Association of vitamin D receptor genetic polymorphism to type 2 diabetes mellitus in local population
By
Naila Abdul Sattar
2006-ag-371
Ph.D. Biochemistry (UAF)
A Thesis submitted in partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHYIN
BIOCHEMISTRY
DEPARTMENT of BIOCHEMISTRY
FACULTY OF SCIENCES
UNIVERSITY OF AGRICULTUREFAISALABAD,
PAKISTAN2016
DECLARATION
I hereby declare that the contents of the thesis,Association of vitamin D receptor genetic
polymorphism to type 2 diabetes mellitus in local populationare the product of my own
research and no part has been copied from any published source (except the references,
standard mathematical or genetic models/equations/formulate/protocols etc.). I further
declare that this work has not been submitted for the award of any other diploma/ degree.
Naila Abdul Sattar 2006-ag-371
1
To,
The Controller of Examination,
University of Agriculture,Faisalabad.
We, the supervisory committee, certify that contents and form of thesis submitted by
Miss. Naila Abdul Sattar, Regd. No. 2006-ag-371 have been found satisfactory and
recommend that it be processed for evaluation by the external examiner(s) for award of
degree.
SUPERVISORY COMMITTEE
1) Chairman ___________________________
Dr. Fatma Hussain
2) Member ___________________________
Prof. Dr. Amer Jamil
3) Member ___________________________
Dr. Raja Adil Sarfraz
2
DEDICATED TO
ALMIGHTY ALLAHHE gave me health and ability to do work
HAZRAT MUHAMMAD(PEACE BE UPON HIM)
He gave the message of ALLAH to mankind so that mankind may
follow the right path
MY LOVING, SWEET & CARING
FAMILY &
MY RESPECTED TEACHERS
They supported me with affectionate and prayers
3
ACKNOWLEDGEMENTS First and foremost, I would like to thank ALLAH for all the blessings He’s given me, and His
Prophet Hazart Muhammad (SAW) to bless me, for my family and friends, for the abilities and
the opportunities and also for the strength and guidance.
I would like to express my sincerest gratitude to my PhD supervisor, Dr. Fatma Hussain and my
second supervisor Prof. Dr. Amer Jamil for their time, constant support and encouragement.
You have taught me how to think critically, write scientifically and present research
meaningfully. I am truly grateful for your mentorship over these past four years and am certain
that they will guide me in my career path. A special thanks as well to my supervisory committee
member Dr. Adil Sarfraz, for sharing their time and knowledge, which have played an essential
role in the successful completion of this dissertation. I would also like to acknowledge the High
Education Commission, Pakistan for granting me a scholarship to fund this research, as well as
the Bristol University, North England, UK., which has also contributed to my professional
development by providing me with supplementary funds to travel to international conferences to
disseminate study findings.
To members of the Clinical and Molecular lab (past and present) - although my time in the lab
with some of you may not have been long, thank you for your friendship and kindness.
Thanks as well to my it was always comforting knowing you were there to experience the
struggles and accomplishments of graduate school with me.
To all my friends for their support and encouragement, and for their understanding when I was
always busy with school.
To my parents - I could not be where I am without your encouragement, support, unconditional
love, and guidance. To my sisters, Shumaila, Bushra and Iqra, and brother Haider thank you
for being patient with me and for supporting me through everything. To my nephews, Arman
and Shehram– for knowing that every call or visit would brighten my spirit, and remind me of
the joy in the simplest things. To my friends –Madiha and Nadia. Thank you for your love,
patience and constant support; and for always being there to listen to my ramblings and
complaints, and sharing in my successes.
It has been a great experience and I am truly grateful for all those who have helped me get to
where I am today.
Naila Abdul Sattar
Dated: 07-10-2016
4
Table of Contents
CHAPTER 1: INTRODUCTION............................................................................................. 1
Need of the following study………………………………………………………………... 5
Objectives…………………………………………………………………………………………. 6
CHAPTER 2: REVIEW OF LITERATURE.......................................................................... 7
2.1 Type 2 diabetes mellitus.......................................................................................................... 7
2.1.1 Background....................................................................................................................... 7
2.1.2 Pathogenesis ................................................................................................................ 7
2.1.3 Risk Factors ...................................................................................................................... 8
2.2 Beta cell dysfunction and insulin sensitivity……......................................................... 8
2.2.1 Role in diabetes mellitus etiology …………… ................................................................ 8
2.3 Vitamin D…………………………. .............................................................................. 10
2.3.1 Background …………………………………………........................................................ 10
2.3.2 Sources of Vitamin D .................................................................................................. 12
2.3.3 Metabolism of Vitamin D ........................................................................................... 13
2.3.4 Factors affecting vitamin D levels... ............................................................................. 16
2.4 Vitamin D and type 2 diabetes mellitus …………………...…. ............................................... 17
2.4.1 Association of vitamin D and insulin resistance .............................................................. 19
2.4.2 Mechanism………………..…............................................................................................ 19
2.4.3 Role of genetics………..................................................................................................... 20
2.4.4 Vitamin D polymorphisms and type 2 diabetes mellitus.................................................. 21
2.5 Rationale/ Summary ......................................................................................................... 23
CHAPTER 3: MATERIALS AND METHODS ………........................................................ 25
3.1 Sample collection…………………………………………………………………………. 25
3.2 Exclusion and inclusion criteria…………………………………………………………. 25
3.3 Physical Parameters……………………………………………………………………… 25
3.4 Reagents, chemicals, kits and instrumentation………………………………………...... 26
3.5 Collection and storage of blood…………………………………………………………. 26
3.6 Biochemical Parameters……………………………………………………………….. 26
3.6.1 Plasma Glucose………………………………………………………………………... 26
3.6.1.1 Procedure……………………………………………………………………………. 26
3.6.2 Glycated hemoglobin (HbA1c)……………………………………………………….. 27
3.6.2.1 Procedure…………………………………………………………………………….. 27
5
3.6.3 Serum Vitamin D3 ……………………………………………………………………... 27
3.6.3.1 Procedure …………………………………………………………………………….. 27
3.6.4 Liver functions tests ……….……………………………………………………………. 28
3.6.5 Renal function tests……………………………………………………………………. 28
3.6.6 Lipid profile……………………………………………………………………………...... 28
3.7 DNA Extraction…………………………………………………………………………… 28
3.8 DNA quantification……………………………………………………………………….. 29
3.9 PCR primer………………………………………………………………………………… 29
3.10 Optimization of amplification condition……………………………….......................... 30
3.10.1 Polymerase chain reaction (PCR)………..…………………………………………… 31
3.11 Enzymatic Digestion……………………………………………………………………. 31
3.12 Electrophoresis…………………………………………………………......................... 32
3.13 Statistical Analysis…………………………………………………………………………. 32
CHAPTER 4: RESULTS AND DISCUSSION……………………………………………… 33
4.1 Demographic and biochemical indices………………………………………………………. 33
4.1.1 Comparison of demographic parameters………………………………………………… 33
4.1.2 Comparison of biochemical parameters………………………………………………… 37
4.1.3 Comparison of biochemical parameters in T2DM complications sub-groups and control groups
…………………………………………………………………………………………… 38
4.1.3.1 Liver function tests ……………………………………………………………………… 39
4.1.3.2 Renal function tests………………………………………………………….................. 39
4.1.3.3 Lipid profile ……………………………………………………………………………. 39
4.2 Association of VDR gene polymorphisms with T2DM and its complications…………… 47
4.2.1 ApaI polymorphisms in T2DM and control groups……………………………………… 47
4.2.2 FokI polymorphisms in T2DM and control groups……………………………………… 53
4.2.3 BsmI polymorphisms in T2DM and control groups……………………………………… 58
4.2.4 TaqI polymorphisms in T2DM and control groups……………………………………… 63
4.3 General discussion on VDR gene polymorphisms…………………………………………. 68
CHAPTER 5: SUMMARY..................................................................................................... 73
5.1 Future direction.................................................................................................................... 74
CHAPTER 6: REFERENCES ................................................................................................. 75
CHAPTER 7: APPENDICES................................................................................................ 103
A.1 TBE buffer……………………………………………………………………………………... 103
A.2 Sodium dodecyl sulfate ………………………………………………………………... 103
6
A.3 Colorless GoTaq® Flexi Buffer……………………………………………………….,,, 103
7
LIST OF TABLES
TABLENO.
TABLE TITLE PAGENO.
2.1 VDR gene polymorphisms associated to T2DM 23
3.1 VDR primers 31
3.2 Enzymatic digestion conditions 32
4.1 Comparison of demographic parameters between diabetic and control groups 36
4.2 Comparison of biochemical parameters between diabetic and control groups 37
4.3 Biochemical characteristics of type 2 diabetic patients with complications and healthy controls 42
4.4 Comparison of means of biochemical parameters in type 2 diabetic complications groups 43
4.5 Analysis of variance (mean squares) for biochemical parameters in type 2 diabetic complications groups 43
4.6 Comparison of means of liver functions tests s parameters in type 2 diabetic complications groups 44
4.7 Analysis of variance (mean squares) for liver function tests in type 2 diabetic complications groups 44
4.8 Comparison of means of renal function tests s parameters in type 2 diabetic complications groups 45
4.9 Analysis of variance (mean squares) for renal function tests in type 2 diabetic complications groups 45
4.10 Comparison of means of lipid profile parameters in type 2 diabetic complications groups 46
4.11 Analysis of variance (mean squares) for lipid profile in type 2 diabetic complications groups 46
8
4.12 Distribution of genotype, allele frequencies and carriage rate of ApaI among patients and controls 49
4.13Distribution of genotype, allele frequencies and carriage rate of ApaI among T2DM complications sub-groups with control group
50
4.14 Probability values for the association of biochemical parameters and ApaI genotypes in T2DM subgroups 51
4.15 Distribution of genotype, allele frequencies and carriage rate of FokI among patients and control 55
4.16Distribution of genotype, allele frequencies and carriage rate of FokI among T2DM complications sub-groups with control group
55
4.17 Probability values for the association of biochemical parameters and FokI genotypes in T2DM sub-groups 56
4.18 Distribution of genotype allele frequencies and carriage rate of BsmI among patients and control 60
4.19 Distribution of genotype, allele frequencies and carriage rate of BsmI among T2DM sub-groups and control group 60
4.20 Probability values for the association of biochemical parameters and BsmI genotypes in T2DM sub-groups 61
4.21 Distribution of genotype, allele frequencies and carriage rate of TaqI among patients and control 65
4.22 Distribution of genotype, allele frequencies and carriage rate of TaqI among T2DM sub-groups and control group 66
4.23Probability values for the association of biochemical
parameters and TaqI genotypes in T2DM sub-groups 66
A.1 Comparison of demographic parameters 104
A.2 Comparison of biochemical parameters 104
A.3 Comparison of liver function tests 105
A.4 Comparison of renal function tests105
A.5 Comparison of lipid profile 106
LIST OF FIGURES
9
FIGURENO.
FIGURE TITLE PAGENO.
1.1Schematic diagram illustrating relative effect of
environmental and genetic risk factors 1
2.1 Chemical Structure of vitamin D 4
2.2 Metabolism of vitamin D 15
2.3Vitamin D metabolism and it biological actions by nuclear
vitamin D receptor16
4.1Electrophoresis of a 2% agarose gel with exon 9 PCR product 48
4.2 Electrophoresis of a 3% agarose gel with ApaI enzymatic digestion of VDR exon 9
49
4.3 ApaI digestion polymorphism in RP group 52
4.4 ApaI digestion polymorphism in NP group 52
4.5 ApaI digestion polymorphism in CP and HP groups 53
4.6Electrophoresis of a 2% agarose gel with exon 2 PCR
product54
4.7 Electrophoresis of a 3% agarose gel with FokI enzymatic digestion of VDR exon 2
55
4.8 FokI digestion polymorphism in RP group 57
4.9 FokI digestion polymorphism in NP group 57
4.10 FokI digestion polymorphism in CP and HP groups 58
4.11 Electrophoresis of a 2% agarose gel with intron 8 PCR product
59
4.12 Electrophoresis of a 3% agarose gel with BsmI enzymatic digestion of VDR intron 8
60
4.13 BsmI digestion polymorphism in and CP and HP groups 62
4.14 BsmI digestion polymorphism in NP group 62
4.15 BsmI digestion polymorphism in RP 62
4.16 Electrophoresis of a 2% agarose gel with exon 9 PCR product
64
4.17 Electrophoresis of a 3% agarose gel with TaqI enzymatic digestion of VDR exon 9
64
10
4.18 TaqI digestion polymorphism in CP and HP 69
4.19 TaqI digestion polymorphism in NP group 69
4.20 TaqI digestion polymorphism in RP 69
11
ABSTRACTType 2 diabetes mellitus (T2DM) is an increasingly common metabolic disorder with a
substantial inherited component. The inheritance pattern is complex and polymorphisms of
several genes might influence genetic susceptibility of the disease that is characterized by
islet dysfunction and insulin resistance. Although various characteristics of diabetes mellitus
in local population have been investigated, progress in defining genetic factors is meager. As
the genetic architecture of T2DM may vary between diverse ethnic populations, it is critical
that such variants are examined in Pakistani population. The present project was aimed to
investigate association of vitamin D receptor (VDR) gene polymorphisms with T2DM in
Pakistan. Methodolgy included documentation of demographic charateristics and
comparative analysis of biochemical parameters (glucose, HbA1c, vitamin D, lipid profile,
liver function tests and renal function tests) in diabetic and normal participants. Genomic
DNA was used for genotyping of four restriction fragment length polymorphism (RFLP)
sites; BsmI, ApaI, TaqI and FokI by polymerase chain reaction (PCR) amplifications and
restriction endonuclease digestion of the products. The digested PCR products were
separated on agarose gel electrophoresis. Among all the demographic parameters, systolic
and diastolic blood pressure and BMI (body mass index) were significantly higher (p<0.001)
in diabetic group as compared to the control group. Hyperglycemia, renal and lipid profiles
were significantly inversely associated (p<0.01) to vitamin D levels in T2DM subjects.
Differences of FokI, BsmI and TaqI genotypes of VDR gene were significant between T2DM
and normal groups (p<0.01). While ApaI showed non-significant association to the T2DM in
local population. No significant association was found between biochemical parameters and
all four restriction sites (ApaI, BsmI, FokI and TaqI) (p>0.01). In addition, VDR gene
polymorphisms were related non-significantly (p>0.05) to the diabetic complications in the
present study. To conclude, VDR gene polymorphisms (BsmI, FokI and TaqI) may contribute
to the onset and progression of T2DM in local Pakistani population but association between
VDR genetic polymorphisms to various diabetic complications is still not clear and warrants
additional functional genomics studies to verify the genetic susceptibility of VDR gene to
T2DM onset and progress.
12
Chapter 1 INTRODUCTIONDiabetes mellitus is a metabolic syndrome, categorized by various etiologies such as
prolonged hyperglycemia through impaired metabolisms of fat, carbohydrate and protein,
resulting from defects in insulin sensitivity and its secretion or both. Approximate global
prevalence of diabetes is about 300 million patients by the year 2025 (Pasquier, 2010).
WHO (world health organization) categorized this disease into type 1 diabetes mellitus
(insulin dependent diabetes mellitus or IDDM), type 2 diabetes mellitus (non-insulin
dependent diabetes mellitus or NIDDM), gestational diabetes mellitus (GDM) and other
specific types related to genetic defects in pancreatic cells and insulin action. Current
classification integrates etiological and clinical criteria and staging of disease (Figure 1.1)
(American diabetes care, 2013).
Figure 1.1: Schematic diagram illustrating relative effect of environmental and
genetic risk factors, LADA: late onset autoimmune diabetes of the adults, MODY:
maturity onset diabetes of the young, T1D: type 1 diabetes, T2D: type 2 diabetes
(Strawbridge et al., 2008).
Most ubiquitous form of diabetes is T2DM. More than 90% of all diagnosed diabetic cases
belong to this type, affecting 246 million people worldwide. It is characterized by insulin
resistance and beta cell dysfunction and is one of the leading causes of death and disability.
Despite the great advances that have been made in the understanding and management of this
complex, multifactorial disease, the one fifth of world population has T2DM. In South Asia,
13
ethnicity is the major risk factor for T2DM onset. With the limited understanding of
underlying cause of T2DM, a comprehensive acquaintance becomes vital (Garduno-Diaz and
Khokhar, 2012; Bakker et al., 2013).
T2DM patients have significantly higher risk for a variety of vascular complications;
retinopathy, neuropathy, atherosclerosis, cardiovascular diseases, hypertension leading to
infections (Shoback and Dolores 2011; Sreelatha et al., 2015). Perhaps there are a number of
different causes of T2DM, though exact etiologies are still not known. Combination of
genetic and environmental factors that contribute to T2DM onset are life style, dietary habit,
BMI, hypovitaminosis D and family history. Physical inactivity and obesity are
consequences of overweight which contribute to prone T2DM through insulin resistance.
Obesity is prevalent in developed and developing countries even in urban part of the world.
Predominant distribution of fat in non-obese people is accountable of T2DM (Waugh et al.,
2010; Herder and Roden, 2011). Although a few etiological hazards for the development of
insulin resistance and dysfunction of beta cell have been recognized, gaps remain in
understanding etiology of such disorders. In addition, factors associated with the longitudinal
evolution of these diseases have received very limited studies (American diabetes care,
2015).
The incidence of T2DM is related with vitamin D status as it participates in glucose
metabolism and insulin release (Ozfirat and Chowdhury, 2010; Talaei et al, 2013).
Hypovitaminosis D predisposes individuals to T2DM. Vitamin D has an influence on
immune system and is also involved in the insulin synthesis and secretion. The diabetic
individuals are more vitamin D deficient than non-diabetic individuals (Sheth et al., 2015).
Until now, majority of research on the relationship of vitamin D with T2DM has been cross-
sectional, revealing significant link of low level vitamin D with higher insulin resistance and
dysfunction of beta cells; although a few studies have reported no association (Gysemans,
2008; Carnevale et al., 2012; Husemoen et al., 2012).
Serum vitamin D levels are associated with insulin resistance and beta cells function in
healthy population (Tao et al., 2013). While in T2DM, elevated vitamin D levels mean better
β-cell function (Kayaniyil et al., 2011). Similarly, vitamin D is known to stimulate aorta
dilation in T2DM (Kuloglu et al., 2013). Over the last decade, vitamin D is gaining more
attention to its potential role in a variety of health conditions; cancer, cardiovascular disease,
14
multiple sclerosis and diabetes. In particular, emerging evidence recommends a possible link
between low levels of vitamin D nutritive status with higher risk of T2DM (Li et al., 2013;
Sheth et al., 2015).
T2DM may be lead to hypovitaminosis D that is frequently accompanied by increasing
inflammatory factors; tumor necrosis factor and interleukin-6. Such abnormalities detected in
systematic inflammation markers can directly affect insulin signaling through various
mechanisms consequently developing insulin resistance (Kolb and Mandrup-Poulsen, 2005).
The bioavailability of vitamin D3 may be good biomarker for the association of vitamin D to
BMD (bone mineral density), nephron osteodystrophy and T2DM (Song et al., 2011; Khan et
al., 2012; Aghajafari et al., 2013; Lim et al., 2013). Vitamin D has anti-proliferative as well
as immune-modulatory attributes (Marques et al., 2010; Nosratabadi et al., 2010).
T2DM is a complex metabolic disorder with strong genetic components. Recent advances in
genome-wide association studies (GWAS) have revolutionized knowledge regarding the
genetics of T2DM. GWAS related genes directly to insulin secretion and indirectly, through
collaborating with other genes, to insulin resistance. There are at least 64 common genetic
variants that are strongly associated with T2DM. However, the pathophysiologic roles of
these variants are mostly unknown and require further functional characterization (Jain et al.,
2013; Kwak and Park, 2013). Mostly candidate genes, familial linkage and genomic analysis
are used to discover T2DM hereditary (Oh and Barrett-Connor, 2002; Filus et al., 2008).
Although, gene variants possess an unpretentious influence accounting for only 10% of the
T2DM heritability, advances in futuristic gene sequencing may explore unique variants with
prominent impact resulting in the better understanding of pathology and therapeutic
approaches (Park, 2011). Variations in the gene sequences such as single nucleotide
polymorphisms (SNP) explain the individual differences in traits like disease susceptibility
and response to treatment (Anuradha, 2013). Candidate genes for T2DM risk present in
specific genome parts are classified as those involved in disease onset, associated pathways
and functions (Hale et al., 2012).
Various molecular epidemiological studies reveal that both T2DM and obesity are important
inherited traits. Though, despite ample research, stratification of specific reasons for these
common conditions at the genetic level is still in its infantile. More detailed ideas for their
molecular mechanisms are considered to increase the chances of a better treatment and in
15
some cases to prevent disease development (Ripsin et al., 2009; Abdullah et al., 2010).
Genetic polymorphism of vitamin D receptor (VDR), vitamin D binding protein (DBP) and
CYP1alpha genes can affect insulin secretion and cause insulin resistance. Furthermore,
these genetic polymorphisms can affect vitamin D synthesis, transportation and action (Sung
et al., 2012). VDR gene is present on chromosome 12q12-q14 (Christakos et al., 2003),
which mediates vitamin D action as it binds to vitamin D response elements (VDRE)
(Maestro et al., 2003; Calle et al., 2008).
Only calcitriol is metabolically able to activate VDR gene. VDR belong to a super-family of
nuclear receptor of the ligand-activated transcription factors including thyroid hormone
receptors, estrogen receptor, peroxisome proliferators-activated receptors and retinoic acid
receptors. Up to 200 genes are activated by 1, 25 (OH) 2D adopting a very complicated
mechanism which is just unrevealed. VDR is extensively expressed in immune system,
stimulates T and B cells, dendritic cells and macrophages are directed to recognition of
central immune-modulatory function of vitamin D and detection of VDR in pancreas leading
to recognition of vitamin D role in the insulin synthesis and secretion (Mathieu and Klaus
2005; Mathieu and Gysemans, 2006; Baeke et al., 2010; Wolden et al., 2011).
VDR acts as transcription factor when bound with vitamin D. These receptors are
predominantly found in beta cells of pancreas necessary for insulin production from pancreas
(Haussler et al., 2011; Vural and Maltas 2012). A number of VDR variants have been
observed in early 1990s; ApaI, BsmI, EcoRV, TaqI, Tru9I, FokI and CDX2. Recently, four
contiguous restriction fragment length polymorphisms for BsmI, TaqI, FokI and ApaI have
been found associated to T2DM (Harne and Hagberg 2005; Lim et al., 2013). Furthermore,
several polymorphisms of VDR encoding gene have been examined in connotation with
insulin secretion, insulin resistance and T2DM. FokI polymorphism is associated to insulin
sensitivity and BsmI polymorphism is related to insulin resistance, function of beta cells and
menaces of T2DM in human (Scragg et al., 2004; Song et al., 2011; Forouhi et al., 2012;
Khan et al., 2012).
Even in type 1 diabetes mellitus (T1DM), numerous VDR polymorphisms are involved. Most
notable among these is human leukocyte antigen locus Fas, Fas-ligand (FasL). It was
demonstrated by Sahin et al. (2012) that FasL -843C/T loci and VDR FokI gene
polymorphisms are linked with T1DM in the Agean region of Turkey. Vitamin D represses
16
T-cell activity and T1DM is a T-cells mediated disease. Nejad et al. (2012) demonstrated that
TaqI VDR polymorphism genotypes might be different in diabetic and control subjects.
However, other VDR SNPs (FokI, BsmI, ApaI) and disease vulnerability were not related.
Yokoyama et al. (2012) proposed that higher vitamin D concentrations may be connected to
chronic kidney condition in patients with T2DM and this connection was enhanced by FokI
polymorphisms.
Thus, a number of polymorphisms in the vitamin D receptor (VDR) gene are reported to
modulate glucose intolerance, insulin secretion and sensitivity in many populations. So far,
BsmI, ApaI, FokI and TaqI are the restriction fragment length polymorphisms (RFLP) at
VDR gene that have been targeted to explain variation in risk of diabetes mellitus
(Manchandra and Bid, 2012). However, studies on association between VDR genetic
polymorphisms and risk of T2DM in different ethnic groups is notconclusive. Progress in
identification of novel VDR gene variants predisposing to diabetes mellitus in Pakistan has
been limited. Comprehensive understanding of VDR genetic polymorphisms would help
uncover their impact in T2DM.
Need of the following studyVitamin D deficiency and vitamin D receptor (VDR) gene polymorphisms are associated to
various health problems and have achieved a lot of emphasis during the last couple of
decades due to potential multifunctional and significant contributor to the health, particularly
in chronic diseases including type 1 and 2 diabetes mellitus (T1DM, T2DM). Complex
inheritance patterns involve polymorphisms of several genes affecting disease susceptibility;
however, the genetic mechanisms that underpin T2DM are still unclear. To date no study at
national level has been performed on VDR genes to understand related clinical translations.
There is a dire need to validate various single nucleotide polymorphisms of VDR gene
proposed in international studies among Pakistani subjects. As the progress in identification
of VDR genetic variants predisposing to T2DM in local population has been limited,
therefore, present research was conducted with the aim to examine this candidate gene in
Pakistani T2DM patients.
17
Objectives
1. Associations of BsmI, ApaI, FokI and TaqI variants of VDR gene with various
biochemical indices among type 2 diabetes mellitus patients
2. Relationship of VDR gene polymorphisms with different diabetic complications and
demographic parameters
18
Chapter 2 REVIEW OF LITERATURE 2.1: Type 2 diabetes mellitus2.1.1: Background
Diabetes mellitus is a serious health problem reaching epidemic extents worldwide. It is an
enduring disease that affected about 387 million people worldwide in 2014. It is estimated
that 455 million people will have this disease by the year 2030 (IDF Diabetes Atlas, 2014).
This theoretical increase in diabetes mellitus prevalence may be endorsed by different factors
such as increased life probability and poor medical management. Financial impact of
diabetes mellitus is immensely arduous as approximate estimated cost of Pakistan’s
healthcare department was $365 billion in 2014 that will probably rise up to $490 billion by
2030 (Hussain et al., 2014). People with this enduring disease have higher risk of various
complications including heart disease, nephropathy, limb amputation, neuropathy and
premature death (Ishaq et al., 2013; Sohail, 2014). Generally these complications arise as a
result of sustained hyperglycemia due to poorly managed and uncontrolled diabetes.
2.1.2: Pathogenesis
Type 2 diabetes mellitus accounts for about 90% of all diabetes mellitus cases. It is
categorized through hyperglycemia and so its diagnosis is based on a fasting blood glucose
(FBG) level of ≥ 120 mg/dL, after two hours oral glucose tolerance test (OGTT) level ≥
160mg/dL and postprandial level ≥ 200mg/dL (American diabetes care, 2015). T2DM is a
chronic disease, when people with normal level of glucose tolerance develop impairment of
fasting or postprandial glucose tolerance that ultimately manifest to T2DM. Prediabetes
includes either impaired glucose tolerance, impaired fasting glucose or both. As levels of
blood glucose are greater as compared to normal however not yet greater enough to diagnose
T2DM in many cases, therefore, it has been assessed that approximately 6.9 million
Pakistanis are living as prediabetic people, many of whom have T2DM (IDF Diabetes Atlas,
2014).
T2DM is a multifactorial disease which develops from main underlying pathophysiological
ailments such as lack of insulin sensitivity and dysfunction of pancreatic β-cells. Insulin
resistance leads to poor insulin function in the liver, muscle and adipose tissue (Orozco et al.,
2008; Martijin et al., 2014). Dysfunction of β-cell is described as the deficient production of
insulin from pancreatic β cells according to requirements of body in conserving glucose
19
homeostasis (Weyer et al., 1999; Schellenberg et al., 2013). The insulin resistance as well as
dysfunction of β -cell have been revealed to envisage the progress of T2DM development
independent to other menaces of this disorder (Weyer et al., 1999; Malik et al., 2010).
Mostly, in early progression to T2DM, lack of insulin sensitivity is established however,
glucose tolerance keeps normal because of compensatory reaction from β-cells of pancreas
which will raise insulin production to reserve glucose homeostasis. But with passage of time
and because of other genetic and environmental influences, this compensatory reaction is
weakened and resulting dysfunction of β-cell ultimately develops hyperglycemia into
diabetes range (Maedler, 2008; Zanuso et al., 2010).
2.1.3 Risk Factors
Genetic and environmental/acquired factors are considered to play very imperative roles in
T2DM risk. Studies revealed that people with first degree affected relatives and monozygotic
twins had established 50% heritability of T2DM (Pierce et al., 1995; Herder and Roden,
2011). Furthermore, current genome wide association studies (GWAS) have described more
than 40 complete diabetes linked loci (Imamura and Maeda, 2011; Wheeler and Barroso,
2011). So, it is flawless that genetic elements play crucial role in T2DM risk. Though,
various environmental/acquired factors also have important role in developing risk of this
enduring disease. Sedentary lifestyle, obesity, smoking, low socioeconomic status and older
age are well considered menaces for T2DM (Fagard and Nilsson 2009; Meigs, 2010). Ethnic
groups such as African, Hispanic, American, Aboriginal and South Asian individuals have
great risk of T2DM than Caucasians, which can be assignable to environmental and genetic
influences. In addition, many dietary factors; whole grains, coffee, dairy, quality of fat and
carbohydrate have also been stated as protective elements against T2DM risk (Hung et al.,
2003; Salas-Salvado et al., 2011), while processed meat and sugar sweetened beverages are
related with greater risk factors (Malik et al.,2010; Micha et al., 2010).
2.2: Beta cell dysfunction and Insulin sensitivity 2.2.1: Role in diabetes mellitus etiology
Both lack of insulin sensitivity and beta cell dysfunction cause initial pathophysiological
disturbances in the typical T2DM history. Lack of insulin sensitivity defines a state where the
usual functions of insulin that are to decrease circulating glucose amounts, enhance glucose
utilization and to restrain hepatic glucose synthesis are impaired (Stumvoll et al., 2005). In
20
normal physiological reaction to increased level of glucose in blood, insulin synthesized from
beta cells binds to the insulin receptors present on plasma membrane of insulin target tissues,
which consequently persuades a cascade of signaling transduction to permit for the
transportation of glucose in cell for glucose consumption. Although, in reduced insulin
sensitivity state, there can be defects at insulin receptor location or in signaling pathway,
which results in diminished insulin action and thus lower amounts of glucose being
transported into the cell. Risk factors for impaired insulin sensitivity are quite similar to those
of T2DM and constitute family history of T2DM, older age, obesity and low physical activity
(Bloomgarden, 1998). In addition, currently described menaces for impaired insulin
sensitivity are subclinical inflammation (Kadowaki et al., 2007; Shoelson and Donath, 2011)
and typical lifestyle features; stress, smoking, less use of dietary fibers and magnesium
(Reaven and Tsao, 2003; Lima et al., 2009). Non- alcoholic liver and polycystic ovary
syndrome have been described as conditions which are categorized by lack of insulin
sensitivity (Bethea and Nestler, 2008; Gronbaek et al., 2008). Pancreatic beta cells constitute
65 to 80% of the total pancreatic cells with their principal role to synthesize, store and secret
insulin to regulate glucose homeostasis. In normal physiological state, enhancement in the
lack of insulin is accompanied through upsurges in insulin production (Kahn et al., 1993).
This compensatory raise in insulin production in response to the lack of insulin sensitivity
may be regulated as long as function of beta cell is not disturbed. With the passage of time,
yet, along the augmented demand on pancreatic beta cells to enhance the production of
insulin and because of a diversity of environmental/acquired and genetic factors, the
pancreatic beta cell compensatory reaction is disturbed in some people along with
insufficient synthesis of insulin (Prentki and Nolan 2006; Lencioni et al., 2008; Maedler,
2008).
In case of dysfunction of beta cells, the concentration of insulin synthesized cannot
overwhelm the lack of insulin sensitivity in multiple organs, subsequently resulting in
hyperglycemia (Maedler, 2008). Therefore, though both dysfunction of pancreatic beta cells
and lack of insulin sensitivity take part in the development of T2DM, it is certainly
dysfunction of beta cells that is serious to the progression of the diseases diabetes mellitus
cannot arise deprived of impairment of the insulin production (Maedler, 2008; Gastaldelli,
2011). Limited knowledge exists about the etiology of dysfunction of pancreatic beta cells
21
however; both environmental and genetic factors are considered to play a role. Few plausible
menaces which have currently been recognized involve glucotoxicity to the beta cell which
would be the outcome of prolonged enduring hyperglycemia, lipotoxicity caused by elevated
levels of free fatty acid that often coexist in people with higher adiposity and lack of insulin
(Bonora, 2008), oxidative stress and long-lasting subclinical inflammation (Greenberg et al.,
2002), additional visceral adipose tissue (Wagenknecht et al., 2003; Utzschneider et al.,
2004), lack of insulin sensitivity of pancreatic beta cells (Bonora, 2008) and low adiponectin
(Kharroubi et al., 2003; Bacha et al., 2004). Family histories as well as genetics are also
considered to take part in defining risk of dysfunction of pancreatic beta cells (Marchetti et
al., 2002; Marchetti et al., 2006). More precisely, GWAS directed to exploration for novel
genes of diabetes susceptibility mostly have been identified from pancreatic beta cell
associated loci (Florez, 2008; Billings and Florez, 2010; Wheeler and Barroso, 2011).
Therefore, though certain etiological menaces for lack of insulin sensitivity and dysfunction
of pancreatic beta cell have been recognized, gaps still persist in thoughtful etiology of such
described disorders. In addition, scarcity of research regarding risk factors associated to the
onset and progression of T2DM demands further studies.
2.3: Vitamin D2.3.1: Background
Vitamin D is necessary for the homeostasis of calcium to prevent rickets and osteomalacia.
The vitamin D3 (cholecalciferol) and vitamin D2 (ergocalciferol) are two major types of this
vitamin.
Vitamin D is similar to secosteroid which contains 27 carbons has molecular structure similar
that of ancient steroid hormones constitute cortisol, estradiol and aldosterone (Norman,
1998). Vitamin D is known for its useful role in regulation and metabolism of calcium. In
teenagers, hypovitaminosis D predisposes to rickets, a disorder of bones characterized by
poor mineralization of skeletal tissues causing retardation of growth and deformities of
skeletal comprising bony projections with rib cage and deformed legs or collided knees. In
old age people, hypovitaminosis D develops osteomalacia, a defect in mineralization
producing tender bone pain and weakness of muscles (Holick, 2003; Holick, 2011).
The detection of useful effects of this vitamin on bone and muscle health hints back to the
end of 19th century, when more than 90% teenagers who belonged to developed cities of
22
Europe and North America had rickets, which directed to find potential cures, containing
liver oil of cod and exposure to sunlight (Holick, 2004), both of them are considered
tremendous sources of this vitamin. However, multi-system implications alongside poor
precise detection of serum phosphorous and calcium levels to diagnosis hypovitaminosis D
are responsible for dramatic increase in the number of people with deficiency of vitamin D in
Pakistan. Moreover, limited studies have been conducted to find the association between
onset of T2DM and vitamin D deficiency in Pakistan (Massod et al., 2010).
Figure 2.1 Chemical structure of Vitamin D
The invention of UV radiation foods for the cure and avoidance of rickets made this vitamin
a new miracle vitamin in this era and led to milk fortification and various food products
(Holick, 2004). Artificially UV radiations are used to produce vitamin D rich food from
ordinary one. But in early 1950s, cases of hypercalcemia in children were considered to be
due of the intoxication of vitamin D from milk (Lightwood, 2001; Stapleton et al., 2007).
Consequently, fortification of vitamin D was prohibited in most of the European countries at
that time. Recently, a few vitamin D fortified food are available in Europe, US and Canada
(Calvo et al., 2005). Nevertheless, some countries, including Pakistan have limited
mandatory food fortification and permit other optional foods fortification (Massod et al.,
2010).
Evolving evidences in past decade have recommended a prospective function of vitamin D in
health of various non-skeletal matrix conditions and disorder phases comprising autoimmune
disorders, cancer, cardiovascular disease as well as T2DM. But different kind of problems
23
bound the explanation and presentation of the recent literature, containing uncertainty with
respect to cut points to describe best or suitable levels of vitamin D and procedures for the
precise measurement of this vitamin.
2.3.2 Sources of Vitamin D
Mostly the cutaneous production of vitamin D after the exposure to sunlight is considered to
be main source in which mainly ultraviolet B (UVB) radiation of sunlight (290 to 315 nm)
commence the photochemical reaction (Holick et al., 2007). Furthermore other than this
endogenous synthesis, humans can also get vitamin D through food supply. Almost all food
sources are initiated from ultra violet radiation of plant ergosterol and sterol, present in
plasma membranes of both fungus and yeast and synthesizing vitamin D2 or ergocalciferol
and vitamin D3 or cholecalciferol by animal sources. Chief natural sources are cod liver oil
which gives 400 IU calciferol per teaspoon and fatty fish such as wild salmon that provides
up to 1000 IU calciferol per 3.5 oz. Mackerel gives 250 IU per 3.5 oz and shitake mushrooms
provides 1600 IU of calciferol per 3.5 oz. However, due to limited supply of vitamin D from
natural sources different countries such as Canada and United States use fortified food to
obtain sufficient concentration of vitamin D (Clavo et al., 2004; Clavo et al., 2005).
In Pakistan specially, the fortified foods with mandatory amount of vitamin D are milk (100
IU per 8oz), fortified vitamin D and multivitamins in the form of pill or liquid syrup (400
IU). These are major non-ultraviolet sources and these types of supplements are easily
available in Pakistan (Mubashra, 2012). However, the efficiency of vitamin D3 as compared
to vitamin D2 is a matter of discussion; most studies have revealed that vitamin D3 is more
effective than vitamin D2 (Armas et al., 2004; Houghton and Vieth, 2006; Vieth, 2007;
Heaney et al.,2011; Autier et al., 2012). But, Holick et al. (2007) argued that both forms of
vitamin D supplementation had equal efficacy in preserving serum levels of vitamin D
(Holick et al., 2007). A current meta-analysis of randomized controlled trials stated that
vitamin D2 is less effective as compared to vitamin D3 in raising serum concentrations of
vitamin D (Tripkovic et al., 2012) and suggested that vitamin D3 can be employed for clinical
as well as nutritional demands (Vieth, 2009).
24
2.3.3 Metabolism of Vitamin D
Exposure to solar ultraviolet rays is responsible for the dermal production of vitamin D3 by
its precursor 7-dehydrocholesterol. Certainly, inactive form of vitamin D3 (previtamin D3) is
generated after exposure to sunlight, which then undergoes non-enzymatic isomerization and
forms vitamin D3. Vitamin D3, either produced endogenously or taken orally, must undergo
hydroxylations, first in liver then in kidney before it converted to active molecules which act
as hormone. Cholcalciferol enters blood stream through binding to protein known as vitamin
D binding protein (DBP) and undergoes hydroxylation through cytochrome P450 enzyme
hydroxylase (CYP2R1) to 25-hydroxyvitamin D also known as calcidiol in liver. Calicidiol is
the chief circulating type of this vitamin in body (DeLuca, 2004; Strushkevich et al., 2008).
Subsequently, 25-hydroxyvitamin D is then transported to kidneys where 1-alpha-
hydroxylase converts 25-hydroxyvitamin D into active metabolite of vitamin D that is 1, 25-
dihydroxyvitamin D or calcitriol (Sakaki et al., 2005).
Diagrammatic representation of vitamin D production and metabolism is elaborated in Figure
2.2 (Holick et al., 2009). Circulating 25-hydroxyvitamin D may be transported to tissues by
two different processes; it may move directly across the cell membrane, or bound to binding
protein in the circulation to reach target tissues, predominantly to kidneys through megalin of
endocytic receptor (Nykjaer et al., 1999). The process of hydroxylation of the vitamin D in
liver is not firmly regulated, while renal production of 1, 25-hydroxyvitamin D is strongly
regulated through calcium, phosphorous, parathyroid hormone and 1, 25-hydroxyvitamin D
itself (Breslau, 2008). When levels of calcium in blood are not sufficient, parathyroid
hormone levels are elevated which stimulate calcitriol production consequently enhancing
the absorption of calcium in intestine (Segersten et al., 2002).
Furthermore, when levels of phosphate are increased, it stimulates the synthesis of fibroblast
growth factor-23 in bones which prohibit the synthesis of calcitriol in kidney (Shimada et al.,
2004). In addition, when levels of 1, 25dihydroxivitamin D are adequate, this metabolite
encourages the catabolic hydroxylase enzyme to produce 24, 25-dihydroxyvitamin D in
kidney, where this metabolite is further converted into biologically inactive form calcitroic
acid which is water soluble and its other carboxylated forms that are defecated through
kidney (DeLuca, 2004; Lips, 2006). Moreover, stimulation of the alpha hydroxylase
(CYP27B1) is also found in many non-renal tissues; skin, prostate, brain, macrophages and
25
pancreas (Zehnder et al., 2001), where it is responsible for the production of extra renal
dihydroxyvitamin D. Maintenance of non-renal dihydroxyvitamin D production is mostly
unknown, however, alpha hydroxylase mRNA is maximum in renal tissues (Omdahl et al.,
2002).
Biological activity of dihydroxyvitamin D may be categorized as either non-genomic or
genomic (Figure 2.3) (Whitfield et al., 2005). About 2-3% of human genome is indirectly or
directly regulated through vitamin D coordination (Bouillon et al., 2008). Furthermore, it has
been established that locally produced dihydroxyvitamin D may control more than 2000
genes which take part in various processes comprising immunity, cell growth, inflammation
and cell proliferation (Nagpal et al., 2005; Norman, 2006). The genomic function of vitamin
D requires the joining of calcitriol to strong affinity receptor, vitamin D receptor (VDR). It is
a member of superfamily of the nuclear hormone receptors which acts as a ligand activated
transcription factor (Ogunkolade et al., 2002). However, the VDR can be present in organs
involve in metabolism of calcium and homeostasis constituting the bone, intestine,
parathyroid glands and kidney. VDRs have also been recognized in many other tissues;
breast, heart, colon, pancreas and prostate (Anderson et al., 2003; Holick et al., 2009).
Vitamin D attaches to the VDR and develops a heterodimer complex as 9-cis retinoic acid
nuclear retinoid-X-receptor. The VDR /retinoid X receptor complex (RXRC) then attaches to
VDRE (vitamin D response element) present in promoter region of respective goal genes. A
protein co-activator complex has been employed and binds to the heterodimeric VDR /RXRC
that resembles with RNA polymerase for transcription (Ogunkolade et al., 2002).
In vitamin D receptive genes, the unloaded VDR /RXRC still attach to the VDRE of
promoter region located in respective target gene, however, a co-repressor complex is
employed that suppress the gene transcription (Anderson et al., 2003). Moreover, besides
genomic function, vitamin D also facilitates a rapid non-genomic function that is found
through the attachment of vitamin D to a cell membrane VDR. Such non-genomic functions
of vitamin D are vital in nuclear transcription activity and membrane associated actions, such
as elevating calcium uptake, secretion of calcium from its intracellular stores and excitement
of protein kinase C action (Fleet, 2004; Norman, 2006).
26
(Holick et al., 2009)
Figure 2.2: Metabolism of vitamin D
Figure 2.3: Vitamin D metabolism and it biological actions by nuclear vitamin D receptor
27
2.3.4 Factors affecting vitamin D levels
A number of factors may affect the production of vitamin D in vivo. Solar zenith angle
(SZA), that is the role of time of day, latitude and time of year significantly affect the
relationship of vitamin D synthesis and sunlight exposure (Kimlin, 2008). The distance
traveled by ultra violet radiation of the sunlight through atmosphere is comparatively longer
in morning than in late afternoon, which results in reduced ultra violet radiation received by
surface of earth during these time intervals than during noon time. The solar zenith angle also
changes during the year; it is least during summer when sun is nonstop overhead, causing
more ultraviolet B rays received by surface of earth than winter when this angle is biggest,
because of least solar angle. Another important factor is latitude where the solar zenith angle
is least near equator and increases as the distance increase from equator while moving toward
poles. Thus less dermal production of vitamin D is in morning, late afternoon and during
winter season in northern hemisphere latitude (Webb et al., 2008; Holick et al., 2009).
Dark skin coloration is also important influential factor for dermal vitamin D production, due
to less absorption of ultra violet radiations with higher melanin component. People with
intensive skin pigmentation contain high melanin component that absorbs ultra violet
photons and therefore contends along 7-dehydrocholesterol (Clemens et al., 2009).
Prevention of sunlight exposure, wrapping of body by clothing as well as sunscreen routine
also decrease dermal synthesis of vitamin D (Matsuoka et al., 2000; Holick et al., 2007).
The composition of body is another significant indicative factor for vitamin D level, as
different studies have constantly revealed that people with higher adiposity have poor levels
of vitamin D (Liel et al., 1999; Arunabh et al., 2003) due to repossession of fat soluble
vitamins in adipocytes, (Wortsman et al., 2003; Blum et al., 2008). A previous study,
although, reported that volumetric reduction as an action of body weight designates decrease
levels of vitamin D in those people who have big body size (Drincic et al., 2012), favored by
another study that storage site of vitamin D is muscle and adipose tissues (Vieth, 2007).
Furthermore, people with disorders of malabsorption; fibrosis, cystic, celiac and Chron’s
diseases have reduced bioavailability of vitamin D because of a diminished capability to
absorb this vitamin (Lo et al., 2005). People with kidney and liver disorders also suffer from
deficiency of vitamin D because of impairments in metabolism of vitamin D (Masuda et al.,
1999; Ishimura et al., 2000). Some medications; anticonvulsants, anti-rejection and
28
glucocorticoids medications may also affect levels of vitamin D, as use of such medications
increase the catabolism of vitamin D metabolites (Godschalk et al., 1992; Holick, 2009;
Skversky et al., 2011). Furthermore, people who have low intake of vitamin D through diet
have decreased vitamin D levels, predominantly in those areas which have seasonal
fluctuation of ultraviolet radiation (Vieth et al., 2001; Rucker et al., 2002; Webb et al.,
2008).
Genetic influences are another important determinant of levels of vitamin D and take part in
inter-individual deviation in vitamin D considerably influencing both quantities and variation
in synthesis (Arguelles et al., 2009; Karohl et al., 2010). Furthermore, two GWAS described
an important relation of circulating vitamin D quantities to polymorphisms for various genes
coding DBP and enzymes comprised in metabolic pathway of this vitamin (Ahn et al., 2010;
Wang et al., 2010). These discoveries were further supported by another systematic
evaluation (McGrath et al., 2010) that also described an important relationship between VDR
gene polymorphisms and vitamin D concentrations.
However, recent data are uneven and limited with respect to the effects of VDR gene and
other genes polymorphisms, as all genes exhibit inclusively variable contributions in
production, function and metabolism of vitamin D. Thus focus on scientific exploration
concerning VDR and vitamin D interactions is increasing.
2.4: Vitamin D and type 2 diabetes mellitusNumerous studies described well-established actions of vitamin D upon skeletal health,
indicating its significant action in many other disorders and health conditions including;
cardiovascular diseases, cancer, autoimmune disorders, and T2DM. Concentrating
specifically at T2DM, initial animal studies revealed that minerals such as magnesium and
calcium, both firmly regulated through vitamin D, have been necessary for insulin secretion
(Boyd et al., 2006; Holick, 2011). Likewise, animal studies also suggested that deficiency of
vitamin D was related with reduced insulin secretion and supplementation of vitamin D
reestablished normal insulin secretion (Norman et al., 2004; Clark et al., 2010). Moreover,
seasonal changes in insulin and glucose concentrations (Behall et al., 2004; de Souza and
Meier, 2007), as well as seasonal changes in diagnosis and management of T2DM have been
noted. There is more diagnosis and lesser glycemic control during winter as compared to
29
summer (Doro et al., 2006). These seasonal variations in T2DM associated traits can be
contributable to vitamin D, assuming the well-considered seasonal variability in serum levels
of vitamin D3. Consistent with such suggestions which recommend a significant function for
this vitamin in T2DM etiology, many cross sectional studies have reported an important
inverse relationship between serum vitamin D and presence of T2DM (Scragg et al., 2004;
Dalgard et al., 2011). Furthermore, most case control research outcomes have also
documented that T2DM patients or those with impaired tolerance of glucose are expected to
have a poor concentrations of vitamin D than to those without T2DM (Pittas et al., 2006;
Scragg et al., 2004). Although conclusions have not been completely consistent with
numerous studies found no such relationship (Snijder et al., 2006; Carnevale et al., 2012).
Moreover, an increasing number of potential studies have reported an important inverse
relationship of baseline serum level of vitamin D with incident of T2DM (Pittas et al., 2006;
Anderson et al., 2010; Thorand et al., 2011; Deleskog et al., 2012). Contrary to that, few
studies have indicated no association (Grimnes et al., 2010; Mitri et al., 2011; Robinson et
al., 2011; Forouhi et al., 2012). These discrepancies may be because of the self -reported
intake of vitamin D (Pittas et al., 2006; Kirii et al., 2009), an expected vitamin D cut-off (Liu
et al., 2010) and the usage of self -reported diabetic status to determine the initial outcome
(Pittas et al., 2006; Knekt et al., 2008; Kirii et al., 2009; Robinson et al., 2011). Almost all
studies to date have evaluated the status of vitamin D at baseline. In fact, only very few
researches consuming repeated calculations of vitamin D have been conducted (Pittas et al.,
2012), which recorded an important inverse association of vitamin D with incidence of DM
after three years of follow-up.
Only two randomized control trials suggesting the effects of this vitamin supplementation on
incidence of T2DM are available in literature (de Boer et al., 2008; Avenell et al., 2009), as
most of these trials stated the effects of vitamin D on insulin resistance, glycemic control and
insulin secretion in preliminary inferences and determined no statistically significant action
of vitamin D supplementation [400 IU/day (de Boer et al., 2008); 800 IU/day (Avenell et al.,
2009)] on occurrence of DM after three to seven years of follow-up.
These studies determined DM status using data of self-reported and post-hoc analyses that
have been designed to evaluate other metabolic disorders or conditions as the initial
consequence. Evidently, additional randomized control trials definitely designed to find
30
effect of the supplementation of vitamin D on risk of T2DM are needed.
2.4.1: Association of vitamin D and insulin resistance
A number of researches have examined the function of vitamin D in initial
pathophysiological conditions underlying T2DM, especially lack of insulin sensitivity and
pancreatic beta cell dysfunction. A significant role of this vitamin with lack of insulin and
pancreatic beta cell function has been derived. Most (Chiu et al., 2004; Scragg et al., 2004;
Kamycheva et al., 2007; Liu et al., 2009; Alvarez et al., 2010; Kelly et al., 2011), but not all
(Erdonmez et al., 2010; Gulseth et al., 2010; Del Gobbo et al., 2011; Hurskainen et al., 2012;
Rajakumar et al., 2012; Rhee et al., 2012), of such studies described such a relationship.
Uneven outcomes have also been stated in cross sectional analyses considering the
relationship of vitamin D with function of beta cell, indicating a positive association
(Boucher et al., 1995; Baynes et al., 1997; Wu et al., 2009) or no significant relationship
(Orwoll et al., 1994; Chiu et al., 2004; Scragg et al., 2004; Gulseth et al., 2010; Del Gobbo
et al., 2011; Rhee et al., 2012). But most of these experiments employed indirect measures of
lack of insulin sensitivity and function of beta cell, fasting or post-prandial level of glucose
or insulin, HOMA-β and HOMA-IR, or many other fasting glucose based processes (Scragg
et al., 2004; Liu et al., 2009; Wu et al., 2009; Del Gobbo et al., 2011; Erdonmez et al., 2011;
Kelly et al., 2011).
2.4.2: Mechanism
Numerous prospective processes have been recommended to describe the relationship of
vitamin D to T2DM and its associated manifestations. Vitamin D can directly increase action
of insulin for the transportation of glucose via exciting the expression of insulin receptors
(Maestro et al., 2000), as VDRE is located in promoter region of insulin receptor gene
(Maestro et al., 2003). Vitamin D cannot directly affect lack of insulin sensitivity by
maintaining intracellular processing of insulin mediated by the regulation of calcium pool
(Draznin et al., 1987; Draznin, 1988). Elevated intracellular calcium may stop insulin target
cells to sense sharp intracellular fluctuations in calcium which are necessary for insulin
action involving glucose transport (Worrall and Olefsky, 2002; Norman et al., 2004). This is
also significant to note that initial determinants of peripheral sensitivity of insulin, skeletal
muscle and adipocytes, express the VDR (Bischoff et al., 2001; Norman, 2006) and like
sensitivity of insulin, the expression of VDR decline in skeletal muscle with age (Bischoff-
31
Ferrari et al., 2008). In addition, the expression of vitamin D α-hydroxylase observed in
various tissues of wistar rats (Li et al., 2008), initiating the local synthesis of vitamin D. With
respect to pancreatic beta cell function, calcitriol can apply direct effects by binding of its
active form in circulation to the beta cell VDR (Johnson et al., 1994; Zeitz et al., 2003).
Instead, activation of this vitamin could happen within the pancreatic beta cell by vitamin D
1-α-hydroxylase that has been designated to express in pancreatic beta cells (Bland et al.,
2006). Furthermore, assuming the occurrence of VDRE in insulin gene promoter region, this
may interpret the transcriptional activation of insulin gene through vitamin D (Maestro et al.,
2003). Vitamin D can also employ indirect effect on beta cell function by maintaining
extracellular calcium and its flux through the beta cell (Sergeev and Rhoten, 1995) as
secretion of insulin is a calcium dependent phenomenon (Holick et al., 2011). Based on the
relationship between T2DM and systemic inflammation (Donath and Shoelson, 2011)
vitamin D may also improve the sensitivity of insulin and promote the function of beta cell
by regulating the generation and actions of cytokines (Pittas et al., 2007). However, limited
data have described the association between vitamin D and T2DM (Cigolini et al., 2006;
Pittas et al., 2007).
2.4.3: Role of genetics
Genetic variations can explain discrepancies in the literature with respect to the relationship
of vitamin D to T2DM. Much research has been focused on various genotypes associated to
the VDR, DBP and vitamin D-1-α-hydroxylase. Polymorphisms that have been recognized in
VDR gene, specifically ApaI, TaqI, FokI and BsmI may be related with T2DM, lack of
insulin sensitivity and dysfunction of pancreatic beta cell. However, recent evidences are
limited and their outcomes have been inconsistent. Studies have found imperative
relationships of specific VDR polymorphisms with higher lack of insulin sensitivity (Chiu et
al., 2001; Oh and Barrett-Connor, 2002; Ortlepp et al., 2002; Filus et al., 2003;Tworowska-
Bardzinska et al., 2008) and insulin secretion (Hitman et al., 1998; Speer et al., 2001;
Ogunkolade et al., 2002). Though, most of these researches have focused on Caucasian
populations and have employed surrogate measures of beta cells functions and lack of insulin
based during fasting. With regard to T2DM specifically, Ortlepp et al. (2001) observed a
greater prevalence of T2DM among those with a certain BsmI genotype for VDR gene as
compared to those deprived of this genotype. Some case-control studies described no
32
significant variations in frequencies of genotype for different VDR genes in T2DM versus
controls (Boullu-Sanchis et al., 1999; Ye et al., 2001; Malecki et al., 2003; Bid et al., 2009;
Dilmec et al., 2010; Vural and Maltas, 2012). Therefore, further investigation into
association between VDR polymorphisms and risk of T2DM is warranted predominantly in
various ethnic populations. Genetic polymorphisms of the vitamin binding protein have been
recognized suggesting an association of these polymorphisms and enhanced risk of T2DM
(Hirai et al., 1998) and lack of insulin sensitivity as calculated through fasting glucose or
levels of insulin (Baier et al., 1998; Szathmary, 2007). Though, there is inadequate data and
insufficient outcomes (Baier et al., 1998; Pratley et al., 1998; Klupa et al., 1999; Szathmary,
2007). However, another gene related to vitamin D studied for a possible association with
T2DM is vitamin D-1-α-hydroxylase (CYP1 alpha), it is accountable for the change of
hydroxyvitamin D to dihydroxyvitamin D (calcitriol). So far, single study has been done to
date (Malecki et al., 2003), which suggested no significant polymorphism in CYP1 alpha
gene in T2DM patients versus controls in the Polish population. Hence, significant
association of specific genotype of CYP1 alpha gene with T2DM was observed in obese
subgroup. However, precise mechanism of this finding was not clear. Earlier, Jorde et al.
(2012) suggested that no significant relationship of T2DM exist with many single nucleotide
polymorphisms (SNP) linked with serum vitamin D level.
2.4.4: Vitamin D receptor polymorphisms
Four allelic variants of vitamin D receptor gene have been recognized: ApaI, FokI, BsmI and
TaqI (Pittas et al., 2007). The functions of these vitamin D receptor polymorphisms have
been comprehensively studied in T2DM patients (Ogunkolade et al., 2002). Polymorphism
genotype ApaI of VDR gene showed relationship to the insulin secretion in Bangladeshi
population, which are at high risk of T2DM with higher prevalence of hypovitaminosis D. A
correlation of ApaI polymorphism with fasting blood glucose level and intolerance of glucose
was evident among those people who had diabetes symptoms at pre-diagnosis stage.
Ogunkolade et al. (2002) illustrated a positive relationship between the BsmI (genotype bb)
and TaqI (genotype TT) polymorphisms with decreased insulin secretory potential. Speer et
al. (2001) proposed that obese T2DM patients have greater levels of C- peptide and VDR
polymorphism of BsmI allele (BB-genotype) indicative of their probable role in pathogenesis
33
of T2DM. Insufficiency of vitamin D was measured in these subjects and polymorphism of
TaqI was an element related to insulin secretion. Though, there is strong evidence of link
between T2DM and VDR polymorphism; conflicting results among different populations are
reported (Malecki et al., 2003).
In T2DM, the vitamin D receptor gene polymorphism of allele ApaI (aa genotype) was
related with impaired secretion of insulin in Caucasian population, thus this population had a
higher risk of developing T2DM (Oh and Barrett-Connor, 2002). Contrary to that, VDR gene
polymorphisms of alleles Fok1, TaqI, ApaI and BsmI had no noteworthy association with
T2DM in a case control research within Bangladeshi population by Islam et al. (2014).
Insulin sensitivity was significantly decreased in T2DM cases of Bangladeshi origin.
It was concluded by Sung et al. (2012) that distributions of VDR gene alleles of the four
SNPs (BsmI, TaqI, Tru9I and ApaI) were same in T2DM patients and controls. These
evidences supporting or opposing a relationship of vitamin D receptor genotypes with
menace of T2DM are conflicting.
Polymorphisms present in intron 8 (BsmI) and exon 9 (TaqI) of vitamin D receptor gene had
substantial linkage with type 2 diabetes mellitus, while distribution as well as frequency of
genotype FokI and ApaI of the VDR were significantly similar in T2DM patients and healthy
people. These results confirmed the previous inferences that VDR gene genotypes BsmI as
well as TaqI polymorphisms are related with onset of type 2 diabetes mellitus (Speer et al.,
2001; Nosratabadi et al., 2010). Furthermore, Al-Daghri et al. (2012) explained that BsmI
and TaqI single nucleotide polymorphisms that are significantly more common in T2DM
patients were allied with elevated levels of cholesterol and lower levels of HDL cholesterol.
However such results are yet not unambiguous as other researchers failed to demonstrate
analogous relationship between FokI, ApaI, BsmI and TaqI polymorphisms and onset of type
2 diabetes mellitus in Indians (Ortlepp et al., 2002), Turkish (Dilmec et al., 2010), Polish
(Malecki et al., 2003) and American populations (Oh and Barrett-Connor, 2002). The reasons
of these discrepancies might be elucidated by the differences in genetic background among
ethnic groups. An overview of VDR gene of the significant allelic variations associated to
T2DM is presented in table 2.1
Although summary depicted below is scarce and not compatible, nonetheless it portrays a
possible association between VDR gene, metabolism of vitamin D and T2DM etiology/traits.
34
Table 2.1: VDR gene polymorphisms associated to T2DMVDR site Country Association Reference
FokI
(BsmI,ApaI,TaqI)
Morocco S
NS
Errouagui et al.(2014)
BsmI,ApaI, TaqI,FokI France NS Ye et al.(2001)
TaqI Iran S Noasratabadi et al.(2010)
BsmI, ApaI, TaqI, USA (San Diego) NS Oh and Barrett-Connor (2002)
BsmI Germany S Ortlepp et al.(2001)
BsmI, ApaI, TaqI, Bangladesh NS Hitman et al.(1998)
BsmI Hungry NS Speer et al.(2001)
BsmI,ApaI, TaqI,FokI Poland NS Malecki,et al.(2003),Cyganek et al.
(2006),
BsmI,ApaI, TaqI,FokI India NS Ortlepp et al.(2003),Bid et al.(2009)
BsmI,FokI Brazil S Cobayashi et al.(2011), Schuch et al.
(2013)
BsmI,ApaI, TaqI,FokI UAE S Al-Anouti, (2013)
BsmI,TaqI Saudi Arabia S Al-Daghri et al.(2012)
BsmI,TaqI,FokI China S Xu et al.(2011), Wang et al.(2012),
Yu et al.(2013)
TaqI
BsmI
Turkey S
NS
Dilmec et al.(2010), Vural and Matlas,
(2012)
BsmI,FokI USA (Maryland) S Harne and Hagberg, (2005)
BsmI
FokI,ApaI,TaqI
Jordan S
NS
Hanash, (2011)
FokI,BsmI Egypt S Mackaway and Badawi, (2014)
TaqI Iran S Nosratabadi et al.(2010)
FokI,TaqI,BsmI
ApaI
Pakistan S
NS
In our study
S: significant, NS: non-significant
2.5: Rational/Summary
The prevalence of T2DM is intensely increasing, both in Pakistan and worldwide.
Furthermore, assuming the lack of correct diagnosis of vitamin D deficiency and multi-
35
system implications in Pakistan, local population are at greater risk of having inadequate
levels of serum vitamin D, with over 75% people having vitamin D deficiency while 18%
reported insufficient vitamin D levels (Massod et al., 2010). Evolving evidence proposes a
prospective role for this vitamin in risk of T2DM as well as its underlying pathophysiological
complications, specifically lack of insulin sensitivity and dysfunction of beta cell. However,
many epidemiological, interventional and biological researches have suggested a probable
relationship of vitamin D to lack of insulin sensitivity and function of beta cell, although
these evidences have been inconsistent. Correspondingly, studies observing the relationship
of vitamin D to the risk factors for T2DM involving metabolic syndrome have increased still
conclusions have also been unpredictable. Limitations of numerous studies held to date
included insufficient sample sizes, employed suboptimal surrogate calculations of outcomes
and mainly Caucasian population. A very few studies have described the function of
calcitriol in the longitudinal evolution of lack of insulin sensitivity and function of beta cell.
Furthermore, regarding the well-known seasonal variation in levels of vitamin D, no research
has yet observed the consequence of seasonal fluctuations in vitamin D on lack of insulin
sensitivity and function of beta cell. Additionally there is dearth of data available on
association between deficiency of vitamin D and risk of onset of the T2DM. Hence, gaps are
present in literature regarding the relationship between menaces of T2DM and vitamin D.
In recent years, a number of polymorphisms, such as BsmI and FokI have been observed in
the vitamin D receptor genes which are able to change the function of VDR protein, while
other polymorphisms in VDR gene found through variation of alleles in sites of restriction
enzyme are TaqI and ApaI. The genetic background of T2DM remains unclear. However, it
is suggested that the vitamin D receptor gene is an innovative candidate gene responsible to
the susceptibility to T2DM.
This thesis will endeavor to address some gaps of the knowledge, particularly through
conducting cross-sectional as well as potential assessments employing validated measures of
VDR gene polymorphisms of genotypes ApaI, BsmI, FokI and TaqI, by evaluating the
function of vitamin D to the novel menaces for T2DM and by observing the effect of VDR
gene polymorphisms on various biochemical parameters among Pakistani T2DM subjects.
36
Chapter 3 MATERIALS AND METHODS 3.1: Sample collection Blood samples of 150 type 2 diabetic patients attending District Headquarters hospital,
Faisalabad, Pakistan were collected on the basis of strict exclusion and inclusion criteria
along with 100 normal individuals between January to February, 2015. Ethical approval of
research protocols was procured from Graduates Studies and Research Board (GSRB) and
Research Medical Council, Bristol, UK. Privacy of research subjects and confidentiality of
their personal information was ensured to minimize the impact of the study on their physical,
mental and social integrity. An information sheet written both in English and Urdu was given
to the participants and all the participants gave written consent. The participants were
screened for hepatitis B (HBS Ag), hepatitis C (HCV) and human immunodeficiency virus
(HIV) prior to scheduled bioassays. The research work was done in Molecular Biochemistry
Lab., Department of Biochemistry, University of Agriculture, Faisalabad, Pakistan and
Molecular Labs., Department of Medical and Dentistry, Southmead Hospital, University of
Bristol, Bristol, UK.
3.2: Exclusion and inclusion criteria
The inclusion criteria for type 2 diabetes mellitus (T2DM) patients was based on WHO,
(2014) criteria; HbA1c (≤ 13.0 %), persistence of hyperglycemia (random blood sugar or
RBS ≤ 200 mg/dL) and body mass index (BMI) (25-40 kg/m2). All the patients of Hepatitis
B, C, HIV, pancreas and kidney failure (other than diabetic nephropathy), pregnant or breast
feeding females or those on medication were excluded. The normal subjects with HbA1c ≤
5.0 %, fasting blood sugar (FBS) ≤ 120 mg/dL and BMI 18-25 kg/m2 were included.
After preliminary biochemical analysis, T2DM patients were subdivided into different
subgroups according to their diabetic complications based upon medical history records and
physician diagnosis as; retinopathy patients (RP), nephropathy patients (NP), cardiac patients
(CP) and hypertensive patients (HP) groups.
3.3: Physical parametersThe biodata was collected from all case and normal participants. It included age (years),
weight (kg), BMI (kg/m2), blood pressure (systolic blood pressure <130 mm Hg and diastolic
37
blood pressure <90 mm Hg were considered non-hypertensive).
3.4: Reagents, chemicals, kits and instrumentation
For blood collection red and purple topped vacutainers and 10 mL syringes were used
(Beckton Dickinson Company). Kits for biochemical analyses of fasting blood sugar (FBS),
vitamin D, total bilirubin (T-bilirubin), direct bilirubin (D-bilirubin) alkaline phosphatase
(ALP), aspartate aminotransferase (ALT), alanine aminotransferase (AST), creatinine, uric
acid, blood urea nitrogen (BUN), cholesterol, high density lipoprotein cholesterol (HDL-C),
low density lipoprotein cholesterol (LDL-C), triglycerides (TG) were purchased from
Merck, Germany. Glycosylated hemoglobin (HbA1c) estimation kit was purchased from
Randox UK. Biochemical analysis was performed using Dade Behring clinical chemistry
system for dimension auto-analyser, Seimen, USA and Diastat auto-analyzer supplied by
Randox, UK. The Dimension® instrument user-defined software application provides a
versatile method of specifying how raw photometric data is transformed prior to conversion
to analyte concentration through the method’s standard curve.
3.5: Collection and storage of blood After 12 hours fasting 10 mL blood sample was collected in a sterile EDTA-coated
vacutainer tube. The 5 mL blood sample was stored at 4°C till molecular analysis. About 2.5
mL blood was centrifuged at 3500 rpm for 10 minutes to separate plasma for HbA1c assay.
The remaining 2.5 mL was allowed to clot for 1-1.5 hours. Then clot was removed carefully
and blood was centrifuged at 3500 rpm for 10 minutes. The supernatant serum was carefully
separated with the help of a pipette. This serum was used for all routine biochemical
parameters, such as liver and renal functions tests and lipid profile.
3.6: Biochemical ParametersThe biochemical parameters were determined by using respective kits (Roche Diagnostics)
following manufacturer’s protocols.
3.6.1: Plasma glucose
3.6.1.1: ProcedureSample, R1and R2 (reagents) were kept into their respective racks in the analyzer. After the
tray is loaded with samples, a pipette aspirates a precisely measured aliquot of sample and
discharges it into the reaction vessel; a measured volume (0.5 mL) of diluent rinses the
pipette. Reagents are dispensed into the reaction vessel. After the solution is mixed and
38
incubated, it is either passed through a colorimeter, which measures its absorbance while it is
still in its reaction vessel, or aspirated into a flow cell, where its absorbance is measured by a
flow-through colorimeter. The analyzer then calculates the analyte’s chemical
concentrations. Standard was run to compare the result. Absorbance was noted at 350 nm and
results were expressed as mg/dL glucose.
3.6.2: Glycated hemoglobin (HbA1c)
3.6.2.1: Procedure
Samples were homogenised and 20 uL of standard and well-mixed whole blood was pipetted
into the properly labelled vials. Hemolysis reagent (1.0 mL) was added to each vial and
vortex. The samples were incubated for 30 minutes at 37°C and loaded onto the Diastat for
glycosylated hemoglobin estimation (%).
3.6.3: Serum vitamin D3
3.6.3.1: Procedure
Sample (60 µL), pre-treatment reagent (60 µL) and assay diluent (0.5 µL) were combined
and inserted on paramagnetic anti-vitamin D coated microparticles ELISA plates to create a
reaction mixture. After incubation a biotinylated vitamin D anti-Biotin acridinium-labelled
conjugate complex was added to the reaction mixture that binds to unoccupied binding sites
of the anti-vitamin D coated microparticles. After washing, pre-trigger and trigger solutions
were added. The resulting chemiluminescent reaction was measured as relative light units
(RLUs). An indirect relationship exists between the amount of vitamin D in the sample and
the RLUs detected by the Architect i System optics at 264 nm and results were expressed as
mg/dL.
3.6.4: Liver functions tests
Total bilirubin, direct bilirubin, alkaline phosphatase, alanine transaminase and aspartate
transaminase were estimated by respective kits. Sample, R1and R2 (reagents) were kept into
their respective racks in the analyzer. After the tray is loaded with samples, a pipette
aspirates a precisely measured aliquot of sample and discharges it into the reaction vessel; a
measured volume (0.5 mL) of diluent rinses the pipette. Reagents are dispensed into the
reaction vessel. After the solution is mixed and incubated, it is either passed through a
colorimeter, which measures its absorbance while it is still in its reaction vessel, or aspirated
into a flow cell, where its absorbance is measured by a flow-through colorimeter. The
39
analyzer then calculates the analyte’s chemical concentrations. Absorbance was noted at 410
nm (total bilirubin), 570 nm (direct bilirubin), 350 nm (alkaline phosphatase), 340 nm
(alanine transaminase) and 532 nm (aspartate transaminase) appropriate wavelength for each
parameter and results were expressed as mg/dL.
3.6.5: Renal functions test
Blood urea nitrogen, uric acid and creatinine were estimated by respective kits. Sample,
R1and R2 (reagents) were kept into their respective racks in the analyzer. After the tray is
loaded with samples, a pipette aspirates a precisely measured aliquot of sample and
discharges it into the reaction vessel; a measured volume (0.5 mL) of diluent rinses the
pipette. Reagents are dispensed into the reaction vessel. After the solution is mixed and
incubated, it is either passed through a colorimeter, which measures its absorbance while it is
still in its reaction vessel, or aspirated into a flow cell, where its absorbance is measured by a
flow-through colorimeter. The analyzer then calculates the analyte’s chemical
concentrations. Absorbance was noted at 340 nm (blood urea nitrogen), 552 nm (uric acid)
and 532 nm (creatinine) appropriate wavelengths for each parameter and results were
expressed as mg/dL.
3.6.6: Lipid profile
Cholesterol, HDL-C, LDL-C and TG were estimated by respective kits. Sample, R1and R2
(reagents) were kept into their respective racks in the analyzer. After the tray is loaded with
samples, a pipette aspirates a precisely measured aliquot of sample and discharges it into the
reaction vessel; a measured volume (0.5 mL) of diluent rinses the pipette. Reagents are
dispensed into the reaction vessel. After the solution is mixed and incubated, it is either
passed through a colorimeter, which measures its absorbance while it is still in its reaction
vessel, or aspirated into a flow cell, where its absorbance is measured by a flow-through
colorimeter. The analyzer then calculates the analyte’s chemical concentrations. Absorbance
was noted at 500 nm (cholesterol), 600 nm (HDL-C), 550 nm (LDL-C) and 500 nm (TG)
appropriate wavelength for each parameter and results were expressed as mg/dL.
3.7: DNA ExtractionBlood samples were collected from patients and healthy donors in 5 mL EDTA coated
vacutainer tubes and stored in refrigerator. Genomic DNA was extracted from the collected
40
blood. Salting out method for DNA extraction was implied using proteinase K, by peptide
hydrolysis and a saturated NaCl solution for cellular dehydration and protein precipitation.
Genomic DNA was recovered by standard salt and ethanol precipitation (Miller et al., 1988).
The blood samples stored in refrigerator/freezer were thawed in 15 mL labelled centrifuge
tubes. 2-3 mL red cell lysis buffer (RCLB) and 2 mL blood were added in centrifuge tubes
and inverted rapidly for 30 seconds then centrifuged at 3000 rpm for 10 minutes. A
disposable Pasteur pipette was used to remove the supernatant (discarding it into a Virkon
beaker) down to approximately 2 cm from the bottom of the tube or to just above the white
pellet when visible (usually on the 3rd or 4th wash). Repeated the wash steps three more times
for a total of four washes (fresh blood might only need 3 washes). After the final wash,
pipette off the supernatant and 300L of dd H2O, 100 L of proteinase K and 50 L of 10%
SDS were added and vortexed. Incubate the samples in the water bath at 55C and again
vortexed every 20 minutes until digestion was completed. Equal volume of ammonium
acetate (4.5M) in each sample was added. In the fume cupboard, doubled the volume of each
tube by adding phenol: chloroform: isoamyl alcohol and vortexed until a white emulsion was
formed and centrifuged at 3000 rpm for 15 minutes. Added 15 mL of ethanol to new labelled
50 mL falcon tubes (one for each sample was being extracted). Discarded the remaining
organic phase into the “phenol: chloroform waste” bottle and incubated the samples over
night at -20C. Centrifuged the samples at 3000 rpm for 18 minutes. Pour off the ethanol; left
the DNA pellet in the tube and allowed the pellets to dry for a few hours or until all the
ethanol had evaporated. When the pellet was dry, add 300 L of Tri EDTA (Ethylene
diamine tetra acetic acid) buffer to each sample (2 mL blood), allowed the DNA pellets to
dissolve before quantification.
3.8: DNA quantificationAfter the DNA extraction from the peripheral blood samples, the quantity and the quality of
the DNA was assessed by spectrophotometric quantification using Nanodrop
(Nanophotometer™, Implen, Germany).
Purity of the DNA was assessed by measuring OD260/OD280 (Sambrook and Russell,
2001).
3.9: PCR primer
PCR primers sequences (Table 3.1) were taken from Dillmec et al. (2010). Primers were
41
diluted to 1 μg/μL stock and these stocks were further diluted to working concentration of 10
pmol/μL. The reactions were set in 0.2 mL PCR tubes.
The PCR reaction mixture (10 μL) contained 5X Colorless GoTaq® Flexi Buffer (Part#
M890A): Proprietary formulation supplied at pH 8.5. The buffer contains 20 mM. Tris HCl;
pH 7.5; 100 mM. NaCl; 0.1 mM. EDTA; 1 mM. dithiothreitol; 1.0 μL of 50% (v/v) glycerol.
Other materials used were 1.0 mM. dNTPs, 0.3 mL of 50 mM. MgCl2, 5 pmol/ μL forward
and reverse primers for their respective DNA fragment (0.4 μL each), Go Taq kit (Promega
Madison, Wisconsin USA) was used for about 1.0 mL of genomic DNA (100 ng/ μL).
3.10: Optimization of amplification condition PCR conditions were optimized for annealing temperature and Mg2+ concentration.
42
Table 3.1: VDR primers
VDRPolymorphism
Primer PCR annealing temperature (°C)
FokI F:CCCTGGCACTGACTCTGCTC
R:GGAAACACCTTGCTTCTTCTCC
60
BsmI F:AGTGTGCAGGCGATTCGTAG
R:ATAGGCAGAACCATCTCTCAG
60
ApaI F:AGCATGGACAGGGAGCAA
R:CCTGTGCCTTCTTCTCTATCC
61.9
TaqI F:CCTGTGCCTTCTTCTCTATCC
R:AGCCTGAGTGCAGCATGA
54
(Delmic et al., 2010)
3.10.1: Polymerase chain reaction (PCR)
The exon 2 of VDR gene was amplified to study the polymorphism for the restriction site
FokI and exon 9 was amplified for ApaI and TaqI polymorphisms evaluation. The exon 8
was amplified to study the BsmI and ApaI polymorphism. The genomic DNA was amplified
using specific primers and according to a specific program (PCR thermocycler T100TM,
BioRad). The initial denaturation step lasted for 3 minutes at 95°C, followed by 35 cycles of
amplification: denaturation at 95°C for 30 seconds, an annealing for 30 seconds with
annealing temperature optimized for each primer set (Table 3.1) and an extension step for 30
seconds at 72°C. Final extension was done for 4 minutes at 72°C.
3.11: Enzymatic digestionAfter amplification of the VDR fragments with the polymerase chain reaction, all the
amplified fragments were digested with specific enzymes under specific time and
temperature conditions, allowing to assess the genotype of each individual.
Four VDR polymorphisms were studied: the FokI (rs10735810 T>C), BsmI (rs1544410
A>G), ApaI (rs7975232 G>T) and TaqI (rs731236 C>T). The digestion conditions are given
in Table 3.2.
43
Table 3.2: Enzymatic digestion conditions
VDR restriction site
Incubation temperature (°C)
Incubation time (minutes)
Digestion fragment size (bp)
ApaI 25 15 217, 528, 745
BsmI 65 15 76, 115, 191
FokI 37 60 70, 197, 267
TaqI 37 60 201, 251, 293, 494
(New England BioLabs®, R0109S)
The 4.0 μL of PCR product was used to proceed with the respective fragment digestion,
added mixture of 1.0 μL of the respective enzyme and 5.0 μL NE-buffer (New England
BioLabs®, R0109S) given appropriate fragments.
3.12: ElectrophoresisAgarose gel (3%) for genomic DNA and 2% for PCR amplified product and digested
fragments was prepared by boiling agarose with 1X TBE (Tris-Borate-EDTA) buffer and
stained with Medori Green Safe Buffer (1 μL/mL) (Bulldog Bio, USA). The migration in the
agarose gel was performed at 110 V for 120 minutes.
To compare the molecular weight of the DNA fragments, a molecular weight marker
(HyperLadder II, Bioline or VC 100 bp Plus DNA Ladder, Vivantis) was used. For agarose
gel visualization, UV light using UVITEC system (Uvitec Cambridge) was used.
3.13: Statistical analysisThe results were expressed as mean ± SD (standard deviation) and mean ± SE (standard
error). Numerical data were analyzed using paired student's t-test, while one way ANOVA
was used to evaluate significant biochemical and molecular results. Allele and genotype
frequencies were calculated and the Pearson s’ chi-square (X2) test (statistical approach used
to compare observed data we would expect to obtain according to a specific hypothesis) was
used to determine their associations in case and control participants. Patients were divided
into different subgroups according to their diabetic complications; retinopathy patients (RP),
nephropathy patients (NP), cardiac patients (CP) and hypertensive patients (HP) in order to
found the associatins between biochemical parameters and genotypes ApaI, BsmI, FokI and
TaqI. The results were considered statistically significant when p-value < 0.05 using the
Statistical Package for Social Sciences (SPSS version 16.0).
44
Chapter 4 RESULTS AND DISCUSSION
Diabetes mellitus is an enduring disease that requires ongoing medical care, patient
education and support to prevent from its acute complications and reduce the risk of chronic
complications. It is well know that the occurrence of type 2 diabetes mellitus (T2DM) is
rapidly rising and producing the socio- economic burden. Recognizing the risk factors and
anticipation of strategies are crucial. Interest in role of vitamin D in T2DM risk has been
rising, but gaps still found in the literature specifically impairment of insulin sensitivity and
dysfunction of beta cells in similar populations produce inadequate prospective data.
The vitamin D can directly improve the action of insulin through enhancing the
expression of receptors related to insulin production, found in VDRE (vitamin D response
element) of promoter region (Maestro et al., 2000). Different mechanisms have been
proposed by which vitamin D can affect the sensitivity of insulin and function of beta cells.
Among all the studied genes, the polymorphisms of VDR (vitamin D receptor) gene; BsmI,
ApaI, FokI and TaqI were related with vitamin D and onset of T2DM (Maestro et al., 2003).
VDR may affect the action of insulin through maintaining the insulin-mediated intracellular
response by regulation of calcium pool (Draznin et al., 1997; Draznin et al., 1998). However,
genetics determines of T2DM in different populations to find the effect of VDR gene
polymorphisms have reported conflicting results. Further studies are needed to reveal the
contribution of vitamin D receptor genetic polymorphisms in T2DM. The main objectives of
the present study were to evaluate the association of VDR genetic polymorphisms; BsmI,
ApaI, FokI and TaqI with demographic and biochemical indices among T2DM patients and
relationship of VDR gene polymorphisms with different diabetic complications, specifically
retinopathy, cardiac diseases, hypertension and nephropathy.
4.1: Demographic and biochemical indices Total 250 subjects were included in the current study. There were 150 T2DM patients and
100 healthy controls. The blood samples were collected from T2DM patients along normal
healthy individuals. Study subjects were interviewed to record demographic parameters.
Whole blood, serum and plasma were used to assess various biochemical parameters.
4.1.1: Comparison of demographic parametersThe demographic parameters; age, gender, BMI (body mass index), systolic and diastolic
45
blood pressure were analyzed to make comparison between healthy controls and type 2
diabetic groups (Table 4.1). Age may be considered one of the risk factor to onset of various
metabolic disorders including T2DM. The selection of subjects in both the groups was done
keeping in view of previous reports about the association of age and onset of type 2 diabetes
mellitus, however statistically no significant difference of age was noted in the present study
between control and T2DM groups (A. Table 1). The prevalence of various metabolic
disorders including type 2 diabetes mellitus may be directly or indirectly related to the elder
age but there are many other factors along with age such as BMI, oxidative stress,
hypertension and cardiac vascular diseases that may also contribute to develop T2DM and
many other metabolic disorders thus age may not be significantly related to the onset of type
2 diabetes mellitus in supporting to the present study (Hanley et al., 2005; Nakanishi et al.,
2005; Gandhe et al., 2013).
Earlier in the last century the occurrence of T2DM was greater in females as
compared to males. However, this inclination has changed, therefore now more males than
females are diagnosed with T2DM, it is because of a more sedentary lifestyle predominantly
among males, resulting in greater obesity, but many recent studies suggested that T2DM
develop without discrimination of gender, although distribution of body fat, blood glucose
levels and insulin resistance may play crucial role in both males and females (Kristine, 2014).
While Nayak et al. (2014) found that gender had no significant association to T2DM onset.
Present study included equal number of females and males to investigate the effect of gender
prone to T2DM in Pakistani population. No significant association of gender to T2DM onset
was found in the present study (A. Table 1).
World widely BMI has been considered as a strong risk factor prone to T2DM after family
history. In the present study, subjects of T2DM group had a significantly high BMI as
compared to normal control group (A. Table 1). BMI is independently related with the hazard
of developing type 2 diabetes mellitus. However, incremental relationship of BMI category
upon the hazard of type 2 diabetes mellitus is stronger for individuals with a greater BMI as
compared to those with normal BMI (Schienkiewitz et al., 2006; Ganz et al., 2014). Bays et
al. (2007) reported that BMI was generally related with increased prevalence of T2DM
including dyslipidaemia and hypertension. Prevalence of all metabolic diseases including
T2DM increased in linear trend as BMI increased with high morbidity rate. Wiliam et al.
46
(2011) has confirmed the previous and current studies that there are more chances to develop
T2DM in obese individuals.
The percentage of patients with obesity was higher than those in normal group
signifying that the detection and control of obesity might be more important in the Pakistani
population. In obese subjects with type 2 diabetes mellitus significant differences of both
systolic and diastolic blood pressure were observed (p<0.01). BMI and blood pressure are
mutually interrelated to each other and high values of blood pressure were observed in obese
people whether they were diabetic or non-diabetic (Marre et al., 2004; Wild et al., 2004;
Connor et al., 2015). By managing both risk factors the menaces of T2DM can be minimized
or better manage the diabetic complications.
Type 2 diabetes mellitus associated demographic parameters such as BMI and blood pressure
(systolic and diastolic) were significant among T2DM subjects as compared to controls.
Therefore, it is emphasized that the better management of disease; good glycemic control and
proper physical activities are exceptionally essential for the prevention of T2DM associated
complications and to improve the quality of life.
47
Table 4.1: Comparison of demographic parameters between diabetic and control
groups
ParameterDiabetic subjects
(n=150)Normal subjects (n=100)
Age (years) 49.5 ± 7.0 50.5 ± 7.6
Gender (M/F) 75/75 50/50
BMI (kg/m2) 35.8 ± 12.5 25.5 ± 5.0
Systolic BP (mm Hg) 140 ± 17 125 ± 15
Diastolic BP (mm Hg) 80 ± 9 77 ± 8
Data expressed as mean ± SD
SD: standard deviation, n: number of subjects, M: male, F: female, BMI:
body mass index, BP: blood pressure
48
4.1.2: Comparison of biochemical parametersThe comparison of biochemical parameters between type 2 diabetes mellitus and control
subjects is mentioned in Table 4.2.
Table 4.2: Comparison of biochemical parameters between diabetic and control groups
ParameterDiabetic subjects
(n=150)
Normal subjects
(n=100)
p - Value
FBS (mg/dL) 145 ± 5.54 80 ± 3.55 > 0.0001
HbA1c (%) 7.43 ±0.69 4.85 ± 0.33 > 0.0001
Vitamin D
(mg/dL)13.69 ± 1.85 22.36 ± 2.34
> 0.0001
T. bilirubin
(mg/dL)1.07 ± 0.42 1.10 ± 0.41
0.5770
D. bilirubin
(mg/dL)0.93 ± 0.23 0.94 ± 0.23
0.7366
ALT (mg/dL) 70.98 ± 15.19 72.94 ± 15.14 0.3179
AST (mg/dL) 33.77 ± 18.76 36.37 ± 19.07 0.2873
ALP (mg/dL) 50.91 ± 10.37 50.93 ± 10.37 0.9881
BUN (mg/dL) 40.53 ± 8.34 14.72 ± 4.34 > 0.0001
Creatinine
(mg/dL)2.02 ± 0.48 0.50 ± 0.33
> 0.0001
Uric acid (mg/dL) 6.19 ± 0.97 2.89 ± 0.78 > 0.0001
Cholesterol
(mg/dL)286.24 ± 28.54 178.03 ± 11.96
> 0.0001
LDL-C (mg/dL) 170.69 ± 10.15 64.40 ± 12.76 > 0.0001
HDL-C (mg/dL) 36.24 ± 3.25 62.12 ± 14.33 > 0.0001
TG (mg/dL) 180.49 ± 9.06 447.39 ± 154.86 > 0.0001
Data expressed as mean ± SD and p value; SD: standard deviation, n: number of subjects, FBS: fasting blood sugar, HbA1c: glycated hemoglobin, T-bilirubin: total bilirubin, D-bilirubin: direct bilirubin, ALT: alanine transaminase, AST: aspartate transaminase, ALP: alkaline phosphatase, BUN: blood urea nitrogen, LDL-C: low density lipoprotein cholesterol, HDL-C: high density lipoprotein cholesterol, TG: triglycerides.
49
Fasting and postprandial blood glucose levels have been used to diagnose both type 1 and 2
diabetes mellitus globally but fasting blood sugar level is one of the best recommended
diagnostic tools (WHO, 2014, 2015). A significantly higher fasting blood sugar (FBS) level
and lower vitamin D level were observed in type 2 diabetes mellitus group as compared to
control subjects. As present study was conducted to investigate the association of vitamin D
to T2DM, inverse significant association of vitamin D with FBS, HbA1c, renal functions
profile and lipid profile was noted. However, the extent of the relationship of vitamin D with
onset of T2DM was variable through BMI, along a weaker relation in people with BMI ≥ 30
kg/m2. Many previous studies have been reported a reduction of relationships of vitamin D
with T2DM/dysglycemia hazard after managing for adiposity (Alhumaidi et al., 2013;
Gandhe et al., 2013; Usluogullari et al., 2013). Vitamin D showed significant effect on
glycemic control via lowering FBS and HbA1c in type 2 diabetes mellitus (A. Table 2).
Fasting blood glucose was found significantly reduced with respect to high levels of vitamin
D by placebo treatment. Thus previous studies revealed that deficiency of vitamin D may be
one of the risk factors to develop type 2 diabetes mellitus (Middleton et al., 2003; Ronald et
al., 2004; Tushuizen et al., 2005; Bevan et al., 2006; Hsin et al., 2010). In addition, it has
been constantly reported that people with higher body fat have low levels of vitamin D. Such
conclusions may be because of the sequestering of vitamin D in adipocytes (Scragg et al.,
2004; Van Linthout et al., 2010; Dalgard et al., 2011).
The prevalence of vitamin D deficiency in present diabetic group was same as in the last
survey conducted on Caribbean T2DM patients which found that 42.6% T2DM patients were
vitamin D deficient (Velayoudom et al., 2011). Thus occurrence of health issues has
increased world widely due to vitamin D deficiency including pathogenesis of T2DM and its
complications (Lemire, 2000; Mathieu et al., 2004; Danescu et al., 2009). Poor management
or poor glycemic control of T2DM are the strongest interpreters for the progression and
development of diabetic complications such as nephropathy, retinopathy, cardiac vascular
disease and hypertension. Diabetic complications including retinopathy are strongly
associated to HbA1c, on the other hand, various macrovascular complication may develop
when HbA1c > 7.0% in T2DM cases (Donald et al., 2003). Barma et al. (2011) found that
the diabetic neuropathy and retinopathy were higher in T2DM subjects as compared to other
diabetic complications.
4.1.3: Comparison of biochemical parameters in T2DM complications sub-groups and control group
50
Overall presentation of biochemical parameters in sub-groups of T2DM complications is
given in Table 4.3. As expected, diabetic participants with complications had hyperglycemia
and hypovitaminosis D as compared to normal control participants. The prevalence of
vitamin D deficiency was significantly higher in T2DM sub groups (>40%) as compared to
control group, although it was non-significant among the complications groups (Table 4.4
and 4.5).
4.1.3.1: Liver function tests
Hepatic impairment is a well-known complication along with fatty liver in T2DM. However,
liver function tests are non-specific markers to determine the future development of T2DM.
Fatty liver may lead to an abnormal liver function profile both in normal individuals and
T2DM cases (Elizabeth and Harris, 2005; Skaaby et al., 2014). In this study, no significant
association of liver function profile, T-bilirubin, D-bilirubin, alanine aminotransferase,
aspartate aminotransferase and alkaline phosphatase were observed (A. Table 3). Such results
can be justified by the fact that none of the case subjects in present study was reported with
fatty liver. T2DM patient with normal liver can have normal liver function profile. It was
found in the present study that none of the diabetic subject showed statistically significant
differences of any liver function tests as observed by Veith et al. (2007) and Park et al.
(2011) (Table 4.6. and 4.7). No statistically significant association of vitamin D with liver
enzymes has been found in previous studies both in T2DM and non-T2DM subjects but
elevated levels of hepatic enzymes such as ALT, AST and ALP were observed in vitamin D
deficient people (Cowie et al., 1995; Gaede et al., 1999; Roszyk et al., 2007; Khattab et al.,
2010). Contrary to these observations, although diabetic complications groups showed
declined vitamin D levels yet their hepatic enzymes (ALT, AST, and ALP) concentrations
were analogous to control group.
4.1.3.2: Renal functions tests
Abnormal renal function profile is found in vitamin D deficient T2DM patients with diabetic
nephropathy (Chadban et al., 2009). Significant differences of all renal profile parameters
were noted in T2DM patients than that of control subjects in the current study (p<0.01) (A.
Table 4). High values of uric acid, creatinine and blood urea nitrogen are indicators for the
onset of diabetic nephropathy (Chan et al., 2000; Rohitash et al., 2014). Uric acid, creatinine
51
and blood urea nitrogen were significantly higher in T2DM patients with nephropathy,
retinopathy, cardiac and hypertension in the present study (Table 4.8 and 4.9) as previously
observed by Chadban et al. (2009) and Rohitash et al. (2014).
4.1.3.3: Lipid profile
Dyslipidaemia is the most common disorder found in T2DM patients, that causes various
cardiac diseases. The comparison of lipid profile was conducted between T2DM and control
groups to investigate the prevalence of lipid profile parameters in T2DM subjects with
cardiac, renal, ocular and hypertensive complications (Table 4.10 and 4.11). Significant
differences (p< 0.01) of lipid profile parameters were observed between both T2DM and
control groups (A. Table 5). It was observed by Samatha et al. (2012) that abnormal lipid
profile and hyperglycemia in T2DM can predispose to microvascular complications than
healthy individuals. Van Linthout et al. (2010) and Ratna et al. (2013) stated that higher
levels of LDL-cholesterol and low level of HDL-cholesterol may cause insulin resistance
which leads to develop T2DM. Shaikh et al. (2010) found elevated level of cholesterol and
fasting blood sugar in T2DM cases. According to Khan et al. (2008) triglycerides are
predominantly high in T2DM subjects in Pakistan. Present study was in agreement to
previous studies with respect to significant association of LDL-cholesterol, triglycerides,
cholesterol and HDL-cholesterol with type 2 diabetes mellitus (Khan et al., 2008; Van
Linthout et al., 2010; Ratna et al., 2012). T2DM not only lowers HDL-C levels, but also
affects its shape and size. Impaired functionality of HDL-C results because of peroxidation
and glycation (Van Linthout et al., 2010). These inferences were in accordance with the
present results. Ratio of LDL-C: HDL-C and TG: HDL-C is good indicator to correlate
various biochemical parameters in postprandial state. T2DM is characteristically related with
a dyslipidaemia characterized through hyper triglyceridaemia as evident in current research
with low levels of HDL-C (Nesto et al., 2005). However, the levels of cholesterol and LDL-
C were not changed significantly both in T2DM cases and healthy individuals according to
Wager et al. (2005). Conversely, cholesterol, triglycerides and LDL-C were found to be
elevated (p<0.01) among T2DM subjects as compared to control subjects in present study.
Nonetheless among diabetic complications sub groups (CP, NP, RP and HP) these three
parameters were non-significant.
52
It may be assumed from the present study that poor glycemic control in T2DM subjects may
cause post diabetic complications. The expected changes in renal function profile, lipid
profile and glucose intolerance (FBS and HbA1c) were observed, that showed a significant
inverse relationship of vitamin D which altered sensitivity of insulin leading to T2DM onset.
Additional studies are required to investigate the processes regarding the constant inverse
relation of vitamin D and adiposity with its probable effect on T2DM risk.
53
Table 4.3: Biochemical characteristics of type 2 diabetic patients with complications and healthy controls Control (n=100) CP (n=80) NP (n=20) RP (n=20) HP (n=30)
Min Max Mean SD Min Max Mean SD Min Max Mean SD Min Max Mean SD Min Max Mean SD
Biochemical parameters
HbA1c 4.00 5.50 4.85 0.33 6.50 8.50 7.42 0.69 6.50 8.50 7.52 0.68 6.50 8.50 7.39 0.70 6.50 8.50 7.40 0.71
Vit.D 19.00 26.00 22.36 2.34 11.00 17.00 13.68 1.82 11.00 17.00 13.55 2.06 11.00 17.00 13.70 1.66 11.00 17.00 13.83 1.97
Liver function tests
T.bilirubin 0.45 1.90 1.10 0.41 0.45 1.90 1.12 0.41 0.45 1.90 0.91 0.40 0.78 1.89 1.27 0.35 0.45 1.90 0.91 0.43
D.bilirubin 0.45 1.23 0.94 0.23 0.45 1.23 0.93 0.24 0.45 1.23 0.92 0.22 0.45 1.23 0.94 0.25 0.56 1.23 0.95 0.21
ALT 37.00 90.00 72.94 15.14 37.00 90.00 70.15 15.34 37.00 90.00 72.25 15.47 47.00 90.00 72.55 13.59 37.00 90.00 71.30 16.20
AST 8.00 76.00 36.37 19.07 8.00 76.00 35.91 19.11 8.00 56.00 26.10 15.74 12.00 76.00 37.05 19.58 8.00 76.00 30.97 18.21
ALP 34.00 67.00 50.93 10.37 34.00 67.00 50.73 10.41 34.00 67.00 51.05 10.46 34.00 67.00 50.10 10.44 34.00 67.00 51.87 10.59
Renal function tests
BUN 12.00 54.00 24.72 10.33 46.00 123.00 76.39 15.70 46.00 123.00 75.70 17.59 54.00 110.00 77.40 15.34 46.00 123.00 76.87 19.76
Creatinine 0.21 1.23 0.50 0.33 1.23 2.50 2.02 0.48 1.23 2.50 2.06 0.47 1.23 2.50 2.01 0.51 1.23 2.50 2.01 0.48
Uric Acid 2.00 4.00 2.89 0.78 4.00 8.00 6.11 0.97 5.00 8.00 6.30 0.92 4.00 8.00 6.20 0.95 4.00 8.00 6.30 1.02
Lipid profile
LDL-C 47.00 87.00 64.40 12.76 156.00 187.00 170.66 10.08 156.00 187.00 169.45 10.61 156.00 187.00 171.70 9.96 156.00 187.00 170.90 10.58
HDL-C 30.00 77.00 62.12 14.33 32.00 41.00 36.29 3.25 32.00 41.00 35.90 3.16 32.00 41.00 36.20 3.43 32.00 41.00 36.37 3.33
TG 167.00 190.00 180.49 9.06 55.00 553.00 448.36 154.56 55.00 553.00 435.10 168.54 55.00 553.00 461.10 144.50 55.00 553.00 443.87 160.04
CHOL. 167.00 200.00 178.03 11.96 230.00 331.00 286.38 28.47 230.00 331.00 284.60 31.30 230.00 331.00 288.15 26.43 230.00 331.00 285.70 29.53
Data expressed as mean ± SD (minimum - maximum ranges)SD: standard deviation, n: number of subjects, CP: cardiac patients, NP: nephropathy patients, RP: retinopathy patients, HP: hypertensive patients, HbA1c: glycated hemoglobin, T-bilirubin: total bilirubin, D-bilirubin: direct bilirubin, ALT: alanine transaminase, AST: aspartate transaminase, ALP: alkaline phosphatase, BUN: blood urea nitrogen, LDL-C: low density lipoprotein cholesterol, HDL-C: high density lipoprotein cholesterol, TG: triglycerides, CHOL: cholesterol
42
Table 4.4: Comparison of means of biochemical parameters in type 2 diabetic complications groups
Group (n) Mean ± SE
HbA1c Vitamin D
Control (100)
CP (80)
NP (20)
RP (20)
HP (30)
4.85 ± 0.033 B
7.42 ± 0.078 A
7.52 ± 0.151 A
7.39 ± 0.156 A
7.40 ± 0.129 A
22.36 ± 0.234 A
13.68 ± 0.203 B
13.55 ± 0.462 B
13.70 ± 0.371 B
13.83 ± 0.359 B
Data expressed as mean ± SEMeans sharing similar letter in a column are statistically non-significant (p>0.05)n: number of observations, SE: standard error, HbA1c: glycated hemoglobin, CP: cardiac patients, NP: nephropathy patients, RP: retinopathy patients, HP: hypertensive patients
Table 4.5: Analysis of variance (mean squares) for Biochemical parameters in type 2 diabetic complications groupsSource of
variation
Degrees of
freedom
Mean squares
HbA1c Vitamin D
Group
Error
Total
04
245
249
99.381S
0.332
1126.9 HS
4.3
S: significant, HS: highly significant, HbA1c: glycated hemoglobin
43
Table 4.6: Comparison of means of liver functions tests parameters in type 2 diabetic complications groups
Group (n) Mean ± SE
T. bilirubin D. bilirubin ALT AST ALP
Control (100)
CP (80)
NP (20)
RP (20)
HP (30)
1.102 ± 0.041 A
1.119 ± 0.046 A
0.912 ± 0.088 A
1.266 ± 0.078 A
0.907 ± 0.078 A
0.939 ± 0.023A
0.928 ± 0.027A
0.921 ± 0.050A
0.945 ± 0.055A
0.946 ± 0.039A
72.94 ± 1.514A
70.15 ± 1.715A
72.25 ± 3.460A
72.55 ± 3.038A
71.30 ± 2.958A
36.37 ± 1.907A
35.91 ± 2.136A
26.10 ± 3.520A
37.05 ± 4.378A
30.97 ± 3.324A
50.93 ± 1.037A
50.73 ± 1.164A
51.05 ± 2.339A
50.10 ± 2.334A
51.87 ± 1.933A
Data expressed as mean ± SEMeans sharing similar letter in a column are statistically non-significant (p>0.05)SE: standard error, n: number of observations, T. bilirubin: total bilirubin, D-bilirubin: direct bilirubin, ALT: alanine transaminase, AST: aspartate transaminase, ALP: alkaline phosphatase, CP: cardiac patients, NP: nephropathy patients, RP: retinopathy patients, HP: hypertensive patients
Table 4.7: Analysis of variance (mean squares) for liver function tests in type 2 diabetic complications groups
Source of
variation
Degrees of
freedom
Mean squares
T. bilirubin D. bilirubin ALT AST ALP
Group
Error
Total
04
245
249
0.5791NS
0.1655
0.00350NS
0.05299
92.6NS
232.4
600.3NS
352.9
10.9NS
108.6
NS: non- significant, HS: highly significant, T. bilirubin: total bilirubin, D-bilirubin: direct bilirubin, ALT: alanine transaminase, AST: aspartate transaminase, ALP: alkaline phosphatase
44
Table 4.8: Comparison of means of renal function tests parameters in type 2 diabetic complications groups
Group (n) Mean ± SE
BUN Creatinine Uric Acid
Control (100)
CP (80)
NP (20)
RP (20)
HP (30)
24.72 ± 1.033 B
76.39 ± 1.755 A
75.70 ± 3.933 A
77.40 ± 3.429 A
76.87 ± 3.608 A
0.50 ± 0.033 B
2.02 ± 0.054 A
2.06 ± 0.106 A
2.01 ± 0.114 A
2.01 ± 0.088 A
2.89 ± 0.078 B
6.11 ± 0.108 A
6.30 ± 0.206 A
6.20 ± 0.213 A
6.30 ± 0.187 A
Data expressed as mean ± SEMeans sharing similar letter in a column are statistically non-significant (p>0.05)n: number of observations, SE: standard error, BUN: blood urea nitrogen, CP: cardiac patients, NP: nephropathy patients, RP: retinopathy patients, HP: hypertensive patients
Table 4.9: Analysis of variance (mean squares) for renal function tests in type 2 diabetic complications groups.
Source of
variation
Degrees of
freedom
Mean squares
BUN Creatinine Uric Acid
Group
Error
Total
04
245
249
40267.0HS
211.0
34.543HS
0.184
163.29HS
0.81
HS: highly significant, BUN: blood urea nitrogen
45
Table 4.10: Comparison of means of lipid profile parameters in type 2 diabetic complications groups
Group (n) Mean ± SE
LDL-C HDL-C TG Cholesterol
Control (100)
CP (80)
NP (20)
RP (20)
HP (30)
64.40±1.276 B
170.66±1.127 A
169.45±2.371 A
171.70±2.227 A
170.90±1.931 A
62.12±1.433A
36.29±0.363 B
35.90±0.707 B
36.20±0.766 B
36.37±0.607 B
180.49±0.906 B
448.36±17.28 A
435.10±37.68 A
461.10±32.31 A
443.87±29.21 A
178.03±1.196 B
286.38±3.183 A
284.60±6.999 A
288.15±5.911 A
285.70±5.391 A
Data expressed as mean ± SE Means sharing similar letter in a column are statistically non-significant (p>0.05)n: number of observations, SE: standard error, LDL-C: low density lipoprotein cholesterol, HDL-C: high density lipoprotein cholesterol, TG: triglycerides, CP: cardiac patients, NP: nephropathy patients, RP: retinopathy patients, HP: hypertensive patients
Table 4.11: Analysis of variance (mean squares) for lipid profile in type 2 diabetic complications groups
Source of
variation
Degrees of
freedom
Mean squares
LDL-C HDL-C TG Cholesterol
Group
Error
Total
4
245
249
169466.0HS
128.0
10047.0 HS
89.0
1070368.0 HS
14589.0
175675.0 HS
553.0
HS: highly significant, LDL-C: low density lipoprotein cholesterol, HDL-C: high density lipoprotein cholesterol, TG:
triglycerides
46
4.2: Association of VDR gene polymorphisms with T2DM and its
complicationsDeficiency of vitamin D has been shown to involve in impairment of insulin production and
function (Zeitz et al., 2003), whereas supplementation of vitamin D may decrease the
cytokine mediated destruction of beta cells (Gysemans et al., 2005). Many studies have
shown that vitamin D deficiency may develop various multifactorial diseases including
T2DM in which genetic and environmental factors play a complex role that is not yet well
defined. A number of studies have focused on the relationship between T2DM onset and
candidate genes. It has been found that deficiency of vitamin D linked with VDR
polymorphisms is predisposed to T2DM (Omdhal et al., 2002; Anderson et al., 2003).
Receptors for vitamin D are located in the antigen presenting cells, T cells and beta cells of
pancreas (Adams et al., 2007). VDR gene has been studied in various populations to find the
relationship with susceptibility to T2DM and its complications but outcomes produced
conflicting results (Omdhal et al., 2002; Anderson et al., 2003; Florez et al., 2008; Ahn et al.,
2010; Billings et al., 2010; Wang et al., 2010; Wheeler et al., 2011). In this study, four VDR
polymorphisms; ApaI, FokI, BsmI and TaqI, were assessed. The aim of this study was to
investigate how VDR genotypes and alleles distribution affect the prevalence of type 2
diabetes mellitus in the Pakistani population. Two different main groups were considered in
the following study: T2DM patients and control.
4.2.1: ApaI polymorphisms in T2DM and control groups
VDR gene (exon 9) was amplified by using PCR to evaluate the ApaI polymorphism in
T2DM and control groups. The product of PCR obtained was loaded on agarose gel. The
fragment of exon 9 of the VDR gene is given in Figure 4.1. To verify all distinctive
genotypes of exon 9 enzymatic digestion was done (Figure 4.2). The presence of all three
fragments (217, 528 and 745 bp) after digestion with restriction enzyme indicates
heterozygosity (Aa) of ApaI genotype. ApaI, BsmI and TaqI genotypes are located in 3’ end
of VDR gene, hence VDR gene polymorphisms related to this region not involved in the
change of VDR protein structure, but they may be involved in pathogenesis of T2DM (Filus
et al., 2008). However, Malecki et al. (2003) found no significant association of ApaI with
T2DM onset and metabolic parameters to support the hypothesis that ApaI may prone
macrovascular diabetic complications. According to the present study no statistically
47
significant differences of ApaI allele distributions observed between T2DM subjects and
control groups (Table 4.12). However overall distribution of ApaI genotypes between healthy
control and T2DM groups are aa 3.2%, Aa 46.0%, AA 50.8%. Many acquired and genetic
factors may associate with T2DM and its complications but the exact reason of those factors
that induce T2DM and its various complications including nephropathy are still not clear
(Nathanson and Nystrom, 2008; Arababadi et al., 2010). Polymorphisms in ApaI genotype of
VDR gene among T2DM complications such nephropathy, retinopathy, cardiac patients and
hypertension were scored in Figures 4.3 to 4.5. No significant association observed between
VDR gene polymorphism (ApaI) and diabetic complications onset in terms of demographic
and biochemical parameters (Table 4.13) as described by Malecki et al. (2003) and
Arababadi et al. (2010). The percentages of ApaI genotypes distribution between T2DM and
healthy control are same as mention in Table 4.12. Impact of VDR gene polymorphisms on
metabolic parameters was also studied to clarify their association underlying the diabetic
complications. Current study investigated the same relationship of VDR gene polymorphisms
with various biochemical parameters (Table 4.14) involved in pathogenesis of various T2DM
complications as found in previous studies (Arababadi et al., 2010; Dilmec et al., 2010;
Nosratabadi et al., 2010). Thus the clinical relevance between biochemical parameters
underlying diabetic complications and ApaI polymorphisms is not yet clear.
Figure 4.1: Electrophoresis of a 2% agarose gel with exon 9 PCR product loaded; 1 – negative control; 2, 3 and 4 – exon 9 fragments with 745 bp
48
Figure 4.2: Electrophoresis of a 3% agarose gel with ApaI enzymatic digestion of VDR exon 9; 5 – heterozygous Aa genotype (217, 528 and 745 bp); 6 – homozygous aa genotype (217 and 528 bp); 7 – homozygous AA genotype (745 bp)
Table 4.12: Distribution of genotype, allele frequencies and carriage rate of ApaI among patients and controls
Genotype
Groups Total
Control Patient
aa4 4 8
4.0% 2.7% 3.2%
Aa54 61 115
54.0% 40.7% 46.0%
AA42 85 127
42.0% 56.7% 50.8%
Total100 150 250
100.0% 100.0% 100.0% Data expressed as X2= 5.1915.08NS, p= 0.075 NS: non- significant
49
Table 4.13: Distribution of genotype, allele frequencies and carriage rate of ApaI among T2DM complications sub-groups with control group
Groups Genotypes
Total aa Aa AA
Control4 54 42 100
4.0% 54.0% 42.0% 100.0%
CP2 35 43 80
2.5% 43.8% 53.8% 100.0%
NP1 11 8 20
5.0% 55.0% 40.0% 100.0%
RP1 8 11 20
5.0% 40.0% 55.0% 100.0%
HP0 7 23 30
0.0% 23.3% 76.7% 100.0%
Total8 115 127 250
3.2% 46.0% 50.8% 100.0%
Data expressed as X2 = 13.168NS, p=0.106
CP: cardiac patients, NP: nephropathy patients, RP: retinopathy patients, HP: hypertensive patients, NS: non-significant
50
Table 4.14: Probability values for the association of biochemical parameters and ApaI genotypes in T2DM subgroups
T2DM Sub-groups
Parameters Control CP NP RP
HP
HbA1c 0.468 0.968 0.611 0.521 0.159
Vitamin-D 0.758 0.218 0.622 0.880 0.972
T. bilirubin 0.560 0.958 0.504 0.078 0.135
D. bilirubin 0.043 0.466 0.527 0.464 0.651
ALT 0.441 0.232 0.571 0.937 0.283
AST 0.995 0.475 0.973 0.160 0.051
ALP 0.001 0.052 0.770 0.449 0.907
BUN 0.100 0.593 0.338 0.647 0.283
Creatinine 0.631 0.841 0.849 0.344 0.684
Uric acid 0.343 0.958 0.114 0.707 0.711
Cholesterol 0.403 0.339 0.153 0.706 0.397
LDL-C 0.094 0.191 0.448 0.826 0.531
HDL-C 0.558 0.239 0.665 0.354 0.843
TG 0.065 0.483 0.618 0.505 0.267
Data expressed as p – value Calculated as
CP: cardiac patients, NP: nephropathy patients, RP: retinopathy patients, HP: hypertensive patients, HbA1c: glycated hemoglobin, T-bilirubin: total bilirubin, D-bilirubin: direct bilirubin, ALT: alanine transaminase, AST: aspartate transaminase, ALP: alkaline phosphatase, BUN: blood urea nitrogen, LDL-C: low density lipoprotein cholesterol, HDL-C: high density lipoprotein cholesterol, TG: triglycerides
51
Figure 4.3: ApaI digestion (Retinopathy patients group): Lane 1; ladder of 100 bp. Lane 2,4,6,7,8,9,10,11 & 12 consist AA of 745 bp. Lane 4,7,9 and 11 consist of 531 and 214 bp fragments
Figure 4.4. ApaI digestion polymorphism products (Nephropathy patients group) : Lane 1,2,3,5,7,8, 9 & 10 have AA homozygous PCR –RFLP product of 745bp. Lane 1,2,3,4,5,6 and 9 consist Aa heterozygous product 531bp . Lane 1,2,3,4,5,6 and 9 consist aa PCR-RFLP product of 214bp.
52
Figure 4.5: VDR gene ApaI digestion polymorphism products (cardiac patients group with HP ) :Lane 2,5,6,8,9,11,12,14 and 15 have AA homozygous PCR-RFLP products of 740bp. Lane 4,7 & 10 have aa homozygous PCR-RFLP products of 530 and 210bp. Lane 1,3 and 13 have Aa heterozygous PCR-RFLP products of 740, 530 and 210bp
4.2.2: FokI polymorphisms in T2DM and control groups
To investigate the FokI polymorphism, exon 2 of VDR gene was amplified by PCR. A PCR
fragment of 267 bp was obtained (Figure 4.6). To confirm heterozygosity, enzymatic
digestion was done by restriction enzyme, FokI digested fragments are given in Figure 4.7.
FokI is considered as another important restriction site of VDR gene polymorphism, located
at 5’ end of the gene. It may be responsible to alter the structure of VDR protein after frame
shift mutation at 5’ end (Naito et al., 2007), produces another translation initiation site that
leads to produce three additional amino acids to the protein of VDR. Such polymorphism of
VDR gene may be involved in susceptibility of various metabolic disorders including T2DM
and pathogenesis of diabetic complications (Whitfield et al., 2001; Naito et al., 2007). In the
present study FokI allele distribution was significantly different in T2DM group as compared
to control group (Table 4.15). Previous studies observed higher prevalence of Ff genotype of
VDR gene in T2DM patients as compared to control group (Ahn et al., 2010; Wang et al.,
2010). Different polymorphisms of VDR gene including FokI may alter the function of VDR
protein (Filus et al., 2008). FokI restriction site found in exon 2, has involved in
transcriptional activity of VDR gene but genetic background of T2DM remains unclear
(Whitfield et al., 2001). These findings suggest that the FokI polymorphism may contribute
to the susceptibility T2DM complications (Figure 4.8 to 4.10). T2DM patients have subtle
changes in glucose metabolism well before onset of the disease. Such VDR polymorphisms
53
influencing the pathogeneses or development can be detected prior to disease onset (Palomer
et al., 2008). According to Speer et al. (2001) and Malecik et al. (2003) no impact of VDR
gene polymorphisms including FokI was found in pathogenesis of T2DM complications and
underlying biochemical parameters. The distribution of FokI allele genotypes in T2DM
complications groups was not statistically different as compared to control group (Table
4.16). Dilmec et al. (2010), Nosratabadi et al. (2010) and Bid et al. (2011) observed that
FokI polymorphism had no association with various clinical or biochemical parameters,
although genetic background of T2DM pathogenesis was not well defined. Current study has
also shown no significant link between FokI genotype and the biochemical parameters (Table
4.17). Thus further studies are required to find the association of VDR gene polymorphisms
to the manifestation of T2DM or related qualitative metabolic parameters in the Pakistani
subjects.
Figure 4.6: Electrophoresis of a 2% agarose gel with exon 2 PCR product loaded; 1 –
negative control; 2, 3 and 4 – exon 2 fragment with 267 bp.
54
Figure 4.7: Electrophoresis of a 3% agarose gel with FokI enzymatic digestion of VDR exon 2; 5 – homozygous TT genotype (70 and 197 bp); 6 – homozygous CC genotype (267 bp); 7 – heterozygous CT genotype (70, 197 and 267 bp
Table 4.15: Distribution of genotype, allele frequencies and carriage rate of FokI among patients and controls
GenotypeGroup Total
Control Patient
ff 11 13 2411.0% 8.7% 9.6%
Ff 36 91 12736.0% 60.7% 50.8%
FF 53 46 9953.0% 30.7% 39.6%
Total 100 150 250100.0% 100.0% 100.0%
Data expressed as X2= 8.33S, p<0.016 S: significant
55
Table 4.16: Distribution of genotype, allele frequencies and carriage rate of FokI among T2DM complications sub-groups with control group
GroupsFokI Total
Ff Ff FF
Control11 36 53 100
11.0% 36.0% 53.0% 100.0%
CP5 47 28 80
6.3% 58.8% 35.0% 100.0%
NP6 9 5 20
30.0% 45.0% 25.0% 100.0%
RP1 16 3 20
5.0% 80.0% 15.0% 100.0%
HP1 19 10 30
3.3% 63.3% 33.3% 100.0%
Total24 127 99 250
9.6% 50.8% 39.6% 100.0% Data expressed as X2 = 30.59NS, p>0.001
CP: cardiac patients, NP: nephropathy patients, RP: retinopathy patients, HP: hypertensive patients, NS: non-significant
Table 4.17: Probability values for the association of biochemical parameters and FokI genotypes in T2DM sub-groups
T2DM Sub-groupsParameters Control CP NP RP HP
HbA1c 0.858 0.606 0.171 0.442 0.695Vitamin-D 0.472 0.536 0.587 0.835 0.640T. bilirubin 0.631 0.047 0.384 0.160 0.360D. bilirubin 0.033 0.791 0.203 0.438 0.407
ALT 0.545 0.151 0.543 0.929 0.634AST 0.839 0.566 0.187 0.026 0.055ALP 0.262 0.394 0.219 0.180 0.868BUN 0.011 0.507 0.048 0.673 0.560
Creatinine 0.183 0.643 0.799 0.146 0.561Uric acid 0.908 0.949 0.915 0.721 0.221
Cholesterol 0.591 0.811 0.239 0.120 0.920LDL-C 0.251 0.214 0.602 0.483 0.561HDL-C 0.730 0.556 0.280 0.328 0.549
TG 0.898 0.025 0.147 0.898 0.269Data expressed as p – valueCalculated as
56
CP: cardiac patients, NP: nephropathy patients, RP: retinopathy patients, HP: hypertensive patients, HbA1c: glycated hemoglobin, T. bilirubin: total bilirubin, D. bilirubin: direct bilirubin, ALT: alanine transaminase, AST: aspartate transaminase, ALP: alkaline phosphatase, BUN: blood urea nitrogen, LDL-C: low density lipoprotein cholesterol, HDL-C: high density lipoprotein cholesterol, TG: triglycerides
Fig 4.8: FokI digestion (Retinopathy patients group): Lane M; ladder 100 bp. Lane 1,3,4,5,6 and 7 consists homozygous fragment FF 273 bp. Lane 2,3,5 and 7 consist heterozygous fragment Ff 198 bp
Fig 4.9: Lane 10 : ladder 100 bp, lane; 1,2,3,4,5,6,7 & 9 have FokI digestion PCR-RFLP product FF 273bp, lane ;4,6,8 and 9 consists 198bp product Ff,lane 7 has ff product of 75bp in NP group.
57
Fig. 4.10: VDR gene FokI digestion polymorphism products (cardiac patients group with HP):Lane 1,2,5 and 6 have FF homozygous PCR-RFLP products of 273bp. Lane 2,3 and 4 have Ff heterozygous PCR-RFLP products of 198 and 50bp. Lane 3 have ff homozygous PCR-RFLP products of 75bp. Lane 7 has ladder of 100bp
4.2.3: BsmI polymorphisms in T2DM and control groups
BsmI polymorphism is found in intron 8 of VDR gene. Amplification of intron 8 was done by
PCR under specific optimized conditions (Figure 4.11). Then, heterozygosity of intron 8 was
confirmed by enzymatic digestion (Figure 4.12).
Three enzymatic digested fragments of BsmI (76, 115 and 191 bp) indicated heterozygosity
(Bb) of BsmI genotype. Important polymorphic restriction sites including BsmI genotype are
located in 3’ end of VDR gene, involved in the change of VDR protein structure but they
may be involved in pathogenesis of T2DM (Houshiarrad et al., 2013). Dilmec et al. (2010)
found significant association of BsmI to T2DM onset in the population of Iran. While Israni
et al. (2009) suggested that higher levels of VDR protein may affect cytokine production
particularly IL-12 as in T2DM subjects, confirmed BsmI polymorphisms. According to the
present study statistically significant differences of BsmI allele distributions observed
between T2DM subjects and control groups (Table 4.17).
A number of factors may be involve in T2DM and its complications onset however the exact
phenomenon that induce T2DM and its various complications are still not clear (Nathanson
and Nystrom, 2008; Zhu et al., 2014). BsmI polymorphisms among T2DM complications
such nephropathy patients group, retinopathy patients group, cardiac patients and
hypertension patients group were scored in Figures 4.13 to 4.15. Effect of VDR gene
polymorphisms on metabolic parameters was also studied to clarify their association
underlying the diabetic complications. No significant association observed between VDR
58
gene polymorphism (BsmI) and diabetic complications onset in terms of demographic and
biochemical parameters (Table 4.18 and 4.19). Current study investigated the same
relationship of VDR gene polymorphisms with various biochemical parameters involved in
pathogenesis of various T2DM complications as found in previous studies by Dilmec et al.
(2010) in population of Turkey and Nosratabadi et al. (2010) in Iranian population. Wang et
al. (2013) found significant association of BsmI polymorphism with T2DM onset however no
evidences were there to support the hypothesis that BsmI polymorphism had link to the
pathogenesis of post diabetic complications and clinical parameters. In conclusion, the
clinical relevance between biochemical parameters underlying diabetic complications and
BsmI polymorphisms is not yet clear.
Figure 4.11: Electrophoresis of a 2% agarose gel with intron 8 PCR product loaded; 1 – negative control; 2, 3 and 4 – intron 8 fragment with 191 bp
Figure 4.12: Electrophoresis of a 3% agarose gel with BsmI enzymatic digestion of
VDR intron 8; 5 – heterozygous Bb genotype (76, 115 and 191 bp); 6 – homozygous bb
genotype (76 and 115 bp); 7 – homozygous BB genotype (191 bp)
59
Table 4.18: Distribution of genotype, allele frequencies and carriage rate of BsmI among patients and controls
Genotype
Group TotalControl Patient
bb2 4 6
2.0% 2.7% 2.4%
Bb64 119 183
64.0% 79.3% 73.2%
BB34 27 61
34.0% 18.0% 24.4%
Total100 150 250
100.0% 100.0% 100.0% Data expressed as X2= 15.08S, p< 0.05
S: significant
Table 4.19: Distribution of genotype allele frequencies and carriage rate of BsmI among T2DM subgroups and control group
GroupsGenotypes Total
bb Bb BB
Control2 64 34 100
2.0% 64.0% 34.0% 100.0%
CP3 59 18 80
3.8% 73.8% 22.5% 100.0%
NP0 18 2 20
0.0% 90.0% 10.0% 100.0%
RP0 19 1 20
0.0% 95.0% 5.0% 100.0%
HP1 23 6 30
3.3% 76.7% 20.0% 100.0%
Total6 183 61 250
2.4% 73.2% 24.4% 100.0%
Data expressed as X2 = 13.158NS, p=0.108CP: cardiac patients, NP: nephropathy patients, RP: retinopathy patients, HP: hypertensive patients, NS: non-significant
60
Table 4.20: Probability values for the association of biochemical parameters and BsmI genotypes in T2DM sub-groups
T2DM Sub-groups
Parameters Control CP NP RP HP
HbA1c 0.775 0.597 0.436 0.256 0.376
Vitamin-D 0.334 0.592 0.165 0.858 0.279
T. bilirubin 0.810 0.999 0.263 0.601 0.585
D. bilirubin 0.850 0.871 0.841 0.037 0.495
ALT 0.229 0.064 0.694 0.051 0.595
AST 0.399 0.291 0.131 0.577 0.870
ALP 0.138 0.180 0.136 0.334 0.622
BUN 0.257 0.855 0.205 0.403 0.123
Creatinine 0.536 0.137 0.620 0.868 0.658
Uric acid 0.336 0.989 0.426 0.422 0.717
Cholesterol 0.335 0.750 0.633 0.966 0.628
LDL-C 0.563 0.358 0.782 0.641 0.369
HDL-C 0.763 0.988 0.178 0.954 0.719
TG 0.206 0.629 0.562 0.914 0.969
Data expressed as p – value Calculated as
CP: cardiac patients, NP: nephropathy patients, RP: retinopathy patients, HP: hypertensive patients, HbA1c: glycated hemoglobin, T. bilirubin: total bilirubin, D. bilirubin: direct bilirubin, ALT: alanine transaminase, AST: aspartate transaminase, ALP: alkaline phosphatase, BUN: blood urea nitrogen, LDL-C: low density lipoprotein cholesterol, HDL-C: high density lipoprotein cholesterol, TG: triglycerides
61
Figure 4.13: VDR gene BsmI digestion polymorphism products (cardiac group with Hypertensive patients) :Lane 11 have BB homozygous 823bp PCR-RFLP product. Lane 5,6,7,8,9,10,11& 12 have Bb heterozygous 648bp PCR-RFLP products. Lane 5 has bb homozygous PCR-RFLP products of 175bp. Lane 1 consists of ladder 100bp.
Figure 4.14: Lane 8 : ladder 100bp, lane; 1,3,5,6 and 7 have BsmI digestion PCR-RFLP product BB 823bp, lane ;1,3,5,6 and 7 consists 648bp product Bb,lane 7 has bb product of 175bp in NP group
Figure 4.15: BsmI digestion (Retinopathy patients group): Lane M; Ladder of 100bp. Lane 3,5,8,9 & 10 consist fragment BB of 823bp. Lane 1,2,3,4,5,6,7,8,9 and 10 consist of Bb fragment of 648bp. Lane 1,2,3,4,5,6,7,8,9 and 10 consist of fragment bb of 175bp
62
4.2.4: TaqI polymorphisms in T2DM and control groups
The TaqI restriction site is located in exon 9 of VDR gene. As ApaI and TaqI belong to same
region of VDR gene so same PCR amplified product (Figure 4.16) was use for enzymatic
digestion to investigate the polymorphism of TaqI among T2DM subjects (Figure 4.17).
Present results confirmed the presence of elevated expression of the VDR genotypes
including TaqI (Tt) in patients with type 2 diabetes as compared to normal subjects group
(Table 4.21). The present data endorsed the association of VDR polymorphism of TaqI
genotype with the risk of T2DM in the Pakistani population. However, molecular explanation
of association between VDR polymorphisms and T2DM is only partially understood (Shi et
al., 2001). Biochemical explanation of VDR polymorphism association with T2DM and its
complication is not well defined to date by Dilmec et al. (2010) in Turkish T2DM patients,
Bid et al. (2011) in Indian T2DM patients and Al-Daghri et al. (2015) in T2DM population
of Saudi Arabia.
Present results suggested that the TaqI polymorphism may contribute to the susceptibility
T2DM complications (Figure 4.20 to 4.22). In present study no significant association of
biochemical parameters underlying diabetic complications with VDR gene polymorphisms
was observed in Pakistani population (Table 4.22). Many previous studies could not explain
the relationship of VDR gene polymorphisms with post diabetic complications, however
onset of T2DM may be influenced by VDR gene polymorphism (Tawfeek et al., 2007; Bid et
al., 2010; Nosratabadi et al., 2010; Macakwy et al., 2014). TaqI polymorphism had no
association with various clinical or biochemical parameters, although genetic background of
T2DM pathogenesis was not well defined (Speer et al., 2001; Palomer et al., 2008). Thus
more investigations regarding these associations are required to the manifestation of T2DM
or related qualitative metabolic parameters in the Pakistani subjects.
63
Figure 4.16: Electrophoresis of a 2% agarose gel with exon 9 PCR product loaded; 1 – negative control; 2, 3 and 4 – exon 9 fragment with 745 bp
Figure 4.17: Electrophoresis of a 3% agarose gel with TaqI enzymatic digestion of VDR exon 9; 5 – heterozygous TC genotype (201, 251, 293 and 494 bp); 6 – homozygous TT genotype (251 and 494 bp); 7 – homozygous CC genotype (201, 251 and 293 bp)
64
Table 4.21: Distribution of genotype, allele frequencies and carriage rate of TaqI among patients and controls groups
Genotype
Group TotalControl Patient
tt 16 22 3816.0% 14.7% 15.2%
Tt 27 81 10827.0% 54.0% 43.2%
TT 57 47 10457.0% 31.3% 41.6%
Total 100 150 250100.0% 100.0% 100.0%
Data expressed as X2= 19.70S, p<0.01 S: significant
Table 4.22: Distribution of genotype, allele frequencies and carriage rate of TaqI
among T2DM sub-groups and control group
GroupsGenotypes Total
tt Tt TT
Control16 27 57 100
16.0% 27.0% 57.0% 100.0%
CP16 37 27 80
20.0% 46.3% 33.8% 100.0%
NP2 7 11 20
10.0% 35.0% 55.0% 100.0%
RP1 15 4 20
5.0% 75.0% 20.0% 100.0%
HP3 22 5 30
10.0% 73.3% 16.7% 100.0%
Total38 108 104 250
15.2% 43.2% 41.6% 100.0%Data expressed as X2 = 35.54NS, p>0.001 Calculated as
CP: cardiac patients, NP: nephropathy patients, RP: retinopathy patients, HP: hypertensive patients, NS: non-significant
65
Table 4.23: Probability values for the association of biochemical parameters and TaqI genotypes in T2DM sub-groups
T2DM Sub-groups
Parameters Control CP NP RP HP
HbA1c 0.183 0.817 0.682 0.535 0.232
Vitamin-D 0.090 0.699 0.337 0.558 0.643
T. bilirubin 0.962 0.931 0.514 0.874 0.261
D. bilirubin 0.129 0.982 0.716 0.105 0.562
ALT 0.365 0.594 0.212 0.150 0.067
AST 0.626 0.339 0.028 0.818 0.599
ALP 0.001 0.301 0.336 0.561 0.727
BUN 0.469 0.593 0.159 0.522 0.163
Creatinine 0.809 0.216 0.428 0.987 0.473
Uric acid 0.521 0.193 0.652 0.726 0.005
Cholesterol 0.225 0.148 0.672 0.978 0.224
LDL-C 0.037 0.740 0.886 0.857 0.850
HDL-C 0.548 0.841 0.324 0.340 0.316
TG 0.582 0.646 0.838 0.895 0.947
Data expressed as p – value Calculated as
CP: cardiac patients, NP: nephropathy patients, RP: retinopathy patients, HP: hypertensive patients, HbA1c: glycated hemoglobin, T. bilirubin: total bilirubin, D. bilirubin: direct bilirubin, ALT: alanine transaminase, AST: aspartate transaminase, ALP: alkaline phosphatase, BUN: blood urea nitrogen, LDL-C: low density lipoprotein cholesterol, HDL-C: high density lipoprotein cholesterol, TG: triglycerides
66
Fig 4.18: VDR gene TaqI digestion polymorphism products (cardiac group) : Lane 1,4,5,6,7 and 11 have TT homozygous PCR –RFLP products of 495 bp and 245 bp. Lane 2 consist tt homozygous products 290, 245 and 210 bp. Lane 3, 8,, 9, 12 and 13 consist Tt heterozygous products 495, 290, 245 and 210 bp. Lane M is Ladder of 100 bp.
Fig 4.19:TaqI digestion polymorphism products (NP group) : Lane 1, 2, 3, 4, 6, 7, 8 and 9 have TT and tt homozygous PCR –RFLP products of 745 bp and 235 bp. Lane 7 consist Tt heterozygous product 496 bp
Fig 4.20: TaqI digestion polymorphism products (Retinopathy group) : Lane 1; Ladder of 100 bp. Lane 2, 4, 5, 8, 9, 10, 11, 12, 13 and 14 cosists homzygous TT 745 bp. Lane 3, 4, 5, 6, 9, 10, 11 and 13 have heterozygous fragment Tt of 496 bp. Lane 3, 4, 5, 6, 8, 9, 10, 11 and 13 consists homozygous tt fragment of 249 bp
67
4.3: General discussion on VDR gene polymorphisms Type 2 diabetes mellitus is a multifaceted disorder that may be developed due to the
interface between environmental or acquired and genetic factors. A number of genes, such as
CRP, Calpain 10, eNOS and VDR genes have been involved to develop T2DM (Puri et al.,
2008; Heaney et al., 2011). However, VDR gene has more associations to the development
of T2DM as compared to all other studied genes.
Vitamin D receptor gene is situated at chromosome 12q12–12q14 (Gyapay et al.,
1994). It has been found that locus on 12q24 (T2DM) associated to the synthesis insulin in
T2DM in an area containing the MODY3 (maturity onset diabetes of the young) locus
(Vaxillaire et al., 1995; Mahtani et al., 1996) MODY3 codes hepatic nuclear factor (HNF-1)
(Yamagata et al., 1995), although the distance between 12q24 and 12q12–12q14 precludes
present observations by relations with either T2DM or MODY3. In scan of genome for traits
linked with T2DM among different Asian populations, 12q12–12q14 was not acknowledged
as a susceptibility portion, however the region consisting VDBP (chromosome 4q12) was
linked to fasting insulin (Baier et al., 1996).
VDR gene is expressed in various tissues of the body including pancreatic tissue that
play important role in synthesis of insulin and homeostasis of glucose. Present study was
conducted to investigate the contribution of VDR genotypes in susceptibly of T2DM in
Pakistani population. To our knowledge it is the first study at national level to investigate the
deficiency of vitamin D in T2DM patients and distribution of VDR genotypes
polymorphisms in association of various demographic and biochemical parameters.
Importance of vitamin D receptor polymorphisms study in different populations was
originated from the variation of genotypes and allele distribution regarding ethnicity. It
requires a comparison between frequencies distribution of genotypes and alleles in patients
and healthy people in each population, then making comparison of the same genotypes in
other populations. Almost 40 % population of T2DM patients in Pakistan is being vitamin D
deficient like Caribbean population (Vélayoudom-Céphise et al., 2011).
The present study validated VDR gene polymorphisms were linked with the
susceptibility of T2DM in Pakistani population that can be elucidated by the differences of
VDR gene variants T2DM and healthy control subjects (p< 0.005).
No existence of an association between T2DM and VDR polymorphisms linked
68
metabolic parameters, including fasting glucose, HbA1c, liver function tests, renal function
tests and lipid profile levels, has been described by observational studies (Nosratabadi et al.,
2010; Bid et al., 2011). FokI genotype polymorphisms and T2DM are not closely associated.
In T2DM patients VDR ff genotype were not significantly lower than in control subjects.
Carrying FokI SNP might be defensive against the deficiency of vitamin D: a previous study
found that such polymorphisms cannot affect the circulating vitamin D levels and may also
affect cardiovascular hazards (Dilmec et al., 2010).
In agreement with present results, a current study has confirmed FokI polymorphisms
of VDR gene as a not possible risk factor for T2DM (Bid et al., 2011). In the current meta-
analysis, Li et al. (2013) studied the relationship among four variants VDR polymorphisms
with T2DM and exhibited that allele f of FokI were not significantly linked with T2DM.
Overall conclusion of the present meta-analysis that the polymorphism of FokI genotype of
vitamin D receptor gene could not be a risk factor for type 2 diabetes mellitus especially in
Pakistani population.
On the conflicting, there are studies describing no relationship between type 2
diabetes mellitus patients and healthy subjects in the allele as well as genotype frequencies in
vitamin D receptor FokI gene polymorphism (Iyengar et al., 1989; Valdivielso Fernandez.
2006). Molecular description for the fictional association between polymorphism of FokI
genotype and T2DM are only partly understood. VDR gene polymorphisms of FokI genotype
can be found by the existence or lack of its restriction site within (ATG) transcriptional start
site of vitamin D receptor gene. The normal length of gene transcribed produced if restriction
site f allele is found while shortened length gene transcribed when such restriction site is
absent. Longer vitamin D receptor protein seems to have reduced transcriptional activity
which leads to decreased activation of the respective target cells (Arai et al., 1997; Jurutka et
al., 2000).
VDR polymorphisms of ApaI genotype were not associated with T2DM in Moroccan
population. Distribution of ApaI genotype showed non-significant statistical difference
between the healthy subjects and T2DM patient (Table 4.14). In agree with present results
the meta-analysis of association between onset of T2DM and ApaI showed non-significant
results (Lei et al., 2013). In contrast, a chines study found that ApaI genotype of VDR gene
polymorphism was linked with T2DM (Xu et al., 2007; Zhang et al., 2008). Previous studies
69
of candidate gene polymorphism and GWAS have concentrated on the relationship between
onset of T2DM and VDR gene, but outcomes have often been fickle among different
populations. Mostly the discrepancies between these studies may be because of false positive
finding, duplication study lacks power, heterogeneity among studies and heterogeneity
transversely studies. However, previous studies have described interactions between vitamin
D and VDR gene polymorphisms in various diseases such as T1DM (Somia et al., 2014),
tuberculosis (Roth et al., 2004) and prostate cancer (Z et al., 2013). Although to present
knowledge, it is the first time an association between VDR gene polymorphisms and levels
of vitamin D has been described in T2DM patients in Pakistani population.
These results propose that each VDR haplotype variants may be related with different
processes that increase the plasma levels of lipid and the hazards of cardiovascular disease.
In agreement with present study, the outcomes of a research done in Caribbean T2DM
patients shown that deficiency of vitamin D was high among T2DM patients and was linked
with the FokI and ApaI genotypes polymorphisms of VDR gene polymorphisms and
measurements of plasma vitamin D levels may help to perceive T2DM patients. The VDR
gene polymorphisms might describe why deficiency of vitamin D is so commonly seen in
some T2DM patients (Bid et al., 2009). Another study revealed that the BsmI genotype of
VDR gene polymorphism predisposed deficiency of vitamin D (Filus et al., 2008). In
addition, results of another study recognized in postmenoposal women exhibited that the
polymorphism of BsmI genotype in VDR gene had no relationship with susceptibility to
insulin resistance and obesity, while it was related to a higher LDL-C level (Tworowska-
Bardzinska et al., 2008).
Genetics determines that deficiency of vitamin D may cause VDR gene
polymorphisms (BsmI, FokI and TaqI) in T2DM subjects (Bid et al., 2009; Dilmec et al.,
2010; Nosratabadi et al., 2010). Prevalence of vitamin D deficiency in T2DM group was
higher as compared to control subjects, showing significant association of BsmI, FokI and
TaqI with T2DM onset in the present study. However, Santosh et al. (2012) observed
divergent results of vitamin D deficiency on VDR polymorphisms in Brazilian population.
Impaired insulin sensitivity is an important constituent of almost all metabolic disorders,
people with impaired insulin sensitivity are at higher risk of T2DM (Kelly et al., 2011). Arai
et al. (1997) described that FokI may affect the activation of transcription that is vitamin D
70
dependent in transfected HeLa tissues. It has been considered that inactive or less active
vitamin D receptor may be linked with higher susceptibility to various autoimmune diseases
including diabetes mellitus (Whitfield et al., 2001).
Many previous studies have demonstrated that VDR polymorphism may affect the synthesis
of insulin in pancreatic tissue and activity of insulin in non-pancreatic tissues (Hitman et al.,
1998; Chiu et al., 2001; Ortelpp et al., 2001). Therefore, the existence of VDR
polymorphisms (TaqI, BsmI, FokI) could lead the people to a higher risk for T2DM (Speer et
al., 2001; Filus et al., 2008). Moreover, the impact of VDR gene polymorphisms on T2DM
and its various complications has been produced conflicting results in different populations.
To date studies observed no significant association of VDR gene polymorphisms (ApaI,
BsmI, FokI and TaqI) with various biochemical and demographic parameters (Palomer et al.,
2008; Bid et al., 2009; Dilmec et al., 2010; Al-Daghri et al., 2015) similar as in present work.
Heterogeneity in different populations and limited data may be responsible for discrepancies
among existing studies (Al-Daghri et al., 2014). People exhibit subtle changes in metabolism
of glucose for a long time before T2DM onset. Factors related to genetics may contribute to
the pathogenesis of T2DM and its complications but the exact mechanism is not yet well
known (Arababadi et al., 2010).
Present study found no statistically significant relationship between ApaI polymorphism and
T2DM, may be due to limited sample size. However, various previous studies examined the
link between VDR polymorphisms and T2DM risks have established inconsistent results (Li
et al., 2013). Biochemical observations may partly explain the present results or even those
of various meta-analysis of same objectives performed in Asia. This study suggests that the
VDR gene polymorphisms (FokI, TaqI, BsmI) may be related with susceptibility to T2DM
subjects but genetic contribution of VDR gene polymorphism for the development or existing
diabetic complications is not clear. Further studies are required with a greater sample size to
elucidate the association between BsmI, FokI, TaqI and ApaI polymorphisms and T2DM in
Pakistani population.
In addition, vitamin D receptor gene consists of many more single nucleotide polymorphisms
(SNPs) other than the four demonstrated in this study. The present study was restricted to
only four SNPs (ApaI, BsmI, FokI, TaqI) most examined polymorphisms. To investigate
whether functional changes of VDR gene may include in risk factors for T2DM, future
71
studies can use tag SNP to explore more diversity within VDR gene (Stram, 2004).
Functional investigations revealing relationships between VDR polymorphisms and T2DM
are limited and clearly warranted. To present new insights into treatment and etiology of
T2DM, it will be essential to do population based or case-control based studies along with
family linkage to clarify the association between the VDR gene polymorphisms and
susceptibility to T2DM in local population.
There are a few limitations of present study. Firstly, our sample numbers considered
relatively small. Secondly, lack of replication studies of the association of VDR gene
polymorphisms and T2DM in Pakistani population. Consequently, further studies including
larger sample numbers and replication of significant findings are necessary to clarify the role
of the VDR gene polymorphism in T2DM.
In conclusion, it is evident that vitamin D deficiency has prevailed in Pakistani population
with T2DM. Alterations in vitamin D action may affect insulin sensitivity, beta-cell function
or both. Moreover our study documents a correlation between VDR BsmI, FokI and TaqI
gene polymorphisms and susceptibility to T2DM in the Pakistani population. The possible
role of vitamin D in the pathogenesis of T2DM is far from being completely understood.
Additionally, further knowledge on this issue may identify new candidate targets in the
treatment and prevention of the disease. Therefore, further investigations on this issue are
warranted.
72
Chapter 5 SUMMARYIt is well known that the occurrence of type 2 diabetes mellitus (T2DM) is swiftly increasing
and causing the socio-economic burden. Therefore, recognizing the menaces and
precautionary strategies is crucial. Although, various hazards have been recognized for lack
of insulin sensitivity and dysfunction of β-cell, gaps still persist in their etiology to
completely understand the underlying mechanisms. Focus on research related to vitamin D
function and VDR gene polymorphisms in T2DM hazard has been emerging, while literature
has limited data regarding association of VDR polymorphisms to diabetes related clinical
parameters in Asian population. The overall purpose of this dissertation was to investigate
the association of VDR polymorphisms (ApaI, BsmI, FokI and TaqI) with T2DM onset and
associated demographic and biochemical parameters. Furthermore, present study also
included an insight into the significant role of VDR gene into progression of T2DM
pathogeneses.
Firstly, the cross-sectional analysis of demographic and biochemical parameters was
conducted among normal and case subjects. In addition, the same parameters were also
studied among diabetic subjects with complications viz. cardiac patients group (CP),
retinopathy patients group (RP), nephropathy patients group (NP) and hypertensive patients
group (HP).
Overall, findings showed a significant inverse relationship of vitamin D levels with body
mass index (BMI), blood pressure (systolic and diastolic), fasting blood sugar (FBS),
glycated hemoglobin (HbA1c), cholesterol, low density lipoprotein cholesterol (LDL-C),
triglycerides (TG), blood urea nitrogen (BUN), uric acid and creatinine. While significant
positive relationship of vitamin D with high density lipoprotein cholesterol (HDL-C) was
observed. Among diabetic and normal participants liver function tests were found to be non-
significant and had no noteworthy relation with vitamin D levels.
Secondly, genetic susceptibility for T2DM and its relation to clinical parameters in four
groups; CP, NP, HP and RP was assessed. T2DM was more prevalent among those
individuals who had significant differences (p<0.001) of BsmI, FokI and TaqI genotypes
polymorphisms of the VDR gene as compared with healthy individuals. While, ApaI
polymorphism was non- significant (p = 0.075).
Furthermore, present study suggested that no statistically significant association
73
existed between VDR gene polymorphisms and diabetic complications; cardiac, renal,
hypertensive and retinal manifestations in Pakistani population.
Thus, VDR gene polymorphisms can be beneficial risk markers for the onset and progression
of T2DM. Current study is not conclusive and warrants additional research to understand
cellular and molecular mechanisms of VDR which remain unclear. Functional genomics
studies may also be helpful to verify that how these polymorphisms may affect the genetic
susceptibility to T2DM.
5.1: Future direction Present dissertation has extended the scientific literature concerning the relationship of
vitamin D and VDR polymorphisms to metabolic pathogenesis underlying T2DM.
Specifically, it is important to consider that mostly cross sectional preliminary data is
available and only limited prospective data has been presented on this topic in Asian
populations. Further longitudinal studies are required to investigate the relationship of VDR
gene polymorphisms with the development of T2DM and its complications over time.
Observational studies, predominantly longitudinal studies will support to find the natural
history of T2DM acquired genetic outcome associations, to define genetic relationships
across a wide range of clinical outcomes and to identify genetic susceptibility with other
probable effect modifiers. Future studies should focus on different populations of multiple
ethnicity that permit for increased generalizability and testing of probable ethno-specific
consequence modifiers. Thus, constant epidemiological investigation into the risk of T2DM
regarding VDR gene polymorphism along with role of vitamin D is warranted, as such
revisions shed light on serious methodological aspects associated with the design of better
randomized controlled trials.
74
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AppendicesA.1:TBE buffer (10 mM Tris HCl, pH 8.3)
Composition
1M TBE (Tris-Borate-EDTA) 1 mL
Distilled water up to 100 mL
Preparation
70 mL of distilled water was taken in a bottle; 1 mL of Tris buffer (1 M) was added in it. pH
was adjusted to 8.0 and to make final volume of 100 mL, distilled water was added. The
solution was autoclaved and stored at 4°C for DNA extraction.
A.2: Sodium dodecyl sulfate (SDS) 10% (w/v)
Preparation
An amount of 100 g of SDS was dissolved in 800 mL of distilled water. The pH was adjusted
to 7.0 by adding several drops of concentrated HCl and water was added to make a final
volume of 1 liter.
A.3: Colorless GoTaq® Flexi Buffer (Part# M890A)
Composition
GoTaq DNA polymerase
Green GoTaq reaction buffer (pH 7.5)
dNTPs
103
A. Table 1: Comparison of demographic parameters
Parameters Group N Mean SD SE p value
Age (years) Control 100 50.5 7.6 0.023 0.091
Patient 150 49.5 7.0 0.056
Gender (M/F) Control 100 50/50 - - >0.01
Patient 150 75/75 - -
Systolic BP (mm Hg) Control 100 125.0 15.0 0.013 <0.01
Patient 150 140.0 17.0 0.036
Diastolic BP (mm Hg) Control 100 77.0 8.0 0.012 <0.01
Patient 150 88.0 9.0 0.034Data expressed as p< 0.01 (Highly significant)N: number of subjects, SD: standard deviation, SE: standard error, BMI: body mass index, M: male, F: female, BP: blood pressure
A. Table 2: Comparison of biochemical parameters
Parameters Group N Mean SD SE p value
HbA1c (%) Control 100 4.85 0.33 0.033 <0.01
Patient 150 7.43 0.69 0.056
Vitamin D (mg/dL) Control 100 22.36 2.34 0.234 <0.01
Patient 150 13.69 1.85 0.151
FBS (mg/dL) Control 100 7.20 9.18 1.23 <0.01
Patient 150 10.16 4.49 0.56
Data expressed as p<0.01 (Highly significant) N: number of subjects, SD: Standard deviation, SE: Standard error, HbA1c: glycated hemoglobin, FBS: fasting blood sugar
A. Table 3: Comparison of liver function tests
BMI (kg/m2) Control 100 25.5 5.0 0.013 <0.01
Patient 150 35.8 12.5 0.066
104
Parameters Group N Mean SD SE t-value p value
T. bilirubin (mg/dL) Control 100 1.10 0.41 0.041 0.63NS 0.53
Patient 150 1.07 0.42 0.034
D. bilirubin (mg/dL) Control 100 0.94 0.23 0.023 0.19NS 0.85
Patient 150 0.93 0.23 0.019
ALT (mg/dL) Control 100 72.94 15.14 1.514 1.00NS 0.32
Patient 150 70.98 15.19 1.240
AST (mg/dL) Control 100 36.37 19.07 1.907 1.07NS 0.29
Patient 150 33.77 18.76 1.532
ALP(mg/dL) Control 100 50.93 10.37 1.037 0.01NS 0.99
Patient 150 50.91 10.37 0.846Data expressed as p> 0.05 N: number of subjects, NS: Non-significant, SD: standard deviation, SE: standard errorT. bilirubin: total bilirubin, D. bilirubin: direct bilirubin, ALT: alanine transaminase, AST: aspartate transaminase, ALP: alkaline phosphatase
105
A. Table 4: Comparison of renal function tests
Parameters Group N Mean SD SE t-value p value
BUN (mg/dL) Control 100 24.72 10.33 1.033 -27.78HS <0.01
Patient 150 76.53 16.62 1.357
Creatinine (mg/dL) Control 100 0.50 0.33 0.033 -27.53 HS <0.01
Patient 150 2.02 0.48 0.039
Uric acid (mg/dL) Control 100 2.89 0.78 0.078 -28.54 HS <0.01
Patient 150 6.19 0.97 0.079Data expressed as p<0.01 N: number of subjects, HS: Highly significant, SD: standard deviation, SE: standard error, BUN: blood urea nitrogen
A. Table 5: Comparison of lipid profile
Parameters Group N Mean SD SE t-value p value
LDL-C (mg/dL) Control 100 64.40 12.76 1.276 -73.10 HS <0.01
Patient 150 170.69 10.15 0.829
HDL-C (mg/dL) Control 100 62.12 14.33 1.433 21.33 HS <0.01
Patient 150 36.24 3.25 0.265
TG (mg/dL) Control 100 180.49 9.06 0.906 -17.20 HS <0.01
Patient 150 447.39 154.86 12.645
Cholesterol (mg/dL) Control 100 178.03 11.96 1.196 -35.86 HS <0.01
Patient 150 286.24 28.54 2.330Data expressed as p<0.01N: number of subjects, HS: Highly significant, SD: Standard deviation, SE: Standard error, LDL-C: low density lipoprotein cholesterol, HDL-C: high density lipoprotein cholesterol, TG: triglycerides
106
