which can take up further cholesterol from cells.

Confirmation that this alternative pathway is quantitatively important in humans came

from a study in which injection of HDL labelled with a CE tracer showed that the

labelled cholesterol was transferred to apoB-containing particles before being excreted

by the liver into the bile (Schwartz et al. 2004). A related enzyme, known as endothelial

lipase, hydrolyses PL on HDL particles, generating smaller HDL particles that are

catabolized faster (Jaye et al. 1999). PL transfer protein (PLTP) transfers PL from TRL

to HDL. In addition to regulating the size of HDL particles, this protein may be involved

in cholesterol metabolism (Jiang 2002). Remodelling of HDL influences the metabolism,

function, and plasma concentrations of HDL. HDL metabolism is outlined in Figure 1.6.

40

Figure 1.6: HDL metabolism. Pre-β HDL consists of apoA-I associated with some PL. FC and further PL is added by interaction with cells resulting in the formation of discoidal HDL particles, these particles in turn acquires more polar lipids (FC and PL) from TRL particles as LPL acts upon them. LCAT convert the polar FC into the hydrophobic CE leading to the transformation of discoidal HDL particles into spherical, cholesterol-rich HDL2 particles. These particles may transfer their cholesterol to the liver. Here, the cholesterol can be secreted in the bile. The lipid-poor apoA-I is thereby regenerated and begins the cycle again. CE, cholesteryl ester; FC, FC; HDL, high density lipoprotein; LCAT, lecithin-cholesterol acyl transferase; LPL, lipoprotein lipase; SR-BI, scavenger receptor class BI; TRL, triacylglycerol rich lipoprotein

1.3 Assembly and secretion of VLDL The assembly of VLDL occurs in the liver, and involves a step by step lipidation of the

apoB100 to form a primordial pre-VLDL, which is converted into a TG-poor VLDL

particle by further lipidation (Olofsson et al. 2000). ApoB100, like other secretory

proteins, is synthesised in the rough endoplasmic reticulum (ER) and it is targeted into

the ER lumen by its N-terminal signal peptide. Translocation into the ER lumen occurs

41

simultaneously with its translation. The assembly of VLDL particles in the ER lumen

involves a stepwise lipidation of the apoB100 (Rustaeus et al. 1999). Initially, nascent

apoB100 is partially lipidated to form a lipid-poor primordial pre-VLDL lipoprotein

particle. This step is facilitated by MTP as mentioned in section 1.2.4 (Rustaeus et al.

1998). MTP has three domains: 1) an apoB binding domain; 2) a lipid transfer domain;

and 3) a membrane association domain (Hussain et al. 2003). MTP can transfer both

neutral and polar lipids to developing VLDL particles. Primordial pre-VLDL lipoprotein

particles are then converted into TG-poor VLDL2 by further lipidation occurring in the ER

(Stillemark-Billton et al. 2005). Lipidation of apoB100 depends on the availability of TG

and without sufficient lipidation a considerable amount of nascent apoB100 can

undergo degradation via different pathways and independently of its translational status

(Liao et al. 1998). This aspect will be further discussed in section 1.4.

The movement of nascent VLDL particles from the lumen of ER to the cis-Golgi is an

important prerequisite for their secretion from hepatocytes. Normally, the transport of

nascent proteins from the ER to the Golgi is mediated by a specific set of proteins

recruited in a certain order to the ER surface. These proteins form a complex known as

coat protein complex II (COP-II) which leads to the formation of a vesicle. However,

these vesicles are too small (55 to 70 nm in diameter) to accommodate VLDL particles.

Recently, independent studies showed that VLDL particles exit the ER in a different

way. Siddiqui et al. showed in an elegant in vitro ER-budding assay that nascent VLDL

particles leave the ER in specialized vesicles called VLDL transport vesicles, which are

bigger (100 to 120 nm in diameter) than the canonical vesicles involved in the transfer

of protein from the ER to the Golgi, thus more suitable to accommodate VLDL particles

(Siddiqi 2008). However, the same group demonstrated that Sar1, a COP-II component

that initiates the process of vesicle generation, is used in both canonical and VLDL

42

transport vesicles budding process (Siddiqi et al. 2003). After leaving the ER

compartment, VLDL transport vesicles translocate to and fuse with the cis-Golgi, in a

process mediated by members of a class of protein known as specific soluble N-

ethylmaleimide-sensitive factor attachment protein receptor (SNARE), responsible for

guiding the targeting, docking and fusion of transport vesicles with their specific target

membrane (Tiwari et al. 2012). These components can be found on both transport

vesicles and in association with their target membranes (Rothman 1994). In humans,

36 members of this family have been characterized and different combinations of four

SNARE proteins can form a number of α-helix coiled-coil structures known as

SNAREpins that can bring two membranes into proximity and lead to their fusion

(Weber et al. 1998). Siddiqui’s group identified the SNARE complex involved in the

VLDL transport vesicles fusion with the cis-Golgi. They first identified Sec22b which

serves as v-SNARE and, by using this as bait in an in vitro VTV-Golgi assay, they were

able to isolate the three t-SNAREs that form the complex which are GOS28, Syntaxin5

and rBet1 found on the cis-Golgi (Siddiqi et al. 2010). VLDL particles undergo some

important modifications in the Golgi lumen, including glycosylation and phosphorylation

of apoB100 (Swift 1996).

TG-poor VLDL2 particles can be transported through the Golgi and then secreted as

VLDL2 or undergo further lipidation to form the mature TG-rich VLDL1 particle

(Stillemark-Billton et al. 2005). For the conversion of a TG-poor VLDL2 particle into a

TG-rich VLDL1 particle, a bulk addition of TG is required (Stillemark-Billton et al. 2005).

Therefore, this process differs from the stepwise lipidation of pre-VLDL particles

described above. The formation of these TG-rich VLDL1 particles is highly dependent

on the availability of TG in the cytosol. There is evidence that the fatty acids used for

43

the synthesis of VLDL-TG come from TG stored in cytosolic lipid droplets (Gibbons et

al. 2000).

1.4 Regulation of VLDL-TG by degradation of apo-B100The level of plasma VLDL-TG depends essentially on the amount of TG packed in each

particle, as well as the number of VLDL particles secreted by the liver. The availability

of apoB100 is a key factor in determining the number of VLDL particles, since VLDL

contains only one molecule of apoB100 per particle. Olofsson and colleagues found

that the rate of disappearance of apoB100 from the ER compartment was much greater

than the rate of apoB100 secretion (Boren et al. 1993). Other studies, in agreement with

the above mentioned results, indicated three important aspects. Firstly, apoB100 is

continuously synthesized (Bostrom et al. 1988), secondly, apoB100 that is not used for

VLDL production is directed to post-translational degradation (Davis et al. 1990) and

thirdly, lipid supply could prevent the post-translational degradation of apoB100 (Dixon

et al. 1991). The prevailing idea at the time was that the level of synthesis of a secretory

protein was the major, if not the only, determinant of the amount of protein secreted

(Olofsson et al. 2012). Therefore, the concept of post-translational degradation of

apoB100 was difficult to accept simply because it was difficult to understand the reason

why a cell should consume energy to produce proteins that were to be sorted to

degradation shortly after.

Three major degradation pathways of apoB100 have been identified and will be

described in this section. The first pathway was reported by Williams and colleagues,

who showed a re-uptake of apoB100 after secretion via the LDL followed by its

44

degradation (Williams et al. 1990). The second pathway to be identified involves the

diversion of apoB100 that is not correctly lipidated during the stepwise lipidation

occurring in the ER lumen, to the ubiquitin-proteasome system. This process is

mediated by Hsp70 (Fisher et al. 1997), and involves a number of other factors, such as

Hsp90, P58IPK, Hsp110, p97, and BiP (Rutledge et al. 2009). Almost all the proteins

that fail to fold correctly as a consequence of structural mutations undergo proteosomal

degradation. In contrast with this general situation, the failure of apoB100, a wild type

protein, to fold correctly may be a consequence of inadequate lipidation (Olofsson et al.

2012). The third pathway was first reported in relation to the ability of omega-3 fatty

acids to decrease apoB100 and VLDL-TG secretion in hepatic cells (Fisher et al. 2001).

This pathway is classified as a post-ER pre-secretory proteolytic process (PERPP).

Other examples of PERPP, such as insulin-stimulated (Sparks et al. 1990) and sortilin

1-mediated apoB100 degradation (Musunuru et al. 2010), were also identified. It is not

clear whether the different examples of degradation via PERPP reported are part of the

same degradative pathway, completely distinct pathways, or pathways that share some

common characteristics (Fisher 2012). However, It has been suggested that at least

some of these cases involves the lysosomal pathway, indicating an autophagic process

(Pan et al. 2008; Musunuru et al. 2010). An overview of the degradation pathways

described above is shown in Figure 1.7.

45

Figure 1.7: VLDL assembly, secretion and regulation by apoB100 degradation. Nascent apoB100 is translocated to the lumen of the ER where stepwise lipidation occurs, aided by MTP. Misfolded or not sufficiently lipidated apoB100 is sorted to proteasomal degradation. Pre-VLDL that is not converted to a mature VLDL2 will be sorted to post-translational degradation, perhaps through autophagy (not shown in the diagram). Once in the Golgi apparatus, VLDL 2 is either converted to VLDL1 through the bulk addition of TG or secreted. At this stage, lipoproteins may be sorted to autophagy by PERPP as a consequence of inadequate lipidation. It has also been reported the degradation of VLDL particles following their re-uptake by LDL-receptor on the plasma membrane. FA, fatty acid; LDL, low density lipoprotein; MTP, microsomal triglyceride transfer protein; PERPP, pre-secretory proteolytic process; TG, triacylglycerol; VLDL, very low density lipoprotein. Based on “Apolipoprotein B secretory regulation by degradation” (Olofsson et al. 2012)

46

1.5 Sources of fatty acids for TG synthesis in the liverSources of fatty acids that can be used for VLDL-TG synthesis in the postabsorptive

state include systemic NEFA, which are all those fatty acids derived from the lipolysis of

subcutaneous adipose tissue, and splanchnic sources. Splanchnic sources of fatty

acids for VLDL-TG include those fatty acids derived from the lipolysis of visceral

adipose tissue draining directly to the liver via the portal vein, the fatty acids derived

from TG storage within the hepatocyte and the de novo lipogenesis (DNL) occurring in

the liver. The majority of fatty acids taken up by the liver are directed into the TG

storage before being directed to other metabolic routes (Gibbons et al. 1992). Although

the hepatic TG storage pool has been reported to turnover rapidly, only a small

proportion of released fatty acids are directed to VLDL synthesis, the remainder being

recycled back to the TG storage pool (Gibbons et al. 2000).

In the postabsorptive state, systemic NEFA pool is the main source of fatty acids used

for VLDL-TG production both in healthy people (Barrows et al. 2006) and in people with

NAFLD (Donnelly et al. 2005). However, in the transition from fasting to the fed state,

fatty acid flux to the liver changes as a result of an increased level of insulin which

decreases NEFA flux into the liver by suppressing lipolysis in the adipose tissue. Thus,

the contribution of endogenous systemic NEFA to VLDL-TG has been shown to

decrease from 77 to 44% from fasting to the fed state in healthy people (Barrows et al.

2006), and from 60 to 28% in people with NAFLD (Donnelly et al. 2005). Furthermore,

in the transition to the fed state, exogenous (dietary) fatty acids can also be used for

VLDL-TG synthesis. These fatty acids can enter the liver through two different

pathways. The first pathway consists of the spillover of fatty acids resulting from the

47

action of LPL on TRL at the capillary endothelium, mainly from the adipose tissue,

where a proportion of fatty acids can escape uptake by the cells, particularly in the late

postprandial period (Bickerton et al. 2007). These fatty acids can enter the liver via a

combination of protein mediated transport and diffusion through the plasma membrane

(Berk 2008). The other pathway is represented by the liver uptake of CM remnants, as

mentioned in section 1.2.5. Barrows et al. reported a dietary fatty acid contribution to

VLDL-TG of about 15% from uptake of CM remnant TG, and 10% from dietary spillover

into the serum NEFA pool (Barrows et al. 2006) in healthy people, whereas Donnelly et

al. found that dietary fatty acid contribution to VLDL-TG was about 15% in people with

NAFLD (Donnelly et al. 2005). It is still unclear whether in the liver fatty acids from

different sources are preferentially routed to the endoplasmic reticulum for secretion in

VLDL or directed to the cytosolic TG pool instead. However, it has been reported that

the contribution of different sources to VLDL-TG and hepatic TG is similar in subjects

with NAFLD (Donnelly et al. 2005).

Although DNL makes only a small contribution to VLDL-TG, representing less than 5%

in the fasted state and about 8% in the fed state in healthy individuals (Timlin et al.

2005), this source has been shown to increase dramatically when a high percentage of

energy is supplied as carbohydrate especially in the form of fructose (see section 1.10).

However, it has been shown that the contribution of DNL fatty acids in the fed state is

substantially higher in hypertriglyceridemic subjects (14 %) (Vedala et al. 2006) and in

subjects with NAFLD (22%) (Donnelly et al. 2005). It has been reported that the

mechanism by which insulin stimulates DNL is by promoting the transcription of

lipogenic genes (e.g. fatty acid synthase and acetyl-CoA carboxylase) via the

transcription factor sterol regulatory element binding protein (SREBP) -1c (Foufelle et

al. 2002). Therefore, the contribution of DNL derived fatty acids to VLDL-TG

48

significantly increases in the fed state (from 5 to >20%) with peak DNL 4 hours after the

meal (Timlin et al. 2005).

An overview of the different sources of fatty acids for TG synthesis in the liver and their

partitioning to different metabolic routes, including VLDL-TG production, is shown in

Figure 1.8.

Figure 1.8: Sources of fatty acids for hepatic and VLDL-TG. Postabsorptive state: FA from the lipolysis of adipose tissue enter the liver and mix with the cytosolic FA pool; FA are then partitioned either toward oxidation or esterification to TG and enter the cytosolic TG pool; FA from the TG pool may also be used for VLDL-TG production. Transition to the postprandial state: FA from the diet can also enter the liver at this stage; Insulin will stimulate DNL and shift FA metabolism from oxidation to esterification. CM, chylomicron; DNL: de novo lipogenesis; ER: endoplasmic reticulum; FA: fatty acids; SAT, subcutaneous adipose tissue; TG: triacylglycerol; VAT, visceral adipose tissue; VLDL, very low density lipoprotein;

49

1.6 The role of insulin in VLDL-TG Metabolism

1.6.1 The role of insulin on VLDL-TG secretion The rate of VLDL-TG production is mainly regulated by the availability of lipid substrate

(Lewis 1997). The insulin-mediated suppression of VLDL production can occur in two

general ways: indirectly, by suppressing lipolysis in the adipose tissue, causing a fall in

systemic NEFA levels (Lewis et al. 1993) and directly, by suppressing VLDL secretion

in a mechanism that does not depend on the availability of fatty acids for VLDL-TG

synthesis (Malmstrom et al. 1998). In adipose tissue, insulin decreases TG lipolysis by

inhibiting hormone sensitive lipase (HSL) and adipose TG lipase (ATGL) (Jaworski et

al. 2007), resulting in a reduction of NEFA flux to the liver. In the case of insulin

resistance, NEFA flux from adipose tissue increases, leading to increased hepatic TG

synthesis and VLDL secretion.

Up to date, not many studies have looked at the direct effect of insulin on VLDL

metabolism and kinetics in humans in vivo. All these studies show that insulin can

suppress VLDL secretion (Lewis et al. 1995; Malmstrom et al. 1998). Insulin seems to

exert a greater effect on VLDL-TG than VLDL-apoB (Lewis et al. 1993). Furthermore,

insulin seems to act differently on the two VLDL subclasses, VLDL1 and VLDL2,

suppressing mainly VLDL1 production (Malmstrom et al. 1997). Overproduction of VLDL

is thought to result in part from loss of insulin-mediated inhibition of VLDL secretion. In

a recent study it was shown that diabetic men have increased VLDL-TG secretion and a

less pronounced decrease in VLDL-TG secretion in response to insulin administration

compared to healthy control group (Sorensen et al. 2011).

50

Several molecular mechanism involved in insulin-mediated VLDL1 suppression have

been elucidated in studies on human and rodent hepatocytes. Sparks and colleagues

found that Insulin-mediated suppression of VLDL1 secretion occurs via activation of

phosphatidylinositol 3-kinase (PI3K) (Sparks et al. 1996). Insulin was shown to

decrease VLDL1 secretion in a PI3K-dependent pathway, promoting apoB degradation

(see section 1.4) and inhibiting second-step bulk lipid addition (see section 1.3) (Brown

et al. 2001). Insulin may also suppress MTP expression via mitogen-activated protein

kinase (MAPK) pathway (Allister et al. 2005) and VLDL secretion by inhibiting Forkhead

transcription factor Foxa-2 (Wolfrum et al. 2003).

1.6.2 The role of insulin on VLDL-TG catabolismThe enzyme LPL is stimulated in adipose tissue by insulin whereas the opposite seems

to happen in the skeletal muscle, as discussed in section 1.2.4. After a fat-rich meal,

CM and VLDL particles compete for hydrolysis by LPL. Since LPL acts preferentially on

larger particles, CM particles represent better substrates than VLDL particles (Fisher et

al. 1995). Therefore, the rapidity of clearance of excess TG from the plasma in the

postprandial period depends on the subject’s VLDL-TG concentration. Increased

postprandial hyperlipidemia, a characteristic of the insulin resistance dyslipidemia has

been shown to result in a slower clearance of postprandial TG (CM-TG) (Ginsberg

1991). Another aspect to consider is the uptake of VLDL remnants in the liver via LDL-

receptor. Studies on hepatocytes have shown that insulin can activate LDL-receptor via

SREBP-1 (Streicher et al. 1996), suggesting that insulin resistance might lead to

reduced activation of LDL-receptors in the liver, thus limiting VLDL remnant removal

(Ginsberg et al. 2005).

51

1.7 The metabolic syndrome

1.7.1 Introduction The metabolic syndrome is a cluster of metabolic abnormalities, which are

phenotypically heterogeneous, and presents as a variable expression of metabolic

defects including abdominal obesity, an atherogenic lipoprotein profile (ALP) (see

section 1.8), raised blood pressure, insulin resistance and pro-thrombotic and pro-

inflammatory states (Kaur 2014). People with metabolic syndrome are at 2-fold

increased risk of developing CVD and at 5-fold increased risk of developing T2DM over

the next 5 to 10 years (Alberti et al. 2009). It was first described in the 1920s by Kylin, a

Swedish physician, as the association of hypertension, hyperglycaemia and gout (Kylin

1923). Two decades later, abdominal obesity was identified as being the most common

type of obesity associated with the metabolic abnormalities seen in patients diagnosed

with CVD and T2DM (Vague 1947). In the late 1980s, Reaven coined the term

“Syndrome X” to describe the proposed interrelationships between resistance to insulin-

stimulated glucose uptake, hypertension, T2DM, and CVD, and proposed insulin

resistance to be a key pathophysiological feature in T2DM, hypertension and

carbohydrate (Reaven et al. 1988). The metabolic syndrome is also known as

dysmetabolic syndrome, Syndrome X, cardio-metabolic syndrome and insulin

resistance syndrome (Miranda et al. 2005).

Several definitions of the metabolic syndrome have been proposed in the last three

decades. The first official definition was proposed by the WHO in 1999 in an attempt to

develop criteria recognized worldwide (World Health Organization 1999). Other

definitions followed from different organisations such as the European Group for the

Study of Insulin Resistance (EGIR 1999), the National Cholesterol Education Program-

52

Adult Treatment Therapy III (ATP III 2001) and the International Diabetes Federation

(IDF 2006). Although these definitions share common feature, some of the parameters

differ, resulting not very practical in terms of applicability. This situation lead several

major organizations (IDF, National Heart, Lung and Blood Institute, American Heart

Association, World Heart Federation, International Atherosclerosis Society and

International Association for the Study of Obesity), in 2009, to work together in an

attempt to harmonise the different criteria. This effort resulted in the publication of a

joint interim statement (Alberti et al. 2009). The different definitions of metabolic

syndrome are shown in Table 1.3. Among these, the ATP III criteria have been the most

widely used in both clinical practice and epidemiological studies (Ritchie et al. 2007).

Insulin resistance, included in WHO and EGIR definitions, can be determined by

glucose tolerance test and hyperinsulinaemic-euglycaemic clamp. Therefore, these

definitions can be more easily applied in research environment.

1.7.2 PrevalenceThe prevalence of metabolic syndrome around the world ranges from <10% to as much

as 84%, depending on several factors such as sex, age, race, and ethnicity, region and

the definition used (Desroches et al. 2007; Kolovou et al. 2007). Genetic factors, diet,

lifestyle, smoking, family history of diabetes, and education all play a role in determining

the prevalence of the metabolic syndrome (Cameron et al. 2004). According to the IDF,

about 25% of the world’s adult population has the metabolic syndrome

(http://www.idf.org/metabolic-syndrome). In addition to those who are clinically

diagnosed with metabolic syndrome, there is a substantial number of free-living and

otherwise healthy individuals who are ‘at risk’ of developing the metabolic syndrome

(Grundy et al. 2004), and while this group will be at increased susceptibility to CVD,

53

http://www.idf.org/metabolic-syndrome

their relatively moderate risk is more likely to be modifiable and responsive to early

dietary and lifestyle modification.

54

Table 1.3: Definitions of the metabolic syndrome by different institutionsWHO 1999 EGIR 1999 NCEP ATP-III 2001 IDF 2006 Consensus 2009

Diabetes or impaired glucose

tolerance or insulin resistance

plus two or more of the

following components:

Central obesity:

BMI>30kg/m2 and/or

WHR>0.9 (♂),

WHR>0.85 (♀)

Dyslipidaemia:

TG≥1.7mmol/L and/or

HDL-C<0.9mmol/L (♂),

HDL-C<1.0mmol/L (♀)

Arterial pressure

>140/90mmHg

Microalbuminuria>20µg/min

or albumin creatinine

ratio≥30mg/g

Insulin resistance or

hyperinsulinaemia (only

non-diabetics subjects)

plus two or more of the


Fasting plasma

glucose≥6.1mmol/L

Dyslipidaemia:

TG≥2.0mmol/L

and/or

HDL-C<1.0mmol/L

Arterial pressure

>140mmHg

Central obesity:

WC≥94cm (♂),

WC≥80cm (♀)

Any three of the following

components:

Fasting plasma

glucose>6.1mmol/L

Hypertriglyceridaemia

TG>1.7mmol/L

HDL-C<1.0mmol/L (♂),


Hypertension: arterial

pressure>130/85mmHg

Central obesity:

WC>102cm (♂),

WC>88cm (♀)

Central obesity

(WC>94cm for European ♂;

>90cm for South Asians &

Chinese ♂; >85 for Japanese

♂, WC>80cm European/South

Asians/Chinese ♀; >90 for

Japanese ♀) plus two of the


HPTG: TG≥1.7mmol/L

Low HDL-C<1.03mmol/L

(♂), HDL-C<1.29mmol/L

(♀)

Elevated blood pressure:

systolic BP≥130mmHg or

diastolic BP≥85mmHg

Elevated fasting plasma

glucose ≥5.6mmol/L or

T2DM

The presence of any three of the following:

Elevated WC (population and

country specific definitions)

HPTG: TG≥1.7mmol/L

Low HDL-C<1.03mmol/L (♂),


Elevated blood pressure:

systolic BP≥130mmHg or

diastolic BP≥85mmHg

Elevated fasting plasma glucose

≥5.6mmol/L

BP, blood pressure; HDL-C, high density lipoprotein cholesterol; HPTG, hypertriglyceridemia; TG, triacylglycerol;T2DM, type 2 diabetes mellitus; WC: waist circumference; WHR, waist to hip ratio; ♂, male; ♀, female

55

1.7.3 PathogenesisIn this section only the most relevant aspects of the pathophysiology of the syndrome

are taken into account. The mechanisms underlying the metabolic syndrome have been

outlined in Figure 1.9.

Figure 1.9: Pathophysiology of the metabolic syndrome. Increased levels of NEFA result from an expanded adipose tissue mass. In the liver, this leads to increased VLDL-TG secretion and increased gluconeogenesis. Expanded VLDL-TG pool may lead to the formation of ALP (see section 1.8). Increased NEFA also reduce insulin sensitivity in muscle by inhibiting insulin mediated glucose uptake. Increased levels of glucose and, to some extent, of NEFA, lead to hyperinsulinaemia and eventually to insulin resistance, which in turn will contribute to the hypertension. Proinflammatory state (resulting in impaired release of adipokines such as IL-6, TNFα and adiponectin), exacerbates both insulin resistance and lipolysis. This will also lead to increased release of fibrinogen (by the liver) and PAI-1 (by the adipose tissue) resulting in a pro-thrombotic state. ALP, atherogenic lipoprotein profile; HDL, high density lipoprotein; IL-6, interleukin-6; NEFA, non-esterified fatty acids; PAI-1, plasminogen activator inhibitor-1; sdLDL, small , dense low density lipoprotein; TG, triacylglycerol; TNFα, tumour necrosis factor α; VLDL, very low density lipoprotein

56

1.7.3.1 Insulin resistance

Insulin-resistance is characterised by impaired glucose metabolism, elevated fasting

glucose levels and/or hyperglycemia, with decreased insulin-mediated glucose

clearance and/or reduction in the suppression of endogenous glucose production

(Petersen et al. 2006). In this condition, normal insulin levels are not able to produce a

normal insulin response in the peripheral target tissues such as adipose tissue, muscle,

and liver. In order to compensate for this defect, β-cells in the pancreas will secrete

more insulin (hyperinsulinemia). However, over time pancreatic β-cells will fail to

produce enough insulin, eventually resulting in hyperglycemia and T2DM (Petersen et

al. 2006). The defects in insulin action in glucose metabolism include deficiencies in the

ability of the hormone to suppress glucose production by the liver and kidney, and to

mediate glucose uptake and metabolism in insulin sensitive tissues such as muscle and

adipose tissue. An important role in the development of insulin resistance is played by

systemic NEFA, which are mainly derived from the lipolysis of adipose tissue TG stores,

and are released by action of HSL. Under normal conditions, insulin suppresses

adipose tissue lipolysis by directly inhibiting HSL, as mentioned in section 1.6.1.

However, when insulin resistance is established, insulin is unable to suppress lipolysis,

resulting in increased systemic NEFA levels in the plasma in a process mediated not

only by HSL (Kraemer et al. 2002) but also by ATGL (Schweiger et al. 2006). Plasma

NEFA can stimulate insulin secretion. However, sustained exposure to high levels of

plasma NEFA can eventually lead to a decrease of insulin secretion (Lee et al. 1994).

Several mechanisms have been suggested to explain the toxic effect of NEFA affecting

insulin secretion in β-cells (Boucher et al. 2004).

57

1.7.3.2 Obesity

The increasing prevalence of obesity worldwide is likely to be one of the causes

underlying the increased incidence of insulin resistance and metabolic syndrome, as

well as CVD and T2DM (Grundy 2005). However, not all overweight or obese

individuals are insulin resistant (Stefan et al. 2008). Waist circumference is an important

component of the most recent and frequently applied diagnostic criteria for the

metabolic syndrome (Alberti et al. 2009). However, waist circumference alone is not

sufficient to obtain information about the type of fat in the abdominal region. Therefore,

in order to distinguish between a large waist due to increases in subcutaneous adipose

tissue versus visceral fat, computed tomography (CT) or magnetic resonance imaging

(MRI) (Lee et al. 2004), or dual-energy x-ray absorptiometry (DEXA) must be used

(Jensen et al. 1995). An expanded visceral adipose tissue would lead to a higher flux of

splanchnic fatty acids to the liver via the portal vein, whereas an expanded abdominal

subcutaneous fat would release fatty acids in the systemic circulation (as systemic

NEFA) (Klein 2004). The former will have a greater impact on hepatic metabolism

(glucose production, lipid synthesis), and on the hepatic secretion of prothrombotic

proteins such as fibrinogen and plasminogen activator inhibitor 1 (PAI-1) (Aubert et al.

2003).

1.7.3.3 Dyslipidaemia

In the liver, insulin, under normal conditions, increases the gene expression of a

number of enzymes involved in TG synthesis (Gonzalez-Baro et al. 2007) but also

reduces VLDL-TG and apoB production and secretion, an effect mainly due to the

suppression of adipose tissue lipolysis (Lewis et al. 1993). Insulin also enhances apoB

degradation (Ginsberg et al. 2005). In the liver of insulin resistant subjects, the

58

increased NEFA flux results in increased TG synthesis and storage, causing more TG

to be secreted as VLDL (Lewis et al. 1996). As already seen in sections 1.2.4 and 1.6.2,

insulin regulates the activity of LPL, which is the most important factor involved in VLDL

clearance. Therefore, hypertriglyceridemia observed in insulin resistance comes as a

result of increased production and/or decreased clearance of VLDL particles. The

atherogenic lipoprotein phenotype (ALP), which is characterized by increased levels of

plasma TG, low level of HDL and a predominance of small, dense LDL (sdLDL)

particles, often found in people with metabolic syndrome, will be covered in more detail

in section 1.8.

1.7.3.4 Hypertension

Hypertension is a condition frequently associated with the metabolic disorders such as

obesity, glucose intolerance, and dyslipidemia (Ferrannini et al. 1991). There are

studies indicating that both hyperglycaemia and hyperinsulinaemia can activate the

cardiac renin angiotensin system, thus contributing to the onset of hypertension

(Malhotra et al. 2001). Furthermore, it has been found that insulin resistance and

hyperinsulinemia may also stimulate the sympathetic nervous system, which in turn will

increase sodium reabsorption by the kidney, cardiac output, and vasoconstriction,

hence resulting in hypertension (Morse et al. 2005).

1.7.3.5 Proinflammatory state

Chronic inflammation is often seen to occur in people with metabolic syndrome,

resulting in higher levels of proinflammatory cytokines such as C-reactive protein

(CRP), tumour necrosis factor-α (TNFα), fibrinogen, and interleukin-6 (IL-6) (Sutherland

et al. 2004). Most of these cytokines are produced within the adipose tissue, and their

overproduction may result as a consequence of expanded adipose tissue mass

59

(Trayhurn et al. 2004). Adipose tissue-derived macrophages may be the primary source

of pro-inflammatory cytokines locally and in the systemic circulation (Weisberg et al.

2003). All this cytokines have been shown to impair insulin sensitivity (Kaur 2014), with

TNFα exacerbating lipolysis in the adipose tissue (Krauss 2004). The prothrombotic

PAI-1, a serine protease inhibitor produced by adipocytes, inhibits the tissue

plasminogen activator (tPA), and therefore higher levels of this adipokine are

associated with impaired fibrinolysis (Alessi et al. 2006). Increased plasma PAI-1 levels

are found in abdominally obese subjects (Cigolini et al. 1996). Adiponectin, an anti-

inflammatory cytokine produced by adipocytes, is decreased in obesity. Adiponectin

can also improve insulin sensitivity (Nawrocki et al. 2004). In the liver, adiponectin

suppresses the expression of gluconeogenic enzymes and down-regulate glucose

production (Combs et al. 2001), whereas, in muscle, adiponectin increases glucose

uptake and promote fatty acid oxidation (Yamauchi et al. 2003).

1.8 Atherogenic lipoprotein phenotype (ALP) and plasma TGIn section 1.7.3.3 the dyslipidemia associated with metabolic syndrome has been

discusses, and the ALP, which is often found in subjects with metabolic syndrome,

introduced. The ALP consists of a collection of abnormalities which confer an increased

risk of coronary heart disease (Austin et al. 1990). The ALP is characterized by

increased levels of plasma TG, low level of HDL and a predominance of smaller and

denser particles called sdLDL (Austin et al. 1996). An expanded VLDL pool (and in

particular TG-rich VLDL1) in the plasma can promote the CETP-mediated exchange of

TG from these particles for both LDL and HDL CE (Sattar et al. 1998). The CE

60

transferred to TRL particles causes these remnants to become more resistant to the

action of LPL and as a result more atherogenic (Ebenbichler et al. 1995). The

hydrolysis of TG-enriched LDL and HDL particles by HL (these particles are a better

substrate for this lipase) will result in a remodelling of LDL and HDL into smaller and

denser sdLDL particles (Austin et al. 1996). This process is outlined in Figure 1.10.

Figure 1.10: Formation of sdLDL. When the plasma concentration of TG is high, due to excessive VLDL secretion and/or impaired LPL action, CETP catalyses the movement of CE from LDL to VLDL and TG in the opposite direction. TG-enriched LDL particles are converted to sdLDL particles by HL. CE, cholesteryl esters; CETP, cholesteryl ester transfer protein; HL, hepatic lipase; LPL, lipoprotein lipase; sdLDL, small, dense LDL; TG, triacylglycerol; TRL, TG-rich lipoprotein; VLDL, very low density lipoprotein

61

A predominance of these particles has been associated with an increased CVD risk (St-

Pierre et al. 2004). These smaller and denser particles have reduced affinity for the

hepatic LDL-receptor, resulting in increased residence time in circulation (Rizzo et al.

2006). They are more likely to cross the endothelial barrier entering the sub-endothelial

space where they may be exposed to oxidative stress (Chait et al. 1993). The uptake of

these particles by macrophage via scavenger receptors will initiate the process of foam-

cell formation that leads to atherosclerosis (Singh et al. 2002). Increased plasma TG

levels may result from increased hepatic TG-rich VLDL (VLDL1) synthesis or impaired

TRL (CM and VLDL remnants) clearance through the action of LPL (Taskinen et al.

2011). Sugar may play an important role in determining this effect either directly by

altering TG metabolism and/or indirectly by delivering excess energy and increasing

body weight (Stanhope et al. 2013). The role of sugar in altering plasma TG levels is

discussed in section 1.10.

1.9 Liver accumulation of TG and non-alcoholic fatty liver disease (NAFLD)

1.9.1 IntroductionNon-alcoholic fatty liver disease (NAFLD) is represented by the accumulation of TG in

the liver (>5% per liver weight) without excessive alcohol consumption (less than 10

g/day), and can range from simple steatosis to non-alcoholic steatohepatitis (NASH),

advanced fibrosis and cirrhosis (Byrne et al. 2009). Importantly, this condition is

strongly associated with insulin resistance(Abdelmalek et al. 2007). It has been

estimated NAFLD affects between 17 and 33% of the general populations up to 70% of

62

people with T2DM (Byrne 2012). NAFLD is associated with important liver (Byrne et al.

2009) and cardio-metabolic disturbances (Scorletti et al. 2011). In the last decades, the

incidence of NAFLD has risen dramatically along with the increasing prevalence of

obesity, and it has been recently estimated that there are approximately one billion

cases worldwide (Loomba et al. 2013). There is evidence that obesity and T2DM

greatly increase the risk of developing NAFLD (Vernon et al. 2011). NAFLD is often

seen as the hepatic manifestation of the metabolic syndrome (Moore 2010). Liver fat

correlates significantly with all components of metabolic syndrome. Although NAFLD

correlates directly with insulin resistance, visceral fat and plasma TG and inversely with

HDL (Hsiao et al. 2007), these interactions are complex and depend on a number of

genetic and socio-environmental factors. Therefore, as a result of this complexity, it is

not unknown that people with NAFLD are not necessarily overweight or obese (Liu

2012) and insulin resistant people do not necessarily develop NAFLD (Guerrero et al.

2009). Since at present a licensed pharmaceutical therapy for the treatment of NAFLD

does not exist, the best way to tackle this condition is represented by lifestyle

modification, aiming in particular to reduce body weight, along with management of

associated pathological conditions where necessary (Nascimbeni et al. 2013).

1.9.2 Diagnosis Currently, liver biopsy remains the gold standard for NAFLD diagnosis and evaluation of

the stage of the disease (Obika et al. 2012). The Kleiner scoring system can is useful

for the evaluation of the severity of the NAFLD (Kleiner et al. 2005). However, the

distribution of lesions in NAFLD can be uneven and therefore considerable sampling

variability for most histologic features can occur (Ratziu et al. 2005). Furthermore, liver

biopsy is not very practical for two important reasons: first, given the high prevalence

63

within the general population, it would be difficult to provide such a test to a large

number of people; second, this procedure has several related risks such as pain,

bleeding, infections and injury to nearby organs (Sumida et al. 2014). Therefore, the

identification of non-invasive markers of NAFLD has become an important area of

research (Obika et al. 2012). Several imaging techniques have been used to date in the

diagnosis of NAFLD, including ultrasound, CT scanning, magnetic resonance imaging

(MRI) (Koplay et al. 2015). Magnetic resonance spectroscopy (MRS) is one of the most

accurate imaging methods for non-invasive evaluation of the degree of fatty infiltration

in the liver (Cassidy et al. 2009).

1.9.3 Pathogenesis The excessive depositions of ectopic fat in the liver can be a result of several factors

alone or in combination: increased NEFA flux to the liver, increased synthesis of fatty

acid via DNL, increased dietary fat, decreased β-oxidation (Byrne et al. 2009). Although

there is a strong link between insulin resistance and excessive deposition of TG in the

liver (Adams et al. 2007), it is still not clear whether insulin resistance causes NAFLD or

whether excessive accumulation of TG, or precursors on the synthetic pathway,

precede and promote insulin resistance. Studies in humans and rodents indicate that

increased delivery of fatty acids from adipose tissue is a significant source of fat

accumulation in the liver (Lewis et al. 2002) supporting the importance of the role that

NEFA flux to the liver plays in the development of NAFLD. Donnelly et al. reported that

approximately 60% of TG deposited in hepatocytes is generated from adipose tissue

sources in patients with NAFLD (Donnelly et al. 2005). Holt et al. found that, systemic

NEFA concentrations during an oral glucose tolerance test were associated with fatty

liver independently of insulin sensitivity (Holt et al. 2006). Liver fat measured by 1H-

64

MRS is closely and positively correlated with measures of total adiposity such as body

mass index (BMI) or percentage body fat. Some studies found even stronger

correlations between liver fat and visceral adipose tissue (VAT) mass, quantified by CT

(Kelley et al. 2003) or by MRS and 1H-MRS (Thamer et al. 2004). As discussed in

section 1.7.3, an expanded adipose tissue is often associated with inflammation. This

state is accompanied with impaired release of adipokines such as IL-6, TNFα and

adiponectin, which exacerbates both insulin resistance and lipolysis. This along with the

impairment of insulin-mediated suppression of lipolysis will result in increased flux of

fatty acids from adipose tissue to the liver. Increased lipolysis in VAT is thought to result

in an elevated flux of fatty acids directly to the liver via the portal vein, a process that is

commonly referred to as the “portal hypothesis” (Lebovitz et al. 2005). However this

hypothesis has been challenged by Nielsen et al. who found that the contribution of

VAT lipolysis to the pool of fatty acids drained into the liver (determined by isotope

dilution and arteriovenous sampling methods) was only 5–10% in normal weight

subjects and only up to 25% in viscerally obese individuals (Nielsen et al. 2004).

Therefore, the main source of systemic NEFA in the fasting state is considered to be

subcutaneous fat (Koutsari et al. 2006). Nevertheless, regardless of the origin of the

fatty acids delivered to the liver, increased hepatic lipid supply is most probably

contributing to hepatic fat accumulation (Harrison et al. 2007).

1.9.4 Dietary sugar and NAFLDSeveral dietary factors are involved in the pathogenesis of NAFLD. However, recently

there has been a growing interest in the role of carbohydrates and in particular of sugar-

sweetened beverages and fructose (Neuschwander-Tetri 2013; Vos et al. 2013). The

role of fructose, that appears to have the strongest effects on hepatic de novo

65

lipogenesis (DNL), has been investigated in part because its metabolism in the liver

differs from glucose. This point will be highlighted in section 1.10. There is evidence

showing that high fructose intakes may alter hepatic insulin sensitivity and promote

lipogenesis and ectopic lipid disposition in humans (Le et al. 2009; Stanhope et al.

2009), supporting the findings in rodents regarding the role of fructose in the

pathophysiology of fatty liver (Kawasaki et al. 2009). The consumption of sucrose and

high-fructose corn syrup has increased dramatically in the Western world in the last

decades, and especially in the US, this may have contributed to the higher incidence of

NAFLD (Elliott et al. 2002). However, Chung et al. in a recent meta-analysis concluded

that hypercaloric fructose and glucose diets might have similar effects on the

accumulation of liver fat (Chung et al. 2014). Moore et al. in a recent review concluded

that the very high doses in intervention trials have been a major confounding factor

(Moore et al. 2014). Therefore, it is still not possible to establish whether or not dietary

sugars (including fructose), at the levels typically consumed by the general population,

can influence hepatic DNL and liver fat accumulation in humans in a way that does not

depend on excess energy. This is also one of the main questions addressed in the

present study.

Energy metabolism within the liver is tightly regulated with transcription factors, sterol

receptor element binding protein-1c (SREBP-1c) and carbohydrate response element-

binding protein (ChREBP) playing important roles in hepatic glucose and lipid

metabolism. The regulation of the SREBP isoforms (SREBP-1a, SREBP-1c and

SREBP2) and ChREBP is very complex and involves both transcriptional and post-

transcriptional mechanisms (Postic et al. 2007; Shao et al. 2012). Insulin can induce

SREBP-1c in the liver promoting the expression of both glycolytic and lipogenic genes,

thus resulting in increased DNL and a parallel decrease in fatty acid oxidation (Lavoie et

66

al. 2006). Similarly, ChREBP can promote glycolytic and lipogenic gene expression and

also up-regulates the expression of genes involved in TG production. ChREBP is up-

regulated by glucose (Iizuka et al. 2004). Dentin et al. found that inhibition of ChREBP

in ob/ob leptin-deficient mice reduced the level of liver fat by decreasing DNL and

enhancing fatty acid oxidation, resulting in a reduction of plasma TG and systemic

NEFA (Dentin et al. 2006). However, although the induction of glycolytic and lipogenic

gene transcription in the liver by insulin and glucose is mainly mediated by SREBP-1c

and ChREBP respectively, there are also interactions with many nutrient-sensitive

nuclear receptors playing an important role (Postic et al. 2007; Shao et al. 2012).

Interestingly, it has been showed that fructose can also induce both SREBP-1c and

ChREBP activities. A study showed that the induction of SREBP-1c by fructose through

animal knock-out experiments depended on the enzyme stearoyl-CoA desaturase

(SCD) (the role of which is discussed in section 1.10) and its production of endogenous

oleate (Miyazaki et al. 2004).

1.10 Carbohydrate induced hypertriglyceridemia (HPTG)Mechanisms underlying carbohydrate induced HPTG and the implications for public

health represent important issues in nutritional research, mainly because of the

importance of carbohydrate to replace fat in treating obesity. As a result of this

exchange, the established cholesterol-lowering effect is accompanied by an elevation of

fasting plasma TG, thus an increased CVD risk (Parks et al. 2000).

The Scientific Advisory Committee on Nutrition (SACN), a committee of independent

experts that advises the UK Government on nutrition issues, in a recent report on

67

carbohydrate, recommended lowering the consumption of “free sugars”, to around 5%

of daily energy intake in the general population (25 g for women and 35 g for men)

(SACN 2014). One benefit of the above mentioned report has been the clarification of

the definitions of “free sugars”, a term which is proposed to replace non-milk extrinsic

sugars (NMES), which are now defined as: all monosaccharides (glucose, fructose,

galactose) and disaccharides (sucrose, lactose, maltose) added to foods by the

manufacturer, cook, or consumer, as well as those sugars naturally present in honey,

syrups, and fruit juices (Te Morenga et al. 2013).

The plasma lipid response to dietary carbohydrate is extremely variable between

individuals and depends on several factors such as the total amount of carbohydrate,

dietary fibre and proportion of free sugars, mainly fructose and sucrose. It has been

shown that high carbohydrate diets made up of monosaccharides, particularly fructose,

induce a more extreme HPTG than those diets made up with oligo and polysaccharides

(Frayn et al. 1995). Furthermore, purified diets induce HPTG more promptly than diets

higher in fibre in which most of the carbohydrate is derived from unprocessed whole

foods (Riccardi et al. 1991). Since the majority of the studies looking at the effect of

carbohydrates on plasma TG were based on diets that were extreme with respect to the

energy contribution from fat and carbohydrate, their outcome, although useful to gain a

better understanding of mechanisms driving this effect, has not been translated into

dietary guidelines because the diets were not related to what people actually eat.

The most likely mechanism for the postprandial HPTG is increased hepatic DNL, which

can lead to increased VLDL production and secretion. DNL is the biochemical process

of synthesising fatty acids from acetyl-CoA produced from non-lipid precursors such as

fructose, glucose or amino acids. DNL can also be viewed as a mechanism for the body

to deal with excessive carbohydrate intake (Schutz 2004). Therefore, DNL may

68

contribute to increased fasting (Schwarz et al. 2003) and postprandial (Timlin et al.

2005) plasma TG concentrations. Although this pathway is present in both liver and

adipose tissue, only the liver has the ability to dramatically increase fatty acid synthesis

in response to changes in dietary macronutrient intake. Carbohydrate and sugar in

particular, have been shown to increase DNL in humans. Positive correlations between

hepatic DNL and high fasting plasma TG concentrations after high-carbohydrate

feeding give further evidence that DNL contributes to carbohydrate-induced HPTG

(Schwarz et al. 2003).

Short-term studies showed that fructose consumption markedly increased circulating

postprandial TG concentrations in adults (Stanhope et al. 2008), suggesting that

postprandial HPTG may represent the early metabolic disruption caused by elevated

fructose consumption. Fructose can promote hepatic DNL in different ways. The liver

represents the main site of fructose metabolism (Mayes 1993). In the liver, fructose can

induce lipogenic genes via SREBP-1c activation, in a fashion that does not depend on

insulin (as previously discussed in section 1.9.4) (Matsuzaka et al. 2004). Most

importantly, fructose can enter the glycolytic pathway via fructose-1-phosphate, thus

bypassing the main control point of glycolysis (the reaction catalysed by

phosphofructokinase) where glucose metabolism is limited by feedback inhibition by

citrate and ATP (energy status). In the liver, the unregulated uptake of fructose will

result in increased production of the lipogenic substrates glyceraldehyde 3-phosphate

and acetyl CoA, therefore leading to increased DNL (Elliott et al. 2002). The mechanism

described above is outlined in Figure 1.11.

69

Figure 1.11: Utilization of fructose and glucose in the liver. Hepatic fructose metabolism begins with phosphorylation by fructokinase. Fructose enters the glycolytic pathway at the triose phosphate level (dihydroxyacetone phosphate and glyceraldehyde-3-phosphate). Therefore, fructose is not affected by the major control point by which glucose enters glycolysis (phosphofructokinase). At this stage, glucose metabolism, but not fructose metabolism, is limited by feedback inhibition by citrate and ATP (energy status). Thus, fructose acts as an unregulated source of acetyl-CoA for DNL in the liver. Newly synthetized FAs can be used for the synthesis of glycerolipids that can be incorporated in VLDL particles.

Palmitate represents the main end product of DNL (Ameer et al. 2014) being the end

product of fatty acid synthase in mammals. Palmitate can also be converted into other

fatty acids via elongation (by adding two carbons units in the form of acetyl-CoA) and

desaturation reactions. The enzyme stearoyl-CoA desaturase 1 (SCD1) converts

saturated fatty acids, mainly palmitate (16:0) and stearate (18:0), into monounsaturated

70

fatty acids (MUFA) in a reaction that is considered the rate-limiting step of the

production of MUFA (Liu et al. 2011). It has been shown that MUFA are required for the

normal production of TG and CE (Ntambi et al. 2004). It has been suggested that

increased DNL is associated with increased SCD1 activity and both processes are

activated by high-carbohydrate diets.

Increases hepatic DNL in response to dietary carbohydrate may occur via increased

insulin secretion, which in turn activates the lipogenic transcription factors liver-X-

receptor (LXR), SREBP-1c and ChREBP (Flowers et al. 2009). It is important to note

that the lipogenic potential of a low-fat, high-carbohydrate diet can be modulated by

different types of fatty acids, with polyunsaturated fatty acids (PUFA) exerting an anti-

lipogenic effect by inhibiting the binding of SREBP-1c to the promoter of SCD1 gene

(Kim et al. 2002), and saturated fatty acids exerting an opposite effect by inducing the

co-activation of SREBP-1c and LXR (Lin et al. 2005). In experiments conducted on

Scd1-deficient mice, Sampath and colleagues showed reduced hepatic DNL in

response to high carbohydrate diets partly via inhibition of SREBP-1c (Sampath et al.

2007). Interestingly, high levels of MUFA, but not of saturated fatty acids, were able to

reactivate SREBP-1c, indicating that the activation of lipogenesis in the liver requires

adequate levels of MUFA. The SCD plasma desaturation product to precursor ratios

such as palmitoleic acid to palmitic acid (16:1n-7/16:0) or oleic acid to stearic acid

(18:1n-9/18:0) have been used in several human studies as a marker for SCD1 activity

(Chong et al. 2008; Peter et al. 2011). A limitation of this index is the fact that it does

not distinguish between liver and adipose tissue SCD1 activity although it has been

shown to have a correlation with plasma TG levels (Paillard et al. 2008) and obesity

(Warensjö et al. 2006). Attie et al. found an increased 18:1/18:0 ratio after a 4-6 week–

long high-carbohydrate diet (carbohydrate: 61-65%; about 50% of carbohydrate was

71

sugar) which explained 44% of the variance in the plasma TG response (Attie et al.

2002). More recently Chong et al. found a parallel increase in hepatic lipogenesis,

desaturation index and plasma VLDL-TG after a short period (3 days) low fat, high

carbohydrate food intervention (Chong et al. 2008). Interestingly, it has been shown that

adipose SCD1 levels correlate with plasma TG, but this does not depend on the

carbohydrate intake and the plasma SCD desaturation index, suggesting that SDC1

activity in liver and adipose tissue influence the plasma TG response by different

mechanisms (Mangravite et al. 2007). Dietary carbohydrate and primarily sugar will

result in increased insulin secretion which leads to activation of both DNL and SCD1

activity in the liver. It has been reported that fructose as well as being more lipogenic

than glucose is also a more potent inducer of SCD1 in the liver (Miyazaki et al. 2004).

Elevated hepatic levels of SCD1 have been suggested to affect the partitioning of fatty

acids away from oxidation and towards storage (Sampath et al. 2007). Increased SCD1

activity and MUFA production may also result in increased production and secretion of

VLDL-TG (Flowers et al. 2009).

1.11 Stable isotopes tracer techniques

1.11.1 General tracer theoryIn order to investigate lipid metabolism it is necessary to gain quantitative information

on their rates of production, conversion and catabolism. A tracer is a compound that is

administered either intravenously or orally (the former is more common) in order to

study the metabolism of a particular molecule (the tracee). A tracer, therefore, should

have the same biological behavior as the tracee, but at the same time, it should have a

distinctive characteristic that allow its identification. This can achieved incorporating

72

stable isotopes into a compound of interest in order to label it. Isotopes of the same

element are atoms with the same number of protons (atomic number) but a different

number of neutrons and thus of a slightly different weight (e.g., 1H and 2H, 12C and 13C).

These elements are chemically identical, since the number of neutrons does not affect

the chemical properties, which are determined by the electron configuration. Although

the use of stable isotope tracers in metabolic studies is based on the assumption that

the tracer and the tracee are metabolically indistinguishable, this is not always the case,

and an “isotope effects” (tracer and tracee behaving differently) has been reported

especially in cases in which heavy isotope loads leads to considerable changes in mass

(Ludke et al. 2008). A mass sensitive analytical technique is required in order to allow

discrimination between labelled and unlabelled compounds. Gas chromatography-Mass

spectrometry (GC-MS) allows the separation of molecules and then the identification of

labelled and unlabelled compounds on the basis of mass. Samples injected into the GC

are vaporized and separated by boiling point and by affinity for the stationary phase

contained in a long column. Then a carrier gas sweeps these compounds one by one

into the MS, where they are ionized and the resulting ions are detected by the mass

spectrometer. This allows the quantification of ions of different masses and eventually it

is possible to determine the enrichment defined as the amount of tracer relative to

tracee (Patterson 1997). The enrichment can be expressed as tracer-to-tracee ratio

(TTR), corresponding to the molar ratio of labeled to unlabeled molecules in the

sample, or atom percent excess (APE), which is the molar ratio of labeled molecule to

the sum of the labeled and unlabeled molecules in the sample (Magkos et al. 2009).

Normally the TTR is calculated by using the GC-MS in selected ion monitoring mode, in

which case the abundances of a particular fragment containing the heavier isotopes

from the tracer and the correspondent fragment coming from the tracee are determined.

73

The use of stable isotope tracers is advantageous compared to the use of radioactive

tracers because it does not result in exposure to ionizing radiation. However, it is

necessary to take into account the natural abundance of these stable isotopes in the

sample (Wolfe 1992). The use of stable isotopes has largely contributed to our current

understanding of lipid and lipoprotein metabolism. All the components of the lipoprotein

such as TG, cholesterol and apolipoprotein, can be analysed by the incorporation of

stable isotopes into their molecular structures.

Stable isotopes can be employed in three general ways in order to investigate a

substrate metabolism: tracer dilution, tracer incorporation and tracer conversion. The

tracer dilution is used to determine the rate of appearance (Ra) of a particular substrate

in the bloodstream. The Ra usually refers to the rate of release of the substrate of

interest into the bloodstream by all tissues of the body (e.g. Ra of fatty acids into the

bloodstream) (Wolfe 1992). In the tracer incorporation method the tracer is used as a

precursor for the synthesis of a product of interest (e.g. labelled fatty acids or glycerol

used for the synthesis of TG) (Parks et al. 2006). Thus, by monitoring the amount of

tracer in the product during a time course it is possible to determine the rate at which

the synthesis of the product occurs. Finally, the tracer conversion consists in measuring

the rate at which a metabolic by-product is produced during the metabolism of a more

complex substrate containing stable isotopes (e.g. labelled CO2 produced during the

oxidation of labelled fatty acids), or the rate at which a tracer is inter-converted (e.g. the

desaturation and elongation of fatty acids leading to the synthesis of very long-chain

polyunsaturated fatty acids) (Emken 2001).

A tracer can be administered intravenously as a single bolus or as a constant infusion

(Wolfe 1992). With the bolus injection, the enrichment curve will quickly reach a peak

and the decline over time depends on the turnover of the molecule of interest. For this

74

reason, this approach is more suitable to investigate those components of lipoprotein

metabolism that turnover slowly (Boren et al. 2012). With constant infusion a longer

time is needed to achieve a plateau of isotopic enrichment and this can be a problem

for those lipoproteins with a slow turnover. This is an important factor to take into

account, especially for those studies using infusion protocols less than 12 hour long, in

which assumptions about the plateau of isotopic enrichment have to be made (Boren et

al. 2012). This approach is therefore more suitable for lipoproteins with rapid turnover,

such as VLDL. A disadvantage related to long infusion protocols is represented by

tracer recycling. However, even when this occurs, there are ways to overcome this

problem, as it will be discussed in section 1.11.2.

1.11.2 Measurement of VLDL-TG kineticsBy using these tracer techniques it is possible to study many different aspects of VLDL

metabolism. For example, by labelling apoB100 it is possible to investigate the

metabolic behaviour of VLDL particles (Parhofer et al. 2006). However, in this section

the focus will be on the possible uses of these techniques in order to investigate VLDL-

TG kinetics. For example, there are studies showing that differences in VLDL-TG

production between groups are not always accompanied by differences in VLDL-

apoB100 secretion (Melish et al. 1980; Mittendorfer et al. 2003), mainly due to the fact

that TG content per particle can vary. Therefore, it is not possible to draw conclusions

regarding the whole particle by studying the kinetics of just one VLDL component. The

secretion of VLDL-TG can be determined in several different ways, such as

arteriovenous balance techniques, ex-vivo labelling of VLDL followed by re-infusion,

and many in-vivo techniques using radioisotopes and stable isotopes. However, the

most common and practical approach is based on the administration of a labelled

75

precursor of TG (glycerol or fatty acids) in order to investigate VLDL-TG kinetics

(Magkos et al. 2004). Constant infusion of glycerol or palmitate tracers has been

employed to determine VLDL kinetics, by fitting the data in a monoexponential rise to

plateau model (Parks et al. 1999; Wang et al. 2001). However, this approach does not

take into account the tracer recycling that can occur during a prolonged constant

infusion (Vedala et al. 2006). Recycling consists of the incorporation of the tracer into a

different pool (e.g. hepatic TG storage pool), from which it is subsequently released and

incorporated in VLDL-TG, or tracer initially incorporated in VLDL-TG, released by the

action of LPL in the plasma and then reincorporated in VLDL-TG (Magkos et al. 2009).

For example, it has been reported that considerable recycling of the tracer in the liver

occurs during a constant infusion of labelled palmitate (Patterson et al. 2002). However,

this approach is still useful for the determination of systemic NEFA contribution to

VLDL-TG, as it will be discussed in section 1.11.3. The bolus injection of labelled

glycerol or fatty acids to determine the slope of decline in VLDL-TG to calculate

important kinetic parameters has also been used (Kekki 1980; Lemieux et al. 1999).

However, as for the constant infusion method, a disadvantage of this approach is that it

does not account for tracer recycling, thus leading to an underestimation of the turnover

rate. Compartmental modelling represents a much accurate way to investigate the

complexity of lipoprotein metabolism compared to those methods based on the

monoexponential slope, since it takes into account the tracer recycling (Magkos et al.

2009). Since the enrichment data in most cases can be described by more than one

model, it is important to find the model that fits better the kinetic curve obtained by

plotting the enrichment (tracer to tracee ratio) against the time (Boren et al. 2012).

Several assumptions have to be made in order for a mathematical model to be used,

even when the model is based on experimental data, implying that the kinetic

76

parameters are not something that can be measured directly, but they rather represent

an indirect approximation. The SAAM II software (SAAM Institute, Seattle, WA, USA) is

widely used for modelling lipoprotein kinetics enrichment data using compartmental

models (Barrett et al. 1998). Patterson and colleagues, by using simultaneous

administration of 2H5-glycerol and 1-13C1-palmitate given as a bolus or a constant

infusion of 2,2-13C2-palmitate, and monitoring the enrichment in VLDL-TG for 12 hours,

found that monoexponential data analysis can underestimate the true VLDL-TG

turnover compared with compartmental modelling (Patterson et al. 2002). They also

observed that this effect was more evident when VLDL-TG turnover was high, whereas

when the turnover was sufficiently slow the two approaches gave similar results.

Furthermore, after using labelled glycerol or fatty acids bolus injections coupled to

compartmental modelling, they found no differences in VLDL-TG turnover. In contrast,

fatty acids bolus injection provided lower turnover rates than glycerol, when used in

conjunction with monoexponential slope analysis, probably due to the fact that fatty acid

tracers recycle more than glycerol (Magkos et al. 2009), and constant infusion of

labelled fatty acids coupled to compartmental modelling did not improve this situation

because the model was not able to resolve the extent of the recycling. In most cases all

the approaches discussed so far have been applied to VLDL as a whole. However, they

can be used to investigate VLDL1 and VLDL2-TG separately. For example, Adiels et al.

developed a multicompartmental mathematical model that allow the assessment of the

kinetics of apoB100 and TG in VLDL1 and VLDL2 simultaneously after a bolus injection

of labelled glycerol and leucine (Adiels et al. 2005). This approach allows the

investigation of many different aspects characterising VLDL subclasses. Nevertheless,

the necessity to separate VLDL into its subclasses by using preparative

77

ultracentrifugation, leads to an increased total measurement variability (Gill et al. 2004)

compared to when VLDL is treated as a single entity (Magkos et al. 2007). Interestingly,

when used simultaneously, tracers for the measurement of TG kinetics and those for

the measurement of apoB100 kinetics can be used to determine an index of nascent

VLDL particle size. This can be achieved by dividing the secretion rate of VLDL-TG by

the secretion rate of VLDL-apoB100 which gives an estimate of the average number of

TG molecules secreted in each VLDL particle (Magkos et al. 2007).

1.11.3 Measurement of the sources of fatty acids for TG synthesis in the liver Tracers can also be used to investigate the sources of different fatty acids used for

VLDL-TG synthesis. By fitting the isotopic enrichment of plasma free palmitate and

palmitate derived from VLDL-TG, it possible to estimate the contribution of systemic

plasma NEFA and non-systemic fatty acids to total VLDL-triglyceride production

(Magkos et al. 2007; Fabbrini et al. 2008). In the post absorptive phase the sources of

fatty acids contributing to VLDL-TG synthesis are represented by splanchnic fatty acids

and systemic NEFA, as discussed in section 1.5. The former includes fatty acids

released from hepatic lipids stores (TG storage pool) and tissues draining directly into

the portal vein (visceral fat stores) and fatty acid derived from hepatic DNL, whereas the

latter come from the lipolysis of subcutaneous adipose tissue and also including those

fatty acids liberated from lipolysis of plasma lipoproteins in other peripheral tissues that

escape uptake (spillover). Mass isotopomer distribution analysis (MIDA) allows to

measure the relative contribution of fatty acids coming from hepatic DNL to VLDL-TG

production (Hellerstein et al. 1991). This methodology is based on combinatorial

probabilities in the synthesis of a fatty acid molecule (palmitate in most cases) from

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acetate units (8 units for palmitate), and relies on the analysis of the incorporation

pattern of a 13C-acetate tracer into the fatty acids used for VLDL-TG synthesis. A

constant infusion is required in order for the precursor pool, represented by cytosolic

acetyl-CoA in the liver, to be at equilibrium. The precursor enrichment is not accessible

and can only be determined indirectly by examining the relative distribution pattern of

the different versions of the product. Therefore, the percentage of palmitate derived

from hepatic DNL contribution to VLDL-TG is derived from the rate at which the

enrichment increases in the product (Hellerstein et al. 1996). An alternative method to

measure DNL contribution to VLDL-TG involves using oral administration of heavy

water (2H2O) (Diraison et al. 1996). This will reach equilibrium very quickly with the total

body water, and its deuterium can be incorporated into fatty acids during their

synthesis. Therefore, the fractional synthesis of palmitate can be determined by

comparing the observed isotopic enrichment with the theoretical isotopic enrichment

that would have been obtained if all the molecules of palmitate were derived from

endogenous synthesis. This theoretical value depends on the isotopic enrichment of

body water and the maximum number of 2H (N) that can be incorporated in the newly

synthesized palmitate. It is important to note at this point, that although palmitate has 31

hydrogens that could be labelled, they are not equivalent since they can have different

biosynthetic origins (7 from H2O, 14 from NADPH and 10 from acetyl-CoA) (Murphy

2006). However, the NADPH and the acetyl-CoA pools do not equilibrate completely

with the labelled body water, and therefore, N cannot be equal to 31. It has been

reported that the N value for palmitate, determined by MIDA, is 21 (Diraison et al.

1996).

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1.12 Proposed work Metabolic syndrome, obesity and type 2 diabetes are characterised by insulin

resistance and atherogenic lipoprotein phenotype (ALP) (raised plasma TG, low HDL

and a predominance of small, dense LDL (sdLDL)), and are associated with an

increased risk of cardiovascular disease (CVD) (Ginsberg et al. 2009). The

accumulation of liver fat can also occur in these metabolic diseases. Furthermore, the

accumulation of liver fat correlates significantly with insulin resistance and ALP

(DeFilippis et al. 2013). Increased intake of “free sugars”, a term which is proposed to

replace non-milk extrinsic sugars (NMES), in combination with insulin resistance, as

represented by increased liver fat, may promote the formation of both TG and sdLDL

(Stanhope 2012). Raised plasma TG levels both in the fasting and in the postprandial

state may lead to the formation of ALP through the remodelling of LDL into smaller and

denser particles, as discussed in section 1.8. The increase in fasting plasma TG may

result from an overproduction of VLDL from the liver and/or an impaired clearance

through the action of lipoprotein lipase (LPL) (Taskinen et al. 2011). As discussed in

section 1.5, the hepatic synthesis of VLDL-TG is mainly regulated by the availability of

systemic NEFA from adipose tissue, de novo lipogenesis (DNL) and lipoprotein

remnants. Although DNL makes a relatively small contribution to VLDL-TG synthesis,

this source has been shown to increase substantially when a high percentage of energy

is supplied as carbohydrate (Schwarz et al. 1995). Insulin plays an important role in the

regulation of all these pathways involved in the production and the clearance of VLDL

particles. Therefore, the differential effects on VLDL-TG kinetics may result from

interplay between the degree of hepatic insulin resistance (as represented by the level

of liver fat) and dietary extrinsic sugar intake. The majority of studies that have

80

investigated the effect of dietary extrinsic sugar on lipid metabolism have tested diets

that were extreme with respect to the energy contribution from liquid formula meals.

Therefore, their outcome, although useful for elucidating mechanisms, has not

translated into dietary recommendations for public health, since the diets were

unrelated to what people actually eat.

In addition to those who are clinically diagnosed with metabolic syndrome, there is a

substantial number of free-living and otherwise healthy individuals who are ‘at risk’ of

developing the metabolic syndrome (Grundy 2005), and while this group will be at

increased susceptibility to CVD, their relatively moderate risk is more likely to be

modifiable and responsive to early dietary and lifestyle modification. This study

investigates for the first time, the effect of dietary sugar at levels of intake actually found

in Western diets (within the lower and upper 2.5 th percentile for men in the UK aged 40

to 65), on fasting plasma TG metabolism in subjects ‘at risk’ of the metabolic syndrome.

Therefore, the present study will focus on this area through the aims listed below.

What are the mechanisms by which dietary sugar affects VLDL-TG kinetics in

these men at higher cardiometabolic risk?

How are the different sources of fatty acids used for VLDL-TG synthesis

affected by dietary sugar?

How does the level of liver fat modulate these effects?

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1.13 Hypothesis A high intake of free sugars, such as fructose and sucrose, will increase the

level of plasma TG, due to increased VLDL-TG secretion (mainly due to higher

VLDL1 TG-rich particles production) driven by increased hepatic DNL and

systemically derived fatty acids.

A high intake of dietary sugar will promote the accumulation of ectopic fat in the

liver

1.14 Study aims The aim of the present study was to examine the effect of two different isocaloric diets

with the same carbohydrate, fat and protein composition in weight and total percentage

energy high and low in sugar (26% and 6% total energy coming from NMES,

respectively), on plasma lipid and lipoprotein levels and IHCL in men at increased risk

of developing metabolic syndrome with either high liver fat or low liver fat (IHCL > or <

5% by magnetic resonance spectroscopy). The main study aimed to recruit 36

participants with an allowance for a 20% drop-out rate (which was equal to three

participants per intervention arm). The aims of this study addressed in each one of the

three results chapters are listed below.

Chapter 3: to compare the levels of liver fat at the end of each dietary

intervention and also to look at the effect of two diets on plasma TG levels and

VLDL-TG levels and composition.

Chapter 4: to investigate the effect of the two diets on VLDL1 and VLDL2-TG

kinetics and the impact of the level of liver fat on these particles kinetics after the

82

two interventions; to compare the two different stable isotope techniques used to

measure VLDL-TG kinetics (bolus injection of bolus of 2H5-glycerol and constant

infusion of U-13C16-palmitate), in order to determine whether there is a good

agreement between them.

Chapter 5: to determine the proportion of VLDL1 and VLDL2-TG derived from

DNL, systemic fat stores and splanchnic fat stores (fatty acids coming from the

hepatic TG storage pools and visceral adipose tissue) in response to the two

diets and the impact of the level of liver fat on these sources of fatty acids after

the two interventions.

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Chapter 2: Subjects and methods

2.1 Study participantsThe study received ethical approval by the Surrey NHS Research Ethics Committee

and by the University of Surrey Ethics Committee and was subsequently registered at

ClinicalTrials.gov (NCT01790984). All the volunteers were told about all the risks, side

effects or discomforts that might be reasonably expected and were asked to provide

written informed consent. Inclusion and exclusion criteria are shown in Table 2.1.

Table 2.1: Inclusion and exclusion criteria for the studyInclusion criteria Exclusion criteria

Ethnicity: Caucasian

Sex: male

Age: 40 – 65

BMI (kg/m2): 26 – 32

Cardio-metabolic risk score: 4 – 10

ApoE genotype: E3 homozygous

IHCL: LLF < 5%; HLF > 5%

T2DM or related diseases

Lipid lowering medications

Unstable body weight for the past three

months

Alcohol consumption (g/day): > 20

Metal implant

Claustrophobia

ApoE, apolipoprotein E; BMI, body max index; HLF, high liver fat; IHCL, intra-hepatocellular lipid; LLF, low liver fat; T2DM, type 2 diabetes mellitus

Participants who were recruited were those at increased risk of developing metabolic

syndrome according to a classification introduced for the RISCK study in which the

effects of dietary fat and carbohydrate on insulin resistance were examined (Jebb et al.

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2010). The criteria used for this study were based on the NCEP ATP-III guidelines

(NCEP ATP-III guidelines, 2001) and produced a weighted score of metabolic risk

based on central obesity/body mass index (BMI), hypertension, raised plasma glucose

and serum insulin levels, and dyslipidaemia (TG/HDL) (see Table 2.2). These criteria

were put in place to identify those individuals at an increased risk of developing

metabolic syndrome although being at a lower level than clinical definitions. Participants

included in the study were those with a score ≥4 but ≤10, classified as being at

increased risk of developing metabolic syndrome and thus suitable for the study.

Inclusion criteria also required participants to be homozygous for the apoE3 genotype.

The different isoforms have different affinities for the LDL-receptor (as mentioned in

section 1.2.1), thus contributing to the variation in lipoprotein concentration and total

cholesterol levels. E3/E3 genotype is also the most common (Eichner et al. 2002).

Table 2.2: The cardio-metabolic risk scoreCharacteristics Value Score

BMI (kg/m2) – Level 1 25 - 30 1

BMI (kg/m2) – Level 2 > 30 2

Waist circumference (cm) – Level 1 > 94 1

Waist circumference (cm) – Level 2 > 102 2

Systolic blood pressure (mmHg) ≥ 140 1

Diastolic blood pressure (mmHg) ≥ 90 1

Fasting glucose (mmol/L) > 5.5 3

Fasting insulin (pmol/L) > 40 3

Fasting HDL-cholesterol (mmol/L) < 1.0 2

Fasting TG (mmol/L) > 1.3 1

Participants with total score between 4 and 10 and apoE3 homozygous were eligible for a liver fat scan by 1H-MRS. Score was adapted from the ‘RISCK’ study (Jebb et al. 2010). Both those with score < 4 (not at risk) or > 10 (elevated risk) were excluded. BMI, body mass index; HDL, high density lipoprotein; TG, triacylglycerol

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Participants were recruited through an existing network of links with GP surgeries in

Guildford area that provided pre-screening data on anthropometrics, serum glucose,

lipids and blood pressure allowing the selection of volunteers suitable for the screening.

After telephone pre-screening, the selected volunteers attended an initial screening visit

at the Centre for Endocrinology and Diabetic Research (CEDAR), where they were

asked to complete a basic health and diet questionnaire, had their height, weight, body

composition and blood pressure measured. They provided a blood sample for the

determination of the metabolic score and for the measurement of basic biochemistry.

Liver enzymes alanine aminotransferase (AST), aspartate aminotransferase (AST) and

gamma-glutamyl transferase (GT), plasma lipids (total TG, and total, LDL and HDL

cholesterol) and apoE3 genotype were all determined at the Royal Surrey County

Hospital. Any subjects taking any drugs known to cause secondary steatohepatitis,

statins or fibrates and those consuming more than 2 units of alcohol per day, were not

accepted into the study. Following an MRS scan performed at the Hammersmith

Hospital in London, the recruited subjects were divided in two groups: low (<5%) and

high (>5%) liver fat, (LLF and HLF respectively). It has been shown that insulin resistant

subjects with a BMI 26-32 have a wide range of liver fat (<2-30%) (Shojaee-Moradie et

al. 2007).

2.2 Study designThe study consisted of a randomised dietary intervention with a cross-over design as

shown in Figure 2.1. The aim was to compare the effects of two diets, high and low in

extrinsic sugar, on VLDL-TG kinetics, sources of fatty acids for VLDL-TG synthesis and

liver fat. All participants underwent a run-in habitual diet for 4 weeks, before being

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randomised to one of the two test diets for 12 weeks using a simple randomization

procedure, with a computer-generated sequence of treatments concealed in sealed

envelopes. First and second dietary interventions were separated by a 4 week wash-out

diet. At the end of each dietary intervention, metabolic studies were performed at the

CEDAR in order to measure lipoprotein and fatty acid kinetics, and DNL.

Figure 2.1: Schematic of the study design. LSP, low sugar phase; HSP, high sugar phase

Both run-in and wash-out diets were based on the habitual diet of the average male in

the UK, according to National Diet & Nutrition Survey (NDNS). The dietary exchange

model was designed according to the mean intakes of two groups of men, aged 35 to

49 and 50 to 64, as published by the NDNS (Hoare et al. 2004). Mean data were used

to provide the intakes for men aged 35 to 65 (Table 2.3). The survey showed that

dietary carbohydrate accounted for 47% food energy (%E), whilst non-milk extrinsic

sugar (NMES) accounted for 13% of total energy.

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Table 2.3: Mean energy and macronutrient intakes of men (NDNS)Mean intakes/age 35 - 49 50 - 64 Mean

Total energy (MJ) 9.93 9.55 9.74

Total carbohydrate (g) [%E] 279 [47] 269 [47] 274 [47]

NMES (g) [% E] 78[13] 70 [12] 74 [13]

Protein (g) [% E] 90 [15] 89 [16] 90 [15]

Saturated fat (g) [%E] 33 [13] 32 [13] 33 [13]

Dietary fibre (g) 16 16 16

Data are mean [%E]. Source: Food Standard Agency and the Department of Health. Summary Report on The National Diet & Nutrition Survey: adults aged 19 to 64 years. Volume 5 (Hoare et al. 2004). %E, percentage of food energy; NMES, non-milk extrinsic sugar

This dietary exchange model aimed to replace two-thirds (66%) of habitual total

carbohydrate intake with study foods (≈ 180 g/day). The remaining balance of 33% of

total carbohydrate was derived from the participants’ habitual diet (usual carbohydrate

foods). Foods were selected from a wide range of supermarket products and divided

by sugar content in low (<10%) and high (>40%) sugar foods. The diets were designed

to be matched for total energy, carbohydrate, fat and protein content and energy.

Target intakes of NMES for the low and high extrinsic sugar diets corresponded to the

lower and upper 2.5th percentile of the intake in males in the two age groups above

mentioned. Those classified as NMES include sugar added to food, table sugar, honey,

glucose and glucose syrups but not the sugar in fruit and lactose (Kelly et al. 2005).

Target percentages of food energy intakes are shown in Table 2.4.

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Table 2.4: Target percentages of food energy intakes on the two dietsLSP HSP

Total carbohydrate (% E) 47 47

NMES (% E) 6 24

Protein (% E) 15 15

Fat (% E) 34 34

Starch/Sugar 6:1 1:1.2

% E, percentage of total energy

2.3 Study procedures

2.3.1 AnthropometricsBody weight, BMI and body fat were measured by electrical bio-impedance, using a

Body Composition Scale (Tanita Body Composition Analyzer BC-418MA).

2.3.2 ApoE genotypeThe apoE genotype was determined at the Clinical Chemistry department at Frimley

Park Hospital by using isoelectric focusing of plasma proteins followed by

immunoblotting. The three isoforms have different isoelectric points due to the

substitution of a single charged amino acid, with apoE2 being the most acidic isoform

and apoE4 the most basic.

2.3.3 Collection of blood samplesBlood samples were taken by venepuncture of an antecubital vein in the forearm, from

all participants after they had fasted overnight for a minimum of 12 hours. The blood

was collected into several different tubes (vacutainers) containing the following

anticoagulants: K2EDTA for the determination of total cholesterol, TG, HDL-cholesterol,

LDL-cholesterol, total apoB and NEFA; fluoride oxalate for plasma glucose, and lithium

89

heparin for plasma insulin. Blood samples were immediately centrifuged (Sorvall

Legend RT Centrifuge, Thermofisher Scientific, Hamphshire, UK) at 2500 rpm

(corresponding to RCF 1439 x g) for 10 minutes at 4˚C in a low speed centrifuge for the

separation of plasma. Aliquots of plasma (0.5 mL) were dispensed into appropriately

labelled cryovials, and stored at -80˚C before analysis. All analyses were completed

within 6 weeks.

2.3.4 Magnetic resonance imaging (MRI) and spectroscopy (MRS) Fat mass (both total and visceral) was determined by MRI, whereas intra-hepatocelluar

lipid (IHCL) was measured by 1H-magnetic resonance spectroscopy (1H-MRS) and the

spectra were acquired using a 1.5 T multinuclear system (Philips Medical Systems,

Best, The Netherlands). Both examinations were conducted at the Robert Steiner, MRI

Unit at the Hammersmith Hospital in London at screening and in the penultimate week

of each dietary intervention. IHCL spectra were acquired from the right lobe of the liver,

using the water signal as an internal reference, as previously described (Thomas et al.

2005). 1H-MRS is a non-invasive, safe, reliable and sensitive method for the detection

of hepatic steatosis, down to a fat percentage (by volume) of less than 5%. It also

provides a more accurate and reproducible estimate of percentage liver fat (with inter

and intra-examination coefficients of variations of 7% and 6% respectively) in

comparison to other methods, such as liver biopsy, which is considered the gold

standard as previously discussed (see section 1.9.2), or ultrasound and CT

(Szczepaniak et al. 2005; Machann et al. 2006; Cowin et al. 2008; Springer et al. 2010).

In order to avoid blood vessels, the gallbladder and other extra-hepatic tissues,

transverse images of the liver have been used to ensure the correct position of the

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8cm3 voxel in the liver. Voxel is the contraction for volume element, which is the basic

unit of MRS reconstruction. The principle of MRS used for the present study was based

on the detection of differences in the signal phases of water and fat, known as the ‘in-

phase/out-of-phase (IP/OP)’ technique (Koplay et al. 2015). 1H-MR spectra were

acquired using a PRESS sequence without water suppression. All the spectra were

analysed by a single trained person using AMARES algorithm included in the MRUI

software package. The content of fat liver is expressed as a ratio to water content.

Participants were asked to fast for 6 hours before the scan.

2.4 Study powerThere are no published data on the measurement of VLDL-TG production rate in

subjects with metabolic syndrome. In a study with non-diabetic men (n=18) with a range

of BMI between 22.4 and 30.1 and liver fat between 1 and 10%, the standard deviation

for VLDL-TG production rate was reported to be 33% of the total VLDL-TG production

rate (mean ± SD: 249 ± 84 mg ∙ kg−1 ∙ day−1) (Adiels et al. 2006). Based on this, with a

data set of 30 men, there was 80% probability that the study would detect a difference

of 26% in VLDL1-TG production rate between the two diets. The main study aimed to

recruit 36 participants with an allowance for a 20% drop-out rate (which was equal to

three participants per intervention arm).

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2.5 Study protocol Participants underwent a clinical study at the CEDAR, after an overnight fast and were

asked to avoid doing vigorous exercise and consume no alcohol for two days before the

study day. An overview of the study protocol is outlined in Figure 2.2.

Figure 2.2: Schematic of the clinical study

All the blood samples taken during the study were immediately transferred into suitable

Vacutainer tubes containing the following coagulants: EDTA (for the lipids), lithium

heparin (for plasma glycerol and insulin) and fluoride oxalate (for glucose). The tubes

were kept on ice, and then centrifuged at 4C for 10 minutes at 2500 rpm

(corresponding to RCF 1439 x g), within 30 minutes of taking the samples. Figure 2.3

shows the tracers used in study protocol and their metabolic fate.

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Figure 2.3: Tracers used in the study protocol and their metabolic fate. DNL, de novo lipogenesis; FA, fatty acid; NEFA non-esterified fatty acids; TG, triacylglycerol; VLDL, very low density lipoprotein

2.5.1 DNLIn the evening prior to the study day, a baseline blood sample was taken and then each

participant was asked to drink a loading dose of 2H2O, half after the evening meal and

half at 10 pm. The amount of heavy water corresponds to 3 g per kg of body water and

aims to produce 0.45% enrichment of body water. This is used to determine the

contribution of DNL to VLDL1 and VLDL2-TG palmitate. From 10 pm in the evening

before the study day onwards subjects were asked to fast until the end of the study and

to drink only water enriched with 2H2O (4.5 g/L) until the following morning, in order to

prevent dilution of labelled water. The following morning at the clinic, a blood sample

93

was taken to measure the plasma water enrichment and VLDL1 and VLDL2-TG

palmitate enrichment.

2.5.2 Palmitate Ra and contribution of systemic palmitate to VLDL-TG and other metabolitesAn intravenous cannula was inserted into the antecubital fossa of one arm for the

administration of isotopes and in the other arm for blood sampling. A baseline blood

sample was taken to measure the baseline VLDL1 and VLDL2-TG glycerol enrichment

and concentrations, plasma glycerol enrichment and concentrations and concentration

of glucose and insulin. At time 0 minutes, a constant intravenous infusion (0.01 µmol ·

kg-1 · min-1) of uniformly labelled U-13C16-palmitate bound to albumin (5%), was

administered with a calibrated IVAC pump for 8 hours. This was carried out for two

purposes. First, to determine the palmitate Ra, which is an index of whole body lipolysis

(the hydrolysis of TG stored in cellular lipid droplets, mainly referring to those within the

adipose tissue, that yields free fatty acids and glycerol) and second, to determine the

contribution of systemic palmitate to the production of VLDL1 and VLDL2-TG, which is

an index of the systemic NEFA contribution to VLDL1 and VLDL2-TG.

2.5.3 VLDL-TG kineticsFurthermore, an intravenous bolus of 2H5-glycerol corresponding to 75 µg per kg of

body weight was administered at time 0 minutes in order to determine the kinetics of

VLDL1 and VLDL2–TG fractions. The constant infusion of U-13C16-palmitate was also

used for this purpose. Blood samples were taken at varying time intervals, depending

on the kinetics of the metabolite, to determine the enrichment and the concentrations of

plasma glycerol and of palmitate and glycerol contained in the lipoprotein fractions.

94

2.6 Laboratory methods All the samples collected during the study day were processes in the laboratory of the

Diabetes and Metabolic Medicine department. The diagram in Figure 2.4 summarise

the main steps for the preparation of glycerol and palmitate samples from plasma and

from VLDL1 and VLDL2 fractions. All these steps will be covered in detail in this section.

Figure 2.4: Preparation and processing of glycerol and palmitate samples from VLDL fractions and plasma. CI, chemical ionisation; EI, electron impact ionisation; GC-MS, gas chromatography-mass spectrometry; TLC, thin layer chromatography

95

2.6.1 Separation of VLDL1 and VLDL2 fractions by ultracentrifugation VLDL1 (Sf 60-400) and VLDL2 (Sf 20-60) were separated by sequential

ultracentrifugation (Beckman Coulter optima LE 80K ultracentrifuge) (James et al.

1990). Three mL of plasma were transferred into an Optiseal tube (Beckman, USA) pre-

coated with polyvinyl alcohol and overlayed with a density 1.006 g/mL solution of

sodium chloride up to 4.5 mL. After ultracentrifugation at 4C, for 66 minutes, at 45 000

rpm, corresponding to average RCF 183 935 x g for the inner row and 218 180 x g for

the outer row of the rotor respectively (Type 50.4 Ti rotor, Beckman, USA), VLDL1

particles were recovered in the upper 1.5 mL volume by tube slicing, which involves

cutting of the upper part of the tube, corresponding to 1.5 cm from the top of the tube.

The blade of the cutting apparatus acts a physical barrier, preventing upper and bottom

fractions to mix. This fraction was transferred into a 7 mL vial and temporarily stored at

4C. The remaining 3 mL volume was then transferred to a fresh Optiseal tube and

again, overlayed with 1.006 g/mL saline so that the total volume to be ultracentrifuged

was 4.5 mL. After ultracentrifugation at 4C, for 16 hours, at 37 000 rpm corresponding

to average RCF 183 935 x g for the inner row and 218 180 x g for the outer row of the

rotor respectively, VLDL2 particles were recovered in the upper 1.5 mL by tube slicing.

As for VLDL1, this fraction was transferred into a 7mL vial and temporarily stored at 4C.

96

2.6.2 Lipid extraction The lipid extraction was based on the Folch method (Folch et al. 1957). VLDL1 and

VLDL2 lipids were extracted with chloroform-methanol 2:1 (volume/volume). The first

lipid extraction was carried out overnight, adding 2 mL and 4 mL (4-fold volume) of

chloroform-methanol 2:1 (volume/volume) solution respectively to 0.5 mL of VLDL1

fraction and 1 mL of VLDL2 fraction. Half a volume of VLDL1 fraction compared to VLDL2

was used in order to avoid overloading of the TLC plate, since VLDL1–TG is much more

abundant than VLDL2-TG. The samples were then spun for 10 minutes at 4C and 2500

rpm (corresponding to RCF 1400 x g) (Centra GP8R, Thermo UK). The infranatant was

transferred into a fresh 10.5 mL vial using a glass Pasteur pipette. The second lipid

extraction was carried out adding a further 2 mL and 1 mL of chloroform-methanol 2:1

(volume/volume) solution respectively to the supernatant of the VLDL1 fraction and the

VLDL2 fraction, and leaving at 4C for 1 hour to settle. Again the samples were

centrifuged for 10 minutes at 4C and 2500 rpm (RCF 1400 x g) and the infranatant

combined with the infranatant from the first extraction. A quarter of the total volume of

chloroform-methanol used, of 0.88% (weight/volume %) potassium chloride was then

added to each vial (750 µL to VLDL1 fractions and 1.5 mL to VLDL2 fractions

respectively). Samples were vortexed and centrifuged for 10 minutes at 4C and 2500

rpm (RCF 1400 x g). The aqueous layer of the solution was discarded and 100 µL of

ethanol were then added to each vial. Samples were dried under oxygen free nitrogen

(OFN) (Parker Filtration Nitroflow) and then reconstituted in chloroform using 200 µL for

VLDL1 and 100 µL VLDL2.

97

2.6.3 Thin layer chromatography (TLC) and hydrolysis of TG The different classes of lipids were then separated on silica gel G60 TLC plates

purchased from Merck (VWR, UK). Samples of extracted lipids were applied as small

bands of 2.5 cm from the bottom of the TLC plate, four samples per plate. Only 100 µL

of the 200 µL of VLDL1 samples were used in order to avoid overloading whereas for

VLDL2 the whole 100 µL were used. After allowing the chloroform to completely

evaporate, the plates were placed into a glass chamber with hexane-diethyl ether-acetic

acid (70:30:2) as the mobile phase. When the front of the solvent travelled to 2.5 cm

from the top of the plates, after about 15-30 minutes from the beginning of the run, the

plates were removed from the chamber to allow the mobile phase to completely

evaporate. The separated lipid classes were visualised by spraying the plates with a

solution of 8-anilo-1-naphtalensulfonic acid (Sigma A1028) in water (100 mg in 100 mL

of distilled water). Plates were left to dry and then placed under UV light in order to

visualise the lipid fraction bands. Figure 2.5 shows the distribution of the different

classes of lipids after separation by TLC and visualisation.

98

Figure 2.5: Separation pattern of different classes of lipids after TLC. CE, cholesteryl esters; DAG, diacylglycerols; FA, free fatty acids; FC, free cholesterol; MAG, monoacylglycerols; PL, phospholipids; TG, triacylglycerol

The bands containing TG were scraped off and hydrolysed overnight in a solution of 1

mL toluene and 2% HCl in methanol (% volume), at 50C, in order to allow the release

of glycerol and lead to the formation of fatty acid methyl esters (FAME). 2 mL of 5%

sodium chloride (weight/volume %) and 3 mL of hexane were added after allowing the

samples to cool. Samples were spun at 2500 rpm (RCF 1400 x g) for 20 minutes. After

centrifugation, the top layer, containing the FAME fraction in hexane, was transferred to

a fresh vial and a further 2 mL of hexane were added for the second extraction. Once

99

again, the samples were spun at 2500 rpm (RCF 1400 x g) for 20 minutes and the top

layer was combined with the top layer from the first extraction. The two sets of vials

containing the fatty acid portion from TG in hexane were left to evaporate until they had

sufficiently decreased in volume. These samples were then transferred into

autosampler vials ready for analysis by GC-MS.

2.6.4 Ion exchange chromatography for glycerol purification The glycerol liberated from hydrolysis of TG, contained in the bottom layer (aqueous

phase), was then purified by ion exchange chromatography. Columns (Evergreen, UK)

were washed with 2 mL of double distilled H2O and then packed with 2 cm AG50W-X8

cation resin (Bio-rad, UK) followed by 2 cm of AG1-X8 anion resin (Bio-rad, UK). The

eluate containing the isolated glycerol was collected in clean glass vials. Once the

whole sample had drained, each column was washed again with 2 mL of double

distilled H2O. Samples were then concentrated by freeze-drying (ModulyoD Freeze

Drier, Thermo Electron Corporation, UK). The same procedure was used to purify the

plasma glycerol samples.

2.6.5 Derivatisation of glycerol The glycerol samples were converted into the triacetate derivative, a more volatile form

which can be analysed by GC-MS (Ackermans et al. 1998). The derivatisation of both

plasma glycerol and glycerol from each fraction was performed by adding 100 µL of

pyridine/acetic anhydride 1/1 (volume/volume) to each sample of freeze dried glycerol.

Samples were then left at room temperature for at least 30 minutes and then dried

under OFN. Samples were then reconstituted in 50 µL of ethyl acetate, vortexed and

transferred into autosampler vials, ready for GC-MS. The triacetate glycerol method,

100

first described by Ackermans presents several advantages. The small size of the

derivative results in a low natural background compared to other derivatives. The use of

chemical ionisation (CI) results in a simple mass spectrum. Furthermore, this method

has the advantages of using relatively cheap reagents and involves a single step and

short reaction times.

2.6.6 Measurement of glycerol enrichment by GC-MS Isotopic enrichment of both plasma and VLDL-TG glycerol was measured on GC-MS

(Agilent 5973 network MSD) (Ackermans et al. 1998; Barrows et al. 2006). The system

was equipped with a 6890N GC fitted with a 30 m, 0.25 mm inner diameter, 0.25 µm

film RTX SMS capillary column (5% phenyl-methylpolysiloxane) (Thames Restek,UK)

and the carrier gas used was helium. 1 µL of sample was injected by splitless injection;

the injector temperature was 75C. The GC temperature was held at 70C for 1 minute

and then increased at 20C/min to 220C. the retention time for glycerol was about 7

minutes and the total run time was 12 minutes. The interface temperature was 175C

and the quadrupole temperature was 150C. The mass spectrometer was operated in

the positive chemical ionization (PCI) mode. Chemical ionisation (CI) is a process in

which the ionisation of compounds requires less energy compared to the electron

impact ionisation (EI). CI is performed in the presence of a large excess of a reagent

gas (methane). This gas is more likely to interact with the electron beam than the

molecule being analysed. The reagent gas molecules are first ionised by collision with

the electron beam and then react with the analyte inducing fragmentation of the sample

molecule yielding sample ions (Figure 2.6).

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Figure 2.6: Derivatisation and fragmentation of glycerol. Derivatisation of glycerol yields the glycerol triacetate derivative which is then fragmented by PCI in GC-MS resulting in the formation of the glycerol fragments m/z 159 (tracee) and 164 (tracer). GC-MS, gas chromatography-mass spectrometry; PCI, positive chemical ionisation

The two ions monitored were those at m/z 159 (ion fragment of triacetyl-glycerol)

representing the tracee and at m/z 164 (ion fragment of triacetyl-2H5-glycerol)

representing the tracer (Figure 2.7). The isotopic enrichment of 2H5-glycerol relative to

unlabelled glycerol in VLDL-TG fractions was measured for each time point as the ratio

(R) between peak area at m/z 164 and peak area at m/z 159. The TTR was then

determined for each time point using the following equation:

TTRt = Rt – R0

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where Rt was the tracer to tracee ratio at a given time point, whereas R0 was the tracer

to tracee ratio at baseline (before administration of the tracer).

Figure 2.7: Selective ion monitoring for ion fragment of triacetyl-glycerol in a typical VLDL-TG sample. At m/z 159 (top), corresponding to the tracee, and ion fragment of triacetyl-2H5-glycerol at m/z 164 (bottom), corresponding to the tracer

2.6.7 Glycerol standard preparation for VLDL-TG fractionsGlycerol standards in the range 0 to 0.035 TTR were prepared using a 25.25 µg/mL

stock solution of glycerol (D0) (Sigma, UK) and 1.47 µg/mL stock solution of 2H5-glycerol

(D5) (Cambridge isotopes, USA) as shown in Table 2.5.

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Table 2.5: Preparation of standard for VLDL-TG glycerol No. D5 (µg) D0 (µg) Theoretical TTR

D5/D0

Observed TTR D5/D0

1 0 7.57 0 0

2 0.04 7.57 0.0058 0.0059

3 0.07 7.57 0.0087 0.0087

4 0.13 7.57 0.0175 0.0167

5 0.21 7.57 0.0272 0.0275

6 0.26 7.57 0.0349 0.0348

D5, 2H5 –glycerol (labelled); D0, unlabelled glycerol

A typical calibration curve is shown in Figure 2.8. The slope of the observed TTR

versus the theoretical TTR was 0.9985.

0.00 0.01 0.02 0.03 0.040.00

0.01

0.02

0.03

0.04

f(x) = 0.998490338748383 x − 4.54853654431753E-05R² = 0.999261586148384

Theoretical TTR

Obs

erve

d TT

R

Figure 2.8: Glycerol standard curve for VLDL-TG fractions. Calibration graph showing the ratio of labelled (D5) to unlabelled glycerol (D0). Results are mean ± SEM (n=3)

2.6.8 Preparation of plasma glycerol samples Blood samples were collected in chilled tubes containing 6.8 IU lithium heparin and

separated by centrifugation immediately after collection (see section 2.5). The plasma

was then transferred into new vials and stored at -20 C. Samples were further

processed in the laboratory of the Diabetes and Metabolic Medicine department. A 0.5

104

mL aliquot of each sample was deproteinized with 1 mL of 3.5 % (w/v) sulphosalicylic

acid in deionised water for 30 minutes at 4C. The supernatant was collected after

centrifugation at 2500 rpm (RCF 1400 x g), for 20 minutes, at 4C and then purified by

ion–exchange chromatography (see section 2.6.4), derivatised (see section 2.6.5) and

analysed by GC-MS (see section 2.6.6). Figure 2.9 shows a typical chromatogram

obtained from a plasma glycerol sample.

Figure 2.9: Selective ion monitoring for ion fragment of triacetyl-glycerol in a typical plasma glycerol sample. At m/z 159 (top), corresponding to the tracee, and ion fragment of triacetyl-2H5-glycerol at m/z 164 (bottom), corresponding to the tracer

105

2.6.9 Glycerol standard preparation for plasma glycerolGlycerol standards in the range 0 to 0.1 TTR were prepared using a 25.25 µg/mL stock

solution of glycerol (Sigma, UK) and 117.6 µg/mL stock solution of 2H5 –glycerol

(Cambridge isotopes, USA) as shown in Table 2.6.

Table 2.6: Preparation of standard for plasma glycerol No. D5 (µg) D0 (µg) Theoretical TTR

D5/D0

Observed TTR D5/D0

1 0 3.79 0 0

2 0.08 3.79 0.0217 0.0198

3 0.21 3.79 0.0543 0.0485

4 1.18 3.79 0.3105 0.3189

5 1.76 3.79 0.4657 0.4789

6 2.59 3.79 0.6831 0.6873

7 4.12 3.81 1.0795 1.0773

D5, 2H5 –glycerol; D0, unlabelled glycerol



-0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.20.0

0.2

0.4

0.6

0.8

1.0

1.2

f(x) = 1.00276806082569 x + 0.00133667710499891R² = 0.999734002455594

Theoretical TTR

Obs

erve

d TT

R

Figure 2.10: Glycerol standard curve for plasma glycerol. Calibration graph showing the ratio of labelled (D5) to unlabelled glycerol (D0). Results are mean ± SEM (n=5)

106

2.6.10 Measurement of palmitate enrichment from VLDL-TG fractions by GC-MS The isotopic enrichment of the palmitate methyl ester (PAME) resulting from the

hydrolysis of VLDL-TG fractions, was measured by electron impact (EI) GC-MS.

Samples were analysed with a GC-MS system (5975 inert XL EI/CI mass selective

detector, Agilent Technologies, Berkshire, UK). Two µL samples were injected into the

column by splitless injection mode (7683 auto-sampler, Agilent, UK) and the injector

temperature was set at 250C. The GC was equipped with a 30 m, 0.25 µm inner

diameter HP 1MS capillary column (100% dimethylpolysiloxane) (J and W Scientific

Inc., USA). Helium was used as carrier gas and the flow rate was kept at 1 mL/ min.

The GC oven temperature was held at 100C for 1 minute and then increased at

25C/min to a final temperature of 300C. The retention time for palmitate was about

14.5 minutes and the total GC run time was 20 minutes. Ions were monitored in EI

mode and U-13C16 palmitate enrichment was determined by selectively monitoring ions

at m/z 286.3 (ion fragment of U13C16-palmitate) representing the tracer, and m/z 270.3

(ion fragment of unlabelled palmitate) representing the tracee (Figure 2.11). The area

under the peak was measured by auto-integration.

107

Figure 2.11: Selective ion monitoring ion fragment of PAME in a typical VLDL-TG sample. At m/z 270.3 (top), corresponding to the tracee, and for ion fragment of U13C16-palmitate at m/z 286.3 (bottom), corresponding to the tracer. PAME, palmitate methyl ester

The isotopic enrichment of U-13C16 palmitate relative to unlabelled palmitate in the

VLDL-TG fractions was measured for each time point as the ratio (R) between peak

areas of ion fragments with m/z 286.3 and m/z 270.3. The TTR was then determined for

each time point using the following equation:

TTRt = Rt – R0

where Rt is the tracer to tracee ratio at a given time point, whereas R0 was the tracer to

tracee ratio at baseline (before administration of the tracer).

108

2.6.11 Palmitate standard preparation for VLDL-TG fractionsPalmitate standards in the range 0 to 0.027 TTR were prepared using a 24.55 µg/mL

stock solution of sodium palmitate (Sigma, UK) and 1.22 µg/mL stock solution of U-

13C16-palmitate (Cambridge isotopes, USA) as shown in Table 2.7.

Table 2.7: Preparation of standard for VLDL-TG palmitate samples No. Tracer

U13C16 (µg)Tracee

12C16 (µg)Theoretical TTR

U13C16/12C16

Observed TTR U13C16/12C16

1 0 7.365 0 0

2 0.037 7.365 0.005 0.005

3 0.055 7.365 0.0075 0.0073

4 0.073 7.365 0.01 0.01

5 0.11 7.365 0.0149 0.0146

6 0.147 7.365 0.0199 0.0201

7 0.196 7.365 0.0266 0.0263

U13C16, uniformly labelled palmitate; 12C16, unlabelled palmitate



0.00 0.01 0.02 0.030.00

0.01

0.02

0.03

f(x) = 0.999014515668645 x − 6.97295592686237E-05R² = 0.999200988828776

Theoretical TTR

Obs

erve

d TT

R

Figure 2.12: Palmitate standard curve for VLDL-TG fractions. Calibration graph showing the ratio of labelled (U13C16) to unlabelled palmitate (12C16). Results are mean ± SEM (n=3)

109

2.6.12 Preparation of plasma palmitate samplesFAME samples were prepared from NEFA in plasma samples. Heptadecanoic acid in

heptane (50µg/mL) was used as an internal standard. Plasma samples were processed

in 3 mL glass vials by adding 250 µL of plasma to 250 µL of heptadecanoic acid and

then gently shaken on horizontal platform vortexer (Shaker orbit 1000, Labnet

international Inc., UK) at 150 rpm speed for 3 minutes. 3 mL of ice cold acetone were

then added and vortexed vigorously on a multi-tube vortexer (VX-2500, Henry

Troemrer, USA) at speed 3 for 10 seconds and then incubated at -20C for 15 minutes,

in order to allow protein precipitation. Samples were then centrifuged 2500 rpm (RCF

1400 x g) for 10 minutes and the supernatant transferred to new 10 mL vials. After

adding 3 mL of hexane and 3 mL of water, vials were gently shaken on the orbital

shaker at 150 rpm speed for 15 minutes and centrifuged at 2500 rpm (RCF 1400 x g)

for 10 minutes to separate the solvent (supernatant) and the aqueous phase. The

supernatant was then transferred into new tubes and the solvent was evaporated under

OFN. First 250 µL of phosphate buffer and then 250 µL of 1:10 (volume/volume)

iodomethane in dichloromethane were added and the vials were vigorously shaken on

the multitube vortexer (speed 3, 1 hour). Three mL of hexane were then added into the

vials before vigorously mixing on the multi-tube vortexer for 30 minutes and centrifuged

at 2500 rpm (RCF 1400 x g) for 10 minutes. The supernatant containing the FAME

fraction was then transferred into fresh tubes and concentrated under OFN to 1.5 mL.

A solid phase extraction (SPE) system was set on a 12-port SPE vacuum manifold

(Restek Corporation, USA) by using SPE cartridges (LC-Si, 3 mL size, Supelco, USA).

2% ethyl acetate in hexane (volume/volume) was used as an elution solution. The SPE

system was washed using an elution solution before each sample was loaded and run

through the column. Each FAME sample was collected in fresh tubes and eluted twice

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with 1.5 mL elution solution each time. The solvent was the evaporated under OFN and

samples were reconstituted with 100 µL hexane and transferred into GC-MS

autosampler vials (0.2 mL, Chromacol Ltd, UK). The isotopic enrichment of U-13C16-

palmitate relative to unlabelled palmitate in the plasma samples (Figure 2.13), was

measured by electron impact (EI) GC-MS using the same procedure used for PAME

resulting from the hydrolysis of VLDL-TG fractions (see section 2.6.10).

Figure 2.13: Selective ion monitoring ion fragment of PAME in a typical plasma palmitate sample. At m/z 270.3 (top), corresponding to the tracee, and for ion fragment of U13C16-palmitate at m/z 286.3 (bottom), corresponding to the tracer. PAME, palmitate methyl ester

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2.6.13 Palmitate standard preparation for plasma palmitatePalmitate standard in the range 0 to 0.009 TTR were prepared using a 30 µg/mL stock

solution of sodium palmitate (Sigma, UK) and 2 µg/mL stock solution of U-13C16-

palmitate (Cambridge isotopes, USA) as shown in Table 2.8.

Table 2.8: Preparation of standards for plasma palmitate No. Tracer

U13C16 (µg)Tracee

12C16 (µg)Theoretical TTR

U13C16/12C16

Observed TTR U13C16/12C16

1 0 7.47 0 0

2 0.003 7.47 0.00053 0.00065

3 0.007 7.47 0.00106 0.00119

4 0.015 7.47 0.00212 0.00221

5 0.023 7.47 0.00319 0.00335

6 0.031 7.47 0.00425 0.00437

7 0.039 7.47 0.00532 0.00539

8 0.047 7.47 0.00638 0.00649

9 0.063 7.47 0.00851 0.00871

U13C16, uniformly labelled palmitate; 12C16, unlabelled palmitate


versus the theoretical TTR was 1.0113 and the linearity was 0.9997 (n=5).

0.000 0.002 0.004 0.006 0.0080.000

0.002

0.004

0.006

0.008 f(x) = 1.0106250104755 x + 7.40888523875767E-05R² = 0.999737101773162

Theoretical TTR

Obs

erve

d TT

R

Figure 2.14: Palmitate standard curve for VLDL-TG fractions. Calibration graph showing the ratio of labelled (U13C16) to unlabelled palmitate (12C16). Results are mean ± SEM (n=5)

112

In order to assess the reliability of the derivatisation and the GC-MS performance, QCs

were prepared using U-13C16 palmitate, 1.5 µmol/mL of 5% human albumin mixed with

human plasma. The CV of the QC was 2.4% (n=10). Furthermore, RM6, a mixture of

FAME (AOCS std 6, Thames Restek Ltd, UK) was run in every assay in order to test

the GC-MS machine reliability and the background enrichment. The TTR from this

standard was expected to be close to zero. Intra-assay and inter-assay CVs were 5.4%

and 8.9% respectively. A different standard was prepared in order to determine the

palmitate concentration. This was made using a 30 µg/mL stock solution of sodium

palmitate (C16) (Sigma, UK) and a 7.6 µg/mL stock solution of heptadecanoic acid (C17)

(Sigma, UK) as an internal standard, as shown in Table 2.9.

Table 2.9: Preparation of standard for measuring plasma palmitate concentrations No. C16 (µg) C17 (µg) Theoretical µg ratio

C16/ C17

Observed µg ratio C16/ C17

1 0 7.6 0 0

2 2.988 7.6 0.39 0.47

3 5.976 7.6 0.78 0.77

4 8.964 7.6 1.17 1.05

5 11.952 7.6 1.57 1.44

6 14.94 7.6 1.96 1.85

7 29.88 7.6 3.92 3.64

C16, palmitate; C17, heptadecanoic acid (internal standard)

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A typical standard curve is shown in Figure 2.15. The slope of the observed versus the

theoretical palmitate to heptadecanoic acid µg ratio was 0.9122.

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.00.00.51.01.52.02.53.03.54.0

f(x) = 0.917201718193636 x + 0.034370739840615R² = 0.998523811445569

Theoretical µg ratio

Obs

erve

d µg

ratio

Figure 2.15: Palmitate standard curve for plasma palmitate concentration. Calibration graph showing the ratio of palmitate (C16) to heptadecanoic acid (C17). Results are mean ± SEM (n=5)

Peak areas of the ion fragment at m/z 270.3 (PAME) relative to peak areas of the ion

fragment at m/z 284.3 (heptadecanoic acid methyl ester) (Figure 2.16) were used to

calculate the plasma palmitate concentration at steady state.

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Figure 2.16: Total ion chromatogram for palmitate concentration. This ion chromatogram shows the palmitate peak at m/z 270.3 (left) relative to the internal standard heptadecanoic acid peak at m/z 284.3 (right)

2.6.14 Measurement of DNLVLDL1 and VLDL2-TG samples were obtained prior to the ingestion of heavy water the

day before the study, and at time -10 and 0 on the study day (see section 2.5). The

PAME from VLDL-TG were prepared as described in section 3.3. The isotopic

enrichment of the PAME resulting from the hydrolysis of the VLDL-TG fraction, was

measured by electron impact (EI) GC-MS, by selectively monitoring ions at m/z 271 (ion

fragment of palmitate in which a deuterium from 2H2O has been incorporated)

representing the newly synthetized palmitate, and m/z 270.3 (ion fragment of unlabelled

palmitate) representing the tracee. The deuterium enrichment that would have been

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obtained if DNL were the only source of fatty acids for VLDL-TG was determined from

the plasma water enrichment. By comparing the observed isotopic enrichment values

with the theoretical values, it is possible to determine the fractional synthesis of VLDL-

TG, which represents a measure of the percent contribution of palmitate derived from

hepatic DNL.

2.6.15 Palmitate standard preparation for DNLPalmitate standards in the range 0 to 0.008 TTR were prepared using a 51.28 µg/mL

(0.2 mM) stock solution of unlabelled sodium palmitate (C16) (Sigma, UK) and 0.257

µg/mL (0.001 mM) stock solution of d1-C16-palmitate (in which deuterium substitutes

only one hydrogen in position 1) (Cambridge isotopes, USA) as shown in Table 2.10.

The observed TTR for each standard was obtained by subtracting the tracer to tracee

ratio of standard No. 1 (in which no d1-C16 was added) from the observed tracer to

tracee ratio in order to account for the background natural enrichment of deuterium in

the unlabelled palmitate.

Table 2.10: Palmitate standards for DNL samples

No.Tracer

d1-C16 (µg)TraceeC16 (µg)

Theoretical TTRd1-C16/C16

Observed TTRd1-C16/C16

1 0 10.26 0 0

2 0.005 10.26 0.000487 0.000803

3 0.013 10.26 0.001267 0.001528

4 0.023 10.26 0.002242 0.002199

5 0.036 10.26 0.003509 0.003675

6 0.051 10.26 0.004971 0.004592

7 0.077 10.26 0.007505 0.007857

d1-C16, labelled palmitate (one deuterium in position 1); C16, unlabelled palmitate

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0.000 0.002 0.004 0.006 0.0080.000

0.002

0.004

0.006

0.008f(x) = 0.995625586177347 x + 0.000108620753688772R² = 0.990756808795278

Theoretical TTR

Obs

erve

d TT

R

Figure 2.17: Palmitate standard curve for DNL samples. Calibration graph showing the ratio of labelled (d1-C16) to unlabelled palmitate (C16). Results are mean ± SEM (n=5)

2.6.16 Measurement of plasma water enrichmentPlasma samples were analysed in duplicate for 2H2O enrichment with a Gasbench II

inlet system and isotope ratio mass spectrometer. 2H2 enrichment was measured using

a platinum catalyst rod. The sample tubes were capped and flushed (100 mL/min) with

the equilibration gas, 5% H2 in helium, and incubated for a minimum of 40 minutes at

22.5oC. Isotopic enrichment was measured relative to laboratory standards previously

calibrated against international standards Vienna Standard Mean Ocean Water and

Standard Light Arctic Precipitation (International Atomic Energy Agency, Vienna,

Austria).

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2.6.17 Measurement of metabolite concentration in plasma and fractionAssays for plasma TG, VLDL1 and VLDL2-TG, total cholesterol, HDL-cholesterol, apoB,

NEFA, 3-hydroxybutyrate (3-OHB) and glucose were performed on a COBAS Mira

auto-analyser (Roche Diagnostics, USA). Baseline (at time 0 hour on study day)

samples were used for glucose and insulin. All the other measurements were the

average of three time points: 0, 5 and 10 hour from the beginning of infusion protocol on

the study day. Pre-calibrations for each assay and low and high quality controls (QCs)

were included within each test. Assays were only performed if the QC values were

within acceptable confidence limits, as defined by the manufacturer. All samples (pre

and post-dietary interventions were analysed within a single batch. Intra-assay CVs

(within-run precision) were calculated using six replicates.

2.6.17.1 Total, VLDL1 and VLDL2-TG

A colorimetric enzymatic assay was used to determine the concentration of total

plasma, VLDL1 and VLDL2-TG concentrations (Triglyceride CP, kit ref: A11A01640;

Horiba ABX, France). The absorbance of the quinoneimine pigment was measured

bichromatically at 500 nm wavelength. Intra-assay precision gave CVs for N (low) and P

(high) QCs of 3.0% and 5.9%, respectively.

2.6.17.2 Total, VLDL1 and VLDL2 cholesterol

Total, VLDL1 and VLDL2 cholesterol was measured using a colorimetric/enzymatic

photometric method using a colorimetric indicator (Kit ref: A11A01634; Horiba ABX,

France). The absorbance of the quinoneimine pigment was measured bichromatically at

500 nm wavelength. The intra-assay CVs were 9.0% and 2.5% for N (low) and P (high)

QCs, respectively.

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2.6.17.3 HDL cholesterol

The method used to measure plasma HDL cholesterol was based on accelerating the

reaction of cholesterol oxidase with non-HDL unesterified cholesterol and dissolving

HDL selectively using a specific detergent called ‘Accelerator Selective Detergent’ (Kit

ref: A11A01636; Horiba ABX, UK). The intensity of the colour development was

measured at a wavelength of 600 nm. The within assay CVs were 12.9% and 3.5% for

low and high quality controls, respectively.

2.6.17.4 LDL cholesterol

Plasma LDL-C was not measured directly on isolated LDL, but was calculated by the

Friedewald Equation: (LDL-C = [TC] – [HDL-C] – ([TG] / 2.2)) (Friedewald et al. 1972).

2.6.17.5 Total apoB

Plasma total apoB was measured by immunoturbidimetry (Kit ref: A11A01688; Horiba

ABX, UK). The principle of this technique is based on the relationship of direct

proportionality between the degree of turbidity produced by the immunoprecipitation of

apoB and plasma apoB concentration. The intra-assay CVs gave 1.5% and 2.4% for

the low and high QCs, respectively.

2.6.17.6 VLDL1 and VLDL2 apoB

VLDL1 and VLDL2 apoB concentrations were determined by an in-house assay. The

VLDL1 and VLDL2 fractions were incubated in a 96 well ELISA plate coated with the

primary antibody, a polyclonal anti apoB. The excess was washed off and the captured

apoB was then sandwiched between the primary and the secondary antibody, a

biotinylated monoclonal antibody (4G3, specific to apoB100). This was then incubated

119

with streptavidin-alkaline phosphatase followed by a wash procedure. Substrate was

added and the colour produced was read at 540nm in the plate reader.

2.6.17.7 Plasma NEFA

The concentration of plasma NEFA was determined by a colorimetric/enzymatic assay

(Kit ref: 0207 D4; Alpha Laboratories Ltd, UK). The intensity of the red pigment was

directly proportional to the concentration of NEFA in the sample. Ascorbic acid was

removed by ascorbic oxidase from the sample, and the intensity of colour was

measured at a wavelength of 550 nm. The intra-assay precision, which was tested

using Control Serum 1 (Alpha Laboratories Ltd, Eastleigh, UK), produced a CV of 0.6%.

2.6.17.8 3-hydroxybutirate (3-OHB)

The concentration of 3-OHB by an enzymatic assay (oxidation of 3-OHB to

acetoacetate) (Kit ref: RB1007; Randox Laboratories Ltd, UK). The change in

absorbance was determined at wavelength of 340 nm.

2.6.17.9 Glucose

Plasma glucose concentration was determined enzymatically using the Trinder method

(Trinder 1969) with the presence of glucose oxidase (Kit ref: A11A01668; Horiba ABX,

UK). The absorbance of quinoneimine dye was quantified bichromatically at a

wavelength of 500 nm. Intra-assay CVs were 4.3% and 5.9% for low and high QCs,

respectively.

2.6.17.10 Insulin

Plasma insulin after each dietary intervention was measured by radioimmunoassay

(RIA). This had four basic requirements; a specific antiserum to the antigen to be

measured, the availability of a radioactive labelled form of the antigen, a method

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whereby antibody-bound tracer can be separated from the unbound tracer, and finally,

the RIA to count radioactivity (Kit ref: HI-1AK; Merck Millipore, MA, USA). A standard

curve was set up. The Millipore Human Insulin assay (Merck Millipore, MA, USA)

utilised 125I-labelled human Insulin and human Insulin antiserum to determine the level

of insulin in plasma using the double antibody/PEG technique (Desbuquois et al. 1971).

2.7 Data analysis

2.7.1 Multi-compartmental model to determine VLDL-TG kinetics

2.7.1.1 Glycerol enrichment data

The enrichment data of plasma free glycerol and of glycerol derived from VLDL1 and

VLDL2 TG was used to determine the lipoprotein kinetic parameters using the modelling

software SAAM II (SAAM Institute, Seattle, WA, USA). A compartmental model

developed by Dr Roman Hovorka (University of Cambridge, UK) was used to obtain the

best fit curves for VLDL1 and VLDL2-TG and all the kinetic parameters. This model also

allows simultaneous modelling of apoB and TG kinetics. This model is based on the

model originally described by Adiels et al (Adiels et al. 2005). Figure 2.18 shows a

schematic diagram of the compartmental model used to determine the turnover kinetics

of VLDL1 and VLDL2-TG.

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Figure 2.18: Compartmental model for the kinetics of VLDL1 and VLDL2-TG by using stable isotopically labelled glycerol. The glycerol tracer is injected into plasma, taken up by the liver and incorporated into VLDL1 and VLDL2-TG. The delay (due to compartments 3 and 4) represents the time after which the label appears in VLDL1 and VLDL2-TG

The model was used to determine VLDL1 and VLDL2-TG kinetic parameters obtained

from the measurements of the enrichment of free glycerol in plasma and glycerol in TG

in VLDL1 and VLDL2 fractions. The model is based on the assumption that a steady

state is maintained throughout the experimental period, hence constant rates of

appearance, disappearance and incorporation of glycerol into the TG pool. This model

also takes into account the intracellular dilution of plasma 2H5-glycerol enrichment and

includes a short and long delay in the system when incorporating glycerol into VLDL-TG

in the liver. Pools 1 and 2 describe the plasma glycerol kinetics. Compartments 3 and 4

within the liver take into account the delay in hepatic TG synthesis and incorporation in

VLDL particles (compartment 5). VLDL can be secreted into plasma as TG-poor VLDL2

or, after undergoing further lipidation, as TG-rich VLDL1 (compartments 6 and 7) (see

section 1.4). The TG in VLDL1 can be hydrolysed and removed from circulation (k 06) or

122

can be retained in the smaller VLDL2 particles. Again, the TG in VLDL2 can be

hydrolysed and removed from circulation or can be retained in the smaller particles with

k 07 representing the sum of both metabolic fates. Rate constants k 06 and k 07

represents the fractional catabolic rate (FCR) of VLDL1 and VLDL2-TG respectively, and

are expressed as pools/day. These constants are the main outcome of the modelling

and were used to determine all the other kinetic parameters as described below, in

section 2.7.2.1.

2.7.1.2 Palmitate enrichment data

A similar multi-compartmental model to the one described above was used to determine

VLDL1 and VLDL2-TG kinetics by modelling plasma pamitate and VLDL1 and VLDL2 TG

palmitate enrichment data. In this case U-13C16-palmitate was given as a constant

infusion, whereas 2H5-glycerol was administered as a single bolus, as shown in section

2.5.

2.7.2 Calculations

2.7.2.1 Kinetic parameters for VLDL1 and VLDL2-TG

The model represents the kinetics of the TTR profile, which changes as the tracer

(either 2H5-glycerol or U-13C16-palmitate) is removed from plasma and incorporated into

VLDL-TG fractions. The assumption is that a steady state of the native glycerol and

palmitate is held throughout the experimental period, which means that there is a

constant incorporation rate of these two in the VLDL-TG fractions. As a consequence

the FCR equals the fractional synthetic rate (FSR).

VLDL1 and VLDL2-TG pools were calculated from VLDL1 and VLDL2-TG concentration

and the plasma volume, which in turn was determined by using the method of Pearson

123

et al. (Pearson et al. 1995). The model identifies the amount of VLDL1-TG FCR which is

catabolised and the amount which is transferred to VLDL2-TG.

VLDL1-TG catabolism is expressed as pools/day.

VLDL1-TG transfer to VLDL2-TG is the number of pools per day of VLDL1-TG that are

converted into VLDL2-TG.

VLDL1-TG production rate (PR), is the input of TG into the VLDL1-TG fraction from the

liver expressed as mg/day and calculated as follows:

VLDL1-TG PR = VLDL1 FCR x VLDL1-TG pool

VLDL1-TG removal is expressed as mg/day and is calculated as:

VLDL1-TG removal = VLDL1-TG PR – VLDL2-TG PR-from VLDL1

VLDL2-TG PR is the total input of VLDL2-TG which is given by:

VLDL2-TG PR = VLDL2-TG PR-from liver + VLDL2-TG PR-from VLDL1

Total VLDL-TG PR was calculated as:

Total VLDL-TG PR = VLDL1-TG PR + VLDL2-TG PR-from liver

2.7.2.2 Contribution of systemic NEFA

The contribution of circulating palmitate to VLDL1 and VLDL2-TG represents a measure

of systemic NEFA contribution, since it is assumed that it is representative of all plasma

fatty acids with regard to turnover (Wolfe 1992). Due to the constant infusion of (U-13C)

palmitate, it was assumed that between 360 and 480 minutes from the beginning of the

infusion the equilibrium between tracer and tracee had been reached. Therefore, the

124

percentage of systemically derived VLDL1 and VLDL2-TG palmitate was calculated in

the following way:

%Systemic VLDL-TG palmitate = (TTR VLDL-TG palmitate/TTR plasma NEFA palmitate) x 100

where TTR VLDL-TG palmitate is the mean enrichment of VLDL1 or VLDL2-TG

palmitate between 360 and 480 minutes from the beginning of the infusion, and TTR

plasma NEFA palmitate is the mean plasma palmitate enrichment between 360 and

480 minutes. Therefore, the absolute rate of VLDL1 and VLDL2-TG palmitate synthesis

derived from systemic NEFA can be determined as follows:

Systemic VLDL-TG palmitate PR = (%Systemic VLDL-TG palmitate x VLDL-TG PR)/100

2.7.2.3 Contribution of DNL

If all VLDL-TG fatty acids are derived from DNL, the enrichment in TG-palmitate

(Maximum palmitate TTR) will be equal to:

Maximum palmitate TTR = 2H2O TTR x N

where 2H2O TTR is the enrichment of the plasma water, and N is the maximum

number of deuterium atoms that can be incorporated into a molecule of palmitate. In

the present study N was 21, based on previous observations (see section 1.11.3)

(Diraison et al. 1996). The percentage of palmitate derived from DNL in VLDL-TG is

calculated as follows:

%DNL VLDL-TG palmitate = (VLDL-TG palmitate TTR / maximum palmitate TTR) x 100

125

Therefore, the absolute rate of VLDL-TG palmitate synthesis derived from DNL can be

determined as follows:

DNL VLDL-TG palmitate PR = (%DNL VLDL-TG palmitate x VLDL-TG PR) / 100

2.7.2.4 Contribution of other splanchnic sources

The splanchnic percentage was assumed to be all other sources of fatty acids except

those derived from DNL and systemic NEFA:

%Splanchnic VLDL-TG palmitate = 100 - %Systemic VLDL-TG palmitate - %DNL VLDL-TG palmitate

As for the systemic and DNL, the splanchnic derived VLDL-TG palmitate synthesis is

calculated as follows:

Splanchnic VLDL-TG palmitate PR = (%Splanchnic VLDL-TG palmitate x VLDL-TG PR) / 100

2.7.2.5 Palmitate kinetics

It was assumed that an isotopic steady state had been reached between 90 and 120

minutes after the beginning of U-13C16-palmitate constant infusion. The rate of

appearance (Ra) of palmitate, which at steady state equals the rate of disappearance

(Rd), was calculated as follows:

Palmitate Ra = Palmitate Rd = F / TTR

where F is the infusion rate of U-13C16-palmitate. The metabolic clearance rate (MCR)

was then calculated as:

Palmitate MCR = Palmitate Rd / Palmitate concentration

126

The percentage of systemic NEFA converted into VLDL-TG palmitate was determined

as follow:

% systemic NEFA in VLDL-TG = Systemic VLDL-TG palmitate PR / Palmitate Ra

2.8 Statistical methodsAll the statistic tests were performed using SPSS 22 (SPSS Inc.; Chicago USA) unless

otherwise specified. The distributions of all data were examined by performing the

Kolmogorov-Smirnov test. Normally distributed variables are presented as mean ±

SEM. These data were analysed using parametric tests. Non-normally distributed

variables are presented as median and interquartile range (IQR). For most of this data,

log transformation was not sufficient to normalise the distribution. Therefore, these

results were analysed by using nonparametric tests.

2.8.1 Parametric tests To compare repeated measurements after the two dietary interventions (HSP vs

LSP) paired-samples two-tailed t test was performed.

To test for differences between the two independent liver fat groups (HLF vs

LLF) independent samples two-tailed t test was performed.

To determine whether the effects of diet were different in the two liver fat groups

an independent samples t test on the differences between diets for the two

groups was carried out.

Testing for associations between variables was carried out by Pearson

correlation analysis.

127

Effect size statistic for normally distributed variables was determined by

calculating manually the eta squared () value for each pair of measurement

and interpreting it using Cohen guidelines (Cohen 1988).

Testing for outliers was carried out by performing Grubbs' test (QuickCalcs

online calculator, GraphPad Software) for some of the most important variable.

Post hoc power analysis was performed by using G*Power 3.1 calculator

(available at http://www.gpower.hhu.de/en.html) for total VLDL-TG PR only.

2.8.2 Non-parametric tests To compare repeated measurements after the two dietary interventions (HSP vs

LSP) Wilcoxon signed ranks test (non-parametric alternative to paired-samples

two-tailed t test) was performed.

To test for differences between the two independent liver fat groups (HLF vs

LLF) Mann-Whitney U test (non-parametric alternative to the t test for

independent samples) was performed.

To determine whether the effects of diet were different in the two liver fat groups

Mann-Whitney U test on the differences between diets for the two groups was

carried out.

Testing for associations between variables was carried out by Spearman rank

correlation analysis.

Effect size statistic for non-normally distributed variables was determined by

calculating manually the r value (not to be confused with r, which is the

coefficient correlation of Pearson) for each pair of measurement and interpreting

it using Cohen guidelines (Cohen 1988).

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Testing for outliers was carried out by performing the Interquartile method for

some of the most important variable: outliers were values < 1st Quartile – 1.5 x

IQR and > 3rd Quartile + 1.5 x IQR.

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Chapter 3: Dietary intake, liver fat, plasma TG and lipoprotein concentrations

3.1 IntroductionEpidemiological and clinical studies suggest plausible mechanisms supporting a role for

sugar in the epidemics of metabolic syndrome, CVD and T2DM. An excessive intake of

dietary sugar can increase plasma TG levels hence increasing the cardiometabolic risk

through adverse changes in plasma lipoproteins, known collectively as an atherogenic

lipoprotein phenotype (ALP) (Te Morenga et al. 2014). Sugar may exert this effect

either directly by altering TG metabolism and/or indirectly by delivering excess energy

and increasing body weight. However, the extent to which the adverse metabolic effects

of dietary sugar consumption result from direct effects of fructose on lipid and

carbohydrate metabolism (discussed in section 1.10), to indirect effects resulting from

increased body weight and adiposity, or to direct metabolic actions that are exacerbated

by weight gain, has not been determined. The most relevant ectopic fat in this respect is

liver fat, since this provides a direct mechanistic link between the impact of sugar on

plasma TG and lipoproteins. While the relative expression of these alternate pathways

may depend upon the amount, type and form of sugar, it may also depend on the

propensity of an individual to gain weight and accumulate ectopic fat.

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3.2 AimsThe aim of the present study was to examine the effect of two different isocaloric diets


energy, which differed in their content of NMES and thus total sugar (see section 2.2)

on plasma lipid and lipoprotein levels and IHCL in men at increased risk of developing

metabolic syndrome. The main end-points of this chapter of results are to compare the

levels of liver fat at the end of each dietary intervention and also to look at the effect of

two diets on plasma TG levels and VLDL-TG levels and composition.

3.3 MethodsThe study consisted of a randomised dietary intervention with a cross-over design as

shown in Figure 2.1. Exclusion criteria were diabetes and any diseases other than

NAFLD, lipid-lowering medication, unstable weight in the preceding 3 months, and an

intake of alcohol exceeding 20g/day. Men were screened for raised cardio-metabolic

risk, as assessed by calculation of a risk score used previously in the ‘RISCK’ study

(Jebb et al. 2010). Inclusion and exclusion criteria for the present study and the study

design have been outlined in section 2.1 and 2.2 respectively. The criteria used for the

present study to identify those subjects at increased risk were based on NCEP ATP-III

guidelines (discussed in section 2.1). Those with a raised metabolic score of ≥4 and

APO E3/E3 genotype, to exclude the confounding effects of E2 and E4 isoforms on lipid

metabolism, underwent an assessment of intra-hepatocellular lipid (IHCL) by magnetic

resonance spectroscopy (MRS) for assignment to high liver fat (IHCL >5%, n=11) or

low liver fat group (<5% IHCL, n=14). Participants were then randomised in a two-way

cross-over design with two 12-week dietary phases. After an initial 4 week run-in period

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on their habitual diet, participants were randomly assigned to either the low or the high

sugar phase (LSP and HSP respectively). Participants returned to their habitual diet for

4 weeks, before crossing-over to the alternative diet for a further 12 weeks.

Intakes for carbohydrate (CHO) and sugar were based on the mean intakes for men

aged 40 to 65 years in the UK’s National Diet & Nutrition Survey (NDNS), with target

intakes for non-milk extrinsic sugars (NMES) on the high and low sugar diets

corresponding to the upper and lower 2.5th percentile of intake in the UK population,

respectively. The dietary exchange model and target percentages of food energy

intakes on the two diets are discussed in section 2.2. Anthropometric (weight, BMI and

body fat mass) were measured by electrical bio-impedance, using a Body Composition

Scale (see section 2.2.1). Metabolite concentrations measurements at baseline and

after each of the two dietary interventions are discussed in section 2.6.17. Fat mass

(both total and visceral) was determined by MRI, whereas intra-hepatocelluar lipid

(IHCL) was measured by 1H-magnetic resonance spectroscopy (1H-MRS) (see section

2.3.4). These were determined at baseline for all the participants and at the end of each

dietary intervention for 17 of 25 participants only.

3.4 Results

3.4.1 Subjects characteristicsA summary of the screening and of subsequent allocation into the two liver fat groups is

shown in Figure 3.1. Sixty eligible male volunteers were booked for a screening visit at

the CEDAR Centre. Of these, 20 were not E3 homozygous, 2 did not do the apoE3

genotype test. Eight volunteers were excluded after anthropometry/biochemistry visit

(see details in Figure 3.1). The remaining 30 were scanned for their percentage of liver

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fat. Following an MRS scan performed at the Hammersmith Hospital in London, the

subjects were divided in two groups: low (< 5%) and high (> 5% and < 40%) liver fat,

(LLF and HLF respectively). One participant dropped-out from the study during the first

dietary intervention due to personal reason, leaving 25 completers. Therefore, although

the study aimed to recruit 36 participants, allowing for a 20% drop-out rate (3

participants per intervention arm), in fact this was not achieved. Of the 25 men who

completed the study 11 subjects started off with the LSP after randomisation whereas

the other 14 began with the HSP.

Figure 3.1: Flow diagram of participants. ApoE, apolipoprotein E; BMI, body mass index; HLF, high liver fat; LLF, low liver fat; MRS, magnetic resonance spectroscopy

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Table 3.1 shows the main physical and physiological characteristics at the screening

visit for the HLF and LLF groups. The two liver fat groups were similar for all

measurements except for plasma TG and liver fat, as expected, which were both higher

in the HLF group compared to the LLF group, (P=0.052 for plasma TG, which is

borderline significant, and <0.001 for liver fat).

Table 3.1: Baseline characteristics of participants at screening visit

All (n=25) HLF(n=11) LLF (n=14) P value

Age (years) * 56 [41-65] 59 [49-64] 54 [41-65] 0.083 0.12

Body weight (kg) 89.8 ± 1.6 90.0 ± 2.2 89.7 ± 2.4 0.923 <0.01

BMI (kg/m2) 28.6 ± 0.3 28.9 ± 0.3 28.4 ± 0.5 0.385 0.03

Total fat mass (kg) 22.8 ± 0.9 24.3 ± 1.1 21.8 ± 1.3 0.190 0.09

Visceral fat mass (kg) 4.32 ± 0.21 4.74 ± 0.37 4.00 ± 0.21 0.079 0.13

Insulin (mU/L) 8.0 ± 0.9 8.9 ± 1.2 7.3 ± 0.7 0.273 0.06

Glucose (mmol/L) 5.6 ± 0.1 5.7 ± 0.1 5.5 ± 0.1 0.134 0.10

r

Liver fat (IHCL) (%) ** 4.2 [2.4-14.2] 14.4 [10.9-24.8] 2.9 [1.1-3.6] <0.001 0.84Plasma TG (mmol/L) ** 1.60 [1.10-2.33] 1.80 [1.21-2.60] 1.15 [0.98-1.93] 0.052 0.39

Data (mean ± SEM) were analysed by independent samples two-tailed t test; * data are mean [range]; ** data (median [IQR]) were analysed by Mann-Whitney U test. P values ≤0.050 and correspondent or r values are in bold. Effect size using Cohen criteria: =0.01, small; =0.06, medium; =0.14, large or r=0.1, small; r=0.3, medium; r=0.5, large. For Total fat mass: HLF (n=8), LLF (n=13); for Insulin: LLF (n=13). BMI, body mass index; IHCL, intra-hepatocellular lipid; HLF, high liver fat; HSP, high sugar phase; IQR, interquartile range; LLF, low liver fat; LSP, low sugar phase; TG, triacylglycerol

The main characteristics of participants after the two dietary interventions for the two

groups are summarised in Table 3.2. Body weight, BMI and total fat mass were

consistently higher after the HSP than the LSP in the whole cohort (P=0.001, 0.001 and

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0.002 respectively) as well as in men with HLF (P=0.005, 0.005 and 0.030 respectively)

and in those with LLF (P=0.003, 0.002 and 0.039 respectively). Liver fat and plasma TG

levels are shown in sections 3.4.3 and 3.4.4 respectively.

Table 3.2: Characteristics of participants after each dietary intervention LSP HSP P value

All Body weight (kg) 87.1 ± 1.9 89.3 ± 1.8 0.001 0.57(n=25) BMI (Kg/m2) 27.7 ± 0.4 28.4 ± 0.3 0.001 0.57

Total fat mass (kg) 21.9 ± 0.7 23.2 ± 0.8 0.002 0.32Visceral fat mass (Kg) * 4.1 ± 0.4 4.2 ± 0.2 0.574 0.02

Insulin (mU/L) 19.3 ± 1.4 19.3 ± 1.4 0.998 <0.01

Glucose (mmol/L) 5.2 ± 0.1 5.2 ± 0.1 0.715 0.01

HLF Body weight (kg) 87.6 ± 2.4 89.8 ± 2.5 0.001 0.66(n=11) BMI (Kg/m2) 28.1 ± 0.5 28.8 ± 0.4 0.001 0.67


Insulin (mU/L) 21.4 ± 1.0 21.2 ± 2.6 0.930 <0.01

Glucose (mmol/L) 5.3 ± 0.1 5.4 ± 0.1 0.828 <0.01

LLF Body weight (kg) 86.7 ± 2.9 88.9 ± 2.7 0.003 0.51(n=14) BMI (Kg/m2) 27.4 ± 0.6 28.1 ± 0.5 0.002 0.52


Insulin (mU/L) 17.7 ± 2.4 17.9 ± 1.4 0.948 <0.01

Glucose (mmol/L) 5.1 ± 0.1 5.1 ± 0.1 0.775 0.01

Data (mean ± SEM) were analysed by paired-samples two-tailed t test for differences between diets; P values ≤0.050 and correspondent values are in bold; effect size using Cohen criteria: =0.01, small; =0.06, medium; =0.14, large. * HLF group: n=7, LLF group: n=10. BMI, body mass index; HLF, high liver fat; HSP, high sugar phase; LLF, low liver fat; LSP, low sugar phase

When looking at the effect of diet in the two groups for the mean differences of all the

above measurements, none of them differed significantly (results not shown).

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3.4.2 Achieved composition of the two diets Table 3.3 shows the achieved dietary intakes of energy and macronutrients at baseline

and during the LSP and HSP. No significant differences in energy intake were found

between LSP and HSP, or between the baseline and LSP and baseline and HSP. The

HSP resulted in a higher intake of total carbohydrate and sugar compared to both

baseline and LSP (P<0.01 in all cases). The achieved energy intake from NMES on the

LSP and HSP was about 10% below and 10% above the energy intake from NMES in

the baseline diet respectively. The HSP diet was significantly lower in total fat compared

to both baseline and LSP diets (P<0.01 in both cases). The total sugar was raised from

21 ± 1% of total energy intake at baseline to 28 ± 1% on the HSP, and lowered to 9 ± 1

% on the LSP.

Table 3.3: Intake of energy and macronutrientsBaseline LSP HSP

Total energy (MJ/d) 9.8 ± 0.5 9.9 ± 0.4 10.6 ± 0.5

Carbohydrate (g/d) 264 ± 15 257 ± 12 329 ± 14 a, c

% E 45 ± 1 44 ± 1 [47] 53 ± 1 a, c [47]

Sugar (g/d) 120 ± 10 56 ± 4 b 173 ± 10 a, c

% E 21 ± 1 9 ± 1 b 28 ± 1 a, c

NMES (g/d) 92 ± 8 32 ± 2 159 ± 10

% E 16 ± 1 6 ± 1 [6] 26 ± 1 [24]

Protein (g/d) 90 ± 5 97 ± 5 92 ± 5

% E 16 ± 1 16 ± 1 [15] 15 ± 1 [15]

Fat (g/d) 87 ± 6 89 ± 5 77 ± 6

% E 33 ± 1 34 ± 1 [34] 27 ± 1 d, e [34]

Starch to sugar ratio 1 : 0.9 4 : 1 1 : 1.1

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Data (mean ± SEM [target % E]) were analysed using one-way ANOVA with post-hoc Tukey test by Ahmad; a P<0.001, d P<0.01 Baseline vs HSP; b P<0.001 Baseline vs LSP; c P<0.001, e P<0.01 HSP vs LSP. Adapted from Ahmad’s PhD thesis (Ahmad 2012). % E, percentage of total food energy; HSP, high sugar phase; LSP, low sugar phase; NMES, non-milk extrinsic sugar

3.4.3 The effect of extrinsic sugar on liver fatAt the screening visit all participants had their first liver fat measurement, although only

17 participants had their liver fat measured after both 12 week dietary interventions.

Liver fat was significantly higher after the HSP compared to the LSP in both groups, as

shown in Figure 3.2. Liver fat levels remained significantly different when comparing the

two groups after each dietary intervention (P=0.018 for HLF and =0.025 for LLF).

No outliers were found after either dietary intervention in the HLF group. In the LLF

group one outlier was found in the upper quartile after the LSP only. However, re-

analysis without this outlier did not change the outcome for the LLF group (data not

shown).

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Figure 3.2: IHCL level expressed as % of liver volume after the two dietary interventions LSP and HSP. (A) HLF group: LSP (median: 11.4%, IQR: [8.2-25.6] %) vs HSP (median: 15.3%, IQR: [11.8-45.7] %); LLF group: LSP (median: 1.4%, IQR: [0.7-1.9] %) vs HSP (median: 1.7%, IQR: [1.0-6.6] %). (B) Liver fat content for each subject in HLF group (n=7) and LLF group (n=10). Data were analysed by Wilcoxon signed ranks test (for differences between diets) and by Mann-Whitney U test (for differences between liver fat groups); P values ≤0.050 and correspondent r values are in bold; effect size using Cohen criteria: r=0.1, small; r=0.3, medium; r=0.5, large. HLF, high liver fat; HSP, high sugar phase; IHCL, intra-hepatocellular lipid; IQR, interquartile range; LLF, low liver fat; LSP, low sugar phase

138

The effect of diet in HLF and LLF groups was also examined for the median differences

of IHCL (median of Δ values between diets for each paired measurement [HSP–LSP])

for the two liver fat groups, and the analysis resulted in a significantly greater effect in

the HLF group than in the LLF group, after the HSP (P=0.019, r=0.57).

In the current study no correlation was found between the difference in liver fat and the

difference in body weight when comparing the two dietary interventions. The level of

IHCL was positively correlated with the content of visceral fat only after the LSP, in the

whole cohort (ρ=0.691, P=0.002), as shown in Figure 3.3.

Figure 3.3: Relation between the liver fat content (IHCL) and total visceral fat at the end of each dietary intervention. LSP (A), HSP (B); open circles, LLF group (n=10); closed circles, HLF group (n=7). Data were analysed by Spearman rank correlation analysis. HLF, high liver fat; HSP, high sugar phase; IHCL, intra-hepatocellular lipid; LLF, low liver fat; LSP, low sugar phase

Furthermore, no association was found between the differences in liver fat levels (Δ

IHCL: IHCL after HSP – IHCL after LSP) and the differences between the visceral fat

levels (Δ Visceral fat = Visceral fat after HSP – Visceral fat after LSP) (ρ = -0.370, P =

0.144).

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3.4.4 The effect of extrinsic sugars on plasma TG levels

3.4.4.1 Between dietary intervention

The concentration of plasma TG in the whole cohort was found significantly higher after

the HSP compared to the LSP as shown in Figure 3.4 A. In the HLF group the

concentration of plasma TG was also found significantly higher after the HSP compared

to the LSP as shown in Figure 3.4 B. No statistically significant difference was found in

the LLF when group comparing the concentration of plasma TG measured after the two

dietary interventions, as shown in Figure 3.4 C.

No outliers were found after either dietary intervention in the HLF group. In the LLF

group two outliers were found after the HSP only, both in the upper quartile. However,

re-analysis without these outliers did not change the outcome for the LLF group (data

not shown).

3.4.4.2 Between liver fat groups

Plasma TG levels were significantly higher in the HLF group than the LLF group after

both interventions as shown in Figure 3.4 B and C.

3.4.4.3 Response to diet in the HLF and LLF groups

The effect of diet in the two liver fat groups was examined for plasma TG. The median

differences (median of Δ values between diets for each paired measurement [HSP–

LSP]) were not significantly different between groups (results not shown).

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Figure 3.4: Effect of HSP and LSP on plasma TG concentrations. (A) Whole cohort (n=25): HSP (median: 1.58 mmol/L, IQR: [1.24-2.02] mmol/L) vs LSP (median: 1.27 mmol/L, IQR: [1.03-1.65] mmol/L); (B) HLF group (n=11): HSP (median: 1.75 mmol/L, IQR: [1.70-3.09] mmol/L) vs LSP (median: 1.64 mmol/L, IQR: [1.34-2.74] mmol/L); (C) LLF group (n=14): HSP (median: 1.28 mmol/L, IQR: [1.08-1.55] mmol/L) vs LSP (median: 1.08 mmol/L, IQR: [0.93-1.36] mmol/L. Data were analysed by Wilcoxon signed ranks test (for differences between diets) and by Mann-Whitney U test (for differences between liver fat groups); P values ≤0.050 and correspondent r values are in bold; effect size using Cohen criteria: r=0.1, small; r=0.3, medium; r=0.5, large. HLF, high liver fat; HSP, high sugar phase; IQR, interquartile range; LLF, low liver fat; LSP, low sugar phase; TG, triacylglycerol

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3.4.5 The effect of extrinsic sugar on plasma cholesterol and total apoB

3.4.5.1 Between dietary interventions

Table 3.4 shows total, LDL and HDL cholesterol concentrations and apoB levels after

the dietary intervention in the whole cohort and in the two liver fat groups. Total

cholesterol and LDL cholesterol concentrations were higher after the HSP than the LSP

in the whole cohort. In the HLF group no significant differences were found. In the LLF

group total cholesterol concentration was higher after the HSP than the LSP, although

this difference was borderline significant. In the same group, LDL cholesterol was also

higher in the HSP than the LSP.

Table 3.4: Total, LDL and HDL cholesterol and total apoB levels after each dietary intervention

LSP HSP P value

All (n=25) Cholesterol (mmol/L) 5.01 ± 0.19 5.31 ± 0.20 0.007 0.27LDL cholesterol (mmol/L) 3.18 ± 0.18 3.33 ± 0.15 0.053 0.15

HDL cholesterol (mmol/L) 1.16 ± 0.05 1.19 ± 0.05 0.155 0.08

ApoB (mg/L) 1006 ± 41 1075 ± 42 0.007 0.27HLF (n=11) Cholesterol (mmol/L) 5.24 ± 0.29 5.58 ± 0.33 0.071 0.29

LDL cholesterol (mmol/L) 3.23 ± 0.27 3.40 ± 0.26 0.268 0.12


ApoB (mg/L) 1057 ± 70 1124 ± 77 0.089 0.26

LLF (n=14) Cholesterol (mmol/L) 4.82 ± 0.26 5.10 ± 0.25 0.058 0.25

LDL cholesterol (mmol/L) 3.14 ± 0.21 3.27 ± 0.20 0.094 0.20


ApoB (mg/L) 965 ± 47 1036 ± 44 0.045 0.27

Data (mean ± SEM) were analysed by paired-samples two-tailed t test for differences between diets; P values ≤0.050 and correspondent values are in bold; effect size was determined using Cohen criteria: =0.01, small; =0.06, medium; =0.14, large. ApoB, apolipoprotein B100; HDL, high density lipoprotein; HLF, high liver fat; HSP, high sugar phase; LDL, low density lipoprotein; LLF, low liver fat; LSP, low sugar phase

142


Total, LDL and HDL cholesterol concentrations and apoB levels did not differ between

the groups on either dietary intervention (data not shown).


The effect of diet in the two liver fat groups was also examined for total, LDL and HDL

cholesterol and apoB. The mean differences (mean of Δ values between diets for each

paired measurement [HSP–LSP]) were not significantly different between groups for

any of these measurements (results not shown).

3.4.6 The effect of extrinsic sugar on VLDL1 and VLDL2

composition


Table 3.5 shows VLDL1 and VLDL2 apoB, cholesterol and TG levels after the dietary

intervention in the whole cohort and in the two liver fat groups. Both VLDL1 cholesterol

and TG were significantly higher after the HSP compared to the LSP in the whole

cohort (P=0.029 and =0.002 respectively). When looking at the HLF group, only VLDL2-

TG levels were significantly higher after the HSP compared to the LSP (P=0.025). In the

LLF group, both VLDL1 cholesterol and TG were significantly higher after the HSP

compared to the LSP (P=0.003 in both cases). In the same group, VLDL2 apoB was

also found significantly higher after the HSP compared to the LSP (P=0.024).

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Table 3.5: VLDL1 and VLDL2 composition after each dietary interventionLSP HSP P value r value

All VLDL1 apoB (mg/L) 10.7 [7.8-19.3] 12.5 [8.4-22.5] 0.600 0.07

(n=25) VLDL2 apoB (mg/L) 8.5 [5.3-14.4] 8.6 [6.2-15.4] 0.056 0.27

VLDL1 cholesterol (mmol/L) 0.15 [0.11-0.24] 0.22 [0.16-0.34] 0.029 0.31VLDL2 cholesterol (mmol/L) 0.08 [0.05-0.11] 0.09 [0.06-0.15] 0.072 0.25

VLDL1 TG (mmol/L) 0.47 [0.35-0.73] 0.58 [0.51-082] 0.002 0.43VLDL2 TG (mmol/L) 0.09 [0.07-0.15] 0.13 [0.08-0.16] 0.067 0.26

HLF VLDL1 apoB (mg/L) 16.2 [9.5-20.2] 14.2 [9.2-21.7] 0.534 0.13

(n=11) VLDL2 apoB (mg/L) 8.8 [8.0-14.8] 11.1 [7.9-16.7] 0.534 0.13

VLDL1 cholesterol (mmol/L) 0.25 [0.20-0.33] 0.22 [0.20-0.48] 0.594 0.11

VLDL2 cholesterol (mmol/L) 0.09 [0.07-0.13] 0.14 [0.08-0.24] 0.075 0.38

VLDL1 TG (mmol/L) 0.76 [0.39-0.99] 0.81[0.57-1.13] 0.266 0.24

VLDL2 TG (mmol/L) 0.11 [0.08-0.14] 0.15 [0.11-0.20] 0.025 0.48LLF VLDL1 apoB (mg/L) 10.4 [6.9-13.0] 11.6 [8.1-23.6] 0.109 0.30

(n=14) VLDL2 apoB (mg/L) 6.1 [4.4-15.5] 7.6 [5.3-17.1] 0.024 0.43VLDL1 cholesterol (mmol/L) 0.13 [0.07-0.16] 0.20 [0.14-0.25] 0.003 0.55VLDL2 cholesterol (mmol/L) 0.07 [0.04-0.11] 0.07 [0.05-0.10] 0.701 0.07

VLDL1 TG (mmol/L) 0.40 [0.25-0.48] 0.52 [0.43-0.61] 0.003 0.55VLDL2 TG (mmol/L) 0.09 [0.06-0.16] 0.09 [0.07-0.14] 0.648 0.09

Data (median [IQR]) were analysed by Wilcoxon signed ranks test for differences between diets; P values ≤0.050 and correspondent r values are in bold; effect size was determined using Cohen criteria: r=0.1, small; r=0.3, medium; r=0.5, large. ApoB, apolipoprotein B100; HLF, high liver fat; HSP, high sugar phase; LLF, low liver fat; LSP, low sugar phase; VLDL, very low density lipoprotein; TG, triacylglycerol


VLDL1 cholesterol was significantly higher in the HLF than the LLF group after the LSP

(P=0.001, r=0.67), although no difference was found when comparing the two groups

after the HSP.

144


The effect of diet in the two liver fat groups was also examined for the differences of

VLDL1 and VLDL2 apoB, cholesterol and TG levels. Therefore, the mean differences

(mean of Δ values between diets for each paired measurement [HSP–LSP]) in the two

liver fat groups were compared, and only the mean difference for VLDL2 cholesterol

was significantly higher in men with high liver fat (P=0.040, r=0.41).

3.4.6.3 VLDL TG-apoB and TG-cholesterol molar ratios

VLDL1 and VLDL2 TG-apoB and TG-cholesterol molar ratios were also determined after

each dietary intervention, as shown in Table 3.6. No significant differences were found

in the whole cohort when comparing the two diets. In the HLF group VLDL2 TG/apoB

was higher in the HSP compared to the LSP, although significance was borderline

(P=0.050), whereas, in the LLF group this was significantly lower after the HSP than the

LSP (P=0.048).

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Table 3.6: VLDL1 and VLDL2 TG-apoB and TG-cholesterol molar ratios after each dietary intervention

LSP HSP P value r valueAll VLDL1 TG/apoB 20222 [15781-26124] 23985 [16404-32513] 0.035 0.30(n=25) VLDL2 TG/apoB 5614 [4453-8116] 6578 [4455-8467] 0.778 0.04

VLDL1 TG/cholesterol 2.77 [1.94-4.42] 2.53 [1.53-4.67] 0.788 0.04


HLF VLDL1 TG/apoB 21393 [16505-32209] 30934 [17139-41312] 0.075 0.38

(n=11) VLDL2 TG/apoB 5123 [4450-5614] 6578 [4618-10389] 0.050 0.42VLDL1 TG/cholesterol 2.37 [1.85-4.31] 2.99 [1.19-5.57] 0.266 0.24


LLF VLDL1 TG/apoB 17061 [15643-25002] 20819 [14762-27984] 0.198 0.24

(n=14) VLDL2 TG/apoB 7663 [4151-8612] 6253 [3905-7187] 0.048 0.37VLDL1 TG/cholesterol 2.96 [1.95-4.96] 2.49 [1.90-4.38] 0.594 0.10


Data (median [IQR]) were analysed by Wilcoxon signed ranks test for differences between diets; P values ≤0.050 and correspondent r values are in bold; effect size was determined using Cohen criteria: r=0.1, small; r=0.3, medium; r=0.5, large. ApoB, apolipoprotein B100; HLF, high liver fat; HSP, high sugar phase; LLF, low liver fat; LSP, low sugar phase; VLDL, very low density lipoprotein; TG, triacylglycerol

When looking at the differences in the two liver fat groups after each dietary

intervention, only VLDL2 TG-apoB molar ratio after the LSP was higher in the LLF group

than the HLF group, although this difference did not reach statistical significance

(P=0.063).

The effect of diet in the two liver fat groups was determined for VLDL1 and VLDL2 TG-

apoB and TG-cholesterol molar ratios. Only the median difference for VLDL2

TG/cholesterol between the two dietary interventions was significantly higher in men

with high liver fat (P=0.006, r=0.55).

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3.4.7 Effect of extrinsic sugar on VLDL particle sizeThe number of TG molecules per apoB was determined and represents a measure of

the average size of VLDL particles in each subject. The average size of VLDL was

measured at the end of each phase.

3.4.7.1 VLDL1: between dietary interventions

In the whole cohort, the number of TG molecules per apoB was significantly higher after

the HSP compared to the LSP (P=0.035), as shown in Figure 3.5 A. When considering

the two liver fat groups separately, no statistically significant differences were found, as

shown in Figure 3.5 B and C for HLF and LLF group respectively.

3.4.7.2 VLDL1: between liver fat groups

The number of TG molecules per apoB in VLDL1 did not differ significantly between the

liver fat groups on either dietary intervention (Figure 3.5 A and B).

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Figure 3.5: Effect of HSP and LSP on the average number of TG molecules per ApoB in VLDL1 particles. (A) Whole cohort (n=25): HSP (median: 23985 TG/apoB, IQR: [16404-32513] TG/apoB) vs LSP (median: 20222 TG/apoB, IQR: [15781-26124] TG/apoB); (B) HLF group (n=11): HSP (median: 30934 TG/apoB, IQR: [17139-41312] TG/apoB) vs LSP (median: 21393 TG/apoB, IQR: [16505-32209] TG/apoB); (C) LLF group (n=14): HSP (median: 20819 TG/apoB, IQR: [14762-27984] TG/apoB) vs LSP (median: 17061 TG/apoB, IQR: [15643-25002] TG/apoB). Data were analysed by Wilcoxon signed ranks test (for differences between diets) and by Mann-Whitney U test (for differences between liver fat groups); P values ≤0.050 and correspondent r values are in bold; effect size using Cohen criteria: r=0.1, small; r=0.3, medium; r=0.5, large. ApoB, apolipoprotein B100; HLF, high liver fat; HSP, high sugar phase; IQR, interquartile range; LLF, low liver fat; LSP, low sugar phase; TG, triacylglycerol; VLDL, very low density lipoprotein

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No significant differences were found when comparing the number of TG molecules per

apoB after the two dietary interventions when examining the whole cohort, as shown in

Figure 3.6 A. The number of TG molecules per apoB was higher after the HSP

compared to the LSP, in the HLF group, and the level of significance was borderline

(P=0.050), as shown in Figure 3.6 B. On the other hand, in the LLF group the number

of TG molecules per apoB was significantly lower after the HSP compared to the LSP

(P=0.048), as shown in Figure 3.6 C.


The number of TG molecules per apoB in VLDL2 did not differ significantly between the

liver fat groups on the HSP. On the other hand, the number of TG molecules per apoB

in VLDL2 on the LSP, was higher in the LLF group than in the HLF group, and although

this difference was not statistically significant, it showed a trend (P=0.063). Results are

shown in Figure 3.6 B and C

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Figure 3.6: Effect of HSP and LSP on the average number of TG molecules per ApoB in VLDL2 particles. (A) Whole cohort (n=25): HSP (median: 6578 TG/apoB, IQR: [4455-8467] TG/apoB) vs LSP (median: 5614 TG/apoB, IQR: [4453-8116] TG/apoB); (B) HLF group (n=11): HSP (median: 6578 TG/apoB, IQR: [4618-10389] TG/apoB) vs LSP (median: 5123 TG/apoB, IQR: [4450-5614] TG/apoB); (C) LLF group (n=14): HSP (median: 6253 TG/apoB, IQR: [3905-7187] TG/apoB) vs LSP (median: 7663 TG/apoB, IQR: [4151-8612] TG/apoB). Data were analysed by Wilcoxon signed ranks test (for differences between diets) and by Mann-Whitney U test (for differences between liver fat groups); P values ≤0.050 and correspondent r values are in bold; effect size using Cohen criteria: r=0.1, small; r=0.3, medium; r=0.5, large. ApoB, apolipoprotein B100; HLF, high liver fat; HSP, high sugar phase; IQR, interquartile range; LLF, low liver fat; LSP, low sugar phase; TG, triacylglycerol; VLDL, very low density lipoprotein

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3.4.8 The effect of extrinsic sugars on VLDL-TG levels


The concentration of VLDL1-TG was found significantly higher after the HSP compared

to the LSP in the whole cohort (P=0.002), as shown in Figure 3.7 A. In the HLF group,

no statistically significant differences in the concentration of VLDL1-TG were observed

when comparing levels measured after the two dietary interventions (Figure 3.7 B). In

the LLF group the levels of VLDL1-TG were significantly higher after the HSP compared

to the LSP (P=0.003), as shown in Figure 3.7 C.

No outliers were found after either dietary intervention in the HLF group. On the other

hand, two outliers (one in the upper and one in the lower quartile) were found in the LLF

group after the HSP only. However, re-analysis after excluding these values did not

change the outcome (data not shown).


VLDL1-TG was significantly higher in the HLF group than the LLF group after both

dietary interventions (P=0.003 for LSP and 0.010 for HSP), as shown in Figure 3.7 B

and C.


The effect of diet in the two liver fat groups was determined for VLDL1-TG levels and

the median differences (median of Δ values between diets for each paired

measurement [HSP–LSP]) in the two liver fat groups were not significantly different

(results not shown).

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Figure 3.7: Effect of HSP and LSP on VLDL1-TG concentrations. (A) Whole cohort (n=25) HSP (median: 0.58 mmol/L, IQR: [0.51-0.82]] mmol/L) vs LSP (median: 0.47 mmol/L, IQR: [0.35-0.83] mmol/L); (B) HLF group (n=11): HSP (median: 0.81 mmol/L, IQR: [0.57-1.13] mmol/L) vs LSP (median: 0.76 mmol/L, IQR: [0.39-0.99] mmol/L); (C) LLF group (n=14): HSP (median: 0.52 mmol/L, IQR: [0.43-0.61] mmol/L) vs LSP (median: 0.40 mmol/L, IQR: [0.25-0.48] mmol/L). Data were analysed by Wilcoxon signed ranks test (for differences between diets) and by Mann-Whitney U test (for differences between liver fat groups); P values ≤0.050 and correspondent r values are in bold; effect size using Cohen criteria: r=0.1, small; r=0.3, medium; r=0.5, large. ApoB, apolipoprotein B100; HLF, high liver fat; HSP, high sugar phase; IQR, interquartile range; LLF, low liver fat; LSP, low sugar phase; TG, triacylglycerol; VLDL, very low density lipoprotein

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The concentration of VLDL2-TG was higher after the HSP compared to the LSP in the

whole cohort, although this did not reach statistical significance (P=0.067). Results are

shown in Figure 3.8 A. In the HLF group the levels of VLDL2-TG were significantly

higher after the HSP compared to the LSP (P=0.025), as shown in Figure 3.8 B. In the

LLF group the levels of VLDL2-TG did not differ significantly when comparing the HSP

to the LSP, as shown in Figure 3.8 C.

No outliers were found after either dietary intervention in either liver fat group.


VLDL2-TG was not statistically different between the two liver fat groups on the LSP. In

contrast, it was higher in the HLF group than the LLF group on the HSP, but this

difference did not reach statistical significance (P=0.058). Results are shown in Figure

3.8 B and C.


The effect of diet in the two liver fat groups was determined for VLDL2-TG levels. The

median difference (median of Δ values between diets for each paired measurement

[HSP–LSP]) for VLDL2 TG/cholesterol was significantly higher in men with high liver fat

(P=0.018, r=0.42).

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Figure 3.8: Effect of HSP and LSP on VLDL2-TG concentrations. (A) Whole cohort (n=25): HSP (median: 0.13 mmol/L, IQR: [0.08-0.16] mmol/L) vs LSP (median: 0.09 mmol/L, IQR: [0.07-0.15] mmol/L); HLF group (n=11): HSP (median =0.15 mmol/L, IQR = [0.11-0.20] mmol/L) vs LSP (median = 0.11 mmol/L, IQR = [0.08-0.14] mmol/L); (C) LLF group (n=14): HSP (median = 0.09 mmol/L, IQR = [0.07-0.14] mmol/L) vs LSP (median = 0.09 mmol/L, IQR = [0.06-0.16] mmol/L). Data were analysed by Wilcoxon signed ranks test (for differences between diets) and by Mann-Whitney U test (for differences between liver fat groups); P values ≤0.050 and correspondent r values are in bold; effect size using Cohen criteria: r=0.1, small; r=0.3, medium; r=0.5, large. ApoB, apolipoprotein B100; HLF, high liver fat; HSP, high sugar phase; IQR, interquartile range; LLF, low liver fat; LSP, low sugar phase; TG, triacylglycerol; VLDL, very low density lipoprotein

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3.5 Discussion

3.5.1 DietThe aim of the present study was to examine the effect of two different isocaloric diets


energy, which differed in their content of non-milk extrinsic sugar (NMES) (also known

as “free sugars”), and thus total sugar (see section 2.2), on lipoprotein metabolism in

men at increased risk of metabolic syndrome. The diets were designed to be matched

for total energy, carbohydrate, fat and protein content and energy. Target intakes of

NMES for the low and high extrinsic sugar diets corresponded to the lower and upper

2.5th percentile of the intake in men aged 35-65 (see section 2.2). In fact, the high sugar

intervention resulted in a higher intake of total carbohydrate and sugar compared to

both baseline and LSP (P<0.01 in all cases), and also significantly lower in total fat

compared to both baseline and LSP diets (P<0.01 in both cases) (see section 3.4.2 and

later in this section). Although there was a difference in body weight between the two

dietary interventions, this difference was the same in both liver fat groups (2.5% higher

after the high sugar phase in both liver fat groups). Therefore, any differences in the

lipid response between the two groups were not due to effects of body weight.

Furthermore, no differences were found with regard to the visceral fat content. The

association of sugar-sweetened beverage consumption with weight gain has been

consistently observed in several large cross-sectional and prospective cohort studies in

the last five decades, as reviewed by Malik et al. (Malik et al. 2006). Rolls et al. found

that giving sucrose-sweetened drinks (containing about 225 g or double this amount)

together with a meal, increased total energy intake in non-dieting adult males (n=42)

(Rolls et al. 1990).

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In the current study, the dietary exchange model achieved an intake of NMES on the

low sugar phase (NMES 6 ± 1% total energy) that was similar to the recommendations

form the UK’s Scientific Committee on Nutrition (SCAN), that in a recent report on

carbohydrate, recommended lowering the consumption of NMES, to around 5% of daily

energy intake in the general population (SACN 2014). On the other hand, the intake of

sugar achieved on the high sugar phase (NMES 26 ± 1% total energy) was 5-fold that

on the low sugar phase. However, this intake still fell within the highest 2.5th percentile

of sugar intake in the UK population. The intake of fat was 27% of total energy on the

high sugar diet, whereas the target was 34%. This may be in part due to the

composition of the high sugar drinks included as part of the study foods on the high

sugar phase, since these drinks contained no fat, but similar amounts of carbohydrate

with 100% sugar relative to foods. For this reason, those participants who consumed

more high sugar drinks and fewer foods had a lower intake of fat. The intake of high

sugar drinks might also help to explain the lower intake of fibre on the high sugar

phase, relative to the low sugar phase (data not shown). However, although it is not

possible to rule out the possible effect of this difference on the outcome, it seems that

that the metabolic response to diet was consistent with the different intake of free sugar

in the two diets.

3.5.2 Liver fatThe cut-off level of IHCL used in the current study to define hepatic steatosis was 5%

which is very similar to what was considered normal in the Dallas Heart Study

(Szczepaniak et al. 2005), a population based study in which 1H-MRS was applied to

measure liver fat in a large sample of US adults (2349 aged 30 to 65). In this study

5.6% was considered the upper limit of normal liver fat. In the current study, men with

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high liver fat showed a much greater response to the high sugar diet than men with low

liver fat. Interestingly, three of the participants who were allocated in the low liver fat

group had a liver fat level higher than 5% after the high sugar diet, indicating that they

had developed hepatic steatosis during the dietary intervention.

This is the first study to investigate the effect of sugar on liver fat in men at increased

risk of metabolic syndrome. These people are free-living and otherwise healthy

individuals who are ‘at risk’ of developing the metabolic syndrome (Grundy 2005), and

while this group will be at increased susceptibility to cardiovascular disease, their

relatively moderate risk is more likely to be modifiable and responsive to early dietary

and lifestyle modification. In a previous study, Kotronen et al. found that liver fat was

more than 4-fold higher in men with the metabolic syndrome (median 9.3%; IQR 16.5%)

than in men without the metabolic syndrome (median 2.0%; IQR 5%) in a group of 109

middle-aged (20-65 years) non-diabetic men (Kotronen et al. 2007). In the present

study, the levels of liver fat found in men with low liver fat were similar to those found in

men without metabolic syndrome in the above mentioned study, and this was true both

at baseline and after both diets (median: 1.4%, IQR: 1.2% after low sugar diet; median:

1.7%, IQR: 5.5% after high sugar diet). On the other hand, liver fat levels in the people

with high liver fat after high sugar diet (median: 15.3%, IQR: 33.9%) were higher than

those found in men with metabolic syndrome in the Kotronen study, but similar after the

low sugar diet (median: 11.4%, IQR: 17.4%). Sevastianova et al. investigated whether

overfeeding overweight subjects (n=5 male, 11 female) with a hypercaloric diet (>1000

Kcal from simple carbohydrate for 3 weeks) increased the content of liver fat (measured

by 1H-MRS as in the current study) (Sevastianova et al. 2012). Moreover, they

examined whether weight loss (hypocaloric diet for 6 months after the hypercaloric diet)

reversed this process. They found that the hypercaloric diet induced a 27% increase in

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liver fat (from 9.2 to 11.7%) and a 2% increase in body weight (this was similar to the

difference in body weight that was found in the present study in both liver fat groups

after the high sugar intervention). During the hypocaloric diet they observed a 4%

decrease in body weight and a 25% decrease in liver fat (from 11.7 to 8.8%),

suggesting that weight loss can reverse the accumulation of liver fat. They showed a

positive correlation between change in body weight and that in liver fat (r=0.47, P=0.06

during the hypercaloric diet; r=0.57, P<0.05 during weight loss). In contrast with these

results, in the current study no correlation was found between the difference in liver fat

and the difference in body weight when comparing the two diets (data not shown). This

may be the result of the metabolic adaptations to the high liver fat content or may be

due to differences in genotype which predisposes some men to the accumulation of

liver fat. The level of liver fat was positively correlated to the content of visceral fat after

the low sugar diet, in the whole cohort (ρ=0.691, P=0.002), as shown in Figure 3.3. This

association was not found after the high sugar diet probably because of the effect of

sugar consumption on the level of liver fat. Importantly, no association was found

between the difference in liver fat and the difference in visceral fat between dietary

interventions (results not shown).

So far, there have been only a few interventional studies looking at the effect of

isocaloric sugar intake (particularly sucrose, high-fructose corn syrup, glucose and

fructose) on liver fat and other indexes of liver health. In general, in those studies

based on short term interventions (up to 6-7 days), fructose feeding corresponding to

30-35% of total energy intake have shown to increase the level of liver fat with

differences between the high sugar diet and the control diet ranging widely, as reviewed

by Moore (Moore et al. 2014). When compared to weight-maintenance diets,

hypercaloric fructose diets significantly increased liver fat levels. Theytaz et al. found

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that a high-fructose dose (3 g fructose · kg-1 · day-1) as a part of an essential amino acid

supplementation diet in 9 healthy male subjects, in a randomised crossover design after

a 6-day hypercaloric dietary intervention, significantly increased the level of liver fat

(2.74 ± 0.55 [mean ± SEM] % volume) compared to the control diet (1.27 ± 0.31 [mean

± SEM] % volume) (Theytaz et al. 2012). Overall, these studies present several

common limitations, including small sample size, short intervention time, failure in

effectively controlling body weight and energy intake, the use of pure fructose or other

sugars, often as liquid formula, rather than solid food, and sugar loads that are extreme.

Moreover, for many studies it is not possible to establish whether the levels of liver fat

increase as a result of fructose consumption or simply because of hypercaloric

conditions. Interestingly, Ngo Sock et al. compared the effects of hypercaloric diets

enriched with fructose (3.5 g fructose · kg-1 fat-free mass · d-1, +35 % energy intake) or

glucose (3.5 g glucose · kg-1 fat-free mass · d-1, +35 % energy intake) in a group of 11

healthy men in a randomised, crossover design after a 7-day dietary intervention (Ngo

Sock et al. 2010), and they found that the liver fat levels increased after both high-

fructose and high-glucose diets (+52 and +58% respectively). In contrast with these

results in another study Lecoultre and colleagues compared the effects of hypercaloric

diets enriched with fructose or glucose (3g · kg-1 fat-free mass · d-1, +31 % energy

intake in both cases) in healthy men (n=17 in fructose group and 11 in glucose group),

in a randomised crossover design after a 7-day dietary intervention (Lecoultre et al.

2013). They found that both glucose and fructose significantly increased the level of

liver fat when compared to a control diet. However, the liver fat was significantly higher

after fructose feeding (+113%) compared to glucose feeding (+59%). The results from

those studies that have investigated the effect of sugar intake on liver fat based on long

term interventions are inconsistent. Most interventional studies have looked at the effect

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of hypercaloric fructose diets (compared to control diets) where fructose was mainly

given as liquid formula. In a 2-week parallel randomized controlled trial, Johnston et al.

examined the effect of two isocaloric diets in which 25% of energy came from either

glucose (n=15) or fructose (n=17) (consumed 4 times a day mixed with 500 mL of

water) on liver fat in 32 centrally overweight male subjects (Johnston et al. 2013), and

they found no alterations in liver fat (liver TG determined by 1H-MRS as in the current

study) after the two diets. In the same study, the same amount of either glucose or

fructose given in addition to the control diet (hypercaloric conditions), significantly

increased the level of liver fat (+24% after fructose diet and +26% after glucose diet). In

contrast, Silbernagel and colleagues in a 4-week randomised, single-blinded parallel

intervention with either glucose or fructose (healthy males and females; n=10 for both

groups) given in addition to the control diet (+25% of energy intake), showed that both

diets did not result in significant increases in the level of liver fat (Silbernagel et al.

2011). However, these results are not conclusive in terms of the effect of sugar on

increasing the liver fat in long term studies (>7 days), and this is true for both

hypercaloric and isocaloric interventions

In the present study, the total sugar was 28% of total energy intake in the high sugar

diet and 9% in the low sugar diet. However, the consumption of fructose was not

determined. Therefore, it was not possible to differentiate between the effect of this

monosaccharide and glucose, although these two monosaccharides are found together

in sucrose.

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3.5.3 Plasma TGIn the present study, the levels of plasma TG were significantly higher after the high

sugar phase only in men with high liver fat, although the same trend was observed for

the other liver fat group. This effect may be in part due to the small sample size when

considering the two groups separately. Men with high liver fat showed higher plasma

TG levels than men with low liver fat, after both dietary interventions. The higher grade

of insulin resistance associated with the higher liver fat content in the high liver fat

group may have played a role in determining this effect. However, the two diets did not

appear to have a differential effect on the two liver fat groups, therefore, it is only

possible to say that plasma TG tended to be higher after the high sugar intervention,

and this effect was slightly more marked in men with high liver fat.

Although VLDL-TG kinetics will be discussed in details in chapter 4, VLDL-TG levels,

being an important component of fasting plasma TG, are considered here. The levels of

VLDL1-TG were significantly higher after the high sugar diet only in men with low liver

fat. In men with high liver fat the VLDL1 particles tended to be larger in size, but this

difference did not reach significance. On the other hand, the level of VLDL2-TG was

higher in men with high liver fat, but not in men with low liver fat, after the high sugar

diet. This group also showed larger VLDL2 particle size after the high sugar diet. This

results support the idea that TG-rich VLDL1 and TG-poor VLDL2 are regulated

differently. However, in the present study the other components of the fasting plasma

TG were not investigated, therefore it is not possible to determine how these

components, and in particular the chylomicron remnants affected the response of

fasting levels of plasma TG to dietary sugar.

Several groups examined the effect of exchanging starches for sugar on fasting plasma

TG in healthy subjects, with ad libitum diets. Albrink et al. observed a dose-dependent

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effect on plasma TG when exchanging sucrose for starch in the content of a very high

carbohydrate and a very low fat diet (70 and 15% of total energy respectively), in six

healthy normolipidemic young men (Albrink et al. 1986). When compared with baseline

concentrations with the men’s usual high-fat diet (40% of energy), the 0% sucrose diet

tended to decrease fasting plasma TG concentrations, and the 18% sucrose diet had

no effect. The 36% and 52% sucrose diets led to a sustained increase in plasma TG

over the 11-day dietary intervention. In particular, the greatest effect was observed in

the 36% sucrose diet after 8 days (about 65% higher than baseline), whereas in the

52% sucrose the peak was observed after 4 days (about 25% higher than baseline). On

the other hand, high amounts of dietary fibre (≥34 g/day) prevented the rise in plasma

TG with the 36% sucrose diets (only 15% higher than baseline) but had no effect with

the 52% sucrose. However, only limited data are available regarding the dose-

dependent effect of sugars on fasting plasma TG in the context of diets more moderate

in terms of carbohydrate content. Marckmann et al investigated the effects of two diets

both containing 29% of energy from fat and 59% of energy from carbohydrate, high and

low in sucrose (23 and 2.5% of total energy respectively), in a 14-day dietary

intervention, in healthy, nonobese women (Marckmann et al. 2000). Diets were fed ad

libitum, and the subjects were allowed to eat to satiety. Although both diets increased

fasting plasma TG concentrations when compared to a control high fat diet (46% of

energy from fat, 41% of energy from carbohydrate of which 2.2% from sugar), the high

sucrose diet produced significantly higher concentrations of fasting TG (+19%) than the

low sucrose diet. This study is similar to the current study in terms of composition of the

low and high sugar diets and also the effect of the two diets on fasting TG is

comparable. It is important to note the fact that in this study, as in many other studies,

the covariation of sugar and total carbohydrate can represent a confounding factor. In

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the present study, the exchange of sugar for starch in the high sugar diet also led to a

higher intake of total carbohydrate although the aim was to leave the carbohydrate

energy contribution unchanged (53% achieved against 47% target). Jeppesen et al. in a

randomized crossover study, measured the effects of two 3-week isoenergetic diets, a

high fat diet (40% carbohydrate and 45% fat) and a high carbohydrate diet (60%

carbohydrate, and 25% fat ) on plasma TG levels, in 10 healthy, postmenopausal

women (Jeppesen et al. 1997). The intake of total sugars in the high carbohydrate diet

was 18% of energy, compared with 12% of energy in the low carbohydrate diet and the

ratio of sugar to starch was the same in both cases (1:2). They found increased fasting

TG concentrations on the high carbohydrate diet compared to the high fat diet (+53%).

In contrast with these finding, other groups did not observed the same effect when

comparing similar dietary interventions. For example Vidon et al. in a randomized

crossover study, measured the effects of two 3-week isoenergetic diets, a high fat diet

(40% carbohydrate and 45% fat) and a high carbohydrate diet (55% carbohydrate, and

30% fat ) on plasma lipid concentrations, in 7 healthy subjects (Vidon et al. 2001). The

content of sugar in both diets ranged between 20 and 25% of total energy. They found

no effect on fasting plasma TG concentrations when comparing the two diets. However,

an important limit of the study described above is the very small sample size.

Other studies have investigated the effect of a high sugar diet on plasma lipids in

unhealthy subjects. Saris et al. looked at the effects of replacing one-quarter of daily fat

intake by complex or simple carbohydrate in 46 overweight middle-aged subjects with

metabolic syndrome (≥ 3 risk factors) (Saris et al. 2000). Subjects were randomly

assigned to one of three diets (free living, ad libitum): 1) control (habitual fat intake:

≈35–40% of energy); 2) low fat (fat intake -10% than control), high complex

carbohydrate (ratio of simple to complex carbohydrate to 1:2); 3) low fat (fat intake -

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10% than control), high sugar (ratio of simple to complex carbohydrate to 2:1). Body

weight, body fat and lipid profile were measured every two months for 6 months.

Fasting plasma TG concentrations were higher in the low fat, high sugar group (+9%

than baseline) than in the other two groups (-20% than baseline) (diet effect, P < 0.05).

Taken together, these studies suggest that the increase in fasting plasma TG may be

due in part to the increase in sugar rather than in total carbohydrate. There have been a

few studies in subjects with type 2 diabetes mellitus. In a randomized controlled trial,

people with type 2 diabetes mellitus (5 male, 7 female) were fed three isocaloric diets

(30% fat, 55% carbohydrate) for eight days each: 1) high fructose (21% of total energy

intake); 2) high sucrose (23% of total energy intake) and 3) starch diets (Bantle et al.

1986). It resulted that fructose and sucrose diets did not significantly increase plasma

TG levels when compared with the starch diet. Although these diets are comparable to

those in the current study, the main limitation is the short intervention and the small

sample size. In another study with longer dietary intervention, a double-blind

randomized crossover design, people with type 2 diabetes mellitus (4 male, 6 female)

were administered crystalline fructose (20% of total energy intake) or placebo (starch

replaced fructose) with their meals for 4 weeks with both isocaloric diets containing 30%

of total energy intake as fat and 50% as carbohydrate (Koivisto et al. 1993). No

significant differences were observed in plasma TG when comparing the two diets.

3.5.4 Other outcomesIn the present study VLDL1 cholesterol was significantly higher after the high sugar diet

compared to the low sugar diet in men with low liver fat (+63%). As Parks suggests, if

carbohydrate induced hypertriglyceridemia results from increased production of VLDL

particles, this mechanism also leads to an increased secretion of cholesterol in VLDL

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because hepatic cholesterol secretion rate is proportional to the VLDL particle secretion

rate (Parks 2001). On the other hand, the cholesterol concentration is less likely to

increase if carbohydrate induced hypertriglyceridemia results from reduced clearance of

TG rather than from increased secretion. However, although both VLDL1-TG and

VLDL1-cholesterol levels were significantly higher after the high sugar diet only in men

with low liver fat, further statistical analysis showed that the effect of diet was not

different in the two liver fat groups, therefore it is not possible to conclude that the high

sugar diet determined higher secretion of both TG and cholesterol in VLDL1 particles.

Eisenberg et al. found that in hypertriglyceridemic men (plasma TG: 8.48 ± 1.72 [mean

± SEM] mmol/L; n=16), large VLDL particles were found to contain more cholesterol

(+40% of cholesteryl ester and +48% of free cholesterol; P<0.001) than in

normolipidemic men (plasma TG: 1.38 ± 0.24 [mean ± SEM] mmol/L; n=7) (Eisenberg

et al. 1984).

3.6 ConclusionThe dietary exchange model achieved an intake of free sugar with the low sugar diet

that was similar to the recommendations from the UK’s Scientific Committee on

Nutrition (SCAN) (5% energy intake), whereas in the high sugar diet the intake of free

sugar was 5-fold that on the low sugar phase (6 and 26% energy intake respectively).

However, in both diets the sugar intake fell within the lower and upper 2.5th percentile of

the UK intake in men aged 35-65, therefore being representative of what these people

really consume, not only in terms of sugar but also the other macronutrients and energy

intake. Although the diets were designed to be matched for total energy, carbohydrate,

fat and protein content and energy, the high sugar intervention resulted in a higher

intake of total carbohydrate and lower intake of total fat compared to target values.

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Although it is not possible to rule out the possible effect of this difference on the

outcome, the metabolic response was consistent with the different intake of free sugar

in the two diets.

Liver fat was higher in both liver fat groups after the high sugar diet, although the

magnitude of this effect was greater in men with high liver fat than in man with low liver

fat. In contrast with previous studies, in the current study no correlation was found

between the levels of liver fat and the body weight, and between liver fat and visceral

fat, after the two diets. Fasting levels of plasma TG tended to be higher after the high

sugar diet, and although this effect was slightly more marked in men with high liver fat,

it was not possible to establish that the two liver fat groups responded differently.

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Chapter 4: VLDL-TG kinetics

4.1 IntroductionAn excessive intake of dietary sugar can increase plasma TG concentration which

increases cardiometabolic risk through adverse changes in plasma lipoproteins, known

collectively as an atherogenic lipoprotein phenotype (ALP) (Austin et al. 1990) as

discussed in see sections 1.8 and 3.1. Increased plasma TG concentrations, in the

postabsorptive and post-prandial states, is a pre-requisite for the development of an

ALP via the remodelling of LDL into small and dense particles with increased potential

to promote atherosclerosis (Sattar et al. 1998). Elevated plasma TG may result from

increased hepatic production and secretion of VLDL, and/or impaired clearance of

VLDL-TG from the plasma via the action of lipoprotein lipase (LPL) (Taskinen et al.

2011). Sugar may play an important role in determining this effect either directly by

altering TG metabolism and/or indirectly by delivering excess energy and increasing

body weight (Stanhope et al. 2013). However, at present, only few studies have

investigated the effect of sugar intake on the kinetic of VLDL1 and VLDL2 separately and

results have been contradictory.

4.2 AimsThe aim of this study was to investigate the impact of liver fat in people at increased risk

of metabolic syndrome with either high (IHCL: >5%, <40%) or low (IHCL: <5%) liver fat,

on VLDL1 and VLDL2-TG kinetics, after high and low intakes of sugar found in Western

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diets. Two different stable isotope techniques (bolus injection of bolus of 2H5-glycerol

and constant infusion of U-13C16-palmitate) were used for this purpose. Importantly, the

compartmental model used to determine the kinetic parameters such as fractional

catabolic rate (FCR) and production rate (PR) allowed the investigation of VLDL1 and

VLDL2-TG kinetics independently. A secondary aim of this study was the comparison

between the two different techniques in order to determine whether there is a good

agreement between them.

4.3 MethodsMen at increased risk of developing metabolic syndrome with either high liver fat (HLF,

n=11) or low liver fat (LLF, n=14) (liver fat > or < 5% by magnetic resonance

spectroscopy), matched for age and BMI, were assigned to high and low sugar diets

(26% and 6% total energy, respectively) for 12 weeks in a randomised crossover trial.

Subjects, study design and dietary interventions have been discussed in detail in

sections 2.1 and 2.2 and outlined in section 3.3. An intravenous bolus of 2H5-glycerol

was administered at the beginning of the clinical study in order to determine the kinetics

of VLDL1 and VLDL2–TG fractions. A constant infusion of U-13C16-palmitate was also

used as an alternative way to determine the kinetics of VLDL1 and VLDL2–TG fractions.

Blood samples were taken at varying time intervals, depending on the kinetics of the

metabolite, to determine the enrichment and the concentrations of plasma glycerol and

palmitate as well as of glycerol and palmitate contained in the VLDL-TG fractions. An

overview of the clinical study is shown in Figure 2.2. VLDL1 and VLDL2 fractions were

separated by sequential ultracentrifugation (see section 2.6.1). These fractions

underwent lipid extraction based on Folch method (see section 2.6.2). The different

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classes of lipids were then separated by TLC and the collected TG fraction was

hydrolysed to yield glycerol and FAME (see section 2.6.3). At this point the FAME

fraction was ready for analysis by GC-MS whereas the glycerol fraction underwent

purification by ion exchange chromatography (see section 2.6.4) and subsequent

derivatisation by using the triacetate glycerol method (see section 2.6.5). The

preparation of plasma glycerol and plasma palmitate is described in sections 2.6.8 and

2.6.12. The isotopic enrichment of both plasma and VLDL-TG derived glycerol was

measured by chemical ionisation GC-MS whereas the isotopic enrichment of both

plasma and VLDL-TG derived palmitate was determined by electron impact ionisation

GC-MS. The enrichment data of both plasma glycerol and palmitate, as well as both

glycerol and palmitate derived from VLDL1 and VLDL2 TG was used to determine the

lipoprotein kinetic parameters using the modelling software SAAM II (see section 2.7.1).

A compartmental model analysis was then used determine VLDL1 and VLDL2-TG and all

the kinetic parameters.

4.4 Results: the effect of extrinsic sugar on VLDL-TG kinetics

4.4.1 VLDL-TG kinetics by modelling glycerol enrichment data Plasma glycerol and VLDL-TG glycerol enrichment curves after the two dietary

interventions are showed in Figure 4.1. Time 0 min corresponds to the bolus injection of

labelled glycerol. The kinetic results for glycerol enrichment data were obtained from 24

out of 25 subjects due to a problem with processing the samples of one of the

participants belonging to the HLF group.

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Figure 4.1: Plasma glycerol and VLDL-TG glycerol enrichment curves after the two dietary interventions in the whole cohort. Results are mean TTR for each time point ± SEM shown as error bar; the solid lines indicate the curve fit. Plasma (green), VLDL1-TG (blue) and VLDL2-TG (red) glycerol enrichment curves after the LSP (A) and HSP (B); n=24. HSP, high sugar phase; LSP, low sugar phase; TTR, tracer to tracee ratio


The main kinetic parameters of VLDL1 and VLDL2-TG were determined at the end of

each dietary intervention by modelling the glycerol enrichment data. When considering

the whole cohort, both VLDL1-TG PR and total VLDL-TG PR were both significantly

higher after the HSP compared to the LSP (P=0.001, =0.40 in both cases), as shown

in Table 4.1 and outlined in Figure 4.2. VLDL1-TG removal was also significantly higher

after the HSP compared to the LSP (P=0.001, =0.36).

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Table 4.1: VLDL-TG kinetics measured using glycerol tracer, comparing LSP vs HSP in the whole cohort

LSP HSP P value

VLDL1 PR (mg/day) 15090 ± 1259 18377 ± 1365 0.001 0.40VLDL1 FCR (pools/day) 10.86 ± 0.65 10.31 ± 0.63 0.199 0.07

VLDL1 catabolism (pool/day) 8.14 ± 0.53 8.08 ± 0.54 0.839 <0.01

VLDL2 PR (mg/day) 3862 ± 305 4161 ± 344 0.304 0.05

VLDL2 FCR (pools/day) 13.43 ± 0.74 12.43 ± 0.80 0.070 0.14

VLDL1 transfer to VLDL2 (pools/day) 2.71 ± 0.28 2.23 ± 0.26 0.073 0.13

VLDL2 PR-from liver (mg/day) 468 ± 66 561 ± 72 0.258 0.05

VLDL2 PR-from VLDL1 (mg/day) 3394 ± 270 3600 ± 366 0.425 0.03

Total VLDL PR (mg/day) 15558 ± 1237 18823 ± 1352 0.001 0.40VLDL1 removal (mg/day) 11696 ± 1220 14777 ± 1302 0.001 0.36

Data (mean ± SEM) were analysed by paired-samples two-tailed t test for differences between diets; P values ≤0.050 and correspondent values are in bold; effect size determined using Cohen criteria: =0.01, small; =0.06, medium; =0.14, large; n=24. FCR, fractional catabolic rate; HSP, high sugar phase; LSP, low sugar phase; PR, production rate; TG, triacylglycerol; VLDL, very low density lipoprotein

Figure 4.2: Overview of VLDL-TG kinetics after the two dietary intervention in the whole cohort. Results are mean (mg/day) as reported in Table 4.1 for LSP (A) and HSP (B); green arrows: VLDL1-TG PR; yellow arrows: VLDL2 PR-from VLDL1; red arrows: VLDL2 PR-from liver; blue arrows: VLDL1 removal; significantly different results are shown in bold; n=24. HSP, high sugar phase; LSP, low sugar phase; PR, production rate; TG, triacylglycerol; VLDL, very low density lipoprotein

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Furthermore, there was a positive correlation between both total VLDL-TG and VLDL1-

TG PR and IHCL in the whole cohort, after the LSP (ρ=0.600, P=0.014 in both cases),

but not after the HSP, as shown in Figure 4.3.

Figure 4.3: Relation between VLDL-TG PR and IHCL at the end the two dietary interventions. Between total VLDL-TG PR and IHCL at the end of LSP (A) and at the end of HSP (B); between VLDL1-TG PR and IHCL at the end of LSP (C) and at the end of HSP (D). Open circles, LLF group (n=10); closed circles, HLF group (n=6). Data were analysed by Spearman rank correlation analysis. HLF, high liver fat; HSP, high sugar phase; IHCL, intra-hepatocellular lipid; LLF, low liver fat; LSP, low sugar phase; PR, production rate; TG, triacylglycerol; VLDL, very low density lipoprotein

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In the HLF group (results shown in Table 4.2 and Figure 4.4) no significant differences

were observed in the total VLDL and VLDL1-TG PR when comparing the LSP with the

HSP. On the other hand, VLDL2-TG PR was significantly higher after the HSP than the

LSP (P=0.019, =0.48). VLDL2-TG PR-from liver was significantly higher after the HSP

compared to the LSP (P=0.001, =0.76). VLDL2-TG PR-from VLDL1 was higher after

the HSP compared to the LSP, and this difference was borderline significant (P=0.053,

=0.36). No significant outliers were found in this group for total VLDL-TG PR, VLDL1-

TG PR and VLDL2-TG PR (total, from liver and from VLDL1).

Correlation analysis showed that there was no association between the difference in

VLDL1-TG PR after the two diets (Δ VLDL1-TG PR: VLDL1-TG PR after HSP – VLDL1-

TG PR after LSP) and the difference between the liver fat levels after the two diets (Δ

IHCL = IHCL after HSP – IHCL after LSP) (ρ = -0.471, P = 0.066).

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Table 4.2: VLDL-TG kinetics measured using glycerol tracer, comparing LSP vs HSP in the HLF group

LSP HSP P value

VLDL1 PR (mg/day) 18915 ± 2059 20860 ± 2500 0.249 0.14


VLDL1 catabolism (pool/day) 7.53 ± 0.84 6.79 ± 0.89 0.151 0.21


VLDL1 transfer to VLDL2 (pools/day) 1.91 ± 0.36 2.16 ± 0.46 0.502 0.05

VLDL2 PR-from liver (mg/day) 292 ± 82.61 584 ± 81.00 0.001 0.76VLDL2 PR-from VLDL1 (mg/day) 3412 ± 373 4317 ± 650 0.053 0.36

Total VLDL PR (mg/day) 19207 ± 2029 21170 ± 2508 0.240 0.15

VLDL1 removal (mg/day) 15503 ± 2102 16543 ± 2412 0.508 0.05

Data (mean ± SEM) were analysed by paired-samples two-tailed t test for differences between diets; P values ≤0.050 and correspondent values are in bold; effect size determined using Cohen criteria: =0.01, small; =0.06, medium; =0.14, large; n=10. FCR, fractional catabolic rate; HLF, high liver fat; HSP, high sugar phase; LSP, low sugar phase; PR, production rate; TG, triacylglycerol; VLDL, very low density lipoprotein

Figure 4.4: Overview of VLDL-TG kinetics after the two dietary intervention in the HLF group. Results are mean (mg/day) as reported in Table 4.2 for LSP (A) and HSP (B); green arrows: VLDL1-TG PR; yellow arrows: VLDL2 PR-from VLDL1; red arrows: VLDL2 PR-from liver; blue arrows: VLDL1 removal; significantly different results are shown in bold; n=10. HLF, high liver fat; HSP, high sugar phase; LSP, low sugar phase; PR, production rate; TG, triacylglycerol; VLDL, very low density lipoprotein

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When analysing the LLF group (results shown in Table 4.3), VLDL1-TG PR as well as

total VLDL-TG PR were significantly higher after the HSP compared to the LSP

(P=0.001 in both cases). VLDL1-TG removal was also significantly higher after the HSP

compared to the LSP (P<0.001). Furthermore, VLDL1-TG transfer to VLDL2-TG was

significantly lower after the HSP compared to the LSP (P<0.005).

One outlier was found in this liver fat group after both dietary interventions for total

VLDL-TG PR and VLDL1-TG PR. Re-analysis after excluding this outlier did not change

the outcome (data not shown). No significant outliers were found in this group for

VLDL2-TG PR (total, from liver and from VLDL1).

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Table 4.3: VLDL-TG kinetics measured using glycerol tracer, comparing LSP vs HSP in the LLF group

LSP HSP P value


VLDL1 catabolism (pool/day) 8.58 ± 0.69 9.00 ± 0.59 0.341 0.07

VLDL2 PR (mg/day) 3975 ± 433 3632 ± 265 0.253 0.10


VLDL1 transfer to VLDL2 (pools/day) 3.28 ± 0.34 2.28 ± 0.31 0.004 0.48VLDL2 PR-from liver (mg/day) 594 ± 83 544 ± 111 0.683 0.01

VLDL2 PR-from VLDL1 (mg/day) 3381 ± 391 3087 ± 292 0.278 0.09

Total VLDL PR (mg/day) 12952 ± 1164 17147 ± 1385 0.001 0.64VLDL1 removal (mg/day) 8976 ± 981 13515 ± 1404 0.001 0.70

Data (mean ± SEM) were analysed by paired-samples two-tailed t test for differences between diets; P values ≤0.050 and correspondent values are in bold; effect size determined using Cohen criteria: =0.01, small; =0.06, medium; =0.14, large; n=10. FCR, fractional catabolic rate; HSP, high sugar phase; LLF, low liver fat; LSP, low sugar phase; PR, production rate; TG, triacylglycerol; VLDL, very low density lipoprotein

Figure 4.5: Overview of VLDL-TG kinetics after the two dietary intervention in the LLF group. Results are mean (mg/day) as reported in Table 4.3 for LSP (A) and HSP (B); green arrows: VLDL1-TG PR; yellow arrows: VLDL2 PR-from VLDL1; red arrows: VLDL2 PR-from liver; blue arrows: VLDL1 removal; significantly different results are shown in bold; n=14. HSP, high sugar phase; LLF, low liver fat; LSP, low sugar phase; PR, production rate; TG, triacylglycerol; VLDL, very low density lipoprotein

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Total VLDL-TG PR was higher in the HLF group than in the LLF group, after the LSP

(P=0.009) but not after the HSP. This was due to a higher VLDL1-TG PR (P=0.007).

VLDL1-TG removal was higher in the HLF group after the LSP (P=0.005) but not after

the HSP. VLDL2-TG PR-from liver was higher in the LLF group than in the HLF group,

after the LSP (P=0.020) but not after the HSP.


The effect of diet in the HLF and LLF groups was also examined in order to assess if

the two groups responded differently to the two dietary interventions. Therefore, the

mean differences (mean of Δ values between diets for each paired measurement

[HSP–LSP] in the two liver fat groups) were compared. It resulted that the mean

differences for VLDL2-TG PR (P=0.005, =0.31), VLDL2-TG PR-from liver (P=0.032,

=0.19), VLDL2-TG PR-from VLDL1 (P=0.016, =0.24) were significantly higher in the

HLF group than the LLF group. On the other hand the mean difference for VLDL1-TG

removal (P=0.040, =0.18) was significantly higher in the LLF group than the HLF

group.

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4.4.2 VLDL-TG kinetics by modelling palmitate enrichment dataPlasma and VLDL-TG palmitate enrichment curves after the two dietary interventions

during constant infusion of labelled palmitate are shown in Figure 4.6. Time 0 min

corresponds to the beginning of the constant infusion of labelled palmitate. The kinetic

results for palmitate enrichment data were obtained from 24 out of 25 subjects due to a

problem with processing the samples of one of the subjects belonging to the HLF

group.

Figure 4.6: Plasma and VLDL-TG palmitate enrichment curves at the end of the two dietary interventions in the whole cohort. Results are mean TTR for each time point ± SEM shown as error bar; the solid lines indicate the curve fit. Plasma (green), VLDL1-TG (blue) and VLDL2-TG (red) palmitate enrichment curves after the LSP (A) and HSP (B); n=24. HSP, high sugar phase; LSP, low sugar phase; TTR, tracer to tracee ratio

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When considering the whole cohort, both VLDL1-TG PR and total VLDL-TG PR were

significantly higher after the HSP compared to the LSP(P=0.015 and =0.014

respectively), as shown in Table 4.4. VLDL1-TG removal was also significantly higher

after the HSP compared to the LSP (P=0.019).

Table 4.4: VLDL-TG kinetics measured using palmitate tracer, comparing LSP vs HSP in the whole cohort

LSP HSP P value

VLDL1 PR (mg/day) 13640 ± 1192 17581 ± 1756 0.015VLDL1 FCR (pools/day) 10.18 ± 0.91 10.02 ± 0.86 0.838

VLDL1 catabolism (pool/day) 7.91 ± 0.93 7.84 ± 0.76 0.927

VLDL2 PR (mg/day) 3437 ± 374 3932 ± 404 0.271

VLDL2 FCR (pools/day) 11.33 ± 0.78 11.38 ± 0.91 0.953

VLDL1 transfer to VLDL2 (pools/day) 2.26 ± 0.31 2.18 ± 0.38 0.800

VLDL2 PR-from liver (mg/day) 528 ± 111 593 ± 95 0.569

VLDL2 PR-from VLDL1 (mg/day) 2904 ± 360 3340 ± 430 0.352

Total VLDL PR (mg/day) 14167 ± 1213 18174 ± 1783 0.014VLDL1 removal (mg/day) 10735 ± 1262 14242 ± 1676 0.019

Data (mean ± SEM) were analysed by paired-samples two-tailed t test for differences between diets; P values ≤0.050 are in bold; n=24. FCR, fractional catabolic rate; HSP, high sugar phase; LSP, low sugar phase; PR, production rate; TG, triacylglycerol; VLDL, very low density lipoprotein

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In the HLF group (results shown in Table 4.5) no significant differences were observed

in any of the kinetic parameters calculated at the end of each intervention. However,

VLDL2-TG PR was higher on the HSP than the LSP, and although this difference was

not significant (P=0.073), there was a trend.

Table 4.5: VLDL-TG kinetics measured using palmitate tracer, comparing LSP vs HSP in the HLF group

LSP HSP P value

VLDL1 PR (mg/day) 17070 ± 1834 21910 ± 3310 0.144



VLDL2 PR (mg/day) 3009 ± 241 4205 ± 728 0.073





Total VLDL PR (mg/day) 17439 ± 1858 22472 ± 3351 0.121

VLDL1 removal (mg/day) 14442 ± 1790 18267 ± 3044 0.185

Data (mean ± SEM) were analysed by paired-samples two-tailed t test for differences between diets; P values ≤0.050 are in bold; n=10. FCR, fractional catabolic rate; HLF, high liver fat; HSP, high sugar phase; LSP, low sugar phase; PR, production rate; TG, triacylglycerol; VLDL, very low density lipoprotein

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When analysing the LLF group, VLDL1-TG PR was significantly higher after the HSP

than the LSP (P=0.047), as shown in Table 4.6. Total VLDL-TG PR was higher after the

HSP compared to the LSP, and although these difference was not statistically

significant a trend was observed (P=0.056). VLDL1-TG removal was also higher after

the HSP compared to the LSP but this difference was not statistically significant

(P=0.056).

Table 4.6: VLDL-TG kinetics measured using palmitate tracer, comparing LSP vs HSP in the LLF group

LSP HSP P value

VLDL1 PR (mg/day) 11189 ± 1241 14489 ± 1474 0.047



VLDL2 PR (mg/day) 3742 ± 615 3738 ± 475 0.994





Total VLDL PR (mg/day) 11830 ± 1322 15104 ± 1534 0.056

VLDL1 removal (mg/day) 8088 ± 1396 11366 ± 1553 0.054

Data (mean ± SEM) were analysed by paired-samples two-tailed t test for differences between diets; P values ≤0.050 are in bold; n=14. FCR, fractional catabolic rate; HLF, high liver fat; HSP, high sugar phase; LLF, low liver fat; LSP, low sugar phase; PR, production rate; TG, triacylglycerol; VLDL, very low density lipoprotein

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Total VLDL-TG PR was higher in the HLF group than in the LLF group, after the LSP

(P=0.019) and after the HSP (P=0.034). This was due to a higher VLDL1-TG PR

(P=0.019 and =0.039 after the LSP and HSP respectively). VLDL1-TG removal was

higher in the HLF group after the LSP (P=0.010) and after the HSP (P=0.039). VLDL2-

TG PR-from liver was higher in the LLF group than in the HLF group, after the LSP

(P=0.010) and after the HSP (P=0.039).

4.4.3 Comparing VLDL-TG kinetics from glycerol and palmitate modelling

4.4.3.1 Whole cohort

The results of VLDL-TG kinetics obtained from modelling the glycerol enrichment data

and those obtained from modelling the palmitate enrichment data in the whole cohort

were similar. In both models VLDL1-TG PR and total VLDL-TG PR were both

significantly higher after the HSP compared to the LSP in the whole cohort (compare

results shown in Tables 4.1 and 4.4). VLDL1-TG removal was also significantly higher

after the HSP compared to the LSP in both cases. The measured parameters were in

most cases higher when determined from the glycerol enrichment data compared to

palmitate enrichment data. For example, VLDL1-TG PR obtained from modelling the

glycerol enrichment data was 5 to 10% higher than the palmitate counterpart, VLDL1-

TG FCR from glycerol enrichment data was 0.5 to 3% higher than that from palmitate.

Total VLDL2-TG PR as well as VLDL2-TG PR-from liver gave similar results in both

cases, whereas VLDL2-TG FCR from glycerol data was 9 to 18% higher than the

palmitate counterpart.

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Correlation analysis showed that there was a significant positive correlation between

measurements determined by the two different methods (r=0.64 for VLDL1-TG PR and

=0.79 for VLDL2-TG PR, P<0.001 in both cases). Bland-Altman analysis (Altman et al.

1983) was also carried out in order to analyse the agreement between the two

measurement techniques since the high correlation observed does not automatically

imply that there is good agreement between the two methods. The difference plots for

both VLDL1 and VLDL2-TG PR are shown in Figure 4.7. The average discrepancy

between the two methods was 1450 mg/day for VLDL1-TG PR and 425 mg/day for

VLDL1-TG PR. In both cases, the mean was not significantly different from 0, which

corresponds to no discrepancy between the two methods (P=0.175 for VLDL1 and

=0.078 for VLDL2)

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Figure 4.7: Bland–Altman analysis of the difference of VLDL-TG PR obtained from glycerol and palmitate modelling. VLDL-TG PR obtained from glycerol enrichment data is plotted against VLDL-TG PR obtained from palmitate enrichment data in the 24 paired measurements for VLDL1-TG PR (A) and VLDL2-TG PR (C); the difference between VLDL-TG PR obtained from glycerol enrichment data and VLDL-TG PR obtained from palmitate enrichment data (PR_glycerol-PR_palmitate) is plotted against the mean of the two measurements ((PR_glycerol+PR_palmitate)/2) in the 24 paired measurements for VLDL1-TG PR (B) and VLDL2-TG PR (D). Linear regression line (——); line of equality (——); mean difference (------); 95% limits of agreement (∙∙∙∙∙∙∙∙). PR, production rate; TG, triacylglycerol; VLDL, very low density lipoprotein

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4.4.3.2 HLF group

When examining the HLF group, both total VLDL2-TG PR and VLDL2-TG PR-from liver

were significantly higher after the HSP compared to the LSP when determined from

glycerol enrichment data. However, only total VLDL2-TG PR obtained from modelling

palmitate enrichment data was higher after the HSP than the LSP, and although these

difference was not statistically significant, a trend was observed (P=0.073) (compare

results shown in Tables 4.2 and 4.5).

4.4.3.3 LLF group

When looking at the LLF group, VLDL1-TG PR was significantly higher after the HSP

compared to the LSP with both models (compare results shown in Tables 4.3 and 4.6).

Total VLDL-TG PR and VLDL1-TG removal were both significantly higher after the HSP

compared to the LSP when determined from glycerol enrichment data, whereas the

same parameters obtained from palmitate enrichment data gave the same trend but

were border line in terms of statistical significance (P=0.056 and 0.054 respectively).

4.4.4 Overview of VLDL-TG production and liver fat changesIn this section the levels of liver fat (see Figure 3.2) and the total VLDL-TG PR (see

Tables 4.2 and 4.3) at the end of each dietary intervention are considered together in

order to offer a synoptic view of these two important measurements and to show the

magnitude of the effects of the diets. The levels of liver fat and total VLDL-TG

production after each diet and the correspondent Δ values (the differences between

levels after HSP and LSP) are shown in Figure 4.8. To note the fact that in this instance

IQR, SEM and P values are not reported. For these, refer back to figure 3.2 and Tables

4.2 and 4.3.

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Figure 4.8: Effect of diet on liver fat and total VLDL-TG production. Median IHCL after the LSP and after the HSP (A) and Δ values (median IHCL after HSP – median IHCL after LSP) (B) for the two liver fat groups; mean total VLDL-TG PR after the LSP and after the HSP (C) and Δ values (mean total VLDL-TG PR after HSP – mean VLDL-TG PR after LSP) (D) for the two liver fat groups. HLF, high liver fat; HSP, high sugar phase; IHCL, intra-hepatocellular lipid; LLF, low liver fat; LSP, low sugar phase; PR, production rate; TG, triacylglycerol; VLDL, very low density lipoprotein

4.5 Discussion

4.5.1 Comparing VLDL-TG kinetics from glycerol and palmitate enrichment dataIn this study, VLDL-TG turnover rates were measured in two ways: either by using a

bolus injection of 2H5-glycerol or a constant infusion of U-13C16-palmitate. In both cases

the enrichment data were used in conjunction with compartmental modelling analysis in

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order to determine the kinetic parameters. Constant infusion of glycerol or palmitate

tracers has been used to determine VLDL-TG turnover by fitting the data a

monoexponential equation to the rise to plateau (Parks et al. 1999; Wang et al. 2001).

The main issue with this approach is that it does not account for the considerable tracer

recycling that occurs during a prolonged constant infusion (see section 1.11.2) leading

to an underestimate of the turnover of VLDL-TG compared to compartmental modelling.

Using a bolus injection of palmitate or glycerol tracers in conjunction with

compartmental modelling analysis is a more accurate method of measuring VLDL-TG

flux since tracer recycling can be accounted for, as shown by Patterson and colleagues

(Patterson et al. 2002). In this study they also showed that monoexponential data

analysis underestimates the turnover of VLDL-TG compared with compartmental

modelling (more details on this study are found in section 1.11.2). In the current study, it

was found that the measured parameters were in most cases higher when determined

from the glycerol enrichment data compared to palmitate enrichment data (see section

4.4.3). In particular, correlation analysis showed that VLDL-TG production rates

determined by using the two methods were closely related indicating that there was a

linear relationship between the two sets of measurements. However, this does not imply

that there is good agreement between the two methods. Therefore, Bland-Altman

analysis was carried out in order to determine if there was good agreement between the

VLDL1 and VLDL2-TG production rates measured by using the two different methods.

The Bland–Altman analysis yields the mean difference between two methods of

measurement (the ‘bias’), and 95% limits of agreement (2 SD) (Altman et al. 1983). In

the present study, the mean difference between the two methods was not significantly

different from 0 (corresponding to the situation in which the two methods can be

considered equivalent) for both VLDL1 and VLDL2-TG production rates. However, this

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analysis confirmed that the production rate, as most of the other parameters determined

by the model, was in most cases higher when determined from the glycerol enrichment

data compared to palmitate enrichment data. The 95% limits of agreement are for visual

judgement of how well the two methods agree. The smaller the range between these

two limits the better the agreement is. There was not a trend in the VLDL1-TG PR

Bland-Altman plot. On the other hand, the VLDL2-TG PR plot showed that there was a

trend as the spread of the difference between the two methods was higher for higher

values of the production rate. Therefore, it seems that the two methods tend to show a

better agreement for lower production rate values (hence slower turnover) of VLDL2-TG.

This finding is in agreement with previous studies showed a that constant infusion of a

fatty acid tracer and compartmental data analysis cannot adequately resolve the extent

of recycling (Magkos et al. 2009), therefore leading to underestimation of the true

turnover rate of VLDL-TG. For this reason, this discussion that follows will focus on the

VLDL-TG kinetics obtained from modelling the glycerol enrichment data.

4.5.2 VLDL-TG kinetics and liver fat Liver fat was higher in both liver fat groups after the high sugar diet, although the


fat, as shown in Figure 4.8 A and B. By comparing the median of liver fat (expressed as

% IHCL) after the two dietary interventions and between the two liver fat groups, and by

looking at the Δ values, it is clear that men with high liver fat accumulated a much

greater amount of ectopic fat in the liver as a result of the high sugar diet compared to

men with low liver fat. The other important outcome of the present study is represented

by the response to diet in the production of VLDL1 and VLDL2-TG. At this point it would

be interesting looking at the total output of VLDL-TG by the liver that is represented by

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the total VLDL-TG production rate. This includes the VLDL1-TG and the VLDL2-TG

production by the liver, thus not taking into account the VLDL2-TG directly derived from

the delipidation of VLDL1 particles. The high sugar diet resulted in a greater production

of VLDL-TG in the low liver fat group compared to the low sugar diet, as shown in

Figure 4.8 C and D. Also in men with high sugar liver VLDL-TG production was higher

after the high sugar diet than after the low sugar diet, but this difference did not reach

significance. However, it is worth looking at the difference in the magnitude of the

response in the two lever fat groups and comparing these results to the effect of diet on

liver fat. Interestingly, the two variables show an opposite behaviour that could be

explained in these terms: men with low liver fat tended to cope better with the high

sugar diet with regard to liver fat accumulation, although the high sugar intake resulted

in greater export of TG by the liver in VLDL particles; on the other hand, men with high

liver fat were less able to respond to the high sugar diet by increasing VLDL-TG

production and were also more prone to accumulate further fat in their liver.

Previous studies have reported an association between liver fat and VLDL-TG

secretion. Hepatic fat content has been shown to positively correlate with insulin

resistance as discussed in section 1.9.3. One study reported that an insulin infusion

suppressed both hepatic VLDL1-TG and apoB production rates in men with low liver fat

(2.1 ± 1.5 [mean ± SEM] % volume; n=10, healthy) by 61% (Adiels et al. 2007). In

contrast, in men with high liver fat (11.4 ± 4.5 [mean ± SEM] % volume; n=10 of which 8

T2DM and 2 healthy) neither VLDL1-TG nor apoB production rates changed

significantly. On the other hand, both VLDL2-TG and apoB production rates increased

rapidly during insulin infusion and were increased both by 73% at the end of the

infusion (at 510 min) in men with low liver fat, although no significant differences were

observed in those with high liver fat. Another study showed that VLDL-TG production

189

rate was almost double in subjects with higher than normal liver fat levels (25.3% vs

3.6% volume), matched on visceral adipose tissue, in a group of obese men and

women (Fabbrini et al. 2009). In agreement with these results, in the current study, total

VLDL-TG secretion was also 48% higher in men with high liver fat than in those with

low liver fat, after the low sugar diet as a result of a higher VLDL1-TG production

(+53%). The lack of a significant difference when comparing groups after the high sugar

diet may be a consequence of the accumulation of the liver fat in the men with low liver

fat during this dietary intervention. The finding of a positive correlation between VLDL1-

TG production and the level of liver fat in the whole cohort after the low sugar diet

(shown in section 4.4.1.1 and in Figure 4.3) is in agreement with a previous study

where a positive correlation was found between VLDL1-TG production and the level of

liver fat (determined by Image-guided magnetic resonance spectroscopy) in both the

whole cohort (10 T2DM and 18 non-diabetic; r=0.58; P<0.01) and in the non-diabetic

group (r=0.48; P<0.05) (Adiels et al. 2006). They also found that liver fat was positively

correlated with VLDL2-TG production from liver (but this was not the case in the current

study) and with both VLDL1 and VLDL2-TG apoB production. In the present study, the

lack of association between VLDL1-TG production and the levels of liver fat in the whole

cohort, after the high sugar diet, suggests that the high sugar intake may have

disrupted this relationship. More importantly, no association was found between the

difference in VLDL1-TG production after the two diets and the difference between the

liver fat levels after the two diets. Therefore, VLDL1-TG production and liver fat

response to dietary sugar does not seem to be related in the current study when

considering the whole cohort. Furthermore, this outcome does not change when

considering the two liver fat groups separately (results not shown). The small size of the

two groups and the fact that only 17 participants out of 25 underwent MRI scan for liver

190

fat content determination at the end of each dietary intervention, contributed to reduce

the chance of finding a relationship between VLDL1-TG production and liver fat levels in

the present study.

4.5.2 The effect of dietary sugar on VLDL-TG kinetics So far, not many studies have looked at the effect of sugar intake on VLDL-TG kinetics.

The studies that have investigated the mechanism of carbohydrate-induced

hypertriglyceridemia are contradictory. Parks et al. looked at the effect of two

isoenergetic diets on VLDL-TG metabolism in a study in which participants (6

normolipidemic and 5 with moderately elevated TG) underwent a 1-week control dietary

intervention (35% fat, 50% carbohydrate, with sugar being 22% of total energy) followed

by a 6-week high carbohydrate, low fat diet (15% fat, 68% carbohydrate with sugar

being 30% of total energy) (Parks et al. 1999). They found that the elevation of plasma

TG after the high carbohydrate diet was mainly caused by a reduced clearance of

VLDL-TG rather than increased production. In contrast with these findings, in the

current study total VLDL-TG production was 21% higher after the high sugar diet than

the low sugar diet in the whole cohort, but no significant differences in VLDL-TG

catabolism were observed, suggesting that the increased levels of VLDL-TG found

were a result of increased synthesis rather than decreased clearance. These results are

in agreement with another study in which 6 healthy subjects were studied after a 2-

week high carbohydrate diet (10% fat, 75% carbohydrates with sugar being 35% of total

energy) and after a 2-week isoenergetic high-fat diet (55% fat, 30%carbohydrates with

sugar being 12% of total energy) (Mittendorfer et al. 2001). They found significantly

higher levels of VLDL-TG after the high carbohydrate diet due to a higher VLDL-TG

production (+70%), with no significant differences in VLDL-TG clearance. Discrepancies

191

among these studies may be due to several factors such as differences in the

participants, the composition of the diets, the duration of the studies, the sample size

and different methodologies used to determine VLDL-TG kinetics. In the current study,

total VLDL-TG production in men with low liver fat was 32% higher after the high sugar

diet than the low sugar diet, and this effect was due to higher VLDL1-TG production rate

(+34%). Surprisingly, total VLDL-TG and VLDL1-TG production rate did not differ

significantly in men with high liver fat when comparing the two diets. However, this

might be a consequence of insufficient power. Post hoc power analysis on total VLDL-

TG production rate showed that in order to achieve a statistical power of 0.80 a much

larger sample size would be needed for the high liver fat group (n=50). In the high liver

fat group VLDL2-TG production rate was higher after the high sugar diet than the low

sugar diet, pointing to a differential regulation of VLDL1 and VLDL2-TG. Insulin plays a

pivotal role in regulating VLDL particle assembly and secretion. It has been shown that

insulin can suppress VLDL1 apoB production but has no effect on VLDL2 apoB

production, indicating that VLDL1 and VLDL2 particles are independently regulated in

the liver (Malmstrom et al. 1997). The effect of diet in the two liver fat groups was also

examined in order to assess if the two groups responded differently to the two dietary

interventions. This showed that the mean differences (means of Δ values between diets

for each paired measurement in the two liver fat groups) for VLDL2-TG production rate

were significantly higher in the men with high liver fat than in men with low liver fat, and

this was due to a greater response of both VLDL2-TG direct production by the liver and

VLDL2-TG production from VLDL1 to the high sugar diet. Surprisingly, the same analysis

showed that the effect of the diet was not different in the two liver fat groups for VLDL1-

TG production rate. Therefore, it is not possible to conclude that the two groups

responded differently, although, as mentioned above, VLDL1-TG production rate was

192

higher in men with low liver fat after the high sugar phase but not in men with high liver

fat. However, it might be possible that the sample size of the high liver fat group was

not sufficiently large to detect a difference in VLDL1-TG production rate as for the whole

cohort and low liver fat group.

4.6 ConclusionThe two different stable isotope techniques used to measure VLDL-TG kinetics (bolus

injection of bolus of 2H5-glycerol and constant infusion of U-13C16-palmitate), showed

good agreement, although the constant infusion of U-13C16-palmitate led to

underestimation of the true turnover rate of VLDL-TG.

Only a few studies have investigated the effect of dietary sugar on VLDL-TG kinetics,

and in those that have, the results are contradictory. In the present study, dietary sugar

affected VLDL-TG metabolism in a way that depended on the level of liver fat, in men at

increased risk of metabolic syndrome. In the whole cohort, the high sugar diet resulted

in a higher VLDL1-TG production with no effect on its clearance. However, when looking

at the two liver fat groups, the only significant effect was the higher VLDL2-TG

production as a result of the high sugar diet that was only found in men with high liver

fat. This result was accompanied by a correspondent higher level of VLDL2-TG, as seen

in the previous chapter (see section 3.4.8). Surprisingly, although men with low liver fat

had higher VLDL1-TG production after the high sugar diet than the low sugar diet, it was

not possible to demonstrate that the two liver fat groups responded differently to the

diet.

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Chapter 5: Different sources of fatty acids for VLDL-TG

5.1 IntroductionDuring fasting, the production of VLDL-TG in the liver is mainly regulated by the

availability of NEFA from peripheral adipose tissue (systemic sources) or splanchnic

sources, as discussed in section 1.5. The latter includes splanchnic fat stores including

visceral adipose tissue and intra-hepatic stores and the new synthesis of fatty acids by

de novo lipogenesis (DNL) in the liver. In the postabsorptive state, systemic NEFA

represents the main source of fatty acids used for VLDL-TG production both in healthy

people (Barrows et al. 2006) and in people with NAFLD (Donnelly et al. 2005). DNL

contribution to VLDL-TG production in the fasting state is relatively small (<5%) (Timlin

et al. 2005). However, DNL contribution may be considerably increased when a very

high proportion of energy is supplied as sugar, and in particular sucrose and fructose,

as discussed in section 1.10.

194

5.2 AimsThe aim of the present study was to determine the proportion of fasting VLDL-TG

derived from systemic NEFA, DNL and other splanchnic sources in response to two

diets that delivered the higher and lower 2.5th percentiles of sugar intake in the UK, high

and low sugar respectively, in men with either low liver fat or high liver fat, and

therefore, the influence of liver fat on these sources of fatty acids.

5.3 MethodsThe day before the metabolic study day, a blood sample was taken to measure

baseline deuterium enrichment in plasma water and VLDL palmitate. Subjects were

given two bottles of 2H2O to drink in the evening (see section 2.5.1). From then until the

end of the study, they were asked to fast and to drink only water enriched with 2H2O.

The following morning they attended for the metabolic study (outlined in Figure 2.2). A

blood sample was taken to measure deuterium enrichment of palmitate in VLDL1 and

VLDL2-TG and plasma water to measure DNL. An 8-hour constant infusion of U-13C16-

palmitate bound to human albumin (5%) was administered to measure palmitate

production rate (assumed to be mainly from systemic adipose tissue lipolysis) and the

percentage contribution of systemic NEFA to VLDL1 and VLDL2-TG (see section 2.5.2).

195

5.4 Results

5.4.1 Contribution of systemic NEFA to VLDL-TG synthesis

5.4.1.1 VLDL1-TG: between dietary interventions

The percentage of palmitate derived from systemic NEFA in VLDL1-TG palmitate was

significantly lower on the HSP than on the LSP in the whole cohort (Figure 5.1 A) and

when looking at the two liver fat groups, in men with HLF but not in men with LLF

(P=0.037) (Figure 5.1 C). However, this did not correspond to a significantly lower

absolute synthesis rate of VLDL1-TG palmitate directly from systemic NEFA, either in

the whole cohort (Figure 5.1 B) or in the HLF group (Figure 5.1 D).

5.4.3.2 VLDL1-TG: between liver fat groups

The percentage of palmitate derived from systemic NEFA to VLDL1-TG was higher in

the LLF group after both dietary interventions (P=0.025 for LSP and =0.007 for HSP)

(Figure 5.1 C). However, no significant differences were observed when comparing the

synthesis rate of VLDL1 and VLDL2-TG palmitate directly from systemic NEFA between

the two liver fat groups in either dietary intervention (Figure 5.1 D).

5.4.1.3 Response to diet in HLF and LLF groups


the two groups responded differently to the two dietary interventions. The mean

differences for the systemic NEFA contribution to VLDL1-TG absolute rate of synthesis

did not differ significantly in the two liver fat groups (data not shown).

196

Figure 5.1: Systemic NEFA contribution to VLDL1-TG. Percentage of VLDL1-TG palmitate derived from systemic NEFA (A) and correspondent absolute rate of synthesis (B) in the whole cohort (n=23) after both dietary interventions; Percentage of VLDL1-TG palmitate derived from systemic NEFA (C) and correspondent absolute rate of synthesis (D) in HLF (n=10) and LLF (n=13) groups after both dietary interventions. Data (mean ± SEM) were analysed by paired-samples two-tailed t test (for differences between diets) and independent samples two-tailed t test (for differences between liver fat groups); P values ≤0.050 and correspondent values are in bold; effect size determined using Cohen criteria: =0.01, small; =0.06, medium; =0.14, large. HLF, high liver fat; HSP, high sugar phase; LLF, low liver fat; LSP, low sugar phase; NEFA, non-esterified fatty acids; TG, triacylglycerol; VLDL, very low density lipoprotein


When considering the whole cohort, the percentage of palmitate derived from systemic

NEFA to VLDL2-TG palmitate was significantly lower on the HSP than on the LSP

(P=0.020) (Figure 5.2 A). However, no significant differences were observed in the

197

correspondent absolute rate of synthesis (Figure 5.2 B). When looking at the two liver

fat groups, both the percentage of palmitate derived from systemic NEFA and the

absolute synthesis rate of VLDL2-TG palmitate directly from systemic NEFA did not

differ significantly on the two dietary interventions in either group (Figure 5.2 C and D).


The percentage of palmitate derived from systemic NEFA to VLDL2-TG was higher in

the LLF group after both dietary interventions (P=0.029 for LSP and =0.007 for HSP)

(Figure 5.2 C). However, no significant differences were observed when comparing the

synthesis rate of VLDL2-TG palmitate directly from systemic NEFA between the two

liver fat groups in either dietary intervention (Figure 5.2 D).




differences for the systemic NEFA contribution to VLDL2-TG absolute rate of synthesis

did not differ significantly in the two liver fat groups (data not shown).

198

Figure 5.2: Systemic NEFA contribution to VLDL2-TG. Percentage of VLDL2-TG palmitate derived from systemic NEFA (A) and correspondent absolute rate of synthesis (B) in the whole cohort (n=23) after both dietary interventions; Percentage of VLDL2-TG palmitate derived from systemic NEFA (C) and correspondent absolute rate of synthesis (D) in HLF (n=10) and LLF (n=13) groups after both dietary interventions. Data (mean ± SEM) were analysed by paired-samples two-tailed t test (for differences between diets) and independent samples two-tailed t test (for differences between liver fat groups); P values ≤0.050 and correspondent values are in bold; effect size determined using Cohen criteria: =0.01, small; =0.06, medium; =0.14, large. HLF, high liver fat; HSP, high sugar phase; LLF, low liver fat; LSP, low sugar phase; NEFA, non-esterified fatty acids; TG, triacylglycerol; VLDL, very low density lipoprotein

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5.4.2 Contribution of hepatic DNL derived fatty acids to VLDL-TG synthesis


In the whole cohort the absolute rate of DNL derived VLDL1-TG palmitate synthesis was

higher after the HSP than the LSP although this difference was not significant

(P=0.073) (Figure 5.3 B). No significant differences were found in men with HLF

between the LSP and the HSP (Figure 5.3 C and D). In contrast, in the LLF group the

percentage of DNL derived VLDL1-TG palmitate was significantly higher after the HSP

than the LSP (P=0.034) (Figure 5.3 C). This also corresponded to a significantly higher

absolute synthesis rate of VLDL1-TG palmitate directly from DNL in the same group

(P=0.032) (Figure 5.3 D).


After the LSP, there was a higher percentage (P=0.038) and a correspondent higher

contribution of DNL (P=0.012) to VLDL1-TG palmitate production in men with HLF

compared to those with LLF (Figure 5.3 C and D respectively).




differences for the hepatic DNL contribution to VLDL1-TG absolute rate of synthesis did

not differ significantly in the two liver fat groups (data not shown).

200

Figure 5.3: Hepatic DNL contribution to VLDL1-TG. Percentage of VLDL1-TG palmitate derived from hepatic DNL (A) and correspondent absolute rate of synthesis (B) in the whole cohort (n=23) after both dietary interventions; Percentage of VLDL1-TG palmitate derived from hepatic DNL (C) and correspondent absolute rate of synthesis (D) in HLF (n=10) and LLF (n=13) groups after both dietary interventions. Data (mean ± SEM) were analysed by paired-samples two-tailed t test (for differences between diets) and independent samples two-tailed t test (for differences between liver fat groups); P values ≤0.050 and correspondent values are in bold; effect size determined using Cohen criteria: =0.01, small; =0.06, medium; =0.14, large. DNL, de novo lipogenesis; HLF, high liver fat; HSP, high sugar phase; LLF, low liver fat; LSP, low sugar phase; TG, triacylglycerol; VLDL, very low density lipoprotein

201


As for VLDL1-TG, percentage and the absolute rate of DNL derived VLDL2-TG palmitate

synthesis did not differ on the two dietary interventions in the whole cohort (Figure 5.3 C

and D respectively) and in men with HLF (Figure 5.4 C and D respectively). A

significantly higher percentage of VLDL2-TG palmitate derived from DNL was observed

in men with LLF on the HSP compared to the LSP (P<0.04) (Figure 5.4 C), but this did

not correspond to a significantly higher absolute synthesis rate of VLDL2-TG palmitate

from DNL (Figure 5.4 D).


Percentage and the absolute rate of DNL derived VLDL2-TG palmitate synthesis did not

differ in either dietary intervention when comparing the two liver fat groups (figure 5.4 C

and D respectively).




differences for the hepatic DNL contribution to VLDL2-TG absolute rate of synthesis did

not differ significantly in the two liver fat groups (data not shown).

202

Figure 5.4: Hepatic DNL contribution to VLDL2-TG. Percentage of VLDL2-TG palmitate derived from hepatic DNL (A) and correspondent absolute rate of synthesis (B) in the whole cohort (n=23) after both dietary interventions; Percentage of VLDL2-TG palmitate derived from hepatic DNL (C) and correspondent absolute rate of synthesis (D) in HLF (n=10) and LLF (n=13) groups after both dietary interventions. Data (mean ± SEM) were analysed by paired-samples two-tailed t test (for differences between diets) and independent samples two-tailed t test (for differences between liver fat groups); P values ≤0.050 and correspondent values are in bold; effect size determined using Cohen criteria: =0.01, small; =0.06, medium; =0.14, large. DNL, de novo lipogenesis; HLF, high liver fat; HSP, high sugar phase; LLF, low liver fat; LSP, low sugar phase; TG, triacylglycerol; VLDL, very low density lipoprotein

203

5.4.3 Contribution of other splanchnic sources of fatty acid to VLDL-TG synthesis


In the whole cohort, the percentage of splanchnic derived VLDL1-TG palmitate and the

correspondent absolute synthesis rate were higher after the HSP than the LSP

(P=0.021 and =0.001 respectively) (Figure 5.5 A and B). The percentage of splanchnic

derived VLDL1-TG palmitate was higher after the HSP than the LSP in the HLF group

(P=0.029) but not in the LLF group (Figure 5.5 C). However, a correspondent higher

absolute synthesis rate of VLDL1-TG palmitate directly from splanchnic sources after

the HSP compared to the LSP, was observed not only in men with HLF (P=0.051) , but

also in men with LLF (P=0.008) (Figure 5.5 D).


After the LSP, splanchnic contribution to VLDL1-TG palmitate synthesis was higher in

the HLF group than the LLF group (P=0.002) (Figure 5.5 D). After the HSP, there was a

higher splanchnic percentage and correspondent absolute synthesis rate contribution to

VLDL1-TG palmitate production (P=0.018 and =0.031 respectively) in men with HLF

than in men with LLF (Figure 5.5).




differences for other splanchnic (non-DNL) sources contribution to VLDL1-TG absolute

rate of synthesis did not differ significantly in the two liver fat groups (data not shown).

204

Figure 5.5: Other splanchnic sources contribution to VLDL1-TG. Percentage of VLDL1-TG palmitate derived from other splanchnic sources (different from hepatic DNL) (A) and correspondent absolute rate of synthesis (B) in the whole cohort (n=23) after both dietary interventions; Percentage of VLDL1-TG palmitate derived from other splanchnic sources (different from hepatic DNL) (C) and correspondent absolute rate of synthesis (D) in HLF (n=10) and LLF (n=13) groups after both dietary interventions. Data (mean ± SEM) were analysed by paired-samples two-tailed t test (for differences between diets) and independent samples two-tailed t test (for differences between liver fat groups); P values ≤0.050 and correspondent

values are in bold; effect size determined using Cohen criteria: =0.01, small; =0.06, medium; =0.14, large. DNL, de novo lipogenesis; HLF, high liver fat; HSP, high sugar phase; LLF, low liver fat; LSP, low sugar phase; TG, triacylglycerol; VLDL, very low density lipoprotein

205

Correlation analysis on the whole cohort showed that there was a significant positive

association between the contribution of splanchnic sources of fatty acids to VLDL1-TG

production and total visceral fat on both dietary interventions (for LSP r=0.66, P<0.01;

for HSP r=0.58, P<0.02), as shown in Figure 5.6 A and B. There was also a positive

correlation between the contribution of splanchnic sources of fatty acids to VLDL1-TG

production and the level of liver fat on both dietary interventions (for LSP ρ=0.71,

P<0.005; for HSP ρ=0.54, P<0.05), as shown in Figure 5.6 C and D.

Figure 5.6: Correlation found for non-DNL splanchnic sources contribution to VLDL1-TG. Relation between contribution of non-DNL splanchnic sources to VLDL1-TG palmitate production and total visceral fat at the end of LSP (A) and HSP (B), and the level of liver fat (IHCL) at the end of LSP (C) and HSP (D). Open circles, LLF group (n=10); closed circles, HLF group (n=6). DNL, de novo lipogenesis; HLF, high liver fat; HSP, high sugar phase; IHCL, intra-hepatocellular lipids; LLF, low liver fat; LSP, low sugar phase; TG, triacylglycerol; VLDL, very low density lipoprotein

206

However, no association was found between the difference in non-DNL splanchnic

sources contribution to VLDL1-TG after the two diets (Δ splanchnic VLDL1-TG palmitate:

splanchnic VLDL1-TG palmitate after HSP – splanchnic VLDL1-TG palmitate after LSP)

and the difference between the visceral fat levels after the two diets (Δ Visceral fat =

Visceral fat after HSP – Visceral fat after LSP) (r = 0.169, P = 0.531). Furthermore, no

association was found between the Δ splanchnic VLDL1-TG palmitate and the

difference between the liver fat levels after the two diets (Δ IHCL = IHCL after HSP –

IHCL after LSP) (ρ = -0.120, P = 0.966).

207


In the whole cohort, the percentage of splanchnic derived VLDL2-TG palmitate and the

correspondent absolute synthesis rate were higher after the HSP than the LSP

(P=0.022 and 0.009 respectively) (Figure 5.7 A and B). In the HLF group (P=0.032), but

not in the LLF group, the percentage of splanchnic derived VLDL2-TG palmitate

production was higher after the HSP than the LSP (P=0.032) (Figure 5.7 C). There was

also a correspondent higher absolute synthesis rate of VLDL2-TG palmitate derived

from splanchnic sources in the same group (P=0.002) (Figure 5.7 D).


After the HSP, there was a higher splanchnic percentage and correspondent absolute

synthesis rate contribution to VLDL2-TG palmitate production (P=0.014 and =0.016

respectively) in men with HLF than in men with LLF (Figure 5.7 B and D respectively).




differences for other splanchnic (non-hepatic DNL) sources contribution to VLDL2-TG

absolute rate of synthesis was significantly higher in the HLF group than the LLF group

(P=0.002, =0.40).

208

Figure 5.7: Other splanchnic sources contribution to VLDL2-TG. Percentage of VLDL2-TG palmitate derived from other splanchnic sources (different from hepatic DNL) (A) and correspondent absolute rate of synthesis (B) in the whole cohort (n=23) after both dietary interventions; Percentage of VLDL2-TG palmitate derived from other splanchnic sources (different from hepatic DNL) (C) and correspondent absolute rate of synthesis (D) in HLF (n=10) and LLF (n=13) groups after both dietary interventions. Data (mean ± SEM) were analysed by paired-samples two-tailed t test (for differences between diets) and independent samples two-tailed t test (for differences between liver fat groups); P values ≤0.050 and correspondent

values are in bold; effect size determined using Cohen criteria: =0.01, small; =0.06, medium; =0.14, large. DNL, de novo lipogenesis; HLF, high liver fat; HSP, high sugar phase; LLF, low liver fat; LSP, low sugar phase; TG, triacylglycerol; VLDL, very low density lipoprotein

209

5.4.4 Summary

5.4.4.1 Between dietary interventions: LSP vs HSP

In men with HLF there was a higher contribution of splanchnic sources to VLDL1-TG on

the HSP compared to the LSP (Figure 5.8 A). However, this did not correspond to a

significantly higher VLDL1-TG PR (see Table 4.2 and Figure 4.4). In the same group,

the higher production of VLDL2-TG with the HSP was mainly due to a higher

contribution of splanchnic sources (Figure 5.8 B).

In men with LLF the higher production of VLDL1-TG on the HSP (see Table 4.3 and

Figure 4.5) was a result of a higher contribution of hepatic DNL and other splanchnic

sources (Figure 5.8 A). In this group VLDL2-TG production and the contribution of

different sources of fatty acids did not differ significantly on the two dietary interventions

(Figure 5.8 B).

5.4.4.2 Between liver fat groups: HLF vs LLF

After the LSP, VLDL1-TG production was higher in the HLF group than in the LLF group

(P=0.007) (see section 4.4.1). This was due to a higher contribution of splanchnic

sources and a higher contribution of DNL as shown in Figure 5.8 A.

After the HSP, a higher contribution of splanchnic sources to both VLDL1-TG (figure 5.8

A) and VLDL2-TG (Figure 5.8 B) production was found in HLF group than LLF group.

However, VLDL1 and VLDL2-TG PR did not differ significantly after the HSP in the two

liver fat groups (see section 4.4.1).

210

Figure 5.8: Contribution of different sources of fatty acids to VLDL-TG. Contribution of different sources of fatty acids to VLDL1-TG PR (A) and VLDL2-TG PR (B) after both dietary interventions, in HLF (n=10) and LLF (n=13) groups. Data are mean contributions (different colours for different sources-see above); data were analysed by paired-samples two-tailed t test (for differences between diets) and independent samples two-tailed t test (for differences between liver fat groups); P values ≤0.050 and correspondent values are in bold; Effect size using Cohen criteria: =0.01, small; =0.06, medium; =0.14, large. DNL, de novo lipogenesis; HLF, high liver fat; HSP, high sugar phase; LLF, low liver fat; LSP, low sugar phase; NEFA, non-esterified fatty acids; TG, triacylglycerol; VLDL, very low density lipoprotein

211

5.4.5 Adipose tissue lipolysis and fat oxidation in the liverPalmitate rate of appearance (Ra) was used as an index of whole body lipolysis. At

isotopic steady state palmitate Ra is equal to the rate of loss from the pool (Rd) also

known as the flux. Palmitate metabolic clearance rate (MCR), a measure of the

efficiency of palmitate removal, was also calculated. Palmitate and plasma NEFA

concentrations were measured during the constant infusion of (U-13C) palmitate.

Plasma concentration of 3-hydroxybutyrate (3-OHB) which reflects hepatic ketogenesis

(Havel et al. 1970), was used as an index of hepatic fatty acid β-oxidation. Results are

shown in Table 5.1.


In the whole cohort, palmitate Ra did not differ significantly after the two dietary

interventions. However, palmitate MCR was higher after the HSP (P=0.007). In men

with HLF, both palmitate Ra and MCR were higher after the HSP than the LSP

(P=0.001 in both cases). Plasma NEFA concentration was also higher after the HSP

than the LSP (P=0.020). Furthermore, 3-OHB concentration was higher on the HSP

than the LSP (+27%), and this difference was borderline significant (P=0.050). In men

with LLF, palmitate kinetics did not differ on the two dietary interventions. However,

plasma NEFA concentration was lower after the HSP than the LSP, and this difference

was borderline significant (P=0.050). The percentage of systemic NEFA converted into

VLDL1-TG (see section 2.7.2.5 for calculation) was 17% after the LSP and 13% after

the HSP, in the HLF group, and 12% after the LSP and 14% after the HSP, in the LLF

group. The percentage of systemic NEFA converted into VLDL2-TG palmitate from

systemic NEFA was approximately 3% in both groups after both dietary interventions.

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Table 5.1: Palmitate kinetics and concentration of palmitate, plasma NEFA and plasma 3-OHB after LSP and after HSP in whole cohort, HLF and LLF groups

LSP HSP P value

All (n=25) Palmitate Ra (mg/day) 58632 ± 3971 62088 ± 3547 0.105 0.10

Palmitate MCR (L/day) 1095 ± 92 1243 ± 99 0.007 0.90

Palmitate (µmol/L) 227 ± 19 217 ± 22 >0.05

Plasma NEFA (µmol/L) 536 ± 30 535 ± 31 0.982 <0.01

3-OHB (µmol/L) 102 ± 16 103 ± 15 0.839 <0.01

HLF (n=11) Palmitate Ra (mg/day) 54374 ± 4404 62292 ± 4018 0.001 0.68


Palmitate (µmol/L) 238 ± 25 220 ± 39 >0.05

Plasma NEFA (µmol/L) 548 ± 44 658 ± 30 0.020 0.44

3-OHB (µmol/L) 73 ± 12 93 ± 15 0.050 0.40

LLF (n=14) Palmitate Ra (mg/day) 61978 ± 6189 61928 ± 5632 0.988 <0.01


Palmitate (µmol/L) 218 ± 28 214 ± 25 >0.05

Plasma NEFA (µmol/L) 526 ± 42 438 ± 31 0.050 0.26

3-OHB (µmol/L) 125 ± 26 110 ± 24 0.279 0.10

Data (mean ± SEM) were analysed by paired-samples two-tailed t test for differences between diets; P values ≤0.050 and correspondent values are in bold; effect size determined using Cohen criteria: =0.01, small; =0.06, medium; =0.14, large. HLF, high liver fat; HSP, high sugar phase; LLF, low liver fat; LSP, low sugar phase; MCR, metabolic clearance rate; NEFA, non-esterified fatty acids; NEFA, non-esterified fatty acids; Ra, rate of appearance; 3-OHB, 3-hydroxybutyrate


The plasma NEFA concentration was higher in the HLF group than the LLF group after

the HSP (P<0.001, =0.52), but not after the LSP. No other significant differences were

found.




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differences for the following measurements were all significantly higher in the HLF

group than the LLF group: palmitate Ra (P=0.039, =0.20), palmitate MCR (P=0.002,

=0.34), plasma NEFA (P=0.002, =0.34) and 3-OHB (P=0.037, =0.20).

5.5 Discussion

5.5.1 Systemic NEFAThe contribution of systemic NEFA to VLDL-TG production did not differ between the

two diets in the two liver fat groups. This is surprising in the men with high liver fat, who

showed a higher palmitate production and clearance rate after the high sugar dietary

intervention, and also a greater response to the high sugar diet than men with low liver

fat. This might be expected to lead to an increased supply of systemic NEFA to the liver

and therefore, a higher contribution to VLDL-TG, but in fact, this did not occur. The

higher palmitate production and clearance rate may not have affected the contribution

of systemic NEFA to VLDL-TG because only a small fraction of systemic NEFA is

incorporated in VLDL-TG. Therefore, a significant difference in lipolysis between the

two interventions would not necessarily result in a significant effect on the contribution

of systemic NEFA to VLDL-TG. One study reported that only approximately 7% of

systemic NEFA was converted in VLDL-TG in the postabsorptive state in lean (n=12) or

overweight/obese but otherwise healthy (n=9) subjects (Koutsari et al. 2013). In the

present study, the percentage of systemic NEFA converted to VLDL1-TG was higher

than in the latter study (17% after the low sugar diet and 13% after the high sugar diet,

in men with high liver fat, and 12% after the low sugar diet and 14% after the high sugar

diet, in men with low liver fat). The percentage of systemic NEFA converted to VLDL2-

TG was approximately 3% in both groups after both dietary interventions. Therefore, the

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percentage of total VLDL-TG palmitate from palmitate Ra in the current study ranges

between 15 and 20%, which is somehow higher than the value found in the above

mentioned study. This might be due to the fact that the participants in the current study

were more insulin resistant than those in Koutsari study, and this might have resulted in

higher levels of systemic NEFA delivered to the liver.

Parks et al. measured the contribution of systemically derived NEFA to total VLDL-TG

palmitate in normolipidemic (n=6) and hypertriglyceridemic (n=5) subjects, after two

isoenergetic diets for 2 weeks: a control diet (carbohydrate 50% E, 45% of which sugar)

and a low fat/high carbohydrate diet (carbohydrate 68% E, 44% of which sugar) (Parks

et al. 1999). They used a constant infusion of [1,2,3,4-13C4] palmitate and determined

systemic NEFA contribution to VLDL-TG by dividing the enrichment of labelled

palmitate in VLDL-TG fractions by the steady state enrichment of plasma palmitate.

They found that the contribution of systemic NEFA was 94% in normolipidemic subjects

and 84% in hypertriglyceridemic subjects after the control diet (mean plasma TG: 1.7

mmol/L), in which the content of simple sugars was similar to the HSP in the current

study. This is higher than in the present study in which the contribution of systemic

NEFA to VLDL1-TG was 38% in the men with high liver fat and 54% in men with low

liver fat after the high sugar diet. Vedala et al. reported a systemic NEFA contribution of

64% in normolipidemic subjects (n=6; mean plasma TG: 0.7 mmol/L), 33% in

hypertriglyceridemic subjects (n=6; mean plasma TG: 2.0 mmol/L) and 58% in

hypertriglyceridemic subjects with type 2 diabetes, after a 2-week eucaloric weight-

maintaining, controlled diet (50% carbohydrate; 35% fat; 15% protein) (Vedala et al.

2006). In this study the systemic NEFA contribution was also determined from a

constant infusion of [1,2,3,4-13C4] palmitate. Interestingly, the systemic NEFA

contribution found in normolipidemic subjects was somehow comparable to the

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contribution to VLDL1-TG found in men with low liver fat after the high sugar diet (64%

and 54% respectively), whereas the systemic NEFA contribution in the

hypertriglyceridemic subjects was very similar to the contribution found in men with high

liver fat after the high sugar diet (38% and 33% respectively).

5.5.2 Hepatic DNLIn the current study, the contribution of hepatic DNL to VLDL1 and VLDL2-TG was small

in both groups and after both dietary interventions, ranging from 4 to 8%. The biggest

difference between the two dietary interventions was observed in men with low liver fat

(4% after the low sugar diet, 7% after the high sugar diet), whereas there was no effect

of sugar intake on hepatic DNL in men with high liver fat, indicating that this pathway is

not flexible in the latter group. Timlin et al. found that the contribution of DNL to VLDL1-

TG was approximately 5% in the fasting state and 13% in the fed state (average value

during the 10-hour study in which subjects received two identical liquid formula meals

with the following composition: 55% E carbohydrate, 30% E fat) in healthy male (n=8,

BMI: 25) (Timlin et al. 2005). In another study from the same group, similar results were

found. The contribution of DNL to VLDL1-TG was approximately 4% in the fasting state

and 8% in the fed state (liquid feeding through tube: 55% E carbohydrate, 30% E fat) in

healthy men (n=12, BMI: 24.4) (Barrows et al. 2006). In the previous two studies the

subjects underwent a run-in diet for the 3 days immediately before the metabolic study,

which was representative of the typical American population (55% E carbohydrate, 30%

E fat). Parks et al. showed that DNL did not contribute substantially to VLDL-TG,

representing less than 5% on either dietary intervention (see section 5.6.1) not only in

normolipidemic subjects but also in hypertriglyceridemic subjects (Parks et al. 1999).

All these studies have reported that the contribution of DNL derived fatty acids to VLDL-

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TG is approximately 5% in healthy people, which is somewhat similar to the results of

the current study. However, other studies have reported a higher contribution of DNL to

VLDL-TG in hypertriglyceridemic individuals. Vedala et al. (see section 5.6.1) reported

that fractional contribution of DNL to VLDL-TG was 3% in normolipidemic subjects, 14%

in hypertriglyceridemic subjects and 13% in T2DM hypertriglyceridemic subjects

(Vedala et al. 2006). Donnelly et al. showed that the fractional contribution of DNL to

fasting VLDL-TG was 22% (approximately 12% over a comparable time period to the

present study) in subjects with NAFLD (n=9, mean plasma TG: 1.7 mmol/L) (Donnelly

et al. 2005). In a double-blinded parallel arm study, Stanhope et al. measured DNL in

overweight and obese subjects who had consumed a hypercaloric diet in the form of

fructose or glucose sweetened beverages for 8 weeks along with their usual ad libitum

diet (fat 30% E, carbohydrate 55% E with fructose or glucose 25% E) (Stanhope et al.

2009). Surprisingly, they found a significant increase of fasting plasma TG level after

the glucose dietary intervention but not after fructose. However, they found that fasting

fractional DNL did not change during either diets (ranging approximately from 8 to 10%,

very similarly to the current study), whereas postprandial DNL was significantly

increased after the fructose diet (from 11 to 19%). In another study, healthy normal

weight men were studied after a control diet (15% proteins, 35% fat, 40% starch, 10%

mono- and disaccharides) and after a 6-day high fructose hypercaloric diet (11%

proteins, 26% fat, 30% starch, 8% glucose and disaccharides, and 25% fructose) (Faeh

et al. 2005), and they found that fasting DNL was significantly higher after the high-

fructose than the control diet (9% vs 2%). In most of these sugar feeding studies, the

percentage of DNL derived fatty acids in VLDL-TG fractions has not been used to

calculate quantitative results such as the DNL derived VLDL-TG palmitate production,

as done in the current study. Furthermore, the above studies used mass isotopomer

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distribution analysis (MIDA) to measure the contribution of hepatic DNL to VLDL-TG,

whereas in the present study a different method based on orally administration of 2H2O

was used (see section 1.11.3 for comparison between the two methodologies).

5.5.3 Other splanchnic sourcesThe most affected source of fatty acids for VLDL-TG by the sugar intake was the

splanchnic source (excluding hepatic DNL). Although the contribution of this source to

VLDL1-TG was higher after the high sugar diet only in men with low liver fat, this did not

correspond to a different response to diet in the two liver fat groups. On the other hand,

the contribution of this source to VLDL2-TG was greater in men with high liver fat after

the high sugar diet, and accounted for the higher VLDL2-TG production in this group

(see section 4.4.1). As discussed in section 1.5, this source includes those fatty acids

draining from the visceral adipose tissue directly to the liver via the portal vein and

those coming from the hepatic depots of TG. The latter will include those fatty acids

coming from the uptake in the liver of TRL remnants. In particular, in the postprandial

state the TG storage in the liver will expand due to the uptake of CM remnants. Some

interesting insights on the uptake of CM-TG come from animal studies. Hultin et al.

estimated that about 10% of dietary fat is taken up by the liver in this form, in rats

(Hultin et al. 1996). In a previous study, Bergman et al. found that approximately 10% of

TG contained in CM was directly taken up by the liver in sheep as compared with about

22% taken up by dog liver (Bergman et al. 1971). Some of the fatty acids coming from

the TG contained in CM remnants will be stored in the hepatic TG storage pool and

may represent a potential source of unlabeled lipid for TG assembly in the liver, which

can be then used for VLDL synthesis in the postabsorptive state. An increased supply

of these CM remnants to the liver postprandially may result from an increased

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postprandial lipemia. It has been shown that dietary sugar has an acute TG elevating

effect.

Nielsen et al. used isotope dilution/hepatic vein catheterization to examine the

contribution of fatty acids delivered to the liver coming from visceral lipolysis (Nielsen et

al. 2004), and found that the splanchnic fatty acid flux accounted for 17% in obese men

and only 6% in lean men. They also found that the release of fatty acids into the portal

vein from lipolysis in visceral fat depots increased with increasing amounts of visceral

fat (visceral fat area determined by computer tomography). In the present study, there

was a significant positive association between the contribution of splanchnic sources of

fatty acids to VLDL1-TG production and total visceral fat in the whole cohort, on both

dietary interventions, although the difference between diets (Δ values) of splanchnic

sources of fatty acids to VLDL1-TG production and total visceral did not differ

significantly. However, there was no difference in the level of visceral fat between the

diets, indicating that the greater contribution of splanchnic sources observed in the

present study after the high sugar phase was mainly due to hepatic TG storage pool

rather than from those fatty acids derived from the visceral fat depots draining directly to

the liver. In their study, Nielsen and colleagues observed that the relative contribution at

any individual visceral fat mass was quite variable (Nielsen et al. 2004). For example,

the relative contribution of fatty acids from visceral fat was lower in some subjects with

a larger amount of visceral fat than it was in others with a smaller amount. Therefore,

although there is a direct relationship between the amount of visceral fat and its

contribution to hepatic fatty acid metabolism, it is impossible to identify those individuals

with a high rate of visceral fatty acid flux based on analysis of body composition and fat

distribution alone.

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5.5.4 Adipose tissue lipolysis and fat oxidation in the liverIn the present study men with high liver fat showed a higher palmitate production and

clearance rate after the high sugar dietary intervention, and also a greater response to

diet than in man with low liver fat. However, as previously discussed in section 5.5.1,

surprisingly, this did not lead to an increased supply of systemic NEFA to the liver and

therefore, a higher contribution to VLDL-TG from this source. The ketone body 3-

hydroxybutyrate (3-OHB) is a product of acetyl-CoA that comes from β-oxidation of fatty

acids in the liver, and blood levels of 3-OHB, which reflect hepatic ketogenesis (Havel

et al. 1970), were used here as an index of hepatic fatty acid β-oxidation, was 27%

higher after the high sugar diet than the low sugar diet in men with high liver fat only

(P=0.050). The higher β-oxidation of fatty acids in the liver may be an adaptive

mechanism to prevent further liver fat accumulation during the HSP as suggested by

Hodson et al. in a study in which they found that ketogenesis was greater in

abdominally obese men (n=9; BMI: 31; fasting plasma TG: 1.2 mmol/L) than in healthy

lean men (n=10; BMI: 22; fasting plasma TG: 0.9 mmol/L) in the postprandial state

(Hodson et al. 2010). In this study, subjects fasted overnight prior to the study day and

received three isoenergetic mixed test meals at 0, 5 and 10 h of the 24-hour study

protocol, and ketone body production arising from the oxidation of dietary fatty acids,

was assessed by measuring the isotopic enrichment from U-13C-labeled fatty acids

appearing in 3-OHB using a procedure based on the method of Beylot et al (Beylot et

al. 1986).

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5.5 ConclusionThis study showed that the sources of fatty acids for VLDL-TG synthesis most affected

by dietary sugar were represented by the splanchnic sources, in men at increased risk

of metabolic syndrome. Surprisingly, no significant effect on the contribution of those

fatty acids coming from the systemic NEFA pool and from hepatic DNL, were found.

Furthermore, the two liver fat groups responded differently to the diets. In fact, the high

sugar diet only produced a significant effect in men with high liver fat, in which the

contribution of splanchnic sources of fatty acids to VLDL2 (but not to VLDL1)-TG

production was higher than the low sugar diet. The hepatic TG storage pool appears to

be the component of splanchnic sources most likely determining this response.

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Chapter 6: General discussion

6.1 Response to low and high sugar dietsThe present study was part of a project funded by the BBSRC to investigate the effects

of dietary sugar on lipoprotein metabolism. The main focus of this thesis was to

investigate the relationship between dietary sugar and VLDL-TG metabolism in the

fasting state in men at increased risk of metabolic syndrome. At the same time, the

impact of liver fat on the response to diets, low and high in extrinsic sugars, was

investigated. A novel aspect of this study was the development of a dietary model for

the isocaloric exchange of starch for sugar that achieved intakes of sugar that were

representative of the lower 2.5th percentile for the low sugar diet and the upper 2.5th

percentile for the high sugar diet. The mean intake of extrinsic sugars during the low

sugar phase was about 6% of total energy intake, that is close to the recent guidelines

of SACN in the UK (about 5% of daily energy intake in the general population) reported

in the Draft Carbohydrates and Health report (SACN 2014), whereas, the mean intake

of extrinsic sugar during the high sugar phase was about 5-fold greater than the

recommended intake. The present study supports the idea that high sugar intake can

have a detrimental effect on plasma lipids and on the accumulation of liver fat.

Furthermore, this study showed that the level of liver fat plays an important role in the

way men at increased metabolic risk respond to dietary sugar. These differences may

be the consequence of metabolic adaptations to the high liver fat content, although it is

not possible to exclude that there might be underlying genetic factors that predispose

some men to the accumulation of liver fat.

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Liver fat was higher in both liver fat groups after the high sugar diet, although the


fat, as shown in Figure 4.8 A and B. Men with high liver fat accumulated a much greater

amount of ectopic fat in the liver as a result of the high sugar diet compared to men with

low liver fat. On the other hand, the high sugar diet resulted in a greater production of

VLDL-TG in the low liver fat group compared to the low sugar diet, as shown in Figure

4.8 C and D. Also in men with high sugar liver VLDL-TG production was higher after the

high sugar diet than after the low sugar diet, but this difference did not reach

significance Interestingly, the two variables show an opposite behaviour that could be

explained in these terms: men with low liver fat tended to cope better with the high

sugar diet with regard to liver fat accumulation, although the high sugar intake resulted

in greater export of TG by the liver in VLDL particles; on the other hand, men with high

liver fat were less able to respond to the high sugar diet by increasing VLDL-TG

production and were also more prone to accumulate further fat in their liver. The effect

of diet in the two liver fat groups was also examined in order to assess if VLDL-TG

metabolism showed a different response to dietary sugar. Surprisingly, it was found that

the effect of the diet was not different in the two liver fat groups for VLDL1-TG

production, although this was significantly higher in men with low liver fat (but not in

men with high liver fat group) after the high sugar phase than the low sugar phase

(+34%).

The mean differences (means of Δ values between diets for each paired measurement

[high sugar – low sugar] in the two liver fat groups) for VLDL1-TG production rate were

not significantly different, therefore it is not possible to conclude that the two liver fat

groups responded differently. On the other hand, VLDL2-TG production was significantly

higher in men with high liver fat (but not in men with low liver fat group) after the high

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sugar phase than the low sugar phase (+34%). Furthermore, the mean differences for

VLDL2-TG production rate were significantly higher in the men with high liver fat than in

men with low liver fat, and this was due to a greater response of both VLDL2-TG direct

production by the liver and VLDL2-TG production from VLDL1 to the high sugar diet.

These results show that the response of VLDL2-TG production was greater in men with

low liver fat.

A priori power analysis showed that with a data set of 30 men, there was 80%

probability that the study would detect a difference of 26% in VLDL1-TG production rate

between the two diets (see section 2.4). The objective for the present study was initially

to recruit 36 participants with an allowance for a 20% drop-out rate (which was equal to

three participants per intervention arm). However, only 25 participants were eventually

recruited and of these it was possible to obtain the kinetics result only from 24

participants (14 with low liver fat and 10 with high liver fat). In order to determine if the

study was underpowered, a post hoc power analysis was done. When considering the

whole cohort, the analysis showed that the study was sufficiently powered for total

VLDL-TG production rate, indicating that the observed difference between the two

dietary interventions was reliable (21% higher after the high sugar diet than the low

sugar diet). However when considering the two liver fat groups separately, it resulted

that the study was sufficiently powered only for the low liver fat group. In the high liver

fat group the power was 0.20. Therefore, it was possible to extrapolate that 14 subjects

would have been needed in this group in order to achieve a power of 0.80 and be able

to detect at a significant level (P<0.05) a difference of 26% between dietary

interventions. This corresponds to the difference observed in the high liver fat group

between the production rate after the high sugar diet and the production rate after the

low sugar diet for total VLDL-TG. The same would apply to VLDL1-TG production rate

224

since this account for most of the total VLDL-TG production in the present study, and

furthermore, the two parameters are highly correlated (data not shown).

Surprisingly, systemically derived fatty acids and those coming from hepatic DNL were

not the main player in determining the effects observed in VLDL-TG production after the

high sugar diet. Hepatic DNL was affected by dietary sugar only in the men with low

liver fat, and this source only slightly contributed to the higher VLDL1-TG production

observed in this group after the high sugar diet. The source of fatty acids that was most

affected by dietary sugar in the present study was the splanchnic (non-hepatic DNL)

source that includes those fatty acids draining from the visceral adipose tissue directly

to the liver via the portal vein and those coming from the hepatic depots of TG. This

source was responsible for the higher VLDL1-TG production seen in men with low liver

fat as well as for the higher VLDL2-TG production in men with high liver fat observed

after the high sugar diet. Interestingly, the mean difference for the contribution of this

source to VLDL2-TG production rate was significantly higher in the men with high liver

fat than in men with low liver fat showing that the response to diet for this source was

greater in men with high liver fat. These findings are in contrast with what was initially

hypothesised, which was that the high sugar diet would result in a higher VLDL-TG

secretion (due to higher production of TG-rich VLDL1 particles), and that the major

contributor to this effect would have been those fatty acids coming from systemic NEFA

and hepatic DNL sources. Furthermore, the level of visceral fat was not significantly

different in either group, on either diet, pointing to the fact that the greater contribution

of splanchnic sources observed in the present study after the high sugar phase was

mainly due to hepatic the TG storage pool rather than to those fatty acids derived from

the visceral fat depots draining directly to the liver. Figure 6.1 shows how the different

sources influence VLDL-TG production on the two interventions.

225

Figure 6.1: Effect of diet on VLDL-TG metabolism. The effect of the HSP compared to the LSP in the HLF (A) and the LLF (B) groups. In men with HLF there was a higher contribution of non-DNL splanchnic sources to VLDL-TG, larger VLDL1 particles, higher VLDL2-TG production rate (PR) and larger VLDL2 particles. In men with LLF there was a higher contribution from hepatic DNL and other splanchnic sources to VLDL-TG, a higher VLDL1-TG PR and smaller VLDL2 particles although higher in number. No differences were observed in the clearance in both groups and after both dietary interventions. ApoB, apolipoprotein B100; DNL, de novo lipogenesis; HLF, high liver fat; HSP, high sugar phase; LLF, low liver fat; LSP, low sugar phase; NEFA, non-esterified fatty acids; PR, production rate; TG, triacylglycerol; VLDL, very low density lipoprotein

226

It is worth at this point taking into account also other outcomes of the study in order to

gain a better insight into the response of lipid metabolism to dietary extrinsic sugars. It

has been suggested that high levels of plasma TG (as for men with high liver fat in the

present study) may increase lipoprotein remodelling leading to the formation an

atherogenic lipoprotein phenotype (Griffin et al. 1995). The ability of plasma lipoproteins

to penetrate the endothelial lining and enter the arterial intima depends largely on their

particle size (Nordestgaard et al. 1995). In this study IDL and LDL-apoB kinetics and

hepatic lipase activity were also determined by others in the research team. The results

that follow have not been published yet. IDL-TG levels and IDL-apoB production rate as

well as LDL2 and LDL3-apoB production rates were significantly higher after the high

sugar diet than the low sugar diet only in men with high liver fat. This might be a

consequence of the higher flux to these particles due to the larger VLDL2-TG pool size

after the high sugar diet. Furthermore, a higher activity of hepatic lipase was found after

the high sugar diet, which could have contributed to this effect, since this enzyme plays

an important role in the remodelling of VLDL particles to IDL and LDL (Zambon et al.

2003). In line with these results, higher levels of the atherogenic sdLDL particles (one of

the major features of the atherogenic lipoprotein phenotype) were also found after the

high sugar diet compared to the low sugar diet in the men with high liver fat but not in

those with low liver fat. This finding suggests a beneficial effect of a reduction of sugar

intake in men with high liver fat on the production of atherogenic lipoproteins and on the

liver fat. Since the latter cannot be targeted pharmacologically, this work shows that a

lifestyle intervention, such as a reduced intake of sugar in the diet, offers a possibility

for the treatment of this condition.

In a recent study, 15 patients with NAFLD were asked to reduce the intake of fructose

by 50% for 6 months in order to determine the effect of this measure on the level of liver

227

fat (Volynets et al. 2013). This intervention resulted in a reduction of the levels of liver

fat (-36%, determined by 1H-MRS) at the end of the study. However, since this change

was accompanied by a small but significant reduction of body weight, as well as a

substantial reduction in sucrose (-70%) and glucose (-63%) intakes along with a

considerable decrease total energy intake (-37%), it is not possible to establish whether

this effect was a direct result of fructose (or other sugars) reduction or was due to the

changes in dietary composition and energy restriction.

6.2 LimitationsSome limitations and practical constraints were identified in the present study. Some of

the outcomes from the present study and especially those from the analysis of the two

liver fat groups separately, would be more reliable with a larger sample size, as resulted

by size effect analysis. Another limitation was the short length of the wash out diet that

in the present study was 4 weeks. Since the present study involved the free-living

consumption of foods in the homes of participants, this may have posed a problem

related to the control of dietary compliance. However, efforts were made in order to

minimise this effect through regular home visits (every fortnight during each

intervention) carried out in order to deliver the study food, to measure body weight and

to control dietary compliance. Participants were also instructed to avoid any deviation

from the study protocol and to maintain their body weight. Furthermore, in order to

obtain accurate dietary intake, the participants were asked to provide labels and

packaging from the food they had consumed and detailed recipes and size portions.

There was no significant difference in energy consumption between the two diets. On

the other hand, despite the fact that efforts were made in order to maintain isocaloric

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condition, there was a difference in body weight between the two dietary interventions.

However, this difference was the same in both groups, so any differences in the lipid

response between the two groups were not a direct effect of changes in body weight.

Since the consumption of fructose was not determined, it was not possible to

differentiate between the effect of this monosaccharides and glucose, although these

two are found together in sucrose.

6.3 Future workA larger number of subjects would be needed in a future study looking at the effect of

dietary sugar on liver fat and lipoprotein metabolism in order to obtain a higher

statistical power. In planning a study with a similar design it would be possible to

minimise any order or carry-over effects between dietary interventions by extending the

duration of the wash-out period for 2 to 3 months. By extending the duration of the

dietary interventions it would also be possible to investigate the effect of sugar

consumption on lipoprotein metabolism and liver fat (and visceral fat) accumulation over

a longer period. For instance, in a 6-month intervention it would be possible to study

the effect of the diets at intermediate time points (e.g. after 2 and after 4 months),

allowing an insight into how lipid metabolism evolves over time. It would be also

interesting to investigate possible gender differences in the response to dietary sugar in

men and women with similar cardiometabolic characteristics. Another important aspect

to consider in future studies is the determination of fructose consumption along with the

consumption of other sugars in order to be able to differentiate between the effect of

glucose and fructose.

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This study highlights the importance of non-hepatic DNL splanchnic sources, since this

component was responsible for most of the differences observed when comparing the

two diets. For this reason, it would be interesting to gain a better understanding of the

role of non-DNL splanchnic sources in VLDL-TG metabolism. Since this source

includes fatty acids from the visceral fat draining to the liver via the portal vein and

those originated from CM remnants (stored in the TG depots within the hepatocyte

before being secreted as VLDL-TG), it would be interesting to investigate all these

sources at the same time. For example, it would be interesting to label CM-TG by giving

a labelled fatty acid as part of a meal and then follow its metabolic fate in the fasting

state. However, it would be most feasible to label CM and follow their entry into VLDL in

the postprandial period (and then possibly postabsorptive the next morning) (Hodson et

al. 2010). In order to determine the contribution of fatty acids from visceral fat, it is

necessary to determine the percentage of fatty acids being delivered to the liver from

visceral adipose tissue lipolysis. A possible approach would consist of the infusion of

two different tracers, one for systemic derived fatty acids the other for visceral fat

derived fatty acids, to calculate the dilution of visceral fatty acids in the hepatic vein

(Nielsen et al. 2004). However, several assumptions have to be made, such as that the

fractional hepatic uptake of fatty acids is the same regardless of whether they reach the

liver via the portal vein or the hepatic artery. The major limitation of this approach is the

fact that it is much more invasive than the infusion protocol used for the present study,

since catheters have to be surgically placed in the hepatic vein and portal vein in order

to infuse the tracer and to collect blood samples.

Another important point to consider is that in the present study only men homozygous

for E3 were recruited. Since the three different isoforms show different affinity for the

LDL-receptor, it would be interesting to investigate how different isoforms affect the

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response to dietary sugar in a similar study. And also, following the same concept, it

would be possible to apply this approach in groups selected according to a particular

genotype associated with an increased/decreased cardiometabolic risk.

6.4 ConclusionIn conclusion, those men at increased risk of metabolic syndrome with high liver fat

showed a greater response in terms of liver fat and atherogenic lipoprotein phenotype

compared to the low liver fat group. However, the high sugar diet also caused negative

consequences in men with low liver fat, including the accumulation of liver fat.

Importantly, a low sugar intake, close to the latest guidelines for sugar consumption in

the general population (5% total energy intake according to the World Health

Organisation and UK’s Scientific Advisory Committee on Nutrition), which is similar to

the intake of sugar in the present study, may produce beneficial changes both to

lipoprotein metabolism and liver fat, thus improving the cardiometabolic health in these

individuals.

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