Post on 05-Aug-2020
transcript
RENAL AND METABOLIC FUNCTION IN RENAL
TRANSPLANT RECIPIENTS RECEIVING
CALCINEURIN INHIBITORS AND THE EFFECTS OF
CONVERSION TO SIROLIMUS
Ramyasuda SWAMINATHAN MBBS FRACP
This thesis is presented in partial fulfillment of the requirements for the degree of
Master of Clinical Research of the University of Western Australia
School of Medicine and Pharmacology
Faculty of Medicine Dentistry and Health Sciences
The University of Western Australia
February 2012
ii
Abstract
iii
Renal transplantation is the best form of renal replacement therapy for patients
with end-stage kidney disease. Refinements in immunosuppressive treatments
and new immunology techniques have reduced early rejection episodes and
improved the short term survival of renal allografts. Patients with successful
renal transplant remain at high risk of long-term cardiovascular complications
which may be mediated, in part, by hyperlipidaemia, hypertension and diabetes.
The rate of late allograft loss due to cumulative immune and non-immune
mediated injury (defined as chronic allograft nephropathy - ‘CAN’) remains
largely unchanged. The immunosuppressive drugs known as the calcineurin
inhibitors (CNI) and the mTOR inhibitors (mammalian Target of Rapamycin
inhibitors “mTOR-I”) may have effects upon the potential non-immune mediators
of CAN (glucose metabolism, lipid profile, proteinuria and blood pressure),
which are also significant cardiovascular risk factors.
This thesis uses retrospective and prospective studies to test the hypothesis
that in renal transplant recipients (RTR) with histological evidence of CAN, the
non-immune mediators of CAN especially proteinuria and/or histological
characteristics of allograft injury determined by BANFF criteria, determine
whether the conversion from a CNI to a mTOR-I is associated with improved
renal function. It also tests whether despite an improvement in renal function
there are any potentially adverse effects upon glucose and lipid metabolism.
Study 1 retrospectively examined the effects of the mTOR-I, Sirolimus (SRL)
upon changes in renal allograft function measured as estimated Glomerular
Filtration Rate (eGFR) and proteinuria and determined the relationship between
iv
pre conversion renal histology and post conversion eGFR & proteinuria to
identify clinical and histological predictors of successful conversion to SRL from
a CNI. 85 RTRs with biopsy proven CAN, a median of 5 years post
transplantation were studied. 51 were electively converted to SRL (SG) and 34
remained on CNI (CG). This was a clinician initiated non-randomised
conversion. After a median follow-up of 5.3 years the eGFR stabilised in the SG
and continued to decline in the CG. Death censored graft loss was significantly
lower in SG compared with the CG. Baseline proteinuria and histological grades
of tubular atrophy predicted the post conversion eGFR. Sirolimus conversion
was also associated with an increase in proteinuria. Pre-conversion protein:
creatinine ratio of ≥ 50mg/mmol was associated with increase in proteinuria
post-conversion. This study concluded that in RTRs with CAN, conversion to
SRL at a median of 5 years post- transplantation stabilised the eGFR and that
successful conversion was associated with lower histological grades of chronic
tubular atrophy and lower proteinuria at the time of conversion.
Study 2 prospectively studied the effects of SRL upon measures of glucose and
lipid metabolism in stable RTRs without diabetes. 25 RTRs on a CNI based
immunosuppressive regimen (8 Tacrolimus “TAC”, 17 CyclosporineA “CyA”)
with stable renal function were electively converted to SRL. Standard oral
glucose tolerance test (OGTT), fasting lipids, free fatty acids (FFA), apo-
lipoproteins A1 and B, Body Mass Index (BMI) and eGFR were measured
before conversion and at 3 and12 months post conversion to SRL. OGTT was
used to derive i) Insulin sensitivity Index (ISI TX), ii) Homeostasis Model
Assessment score for Insulin Resistance (HOMA- IR), iii) Metabolic Clearance
rate of glucose (MCR). 21/ 25 patients were maintained on statin therapy. The
v
mean SRL level was 6.7 +/- 2.2ng/ml and the mean dose was 1.59 +/-0.8
mg/day. After conversion to SRL, there was no change in the BMI, HOMA-IR,
Glycated haemoglobin (HbA1C), ISItx, MCR, high density lipoprotein (HDL), FFA
or Apo A1 at 3 or 12 months post-conversion compared with baseline. However
there was a significant increase in the total cholesterol (TC), low density
lipoprotein (LDL), Triglycerides (TGL), non-HDL cholesterol and Apo B. This
study concluded that in stable RTR with CAN, SRL did not affect the glucose or
insulin homeostasis in this selected patient cohort without diabetes. SRL
induced dyslipidaemia was primarily associated with altered metabolism of Apo
B containing lipids and not with altered glucose or insulin disposition.
Overall this research helps to further clarify the role of mTOR inhibitors in renal
transplant setting, improve the understanding of the factors that contribute to a
successful conversion to mTOR inhibitors in the setting of CAN and understand
the possible mechanisms that effect glucose and lipid metabolism after renal
transplantation, which may have important implications for understanding the
long-term patient and renal allograft survival and assist clinicians in the
appropriate timing and patient selection for the optimal use of the mTOR-I
Sirolimus.
vi
Table of Contents
Abstract ............................................................................................................. ii
Table of Contents ............................................................................................. vi
List of Tables ................................................................................................... xii
List of Figures ................................................................................................ xiv
Abbreviations ................................................................................................. xvi
Acknowledgements ....................................................................................... xix
Statement of Candidate Contribution: ......................................................... xxi
Introduction ..................................................................................................... 22
Chapter 1 Review of Literature ...................................................................... 26
1.1. End Stage Kidney Disease in Australia .................................................. 27
1.2. Rejection of renal allograft: ..................................................................... 29
1.2.1 Cell Mediated Rejection (CMR): ............................................................. 30
1.2.2 Antibody Mediated Rejection (AMR): ...................................................... 31
1.2.3 Chronic Rejection: .................................................................................. 32
1.3. Mechanism of action of different Classes of Immunosuppressive agents:
............................................................................................................... 33
1.4. Overview of Chronic Allograft Nephropathy ............................................ 37
1.4.1 Definition of CAN: ................................................................................... 37
1.4.2 Nomenclature of CAN – A historical perspective ..................................... 37
1.4.3 Mediators of CAN: .................................................................................. 38
1.4.4 Pathogenesis of CAN ............................................................................. 46
1.4.5 Mechanisms of allograft injury: ............................................................... 48
1.4.6 Banff Histological Classification of CAN .................................................. 50
1.4.7 Role of CNI in CAN ................................................................................. 53
1.4.8 The Natural History of CAN .................................................................... 57
1.5 Role of mTOR inhibitors in CAN: ............................................................. 58
vii
1.6. Proteinuria post Transplantation: ............................................................ 62
1.6.1 Introduction ............................................................................................. 62
1.6.2 Post Transplant Proteinuria: ................................................................... 63
1.6.3 Sirolimus and Proteinuria: ....................................................................... 63
1.7 Overview of Post Transplant Diabetes Mellitus ....................................... 66
1.7.1 Introduction ............................................................................................. 66
1.7.2 Steroids and PTDM: ............................................................................... 67
1.7.3 CNI and PTDM: ...................................................................................... 67
1.7.4 SRL and PTDM: ..................................................................................... 68
1.8. The mTOR pathway and mTOR inhibitors: ............................................. 69
1.8.1 The upstream components of mTOR ...................................................... 69
1.8.2 The downstream effectors of mTOR pathway ......................................... 71
1.8.3 Positive and Negative Feed-back in mTOR pathway: ............................. 71
1.8.4 Role of mTOR signaling in glucose metabolism: ..................................... 73
1.8.5 Conflicting role of mTOR inhibition on glucose metabolism:.................... 77
1.8.6 Effect of mTOR inhibition on glucose metabolism in RTRs ..................... 77
1.8.7 Limitations of the existing studies ........................................................... 81
1.9 Post Transplant Dyslipidaemia: ............................................................... 82
1.9.1 Overview of the lipid metabolism: ........................................................... 82
1.9.2 Lipid Transport:....................................................................................... 86
1.9.3 Post Transplant Dyslipidaemia: .............................................................. 88
1.10 Summary: .............................................................................................. 91
Chapter 2: Research Methodology ................................................................ 92
2.1 Introduction: ............................................................................................. 93
2.2 Study Hypotheses: .................................................................................. 93
2.3 Aims: ....................................................................................................... 94
2.4 Study Design ........................................................................................... 95
2.4.1 Inclusion Criteria: .................................................................................... 95
viii
2.4.2 Exclusion Criteria .................................................................................... 95
2.4.3 Study Population .................................................................................... 95
2.5 Study Methods: ....................................................................................... 97
2.5.1 Data collection: ....................................................................................... 98
2.5.2 Prospective Group Study Protocol .......................................................... 99
2.6 Laboratory Methods: ............................................................................. 100
2.7 Study Protocols ..................................................................................... 102
2.7.1 Standard Oral Glucose Tolerance Test: ................................................ 102
2.7.2 Sirolimus conversion Protocol: .............................................................. 102
2.7.3 MPA / AZA dosing ................................................................................ 102
2.7.4 Statin Use in PG ................................................................................... 103
2.8 Follow- up .............................................................................................. 103
2.8.1 Retrospective Study ............................................................................. 103
2.8.2 Prospective Study ................................................................................. 103
2.9 End points: ............................................................................................ 103
2.9.1 Retrospective Study ............................................................................. 103
2.9.2 Prospective Study ................................................................................. 103
2.10 Definitions: ........................................................................................... 104
2.11 Statistical Methods: ............................................................................. 107
2.11.1 Descriptive Statistics:.......................................................................... 107
2.11.2 Additional tests used in Retrospective Study: ..................................... 107
2.11.3 Additional Tests used in the Prospective Study: ................................. 109
2.12 Ethical issues: ..................................................................................... 110
2.13 Results: ............................................................................................... 110
Chapter 3: Evaluation of Renal Outcomes in Renal Transplant Recipients
with Chronic Allograft Dysfunction following conversion from Calcineurin
inhibitors to Sirolimus and the Predictors of Successful Conversion ..... 111
3.1 Hypothesis and Aims ............................................................................. 112
ix
3.1.1 Hypothesis: ........................................................................................... 112
3.1.2 Aims: .................................................................................................... 112
3.2 Methodology: ......................................................................................... 113
3.2.1 Statistical Methods ............................................................................... 113
3.3 Results .................................................................................................. 114
3.3.1 Baseline Clinical Characteristics: .......................................................... 114
3.3.2 Baseline Histological Characteristics: ................................................... 115
3.3.3 CNI dosing in the CG ............................................................................ 120
3.3.4 SRL dosing in the SG: .......................................................................... 120
3.3.5 eGFR Outcomes: .................................................................................. 121
3.3.6 Proteinuria Outcomes: .......................................................................... 131
3.3.7 Blood Pressure outcomes: .................................................................... 139
3.4 Discussion ............................................................................................. 141
3.4.1 eGFR outcomes: .................................................................................. 141
3.4.2 Proteinuria Outcomes: .......................................................................... 145
3.4.3 Other Outcomes: .................................................................................. 146
3.5 Strengths of the Study: .......................................................................... 147
3.6 Study Limitations ................................................................................... 148
3.6.1 Study Design ........................................................................................ 148
3.6.2 Outcome measures .............................................................................. 148
3.6.3 Statistical Methods ............................................................................... 148
3.7 Conclusion ............................................................................................. 149
3.8 Validation of Study Hypothesis: ............................................................. 150
3.9 Directions for Future Research .............................................................. 150
Chapter 4: Effect of mTOR INHIBITOR Sirolimus Upon Glucose & Lipid
Metabolism in Stable Renal Transplant Recipients. .................................. 151
4.1 Hypothesis and Aims: ............................................................................ 152
4.1.1 Hypothesis: ........................................................................................... 152
x
4.1.2 Aim: ...................................................................................................... 152
4.2 Methods: ................................................................................................ 152
4.2.1 Statistical Methods: .............................................................................. 153
4.3 Results: ................................................................................................. 153
4.3.1 Entry Clinical Characteristics ................................................................ 153
4.3.2 Entry Histological Characteristics: ........................................................ 155
4.3.3 Concomitant use of other immunosuppressants: .................................. 156
4.3.4 Use of Antihypertensive agents: ........................................................... 157
4.3.5 Use of statin: ........................................................................................ 158
4.3.6 Renal Function (eGFR): ........................................................................ 158
4.3.7 Proteinuria ............................................................................................ 158
4.3.8 Effect of SRL conversion upon measures of glucose metabolism: ........ 160
4.4 Discussion: ............................................................................................ 168
4.4.1 eGFR .................................................................................................... 168
4.4.2 Proteinuria vs. albuminuria. .................................................................. 168
4.4.3 Effect of SRL conversion upon Glucose and Insulin metabolism: ......... 170
4.4.4 Effect of SRL conversion upon Lipid Metabolism: ................................. 173
4.4.5 Cardio-vascular risk profile following conversion to SRL ....................... 177
4.5 Strengths of the study............................................................................ 178
4.6 Limitations of this Study:........................................................................ 179
4.6.1 Study Design: ....................................................................................... 179
4.6.2 Outcome measures: ............................................................................. 179
4.6.3 Statistical Methods ............................................................................... 180
4.7 Conclusion ............................................................................................. 180
4.8 Validation of the study Hypothesis: ....................................................... 181
4.9 Directions for Future Research .............................................................. 182
Bibliography .................................................................................................. 183
xi
Appendices.................................................................................................... 213
Appendix 1 (Ethics approval)....................................................................... 214
Appendix 2 (Patient Information Sheet) ....................................................... 215
Appendix 3 (Abstracts and Presentations) .................................................. 220
xii
List of Tables
Table 1.1 Long term renal allograft survival rates ....................................... 36
Table 1.2 Banff 2007 Classification ............................................................... 52
Table 1.3 Summary of renal outcomes and side effect profile following
mTOR-I conversion in transplant recipients................................................. 60
Table 1.4 SRL and PTDM in RTRS- Summary of published studies ........... 79
Table 1.5 Function of Apo-lipoproteins ........................................................ 85
Table 1.6 Studies in RTRs showing SRL induces dyslipidaemia ............... 89
Table 2.1 Data Collected in the Study Population ........................................ 98
Table 2.2 Additional data collected in the Prospective Group .................... 99
Table 2.3 Laboratory Methods ..................................................................... 100
Table 3.1 Baseline Characteristics of RTRs in SG and CG ....................... 114
Table 3.2 Linear mixed modelling of eGFR comparison in the two groups
........................................................................................................................ 122
Table 3.3Comparison of predicted eGFR at specific time-points ............. 123
Table 3.4 Univariate analysis of pre-conversion clinical features that may
predict eGFR response post SRL conversion. ........................................... 124
Table 3.5 Univariate analysis of pre-conversion histological ................... 126
features that may predict eGFR response post SRL conversion. ............ 126
Table 3.6 Model predicting eGFR outcome................................................. 127
Table 3.7 Linear mixed modelling of graft loss censored proteinuria ...... 133
Table 3.8 Baseline PCR correlates with post-conversion PCR ................. 136
Table 4.1 Entry Clinical characteristics of PG ............................................ 154
Table 4.2 Entry Histological Characteristics .............................................. 155
Table 4.3: Concomitant use of other immunosuppressants ..................... 156
xiii
Table 4.4: Use of anti-hypertensive agents and Prednisolone before and
after SRL conversion .................................................................................... 157
Table 4.5 Fasting Glucose, Insulin, C-peptide and Glycated Hb levels post
SRL conversion ............................................................................................. 160
Table 4.6 Changes in Fasting C-peptide levels with time .......................... 161
Table 4.7 Improved Impaired Glucose tolerance and insulin resistance
post-conversion ............................................................................................ 162
Table 4.8 OGTT derived indices post SRL conversion .............................. 164
Table 4.9 Changes in BMI, Hs CRP & FFA post SRL conversion ............. 165
Table 4.10 Changes in Lipids and Lipoproteins post SRL Conversion ... 166
Table 4.11 Significance of the difference among baseline, 3 and 12 month
lipid and lipoprotein values .......................................................................... 167
xiv
List of Figures
Fig 1.1: Age specific mortality rate for Australians on treated with dialysis
or renal transplantation. ................................................................................. 28
Fig 1.2 Mechanism of T-cell mediated rejection ........................................... 30
Fig 1.3 Mechanism of Antibody Mediated Rejection .................................... 31
Fig 1.4 Structure of T-cell and sites of action of immunosuppressants. ... 33
Fig 1.5 Renal allograft survival rates 1965-2008 ........................................... 35
Fig 1.6 Pathogenesis of CAN. ....................................................................... 46
Fig 1.7 Histological changes of CAN ............................................................. 51
Fig 1.8 Mechanism of Chronic CNI toxicity................................................... 54
Fig 1.9 Histological Features of CNI Toxicity ............................................... 56
Fig 1.10 The mTOR pathway .......................................................................... 70
Fig 1.11 Basic Structure of Lipoprotein ....................................................... 83
Fig: 1.12 Endogenous and Exogenous Lipid Transport .............................. 86
Fig 2.1 Study Design ....................................................................................... 96
Fig 3.1 Baseline ‘ci” scores between SG and CG ...................................... 116
Fig 3.2 Baseline ‘ct” scores between SG and CG ...................................... 117
Fig 3.3 Baseline ‘‘cv” scores between SG and CG .................................... 118
Fig 3.4 Baseline ‘ah” scores between SG and CG ..................................... 119
Fig 3.5 Rate of decline in eGFR ................................................................... 121
Fig 3.6 Kaplan-Meier plot of Graft loss ....................................................... 129
Fig 3.7 Changes in PCR CG vs. SG ............................................................. 131
Fig 3.8 Graft loss censored PCR Changes ................................................. 132
Fig 3.9 Comparison of Baseline with post-conversion PCR ..................... 135
Fig 3.10 “ci” Changes at baseline ............................................................... 138
Fig 3.12 “ct” Changes at baseline ............................................................... 138
xv
Fig 3.11 “cv” Changes at baseline .............................................................. 138
Fig 3.13 “ah” Changes at baseline .............................................................. 138
Fig 3.14 Comparison of Systolic Blood Pressures in both groups .......... 139
Fig 3.15 Comparison of Diastolic Blood pressure in both groups ........... 140
Fig 4.1 Proteinuria Change post SRL conversion ...................................... 159
xvi
Abbreviations
Abbreviation Expansion
4E-BP1 Eukaryotic translation initiation factor – Binding Protein1 ABO Major blood Group Antigen s A,B,O ACE- I Angiotensin Converting Enzyme Inhibitor(s) ACR Albumin : Creatinine Ratio ADA American Diabetes Association AMP Adenosine Mono phosphate AMPK AMP Kinase AMR Antibody Mediated Rejection ANOVA Analysis of Variance ANZDATA Australia and New Zealand Dialysis and Transplant
Registry AP-1 Activator Protein-1 APC Antigen Presenting Cells Apo A /B/C Apo lipoprotein A/B/C ARB Angiotensin Receptor blocker(s) ATN Acute Tubular Necrosis AZA Azathioprine BD Brain death BMI Body Mass Index CAN Chronic Allograft Nephropathy CCB Calcium Channel Blocker(s) CD 3/4/8 Cluster of Differentiation (3/4/8) CE Cholesterol Ester CG Control Group CM Chylomicron CMR Cell Mediated Rejection CNI Calcineurin Inhibitor(s) CyA CyclosporineA DBD Donation after Brain Death DCD Donation after Cardiac Death DGF Delayed Graft Function DI Disposition Index DNA Deoxy Ribo Nucleic Acid eGFR Estimated Glomerular Filtration Rate EIF4G Eukaryotic translation initiation Factor 4 gamma ESKD End stage Kidney Disease EVR Everolimus FFA Free Fatty Acids FKBP12 FK Binding Protein 12 g Gram(s) GN Glomerulonephritis H and E Haematoxylin and Eosin HbA1C Glycated Haemoglobin HDL High Density Lipoprotein HLA Human Leukocytic antigen HOMA – IR Homeostasis Model Assessment score for Insulin
Resistance HPL Hepatic Lipase HsCRP Highly-Sensitive C-Reactive Protein
xvii
IDL Intermediate Density Lipoprotein IFG Impaired Fasting Glucose IFN Interferon IFTA Interstitial Fibrosis and Tubular atrophy IGT Impaired glucose tolerance IKK Inhibitor of Kappa B Kinase IL-2 Interleukin 2 IQR Inter Quartile Range IR Insulin Resistance IRI Ischaemia Reperfusion injury IRS-1 Insulin Receptor Substrate 1 ISI Tx Insulin Sensitivity Index for Transplantation ITT Intent to Treat kg Kilogram(s) l Litre(s) LCAT Lecithin- Cholesterol Acyl Transferase LDL Low Density Lipoprotein LDLr LDL receptor Lp(a) Lipoprotein(a) LPL Lipoprotein Lipase LRP LDLr related Protein MACE Major Coronary Event MAP Membrane Associated protein MCR Metabolic Clearance Rate of Glucose mg Milligram(s) MHC Major Histo-compatibility Antigen MHC Major Histocompatibility Complex Min minute ml Millilitre(s) MMF Mycophenolate Mofetil mmol Millimol(s) MPA Mycophenolic acid mRNA Messenger Ribonucleic Acid mTOR Mammalian Target of Rapamycin mTOR-I mTOR inhibitor(s) NFAT Nuclear Factor of activated T cell
NFĸB Nuclear Transcription Factor β NK Natural Killer NODAT New Onset Diabetes after Transplantation OGTT Standard Oral Glucose Tolerance Test PAS Periodic acid schiff PCR Protein: Creatinine Ratio PI3K Phosphoinositide-3 Kinase PL Phospholipids PMN Polymorphonuclear neutrophil PPAR- Peroxisome proliferator activated receptor - PRA Panel Reactive Antibodies PTDM Post Transplant Diabetes Mellitus RAAS Renin aldosterone Angiotensin System RPH Royal Perth Hospital RTR Renal Transplant Recipient(s)
xviii
SG Sirolimus Group SRL Sirolimus T- Reg Regulatory T Cell
T2DM Type 2 Diabetes Mellitus TAC Tacrolimus TC Total Cholesterol TCR T Cell receptor TG Transplant Glomerulopathy TGF-β1 Tumour Growth Factor TGL Triglycerides Th (1/2) T Helper subset (1/2) TLR Toll Like Receptors TSC1/2 Tuberous Sclerosis Gene 1/2 VEGF Vascular Endothelial Growth Factor VLDL Very Low Density Lipoproteins. WHO World Health Organisation
xix
Acknowledgements
I would like to sincerely thank my supervisor Dr. Ashley Irish for his support,
guidance, encouragement and for mentoring me during the past four years. It
has been a great privilege to work with such a highly talented clinician and
researcher, one who has been incredibly tolerant and patient towards my short-
comings and has constantly motivated me to complete this research. His sense
of humour has been a huge encouraging factor.
I thank my co-supervisor Professor Rajalingam Sinnaih, who in spite of his busy
work schedule examined and re-coded all the renal biopsies for the purposes of
this research.
I express my sincere thanks to Ms Sally Burrows, Bio-statistician, University
Department of Medicine, Royal Perth Hospital, for providing statistical support. I
would also like to thank my cousin and bio-statistician Mrs. Sowmya Anand for
helping with statistical analysis.
My special thanks to Mr James Goodchild and Mr Ralph Baker, Department of
Medical Illustration, Royal Perth Hospital for the illustrations published in this
thesis.
I would like to thank the Department of Nephrology and the nephrologists at
Royal Perth Hospital for their support.
Ms Sam Fidler, Senior scientist, Department of Immunology has been of great
help and support with immunological data and I thank her for that.
xx
I would like to sincerely extend my thanks to Pathwest Laboratories, Royal
Perth hospital and especially to Ms Linda Gregory for the laboratory analysis.
I thank the friendly staff at central specimen collection at Royal Perth Hospital.
Lisa Burnette, my friend and renal research manager has been very supportive.
I thank the renal pharmacists and our library staff for their support.
My sincere thanks, to all the patients, without whose participation, this research
would not have been possible.
This research endeavour would not have been possible without the help,
support, encouragement and motivation of my dear husband, Venkatesan
Narayanaswamy (Venky). Not only did he support me emotionally and shared
the domestic duties during my course-work, he also helped me with the Visio-
drawings published in this thesis. My daughters, Madu and Preetha have been
very patient and have been encouraging me to work at home during the
holidays. I would also like thank my beloved parents Chandra and
Swaminathan, who have dedicated their lives solely for my well-being, have
been of immense support through out my career. They have relieved me of all
my domestic duties during the past few months and this has enabled me to
complete my thesis. To my family, I dedicate this thesis.
Finally I thank all my friends and colleagues whose constant encouragement
has enabled me to complete this work.
xxi
Statement of Candidate Contribution:
I was involved in the concept development and study design for both the studies
in this thesis.
I was responsible for recruiting patients performing clinical examination and
data collection for both the studies in this thesis.
I performed the statistical analyses for the prospective study.
I have presented the results from both studies at national and international
meetings. (Appendix 3).
Dr. Ramyasuda Swaminathan C/Prof. Ashley Irish
Candidate Co-ordinating Supervisor
20 February, 2012
22
Introduction
23
Kidney transplantation is the best form of renal replacement therapy for those
suffering from end stage kidney disease (ESKD). Recipients of kidney
transplantation have a better quality of life and also extended life expectancy
compared with those who continue on dialysis. The major limitation for
increasing the number of patients who can benefit from kidney transplantation is
the limited availability of donor organs, with the number of people waiting for a
kidney transplant exceeding the organs available for transplantation.
Following renal transplantation kidney function is not indefinite. There is a slow
loss of function over time; with the half life a transplanted kidney reported as
about 12-15 years (defined as the period of time taken for 50% of the
transplanted kidneys to stop functioning). The reason for the chronic attrition of
renal function and eventual return to dialysis or need for further transplantation
is complex and incompletely studied, and has been attributed to several
immunological and non-immunological factors.
A major focus of research in renal transplantation is to examine the patho-
physiology of renal allograft injury and develop ways to improve the longevity of
the renal allograft. With improvements in the immunosuppressive medications
and advances in immunology, there has been an improvement in the
understanding and managing acute rejection. With the introduction calcineurin
inhibitors (CNI) [cyclosporine (CyA) and Tacrolimus (TAC)] as
immunosuppressive agents, acute rejections rates and early (1 & 5 year) graft
survival rates have improved significantly. However, the rates of late graft loss
due to cumulative immune and non-immune mediated injury (chronic allograft
nephropathy- CAN) remains unchanged.
24
Strategies to reduce the incidence of CAN and hence prolong the longevity of
the allograft are therefore the focus of much research. Sirolimus (SRL) is a
relatively new immunosuppressant that has demonstrated promising results in
reducing CAN. This belongs to a class of immunosuppressants called mTOR
inhibitors (mTOR-I). Compared with the CNI such as CyA and TAC, SRL is not
nephrotoxic. Additionally, due to its anti-proliferative properties, SRL has the
advantage of a lower risk of malignancy and was not considered to have an
adverse impact on glucose metabolism in renal transplant recipients (RTR).
Hence SRL was perceived as a major advantage in immunosuppression that
could potentially improve long term renal transplant outcomes. However with
increasing clinical use and longer follow up, some of the side-effects of this drug
such as impaired wound healing, proteinuria, oedema and dyslipidaemia have
become more apparent and limited its use.
In 2006 I commenced clinical research into the effects of SRL upon factors that
may impact long-term patient and kidney survival and identified several areas of
clinical uncertainty:
Which factors predict a favourable response to introduction of SRL with
regard to allograft function?
Does SRL affect glucose metabolism?
What are the characteristic lipid and lipoprotein changes associated with
SRL and what is the aetiology of this dyslipidaemia?
I have addressed and developed these 3 key questions by means of
retrospective and prospective clinical study, the results of which are presented
25
in this thesis entitled “Renal and Metabolic function in Renal Transplant
Recipients receiving Calcineurin Inhibitors and the effects of conversion to
Sirolimus”
This thesis is structured in 4 chapters:
Chapter 1: The literature on renal allograft loss due to CAN is reviewed
focusing on the role of the modifiable non-immunological risk factors of CAN
including post-transplant diabetes, dyslipidaemia, proteinuria and the impact of
various class of immunosuppressants upon these factors, with major focus on
SRL.
Chapter 2: Research methodology
Chapter 3: Results of the retrospective study which compares measures of
renal function in RTRs with CAN who are converted to SRL with those who
continue on a CNI. The clinical and histological factors that may predict these
clinical outcomes following conversion to SRL are described.
Chapter 4: Results of the prospective study, which examines the effects of
conversion from a CNI to SRL upon measures of glucose and lipid metabolism,
are presented.
This research will help to clarify the role of mTOR-I in the renal transplant
setting and improve the understanding of the factors that contribute to
successful conversion to mTOR-I in the setting of CAN and their effect upon
glucose and lipid metabolism. This will assist clinical decision making by
allowing better understanding of the benefits and risks of the use of mTOR-I in
clinical practice.
26
Chapter 1 REVIEW OF LITERATURE
27
1 .1 . End Stage Kidney Disease in Australia
ESKD is a major public health issue with approximately 10% of Australians
having signs of chronic kidney disease. (Chadban, Briganti et al. 2003).
National data presented in the Australia and New Zealand Dialysis and
Transplant (ANZDATA) registry reports indicate that more than 2000
Australians commence on renal replacement therapy every year and this
number has been increasing at a rate significantly higher than the population
growth over the last few decades. Dialysis and renal transplantation are the two
modes of renal replacement therapies available. In 2009, 2337(107 per million)
Australians commenced on renal replacement therapy; 1565 (72/million)
commenced dialysis and 772 (35/million) had renal transplantation. As of
December 2009 18,243(834/million) Australians have ESKD, out of which
10,341(473/million) were maintained on dialysis and 7902 (361/million) had
functioning transplants (ANZDATA 2010).
In Australia, diabetic nephropathy is the leading cause of ESKD accounting for
33% of all those with ESKD, followed by glomerulonephritis (23%) and
hypertension (13%) (ANZDATA 2010).
Renal transplantation is the best form of renal replacement therapy. Compared
with dialysis, transplantation not only improves the quality of life but also the
longevity (Ogutmen, Yildirim et al. 2006). Patient survival rates are better for
those with ESKD who are accepted for transplantation when compared with
28
those who are maintained on dialysis even after adjusting for all degrees of co-
morbidity.(Schnuelle, Lorenz et al. 1998; McDonald and Russ 2002)
In the Australian population, overall mortality rates on dialysis is 15.4/100
patient years compared with 1.23/100 patient years in the transplant population.
(Fig 1.1)
Fig 1.1: Age specific mortality rate for Australians on treated
with dialysis or renal transplantation.
Fig 1.1 depicts the age specific mortality rates for dialysis and transplant population compared with the general Australian population. Mortality rates are highest for the dialysis population. Source (ANZDATA 2010)
The source of a renal allograft can be either a deceased or living (related or
unrelated) donor. In Australia, 60% of the kidney transplants are from
deceased donors and 40% from live donors. (McDonald and Russ 2002)
(ANZDATA 2010). Strategies to improve the longevity of renal allograft function
will help reduce demand by reducing the need for repeat transplantation.
29
1.2. Reject ion of renal a llograft :
Transplantation of a donor kidney into the recipient evokes an immune
response because of the genetic differences between the donor and recipient.
This process is termed “rejection” and can be classified into 2 groups based on
the principle mechanisms of evoking the host immune response.
o T-cell mediated
o Antibody mediated
Renal transplantation requires life-long use of immunosuppressive agents to
suppress innate and acquired mechanisms of immunological graft rejection and
allow the recipient to accommodate the foreign kidney.
30
1.2.1 Cell Mediated Reject ion (CMR):
Cell mediated rejection is the most commonly encountered type of kidney
allograft rejection.
Fig 1.2 Mechanism of T-cell mediated rejection
Fig 1.2 is a simplified depiction of the cellular mechanisms involved in cell mediated rejection. See section 1.2.1 for further explanation. Adapted from (Nankivell and Alexander 2008) Abbreviations: APC- Antigen Presenting Cell; MHL- Major Histocompatibility Ligand; CD – Cluster of
Differentiation; TCR – T Cell Receptor; T reg – Regulator T Cell; IFN- Interferon; TGF β – Transforming Growth Factor β
Donor antigens are presented to the recipient lymphocytes by the antigen
presenting cells (APCs). Dendritic cells and macrophages are the predominant
APCs but B cells, tubular epithelial or endothelial cells can also present
antigens. Antigen presentation activates the naive CD4 T cells and requires a
co-signal which allows the activation of the T lymphocytes which can then
proliferate and enter the interstitial compartment of the allograft and invade the
31
tubular basement membrane (Fig 1.2). Histological hall mark of T-cell mediated
rejection is dense lymphocytic inflammatory infiltrate and mononuclear cells
invading the tubular basement membrane causing tubulitis. In general CMR
responds to glucocorticoids and resolves without any residual damage to the
renal allograft. (Nankivell and Alexander 2008)
1.2.2 Antibody Mediated Reject ion (AMR):
Fig 1.3 Mechanism of Antibody Mediated Rejection
Fig 1.3 is a simplified depiction of the cellular mechanisms involved in antibody mediated rejection. See section 1.2.2 for further explanation. Adapted from (Nankivell and Alexander 2008) Abbreviation: PMN- Polymorphonuclear neutrophil
Preformed or de-novo antibodies can mediate rejection without involving the
APCs. Antibodies that can trigger the immune response in the kidney allograft
include HLA antibodies, anti endothelial cell antibodies and ABO blood group
antibodies.
AMR can either be hyperacute or acute.
Hyperacute rejection: This occurs almost immediately after the graft is
implanted and the vascular clamps are released. This is usually due to
32
prior sensitization and preformed antibodies. Improvements in transplant
immunology cross match techniques and immunosuppressive treatments
have prevented hyper-acute rejection.
Acute AMR: Previous exposure to relevant HLA antigens commonly
causes generation of high titres of complement fixing antibodies which
target the major histocompatibility complex (MHC) antigens displayed by
the donor peri-tubular and glomerular capillary endothelium (Fig 1.3).
(Nankivell and Alexander 2008)
1.2.3 Chronic Reject ion:
Chronic rejection is defined as ongoing immune T and B cell mediated injury to
the allograft usually due to inadequate immunosuppression and /or antibodies
against the allograft. There is a progressive decline in renal function.
Histologically, this is characterized by invasion of renal the parenchyma with T-
cells. In chronic antibody mediated rejection there is an association with donor
specific HLA antibodies or non HLA antibodies and histological features such
as expansion and double- contouring of the glomerular basement membrane
and evidence for complement deposition known as Transplant Glomerulopathy
(TG) (Nankivell and Alexander 2008).
Chronic Rejection is difficult to treat and to date there are no established
therapies. Chronic rejection can lead to chronic allograft nephropathy (CAN)
which is the main focus of this thesis and is discussed in detail in section 1.4
33
1.3. Mechanism of act ion of different Classes of
Immunosuppressive agents:
The drugs used in long term maintenance immunosuppression are steroids,
CNI such as CyA and TAC, mTOR-I such as SRL and Everolimus (EVR), anti-
proliferative agents such as Azathioprine (AZA) and Mycophenolic acid (MPA).
The mechanisms of action of these agents are discussed further and
summarised in Fig 1.4.
Fig 1.4 Structure of T-cell and sites of action of
immunosuppressants.
Fig 1.4 is a simplified depiction of the sites of action of various immunosuppressants. See section 1.3 for further explanation. Source: Adapted from (Halloran 2004); Abbreviations: CD- Cluster of Differentiation; NFAT- Nuclear Factor of activated T cell; IKK – Inhibitor of Kappa B Kinase; MAP – Membrane Associated protein; AP-1- Activator Protein 1; NF –ĸB – Nuclear Factor ĸ B;
34
Glucocorticoids: They bind to the glucocorticoids receptors and
affect DNA transcription factors activator protein 1 (AP1) and nuclear
factor- KB (NF-ĸB).
Calcineurin Inhibitors (CNI): These agents inhibit the calcineurin
receptors which are activated by the antigen presenting cell (APC) via
the TCR/CD3 complex. CyA binds to cyclophilin and TAC to FKBP12
which prevents T cell activation and proliferation.
mTOR Inhibitors (mTOR-I): These act at a different site to the CNI
by inhibiting the Mammalian Target of Rapamycin (mTOR) and thus
arresting the cell cycle at the G1 phase. SRL prevents Inter leukin-2 (IL-
2) dependent T cell proliferation, by arresting the cell cycle at G1 phase.
The mechanism of action of mTOR-I are discussed in detail in
subsequent sections.
Mycophenolic Acid (MPA): MPA is the active form of the pro-
drugs Mycophenolate Mofetil and Mycophenolate Sodium. MPA inhibits
the nucleotide synthesis and prevents proliferation of the T and B cells.
Azathioprine (AZA): Acts by interfering with DNA synthesis and thus
inhibits proliferation of T and B lymphocytes. (Halloran 2004)
Modern immunosuppressive medications have revolutionized transplantation by
significantly improving the 1 year and 5 year graft survival rates because they
reduce early rejection rates to as low as 10% as shown in the SYMPHONY trial
(Ekberg, Tedesco-Silva et al. 2007). Despite impressive reductions in early
rates of allograft rejection, it is notable that long term graft survival has not
changed significantly over the past decades. (Fig 1.5 and Tab 1.1)
35
Fig 1.5 Renal allograft survival rates 1965-2008
Fig 1.5 shows the long term outcomes of the renal allograft survival stratified by era Source:(ANZDATA 2010)
Overall, the annual graft loss after 1 year post transplantation remains constant
with a rate of 3-5%. During the past 3 decades this has not significantly
improved despite the introduction of newer immunosuppressive agents.
(Pascual, Theruvath et al. 2002)
0.00
0.25
0.50
0.75
1.00
Gra
ft S
urv
iva
l
0 5 10 15 20 25 30 35 40
Years
1965-9 1970-4 1975-9 1980-4 1985-9
1990-4 1995-9 2000-4 2005-8
Primary Deceased Donor Grafts
Graft Survival - Australia and New Zealand
36
Table 1.1 Long term renal allograft survival rates
Survival rates (%) 1year 5 years 10 years 15 years 20 years
1970-1974 58.2 41.9 30.3 22.8 14.6
1975-1979 51.7 36.0 25.6 17.7 12.6
1980-1984 63.6 45.4 32.1 23.0 16.2
1985-1989 80.8 65.8 47.2 32.8 21.3
1990-1994 85.0 70.9 50.7 33.8
1995-1999 88.6 76.2 58.6
2000-2004 91.6 80.8
2005-2009 91.6
Table 1.1 The data in Fig 1.5 in tabulated. Source:(ANZDATA 2010)
In RTR, the main cause of graft loss after 5 years is CAN (44%). Death with a
functioning graft (40% - Patient death due to causes other than renal allograft
failure) is the next major cause of graft loss and malignancy (35%) and cardio-
vascular disease (30%) are major causes of death with functioning graft.
(ANZDATA 2010)
Because, CAN remains the leading cause of graft loss, any strategy to improve
long-term graft survival should include mechanisms to prevent or minimise the
progression of CAN.
37
1.4. Overview of Chronic Allograft Nephropathy
1.4.1 Definit ion of CAN:
Chronic allograft nephropathy is defined as a clinico-pathologic syndrome
consisting of
progressive allograft dysfunction which manifests as rising creatinine
(declining eGFR), proteinuria and hypertension
chronic histological damage defined as the presence of progressive
interstitial fibrosis and tubular atrophy.
The consequences of CAN and their progression eventually lead to graft loss
and the need to return to dialysis or further renal transplantation. (Nankivell and
Chapman 2006)
1.4.2 Nomenclature of CAN – A historical perspect ive
The terminology CAN represented different meanings depending upon the era
of literature reviewed. The distinction between acute allograft rejection (intense
inflammatory response and usually reversible) and chronic rejection (chronic
injury resulting in arterial intimal fibrosis usually due to an allo antibody) was
recognized as early as 1960s and was first described by Hume, Porter,
Jeannette et.al.(Hume, Merrill et al. ; Jeannet, Pinn et al.). This immune
mediated late allograft loss was referred as CAN.(Halloran, Melk et al. 1999)
With improvements in immunosuppressive medications and advances in
transplant immunology it became apparent that the late allograft injury is not
entirely due to immune mediated injury. There was shift in paradigm and the
38
term CAN denoted late allograft loss due to either immune or non-immune
mediated damage. In the early 1990s the nephrotoxicity due to CNI was thought
to be a major non- immune mediator of CAN. The terms CAN and CNI toxicity
were used interchangeably. Now it is being recognized that factors other than
CNI are associated with non-immune mediated late renal allograft injury. (Matas
2011). The change in paradigm is also reflected in the histological classification
of late allograft loss. The Banff 1997 histological classification of CAN does not
distinguish between immune/ non-immune mediated injury. (Racusen, Solez et
al. 1999). It is now recognized late allograft dysfunction is not synonymous
either with chronic rejection or chronic CNI toxicity. But it is represents
irreversible allograft damage which is a cumulative result of several immune
and non-immune mediated factors. The histological findings of interstitial
fibrosis and tubular atrophy are pathognomonic of CAN. As a consequence of
this understanding, Banff 2007 has introduced the terminology IFTA- NOS
(Interstitial Fibrosis/ Tubular Atrophy – Not Otherwise Specified) to denote the
histological damage that cannot be attributed to either immune factors or CNI
toxicity. (Solez, Colvin et al. 2008)
1.4.3 Mediators of CAN:
Because most of the late renal allograft loss has been attributed to CAN, the
risk factors associated with allograft loss are traditionally used as surrogate
determinants of CAN. The immune and non-immune mediated factors that
influence and contribute to the development of CAN can be further divided into
donor or recipient dependant factors (Fig 1.6). In general donor factors
contributing to CAN, are usually non-modifiable unless a recipient has more
than one potential live donor, has the opportunity to chose and hence to some
39
extent able modify some of the donor related risk factors. Similarly some of the
recipient factors such as preexisting diabetes, hypertension and family history
are non-modifiable. In the following sections risk factors of CAN are discussed
as immune and non-immune mediators and further divided into donor or
recipient factors.
1 .4 .3 .1 Immune Mediators of CAN
Donor factors
HLA mismatch and HLA antibodies:
Terasaki and Patel published a land mark paper in 1969 which showed the
presence of preformed cytotoxic antibodies against the donor was associated
with graft loss.(Patel and Terasaki 1969). Over the past few years the
significance of HLA antibodies against both donor specific and non- donor
specific antigens have been studied. Degree of HLA mis-matches between the
donor and recipient and the presence of anti-HLA antibodies are associated
with higher incidences of antibody mediated rejection and CAN. Presence
either de-novo or pre existing of HLA antibodies is associated with a higher
incidence of AMR and also late graft loss. (Mao, Terasaki et al. 2007). The
higher pre-transplant cumulative HLA antibody burden (as measured by Mean
Fluorescence Intensity - MFI) is also associated with greater incidence of late
graft loss. (Lefaucheur, Loupy et al. 2010; Loupy, Hill et al. 2012). Female sex,
multi parity, blood transfusions, higher degree of HLA mis matches and prior
transplantation predispose to the formation of anti HLA antibodies.
40
Role of Non-HLA antibodies:
Antibodies against non-HLA antigens have been identified in renal transplant
recipients and shown to be detrimental to graft function and lead to CAN. Non
HLA antibodies include those against MHC Class 1 chain related gene A
(MICA), Glutathione S-Transferase T, angiotensin II type 1 (AT1R) receptor and
endothelial cell antigens. These antibodies have also been implicated in
progressive graft loss. In addition other factors involved in variation in recipient
immune responses due to polymorphisms in the cytokines IL6, TNF-α,
coagulation factors (prothrombin and Factor V) and the fibrotic growth factor
caveolin contribute to the development of CAN.(Reinsmoen, Lai et al. 2010;
Sigdel, Li et al. 2012). (Dragun 2008; Moore, McKnight et al. 2010)
Recipient Factors
Rejection episodes including Sub-Clinical rejection:
Sub-clinical rejection (SCR) is defined as the presence of histological lesions of
rejection in clinically well functioning grafts i.e. stable creatinine and absent
proteinuria. In a series of protocol biopsies it has been shown that SCR
precedes the development of CAN. (Moreso, Ibernon et al. 2006). The number
of episodes of acute rejection is also associated with poor long term allograft
function (McDonald, Russ et al. 2007). This is thought to be due to the intense
allo-immune response and subsequent inflammatory damage which then
progresses to CAN.
Adequacy of Immune suppression:
Inadequate immunosuppression can lead to development of allo-antibodies.
This can result in an allo immune response which manifests as acute rejection
41
episodes, sub- clinical rejections or chronic anti body mediated rejection all of
which can lead to CAN. Non- compliance with immunosuppressants is a cause
of inadequate immunosuppression and allograft biopsies show higher degrees
of interstitial fibrosis and tubular atrophy scores in non-compliant RTRs than
those who were compliant with the immunosuppressive medications.(Lerut,
Kuypers et al. 2007)
1.4.3 .2 Non- Immune Mediators of CAN
Donor Factors
Donor Source:
Kidneys from live donors (related or unrelated) survive longer than those from
deceased donors.(Terasaki, Cecka et al. 1995; Fuggle, Allen et al. 2010) . The
allografts from live donors are less prone to peri-transplant injury compared with
the deceased donor allografts. Prolonged cold ischaemic time (time elapsed
between the kidney allograft clamped from the donor to being reperfused by the
recipient, during which time the organ is maintained in cold storage) is a strong
predictor of late allograft loss. Deceased donor allografts have significantly
higher cold ischaemic times(Salahudeen, Haider et al. 2003). Even after
correcting for cold ischaemic times the survival rates for live donor allografts are
superior to the deceased donor allografts.(Roodnat, van Riemsdijk et al. 2003).
Kidney allograft from donors who die of brain damage due to non hypoxic
causes have better survival rates than those who die of hypoxic brain injury.
(Halloran, Melk et al. 1999). Kidney allografts from non heart beating donors
have increased delayed graft function and lower eGFR at 1 and 3 years
compared with allografts from brain dead donors.(Pine, Goldsmith et al. 2010).
42
Cardiovascular instability, use of vasopressins and pro-inflammatory cytokine
release as a consequence of brain death which are different in not only the
setting of deceased vs. live donor, but also depending upon the cause of donor
death (brain vs. cardiac; hypoxic vs. non hypoxic brain injury) all lead to delayed
graft function and be associated with late allograft loss (Pratschke, Weiss et al.
2008). Kidneys from a live donor source have great longevity than from a
deceased donor; non hypoxic brain death in a donor source is associated with
better long term outcomes compared with a kidney allograft received from a
donor secondary to hypoxic brain injury or from a non-heart beating donor.
Donor Age
Increasing donor age is associated with higher risk of late graft loss in both live
and deceased donor kidney allografts.(Chavalitdhamrong, Gill et al. 2008;
Fuggle, Allen et al. 2010). The normal physiological phenomenon of ageing is
associated with glomerulosclerosis, tubular atrophy, interstitial fibrosis and
thickening of vessel wall. These lead to an effective decrease in nephron
number as donor age increases. Implantation biopsies show that the severity of
the histological changes in the donor is associated with reduce renal allograft
survival, in kidneys transplanted from older donors.(Remuzzi, Cravedi et al.
2006).
Donor Sex
The kidney allografts from male donor survive longer than from female donors.
Female kidneys are up to 15% smaller and hence have lower nephron number
and size. This difference in nephron mass contributes to increased
hyperfiltartion injury and subsequent graft loss. (Pratschke, Weiss et al. 2008)
43
Peri-transplant Factors
Delayed Graft Function (DGF)
Delayed graft function (defined as the need for dialysis any time during the
immediate post transplant period) is associated with increased episodes of
acute rejection episodes during the first year post transplantation. (Gentil,
Alcaide et al. 2003) . DGF is also independently associated with lower eGFR
and increased graft loss.(Yarlagadda, Coca et al. 2009). Prolonged cold
ischaemic time and transplantation from a deceased donor (Non-heart beating
donors > Brain Dead Donors) are independent risk factors for DGF and
contribute to the development of CAN.
Ischaemia reperfusion Injury (IRI)
Ischaemia to an allograft occurs during organ retrieval and subsequent
reperfusion by the recipient results in a cascade of inflammatory response. This
phenomenon in the recipient is termed as IRI. IRI leads to the adhesion of the
leukocytes to the vascular endothelium, infiltration of the these leukocytes into
the allograft tissue, increased graft immunogenicity and accelerated host
immune responses which trigger complex injury/repair pathways that promote
graft fibrosis (Timsit, Yuan et al. 2010) (Pratschke, Weiss et al. 2008) and
subsequent development of CAN.
Recipient factors
Hypertension, Post transplant Diabetes Mellitus (PTDM), dyslipidaemia and
proteinuria are not only non-immune mediators of CAN but also significant
cardiovascular risk factors. As in the general population the prevalence of these
risk factors increase with increasing recipient age.
44
Hypertension:
Activation of the Renin Angiotensin – Aldosterone System (RAAS) is associated
with preferential vasoconstriction of the efferent arterioles, sodium retention and
systemic hypertension. It also up regulates Transforming Growth Factor β (TGF
β) and promotes interstitial fibrosis and subsequently leads to IFTA. (Remuzzi,
Perico et al. 2005). Thus hypertension is not only a manifestation of CAN but is
also is an important risk factor for CAN, late renal allograft and patient loss.
(Opelz, Wujciak et al. 1998).
Post Transplant Diabetes Mellitus (PTDM) & Dyslipidaemia;
PTDM adversely affects long term allograft function and patient survival
(Helanterä, Ortiz et al.), (Demirci MS and A 2010) because of an increased
cardio vascular morbidity and mortality post transplantation.(Cosio, Kudva et al.
2005). Dyslipidaemia is a significant cardiovascular risk factor in RTR and
reduction in lipid levels with statins have shown an improvement in
cardiovascular morbidity in RTR (Jardine, Gaston et al. 2011) (Lentine and
Brennan 2004). Though dyslipidaemia has not been shown to impact on the
renal allograft dysfunction directly, it is possible that post transplant diabetes
and dyslipidaemia, by increasing the oxidative stress affect the vascular
endothelium and contribute to late allograft loss.ive stress. PTDM and
dyslipidaemia are discussed in detail in sections 1.7 and 1.9
Viral infections:
Viral infections such as BK virus nephropathy can lead to allograft damage by
activation of fibrotic pathways in the renal allograft. The role of cytomegalovirus
in chronic renal allograft damage is uncertain.(Li and Yang 2009).
45
Proteinuria:
Proteinuria is a manifestation of allograft damage and persistent proteinuria has
also been directly implicated in tubular injury and epithelial mesenchymal
transition leading to fibrosis and progressive allograft damage initiating a
vicious cycle.(Fernandez-Fresnedo, Plaza et al. 2004), (Li and Yang 2009). This
is discussed in detail in section 1.6
There is a complex interplay between various modifiable and non-modifiable
(both immune or-non-immune mediated) factors that can cause allograft
damage. (Fig1.6). Most of the donor and immune mediated factors are not
modifiable. However the use of immunosuppressive medications and host
factors such as diabetes, hypertension and dyslipidaemia are modifiable risk
factors and can significantly influence the renal allograft outcomes. It is these
modifiable metabolic factors that will be addressed in this thesis.
46
Fig 1.6 Pathogenesis of CAN.
Renal Allograft
Tubular DamageChronic Interstitial
Damage
Glomerular
Damage
CAN
(Allograft Dysfunction &
Failure)
Donor Factors
Age
Live vs. deceased
Cold Ischaemia
Co-morbidites
Recipient Factors
Age
HLA mismatch
PRA Level
Co-morbiditesInadequate
Immunosuppression
Inflammation
Sub-Clinical
Rejection
CyA**
&
TAC**
Cytokines and growth factors
Proteinuria Senescence**
Blood Vessels
Hypertension**
Diabetes Mellitus**
Dyslipidaemia**
Other Causes:
Recurrent GN
Transplant Glomerulopathy
Atubular
glomeruli
Cortical
Ischaemia
Ischaemia
DGF/ATN
CNI Toxicity**
Nephron Loss
Disruption of Internal
Architecture
** - Modifibale risk factors of CAN
Fig 1.6 shows the interactions of various immune and non-immune mediators of CAN Adapted from (Nankivell and Chapman 2006) Abbreviations: DGF – Delayed Graft Function; CNI – Calcineurin Inhibitor(s); ATN – Acute Tubular
Necrosis; PRA – Panel Reactive Antibodies; GN – Glomerulonephritis; CAN – Chronic Allograft Nephropathy;
1.4.4 Pathogenesis of CAN
The allograft damage in CAN is usually considered as the end result of
cumulative immune and non-immune mediated insults, which can be host or
donor related. Several models, complementary to each other have been have
been proposed to explain the pathogenesis of CAN.
1.4.4 .1 Chronic Reject ion Model:
This model suggests that chronic immune mediated allograft injury due to
inadequate immunosuppression of host immune responses results in allograft
47
damage. This mechanism was considered a key player in the development of
CAN in the pre CNI era. (Hume, Merrill et al. 1955) (Nankivell and Chapman
2006)
1.4.4 .2 Input-Stress model:
This model takes into account factors influencing the quality of the allograft and
includes donor age, source, cold ischaemic times (input factors) and post
transplant immune and non-immune stressors such as (episodes of rejection,
hypertension, diabetes- mellitus, proteinuria and dyslipidaemia). (Grinyo, Saval
et al.) (Timsit, Yuan et al. 2010).These factors may drive individual nephrons to
senescence and deplete the finite nephron mass leading to CAN. (Halloran,
Melk et al. 1999; Melk 2003; Nankivell and Chapman 2006)
1.4.4 .3 Cumulat ive damage Model:
Several immune and non-immune mediated time-dependent factors lead to
permanent damage of the nephrons Example: repeated episodes of acute
rejection, sub-clinical rejection, hypertension, diabetes etc. The allograft
dysfunction is the result of cumulative catastrophic failure of many individual
nephrons resulting in incremental loss of architectural integrity. (Halloran, Melk
et al. 1999; Melk 2003)
In summary it is important to note that all of these models consider that more
than one factor is responsible for the development of CAN and that it is the
resultant cumulative injury over time which leads to the irreversible damage of
the allograft.
48
1.4.5 Mechanisms of allograft injury:
There are several proposed mechanisms by which injury to specific segments
of the nephron leads to allograft failure.
1.4.5 .1 Internal architectural degradation:
All the mediators of CAN described above can cause damage to specific
segments of the nephron and interstitium.
Individual nephrons may fail due to glomerular, tubular or interstitial damage.
Summated damage of individual nephrons leads to ultimate architectural
degradation and failure of the allograft. (Kriz, Hartmann et al. 2001)
Glomerular damage:
This occurs due to glomerulosclerosis, transplant glomerulopathy or atubular
glomeruli. Atubular glomeruli are formed after irreversible tubular damage
results in disconnection of the glomerulus from the downstream nephron.
(Nankivell 2004) Chronic hypertension, prolonged CNI exposure are some of
the causes of renal allograft glomerulosclerosis, chronic antibody mediated
rejection leads to transplant glomerulopathy.
Tubular damage:
This occurs secondary to localized apoptosis or luminal obstruction by cellular
debris (Nankivell 2004).Tubulitis secondary to cell mediated rejection or
obstruction due to casts such as in oxalate nephropathy may cause of tubular
damage.
49
Interstitial damage:
Adhesions from segmentally injured glomeruli can cause the ultra-filtrate to
escape into the para-glomerular and para-tubular interstitial spaces, trigger
inflammatory process leading to interstitial fibrosis. (Nankivell 2004)
1.4.5 .2 Cort ical Ischaemia:
Tubular cells are metabolically active and hence susceptible to ischaemia of
any cause. Damage to the peri-tubular capillary (PTC) network results in tubulo-
interstitial damage and subsequently allograft dysfunction. (Basile 2004; Yasuo,
Tokihiko et al. 2005). Glomerulosclerosis, arteriolar hyalinosis due to chronic
CNI toxicity, vaso-constriction secondary to acute CNI toxicity, fibro-intimal
hyperplasia due to chronic hypertension and peri-tubular capillaritis in AMR are
some of the examples of the disease processes that lead to cortical ischaemia
and subsequent fibrosis.
1.4.5 .3 Chronic Inflammation:
Acute injury of the renal allograft due to any cause results in inflammation. As a
part of the injury repair pathway, partial or incomplete resolution of inflammation
leads to a vicious cycle of perpetual injury→ inflammation → enhanced allo-
recognition → further injury until allograft eventually fails.(Shishido, Asanuma et
al. 2003; Nankivell 2004)
1.4.5 .4 Epithelia l – Mesenchymal Transit ion (EMT):
Tubular injury resulting in loss of cell adhesion and transformation of tubular
cells to myo-fibroblasts, (under the influence of hypoxia or cytokines like TGF-
50
ß1, interleukin 1) has been proposed as a mechanism of progressive fibrosis
and allograft dysfunction. (Strutz ; Vongwiwatana, Tasanarong et al. 2005)
It is important to note that these proposed mechanisms of injury are not
independent of each other. In RTRs many of these mechanisms contribute to
allograft damage. For example chronic CNI toxicity can cause vasoconstriction
leading to ischaemia, tubular injury and release of cytokines causing EMT,
progressive fibrosis and allograft dysfunction.
1.4.6 Banff Histologica l Classificat ion of CAN
The histological features of CAN have been described by a consensus of
pathologists, nephrologists and immunologists further determined the criteria
denoted as the Banff ‘97 classification. This was later revised in 2007, called
Banff 2007. (Table 1.2)
The predominant histopathological changes of CAN are non-specific interstitial
fibrosis (ci), and tubular atrophy (ct), where the pre-fix ‘c’ denotes the chronicity
of the lesions. Additional features of CAN include glomerulopathy (cg),
expansion of mesangial matrix (mm) and vascular fibrous intimal thickening
(cv). The latter 3 attributes are considered to represent immune mediated
damage of the allograft. (Fig 1.7)
51
Fig 1.7 Histological changes of CAN
1.7a CAN. There is interstitial fibrosis, tubular atrophy and glomerulosclerosis in a patchy pattern. Infiltrates of small lymphocytes are present in the fibrotic interstitium and not in the non atrophic tubules. (PAS –Silver Stain x5)
1.7b Transplant obliterative arteriopathy with thickened intima infiltrated with inflammatory cells and thickened muscle walls. (PAS x5)
52
Table 1.2 Banff 2007 Classification
Table 1.2 explains the salient features of the BANFF 2007 histological classification.
Grade 0 Grade I
(Mild)
Grade II
(Moderate)
Grade III
(Severe)
Interstitial fibrosis(ci):
cortical area affected
ci0: ≤ 5% ci1: 6-25% ci2: 26-50% ci3: >50%
Tubular Atrophy (ct): area of cortical
tubules are atrophied
ct0: No tubular atrophy
ct1: up to 25%
ct2: 25-50%
ct3: >50%
Glomerulopathy(cg): Capillary loops
with double contours
cg0: <10%
cg1: 11- 25%
cg2: 26-50%
cg3: >50%
Vascular fibrous intimal thickening(cv):
Mesangial Matrix thickening (mm)
cv0: No change
mm0: No increase
cv1: up to 25%
mm1: up to 25%
cv2: 26-50%
mm2: 26-505
cv3: > 50%
mm3: >50%
53
1.4.6 .1 Banff 2007 updates
The Banff working group reviewed its classification of CAN in 2007 and the term
chronic allograft nephropathy was replaced with interstitial fibrosis and tubular
atrophy (IFTA) but the existing histological scoring categories remain
unchanged (Solez, Colvin et al. 2008). The main reason for this change in
nomenclature was to differentiate CAN from chronic cellular or antibody
mediated rejection and calcineurin inhibitor toxicity. In this thesis the term CAN
will be used and not IFTA.
1.4.7 Role of CNI in CAN
The CNI have significant effects upon renal micro-vascular function. In animals
and humans they cause renal afferent arteriolar vasoconstriction, reduced renal
blood flow and this leads acutely to reduced renal function. Acute CNI toxicity is
characterized by smooth muscle necrosis and early hyalinosis in afferent
arterioles and/or isometric vacoulation of proximal tubules. These early changes
are reversible (Liptak and Ivanyi 2006). However, the chronic damage due to
prolonged CNI exposure may play a role in progressive renal allograft damage
by immunological or non-immunological pathways. CNI induced
vasoconstriction leads to hypoxia and release of pro-inflammatory and pro-
fibrotic factors such as angiotensin 2, transforming Growth Factor – TGF β1.
CNI can also cause up-regulation of Toll like receptors (TLR) leading to
activation of Nuclear Transcription factor NFĸB and AP1 which activates
dendritic cells and T lymphocytes and eventually lead to chronic allograft
damage.(Li and Yang 2009). The patho-physiology of CNI nephrotoxicity is
depicted below. (Fig 1.8).
54
Fig 1.8 Mechanism of Chronic CNI toxicity
Calcineurin Inhibitors
(CyA & TAC)
Vasoconstriction
Ischaemia
Release of Angiotensin
II & TGF – β1
Inflammation and
Fibrosis
CAN
Activation of NFkβ and
AP1
Up regulation of TLR2 &
TLR4 and their Ligands
Fig 1.8 depicts the immune (right) and vascular mechanisms (left) through which chronic CNI exposure leads to allograft injury. Adapted from (Li and Yang 2009); Abbreviations: TLR- Toll Like Receptors; NFĸβ - Nuclear Transcription factor ĸB;
AP1- Activator Protein 1; TGF – β1 – Transforming Growth Factor β1
55
1.4.7 .1 Histological Changes of CNI Nephrotoxicity:
The histological changes of chronic CNI toxicity include arteriolar hyalinosis,
striped interstitial fibrosis and tubular atrophy (Fig 1.9). The glomeruli exhibit
non-specific changes such as glomerular hypertrophy, expansion of mesangial
matrix, focal or global sclerosis. (Liptak and Ivanyi 2006; Shimizu, Ishida et al.
2008). Acute CNI nephrotoxicity seems to correlate well with the serum drug
levels and responds to dose reduction, but the chronic histological changes do
not regress after CNI withdrawal.(Servais, Toupance et al. 2009)
Banff Classification of CNI Toxicity:
The Banff’97 classification has described the following quantitative criteria for
arteriolar hyaline thickening (ah)
Ah0: No PAS positive hyaline thickening
Ah1: Mild to moderate PAS positive hyaline thickening in at least one arteriole
Ah2: Moderate to severe PAS positive hyaline thickening in more than one
arteriole
Ah3: Severe PAS positive hyaline thickening in many arterioles
Banff 2007 has recommended alternate scoring for arteriolar hyalinosis as
described below.
Banff 2007 modification of ‘ah’
Aah0: No typical lesions of CNI arteriolopathy
Aah1: Replacement of degenerated smooth muscle cells by hyaline deposits
present in only one arteriole, no circumferential involvement.
Aah2: Replacement of degenerated smooth muscle cells by hyaline deposits
present in more than one arteriole, no circumferential involvement.
56
Aah3: Replacement of degenerated smooth muscle cells by hyaline deposits
with circumferential involvement irrespective of the number of arterioles.
Fig 1.9 Histological Features of CNI Toxicity
1.9a: CNI Toxicity. The arterioles show hyaline deposits (H&E x20)
1.9 b CNI Toxicity: The nodular hyaline deposits are seen in the afferent and efferent arterioles. There is glomerular mesangial expansion. (PAS x20)
57
1.4.8 The Natural History of CAN
The histological changes of CAN can be detected as early as 3 months post
transplantation and usually pre dates clinical evidence of allograft dysfunction
and frequently progressive and ultimately lead to allograft failure. (Li and Yang
2009). At one year post transplantation approximately 95% of renal allografts
show evidence of at least Banff grade I CAN and 75% show evidence of CNI
nephrotoxicity. By 5 years post transplantation these figures increase to 100%
and 93% respectively. (Nankivell 2003). These studies were based on earlier
use of CNI with higher doses. More recently the use of CNI minimization
protocols has been shown to improve graft function in the early transplant
period (Ekberg, Tedesco-Silva et al. 2007). Because chronic CNI nephrotoxicity
is not directly related to the serum levels of the drug, the long-term effects of
CNI minimisation upon CAN remain to be elucidated.
In addition to the direct nephrotoxicity and contribution to the development of
the histological features of CAN, CNI have additional deleterious effects upon
lipids, blood pressure and glucose metabolism which are independent risk
factors for the development of CAN and adversely affect allograft and patient
outcomes. CyA and TAC have different effects on glucose and lipid metabolism.
TAC is associated with a higher rates of PTDM, due to its effects upon the
pancreatic beta cells and insulin deficiency but has less effect on LDL-
cholesterol levels and blood pressure(Dmitrewski 2001). CyA has higher rates
of hypertension and increased LDL-cholesterol levels but a lower rate of post-
transplant diabetes. Diabetes and dyslipidaemia are risk factors for CAN and
have significant impact on long-term graft and patient survival. Therefore
measures to minimize these effects by the use of alternative
58
immunosuppressive strategies such as mTOR inhibition, or CNI minimization /
avoidance have been developed (Haller and Oberbauer 2009; Oberbauer and
Haller 2009). In addition, the early detection and intervention upon modifiable
risk factors such as diabetes, hypertension and dyslipidaemia may prevent or
slow the progression of CAN.(Sharif, Shabir et al. 2011) (Campistol 2009)
1.5 Role of mTOR inhibitors in CAN:
mTOR-I are a relatively new class of immunosuppressive agents that bind with
the specific cytosolic protein FKBP-12. The FKBP-12-sirolimus complex inhibits
the activation of the mammalian target of rapamycin (mTOR), a critical kinase
for cell cycle progression. The inhibition of mTOR results in blockage of several
specific signal transduction pathways. The net result is the inhibition of
lymphocyte activation, which results in immunosuppression (Fig 1.4).
mTOR-I are not nephrotoxic because they do not influence renal blood flow or
micro-vascular function. SRL has been shown to reduce inflammation and
improve endothelial dysfunction (Maamoun, Esmail et al. 2011) compared with
CNI, (Sancho, Pastor et al. 2010) For these reasons mTOR-I were considered
as an alternative immunosuppressive to prevent or reduce CAN or to replace
CNI therapy in those patients with established CAN. In addition to their
immunosuppressive properties SRL also has anti-proliferative properties and
has been shown to decrease the incidence of malignancy, especially non-
melanoma skin cancers. Hence in the last few years there is an increasing trend
to convert RTRs to mTOR-I with the aim to preserve allograft function and
prevent malignancies (Schena and Pascoe 2009). There are several studies
59
that have demonstrated that conversion from CNI based immunosuppression to
mTOR-I improve renal function. Oberbauer et.al for the Rapamune
Maintenance Regimen (RMR) Study (Oberbauer, Segoloni et al. 2005), Kreis
et.al for the Sirolimus European renal Transplant Study (Kreis, Cisterne et al.)
and Flechner et.al (Flechner, Goldfarb et al. 2002) published some of the early
randomized control trials which showed that mTOR-I based
immunosuppression improved eGFR in RTR compared with maintenance on a
CNI. Some of the more recent studies that have evaluated the renal function
and adverse effects of mTOR-I are summarised in Table.1.3.
60
Table 1.3 Summary of renal outcomes and side effect profile following mTOR-I conversion in transplant
recipients
1. (Uslu, Töz et al. 2009) 2. (Lebranchu 2009) 3. (Schena and Pascoe 2009) 4. (Teperman 2009) 5. (Egbuna 2009)
Table 1.3 summarises some of the most recent studies. The renal outcomes and side-effect profile following conversion to mTOR-I in transplant recipients are reported.
Publication
Year
Study design Improvement
in eGFR
Proteinuria Other side effects Comments
Uslu1
2009 Retrospective Yes Yes Dyslipidaemia Had baseline and 12 months post
conversion histology follow-up.
CONCEPT2
2009 Randomised Control
Trial; Conversion to
SRL at 12 weeks post
transplantation
Yes No Aphthous Ulcers, Diarrhoea oedema Included low immunological risk
patients. Excluded patients with
Creatinine clearance of less 40ml/min
and Proteinuria>1g/day
CONVERT3
2009 Randomised Control
Trial
Yes Yes Aphthous ulcers, Dyslipidaemia ,
infections
Showed benefit in RTR with a baseline
GFR of>40ml/min.
Lower incidence of malignancy
STN4
2009 Randomised Control
trial
Yes Not mentioned Dyslipidaemia Renal function in liver Transplant
recipients; 12 month follow-up
Egbuna5
2009 Retrospective Yes Yes Mucosal and skin lesions;
Dyslipidaemia
Compared SRL conversion steroid
withdrawal or steroid continuation.
Follow-up was 12 months
61
The relevant studies are discussed in detail in Chapter 3, section 3.4.
Though there is significant improvement in eGFR following conversion to SRL,
these studies also demonstrate that SRL has several side effects that have the
potential to limit their widespread use or replace entirely CNI based treatments.
The two major side effects of SRL, proteinuria and dyslipidaemia can be
detrimental to both graft function and patient morbidity. Additional and common
side effects including skin rash, GI symptoms, lymphoedema, interstitial
pneumonitis, impaired wound healing and reduced male fertility, have also
limited the wider use of mTOR-I (Remuzzi, Ruggenenti et al. 2009) (Cravedi,
Ruggenenti et al. 2010). mTOR-I with or without CNI are not optimal when used
as first-line immunosuppression because of an increase risk of rejection in the
early transplantation period with or without a CNI. (Flechner 2008; Budde,
Becker et al. 2011). The discussion about the suitability of mTOR-I as primary
immunosuppression is beyond the scope of this review. However the use of
CNI in the early transplant period and later conversion to mTOR-I before
permanent allograft injury occurs is an accepted paradigm.(Sharif, Shabir et al.
2011) (Flechner, Kobashigawa et al. 2008)
The effect of SRL on glucose metabolism in RTR however is uncertain
(discussed in section 1.7) and this thesis will study the effect of SRL upon
proteinuria, glucose and lipid metabolism, because of their significant risk to
patient and allograft health, and also because they have the potential for
modification by therapeutic interventions.
62
1.6. Prote inuria post Transplantat ion:
1.6.1 Introduct ion
Proteinuria refers to the detection of increased of protein levels in the urine.
Proteinuria can be quantified by 24 hour collections or random sampling
corrected for urine flow by the ratio of protein: creatinine. Up to 150mg of
protein/day is considered normal. Proteinuria of more than 150mg/day (Protein:
Creatinine ratio >21 mg/mmol) is considered pathological and is a result of one
or more of the following mechanisms (Cameron JS, 1998).
o increase glomerular permeability (glomerular proteinuria)
o tubulo-interstitial disease (tubular proteinuria)
o Increase filtration through normal glomeruli (overflow proteinuria)
1.6.1 .1 Glomerular Proteinuria:
Glomerular Proteinuria is caused by the loss of glomerular charge barrier or
glomerular size barrier or a combination of both. The extent of proteinuria can
range from as little as 150 mg/ day to >50g /day or more in extreme cases.
Protein excretion greater than 3.0g/ day (PCR >360mg/mmol) is described as
nephrotic range proteinuria. Glomerular proteinuria is non-selective and albumin
is the predominant protein.
1.6.1 .2 Tubular Proteinuria:
Tubular proteinuria occurs when the primary site of injury is tubulo-interstitium
and failure of the tubules to reabsorb the filtered proteins. Proteinuria in these
63
conditions is usually low-grade i.e. < 2g/day (~PCR 200mg/mmol). In addition to
tubular proteins such as the microglobulin, and other low molecular weight
proteins, only low levels of albuminuria will be present due to impairment in the
tubular re-absorption.
1.6.1 .3 Overflow Proteinuria:
This is due to excess amounts of an abnormal protein filtered through normal
glomeruli e.g. light chains in multiple myeloma.
1.6.2 Post Transplant Proteinuria:
Proteinuria is one of the clinical features of CAN. The major cause of proteinuria
post transplantation is glomerular proteinuria (Chung, Kil Park et al. 2000). In
RTR proteinuria is a predictor of graft loss and cardiovascular mortality
(Fernandez-Fresnedo, Plaza et al. 2004). Immunosuppressants themselves can
directly influence the extent of proteinuria in RTRs CNIs can reduce proteinuria
whilst mTOR-I increase proteinuria. (Barama 2008).
1.6.3 Sirolimus and Proteinuria:
One of the major physiological effects of SRL in RTRs is the development of
significant proteinuria, which may require cessation of therapy (Dervaux,
Caillard et al. 2005; Moore, Light et al. 2007) (Sahin, Sahin et al. 2006). The
exact mechanism by which SRL causes proteinuria is not clear. Most studies
have demonstrated proteinuria in the setting of CNI withdrawal and CAN
(Letavernier, Pe'raldi et al. 2005; ska, Banasik et al. 2006) (Ruiz, Diekmann et
64
al. 2005; Boratynska 2006). Under these circumstances, whether SRL causes
proteinuria by direct nephrotoxic effects, or whether the proteinuria is due to the
effects of CNI withdrawal, or both mechanisms is difficult to determine. There
have been reports of new onset proteinuria including nephrotic range
proteinuria and rarely, de-novo glomerulonephritis (Dittrich, Schmaldienst et al.
2004) following conversion from CNI to SRL, consistent with a direct
nephrotoxic effect. SRL may cause proteinuria by glomerular injury including
direct damage to podocytes (Torras, Herrero-Fresneda et al. 2009), down
regulation of Vascular Endothelial Growth Factor - VEGF receptors (Izzedine,
Brocheriou et al. 2005) or loss of nephrin expression in the glomeruli (Biancone,
Bussolati et al. 2010). In animal models, SRL causes worsening of the
glomerular proteinuria due to podocyte injury but its anti-proliferative effects
may reduce tubulo-interstitial proteinuria by reducing the tubulo-interstitial
fibrosis (Torras, Herrero-Fresneda et al. 2009). However, there is conflicting
evidence about the effects of SRL on the tubular interstitium, with some studies
showing that SRL causes tubulo interstitial damage, preventing protein re-
absorption and leading to tubular proteinuria. (Straathof-Galema 2006).
The mechanism(s) by which SRL causes proteinuria in-vivo still remain to be
fully elucidated. It is notable that in some studies, a pre-conversion proteinuria
more than 800mg/day has been shown to predict the development of significant
proteinuria post conversion suggesting that pre-existing injury may predispose
to this complication (Diekmann, Budde et al. 2004; Diekmann 2008). However,
there are no studies in RTR, which have identified whether the proteinuria
secondary to SRL is glomerular, tubular or a combination of both. There are no
studies that have specifically examined the histological features or the dominant
65
damage to specific histological compartment that predict post conversion
proteinuria in RTRs. This research has studied pre conversion clinical and
histological factors that can predict proteinuria post conversion to SRL. The
results are reported in Chapter 3
66
1.7 Overview of Post Transplant Diabetes
Mellitus
1.7.1 Introduct ion
Post transplant diabetes mellitus (PTDM - the total of prevalent and incident
diabetes) is a frequent cause of complications after kidney transplantation and
has been associated with both poor graft outcomes and an increase in patient
cardiovascular morbidity and mortality (Crutchlow and Bloom 2007; Balla 2009).
Death with a functioning graft due to cardiovascular causes is a major cause of
graft loss. New onset diabetes after transplant (NODAT) is the term used to
identify incident diabetes occurring after renal transplantation in distinction to
those where diabetes predates the transplant. The incidence rates of NODAT in
RTRs are in the order of 10 to 40 %.(Balla 2009). The non modifiable risk
factors for NODAT include age, genetic background, family history of diabetes
and pre transplant impaired glucose tolerance. Modifiable risk factors include
obesity, viral infections and immunosuppressants (Shah, Kasravi et al. 2006;
Balla 2009) (Hur, Kim et al. 2007; Roland 2008; Sharif and Baboolal 2010).
NODAT, similar to Type 2 Diabetes Mellitus (T2DM) is characterized by chronic
hyperglycaemia, insulin resistance and relative insulin deficiency. Some
immunosuppressants commonly used after kidney transplantation such as
glucocorticoids, CNI and possibly mTOR-I have significant impact on glucose
metabolism, whilst others such as Azathioprine and Mycophenolic acid are
neutral. (Chow and Li 2008).
67
1.7.2 Steroids and PTDM:
Steroid treatment has been shown to be a determinant of insulin resistance post
transplantation. Glucocorticoids increase hepatic glucose production, decrease
peripheral insulin sensitivity, increase weight and increase insulin resistance
and increase the risk of NODAT. Dose reduction of steroids has been shown to
improve insulin resistance (Crutchlow and Bloom 2007) (Oterdoom, de Vries et
al. 2007; Roland 2008). However complete steroid withdrawal has not shown to
reduce the risk of NODAT compared with low dose prednisolone (Midtvedt,
Hjelmesaeth et al. 2004).
1.7.3 CNI and PTDM:
CyA and TAC impair insulin secretion and predispose to NODAT. Animal and
human studies have shown that CNI, especially TAC is directly toxic to the
pancreatic islet cells. In transplant recipients with hyperglycaemia, biopsy of the
pancreas has demonstrated that CNI exposure is associated with cytoplasmic
swelling and vacuolisation, implying toxicity to pancreatic β cells. This effect
was more pronounced in those patients receiving TAC compared with CyA.
TAC is more diabetogenic than CyA.(Drachenberg 1999). The hyperglycaemia
associated with TAC is also dose-dependent with higher doses causing more
pancreatic islet cell damage and the effect of CNI is independent and additive to
the effects of steroids causing PTDM. (Marchetti p 2000; Larsen 2006) (Heisel,
Heisel et al. 2004; Webster A 2005; Shah, Kasravi et al. 2006; Chadban 2008;
Roland 2008; Lee 2010; Morales 2010).
68
1.7.4 SRL and PTDM:
The role of SRL on glucose metabolism is uncertain. mTOR inhibition has been
reported to have variable effects on varied effect on glucose metabolism. To
understand how mTOR-I may impact upon glucose metabolism, it is essential to
review the mTOR pathway The mTOR pathway and the impact of mTOR
inhibition on glucose metabolism are described in detail in section 1.8
69
1.8. The mTOR pathw ay and mTOR inhibitors:
The mTOR protein is a serine-threonine kinase that is a part of a complex
intracellular signaling pathway which modulates cell growth and proliferation by
regulating protein synthesis. The upstream components of the signaling
pathway(s) activate the mTOR and the downstream components are effector
pathways of protein synthesis.(Hay and Sonenberg 2004; Hartford and Ratain
2007; Vodenik, Rovira et al. 2009) (Wullschleger, Loewith et al. 2006) (Fig 1.10)
1.8.1 The upstream components of mTOR
The upstream activators of the mTOR pathway include:
1. Insulin via the insulin receptor substrate -1 (IRS-1)
2. Amino acids via Phosphoinonositide-3 kinase (PI3K ) pathway
3. Other growth factors via the phospoinositide-3 kinase (PI3K) / Akt
pathway
4. Energy status of the cell via LKB1 and AMP- activated kinase (AMPK)
The upstream inhibitors of the mTOR
1. Stress signals such as DNA damage and hypoxia act via the TSC
pathway to cause inhibition of mTOR pathway
70
Fig 1.10 The mTOR pathway
IR
Insulin
IRS - 1
Class 1
PI 3K
Protein Kinase B
Akt
TSC 1 & 2
Rheb
mTOR/Raptor
Complex
S 6K4E – BPs 1
eIF 4E eIF 4B S6 e EF 2K
Protein Translation
Glucose
Energy Status
AMPK
Stress
(DNA damage
Hypoxia)
Growth Factors
& Harmones
mTOR/Rictor
Complex
GTP
Class 3
PI 3K
Amino Acids
Siro
limu
s &
Oth
er
mT
OR
In
hib
ito
rs
1
2
1
3
4
5
Positive Feedback
Inhibitory Feedback
Adapted from (Hartford and Ratain 2007), (Wullschleger, Loewith et al. 2006);
Fig 1.10 depicts the simplified version of the mTOR pathway. Pathways 1, 2, 3, 4 & 5 are the upstream regulators of the mTOR complex. Abbreviations: IR – Insulin receptor; IRS – Insulin receptor substrate; PI3K – Phosphoinositide – 3
Kinase; AMPK- adenosine Mono-phosphate Kinase; TSC – Tuberous Sclerosis gene; 4E-BP- Eukaryotic translation initiation factor – Binding protein; eIF-4E - Eukaryotic translation initiation factor; S6 k- S6 Kinase.
71
1.8.2 The dow nstream effectors of mTOR pathw ay
These pathways control the translation of specific mRNAs and protein
synthesis.
1. Phosphorylation of eukaryotic translation initiation factor – Binding
Protein1 (4E-BP1), which in turn inhibits the eIF-4E (Eukaryotic
translation initiation factor) – Binding Protein1, resulting in protein
synthesis
2. Phosphorylation and activation of S6 Kinase 1 (S6K1). This also has an
inhibitory feed back by inhibiting IRS-1
3. Activation of eukaryotic translation initiation factor 4 gamma (e-IF4G)
1 .8 .3 Posit ive and Negat ive Feed-back in mTOR
pathw ay:
mTOR signaling is also controlled by several negative and positive feed-back
loops. In the cell the mTOR forms a complex to either GBL protein (raptor
proteins-mTOR1) or the rictor proteins (mTOR2) (Hay and Sonenberg 2004;
Hartford and Ratain 2007; Varma 2008; Vodenik, Rovira et al. 2009). (Fig1.11)
The rictor complex is not inhibited by the mTOR-I and hence will not be
discussed further. The mTOR GBL / raptor complex receives input from the
upstream pathways like PI3K/Akt, TSC1/TSC2 and AMPK and acts through
downstream effectors S6 kinase (S6K, and translation factor inhibitor E4BP1).
This pathway is critical for cell growth, cell cycle progression and regulation of
organ size. (Hay and Sonenberg 2004) (Wullschleger, Loewith et al. 2006). SRL
72
and other mTOR-I bind to the FK506 intracellular binding protein (FKBP12). The
rapamycin – FKBP12 complex that is formed inhibits the mTOR by
destabilization of mTOR/raptor complex thereby interfering with the ability of the
mTOR / raptor complex to signal the downstream effectors. (Di Paolo,
Teutonico et al. 2006)
73
1.8.4 Role of mTOR signaling in glucose metabolism:
1.8.4 .1 Mechanisms by w hich mTOR activat ion may cause
glucose intolerance and insulin resistance:
Reduced function of Insulin Receptor (IR) and IR substrate (IRS)- 1contributes
to NIDDM in humans (White 1998). Nutritional excess, chronic hyperglycaemia
and/or obesity which are predisposing factors for the development of insulin
resistance and non-insulin-dependent diabetes mellitus (NIDDM) are also
potent activators of mTOR (pathways 2 & 4, Fig 1.10).
Over activation of mTOR pathways even in physiologic conditions involving
excess nutrient supply or hyper-insulinaemia promote phosphorylation of IRS-1
and IRS-2 that inhibit their function, promote degradation and possibly inhibit
protein translation. Hyperstimulation of downstream S6K pathway can stimulate
the inhibitory phosphorylation of IRS-1 (i.e. negative feedback) resulting in
insulin resistance.
The mTOR/GBL/raptor complex can be activated by insulin (via the insulin
receptor substrate IRS 1) or other growth factors via the PI3K/Akt complex
(Hartford and Ratain 2007). IRS 2 expression results in β-cell growth,
proliferation and survival, whereas reduction of IRS-2 causes beta cell
apoptosis. mTOR activation by hyperglycaemia results in the inhibition of IRS-2
and hence beta cell apoptosis. IRS-1 and PI3K play an important role insulin
regulated metabolic process and reduced function of insulin receptor and IRS-1
can contribute to non- insulin dependent diabetes mellitus in humans (White
1998)
74
Activation of mTOR complex by amino-acids and hyperglycaemia have all
shown to increase insulin resistance through PI3K /S6K pathway and
suppression of IRS-1-dependent PI3-kinase/Akt signaling. (Tremblay and
Marette 2001) (Tzatsos and Kandror 2006).
Chronic activation of mTOR by glucose (and/or IGF-1) in β-cells can lead to
increased Ser/Thr phosphorylation of IRS-2 that targets it for proteosomal
degradation, resulting in decreased IRS-2 expression and increased β-cell
apoptosis and lead to decrease β-cell mass and hence predispose to diabetes.
(Briaud, Dickson et al. 2005)
These studies demonstrate that mTOR activation results in altered signaling
that would promote or increase insulin resistance suggesting that mTOR
inhibition (by SRL) should improve insulin resistance. The complex pathways of
mTOR activation also has other feed-back mechanisms that modify these
effects and these changes may mean that mTOR inhibition have the capacity to
increase or reduce insulin resistance as discussed in the following sections.
75
1.8.4 .2 Mechanisms by w hich mTOR inhibit ion may cause
glucose intolerance and insulin resistance:
If mTOR stimulation increases insulin resistance then blocking the mTOR
complex with SRL should decrease insulin resistance. However, there are
mechanisms by which mTOR inhibition can cause insulin resistance. SRL can
worsen insulin resistance and cause diabetes by direct toxic effects pancreatic
islet cells, by inhibiting the growth cycle. (Bell 2003). Animal studies show,
mTOR / S6K pathway which is critical for beta cell adaptation to hyperglycaemia
when chronically inhibited by mTOR-I augment insulin resistance, beta cell
dysfunction and death. (Fraenkel 2008). Long term administration of SRL is
associated with hyper-insulinaemia and worsening glucose tolerance in the
mouse model of nutrition dependent T2DM. Decrease in insulin stimulated Akt
Phosphorylation and glucose transporter protein synthesis by chronic mTOR
inhibition has been suggested as one possible mechanism. (Chang 2009)
Studies in humans have also demonstrated that mTOR inhibition promotes
insulin resistance. Studies in RTR show that when challenged in vivo with
insulin, chronic inhibition of the mTOR/S6K pathway by SRL is associated with
an impaired activation of IRS-1, IRS-2 and AKT pathways causing insulin
resistance. (Di Paolo, Teutonico et al. 2006).
76
1.8.4 .3 Mechanisms by w hich mTOR Inhibit ion may improve
insulin resistance:
There is also evidence that mTOR inhibition can reduce insulin resistance.
Animal studies have shown that SRL prevents the onset of Type 1 diabetes
(Baeder, Sredy et al. 1992).
mTOR inhibition in humans by SRL promotes insulin mediated glucose uptake
by skeletal muscles in the setting of amino-acid excess. (Tremblay and Marette
2001). mTOR-I stimulate insulin mediated glucose uptake in humans in the
setting of hyper-insulinaemia and nutrient abundance, conditions which are
known to activate the mTOR /S6K pathway. (Krebs 2007). SRL can prevent
insulin resistance caused by chronic insulin treatment by preventing
i) reduction of IRS-1 protein levels
ii) down regulation of acute insulin-induced Protein Kinase B (PKB)
phosphorylation and
iii) down regulation of insulin stimulated glucose transport. (Berg
2002)
The pro inflammatory cytokine interleukin 6 (IL6) levels correlate with obesity,
insulin resistance and predict the development of T2DM. In vitro, mTOR
inhibition is shown to ameliorate IL-6 induced insulin resistance in liver cells
through a pathway independent of S6K1 protein (Kim 2008)
77
1.8.5 Conflict ing role of mTOR inhibit ion on glucose
metabolism:
Mechanisms described in sections 1.8.4.1 – 1.8.4.3 are mostly derived from in-
vitro studies, animal models or in vivo studies conducted under strict
experimental conditions using healthy volunteers. It is not clear whether same
mechanisms function in the setting of chronic mTOR inhibition in the post renal
transplantation setting, where multiple other factors affecting glucose and
insulin regulation are involved.
Chronic and long-term hyperglycaemia causes pancreatic beta cell apoptosis,
whereas in the short term hyperglycaemia promotes beta cell stimulation.
mTOR signaling has varied effects on glucose metabolism depending upon the
nutritional status, insulin and glycaemic status weight and adiposity and whether
the duration of mTOR inhibition is acute or chronic. Hence mTOR inhibition can
either alleviate or aggravate insulin resistance depending upon the baseline
conditions.
1.8.6 Effect of mTOR inhibit ion on glucose metabolism
in RTRs
The use of mTOR-I, especially in the setting of CNI withdrawal is anticipated to
have a significant impact upon glucose metabolism. Whether mTOR-I will
ameliorate or accentuate insulin resistance and its effect upon the cause or cure
NODAT, may depend upon the baseline characteristics of RTRs and additional
risk factors for NODAT and the duration and intensity of mTOR inhibition. Table
78
1.4 summarises the some of the available and most relevant studies and the
clinical implications of MTOR inhibitors on glucose metabolism in renal
transplant recipients and highlights the variability in the outcome measures
reported and conclusions obtained.
79
Table 1.4 SRL and PTDM in RTRS- Summary of published studies
Continued…
Study Study design Comparator NODAT as
primary
end point
Follow-up
(months)
SRL associated
with
PTDM/NODAT
Comments
Kahan1 Randomised Control
Trial;
CyA with SRL 2 or
5mg
No 12 NO NODAT not defined
Kasiske2 Retrospective
USRDS data
CyA, TAC& SRL No No Based on anti-diabetic
prescriptions
Teutonico3 Prospective CyA, TAC& SRL Yes 6 Yes
Araki4 Retrospective CyA, TAC& SRL Yes 39 No Used ADA guidelines
Arellano5 Retrospective None No 35 No NODAT not defined
80
1. (Kahan 2000) 2. (Kasiske, Snyder et al. 2003) 3. (Teutonico, Schena et al. 2005) 4. (Araki, Flechner et al. 2006) 5. (Arellano, Campistol et al. 2007) 6. (Veroux, Corona et al. 2008) 7. (Johnston, Rose et al. 2008) 8. (Laecke 2009)
Table 1.4 summarises studies which have evaluated post transplant diabetes in the setting of mTOR-I. Abbreviations: USRDS – United States Renal Data System; CyA- Cyclosporine A; TAC – Tacrolimus; SRL – Sirolimus; NODAT – New onset Diabetes after transplantation.
Study Study design Comparator NODAT as
primary end
point
Follow-up
(months)
SRL associated
with NODAT
Comments
Veroux6 Retrospective CyA, TAC& SRL Yes 21 No New onset fasting
hyperglycaemia was
defined as NODAT
Johnston7 Retrospective
USRDS
CyA, TAC& SRL Yes Yes Based on anti-diabetic
prescriptions
Van Laecke8 Retrospective CyA, TAC& SRL No 3 Yes Compared RTRs with
or without NODAT and
analysed risk factors
81
1.8.7 Limitat ions of the exist ing studies
These studies have several major limitations. Most are retrospective and the
criteria for diagnosing NODAT are not standardised. Use of other
immunosuppressive agents, steroids BMI and age (which are significant risk
factors for development of diabetes & insulin resistance) and target were SRL
levels not reported in many of the studies. (Pavlakis and Goldfarb-Rumyantzev
2008)
Therefore interpretation of these conflicting studies is difficult and the
conclusions drawn are open to criticism and require additional study. The
variability in analysis and reporting of these trials has influenced the design and
reporting of the prospective study of SRL conversion from a CNI upon
measures of glucose and insulin metabolism in RTRs described in Chapter 4 of
this thesis.
82
1.9 Post Transplant Dyslipidaemia:
1.9.1 Overview of the lipid metabolism:
The major lipids in human plasma are free fatty acids (FFA), Cholesterol (CHL),
Cholesterol-Esters (CE), triglycerides (TGL) and phospholipids (PL).
1.9.1 .1 Lipids:
All lipids except FFA are transported in the form of lipoproteins (LP) in plasma.
LP are spherical particles which are made of lipid and protein molecules. TGL
and CE (hydrophobic) which are non-polar lipids constitute the core of the
lipoproteins. PL and CHL (which are amphipathic) cover the surface of the
lipoprotein. Apolipoproteins are present on the surface of the lipoproteins.
Figure 1.11 shows the basic structure of a lipoprotein.
83
Fig 1.11 Basic Structure of Lipoprotein
Fig 1.11 depicts the simplified version of the structure of a lipoprotein. Phospholipids, cholesterol and Apo lipoproteins are present on the surface the lipo protein.;
1.9.1 .2 Lipoproteins:
Lipoproteins are classified into five major classes based on their densities:
Chylomicrons (CM) transport dietary cholesterol and triglycerides
from intestine to adipose tissue, muscles and liver. The major lipid is
TGL
Very Low Density Lipoprotein (VLDL) is synthesized in the liver from
endogenous lipids.
Intermediate Density Lipoprotein (IDL) is formed during lipolysis of
VLDL to LDL and contains relatively less triglyceride and more
cholesterol.
84
Low Density Lipoprotein (LDL) is synthesized in the liver and is the
major carrier of plasma cholesterol and has very low TGL content
High Density Lipoprotein (HDL) is synthesized in the liver and
intestine and plays a major role in mobilizing the cholesterol from the
peripheral tissues.(Rader D.J. 2012)
1.9.1 .3 Apolipoproteins:
Apolipoproteins are a class of proteins that are present on the surface of the
lipoproteins, provide stability to the lipoproteins and play a major role in their
metabolism.
Apolipoprotein A (Apo-A):
Apo A is found predominantly in HDL. Isoform includes AI, AII and AIV.
Apolipoproteins B (Apo-B):
Apo-B comprises of two isoforms, Apo B 100 rich in VLDL particles, VLDL
remnant particles and LDL particles and Apo B48 rich in Chylomicron and
Chylomicron- remnants. Because of the longer half life of LDL particle (3-4days)
compared with VLDL particles, LDL particles account for 90% of apo B in the
plasma. Apo B is synthesized by the liver and has the region that binds to the
LDL receptor (LDLr) (Sniderman, Couture et al. 2010) .
Apo C Lipoprotein (Apo- C):
Apo-C Synthesized by the liver and is present in all the lipoproteins except LDL.
85
Table 1.5 Function of Apo-lipoproteins
Apolipoprotein Lipoprotein Function
AI,II and IV HDL,CM AI is Structural component of
HDL; LCAT activator; Role of AII
and IV unknown
B48
B100
CM
VLDL, IDL, LDL
B48 is structural component of
CM;
B100 is the structural
component of VLDL,IDL,LDL;
Ligand for LDL receptor
CI,CII,CIII All lipoproteins
except LDL
C1 inhibits uptake of CM and
VLDL remnants by the liver;
C2: activator of LPL;
CIII Inhibitor of LPL
Apo E All lipoproteins
except LDL
Binds lipoproteins to the LDLr,
LRP and ApoE receptor
Table 1.5 explains the function of the different classes of apo lipoproteins; Adapted from (Rader D.J. 2012) Abbreviations: Apo – Apolipoproteins; LDL – Low density lipoprotein; VLDL – Very Low density lipoprotein; HDL – High density Lipoprotein; LDLr- LL receptor; LRP – LDLr relate protein
Apolipoprotein E (Apo-E):
Apo-E is synthesized by the liver and mediates the uptake of chylomicron, IDL,
VLDL and LDL by the liver by LDLr and LDLr related protein. It exists in 3 major
isoforms E2, E3 and E4. Absence of Apo E causes increase in plasma levels of
chylomicron and VLDL remnants and cause premature atherosclerosis. (Rader
D.J. 2012).
86
1.9.2 Lipid Transport:
Fig: 1.12 Endogenous and Exogenous Lipid Transport
Fig 1.12 shows the endogenous and exogenous pathways of the Lipid Cycle Adapted from: (Vaziri 2003); Abbreviations :C- cholesterol; CE- Cholesterol ester; TG- triglyceride; HDL- High density Lipoprotein; IDL
- Intermediate density Lipoprotein; VLDL- Very low density Lipoprotein; LDL- Low Density lipoprotein.
87
1.9.2 .1 Exogenous Pathw ay:
This is used to distribute dietary lipids to various parts of the body. (Fig 1.12)
Dietary triglyceride and cholesterol are incorporated into the chylomicrons and
are delivered to the adipose tissue and skeletal muscles in the form of FFA.
This is mediated by Apo C proteins. Lipoprotein lipase is the enzyme that splits
the TGL into glycerol and FFA. A deficiency in this enzyme causes
hypertriglyceridaemia, which is one of the mechanisms proposed for the
hypertriglyceridaemia associated with ESKD. (Vaziri 2003)
1.9.2 .2 Endogenous pathw ay:
The source of the lipids (TGL, CE &PL) is the liver rather than the intestine. (Fig
1.14) Liver incorporates these lipids to form VLDL incorporating Apo-B. The
hepatic lipoprotein lipase is the enzyme that splits the VLDL to release TGL to
the tissues. Deficiency of hepatic LPL causes a delay in the breakdown of VLDL
and can contribute to hypertriglyceridaemia.(Rader D.J. 2012)
88
1.9.3 Post Transplant Dyslipidaemia :
Dyslipidaemia is a major risk factor for cardio vascular events post renal
transplantation (Jardine, Fellstram et al. 2005). Immunosuppressants play a
major role in post transplant dyslipidaemia.(Kanbay, Yildirir et al. 2006). Steroid
induced dyslipidaemia is a consequence of weight gain, peripheral insulin
resistance and increased hepatic synthesis of VLDL. CyA causes
hypercholesterolaemia by reducing the activity of 7 alpha hydroxylase in the
liver which is the rate limiting step for cholesterol catabolism. CYA increases
LDL and TC levels by increasing hepatic lipase activity and decreased
lipoprotein lipase activity which results in the impaired clearance on LDL and
VLDL. (Vaziri, Liang et al. 2000). Tacrolimus does not significantly alter the lipid
or lipoprotein profile of renal transplant recipients. (Deleuze, Garrigue et al.
2006).
1.9.3 .1 Effect of MTOR inhibit ion on lipid metabolism in RTRs:
Several studies in RTRs have consistently shown that SRL induces mixed
hyper-cholesterolaemia and hypertriglyceridaemia. Dyslipidaemia is one of the
major side-effects that limits the use of mTOR-I in RTRs. Mixed dyslipidaemia is
also seen in heart and liver transplant recipients receiving mTOR-I.(Tenderich
2007; Watson, Gimson et al. 2007). The dyslipidaemia associated with mTOR-I
is neither dependant on the age, sex, pre-existing lipid profile of RTR nor the
dose of SRL. Some of the older and recent studies in RTRs demonstrating the
effect of SRL on lipid metabolism are presented in Table 1.6. It is worth noting
that the earlier studies used higher target levels of SRL compared with more
recent studies, but in both instances SRL was associated mixed dyslipidaemia.
89
Table 1.6 Studies in RTRs showing SRL induces dyslipidaemia
1. (Groth 1999) 2. (Kahan 2000).. 3. (Morrisett, Abdel-Fattah et al. 2002) 4. (Wlodarczyk, Vitko et al. 2005) 5. (Kasiske 2008)
Table 1.6 summarises some of the relevant studies in RTRs showing SL induced dyslipidaemias. Abbreviations; N/A – Information not available/ provided; TC – Total Cholesterol; TGL - Triglyceride
Study SRL level
ng/ml)
Increase TC Increase TGL Comments Statin Use
Groth-19991 15-30 Yes Yes RCT 40%
Kahan -20002 15-30 Yes Yes RCT 60%
Morisset-20023 N/A Yes Yes Prospective
SRL 10mg/day
N/A
Wlodarczyk-20054 N/A
Yes Yes Prospective
SRL+TAC(2doses 0.5
or 2mg/day)
N/A
Kasiske-20085 N/A Yes Yes Systematic Review Variable
90
1.9.3 .2 Limitat ions of exist ing studies describing the e ffect of
mTOR–I on lipid metabolism in RTRs:
Dyslipidaemia was not primary end-point in many of the trials and the majority
did not measure a more comprehensive analysis of lipid classes, sub fractions
or lipoproteins levels. Hence the mechanism of lipid abnormalities caused by
SRL has not been comprehensively studied.
Animal studies have shown mTOR stimulation promotes fat storage by
suppressing lipolysis and stimulation of de-novo lipogenesis and mTOR
inhibition stimulates lipolysis with elevated FFA levels (Chakrabarti, English et
al.). In vitro studies have demonstrated that mTOR inhibition causes reduced
lipid uptake and reduce fat cell number there by impairing the capacity of
adipose tissue for plasma clearance and reduction in Peroxisome proliferator –
activated receptor (PPAR- ) expression contributing to hypertriglyceridaemia.
(Houde, Brule et al. 2010). However studies exploring the mechanism of SRL
induced dyslipidaemia in RTR are very limited. The relevant studies are
discussed in detail in Chapter 4, section 4.4. Understanding how SRL affects
lipid metabolism in RTR will help us understand and manage this common and
important cardiovascular risk factor. The effect of SRL on cardiac risk factors in
RTRs and the effect of SRL conversion upon lipids and lipoproteins in routine
clinical (non-experimental) settings and their relationship with possible
mechanisms of SRL induced dyslipidaemias form part of the prospective study
and reported in Chapter 4.
91
1.10 Summary:
mTOR-I improve renal allograft function (GFR) and because of their anti-
proliferative properties have been effective in reducing malignancies post
transplantation. However some of their side-effects preclude their wide-spread
clinical use in RTR. Proteinuria and dyslipidaemia are two major side-effects of
mTOR-I. These are also important cardio-vascular risk factors. The effect of
mTOR-I upon PTDM, which is another significant cardiovascular risk factor, is
uncertain. Over recent years, much research has focused on the improvement
in the renal allograft function and the side-effects of mTOR-I in RTR. However
work in RTR, focusing on the predictors of improved renal allograft function, the
mechanisms of mTOR-I induced proteinuria, dyslipidaemia and glucose
intolerance, in the clinical setting are limited and should be the focus of future
studies.
92
Chapter 2 RESEARCH METHODOLOGY
93
2.1 Introduct ion:
In 2002, the renal unit at Royal Perth Hospital (RPH) commenced the use of the
mTOR-I SRL as a component of therapeutic agents for maintenance
immunosuppression. It is the policy of our centre to use SRL after an initial
period of CNI use and to biopsy the renal allograft prior to mTOR-I conversion in
order to determine that conversion is clinically appropriate and that when
relevant, the cause of allograft dysfunction is known to be potentially responsive
to elimination of CNI and introduction of an mTOR-I. RTRs with biopsy proven
CAN or normal renal histology were eligible for conversion, while patients with
active glomerular disease or rejection were excluded.
2.2 Study Hypotheses:
In RTR with biopsy confirmed CAN who are converted to SRL from a CNI
1. Baseline proteinuria independently determines the degree of change in renal
function (eGFR).
2. Evidence of allograft injury scored by renal histology will independently
predict the post-conversion change in eGFR and occurrence of proteinuria.
3. SRL has independent and potentially adverse effects upon glucose and lipid
metabolism.
94
2.3 Aims:
In a cohort of RTRs with CAN
1) To retrospectively
compare the graft function (eGFR) and proteinuria between
those converted to SRL and those maintained on a CNI.
determine the relationship between pre conversion renal
histology (as defined by BANFF criteria) and post conversion
eGFR and proteinuria
identify clinical and histological predictors of successful
conversion to SRL from a CNI.
2) To prospectively determine the effect of SRL upon glucose and lipid
metabolism at 3 and 12 months following conversion from a CNI
based immunosuppressive regimen.
95
2.4 Study Design
This study comprises a retrospective and a prospective component.
2.4.1 Inclusion Criteria:
Adult RTR with
Exposure to CNI (CyA or TAC) for at least 6 months post
transplantation.
Biopsy proven and/ or clinical CAN.
2 .4 .2 Exclusion Criteria
RTR who were converted to SRL due to indications other than CAN
BK virus nephropathy.
Non-skin malignancies
Salvage therapy for recurrent rejections
2 .4 .3 Study Populat ion
The biopsy database maintained at RPH renal unit was interrogated and RTRs
who underwent renal biopsy and had a diagnosis of CAN were identified.
Between January 2002 and December 2009, 97 RTRs had biopsy proven CAN
and satisfied the inclusion and exclusion criteria. From the pharmacy database
an additional 13 RTRS who were maintained on SRL and either had declined
biopsy or had a medical contra-indication but had a clinical scenario consistent
96
with the diagnosis of CAN and satisfied other inclusion criteria were also
identified. These 110 RTRs comprised the study population. The study
population was divided into a retrospective and a prospective group. (Fig 2.1)
Fig 2.1 Study Design
Fig: 2.1 Shows patient screening, allocation and study design Abbreviations: EVR – Everolimus: RTR – Renal Transplant Recipient: SG – Sirolimus Group: CG-
Control Group; CAN- Chronic allograft nephropathy
97
2.4.3 .1 Retrospective Study Group
85 RTRs satisfied the entry criteria and entered the retrospective study group.
51/ 85 were converted from a CNI based regimen to SRL (defined as Sirolimus
Group =SG) and the remaining 34 RTRs continued on CNI (defined as the
control group =CG). Conversion to SRL or continuing on a CNI was based on a
clinical decision by the treating nephrologist.
2 .4 .3 .2 Prospective Study Group:
Between June 2006 and December 2009 25 /110 RTRS, who were electively
converted to SRL and satisfied the selection criteria, entered the prospective
study (the prospective group = PG). Patients entering this study had to fulfill the
following additional inclusion criteria:
Not diabetic at the time of conversion.
Signed Informed consent to participate in the study (Appendix 2- PIS)
2.5 Study Methods:
Patients’ medical records were reviewed and the data shown in Table 2.1 were
collected for all 110 patients.
98
2.5.1 Data collect ion:
The following data were collected retrospectively by reviewing the patient
records.
Table 2.1 Data Collected in the Study Population
Data Characteristic Specific Data Collected
General Baseline demographics ( age , sex, date of
transplant)
Primary Renal Disease
Duration of dialysis
Immune mediators of
graft function
Number of previous transplants
HLA mis-matches
Maintenance Immunosuppression
Duration of CNI exposure
Non-immune mediators
of graft function
Donor source (cadaveric vs. live),
Donor age
Diabetic Status
Histology* Based on BANFF 2007 criteria
Clinical Parameters* Serial Blood pressure readings
Laboratory Parameters* Serum Creatinine
Urine protein: Creatinine Ratio
Table 2.1 shows the list of all the clinical, laboratory and histological data that was collected from the study population (retrospective and prospective studies) * Data not specifically measured for the purposes of this study, but collected from patient records
99
2.5.2 Prospect ive Group Study Protocol
Table 2.2 Additional data collected in the Prospective Group
Data
Characteristic
Additional Data Collected
Laboratory
parameters*
OGTT (glucose and insulin), FFA, HbA1c
Lipids: Total Cholesterol, HDL, LDL, Apo-
lipoprotein A& B,
Trough SRL levels
Hs CRP
Urinary Spot PCR/ ACR (Albumin: Creatinine
ratio
Clinical
Parameters*
Blood pressure, height and weight
Table 2.2 shows the list of all additional clinical and laboratory data that was collected only in the prospective group. *Collected for the purposes of this study at the time of conversion and at 3 and 12 months post conversion to SRL
Blood Pressure: Blood Pressure was measured with wall mounted aneroid
sphygmomanometer, in the clinic setting with patient sitting after 5 min of rest.
Cuff size adjusted to patient arm circumference.
Lipids: Samples were obtained after 12 hours fasting.
Spot PCR/ ACR: Random sample was collected.
100
2.6 Laboratory Methods:
All biochemical tests used were performed as routine clinical care using
standard methods at Path-west Laboratories. (Table 2.3)
Table 2.3 Laboratory Methods
Test
Methodology
Apo lipoprotein A1 The assay is done by BNII, an immunochemical reaction
using Siemens reagent (Siemens Healthcare Diagnostics
Inc. Newark, DE 19714 USA
Apo-lipoprotein B The assay is done by BNII, an immunochemical reaction
using Siemens reagent (Siemens Healthcare Diagnostics
Inc. Newark, DE 19714 USA
Total Cholesterol HITACHI 917 using cholesterol esterase (Roche
Diagnostics, Indianapolis, IN USA)
C-Peptide Immulite 2000 C-Peptide is a solid phase, two site
chemiluminescent immunometric assay
Creatinine By Creatinine Reagent (Abbott Diagnostics, Abbott
Laboratories, Abbott Park, IL 60064, USA)
FFA Cobas Mira Analyser (Roche Diagnostics, Basel,
Switzerland)
Glucose by HITACHI 917 using Hexokinase in Roche reagent
(Roche Diagnostics, Indianapolis, IN USA)
Hb A1C The HbA1c assay method is the Biorad Variant II
101
Test
Methodology
Haemoglobin A1c program. This is a HPLC method, using
a cation exchange cartridge. HbA1c is identified on the
basis of it's elution time, and labile A1c and carbamylated
haemoglobin elute earlier and do not interfere.
HDL/LDL by HITACHI 917 using PEG-modified enzymes and
dextran sulphate in Roche reagent (Roche Diagnostics,
Indianapolis, IN USA)
Insulin Immulite 2000 Insulin (Siemens Medical Solutions
Diagnostic, 5210 Pacific Concourse Drive, Los Angeles,
CA 90045-6900-USA)
SRL Micro particle enzyme immunoassay (MEIA) using the
Abbott Imx analyser (Abbott Diagnostics, NSW Australia)
Triglycerides by HITACHI 917 using lipoprotein lipase in Roche reagent
(Roche Diagnostics, Indianapolis, IN USA)
Urea by HITACHI 917 using urease in Roche reagent (Roche
Diagnostics, Indianapolis, IN USA)
Urine Albumin by HITACHI 917 using bromocresol green in Roche
reagent (Roche Diagnostics, Indianapolis, IN USA)
Urine protein by HITACHI 917 using Benzethonium chloride in Roche
reagent, turbidimetric method (Roche Diagnostics,
Indianapolis, IN USA)
Table 2.3 explains the laboratory methods and the reagents used for the tests used in this research. .All tests n the retrospective study are routine clinical care. Blood samples for the additional tests in the prospective study, were frozen stored under standard conditions at Royal Perth Hospital Biochemistry department and analyzed in batches.
102
2.7 Study Protocols
2.7.1 Standard Oral Glucose Tolerance Test:
This was performed according to WHO standards. Glucose and insulin levels
were measured after 8 hours of fasting, 60 and 120 min post intake of 75g of
glucose.(American Diabetes Association). Fasting C-peptide was also
measured.
2.7.2 Sirolimus conversion Protocol:
The CNI was stopped abruptly and replaced with SRL – there was no
overlapping period. Some patients were given 5 mg of SRL (loading dose) on
day 1 and subsequently 2mg per day. SRL levels were measured day 5 of
conversion aiming to achieve a trough level of 5-10 ng/ml. The dose of anti-
proliferative agents (Mycophenolic Acid “MPA” or Azathioprine “AZA”) and
steroids were maintained or altered according to the clinicians’ discretion.
2.7.3 MPA / AZA dosing
The RTRs were maintained on one of the two types of Mycophenolate
preparations commercially available [Mycophenolate Mofetil (MMF) - Cellcept®
Roche Pharma, Italy and Mycophenolate Sodium- Myfortic® Novartis]
depending upon the clinician preference. For the purposes of this study the
Myfortic doses were converted to equivalent MMF doses (dose equivalence
was calculated on the basis that 1000mg MMF = 720mg Myfortic) and reported
as the dose of MPA.
103
2.7.4 Stat in Use in PG
The RTRs who were maintained on a statin were not required to stop the statin
for the purposes of entering the prospective study. Though this is a limitation of
the research methodology, it was felt that in this high cardio-vascular risk
population, cessation of statin therapy for the purposes of clinical research was
inappropriate.
2.8 Follow - up
2 .8 .1 Retrospect ive Study
Patients were followed up until June 2011.
2.8.2 Prospect ive Study
12 months from time of entry.
2.9 End points:
2 .9 .1 Retrospect ive Study
eGFR and Proteinuria at census
Graft Failure
Conversion Failure
2.9.2 Prospect ive Study
The PG was followed up for 12 months post conversion and measures of
glucose and lipid metabolism were collected as detailed in table 2.2
104
2.10 Definit ions:
Time “0”:
Date of biopsy or date of conversion to SRL (if biopsy was not done).
eGFR:
The eGFR is calculated using the abbreviated MDRD Formula:
eGFR (ml/min/1.73m2) = 186 x (S Cr/88.4-1.154 x (Age)-0.203 x (0.742 if female)
We have chosen abbreviated MDRD to calculate the eGFR as this it is the most
validated equation of the commonly used equations which is routinely used by
our laboratories to report eGFR using serum creatinine.(Mariat 2005; The
Australasian Creatinine Consensus Working Group 2005; Jong 2006)
Proteinuria and albuminuria:
Measured as spot PCR and ACR mg/mmol.
Significant Proteinuria:
PCR >100mg/mmol. An arbitrary cut-off of 100 was chosen because this
approximates to 1g of proteinuria/ day, which most clinicians would consider
significant.
Nephrotic range Proteinuria:
PCR >300mg/mmol
Graft Failure:
Progressive deterioration in renal allograft function resulting in initiation of renal
replacement therapy.
105
Conversion Failure:
RTRs in SG who discontinued SRL due to adverse outcome or side-effects of
SRL therapy.
Diabetes:
Defined as a American Diabetic Association 2010 guidelines (American
Diabetes Association 2010)
Fasting blood glucose of ≥ 7 mmol/l or 2 hour plasma glucose of > 11.1 mmol.
Measures of Insulin resistance and Insulin Sensitivity:
The gold standard for determining the insulin sensitivity is the euglycaemic
hyperinsulinaemic glucose clamp technique, which was described by De Fronzo
et.al in 1979. This technique is cumbersome and expensive and cannot be used
in larger studies or in day-to- day clinical practice. Several surrogate estimates
of insulin sensitivity and insulin resistance based on the insulin and glucose
values obtained during standard oral glucose tolerance tests have been shown
to correlate well with the gold standard glucose clamp technique. OGTT derived
measures of Insulin sensitivity and Resistance, which include HOMA- IR score
HOMA-IR), Insulin Sensitivity Index (ISItx), Disposition Index (DI) and Metabolic
Clearance rate of Glucose have all been validated in renal transplant
population(Stumvoll, Mitrakou et al. 2000; Hjelmesæth 2001). These measures
have been used in this study to assess the changes in the glucose metabolism.
Insulin resistance was calculated using the Homeostasis Model
Assessment Score (HOMA-IR) = Ins0 (μmol x Glu0 (mmol/l) / 22.5);
106
HOMA –IR has been validated to mirror the glucose clamp technique in
the assessment of insulin resistance (Bonora, Targher et al. 2000).
A Value of >2.5 is defined as IR. Increasing values of the HOMA-IR score
indicate increasing insulin resistance.
OGTT derived Insulin sensitivity indices for transplantation (ISItx): is
calculated using the formula [0.028 - 0 0.0032 x BMI (kg/m2) –
0.0000645 X Ins 120 (pmol/l) – 0.00375 x Glu 120 (mmol/l)]. Decreasing
values means increasing insulin resistance.(Hjelmesæth 2001;
Teutonico, Schena et al. 2005) . Ins120 and Glu120 represent Insulin &
Glucose values measured at 2 hours during the standard OGTT.
Disposition index (DI) is as a measure for the ability of the pancreatic
beta cell to compensate for various degrees of insulin resistance and has
been studied in RTRs. (Teutonico, Schena et al. 2005) In
normoglycaemic individuals DI is a constant whereas it declines with the
development of glucose intolerance
DI = ISItx x Secr*1phaseInsulin release and ß – cell function is estimated using
BMI and insulin & glucose values at 0 and 60 min from OGTT
*Secr1phase = 728 +3.537 x Ins0 -120.3x Glu60 + 1.341 x Ins 60 + 21.27 x
BMI
Metabolic Clearance rate of Glucose (MCR) = 19.24 – (0.281x BMI) –
0.00498x Ins120) – (0.333xGlu120). The MCR values decline with
increasing insulin resistance.
The values ISItx, DI, MCR are generated for specific populations and do not
have a “normal reference range”. Comparisons of the values within the
population and interpretation based on the pre-post values are recommended.
107
2.11 Stat ist ical Methods:
2.11.1 Descript ive Stat ist ics:
All continuous variables were examined for normality of distribution using
histograms and box plots. Normally distributed data were reported as mean +/-
standard deviation for descriptive purposes and mean +/- standard error of the
mean for comparative purposes. Data that were not normally distributed were
logarithmically transformed and re-examined for normality of distribution. If
logarithmic transformation normalized the data distribution they were presented
as geometric mean (95% Confidence Intervals-CI). Non normalised data were
summarised using proportions, medians, and inter-quartile range (IQR) as
appropriate. Differences between baseline characteristics of the two groups
were investigated using T-Test for normally distributed data, Wilcoxon rank sum
tests for continuous variable for skewed distributions and chi square tests for
categorical variables. Fisher's exact test was performed when the number
(25% or more) of expected values in the table was small (<5).
Software used: SPSS v. 19 (Statistical Package for Social Sciences, Chicago,
IL, USA).
Additional statistical methods used to analyze the results in the retrospective
and prospective studies are described in subsequent sections.
2.11.2 Addit ional tests used in Retrospective Study:
Scatter plots for each outcome over time, overlaid with the Locally Weighted
Scatter plot Smoothing (LOWESS) fit were examined to determine if non linear
terms were required to model the data. The fit was assessed to be
108
predominantly linear. Longitudinal outcomes were assessed using Linear
Mixed Modeling with patient and time specified as random effects, generating
models with not only random intercepts but also random slopes. Interaction
terms between time and group were tested to investigate differences in
outcomes over time between CG and SG.
The relationships between baseline histological features and eGFR over time
were investigated using a manual backward step-wise linear mixed model
regression. Each variable was added to a “base” model that incorporated time,
group and their interaction, to determine if a main effects association was
present. Three way interactions between time group and the histological
variable and their lower order terms were then included to identify if the effects
varied over time between groups. Only significant interaction terms are
reported. All factors and their interaction terms with p values <0.1 were added
to the base model and a backward stepwise process was applied to determine
the final model. Variables that were not significant at 0.05 were removed one at
a time and the resulting model investigated for evidence of confounding. This
process continued until all variables in the model were either significant or
important in the model.
Subgroup analyses of outcomes at conversion or at census and their
association with baseline histological features were performed using Fisher’s
exact test due to the small samples involved. Fisher’s exact test was used to
determine the association between baseline histological features, proteinuria,
eGFR and post conversion eGFR and proteinuria measures.
109
Software used: StataCorp2011. Stata Statistical Software Release 12. College
station, TX : Stata Group LP
2.11.3 Addit ional Tests used in the Prospective Study:
The measures of glucose metabolism and lipid fractions were examined in the
same group on more than two separate occasions. Hence, within subjects
ANOVA (repeated measures of ANOVA) was used to compare the differences
among the means.
The Mauchly’s test of sphericity (testing homogeneity of covariance) was used
prior to conducting a within subjects ANOVA to lower the risk of Type 1 error
associated with multiple comparisons. If the data failed the sphericity test (P-
value <0.05), SPSS modified the ANOVA F test to make it more conservative
and less likely to reject the null hypothesis by reducing the degrees of freedom
around the numerator and the denominator of the F ratio. If the r- ANOVA was
significant, paired t-tests were performed with Bonferroni adjustment for 3-way
comparison and P-value of <0.02 was considered significant in the 3 way T-
tests.
Software used: SPSS v. 19 (Statistical Package for Social Sciences, Chicago,
IL, USA).
110
2.12 Ethica l issues:
This study has been approved by the Ethics committee of RPH. (Appendix 1)
2.13 Results:
The results of the retrospective study are presented in Chapter 3 and those of
the prospective study in chapter 4
111
Chapter 3 EVALUATION OF RENAL OUTCOMES IN
RENAL TRANSPLANT RECIPIENTS WITH
CHRONIC ALLOGRAFT DYSFUNCTION
FOLLOWING CONVERSION FROM
CALCINEURIN INHIBITORS TO
SIROLIMUS AND THE PREDICTORS OF
SUCCESSFUL CONVERSION
112
3.1 Hypothesis and Aims
3.1.1 Hypothesis:
In RTRs with biopsy confirmed CAN who are converted to SRL from CNI
Baseline proteinuria will independently determine the degree of change
in renal function (eGFR).
Evidence of allograft injury scored by renal histology will independently
predict the post-conversion change in eGFR and occurrence of
proteinuria.
3.1.2 Aims:
In a cohort of RTRs with CAN, to retrospectively
i. Examine the effects of SRL upon changes in graft function
(eGFR) and proteinuria and compare with those maintained
on a CNI.
ii. Study the relationship between pre conversion renal
histology (as defined by BANFF criteria) and post
conversion eGFR and proteinuria
iii. Identify clinical and histological predictors of successful
conversion to SRL from a CNI.
113
3.2 Methodology:
This has been described in detail in Chapter 2.
85 RTRS with clinical and/or biopsy proven CAN who satisfied the inclusion and
exclusion criteria were followed for a median 64 months (IQR 12-92). 51/85
were converted to SRL (defined as SG) and 34/85 continued on the CNI
(defined as CG). The decision to convert or maintain CNI use was based on a
clinical decision by the treating nephrologist.
3.2.1 Stat ist ical Methods
As explained in Chapter 2, data were summarised using proportions, medians
and IQR. Longitudinal outcomes were analysed using Linear Mixed Modeling.
The relationship between baseline histological features and eGFR over time
was investigated using a manual backward step-wise linear mixed model
regression. Sub-group analysis of PCR outcomes was done using Fisher’s
exact test due to the small samples involved.
114
3.3 Results
3.3.1 Baseline Clinical Characterist ics:
Table 3.1 Baseline Characteristics of RTRs in SG and CG
Characteristics SG CG P- value
Number of RTR@ 51 34 -
Male: Female (%)@ 63 : 37 58: 42 NS
Age at Tx (years)* 41.8+/- 12.6 42.7 +/- 11.5 0.6
Years on CNI ** 4.5 (1.4,9.1) 4.8 (3.1,11.4) 0.2
Pre-Transplant DM@ 9.8 % 8.8% 1.0
DM @ conversion** 22 % (11/51) 38 %(13/34) 0.14
eGFR ml/min** 48.3 (20,55) 40.8( 20,54) 0.06
Urinary PCR(mg/mmol) ** 14.5 .(9,26) 21.5 (6.5,57) 0.42
Use of Steroids (%)@ 70 71 1.0
Total HLA –Mismatch ** 3 (2,4) 3 (2,5) 0.7
*Values expressed as mean +/- Standard Deviation; **Values expressed as median (with 95% Confidence Intervals); @ Values expressed represent actual numbers P ≤ 0.05 is significant. Table 3.1 shows the differences in the baseline characteristics between the two groups. Abbreviations: SG – Sirolimus Group; CG – Control Group; RTR- Renal Transplant recipients; Tx –
Transplantation; CNI - Calcineurin Inhibitors; DM - Diabetes Mellitus; eGFR- estimated Glomerular Filtration Rate; PCR- Protein : Creatinine ratio
115
At baseline the two groups were similar for age, sex distribution, donor age,
HLA mismatches, duration of CNI exposure, prevalence of PTDM and steroid
use. Although not quite attaining statistical significance, the eGFR was lower in
the CG compared with the SG (41 vs. 48 ml/min P=0.06). Both groups had low
levels of proteinuria at baseline [14 (SG) vs. 21 (CG) mg/mmol]. Because of the
retrospective nature of the study ACR measurements were not routinely done
and hence not reported here. This has been addressed in the prospective study
presented in Chapter 4.
3.3.2 Baseline Histological Characterist ics:
41 / 51 of the SG and all 34 CG patients had undergone a renal biopsy prior to
conversion. These biopsies were coded based on the Banff 2007 criteria by a
single histo-pathologist, blinded to the clinical outcomes, for the purposes of this
research. Figures 3.1 to 3.4 depict the frequency distribution of the baseline
histological scores.
116
3.3.2 .1 Tubulo-interst it ia l injury: “ci” and “ct” scores
Fig 3.1 Baseline ‘ci” scores between SG and CG
Fig 3.1 shows the distribution of the severity of Chronic interstitial fibrosis- “ci “ scores as defined by BANFF 2007 between the Sirolimus Group –SG (blue) and Control Group- CG (red)
There was no difference in the chronic interstitial fibrosis – “ci” scores at
baseline between the groups. (Fig 3.1)
SG
CG
0
50
ci'0' ci'1'
ci'2' ci'3'
19
44
32
5
6
47
32
15
%
Chronic interstitial Fibrosis
Baseline 'ci' scores
117
Fig 3.2 Baseline ‘ct” scores between SG and CG
Fig 3.2 shows the distribution of the severity of Chronic tubular atrophy - “ct” scores as defined by BANFF 2007 between the Sirolimus Group –SG (blue) and Control Group - CG (red)
At baseline, the chronic tubular atrophy scores “ct” were different between the
groups with CG having more “ct3” than the SG. (Fig 3.2)
SG
CG
0
50
100
ct'0' ct'1'
ct'2' ct'3'
12
49
39
0
3
50
32
14
%
Chronic Tubular Atrophy
Baseline 'ct' scores
118
3.3.2.2 Vascular intimal injury: “cv” scores
The “cv” scores which indicate vascular damage due to immune mediated
mechanisms were not different between the groups at baseline. (Fig 3.3)
Fig 3.3 Baseline ‘‘cv” scores between SG and CG
Fig 3.3 shows the distribution of the severity of chronic vascular intimal thickening score- “cv” scores as defined by BANFF 2007 between the Sirolimus Group –SG (blue) and Control Group- CG (red)
SG
CG
0
20
40
60
0 1
2 3
32 51
15
2
24 46
21
9
%
Vascular intimal thickening
Baseline "cv' scores
119
3.3.2 .3 Tissue injury due to chronic CNI exposure: “ah” scores
Fig 3.4 Baseline ‘ah” scores between SG and CG
Fig 3.4 shows the distribution of the severity of arteriolar hyalinosis - “ah” scores as defined by BANFF 2007 between the Sirolimus Group –SG (blue) and Control Group- CG (red)
There was no difference in the “ah” scores, which denotes chronic vascular
injury secondary to CNI exposure (Fig 3.4) nor in the glomerulosclerosis score
which denotes overall damage to the glomerular capillaries.(data not shown)
In summary, the CG group had higher proportion grade 3 scores compared with
the SG. But except for the ‘ct’ findings, the ‘ci’, ‘cg’ and ‘ah’ scores did not reach
statistical significance.
SG
CG
0
50
ah'0' ah'1'
ah'2' ah'3'
41
27
17
15
32
24 35
9
%
Arteriolar Hyalinosis
Baseline 'ah' scores
120
3.3.3 CNI dosing in the CG
In response to the biopsy finings, in the CG 50 % (17/34) of the RTRS had the
dose of CNI reduced and in 1 patient the CNI was withdrawn. The mean dose
reduction was 28 % (Range 14-50%). 3 RTRs were switched to a different CNI
(CyA ↔ TAC). In the remaining 13 patients (34%), there was no change in the
CNI use.
3.3.4 SRL dosing in the SG:
In the SG the mean SRL level was 7.2 +/- 1.9ng/ml. The mean SRL dose was
1.54 +/-0.6 mg/day.
121
3.3.5 eGFR Outcomes:
Fig 3.5 Rate of decline in eGFR
Fig 3.5 shows the change in eGFR over time between the two groups; Sirolimus Group –SG (blue) and Control Group- CG (red); X axis: Time in Years Y axis: eGFR expressed in ml/min/1.73 m
2
In the SG the eGFR remained almost stable during the duration of follow-up at
47.8 ml/min compared with 48.3 ml/min at baseline. In the CG there was a
significant decline in the eGFR from a baseline value of 40.8 ml/min to 30.1
ml/min. (Fig 3.5)
The rate of decline of eGFR was significantly higher in the CG. The eGFR
declines at a rate of 0.0402 ml/week (2.1ml/year) in the CG compared with the
0.002ml/week (0.1 ml/year) in the SG. (P=0.02)
10
20
30
40
50
60
Pre
dic
ted e
GR
F (
fixe
d)
Conversion 1 2 3 4 5 6 7Years
SRL 95%CI Control 95%CI
Fitted SRL Fitted Control
122
3.3.5 .1 Linear Mixed- Modeling for Slope of eGFR
Using linear mixed modeling, rates of decline of eGFR were compared in both
the groups. The results are presented in Table 3.2
Table 3.2 Linear mixed modelling of eGFR comparison in the two
groups
eGFR Co-ef Std.Err P>IzI 95%Confidence
Interval
1.CG -7.5 4.0 0.061 -15.4 0.3
time -0.002* 0.01 0.84 -0.02 0.02
CG# c. time 1 -0.04* 0.017 0.02* -0.07 -0.01
Constant 48.31 2.5 0.000 45.93 54.15
*Indicates Slopes in both groups ** P value for difference in slopes P≤ 0.05 is significant Table 3.2 shows the results of linear mixed modeling comparing the two groups; Abbreviations: eGFR – estimated Glomerular Filtration Rate; Co-ef – Coefficient; Std.Err – Standard
Error; Sirolimus Group –SG and Control Group- CG . See section 3.4.1.1 for explanation.
Explanation for Table 3.2
The 1st row (1.CG) indicates that at time ‘0’ (baseline) there is on average a
difference between the CG and SG of 7.5 ml/min. The negative sign indicates
that the GFR in CG is less than the SG.
The 2nd row (time) indicates that when Control=0 (SG) the rate of change of
GFR over time is 0.002ml/min/week. The negative sign indicates that the GFR
declines with time and P value of 0.84 indicates that in the SG the value is not
significantly different from the baseline.
123
The 3rd row is the interaction term and represents the difference between the
slopes between the 2 groups. This term indicates that in the CG the GFR
declines by an additional 0.04 ml/min/week compared with the SG.
3.3.5 .2 Predicted eGFR at specific Time points:
The predicted eGFR values at specific time points and percentage of decline
from baseline in the two groups is shown in Table 3.3
Table 3.3Comparison of predicted eGFR at specific time-points
SG*
ml/min/1.73m2
CG*
ml/min/1.73m2
% decline from Baseline
SG CG
Baseline 48.30 40.79 - -
1 year 48.20 38.66 0.2 5.1
2 years 48.09 36.49 0.43 10.5
3 years 47.98 34.36 0.66 15.7
4 years 47.88 32.23 0.87 21.1
5 years 47.77 30.10 1.1 26.2
*Values expressed as Mean Table 3.3 shows the predicted values of eGFR at specific time points and the percentage of decline from the baseline value between the SG and CG Abbreviations: eGFR – estimated Glomerular Filtration Rate; Sirolimus Group –SG and Control Group-
CG
124
3.3.5 .3 Predictors of improvement in eGFR post conversion:
The significant difference in the slope of the eGFR over time indicates that the
SG has higher eGFR compared with CG over time. In order to evaluate the
factors that may predict the eGFR response to conversion, baseline histological
scores and clinical features that may predict post-conversion eGFR were then
analysed in a 3 –way interaction model, similar to the model explained in Table
3.2
The results of the univariate analysis for clinical factors that may predict
response are shown in Table 3.4 The interaction terms are not shown.
Table 3.4 Univariate analysis of pre-conversion clinical features
that may predict eGFR response post SRL conversion.
eGFR** Co-ef Std. Err P>IzI 95% Confidence
Interval
Duration on CNI 0.12 0.33 0.7 -0.53 0.76
Donor age -0.2 0.12 0.09 -0.43 0.04
PCR @Conversion -.0.04 0.017 0.02* -0.08 -0.008
P≤ 0.05 is significant. *Significant Predictor in univariate analysis ** Interaction terms not shown Table 3.4 shows the results of univariate analysis of the pre-conversion clinical features that may predict post SRL conversion eGFR response Abbreviations: eGFR – estimated Glomerular Filtration Rate; Co-ef – Coefficient; Std.Err – Standard
Error; Sirolimus Group –SG and Control Group- CG
The other factors that were examined in the model and were found to be not
significant included donor sex, age at transplant and degree of HLA mis-
matches (data not shown).
125
In univariate analysis examining the role of parameters of histological injury,
only the histological ‘ct 1-3’ scores, ‘cv 2-3’ scores and cg3 scores predicted the
post conversion eGFR. ‘ah3’ scores almost reached significance
(p=0.06).Baseline ‘ci1-2’, ‘ah1-2’ or ‘gl’ scores did were not predictive of the post
conversion outcomes. (Table 3.5)
126
Table 3.5 Univariate analysis of pre-conversion histological
Features that may predict eGFR response post SRL conversion.
Table 3.5 shows the results of univariate analysis of the pre-conversion histological features that may predict post SRL conversion eGFR response *Interaction terms not shown **Denotes significant P values. P≤ 0.05 is significant Abbreviations: eGFR – estimated Glomerular Filtration Rate; Co-ef – Coefficient; Std.Err – Standard
Error; Sirolimus Group –SG and Control Group- CG ; ct- Chronic tubular atrophy; cv- chronic vascular intimal thickening; cg-chronic glomerulopathy; ah- arteriolar hyalinosis; gl- glomerulosclerosis
eGFR*
Co-ef Std. Err P>IzI 95%Confidence
Interval
ct 1 -15.2 7.1 0.03** -29.1 -1.4
ct2 -31.1 7.2 <0.001** -45.4 -17.0
ct3 -36.3 10.1 <0.01** -56.2 -16.4
cv1 -6.1 4.8 0.2 -15.4 3.3
cv2 -15.7 6.1 0.01** -27.7 -3.4
cv3 -29 9.5 0.002** -47.7 -10.2
ci1 3.3 11.7 0.8 -19.6 26.3
ci2 -10.1 15.7 0.5 -40.9 20.9
ci3 -12.9 21.1 0.5 -54.3 28.4
cg1 0.1 5.8 0.9 -11.4 11.6
cg2 -10.5 7.4 0.15 -25.0 3.9
cg3 -21.7 9.4 0.02** -40.2 -3.2
ah1 -1.0 5.5 0.8 -11.8 9.7
ah2 -1.7 5.7 0.8 -12.6 9.2
ah3 -13.1 7.0 0.06 -26.9 0.8
gl <25% 8.3 9.8 0.4 -10.9 27.6
gl 26-50% 5.9 10.2 0.6 -14.0 25.8
gl >50% -11.6 11.6 0.3 -34.3 11.0
127
3.3.5 .4 Final mult ivariate model predict ing Post -conversion
eGFR:
Multi-variate analysis was performed in a stepwise backward elimination
(manual) as described in Chapter 2. The final model that best predicts eGFR is
shown in Table 3.6.
Table 3.6 Model predicting eGFR outcome
eGFR Co-ef P>IzI 95%Confidence
Interval
1 1.CG -7.5 0.1 -16.9 1.9
2 time 0.02 0.08 -0.003 0.05
3 CG#c.time1 -0.05 0.012* -0.87 0.01
4 PCR@conv -0.072 0.396 -0.24 0.09
5 CG#c.PCR@conv1 0.06 0.516 -0.11 0.22
6 c.time# c.PCR@conv1 -0.001 0.002* -0.002 .0005
7 CGl#c.time# .PCR@conv1 0.001 0.014* 0.0002 0.0002
8 Ct1 -13.4 0.05* -26.9 0.2
9 Ct2 -29.1 <0.001* -42.9 -15.8
0 Ct3 -25.7 0.016* -46.7 -4.7
11 cons 70.7 0.000* 57.9 83.6
P≤ 0.05 is significant *Denotes significant P values. Table 3.6 shows the results of the final multi-variate analysis of the pre-conversion clinical and histological features that may predict post SRL conversion eGFR response using step-wise backwards elimination analysis Abbreviations: eGFR – estimated Glomerular Filtration Rate; Co-ef – Coefficient; Std.Err – Standard
Error; Control Group- CG ; ct- Chronic tubular atrophy; cv- chronic vascular intimal thickening; cg-chronic glomerulopathy; ah- arteriolar hyalinosis; gl- glomerulosclerosis; PCR – Protein : Creatinine ratio; cons – constant; conv- conversion
128
Table 3.6 shows a three way interaction term between, groups, time and PCR.
The higher baseline PCR values are associated with greater decline in eGFR
over time in both groups. (Row 7: 3-way interaction between group, time and
PCR at conversion; Table 3.6). Rows 8-10 show that increasing severity of
tubular damage is associated with increasing decline of eGFR.
3.3.5 .5 Different re lat ionship betw een baseline PCR and eGFR in
both groups:
The 2-way interaction term (row 6) also indicates that the relationship between
baseline PCR and decline in eGFR is different in the SG compared with the CG
(P=0.002).
For a unit increment in PCR the eGFR declines at the rate of 0.07ml/min in the
SG compared with 0.002ml/min in the CG (P=0.02) (Data not shown). This
demonstrates that baseline proteinuria has differing effects upon the eGFR
outcome in both groups and that higher baseline proteinuria is associated with
greater decline in eGFR post conversion to SRL.
129
3.3.5 .6 Graft Loss:
In the SG the actual graft loss was 6% (3/51) compared with 24% (8/34) in CG.
P= 0.02. There were no deaths in either group during the follow-up.
The median time to graft loss in the SG was 3.1 +/- 2.1 years compared with 2.4
+/- 1.5 years in the CG. (P=0.01) (Fig 3.6)
Fig 3.6 Kaplan-Meier plot of Graft loss
Fig 3.6 shows the Kaplan Meier Plot of graft loss between the two groups. SG- Green; CG- blue Abbreviations: SG- Sirolimus Group; CG- Control Group
SG
P=0.01
CG
130
3.3.5 .7 Conversion Failure:
11 out of 54 (20%) ceased SRL due to a complication. The median time to
stopping therapy was 12 months (IQR 5-38). The majority stopped therapy
because of the development of nephrotic range proteinuria.
6 /11 (55%) ceased therapy due to nephrotic range proteinuria.
2/11 due to infection
1/11 due to edema without nephrotic range proteinuria
1/11 due to Transplant Glomerulopathy (DSA)
1/11 due to sexual dysfunction
All conversion failures were assumed to have reached the end point and Intent
to Treat (ITT) analysis was not used to analyze the graft loss.
The lower rates of graft loss in the SG should be interpreted in the context of
high rates of conversion failure.
131
3.3.6 Proteinuria Outcomes:
At baseline, the median proteinuria was similar between the SG and CG (14.5
vs. 21mg/mmol). After a median follow – up of 64 months, the increase in
proteinuria was similar in both groups. (Fig 3.7)
Fig 3.7 Changes in PCR CG vs. SG
Fig 3.7 shows the change in Proteinuria over time between the two groups; Sirolimus Group –SG (red) and Control Group- CG (blue) X axis: Time in years; Y axis: Proteinuria expressed as PCR mg/mmol
0
200
400
600
Pre
dic
ted P
CR
(fixe
d)
Conversion 1 2 3 4 5 6 7Years
Control 95%CI SRL 95%CI
Fitted Control Fitted SRL
132
3.3.6 .1 Graft loss censored Proteinuria
Proteinuria is a marker of allograft dysfunction and allograft injury, irrespective
of the aetiology of graft injury. In order to exclude this confounder that
heterogeneity of advanced allograft injury may mask more subtle changes of
proteinuria in those with less advanced injury, the proteinuria changes were
analysed after excluding those with graft loss in both the groups. (Fig 3.8)
Fig 3.8 Graft loss censored PCR Changes
Fig 3.8 shows the change in Proteinuria over time between the two groups after censoring for graft loss; Sirolimus Group –SG (red) and Control Group- CG (blue) X axis: Time in Years; Y axis: Proteinuria expressed as PCR mg/mmol
-500
0
500
100
0
Pre
dic
ted P
CR
(fixe
d)
Conversion 1 2 3 4 5 6 7Years
Control 95%CI SRL 95%CI
Fitted Control Fitted SRL
P=0.003
133
Table 3.7 Linear mixed modelling of graft loss censored
proteinuria
PCR Co-ef Std.Err P>IzI 95%Confidence
Interval
1.CG 11.6 19.1 0.5 -25.9 49.1
time 1.2 0.39 0.003* 0.39 1.94
CG# c. time 1 -1.1 0.65 0.083 -2.4 0.15
Constant 36.7 11.2 0.001 14.83 58.66
* P value for difference in proteinuria measured as PCR mg/mmol between the two groups P≤ 0.05 is significant Table 3.7 shows the results of linear mixed modeling of change in proteinuria between the groups after censoring for graft loss Abbreviations: PCR – Protein: Creatinine ratio; Co-ef – Coefficient; Std.Err – Standard Error; Sirolimus
Group –SG and Control Group- CG
Table 3.7 shows that the PCR values change with time and in the SG the PCR
increases by 1.2 mg/mmol/ week (P=0.003), compared with 0.1mg/mmol/week
in the CG. (The linear mixed modeling Table 3.7 is interpreted in the same way
as Table 3.2)
134
3.3.6 .2 Proteinuria in SG
Not only there was a significant increase in proteinuria following conversion to
SRL, 11% (6/54) developed nephrotic range proteinuria leading to cessation of
SRL therapy. Patients in the SG were further analysed to find out whether a
specific level of proteinuria at the time of conversion predicted the development
of significant proteinuria (defined as a PCR of>100mg/mmol) after conversion to
SRL. The numbers were too small to generate a ROC analysis, hence requiring
a different statistical approach. A PCR of 50mg/mmol which corresponds to
approximately to 0.5 g of proteinuria/day, was defined as a clinically acceptable
or safe level of proteinuria and used to divide the SG into two groups, based on
the proteinuria at the time of conversion. (Fig 3.9).
o Those with normal PCR at conversion(PCR<50mg/mmol)
o Those with elevated PCR at conversion (PCR>50mg/mmol)
135
Fig 3.9 Comparison of Baseline with post-conversion PCR
Fig 3.9 shows the change in Proteinuria over time between the two SG; Blue: PCR> 50mg/mmol at the time of conversion Green: PCR ≤ 50mg/mmol at the time of conversion X axis: Time in years; Y axis: Proteinuria expressed as PCR mg/mmol; Abbreviations: PCR – Protein: Creatinine ratio
RTRs with the higher baseline PCR (>50mg/mmol creatinine) had a statistically
significant increase in proteinuria compared with those RTRs who had
proteinuria (<50mg/mmol) at baseline (17 vs. 71 % P<0.0001; Fig. 3.10). The
odds of having a PCR greater than100mg/mmol post SRL conversion are 11.5
times (OR) higher than if the PCR at baseline is ≥50mg/mmol (P=0.015).
3.3.6 .3 Predictors of Post Conversion PCR:
The changes in PCR from baseline to the end of follow-up did not differ
between the two groups (Fig 3.7). Hence the linear mixed modeling that was
0
200
400
600
Pre
dic
ted S
RL
PC
R(f
ixe
d)
Conversion 1 2 3 4 5 6 7Years
PCR>50 95%CI PCR<=50 95%CI
Fitted PCR>50 Fitted PCR<=50
P<0.0001
136
used to predict eGFR changes could not be used to predict the PCR changes
post conversion. However, sub –group analysis was performed in the SG to
analyse the baseline histological and clinical features of those who developed
significant proteinuria post conversion, compared with those who did not. The
results are summarised in Table 3.8
3.3.6 .4 Baseline proteinuria predicts post conversion Proteinuria
Table 3.8 Baseline PCR correlates with post-conversion PCR
PCR at end-of follow-up (mg/mmol)
PCR
at
Conversion
(mg/mmol)
<50 51-100 >100
<50 66 17 17
51-100 40 0 60
>100 0 0 100
Table 3.8 shows the correlation between proteinuria at the time of conversion and post conversion proteinuria. Values are expressed as percentages of RTRs who had a PCR <50, 51-100, >100 at the time of conversion and post-conversion to SRL
Table 3.8 shows 17% of those with a PCR of <50mg/mmol, 60% of those with
50-100mg/mmol and 100% of those with a baseline PCR of 100mg/mmol
developed significant proteinuria after conversion to SRL.(P=0.03). Therefore
increasing levels of baseline proteinuria correlates with increased risk of post-
conversion proteinuria. A cut-off of 50mg/mmol appears to be a clinically useful
measure to guide an acceptable clinical decision point for conversion.
137
3.3.6 .5 Durat ion on CNI and post conversion proteinuria
The median time to conversion to SRL was 6.8 years (IQR 4.5 - 7.1) in those
who developed significant proteinuria post conversion compared with 2.6 years
(IQR 1.3- 8.7) in those who did not. Although this did not reach statistical
significance and could be due to the small sample size it suggests that later
conversions with an increased risk of chronic tubular injury due to CNI exposure
may be relevant and requires further exploration.
3.3.6 .6 Histological Injury Scores and post conversion
Proteinuria
Figures 3.11 to 3.14 show the baseline histological changes in those who
developed significant proteinuria and those who did not develop significant
proteinuria post SRL conversion. There is no difference in the histological
changes between the groups.
138
Fig 3.10 “ci” Changes at baseline
Fig 3.12 “ct” Changes at baseline
▀ Group 1: Significant Proteinuria post conversion
▀ Group2: Proteinuria <100mg/mmol post-conversion
Fig 3.10 -3.13 show the comparison of the histological changes between the groups who developed proteinuria after conversion with those who did not.
Fig 3.11 “cv” Changes at baseline
Fig 3.13 “ah” Changes at baseline
0
20
40
60
ci0 ci1 ci2
ci3
11
56
33
0
24 36 36
4
chronic interstitial fibrosis
Baseline "ci" scores : Gp 1 Vs.Gp 2
0
50
ct0 ct1 ct2
ct3
12 44 44
0
16
44 40
4
Axi
s Ti
tle
Baseline "ct" scores: Gp 1 vs.Gp 2
0
100
cv0 cv1 cv2 cv3
22 67
11 0
40 40 16 4 A
xis
Titl
e
Chronic Vascular Intimal Changes
Baseline "cv" scores : Gp 1 Vs.Gp 2
0
50
ah0 ah1 ah2 ah3
27 27 19 27
38 38 14 10
Axi
s Ti
tle
Arteriolar Hyalinosis
Baseline 'ah" scores: Gp 1 vs. Gp 2
P=0.1
139
3.3.7 Blood Pressure outcomes:
Systolic and diastolic blood pressure was similar in both groups and did not vary
during the follow-up. (Fig 3.14)
Fig 3.14 Comparison of Systolic Blood Pressures in both groups
Fig 3.14 shows the change in systolic blood pressure over time between the two groups; Sirolimus Group –SG (red) and Control Group- CG (blue) X axis: Time in years; Y axis: Diastolic BP expressed in mm of Mercury;
The mean systolic BP was 129 +/- 16 mmHg in the SG compared with 129 +/-
18 in the CG.
140
Fig 3.15 Comparison of Diastolic Blood pressure in both groups
Fig 3.15 shows the change in diastolic blood pressure over time between the two groups; Sirolimus Group –SG (red) and Control Group- CG (blue) X axis: Time in years; Y axis: Diastolic BP expressed in mm of Mercury;
The mean diastolic BP was 75 +/- 8 mmHg in the SG compared with 77 +/- 10
in the CG. (Fig 3.15)
3.3.7 .1 Use of anti-hypertensive agents:
In the SG the number of anti-hypertensive medications before conversion was
2.4 +/-1.4 compared with 2.3 +/- 1.5 in the CG. Post conversion these values
were 2.3 +/- 1.3 and 2.6 +/- 1.7; i.e. no change within or in between groups.
70
75
80
85
Pre
dic
ted D
iasto
lic B
P (
fixe
d)
Conversion 1 2 3 4 5 6 7Years
Control 95%CI SRL 95%CI
Fitted Control Fitted SRL
141
3.4 Discussion
3.4.1 eGFR outcomes:
This study demonstrates that in RTRs with CAN, conversion to SRL from a CNI
is associated with a significantly lower rate of decline in eGFR, in fact the eGFR
after a median of 5 years follow up was nearly identical to the baseline.
However in the group who did not receive SRL but continued or reduced the
CNI there was a continued loss of eGFR such that at the end of follow up, they
had lost an eGFR of 10ml/min. which equates to a 26% loss of eGFR from
baseline. (41 ml/min at the time of biopsy to 30ml/min).The absolute graft loss
was also statistically higher in the CG than the SG.
This difference in rate of decline in eGFR between the groups could be
attributed either to the beneficial effects of SRL upon the allograft or due to the
effects of CNI withdrawal or a combination of both. Pontrelli et al examined the
histological changes in renal allograft biopsies before and after SRL conversion
and reported that conversion to SRL decreases the progression of interstitial
fibrosis compared with continuation on a CNI (Pontrelli, Rossini et al. 2008).
The anti-proliferative properties of SRL could be a possible mechanism for
regression in the fibrosis scores. It is possible that reduction in fibrosis similar to
that in Pontrelli, occurred in the SG and /or fibrosis progressed in the CG, which
resulted in improved eGFR in the SG compared with the CG. Follow-up
examination of renal histology may have been informative but was not available
in the two groups studied here. Ruiz et.al compared implantation biopsies and
protocol biopsies at 1 year and showed that RTRs on SRL had less ‘ci’ and ‘ct’
scores than those maintained on a CNI. (Ruiz, Campistol et al. 2002). However
142
Servais et.al demonstrated that SRL conversion, though associated with
improvement in eGFR is not associated with regression of interstitial fibrosis.
(Servais, Toupance et al. 2009). Flechner et.al have shown that continuing CNI
either in the same of reduced dose is associated with persistent histological
damage compared with CNI withdrawal.(Flechner, Kobashigawa et al. 2008).
From the available literature it is unclear if the difference in the chronic
histological damage scores is due to the adverse effects of CNI continuation or
if SRL causes improvement in the histology scores. The current study does not
answer this question, but the multivariate model suggests that being on SRL is
a strong and independent predictor of lower rate of decline of eGFR. This could
be explored in further studies.
The other strong predictor of the difference in eGFR outcomes between the
groups was the degree of ‘ct’ changes and the multi- variate model predicted
that increasing severity of histological scores of ‘ct’ directly and independently
correlated with rate of loss of eGFR declined. The histological scores at
baseline were not significantly different in both groups, except for the chronic
tubular atrophy changes. The CG had higher proportion of ct3 compared to the
SG. (Fig 3.2) One might argue that the decline in eGFR in the CG was because
of the higher proportion of ct3 at baseline, although the distribution of grade 1
and grade 2 changes at conversion were similar in both groups. (50 vs. 49%
and 32 vs.39%. Figure3.2) The multivariate modeling takes in to account for this
variation and suggests that in addition to the ‘ct’ changes, SRL and baseline
proteinuria independently predicted the difference in outcome between the
groups.
The Banff Classification scores of ‘gl’ and ‘ah’ describe the non immune
mediated injury to the vasculature with ‘ah’ denoting exposure to CNI. These
143
scores were not different between the groups at baseline. Though changes of
arteriolar hyalinosis are associated with chronic CNI exposure, these changes
are not exclusive to CNI exposure. Strain upon the vascular endothelium due to
age related factors and hypertension could also produce similar histological
changes. In addition to arteriolar hyalinosis, CNI exposure can lead to chronic
tubular atrophy (Liptak and Ivanyi 2006). In the current study, the duration of
CNI exposure did not predict improvement in the eGFR either in uni or multi
variate analysis suggesting that neither the duration of CNI exposure nor the
histological changes of CNI toxicity, predict the eGFR outcomes post
conversion to SRL. Irrespective of the aetiology of the inciting insult chronic
tubulo-interstitial damage is associated with decline in GFR in kidney disease
in most states (Liu 2011). A similar mechanism could explain why chronic
tubular injury rather than glomerulosclerosis or arteriolar hyalinosis predicts
eGFR outcomes in the current study. This finding should also be interpreted
along with the outcomes of blood pressure, which remained stable during the
period of follow-up in both groups. ‘cg’ and ‘cv’ changes denote allograft injury
due to immune mediated processes. [In the current study both groups (CG and
SG) had similar immunological risk (degree of HLA mismatches) and the
baseline biopsy did not show any difference in these scores]. It is not surprising
that these scores did not predict the eGFR outcome.
Clinically, a lower baseline proteinuria was associated with a decreased rate of
decline in eGFR post conversion to SRL, as shown in the multi variate analysis.
It was also noted that the effect of proteinuria was different in the SG compared
with the CG. Higher baseline proteinuria was associated with decline in eGFR in
both groups. However, for a similar degree of baseline proteinuria the rate of
144
decline in eGFR was significantly higher in the SG. This association in addition
to the histological predictors suggests that conversion to an mTOR-I before
advanced tissue injury occurs may be beneficial.
Several studies have demonstrated improvement in eGFR after conversion to
SRL in renal and other solid organ transplantation (Table 1.3). The CONVERT
trial is one of the largest studies that examined 830 RTRs prospectively after
conversion to SRL from a CNI based regimen. They reported that an
eGFR>40ml/min at the time of conversion was associated with both better graft
and patient survival. The mean time to conversion from transplantation was 38
months and 90% of their study patients had a GFR of>40 ml/min at the time of
conversion. The recruitment of patients with eGFR <40ml/min was stopped
because in this group 17% reached the primary end-points of graft or patient
loss post conversion to SRL. They concluded that it was not safe to convert
patients with an eGFR <40/ml/min. In the post-hoc analysis they identified a
subset of patients with GFR ≥40ml/min and PCR <110mg/mmol who showed
improvement in eGFR at 24 months post conversion. (Schena and Pascoe
2009). Due to smaller patient numbers in the current study we did not stratify
the population according to baseline eGFR. However in the current study the
baseline proteinuria and histological features both independently correlate with
eGFR outcomes after conversion to SRL. The CONVERT trial reported only
baseline histological characteristics in their population, but did not correlate the
histological findings with the clinical outcomes post conversion. There is paucity
in the literature correlating the pre-conversion histological correlates with post
SRL conversion clinical outcomes. The current study demonstrated that
145
baseline histological features, in particular higher ‘ct’ scores predict post
conversion eGFR. This could be confirmed larger prospective randomised trials.
3.4.2 Proteinuria Outcomes:
This study demonstrated conversion to SRL is associated with increasing levels
or proteinuria and this is consistent with the published literature. (Table 1.3). In
the current study a pre-conversion proteinuria of >50 mg/mmol predicts
significant proteinuria post-conversion. This result is also consistent with other
published studies that have demonstrated that higher baseline proteinuria is a
predictor of poor outcome after conversion to SRL.(Diekmann, Budde et al.
2004) (Schena and Pascoe 2009).
In the current study, a greater proportion of patients with post-conversion
proteinuria had a longer duration of CNI exposure and also higher ah2/3 scores
(46% vs.24%; Fig 3.14). This observation was not statistically significant
perhaps due to small sample size. However this is a clinically important
question regarding the duration of exposure to a CNI, with higher histological
changes being associated with higher degrees of proteinuria post-conversion,
and certainly argues for earlier conversion from a CNI to mTOR-I to avoid this
problem. The current study was not adequately powered to fully answer this
question. The study also does not indicate whether the proteinuria secondary to
SRL is due to tubular or glomerular mechanisms and again was not designed to
explore the mechanisms of SRL induced proteinuria. In the prospective study
presented in Chapter 4 some aspects of this issue have been addressed.
146
3.4.3 Other Outcomes:
As mentioned earlier, blood pressure did not change and remained within
recommended ranges for optimal CKD disease management in both the
groups. This suggests that the improvement in eGFR was not related to
concurrent alteration in blood pressure or the use of reno-protective anti-
hypertensive agents which also did not differ between the groups before and
after conversion. The blood pressure outcomes reported in this study are
consistent with the findings in the CONVERT trial where no difference in the
systolic of diastolic BP between conversion and follow-up were noted.
The prevalence of the other side effects reported in this study such as edema,
Infections are similar to the published data. Dyslipidaemia is discussed in detail
in Chapter 4.
147
3.5 Strengths of the Study:
i. The study population reflects a real world situation where the decision to
convert to SRL after CNI exposure was based on a clinical decision
designed to improve renal function
ii. This study is the first in RTR to explore both the histological and clinical
features that may predict improvement in eGFR post conversion to an
mTOR-I. 90% of patients had a renal biopsy allowing accurate
histological coding of allograft injury.
iii. The study had a control group exposed to a CNI who were followed up
for a similar duration and had similar entry characteristics to the SRL
group.
iv. Long follow-up (median 5years)
v. The SRL dose and trough levels are consistent with current practice.
(Kasiske, B. L., B. Nashan, et al.2012). We used a standard dosing
protocol and target levels that are less than previously reported for
pivotal studies with this agent.
148
3.6 Study Limitat ions
3.6.1 Study Design
The conversion to SRL was non-randomised and unbalanced and is the major
limitation of this study.
3.6.2 Outcome measures
Follow-up renal biopsy to correlate post conversion clinical outcomes with
histological outcomes was not performed. Hence this study was unable to
demonstrate if the clinical improvement correlated with histological
improvement. Failure to distinguish between albuminuria and total proteinuria
limits patho-physiological interpretation of changes in proteinuria and site of
injury/action by SRL.
3.6.3 Stat ist ical Methods
Small sample size is a major limitation of this study with reduced power to
detect smaller differences between the groups. Results and interpretation of
certain outcome measures that showed clinically significant differences but
were not statistically significant (Proteinuria, duration of CNI exposure and
histological correlates) could have been due to small sample size. (Type-II
error). ITT analysis was not used.
149
3.7 Conclusion
In conclusion, this study has demonstrated that in RTR with CAN, conversion to
SRL compared with continuing on a CNI:
Stabilises eGFR and was associated with less graft loss over a median
of 5 years follow up.
The severity of chronic tubular injury assessed by renal histology predicts
post conversion eGFR
Proteinuria of greater than 0.5g/day at the time of conversion predicts
post conversion decline in eGFR and worsening of proteinuria.
Conversion to mTOR-I even at 5 years post-transplantation is beneficial.
The combination of renal histology and the clinical parameters of
proteinuria and eGFR provide substantial information to guide clinicians
in making informed clinical decisions regarding continued CNI use or
conversion to an mTOR-I.
150
3.8 Validat ion of Study Hypothesis:
My results confirm the study hypothesis that baseline proteinuria and renal
allograft injury scores independently predict the changes in eGFR and
proteinuria after conversion to SRL.
3.9 Direct ions for Future Research
The optimal timing for SRL conversion based upon a predictive model of
histology and clinical parameters such as eGFR and proteinuria requires
prospective study. This study suggests levels of proteinuria and histology are
useful predictors and should be tested in prospective analysis.
Studies to examine histological changes post mTOR-I conversion and
correlation with clinical and immunological outcomes could be designed.
The mechanisms of mTOR-I induced proteinuria remain to be elucidated and
especially how the changes in tubular injury patterns on histology noted here
associate with proteinuria. Studies designed to elucidate the pathogenesis of
proteinuria and correlating with histological measure could be undertaken. This
will also clarify the role of mTOR-I in proteinuric states both in transplant and
non-transplant population.
Whilst this research mainly focused on the non-immune mediators of CAN,
correlation with immune mediators of CAN and post transplant HLA and non-
HLA antibodies post conversion will help us understand the full spectrum of the
effects of mTOR-I in RTR. This could be explored in future studies.
151
Chapter 4 EFFECT OF MTOR INHIBITOR
SIROLIMUS UPON GLUCOSE & LIPID
METABOLISM IN STABLE RENAL
TRANSPLANT RECIPIENTS.
152
4.1 Hypothesis and Aims:
4.1.1 Hypothesis:
In RTR, SRL has independent and potentially adverse effects upon glucose and
lipid metabolism.
4.1.2 Aim:
To assess the effects of the mTOR inhibitor SRL upon measures of glucose and
lipid metabolism in stable RTR.
4.2 Methods:
This is described in detail in Chapter 2.
In brief, 25 RTR on a CNI based immunosuppressive regimen for a median of 7
years (8 receiving TAC and 17 CyA) who satisfied the inclusion and exclusion
criteria were electively converted to SRL and followed for 12 months.
Standard oral glucose tolerance test (OGTT), fasting lipids, free fatty acids, apo-
lipoproteins A1 and B, Hs - CRP, Body Mass Index (BMI),proteinuria and eGFR
were measured before conversion and at 3 &12 months post conversion to
SRL.
153
OGTT derived i) Insulin sensitivity Index (ISI TX), ii) HOMA- IR, iii) Metabolic
Clearance rate of glucose (MCR) iv) Disposition Index (DI) were calculated at
these time points.
4.2.1 Stat ist ical Methods:
This is described in detail in Chapter 2. Descriptive statistics were summarised
using median, inter-quartile range (IQR) and categorical outcomes were
summarised using frequency distribution. For 3 way comparisons, repeated
measures of ANOVA (r-ANOVA) were used to compare the differences among
the means. If the r- ANOVA was significant, paired t-tests were performed with
Bonferroni adjustment.
4.3 Results:
4.3.1 Entry Clinical Characterist ics
Baseline clinical and histological characteristics are shown in Tables 4.1
and 4.2
24/25 completed 12 months follow-up. One patient stopped SRL and
restarted CNI, 7 months after conversion, because of an acute rejection
episode.
12 months post conversion the mean SRL dosage was 1.59 +/- 0.8
mg/day, with a SRL trough level of 6.7 +/- 2.2ng/ml (therapeutic range =
5-15 ng/ml).
154
Table 4.1 Entry Clinical characteristics of PG
Baseline Characteristics
Age in years* 45 (40,60)
Sex (M:F)** 16 : 9
Duration of CNI exposure in months* 83.4 (32,141)
Pre - conversion CNI (TAC: CyA) **
Pre - conversion MPA : AZA**
Number maintained on Prednisolone**
8: 17
18: 7
17
*Values in median (IQR); ** Values represent actual numbers;
Table 4.1 describes the baseline clinical characteristics of all the RTRs in the prospective study Abbreviations: M – Male; F- Female; CNI – Calcineurin Inhibitor; TAC – Tacrolimus; CyA- Cyclosporin A;
MPA- Mycophenolic Acid; AZA- Azathioprine
155
4.3.2 Entry Histological Characterist ics:
21/25 patients had a renal biopsy prior to conversion. The majority of the
histological changes of allograft injury were coded as mild (grade 1). (Table 4.2)
Table 4.2 Entry Histological Characteristics
Grade 0 Grade1 Grade 2 Grade3
ci * 21 63 16 0
ct * 21 74 5 0
ah * 40 25 25 10
gl * 30 45 15 10
*Values expressed in% Table 4.2 describes the severity of the histological changes at the time of conversion to SRL; Histological changes are based upon Banff 2007 classification Abbreviations: ci- chronic interstitial fibrosis; ct – chronic tubular atrophy; ah- arteriolar hyalinosis; gl-
glomerulosclerosis;
156
4.3.3 Concomitant use of other immunosuppressants:
There was a significant reduction in the dose of the use of the ant-proliferative
agents (AZA & MPA) over the duration of the study however the use of steroids
did not differ significantly. The results are presented in Table 4.3
Table 4.3: Concomitant use of other immunosuppressants
Drug Dose Pre conversion Post-conversion P- Value
MPA* mg 1472+/-469 1208 +/-404 0.03
AZA* mg 68 +/- 19 39 +/- 20 0.005
Prednisolone*mg 5.9 +/- 2.9 4.3 +/-3 0.2
*Values in mg expressed as mean +/- SD; P ≤ 0.05 is significant Table 4.3 shows the prevalent use of other steroids and MPA/AZA before and at 1 year after conversion to SRL Abbreviations: MPA – Mycophenolic Acid; AZA- Azathioprine
157
4.3.4 Use of Antihypertensive agents:
Table 4.4: Use of anti-hypertensive agents and Prednisolone
before and after SRL conversion
Number of RTRS on medications Baseline
(At conversion)
N=25
12 months
(Post-conversion)
n=24
P value
No Anti-hypertensive medications * 3 4 1.0
ACE-I* 12 9 0.3
ARB* 14 18 0.3
β Blocker* 9 5 0.3
Prednisolone*
Diuretics*
17
11
15
13
0.7
0.7
Total anti hypertensive agents** 2 (1, 3.25) 2 (1, 3) 1.0
* Values represent actual number of patients. ** Values expressed as median and IQR P≤0.05 is considered significant Table 4.3 shows the prevalent use of anti-hypertensive agents before and at 1 year after conversion to SRL Abbreviations: ACE-I – Angiotensin Converting Enzyme Inhibitors; ARB – Angiotensin Receptor
blockers;
There was no significant difference in the number or type of anti hypertensive
agents used at baseline and at the completion of the study
158
4.3.5 Use of stat in:
21/25 RTR were maintained on a statin at the time of conversion.
The type and dose of statin used varied depending upon clinician discretion.
3/21 RTR who received statin therapy had their dose increased after conversion
to SRL and one had a fibrate added to statin therapy.
4.3.6 Renal Funct ion (eGFR):
The mean eGFR at the time of conversion was 49.2 +/- 3.8 ml/min. At 3 months
post conversion there was a significant improvement in the eGFR to 55.2 +/- 4.2
ml/min and the effects were sustained at 12 months (53.4 +/- 4.2 ml/min).
4.3.7 Proteinuria
There was a progressive increase in proteinuria measured both as PCR and
ACR. The PCR and ACR increased from 18.7 and 6.9mg/mmol from baseline to
56.7 and 45.2 mg/mmol respectively at 12months post conversion. (Fig 4.1)
159
Fig 4.1 Proteinuria Change post SRL conversion
Fig 4.1 shows the change in protein: Creatinine ratio (blue) and Albumin: Creatinine ratio (red) From baseline and at 3 and 12 months following conversion to SRL X axis: specific time points (Bas: At conversion; 3 and 12 months after conversion to SRL) Y Axis: PCR/ACR measured in mg/mmol Abbreviations: ACR - Albumin: Creatinine ratio; PCR - Protein: Creatinine ratio; Bas – Baseline; 3mon - 3
months post conversion; 12mon- 12 months post conversion
The increase in PCR/ ACR is statistically significant, however the increase is
not considered clinically significant because these levels of proteinuria remain
at low levels. In the cross-sectional study presented in chapter 3, the SG were
divided in two groups based on the baseline PCR of ≤ 50mg/mmol and a
baseline PCR >50mg/mmol was associated with a clinically significant post
conversion PCR. In the current study 1/25 had a baseline proteinuria >
50mg/mmol and at 12months 4 patients, (including the one patient with a PCR >
50 mg/mmol at baseline) had developed a clinically significant proteinuria
(previously defined as PCR >100mg/mmol).
ACR formed 38% of the total PCR at baseline and this value increased to 41%
at 3 months and 80% at 12 months. (ANOVA p-value 0.04). This denotes that
albuminuria increased at a higher proportion than the non-albumin proteinuria.
0
10
20
30
40
50
60
Bas 3mon 12mon
18.1
36.2
56.7
6.9
26.6
45.2
PCR
ACR
P<0.001
160
4.3.8 Effect of SRL conversion upon measures of
glucose metabolism:
4.3.8 .1 Fasting Glucose, Insulin, C-Peptide levels and Glycated
Haemoglobin:
In this study there was no difference in fasting glucose or insulin levels
measured at baseline when compared with those measured at 3 and 12 months
post conversion to SRL. (Table 4.5)
Table 4.5 Fasting Glucose, Insulin, C-peptide and Glycated Hb
levels post SRL conversion
Baseline 3 month 12 month ANOVA
P- Value
Referenc
e Range
Glucose (mmol/L)* 5.0+/-0.1 4.9 +/- 0.1 5.0 +/- 0.1 0.4 <5.6
Insulin mU/L* 8.7 +/- 1 9.0+/- 1.3 8.0 +/- 0.6 0.6 <12
C-Peptide nmol/L* 0.78 +/-0.7 0.72 +/-0.08 0.86+/-0.08 0.03 0.2-0.9
Glycated Hb (%)* 5.67+/- .72 5.6 +/-0.8 5.7 +/- 0.8 0.6 <6%
*Values expressed as mean ± SD P≤0.05 is significant Table 4.5 shows the change in the measures of glucose metabolism from baseline and at 3 and 12 months following conversion to SRL.
161
The mean value of C-Peptide decreased at 3 months compared with baseline
and subsequently increased at 12 months. Since the ANOVA p- value reached
statistical significance for C-peptide measurements, a 3-way, paired sample T-
test was performed. This test did not show any significant difference between
the baseline, 3 and 12 month values (Table 4.6).
Table 4.6 Changes in Fasting C-peptide levels with time
Baseline & 3 mon Baseline & 12 mon 3 & 12 mon
C-Peptide (P-value) 0.3 0.9 0.06
Table 4.6 shows the p values of the 3 way paired T-Test analysis; P ≤0.02 is significant Abbreviations: 3 & 12 mon - 3 and 12 months post conversion to SRL.
The C-peptide findings could be interpreted in two ways:
I. The changes in C-peptide levels were within the reference range and
moved in both directions hence the changes are probably not significant.
II. The small sample size of the study was not powered to detect the
changes in C-Peptide.
It is most likely these changes are not clinically significant particularly when
considered with the absence of change in other parameters of beta-cell function
and glucose and insulin disposition.
162
4.3.8 .2 Strat ificat ion based on ADA /WHO classificat ion of
disturbances in Glucose metabolism:
Based upon the ADA/ WHO classification of Diabetes no RTR was diabetic at
the time of entry and no patient developed new-onset diabetes after transplant
(NODAT) at 12 months.
However at baseline during the standard OGTT, 9/25 (36%) patients either had
a fasting glucose level between 5.6 and 6.9mmol/l or a 2 hour glucose of 7.8 -
11.0 mmol/l. which classifies them at increased risk of diabetes (American
Diabetes Association 2010). At 12 months there was a significant improvement
in these figures and only 3 of the original 9 remained at an increased risk of
diabetes (P= 0.02), while 6/9 resumed normoglycaemia. (Table 4.7) 16/25 had
normal OGTT values throughout.
Table 4.7 Improved Impaired Glucose tolerance and insulin
resistance post-conversion
Baseline 12 months P-value
IGT (ADA)* 9 3 0.02
HOMA-IR>2.5* 6 5 0.6
*Values represent actual number of patients P≤0.05 is significant Table 4.7 shows the changes in the measures of insulin resistance from baseline to 12 months following conversion to SRL. Abbreviations: IGT – Impaired Glucose Tolerance; ADA – American Diabetes Association; HOMA-IR –
Homeostasis Model assessment score for Insulin resistance
163
4.3.8 .3 OGTT derived measures of Insulin resistance and
sensit ivity:
HOMA –IR: There was no significant change in the HOMA scores from baseline
compared with 3 and 12 months post conversion. 6/25 patients had HOMA-IR
scores >2.5, (classifying them as IR) at baseline and 3 months and 5 were IR at
12 months. (Table 4.7)
Disposition Index: There was no change in the DI values measured at baseline
and those measured at 3 and 12 months post conversion. This indicates that
pancreatic beta cell function remains intact and is able to adequately and
appropriately respond to the glucose load. An Increase in the DI, which means
an increase in glucose intolerance due to reduced pancreatic β- cell function,
was not seen in our study. This supports the findings that changes in C-peptide
levels observed in this study over time are not significant. (Table 4.8)
Metabolic Clearance Rate of Glucose: This measures both pancreatic β cell
function and peripheral insulin resistance. Declining MCR values represent
increasing insulin resistance. In my study, there was no change in the metabolic
clearance rate of glucose compared with baseline and at 3 and 12 months post
conversion, indicating that there was no change in the insulin resistance. (Table
4.8)
164
Insulin Sensitivity Index ISItx:
There was no change in the ISItx indices from baseline to 3 and 12 months post
transplantation. ISItx is inversely related to IR and is consistent with the other
measures of IR reported. (Table 4.8)
Table 4.8 OGTT derived indices post SRL conversion
*Values expressed as mean ± SD P≤0.05 is significant Table 4.8 shows the changes in the measures of insulin sensitivity and resistance from baseline to 3 &12 months following conversion to SRL. (Section 4.3.8.3 for explanation) Abbreviations: ISI Tx – Insulin Sensitivity Index; DI- Disposition Index; MCR – Metabolic Clearance rate
of glucose; HOMA-IR –Homeostasis Model assessment score for Insulin resistance
Baseline 3month 12 month ANOVA
P-value
ISI TX * 0.09+/0.01 0.09+/- 0.01 0.09+/- 0.01 0.6
DI* 94.8 +/- 9.2 91.9 +/- *.4 93.8+/- 9.5 0.9
MCR (ml/kg/min)* 9.2 +/- 0.4 9.0 +/- 0.4 9.3 +/- 0.4 0.6
HOMA -IR* 1.97 +/-0. 2 2.0+/- 0.3 1.83 +/- 0.2 0.3
165
4.3.8 .4 BMI, Free Fatty Acid levels and Hs CRP
Table 4.9 Changes in BMI, Hs CRP & FFA post SRL conversion
*Values expressed as mean ± SD P≤0.05 is significant Table 4.9 shows the changes in BMI, FFA and the inflammatory marker Hs- CRP from baseline to 3 &12 months following conversion to SRL. (Section 4.3.8.3 for explanation) Abbreviations: BMI – Body Mass Index; FFA- Free Fatty Acid; Hs CRP- Highly Sensitive C- Reactive
Protein
There was no significant change in the BMI, or FFA between baseline and at 3
and 12 months. However the FFA levels did rise with time and the Hs CRP
showed a trend towards increasing from the baseline value. (Table 4.9) This
might suggest an effect of increased inflammation and a larger patient group
may have been informative.
Baseline 3 months 12 months ANOVA
P-Value
Reference
Range
BMI (kg/m2)* 25.2 +/- 0.9 25.4 +/- 0.7 25.7 +/- 0.8 0.12 20-25
FFA (mmol/l)* 0.21+/- .02 0.24 +/- 0.03 0.27 +/- 0.03 0.2 0.1-0.6
Hs CRP* 3.8 +/- 1 5.4 +/- 1.3 6.8 +/- 1.6 0.07 -
166
4.3.8 .5 Effect of SRL conversion upon lipid and lipoproteins:
There was a significant increase in the Total Cholesterol, Triglycerides, LDL-
Cholesterol and Apolipoprotein B levels evident at 3 and maintained at 12
months post conversion compared with baseline. There was no change in HDL-
cholesterol or Apolipoprotein A1 levels at 3 or 12 months after conversion
compared with baseline. The calculated non-HDL levels increased significantly
and Apo-A1 / Apo-B ratio decreased significantly, indicating that the changes in
the lipids after conversion are due to an increase in Apo-B containing non-HDL
lipid fractions. (Table 4.10)
Table 4.10 Changes in Lipids and Lipoproteins post SRL
Conversion
Baseline* 3months* 12 months * ANOVA
P-Value**
Reference
Range
TC (mmol/l)* 4.4+/- 0.2 5.0+/- 0.2 5.1+/-0.2 0.03 <5.5
LDL (mmol/l)* 2.2 +/- 0.2 2.6 +/- 0.2 2.9 +/- 0.2 0.003 <3.0
Apo-B (g/l)* 0.78+/-0.05 0.92 +/-0.05 0.92+/-0.05 <0.0001 <1
TGL(mmol/l)© 1.42 +/-0.2 1.95 +/-0.3 2.02 +/-0.3 0.008 <1.7
HDL (mmol/l)* 1.45 +/- 0.1 1.58 +/- 0.2 1.36 +/- 0.1 0.06 >1.0
Apo-A1 (g/l)* 1.53+/- 0.08 1.58 +/- 0.1 1.59 +/- 0.1 0.4 >1.15
Non-HDL Cholesterol* 2.9+/-0.2 3.4 +/- 0.2 3.7 +/-0.2 0.008 -
Apo A1/Apo B ratio* 2.1 +/-0.2 1.8 +/-0.2 1.8 +/- 0.1 <0.0001 -
*Values Expressed as Mean +/- SD; © Logarithmic Transformation ** P<0.05 is significant Table 4.10 shows the changes in Lipid sub fractions and lipo proteins from baseline to 3 &12 months following conversion to SRL. (Section 4.3.8.5 for explanation) Abbreviations: TC- Total Cholesterol; LDL – Low Density Lipoprotein; Apo-B- Apo-lipoprotein –B TGL-
Triglyceride; HDL- High density Lipoprotein; Apo-A1- Apo-lipoprotein A1;
167
4.3.8 .6 Changes in Lipids w ith t ime
Table 4.11 Significance of the difference among baseline, 3 and
12 month lipid and lipoprotein values
Base & 3 months Base & 12 months 3 &12 months
TC mmol/L 0.02 0.002* 0.8
LDL mmol/L 0.04 0.001* 0.6
Apo B g/L <0.001* 0.001* 1.0
TGL mmol/L 0.006* 0.007* 0.5
Non-HDL mmol/L 0.007* 0.03 0.2
Apo A1: Apo B <0.0001* 0.001* 0.7
P < 0.02 is statistically significant (Bonferroni Correction for 3-way analysis) *Denotes significant P- values. Table 4.11 shows the Bonferroni correction for the parameters with significant P-value in ANOVA showed in Table 4.10. (Section 4.3.8.6 for explanation) Abbreviations: TC- Total Cholesterol; LDL – Low Density Lipoprotein; Apo-B- Apo-lipoprotein –B TGL-
Triglyceride; HDL- High density Lipoprotein; Apo-A1- Apo-lipoprotein A1;
The changes in the lipid profile were evident by 3 months post conversion and
remained stable by 12 months. (Table 4.11)
168
4.4 Discussion:
4.4.1 eGFR
This study demonstrated that eGFR improved after conversion to SRL. The
improvement in eGFR was seen at 3 months and was sustained at 12 months.
This finding is consistent with other published studies such as CONVERT which
suggest if the eGFR at the time of conversion is >40ml/min there is an
improvement in eGFR post conversion.(Schena and Pascoe 2009). The results
of the retrospective study presented in Chapter 3 show that the eGFR stabilised
rather than improved following conversion. The results of the current
prospective study could be interpreted as an initial improvement in eGFR
following CNI withdrawal. In this study the duration of follow-up was 12 months.
Longer follow-up would help clarify whether the eGFR improvement is sustained
in this population. The current study population also had low PCR and “ct”
scores both of which were predictors of good outcome in the retrospective
study. This could be another possible explanation for the improved eGFR seen
in this cohort.
4.4.2 Proteinuria vs. a lbuminuria .
Following conversion to SRL, there was an increase in proteinuria (PCR and
ACR) with 4/25 patients (16%) of the patients developing significant proteinuria
at 12 months post conversion. The absolute values of both PCR and the ACR
increased and the albuminuria component of increased significantly compared
with the non-albumin proteinuria. There could be several explanations for this
differential change in the composition of urinary protein. The non-selective
169
nature of the proteinuria and increased in albuminuria could suggest either a
glomerular mechanism or altered tubular handling of protein reabsorption or
both. This could be an effect of the CNI withdrawal leading to arteriolar vaso-
dilatation and increased glomerular filtration pressure leading to proteinuria. If
this was only a CNI withdrawal effect one might expect an early increase and
then a stabilisation of proteinuria with time. The ACR changes were still
increasing at 12 months, suggesting that in addition to the effect of CNI
withdrawal other factors might influence these changes. These findings are
consistent with other published studies and as explained in section 1.6.3, SRL
induced proteinuria could be a result glomerular injury either due to podocyte
damage, increased VEGF expression or due to loss of nephrin expression.
Apart from suggesting that proteinuria following SRL conversion is non-selective
and may involve glomerular or tubular mechanisms, defining the mechanism of
proteinuria and correlating this to histological scores could be explored in future
studies.
170
4.4.3 Effect of SRL conversion upon Glucose and
Insulin metabolism:
The results of this study did not demonstrate that conversion from a CNI to SRL
had a significant adverse effect upon glucose metabolism. There was no
significant change in measures of insulin resistance, insulin secretion or
clearance. In fact conversion to SRL seemed beneficial. No patient developed
new onset diabetes and the majority of those with IGT at baseline showed
improvement. The concurrent use of other medications which may also
influence glucose metabolism (corticosteroids, beta-blockers, angiotensin
converting enzyme inhibitors or Angiotensin II receptor blockers) did not differ
between conversion and 12 months post conversion. None of the patients were
on oral hypoglycaemic agents. Overall, these results demonstrate that
cessation of CNI and SRL conversion did not have a significant impact upon
measures of glucose metabolism in RTRs and thus rejects the study hypothesis
that SRL conversion has adverse effects on glucose metabolism in RTRs.
Studies in RTRs examining the effects of SRL upon glucose metabolism have
demonstrated the variable effects of mTOR –I (Table 1.4). Most of these studies
were retrospective and measures of glucose metabolism or NODAT were not
the primary end-points and the limitations of these studies have been described
in detail in 1.4.6
In my study there was no change in glucose homeostasis following CNI
withdrawal and conversion to SRL. Both CNI inhibitors CyA and TAC decrease
insulin sensitivity and increase insulin resistance, although TAC has the more
171
potent effect upon glucose and insulin metabolism. There is evidence that
reducing or stopping these agents may improve glucose homeostasis.
Therefore, if SRL did not have any adverse impact on glucose homeostasis it is
possible there may be improvement in the measures of glucose sensitivity and
insulin resistance due to the CNI withdrawal. However this study demonstrates
that the effect of stopping CNI and immediately starting SRL did not adversely
affect the measures of glucose metabolism. A possible explanation is that SRL
has an effect similar to that of CNI on glucose homeostasis, at least in the
doses reported here. But this study was not designed to examine that
hypothesis which would require randomisation to these agents.
The only study to prospectively examine the impact of CNI withdrawal and
conversion to SRL upon glucose metabolism in RTRs was published by
Teutonico et.al in 2005. (Teutonico, Schena et al. 2005).They followed 26 RTRs
with biopsy proven CAN for 6 months after conversion to SRL from CyA a
median of 38 months after transplantation. They studied another 16 RTRs who
were treated with low dose TAC and SRL combination and studied the effect of
TAC withdrawal. In their study, 2 patients in each group developed PTDM as
defined by ADA criteria. In addition there was a decrease in the ISItx, MCR and
DI after conversion to SRL. They concluded that SRL increased peripheral
insulin resistance and impaired pancreatic beta cell response to glucose, which
resulted in deterioration in glucose homeostasis. (Teutonico, Schena et al.
2005) Compared with the study by Teutonico, where 4 people with IGT
developed NODAT, in my study 67% (6/9) with IGT, normalized after
conversion to SRL and none developed NODAT. There are several reasons
why the study results of Teutonico et al differ from those reported in this thesis.
172
1) The mean dose of SRL was much higher in the Teutonico study (3.8mg/day
vs. 1.6 mg/day) which led to significantly higher achieved trough levels (11.4
ng/dl vs.6.72 ng/dl).
2) Teutonico included more patients with impaired fasting glucose and IGT
(44% and 32 %) at entry compared with 28% and 8% respectively in this study.
The present cohort comprised RTRs with a reduced risk of developing diabetes
and glucose intolerance because we designed the study to exclude existing
diabetes at the time of conversion, although patients with IGT were not
excluded.
3) Teutonico et al may have included RTRs with more severe reduction in renal
function (Creatinine clearance 37ml/min) at conversion than was recorded in
this cohort (eGFR 50ml/min), although due to differing measures of reporting
kidney function (Cr Cl vs. eGFR) the magnitude of this difference is uncertain.
4) Conversion to mTOR-I occurred earlier in their study, at a median of 38 vs.
84 months. It is possible that the late conversion of this study, selected a cohort
of patients who had stable renal function and had inherently lesser risk for
developing diabetes.
5) The biopsy findings or proteinuria outcomes were not mentioned by
Teutonico. It is possible that their cohort consisted of patients with higher
histological grades of CAN and higher degrees of proteinuria in addition to the
reduced Creatinine Clearance.
6) Because reduced GFR and proteinuria are each independently associated
with insulin resistance this may also have contributed to the differing outcome
and conclusion. In RTRs, similar to the general population, IR is associated with
elevated FFA levels. (Armstrong 2005).Following mTOR-I conversion there was
a significant increase in both serum TGL and FFA in the Teutonico study in
173
contrast to this study where conversion to SRL was not associated with a
significant rise in FFA, suggesting that IR was more significant in the Teutonico
patients.
4.4.4 Effect of SRL conversion upon Lipid Metabolism :
Mixed Dyslipidaemia (hyper-cholesterolaemia and hyper-triglyceridaemia) are
adverse effects of SRL therapy (Kasiske 2008) and the severity of this is dose-
dependent.(Blum 2000). Early studies of SRL used much higher target levels of
SRL(15-30ng/ml) than in current use and were associated with often severe
hypercholesterolaemia and triglyceridaemia (Groth 1999), which was cited as
one of the major adverse effects of SRL therapy leading to cessation of the
drug.
The results reported in this thesis are consistent with these previous studies
confirming that SRL induces a mixed pattern of dyslipidaemia. The severity of
these changes may have be attenuated by targeting lower therapeutic levels
(<15ngml). In this study, SRL increased TGL, TC, LDL, non-HDL and Apo B
whilst HDL-cholesterol, Apo A1 and FFA levels did not significantly differ. The
Apo A1: Apo B ratio decreased significantly. These results are consistent with
the study hypothesis, that SRL has significant adverse effect upon lipid
metabolism in RTRs. A rise in Apo B containing lipids measured as an increase
in Total, LDL and non-HDL cholesterol, and the decrease in the ApoA1/Apo B
ratio are potentially atherogenic, and would normally be considered to increase
a patient’s cardiovascular risk. Indeed these changes occurred when the
majority of RTRs were already receiving a lipid modifying agent in recognition of
174
their heightened CVD risk. These changes are therefore potentially adverse and
would warrant additional therapeutic intervention in order to modify CVD risk.
4.4.4 .1 Mechanisms of SRL induced dyslipidaemia:
The pathogenesis of SRL induced dyslipidaemia is not fully elucidated and
studies in RTRs exploring the pathogenesis of SRL induced dyslipidaemias are
limited. Morrisett et.al prospectively examined 6 RTRs who were maintained on
10mg/day of SRL and found that plasma cholesterol levels increased by 50%,
TGL by 90%, Apo B by 28% and plasma FFA by 42.3% after 6 weeks of
treatment. They did not study insulin metabolism but speculated that increase in
TGL and VLDL could be due to an increase in hepatic synthesis of TGL with
increased secretion of VLDL. They concluded that SRL alters the insulin
signaling pathway to increase the plasma adipose tissue lipase activity and
decrease lipoprotein lipase activity, resulting in a mixed dyslipidaemia and an
increase in the FFA pool. (Morrisett, Abdel-Fattah et al. 2002; Morrisett, Abdel-
Fattah et al. 2003) .
The current study has demonstrated that SRL increased TC, LDL, non- HDL
and Apo-B levels without changing the HDL-cholesterol or Apo-A1
concentrations. In contrast to the studies by Morrisett et.al, there was a rise in
TGL without a change in measures of IR or a clear expansion of the plasma
FFA pool (although there was a trend towards an increase in FFA it did not
reach significance). This suggests that the rise in TGL may be caused by
mechanism(s) independent of insulin production or disposition.
175
There are several reasons why the more extreme changes in lipid measures
described by Morrisett et.al were not identified in the present study. These
include the very high dose or SRL (10mg/day) compared with only 1.6mg/day in
this study. In addition, a longer follow-up (12 months vs 6 weeks) and the high
prevalence of statin use in the current patient group are likely important. It is
known that SRL induced dyslipidaemia responds to statins and attenuates with
time (Kasiske 2008). In my study the rise in TC is best explained by an increase
in the non-HDL-cholesterol fractions (including measured LDL-cholesterol as
noted here) and an increase in Apo B particles. This could be due to either an
increase in production or a decreased clearance of Apo B containing lipids.
Hoogeveen et.al performed metabolic studies in 5 RTRs who received high
dose SRL (10mg/day). They performed a kinetic turn over study that showed
that an increase in VLDL-apoB100 concentrations was due to a significant
reduction in the fractional catabolic rate of VLDL- apoB100, rather than an
enhanced VLDL-apoB100 synthesis. They concluded that SRL induced
dyslipidaemia was due to the reduced catabolism of Apo-B containing
lipoproteins. (Hoogeveen, Ballantyne et al.) The findings of this thesis are
consistent with Hoogeveen et.al, that SRL increases Apo-B containing lipids.
Animal studies have demonstrated that chronic SRL treatment causes IR and
hyperlipidaemia by up-regulating the hepatic gluconeogenesis and impaired
lipid deposition in adipose tissue.(Houde, Brule et al. 2010).This effect is
mediated through the mTORC1/S6K1 pathway (Pathway 3 Fig 1.10) Using cell
cultures, Ma et.al have demonstrated that SRL decreases the LDLr expression
by hepatocytes and this could be a possible explanation of SRL induced
hypercholesterolaemia. (Ma, Ruan et al. 2007) LDL comprises 90% of Apo B
176
containing lipids and is metabolized via the LDLr expressed by the hepatocytes.
The current study confirmed a rise in Apo-B containing lipids post conversion to
SRL. Only Apo B100 and not 48 binds to the LDLr. So if SRL induces
hypercholesterolaemia by reduction of LDLr, Apo B100 should be selectively
increased. However, Apo B fractions were not measured in this study and
hence this mechanism could not be specifically tested.
Chakrabarti et.al, using a mouse model have shown that i) mTOR stimulation
promotes fat storage by suppressing lipolysis and promoting lipogenesis and ii)
mTOR inhibition by SRL promotes lipolysis and suggested this as a potential
mechanism of SRL induced hypertriglyceridaemia. In their study they used very
high doses of SRL (5mg/kg). (Chakrabarti, English et al. 2010). In my study
there was no significant evidence of enhanced lipolysis as measured by
expansion of the FFA pool following conversion to SRL, although there was a
small trend in this direction. Smaller patient numbers and the low levels of SRL
achieved in this study are possible explanations.
The other mechanism which could explain hypertriglyceridaemia without
involving insulin pathways, would be direct reduction in lipoprotein lipase activity
(LPL) or hepatic lipase activity (HPL) caused by SRL. However previous studies
have demonstrated that SRL treatment did not alter LPL or HPL levels.
(Hoogeveen, Ballantyne et al.). In this study we did not measure LPL or HPL so
cannot test this potential mechanism.
The increase in TGL may be due to the increase in VLDL secondary to the
decreased catabolism of Apo-B containing lipids. The increase in non-HDL
cholesterol in the current study suggests that this mechanism is possible and
177
this requires further exploration by direct measurements of lipid sub-fractions or
most accurately lipid turnover studies.
4.4.5 Cardio-vascular risk profile follow ing conversion
to SRL
The changes in the lipid profile following conversion to SRL points towards an
increased CV risk. RTRs have a 10-fold increased risk of cardiac deaths
compared with the general population. (Liefeldt and Budde 2010). Hence the
changes in the lipid fractions should be interpreted in the context of the patients
cardio-vascular risk profile. PTDM is associated with increased major coronary
artery events (MACE).(Lentine, Brennan et al. 2005) Holme et.al demonstrated
non-HDL cholesterol is associated with increased incidence of MACE in RTRs.
(Holme, Fellstrom et al. 2010) The other non-traditional risk factors that predict
MACE in RTR include graft dysfunction, graft loss albuminuria, non-albumin
proteinuria and inflammatory markers. (Jardine, Fellstram et al. 2005) (Lentine,
Brennan et al. 2005),.(Halimi, Matthias et al. 2007; Prasad, Bandukwala et al.
2009)
The current study has demonstrated that SRL increases non- HDL cholesterol,
HsCRP, albuminuria and non-albumin proteinuria and decreases the ApoA1 / B
ratio, which are known cardiac risk factors. This study has also demonstrated
that SRL improves eGFR and does not interfere with glucose and insulin
homeostasis. This latter effect of improved eGFR could possibly mitigate to
some extent the CV risk profile and offset the adverse effects of increased
dyslipidaemia and proteinuria upon MACE. Although the current study was not
178
designed to examine the cardiovascular disease risk, nevertheless these factors
remain important considerations for clinical implications of a choice to switch
from a CNI to an mTOR-I. Further this study did not examine other markers of
inflammation such as IL-6 and TNF α and markers of endothelial dysfunction
and hence assessing cardio-vascular risk profile post SRL conversion based
exclusively on the current study results is not possible. This could be explored
in future studies.
4.5 Strengths of the study
1) This study is the first in RTR to prospectively explore the effects of mTOR
inhibitors upon glucose and insulin metabolism and their relationship to the
derangements in lipid metabolism.
2) The study population represent a real world clinical group where the decision
to convert to SRL is based on a clinical indication to improve renal function and
improve allograft survival
3) The SRL dose and trough levels are consistent with current
recommendations and practice. (Kasiske, B. L., B. Nashan, et al.2012).
We used a standard dosing protocol and target levels that are less than
previously reported for pivotal studies with this agent.
179
4.6 Limitat ions of this Study:
4.6.1 Study Design:
The conversion to SRL was non-randomised and is a major limitation of this
study. The clinical decision to convert to SRL could have been influenced by the
clinicians pre-existing bias and only those patients with better graft function
were converted to SRL and this could have impacted upon the outcomes. The
majority of the patients were on a statin therapy and there was no control group.
4.6.2 Outcome measures:
Though the original aim was into include measures of waist: hip ratio because
this measure better correlates with IR, this measurement was obtained in only
50% of the study population at differing time points and hence the results are
not reported.
Lipid and Lipoprotein sub-fractions were not measured. Measuring VLDL and
also LPL/HPL would have further considered the mechanisms of lipid
derangements secondary to SRL.
Measuring Apo-B 100 and Apo B48 and Apo-C levels would have helped in
further elucidating the mechanisms lipid derangements.
180
4.6.3 Stat ist ical Methods
The small sample size is a major limitation of this study and restricts power to
show smaller differences, even when they may be present. Results and
interpretation of certain outcome measure that showed clinically significant
difference but were not statistically significant (C-peptide, HsCRP) could have
been due to small sample size.
Because multiple tests have been performed in this study there is a probability
that some of the tests have shown statistical significance when no such
relationship exists (type 1 error) although where possible we have adjusted for
multiple comparisons.
4.7 Conclusion
In conclusion, this study has demonstrated that in RTRs, conversion to SRL
based on current recommended SRL therapeutic levels in patient s receiving a
statin:
Improved eGFR
Increased proteinuria measured as total protein or albumin
Did not alter insulin or glucose disposition
Did not cause NODAT
Did not worsen markers of IR.
Caused mixed dyslipidaemia, but to a lesser extent than previously
published, which might be due to lower dose of SRL or concomitant
statin use or both
181
Dyslipidaemia secondary to SRL
o was associated with an increase in Apo- B containing lipids either
due to increased production or delayed catabolism
o was not associated with glucose or insulin disposition.
Impact upon CV risk profile is uncertain but potentially adverse.
Further studies are needed to clarify the mechanisms of SRL induced
dyslipidaemia, glucose and insulin metabolism and the impact of SRL
upon traditional and non-traditional cardio-vascular risk factors.
4.8 Validat ion of the study Hypothesis:
The study hypothesis that SRL has independent and potentially adverse
outcomes upon lipid metabolism in RTR is validated but the hypothesis that
SRL has adverse outcomes upon measures of glucose metabolism is rejected.
182
4.9 Direct ions for Future Research
Results of this study results have led to generating hypotheses and potential to
design future studies.
This small non-randomised study showed that SRL did not alter glucose or
insulin metabolism. A larger RCT comparing maintenance on a CNI with SRL
would be required to examine this more comprehensively.
This study has not suggested possible mechanisms by which SRL might
influence glucose homeostasis. mTOR inhibition can have varied effects upon
glucose homeostasis, depending upon the metabolic state of the individual and
chronicity of the inhibition. Future studies can be designed to identify the
characteristics of RTRs who are at higher risk of developing derangements in
glucose homeostasis following mTOR-I.
Studies examining the lipid sub-fractions; apo-lipoprotein sub-fractions and lipid
kinetics could be designed to explore the mechanisms of SRL induced
dyslipidaemia.
The mechanisms and origin of the increased proteinuria demonstrated require
additional evaluation.
Cardio-vascular risk profile of SRL needs to be explored further. This opens up
the possibility of multi-centre trials with hard cardio-vascular endpoints to
explore the association of MACE with traditional and non-traditional risk factors
in RTR.
Studies evaluating the impact of SRL upon inflammatory markers such as IL-6,
TNF α, markers of endothelial dysfunction and examining the association of
these with measures of glucose and Insulin metabolism will help us understand
the vascular risk profile of mTOR-I.
183
Bibliography
American Diabetes Association, A. (2010). "Diagnosis and Classification of
Diabetes Mellitus." Diabetes Care 33(Supplement 1): S62-S69.
ANZDATA (2010). The 33th Annual Report Australia and New Zealand Dialysis
and Transplant Registry, http://www.anzdata.org.au/ Adelaide, South
Australia, ANZDATA Registry.
Araki, M., S. Flechner, et al. (2006). "Posttransplant diabetes mellitus in kidney
transplant recipients receiving calcineurin or mTOR inhibitor drugs."
Transplantation 81(3): 335-341.
Arellano, E. M., J. M. Campistol, et al. (2007). "Sirolimus Monotherapy as
Maintenance Immunosuppression: Single-Center Experience in 50
Kidney Transplant Patients." Transplantation Proceedings 39(7): 2131-
2134.
Armstrong, K. A. e. a. (2005). "Free fatty Acids Are Associated withh Obesity,
Insulin Resistance ans Atherosclerosis in Renal Transplant Recipients."
Transplantation 80(7): 937-944.
Baeder, W. L., J. Sredy, et al. (1992). "Rapamycin prevents the onset of insulin-
dependent diabetes mellitus (IDDM) in NOD mice." Clinical and
experimental immunology 89(2): 174-178.
184
Balla (2009). "New-onset diabetes after transplantation: a review of recent
literature." Current opinion in organ transplantation 14(4): 375-379.
Barama, A. A. (2008). "Mechanisms and Management of Proteinuria in Kidney
Transplant Patients." Drugs Issue: Volume 68 Supplement 1, 2008, pp
33-39: 33-39.
Basile, D. (2004). "Rarefaction of peritubular capillaries following ischemic acute
renal failure: a potential factor predisposing to progressive nephropathy."
Current opinion in nephrology and hypertension 13(1): 1-7.
Bell (2003). "Rapamycin Has a Deleterious Effect on MIN-6 Cells and Rat and
Human Islets." Diabetes 52(11): 2731-2739.
Berg (2002). "Rapamycin partially prevents insulin resistance induced by
chronic insulin treatment." Biochemical and biophysical research
communications 293(3): 1021-1027.
Biancone, L., B. Bussolati, et al. (2010). "Loss of Nephrin Expression in
Glomeruli of Kidney-Transplanted Patients Under m-TOR Inhibitor
Therapy." American Journal of Transplantation 10(10): 2270-2278.
Blum, C. B. (2000). "Effects of Sirolimus on Lipids in Renal Allograft Recipients:
An Analysis Using the Framingham Risk Model." American journal of
transplantation 2: 551-559.
185
Bonora, E., G. Targher, et al. (2000). "Homeostasis model assessment closely
mirrors the glucose clamp technique in the assessment of insulin
sensitivity: studies in subjects with various degrees of glucose tolerance
and insulin sensitivity." Diabetes Care 23(1): 57-63.
Boratynska, M. e. a. (2006). "Conversion to Sirolimus From Cyclosporine May
Induce Nephrotic Proteinuria and Progressive Deterioration of Renal
Function In Chronic Allograft Nephropathy Patients." Transplantation
Proceedings 38: 101-104.
Briaud, I., L. Dickson, et al. (2005). "Insulin receptor substrate-2 proteasomal
degradation mediated by a mammalian target of rapamycin (mTOR)-
induced negative feedback down-regulates protein kinase B-mediated
signaling pathway in beta-cells." The Journal of biological chemistry
280(3): 2282-2293.
Budde, K., T. Becker, et al. (2011). "Everolimus-based, calcineurin-inhibitor-free
regimen in recipients of de-novo kidney transplants: an open-label,
randomised, controlled trial." The Lancet 377(9768): 837-847.
Campistol, J. M. B. I. (2009). "Chronic allograft nephropathy - a clinical
syndrome: early detection and the potential role of proliferation signal
inhibitors." Clinical transplantation 23: 769-777.
186
Chadban, S. (2008). "New-onset diabetes after transplantation—should it be a
factor in choosing an immunosuppressant regimen for kidney transplant
recipients." Nephrology Dialysis Transplantation 23(6): 1816-1818.
Chadban, S. J., E. M. Briganti, et al. (2003). "Prevalence of Kidney Damage in
Australian Adults: The AusDiab Kidney Study." Journal of the American
Society of Nephrology 14(suppl 2): S131-S138.
Chakrabarti, P., T. English, et al. (2010). "Mammalian Target of Rapamycin
Complex 1 Suppresses Lipolysis, Stimulates Lipogenesis, and Promotes
Fat Storage." Diabetes 59(4): 775-781.
Chang (2009). "Long-term Administration of Rapamycin Reduces Adiposity, but
Impairs Glucose Tolerance in High-Fat Diet-fed KK/HlJ Mice." Basic &
clinical pharmacology & toxicology 105(3): 188-198.
Chavalitdhamrong, D., J. Gill, et al. (2008). "Patient and graft outcomes from
deceased kidney donors age 70 years and older: an analysis of the
Organ Procurement Transplant Network/United Network of Organ
Sharing database." Transplantation 85(11): 1573-1579.
Chow, K. and P. Li (2008). "Review article: New-onset diabetes after
transplantation." Nephrology 13(8): 737-744.
187
Chung, J., S. Kil Park, et al. (2000). "Glomerulonephritis is the major cause of
proteinuria in renal transplant recipients: histopathologic findings of renal
allografts with proteinuria1." Clinical Transplantation 14(5): 499-504.
Cosio, F. G., Y. Kudva, et al. (2005). "New onset hyperglycemia and diabetes
are associated with increased cardiovascular risk after kidney
transplantation." 67(6): 2415-2421.
Cravedi, P., P. Ruggenenti, et al. (2010). "Sirolimus for calcineurin inhibitors in
organ transplantation: contra." Kidney Int 78(11): 1068-1074.
Crutchlow, M. F. M. F. and R. D. R. D. Bloom (2007). "Transplant-associated
hyperglycemia: a new look at an old problem." Clinical journal of the
American Society of Nephrology 2(2): 343-355.
Deleuze, S., V. Garrigue, et al. (2006). "New Onset Dyslipidemia After Renal
Transplantation: Is There a Difference Between Tacrolimus and
Cyclosporine?" Transplantation Proceedings 38(7): 2311-2313.
Demirci MS, T. H., Yılmaz F, Ertilav M, Asci G, Ozkahya M, Zeytinoglu and N.
D. A, Ok E (2010). "Risk factors and consequences of post-transplant
diabetes mellitus." Clinical transplantation 24(5): 170-177.
Dervaux, T., S. Caillard, et al. (2005). "Is sirolimus responsible for proteinuria?"
Transplantation Proceedings 37(6): 2828-2829.
188
Di Paolo, S., A. Teutonico, et al. (2006). "Chronic Inhibition of Mammalian
Target of Rapamycin Signaling Downregulates Insulin Receptor
Substrates 1 and 2 and AKT Activation: A Crossroad between Cancer
and Diabetes?" J Am Soc Nephrol 17(8): 2236-2244.
Diekmann (2008). "Conversion to sirolimus for chronic allograft dysfunction:
Long-term results confirm predictive value of proteinuria." Transplant
international 21(2): 152-155.
Diekmann, F., K. Budde, et al. (2004). "Predictors of success in conversion from
calcineurin inhibitor to sirolimus in chronic allograft dysfunction."
American Journal of Transplantation 4(11 ): 1869-1875.
Dittrich, E., S. Schmaldienst, et al. (2004). "Rapamycin-associated post-
transplantation glomerulonephritis and its remission after reintroduction
of calcineurin-inhibitor therapy." Transplant International 17(4): 215-220.
Dmitrewski, J. (2001). "Metabolic and hormonal Effects oftacrolimus(FK506) or
cyclosporin immunosuppression following renal transplantation."
Diabetes,Obesity and Metabolism 3: 287-292.
189
Drachenberg, C. B. K., David K.; Weir, Matthew R; Wiland, Ann; Fink, Jeffrey
C.; Bartlett, Stephen T; Cangro, Charles B.; Blahut, Steven;
Papadimitriou, John C (1999). "ISLET CELL DAMAGE ASSOCIATED
WITH TACROLIMUS AND CYCLOSPORINE: MORPHOLOGICAL
FEATURES IN PANCREAS ALLOGRAFT BIOPSIES AND CLINICAL
CORRELATION1." Transplantation 68(3): 396-402.
Dragun, D. (2008). "Humoral responses directed against non-human leukocyte
antigens in solid-organ transplantation." Transplantation 86(8): 1019-
1025.
Egbuna (2009). "Outcomes with conversion from calcineurin inhibitors to
sirolimus after renal transplantation in the context of steroid withdrawal or
steroid continuation " Transplantation 88(5): 684 -692.
Ekberg, H., H. Tedesco-Silva, et al. (2007). "Reduced Exposure to Calcineurin
Inhibitors in Renal Transplantation." New England Journal of Medicine
357(25): 2562-2575.
Ekberg, H., H. Tedesco-Silva, et al. (2007). "Reduced exposure to calcineurin
inhibitors in renal transplantation." New England Journal of Medicine,
The 357(25): 2562-2575.
Fernandez-Fresnedo, G., J. J. Plaza, et al. (2004). "Proteinuria: a new marker
of long-term graft and patient survival in kidney transplantation."
Nephrology Dialysis Transplantation 19(suppl 3): iii47-iii51.
190
Flechner (2008). "Reviewing the Evidence for De Novo Immunosuppression
With Sirolimus." Transplantation proceedings 40(10): S25-S28.
Flechner, S. M., D. Goldfarb, et al. (2002). "Kidney transplantation without
calcineurin inhibitor drugs: a prospective, randomized trial of sirolimus
versus cyclosporine." Transplantation 74(8): 1070-1076.
Flechner, S. M., J. Kobashigawa, et al. (2008). "Calcineurin inhibitor-sparing
regimens in solid organ transplantation: Focus on improving renal
function and nephrotoxicity
" Clinical transplantation 22(1): 1-15.
Fraenkel (2008). "mTOR Inhibition by Rapamycin Prevents [beta]-Cell
Adaptation to Hyperglycemia and Exacerbates the Metabolic State in
Type 2 Diabetes." Diabetes 57(4): 945-957.
Fuggle, S. V., J. E. Allen, et al. (2010). "Factors affecting graft and patient
survival after live donor kidney transplantation in the UK."
Transplantation 89(6): 694-701.
Gentil, M. A., M. P. Alcaide, et al. (2003). "Impact of delayed graft function on
cadaveric kidney transplant outcome." Transplantation Proceedings
35(2): 689-691.
191
Grinyo, J. M., N. Saval, et al. Clinical assessment and determinants of chronic
allograft nephropathy in maintenance renal transplant patients,
Nephrology Dialysis Transplantation. 26 (11) (pp 3750-3755), 2011. Date
of Publication: November 2011.
Groth, C. G. B., Lars; Morales, José-Maria; Calne, Roy; Kreis, Henri; Lang,
Philippe; Touraine, Jean-Louis0; Claesson, Kerstin; Campistol, Josep M.;
Durand, Dominique; Wramner, Lars; Brattström, Christina; Charpentier,
Bernard (1999). "SIROLIMUS (RAPAMYCIN)-BASED THERAPY IN
HUMAN RENAL TRANSPLANTATION: Similar Efficacy and Different
Toxicity Compared with Cyclosporine1." Transplantation 67(7): 1036-
1042.
Halimi, J.-M., B. Matthias, et al. (2007). "Respective predictive role of urinary
albumin excretion and nonalbumin proteinuria on graft loss and death in
renal transplant recipients." American Journal of Transplantation 7(12):
2775-2781.
Haller, M. and R. Oberbauer (2009). "Calcineurin inhibitor minimization,
withdrawal and avoidance protocols after kidney transplantation."
Transplant International 22(1): 69-77.
Halloran, P. F. (2004). "Immunosuppressive Drugs for Kidney Transplantation."
New England Journal of Medicine: 2715-2729.
192
Halloran, P. F., A. Melk, et al. (1999). "Rethinking chronic allograft nephropathy:
the concept of accelerated senescence." Journal of the American Society
of Nephrology 10(1): 167-181.
Halloran, P. F., A. Melk, et al. (1999). "Rethinking Chronic Allograft
Nephropathy: The Concept of AcceleratedSenescence." Journal of the
American Society of Nephrology 10(1): 167-181.
Hartford, C. M. and M. J. Ratain (2007). "Rapamycin: Something Old,
Something New, Sometimes Borrowed and Now Renewed." Clin
Pharmacol Ther 82(4): 381-388.
Hay, N. and N. Sonenberg (2004). "Upstream and downstream of mTOR."
Genes & development 18(16): 1926-1945.
Heisel, O., R. Heisel, et al. (2004). "New Onset Diabetes Mellitus in Patients
Receiving Calcineurin Inhibitors: A Systematic Review and Meta-
Analysis." American Journal of Transplantation 4(4): 583-595.
Helanterä, I., F. Ortiz, et al. (2009). "Impact of glucose metabolism
abnormalities on histopathological changes in kidney transplant protocol
biopsies." Transplant International 23(4): 374-381.
193
Hjelmesæth, J. M., Karsten ; Jenssen, Trond ; Hartmann, Anders (2001).
"Insulin resistance after renal transplantation: impact of
immunosuppressive and antihypertensive therapy." Diabetes care
24(12): 2121-2126.
Holme, I., B. Fellstrom, et al. (2010). "Comparison of predictive ability of
lipoprotein components to that of traditional risk factors of coronary
events in renal transplant recipients." Atherosclerosis 208(1): 234-239.
Hoogeveen, R. C., C. M. Ballantyne, et al. Effect of sirolimus on the metabolism
of apoB100-containing lipoproteins in renal transplant patients,
Transplantation. 72 (7) (pp 1244-1250), 2001. Date of Publication: 15 Oct
2001.
Houde, V. P., S. Brule, et al. (2010). "Chronic Rapamycin Treatment Causes
Glucose Intolerance and Hyperlipidemia by Upregulating Hepatic
Gluconeogenesis and Impairing Lipid Deposition in Adipose Tissue."
Diabetes 59(6): 1338-1348.
Hume, D. M., J. P. Merrill, et al. Experiences with renal homotransplantation in
the human: report of nine cases, The Journal of clinical investigation. 34
(2) (pp 327-382), 1955. Date of Publication: Feb 1955.
Hume, D. M., J. P. Merrill, et al. (1955). "EXPERIENCES WITH RENAL
HOMOTRANSPLANTATION IN THE HUMAN: REPORT OF NINE
CASES 1." The Journal of Clinical Investigation 34(2): 327-382.
194
Hur, K., M. Kim, et al. (2007). "Risk factors associated with the onset and
progression of posttransplantation diabetes in renal allograft recipients."
Diabetes Care 30(3): 609-615.
Izzedine, H., I. Brocheriou, et al. (2005). "Post-transplantation proteinuria and
sirolimus." The New England Journal Of Medicine 353(19): 2088-2089.
Jardine, A. G., B. Fellstram, et al. (2005). "Cardiovascular Risk and Renal
Transplantation: Post Hoc Analyses of the Assessment of Lescol in
Renal Transplantation (ALERT) Study." American Journal of Kidney
Diseases 46(3): 529-536.
Jardine, A. G., R. S. Gaston, et al. (2011). "Prevention of cardiovascular
disease in adult recipients of kidney transplants." The Lancet 378(9800):
1419-1427.
Jeannet, M., V. W. Pinn, et al. Humoral antibodies in renal allotransplantation in
man, The New England journal of medicine. 282 (3) (pp 111-117), 1970.
Date of Publication: 15 Jan 1970.
Johnston, O., C. L. Rose, et al. (2008). "Sirolimus Is Associated with New-Onset
Diabetes in Kidney Transplant Recipients." J Am Soc Nephrol 19(7):
1411-1418.
195
Jong, P. E. d. (2006). "Equations used to predict glomerular filtration rate
perform poorly in kidney transplant recipients." Nature Clinical Practice
nephrology 2(6): 300-301.
Kahan, B. D. (2000). "Efficacy of sirolimus compared with azathioprine for
reduction of acute renal allograft rejection: a randomised multicentre
study." The Lancet 356(9225): 194-202.
Kanbay, M., A. Yildirir, et al. (2006). "Effects of Immunosuppressive Drugs on
Serum Lipid Levels in Renal Transplant Recipients." Transplantation
Proceedings 38(2): 502-505.
Kasiske, B. L., B. Nashan, et al. (2012). "A Prospective, Multinational
Pharmacoepidemiological Study of Clinical Conversion to Sirolimus
Immunosuppression after Renal Transplantation." Journal of
Transplantation 2012: 16.
Kasiske (2008). "Mammalian target of rapamycin inhibitor dyslipidemia in kidney
transplant recipients." American journal of transplantation 8(7): 1384-
1392.
Kasiske, B. L., J. J. Snyder, et al. (2003). "Diabetes mellitus after kidney
transplantation in the United States." American Journal Of
Transplantation: Official Journal Of The American Society Of
Transplantation And The American Society Of Transplant Surgeons 3(2):
178-185.
196
Kim (2008). "Regulation of interleukin-6-induced hepatic insulin resistance by
mammalian target of rapamycin through the STAT3-SOCS3 pathway."
The Journal of biological chemistry 283(2): 708-715.
Krebs (2007). "The mammalian target of rapamycin pathway regulates nutrient-
sensitive glucose uptake in man." Diabetes 56(6): 1600-1607.
Kreis, H., J. M. Cisterne, et al. "Sirolimus in association with mycophenolate
mofetil induction for the prevention of acute graft rejection in renal
allograft recipients." Transplantation 69(7): 1252-1260.
Kriz, W., I. Hartmann, et al. (2001). "Tracer Studies in the Rat Demonstrate
Misdirected Filtration and Peritubular Filtrate Spreading in Nephrons with
Segmental Glomerulosclerosis." Journal of the American Society of
Nephrology 12(3): 496-506.
Laecke, V. (2009). "Posttransplantation Hypomagnesemia and Its Relation with
Immunosuppression as Predictors of New-Onset Diabetes after
Transplantation." American journal of transplantation 9(9): 2140-2149.
Larsen, J. L. B., Robert G.; Burkman, Tab ; Ramirez, Ana Lisa; Yamamoto,
Sakura; Gulizia, James; Radio, Stanley; Hamel, Frederick G. (2006).
"Tacrolimus ans Sirolimus Cause Insulin Resistance in Normal Sprague
Dawley Rats." Transplantation 82(4): 466-470.
197
Lebranchu (2009). "Efficacy on renal function of early conversion from
cyclosporine to sirolimus 3 months after renal transplantation: Concept
study." American journal of transplantation 9(5): 1115-1123.
Lee, Y.-J. K., Beom; Lee, Jung Eun1; Kim, Yoon-Goo; Kim, Dae Joong; Kim,
Sung-Joo; Joh, Jae-Won; Oh, Ha Young; Huh, Wooseong (2010).
"Randomized trial of cyclosporine and tacrolimus therapy with steroid
withdrawal in living-donor renal transplantation: 5-year follow-up."
Transplant International Issue: Volume 23(2), (2): p 147–154.
Lefaucheur, C., A. Loupy, et al. (2010). "Preexisting Donor-Specific HLA
Antibodies Predict Outcome in Kidney Transplantation." Journal of the
American Society of Nephrology 21(8): 1398-1406.
Lentine, K. L. and D. C. Brennan (2004). "Statin use after renal transplantation:
a systematic quality review of trial-based evidence." Nephrology Dialysis
Transplantation 19(9): 2378-2386.
Lentine, K. L., D. C. Brennan, et al. (2005). "Incidence and predictors of
myocardial infarction after kidney transplantation." Journal of the
American Society of Nephrology 16(2): 496-506.
Lerut, E., D. R. Kuypers, et al. (2007). "Acute rejection in non-compliant renal
allograft recipients: a distinct morphology." Clinical Transplantation 21(3):
344-351.
198
Letavernier, E., M. N. Pe'raldi, et al. (2005). "Proteinuria following a switch from
calcineurin inhibitors to sirolimus " Transplantation 80(9): 1198-1203.
Li, C. and C. W. Yang (2009). "The pathogenesis and treatment of chronic
allograft nephropathy." Nat Rev Nephrol 5(9): 513-519.
Liefeldt, L. L. and K. K. Budde (2010). "Risk factors for cardiovascular disease
in renal transplant recipients and strategies to minimize risk." Transplant
international 23: 1191-1204.
Liptak, P. and B. Ivanyi (2006). "Primer: Histopathology of calcineurin-inhibitor
toxicity in renal allografts." Nature clinical practice nephrology 2(7): 398-
404.
Liu, Y. (2011). "Cellular and molecular mechanisms of renal fibrosis." Nature
Reviews Nephrology 7(12).
Loupy, A., G. S. Hill, et al. (2012). "The impact of donor-specific anti-HLA
antibodies on late kidney allograft failure." Nat Rev Nephrol 8(6): 348-
357.
Ma, K. L. K. L., X. Z. X. Z. Ruan, et al. (2007). "Sirolimus modifies cholesterol
homeostasis in hepatic cells: a potential molecular mechanism for
sirolimus-associated dyslipidemia." Transplantation 84(8): 1029-1036.
199
Maamoun, H., E. Esmail, et al. (2011). "Vascular endothelial function of
sirolimus maintenance regimen in renal transplant recipients."
Transplantation Proceedings 43(5): 1616-1618.
Mao, Q., P. I. Terasaki, et al. (2007). "Extremely High Association Between
Appearance of HLA Antibodies and Failure of Kidney Grafts in a Five-
Year Longitudinal Study." American Journal of Transplantation 7(4): 864-
871.
Marchetti p, N. R. (2000). "The metabolic effects of cyclosporin and tacrolimus."
Journal of endocrinological investigation 23(8): 482-490.
Mariat, C. (2005). "Predicting Glomerular filtration rate in Kidney transplantation:
are the K/DOQI guidelines applicable?" American Journal of
Transplantation 5: 2698-2703.
Matas, A. J. (2011). "Chronic Progressive Calcineurin Nephrotoxicity: An
Overstated Concept." American Journal of Transplantation 11(4): 687-
692.
McDonald, S., G. Russ, et al. (2007). "Kidney Transplant Rejection in Australia
and New Zealand: Relationships Between Rejection and Graft Outcome."
American Journal of Transplantation 7(5): 1201-1208.
McDonald, S. P. and G. R. Russ (2002). "Survival of recipients of cadaveric
kidney transplants compared with those receiving dialysis treatment in
Australia and New Zealand, 1991–2001." Nephrology Dialysis
Transplantation 17(12): 2212-2219.
200
Melk, A. (2003). "Senescence of renal cells: molecular basis and clinical
implications." Nephrology Dialysis Transplantation 18(12): 2474-2478.
Midtvedt, K., J. Hjelmesaeth, et al. (2004). "Insulin Resistance after Renal
Transplantation: The Effect of Steroid Dose Reduction and Withdrawal."
J Am Soc Nephrol 15(12): 3233-3239.
Moore, J., J. Light, et al. (2007). "Clinically significant proteinuria following the
administration of sirolimus to renal transplant recipients." Drug
metabolism letters 1(4): 267-271.
Moore, J., A. J. McKnight, et al. (2010). "Association of caveolin-1 gene
polymorphism with kidney transplant fibrosis and allograft failure." JAMA
303(13): 1282-1287.
Morales (2010). "New-Onset Diabetes After Renal Transplantation. ." Journal of
investigative medicine 58(6): 755 -763.
Moreso, F., M. Ibernon, et al. (2006). "Subclinical Rejection Associated with
Chronic Allograft Nephropathy in Protocol Biopsies as a Risk Factor for
Late Graft Loss." American Journal of Transplantation 6(4): 747-752.
Morrisett, J. D., G. Abdel-Fattah, et al. (2002). "Effects of sirolimus on plasma
lipids, lipoprotein levels, and fatty acid metabolism in renal transplant
patients." Journal of Lipid Research 43(8): 1170-1180.
201
Morrisett, J. D., G. Abdel-Fattah, et al. (2003). "Sirolimus changes lipid
concentrations and lipoprotein metabolism in kidney transplant
recipients." Transplantation Proceedings 35(3 Suppl): 143S-150S.
Nankivell, B. and S. Alexander (2008). "Rejection of the kidney allograft." The
New England journal of medicine 363(15): 1451-1462.
Nankivell, B. J. (2003). "The natural History of Chronic Allograft Nephropathy."
New England Journal of Medicine 349(24): 2326-2333.
Nankivell, B. J. and J. R. Chapman (2006). "Chronic Allograft Nephropathy:
Current concepts and Future Directions." Transplantation 81(5): 643-654.
Nankivell, B. J. B., Richard J.; Fung, Caroline L.S.; O’Connell, Philip J.;
Chapman, Jeremy R.; Allen, Richard D.M. (2004). "Delta Analysis of
Posttransplantation Tubulointerstitial Damage." Transplantation 78(3):
434-441.
Oberbauer, R. and M. Haller (2009). "Calcineurin inhibitor minimization,
withdrawal and avoidance protocols after kidney transplantation."
Transplant international 22(1): 69-77.
Oberbauer, R., G. Segoloni, et al. (2005). "Early cyclosporine withdrawal from a
sirolimus-based regimen results in better renal allograft survival and
renal function at 48 months after transplantation." Transplant
International 18(1): 22-28.
202
Ogutmen, B., A. Yildirim, et al. (2006). "Health-Related Quality of Life After
Kidney Transplantation in Comparison Intermittent Hemodialysis,
Peritoneal Dialysis, and Normal Controls." Transplantation Proceedings
38(2): 419-421.
Opelz, G., T. Wujciak, et al. (1998). "Association of chronic kidney graft failure
with recipient blood pressure." Kidney Int 53(1): 217-222.
Oterdoom, L., A. P. J. de Vries, et al. (2007). "Determinants of insulin resistance
in renal transplant recipients." Transplantation 83(1): 29-35.
Pascual, M., T. Theruvath, et al. (2002). "Strategies to Improve Long-Term
Outcomes after Renal Transplantation." New England Journal of
Medicine 346(8): 580-590.
Patel, R. and P. I. Terasaki (1969). "Significance of the Positive Crossmatch
Test in Kidney Transplantation." New England Journal of Medicine
280(14): 735-739.
Pavlakis, M. and A. S. Goldfarb-Rumyantzev (2008). "Diabetes after
Transplantation and Sirolimus: What's the Connection?" J Am Soc
Nephrol 19(7): 1255-1256.
203
Pine, J. K., P. J. Goldsmith, et al. (2010). "Comparable Outcomes in Donation
after Cardiac Death and Donation after Brainstem Death: A Matched
Analysis of Renal Transplants." Transplantation Proceedings 42(10):
3947-3948.
Pontrelli, P. P., M. M. Rossini, et al. (2008). "Rapamycin inhibits PAI-1
expression and reduces interstitial fibrosis and glomerulosclerosis in
chronic allograft nephropathy." Transplantation 85(1): 125-134.
Prasad, G. V. R., F. Bandukwala, et al. (2009). "Microalbuminuria post-renal
transplantation: relation to cardiovascular risk factors and C-reactive
protein." Clinical Transplantation 23(3): 313-320.
Pratschke, J., S. Weiss, et al. (2008). "Review of nonimmunological causes for
deteriorated graft function and graft loss after transplantation." Transplant
International 21(6): 512-522.
Racusen, L. C., K. Solez, et al. (1999). "The Banff 97 working classification of
renal allograft pathology." Kidney International 55(2): 713-723.
Rader D.J., H. H. H. (2012). "Disorders of Lipoprotein Metabolism. “Chapter
356. Disorders of Lipoprotein Metabolism. In D.L. Longo, A.S. Fauci, D.L.
Kasper, S.L. Hauser, J.L. Jameson, J. Loscalzo (Eds), Harrison's
Principles of Internal Medicine, 18e. Retrieved February 18, 2012 from
http://www.accessmedicine.com.rplibresources.health.wa.gov.au/content
.aspx?aID=9143689.
204
Reinsmoen, N. L., C.-H. Lai, et al. (2010). "Anti-angiotensin type 1 receptor
antibodies associated with antibody mediated rejection in donor HLA
antibody negative patients." Transplantation 90(12): 1473-1477.
Remuzzi, G., P. Cravedi, et al. (2006). "Long-Term Outcome of Renal
Transplantation from Older Donors." New England Journal of Medicine
354(4): 343-352.
Remuzzi, G., N. Perico, et al. (2005). "The role of renin-angiotensin-aldosterone
system in the progression of chronic kidney disease." Kidney Int 68(S99):
S57-S65.
Remuzzi, G., P. Ruggenenti, et al. (2009). "Sirolimus to replace calcineurin
inhibitors? Too early yet." The Lancet 373(9671): 1235-1236.
Roland (2008). "Immunosuppressive medications, clinical and metabolic
parameters in new-onset diabetes mellitus after kidney transplantation."
Transplant international 21(6): 523-530.
Roodnat, J. I., I. C. van Riemsdijk, et al. (2003). "The superior results of living-
donor renal transplantation are not completely caused by selection or
short cold ischemia time: a single-center, multivariate analysis."
Transplantation 75(12): 2014-2018.
205
Ruiz, J. C., J. M. Campistol, et al. (2002). "Early cyclosporine a withdrawal in
kidney transplant recipients under a sirolimus-based immunosuppressive
regimen: pathological study of graft biopsies at 1-year posttransplant."
Transplantation Proceedings 34(1): 92-93.
Ruiz, J. C., F. Diekmann, et al. (2005). "Evolution of Proteinuria After
Conversion From Calcineurin Inhibitors (CNI) to Sirolimus (SRL) in Renal
Transplant Patients: A Multicenter Study." Transplantation Proceedings
37(9): 3833-3835.
Sahin, G. M., S. Sahin, et al. (2006). "Proteinuria After Conversion to Sirolimus
in Renal Transplant Recipients." Transplantation Proceedings 38(10):
3473-3475.
Salahudeen, A. K., N. Haider, et al. (2003). Cold ischemia and the reduced
long-term survival of cadaveric renal allografts, Kidney International. 65
(2) (pp 713-718), 2004. Date of Publication: February 2004.
Sancho, A., M. C. Pastor, et al. (2010). "Posttransplant Inflammation Associated
With Onset of Chronic Kidney Disease." Transplantation Proceedings
42(8): 2896-2898.
206
Schena, F. P. and M. D. A. Pascoe, Josefina3; del Carmen Rial, Maria4;
Oberbauer, Rainer5; Brennan, Daniel C.6; Campistol, Josep M.7;
Racusen, Lorraine8; Polinsky, Martin S.9; Goldberg-Alberts, Robert9; Li,
Huihua9; Scarola, Joseph9; Neylan, John F.9; for the Sirolimus
CONVERT Trial Study Group (2009). "Conversion from calcineurin
inhibitors to sirolimus maintenance therapy in renal allograft recipients:
24-month efficacy and safety results from the CONVERT trial "
Transplantation 87(2): 233 -242.
Schnuelle, P., D. Lorenz, et al. (1998). "Impact of renal cadaveric
transplantation on survival in end-stage renal failure: evidence for
reduced mortality risk compared with hemodialysis during long-term
follow-up." Journal of the American Society of Nephrology 9(11): 2135-
2141.
Servais, A. a., c,*; , O. e. Toupance, et al. (2009). "Fibrosis Quantification in
Renal Transplant Recipients Randomized to Continue Cyclosporine or
Convert to Sirolimus
" American journal of transplantation 9(11): 2552-2560.
Shah, T., A. Kasravi, et al. (2006). "Risk factors for development of new-onset
diabetes mellitus after kidney transplantation." Transplantation 82(12):
1673-1676.
Sharif, A. and K. Baboolal (2010). "Risk factors for new-onset diabetes after
kidney transplantation." Nat Rev Nephrol 6(7): 415-423.
207
Sharif, A., S. Shabir, et al. (2011). "Meta-Analysis of Calcineurin-Inhibitor-
Sparing Regimens in Kidney Transplantation." Journal of the American
Society of Nephrology 22(11): 2107-2118.
Shimizu, T., H. Ishida, et al. (2008). "Clinical and Histological Analysis of
Chronic Tacrolimus Nephrotoxicity in Renal Allografts." Transplantation
Proceedings 40(7): 2370-2372.
Shishido, S., H. Asanuma, et al. (2003). "The Impact of Repeated Subclinical
Acute Rejection on the Progression of Chronic Allograft Nephropathy."
Journal of the American Society of Nephrology 14(4): 1046-1052.
Sigdel, T. K., L. Li, et al. (2012). "Non-HLA antibodies to immunogenic epitopes
predict the evolution of chronic renal allograft injury." Journal of the
American Society of Nephrology 23(4): 750-763.
ska, M. B., M. Banasik, et al. (2006). "Conversion to Sirolimus From
Cyclosporine May Induce Nephrotic Proteinuria and Progressive
Deterioration of Renal Function in Chronic Allograft Nephropathy
Patients." Transplantation Proceedings 38(1): 101-104.
Sniderman, A., P. Couture, et al. (2010). "Diagnosis and treatment of
apolipoprotein B dyslipoproteinemias." Nat Rev Endocrinol 6(6): 335-346.
Solez, K., R. B. Colvin, et al. (2008). "Banff 07 classification of renal allograft
pathology: updates and future directions." American Journal Of
208
Transplantation: Official Journal Of The American Society Of
Transplantation And The American Society Of Transplant Surgeons 8(4):
753-760.
Straathof-Galema, L. (2006). "Sirolimus-Associated Heavy proteinuria in a renal
Transplant Recipient: Evidence for a tubular Mechanism." 2006 6: 429-
433.
Strutz, F. Pathogenesis of tubulointerstitial fibrosis in chronic allograft
dysfunction, Clinical Transplantation. 23 (SUPPL.21) (pp 26-32), 2009.
Date of Publication: December 2009.
Stumvoll, M., A. Mitrakou, et al. (2000). "Use of the oral glucose tolerance test
to assess insulin release and insulin sensitivity." Diabetes Care 23(3):
295-301.
Tenderich (2007). "Comparison of sirolimus and everolimus in their effects on
blood lipid profiles and haematological parameters in heart transplant
recipients." Clinical transplantation 21(4): 536-543.
Teperman, L. S., L.2; Marotta, P.3; Sebastian, A.4; Moonka, D.5; Patel, D.6;
Koneru, B.7; Klintmalm, G.8; Roberts, J (2009). Efficacy of
Mycophenolate Mofetil/Sirolimus Maintenance Therapy after CNI
Withdrawal in Liver Transplant Recipients: 1-Year Outcomes of the
Spare-the-Nephron (STN) Trial. AMERICAN TRANSPLANT
CONGRESS Blackwell Publishing Ltd.
209
Terasaki, P. I., J. M. Cecka, et al. (1995). "High survival rates of kidney
transplants from spousal and living unrelated donors." New England
Journal of Medicine 333(6): 333-336.
Teutonico, A., P. F. Schena, et al. (2005). "Glucose metabolism in renal
transplant recipients: effect of calcineurin inhibitor withdrawal and
conversion to sirolimus." Journal of American Society of Nephrology
16(10): 3128-3135.
The Australasian Creatinine Consensus Working Group , T. (2005). "Chronic
kidney disease and automatic reporting of estimated glomerular filtration
rate: a position statement
" Medical Journal of australia 183(3): 138-141.
Timsit, M.-O., X. Yuan, et al. (2010). "Consequences of transplant quality on
chronic allograft nephropathy." Kidney Int 78(S119): S54-S58.
Torras, J., I. Herrero-Fresneda, et al. (2009). "Rapamycin has dual opposing
effects on proteinuric experimental nephropathies: is it a matter of
podocyte damage?" Nephrology Dialysis Transplantation 24(12): 3632-
3640.
Tremblay, F. and A. Marette (2001). "Amino acid and insulin signaling via the
mTOR/p70 S6 kinase pathway. A negative feedback mechanism leading
to insulin resistance in skeletal muscle cells." The Journal of biological
chemistry 276(41): 38052-38060.
210
Tzatsos, A. and K. Kandror (2006). "Nutrients suppress phosphatidylinositol 3-
kinase/Akt signaling via raptor-dependent mTOR-mediated insulin
receptor substrate 1 phosphorylation." Molecular and cellular biology
26(1): 63-76.
Uslu, A., H. Töz, et al. (2009). "Late Conversion From Calcineurin Inhibitor-
Based to Sirolimus-Based Immunosuppression Due to Chronic Toxicity:
A Prospective Study With Protocol Biopsy Amendment." Transplantation
Proceedings 41(2): 756-763.
Varma (2008). "Long-term effects of rapamycin treatment on insulin mediated
phosphorylation of Akt/PKB and glycogen synthase activity."
Experimental cell research 314(6): 1281-1291.
Vaziri, N. D. (2003). "Molecular mechanisms of lipid disorders in nephrotic
syndrome." Kidney International 63(5): 1964-1976.
Vaziri, N. D., K. Liang, et al. (2000). "Effect of Cyclosporine on HMG-CoA
Reductase, Cholesterol 7α-Hydroxylase, LDL Receptor, HDL Receptor,
VLDL Receptor, and Lipoprotein Lipase Expressions." Journal of
Pharmacology and Experimental Therapeutics 294(2): 778-783.
Veroux, M., D. Corona, et al. (2008). "New-Onset Diabetes Mellitus After Kidney
Transplantation: The Role of Immunosuppression." Transplantation
Proceedings 40(6): 1885-1887.
211
Vodenik, B., J. Rovira, et al. (2009). "Mammalian Target of Rapamycin and
Diabetes: What Does the Current Evidence Tell Us?" Transplantation
Proceedings 41(6, Supplement 1): S31-S38.
Vongwiwatana, A., A. Tasanarong, et al. (2005). "Epithelial to Mesenchymal
Transition During Late Deterioration of Human Kidney Transplants: The
Role of Tubular Cells in Fibrogenesis." American Journal of
Transplantation 5(6): 1367-1374.
Watson, C. J. C. J. E., A. E. A. E. S. Gimson, et al. (2007). "A randomized
controlled trial of late conversion from calcineurin inhibitor (CNI)-based to
sirolimus-based immunosuppression in liver transplant recipients with
impaired renal function." Liver transplantation 13(12): 1694-1702.
Webster A, W. R. T. R. C. J. C. J. (2005). "Tacrolimus versus cyclosporin as
primary immunosuppression for kidney transplant recipients." Cochrane
database of systematic reviews(4): CD003961 -CD003961.
White, M. F. (1998). "The IRS-signalling system: a network of docking proteins
that mediate insulin action." Molecular and cellular biochemistry 182(1-2):
3-11.
Wlodarczyk, Z., S. Vitko, et al. (2005). "Lipid Metabolism in Renal Transplant
Patients Receiving Tacrolimus/Sirolimus Combination Therapy."
Transplantation Proceedings 37(4): 1871-1873.
212
Wullschleger, S., R. Loewith, et al. (2006). "TOR Signaling in Growth and
Metabolism." Cell 124: 471- 484.
Yarlagadda, S. G., S. G. Coca, et al. (2009). "Association between delayed graft
function and allograft and patient survival: a systematic review and meta-
analysis." Nephrology Dialysis Transplantation 24(3): 1039-1047.
Yasuo, I., S. Tokihiko, et al. (2005). "Injury and progressive loss of peritubular
capillaries in the development of chronic allograft nephropathy." Kidney
International 67(1): 321.
213
Appendices
214
Appendix 1
215
Appendix 2
An evaluation of determinants of renal and metabolic functions in Renal
Transplant Recipients and the effects of conversion from Calcineurin Inhibitors
to Sirolimus
Principal Investigator: Dr. Ramyasuda Swaminathan
PATIENT INFORMATION SHEET
Dear Patient,
Your Nephrologist has decided to change your current immunosuppression
medication to a new immunosuppressive medication called Sirolimus. During
this time we will ask you to contribute to an observational study examining the
benefits and risks of changing to this drug.
Study Purpose
Based on your current condition of your kidney transplant your nephrologist has
decided to change you over from your current immunosuppressive treatment to
a drug called Sirolimus. The benefits of Sirolimus in kidney transplantation have
216
been well established and your nephrologist would have explained to you the
benefits and potential side-effects related to Sirolimus.
All the immunosuppressive medications (including Steroids, Cyclosporine,
Tacrolimus and Sirolimus) can have significant effects on the glucose (sugar)
and cholesterol (fats) levels by affecting different metabolic pathways. In
addition sirolimus may affect the production and action of hormones associated
with ovarian and testicular function. The aim of this audit is to monitor if and
how your glucose, cholesterol and certain hormone levels change after you
have been converted to Sirolimus from your previous medication.
217
Will your treatment be affected?
Participation in this audit will not change your treatment in any way. The
decision to convert you to Sirolimus and continue the treatment will be
determined by your nephrologist. Participation in this audit will in no way
influence the decision-making process.
What information is needed from you?
We would need to measure your height, weight and blood pressure, and waist
and hip circumferences at the time of conversion and at approximately three
and twelve months after conversion to Sirolimus. At the same time you would
need to have blood and urine tests. Most of these tests and physical
examination are already part of your routine post transplant care and will be
done during your routine visit to your nephrologist. The main difference will be
that you are requested to do an oral glucose tolerance test on three occasions
over a 12 month period (i.e at the time of change over to Sirolimus, three and
twelve months after change-over). This test determines your risk of diabetes
and measures your glucose and insulin levels when fasting and at 1 and 2
hours after ingestion of a sugar solution. This extra test means that you will
need to provide three additional sets of blood samples (10ml or approx 2 tsp)
over and above what you would normally expect to provide. At the same time
we would also measure the hormones and cholesterol (fats) in your blood. We
would ask you to fill in a simple Questionnaire, which enquires about the
common side effects of Sirolimus and changes in health after the changeover of
medication.
How will the information collected be used?
218
The information collected during this audit will be kept confidential. The data will
be analysed by the Principal Investigator (PI) and used in a thesis titled “An
evaluation of determinants of renal and metabolic functions in Renal Transplant
Recipients and the effects of conversion from Calcineurin Inhibitors to
Sirolimus” which will be submitted to the University of Western Australia. The
results may also be published in a medical journal as intended, but no reader
will be able to identify the individual patients.
219
Further information
If you require any further information you can contact the Principal Investigator
through Royal Perth Hospital on 08 9224 2244 during normal business hours.
This audit has been approved by the Royal Perth Hospital Ethics Committee.
For questions relating to Ethical approval you can contact the Chairman of the
Ethics committee through Royal Perth Hospital on 08 9224 2244 during normal
business hours.
Thank you for agreeing to participate in this study.
CONSENT TO PARTICIPATION IN THE AUDIT
I,........................................................................ agree to participate in the above
study. I have read and understood the attached Information Sheet and I have
retained a copy of the signed document. I have been given the opportunity to
ask questions about the study by the investigator. I give consent for my medical
records being accessed by the Principal Investigator.
Signed................................................………………......................................
Date...........................
Signature of Investigator/Coordinator.............................................................
Date...........................
220
Appendix 3
Abstracts:
1. R. Swaminathan & A. Irish “Outcomes of conversion to Sirolimus from
CNI in renal transplant recipients with Allograft dysfunction due to CNI
toxicity &/or Chronic Allograft Nephropathy” Tumour & Cell Biology,
March 2007
2. R. Swaminathan & A. Irish “Factors predicting renal outcomes after
conversion to Sirolimus from CNI in renal transplant recipients with
allograft dysfunction due to Calcineurin inhibitor toxicity and/ or Chronic
allograft nephropathy” JASN October 2007 (Abstracts)
3. R. Swaminathan & A. Irish “Outcomes of conversion from CNI based
therapy to sirolimus in renal transplant recipients with allograft
dysfunction due to calcineurin inhibitor toxicity and/ or Chronic allograft
nephropathy” JASN October 2007 (Abstracts)
4. R. Swaminathan & A. Irish “Renal outcomes and factor predicting the
outcomes after conversion to Sirolimus form calcineurin inhibitors in
chronic allograft dysfunction” – ATC Boston, June 2009
5. R. Swaminathan & A. Irish “Effect Of M-Tor Inhibitors Upon Glucose &
Lipid Metabolism In Renal Transplant Recipients” TSANZ Canberra,
2010