REVIEWS The genetics of diabetic complications Emma Ahlqvist, Natalie R. van Zuydam, Leif C. Groop and Mark I. McCarthy Abstract | The rising global prevalence of diabetes mellitus is accompanied by an increasing burden of morbidity and mortality that is attributable to the complications of chronic hyperglycaemia. These complications include blindness, renal failure and cardiovascular disease. Current therapeutic options for chronic hyperglycaemia reduce, but do not eradicate, the risk of these complications. Success in defining new preventative and therapeutic strategies hinges on an improved understanding of the molecular processes involved in the development of these complications. This Review explores the role of human genetics in delivering such insights, and describes progress in characterizing the sequence variants that influence individual predisposition to diabetic kidney disease, retinopathy, neuropathy and accelerated cardiovascular disease. Numerous risk variants for microvascular complications of diabetes have been reported, but very few have shown robust replication. Furthermore, only limited evidence exists of a difference in the repertoire of risk variants influencing macrovascular disease between those with and those without diabetes. Here, we outline the challenges associated with the genetic analysis of diabetic complications and highlight ongoing efforts to deliver biological insights that can drive translational benefits. Ahlqvist, E. et al. Nat. Rev. Nephrol. advance online publication 31 March 2015; doi:10.1038/nrneph.2015.37
Introduction
Department of Clinical Sciences, Diabetes and Endocrinology, Clinical Research Centre, Entrance 72, University Hospital in Malmö, Lund University, 205 02 Malmö, Sweden (E.A., L.C.G.). Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK (N.R.v.Z.). Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington, Oxford OX3 7LJ, UK (M.I.M.). Correspondence to: M.I.M. mark.mccarthy@ drl.ox.ac.uk
Diabetes mellitus is associated with a range of microvascular, macrovascular and metabolic complications, which together have a sizeable and increasing impact on the global burden of morbidity and mortality. 1,2 Diabetes is the leading global cause of end-stage renal disease (ESRD), and a major contributor to blindness, lower limb amputation, and cardiac disease.3,4 Despite substantial research efforts, there remains considerable uncertainty over the underlying mechanisms whereby prolonged hyperglycaemia, in concert with the other metabolic features of diabetes, results in such a charac teristic pattern of damage. Although the pathology and epidemiology of diabetic kidney disease (DKD) are well described,5 in particular for type 1 diabetes mellitus (T1DM),5 the processes underlying its development and the factors that influence individual predisposition are poorly understood.6 The extent to which the mechanisms underlying DKD among patients with T1DM or type 2 diabetes mellitus (T2DM) are shared is a matter of debate: whereas the overall clinical presentation is similar for both T1DM and T2DM, the histological findings in T2DM are notably more variable as compared with T1DM. This difference in part reflects the comorbidity and age-associated effects of hypertension and coexisting atherosclerosis.7–10 The degree to which florid cardiovascular disease in patients with poorly controlled diabetes reflects qualitatively distinct processes, Competing interests E.A. and N.R.v.Z. have received salary support, and L.C.G. and M.I.M. have received research support, from the InnovativeMedicines Initiative and the Juvenile Diabetes Research Foundation for research on the genetic basis of diabetic complications.
rather than acceleration of the mechanisms that operate in normoglycaemic individuals, is also far from clear. Uncertainties over the causal mechanisms underlying diabetic complications represent serious obstacles to the development of strategies to address currently unmet medical needs, impeding efforts to identify validated therapeutic targets that can be subsequently modulated for remedial benefit. These challenges are illustrated by aliskiren, a direct inhibitor of renin. Despite considerable prior data indicating that this drug would be a useful adjunct in the management of DKD, the data obtained from clinical trials suggested otherwise.11 The ALTITUDE study found no significant difference in renal and cardiovascular outcomes between patients treated with aliskiren and those who received placebo.11 Absence of a definitive mechanistic framework for disease pathogenesis further limits the capacity to stratify individual risk, for example, through the use of surrogate biomarkers to monitor the development and progression of diabetic complications. The absence of a mechanistic framework also negatively influences individual clinical management and contributes to the excessive costs associ ated with developing novel therapies.12,13 The identification of robust surrogate biomarkers for DKD, approved by the relevant regulatory authorities, enable clinical trials to be more efficient and involve smaller numbers of patients at high risk of complications (selected on the basis of genetic or non-genetic biomarker status) to be followed for shorter intervals than would otherwise be the case for clinical trials of renal outcomes.14 This Review explores the role that human genetics can play in delivering novel insights into the mechanistic basis of diabetic complications, including cardiovascular
NATURE REVIEWS | NEPHROLOGY
ADVANCE ONLINE PUBLICATION | 1 © 2015 Macmillan Publishers Limited. All rights reserved
REVIEWS Key points ■■ The majority of the morbidity and mortality associated with diabetes mellitus is due to complications, such as diabetic kidney disease ■■ A limited understanding of the underlying mechanisms responsible for the development of diabetic complications compromises efforts to develop novel strategies for treatment and prevention ■■ Studies of human genetics offer powerful tools for delivering robust mechanistic insights into complex traits, such as diabetic complications, which have a substantial genetic component ■■ Scientific progress to date has been limited by small sample sizes and/or phenotypic imprecision; however, several major discovery efforts are underway that have been designed to address these issues ■■ The identification of genetic variants with robust effects on the risk of developing diabetic complications will accelerate efforts to develop more effective strategies for treatment and prevention
disease, diabetic retinopathy, diabetic neuropathy and DKD. Compelling evidence has indicated that individual predisposition to DKD in patients with T1DM has a substantial genetic component.15–22 Identification of the specific genetic variants responsible for such predisposition offers a powerful strategy to define key patho genetic mechanisms and generate therapeutic targets with a causal link to human disease. In addition, charac terization of the wider phenotypic impact of a variant of interest provides clues to the potential consequences of therapeutic perturbation of the cognate pathway— information that can help to prioritize among a set of potential therapeutic targets.23
Microvascular complications Diabetic kidney disease DKD affects ~30% of all patients with T1DM or T2DM.24,25 The early phase of DKD is typically charac terized by glomerular hyperfiltration, followed by progressive morphological and ultrastructural changes, including expansion of the extracellular matrix (ECM) and loss of the charge barrier in the glomerular basement membrane.26 The overall result of such changes is protein leakage, characterized initially as microalbuminuria (urinary albumin excretion rate [AER] 20–199 μg/min) that progresses to macroalbuminuria (AER ≥200 μg/min). The estimated glomerular filtration rate (eGFR) decreases, leading to chronic kidney disease (CKD; eGFR 10 million variants across the genome.51 The requirement to make due allowance for extensive multiple testing has led to the adoption of rigorous thresholds for the declaration of statistical significance and a strong impetus towards replication, both of which have limited false claims of association. 52,53 GWAS have proven highly effective in identif ying robustly associated loci for numerous complex traits and diseases, such as T2DM and coronary artery disease (CAD).54,55 In the case of T2DM, >80 disease regions have been identified to date.54 The majority of these disease regions map to intergenic loci,
NATURE REVIEWS | NEPHROLOGY
ADVANCE ONLINE PUBLICATION | 3 © 2015 Macmillan Publishers Limited. All rights reserved
REVIEWS Table 1 | Major consortia addressing the genetic basis of diabetes complications and associated traits Acronym
Full name
Trait of Interest
URL
CARDIoGRAMplusC4D133
Coronary ARtery DIsease Genome wide Replication and Meta-analysis plus The Coronary Artery Disease Genetics consortium
Coronary artery disease
http://www.cardiogramplusc4d.org/
CARe consortium77
Candidate-gene Association REsource Study
Multiple phenotypes
http://www.nhlbi.nih.gov/research/ resources/genetics-genomics/care
DCCT/EDIC134
The Diabetes Control and Complications Trial and the Epidemiology of Diabetes Interventions and Complications study
Complications of diabetes
http://diabetes.niddk.nih.gov/dm/pubs/ control/
DIAGRAM109
DIAbetes Genetics Replication And Meta-analysis consortium
Type 2 diabetes
http://diagram-consortium.org/about.html
FIND
Family Investigation of Nephropathy and Diabetes
Diabetic kidney disease
https://www.niddkrepository.org/studies/ find
GENIE60
GEnetics of Nephropathy —an International Effort
Diabetic kidney disease
http://www.ncbi.nlm.nih.gov/projects/gap/ cgi-bin/study.cgi?study_id=phs000389.v1.p1
GIANT135
The Genetic Investigation of Anthropometric Traits consortium
Anthropometric traits
http://www.broadinstitute.org/collaboration/ giant/index.php/GIANT_consortium
GoKIND136
Genetics of Kidneys in Diabetes study
Diabetic kidney disease
https://www.niddkrepository.org/studies/ gokind/
JDRF–DNCRI
Diabetes Research Foundation– Diabetic Nephropathy Collaborative Research Initiative
Diabetic kidney disease
http://jdrf.org/press-releases/jdrf-formslargest-ever-international-effort-to-researchgenetics-of-diabetic-kidney-disease/
MAGIC137
The Meta-Analyses of Glucose and Insulin-related traits consortium
Glycaemic traits
http://www.magicinvestigators.org/
SUMMIT76
SUrrogate markers for Micro- and Macro-vascular hard endpoints for Innovative diabetes Tools
Complications of diabetes
http://www.imi-summit.eu/
and few of them involve genes previously implicated in disease, an observation that casts further doubt on the value of the candidate gene approach.54 The first application of GWAS to DKD was published in 2009.56 This study, which used pooled DNA from 547 patients with T1DM and ESRD and 549 controls with T1D and no evidence of DKD, identified no significant associations.56 A further study from the GoKinD combined a GWAS of >1,700 individually typed patients with T1DM (820 of whom had DKD) with a replication analysis in 1,304 patients from the DCCT/EDIC trial (Table 1).57 Promising associations were identified in the FRMD3 and CARS loci; however, neither signal reached genome-wide significance nor were the results widely replicated.58 Over the past 5 years, discovery efforts for complex traits have moved towards the development of inter national consortia to support large-scale meta-analysis of multiple individual GWAS; for some diseases, these efforts include many tens of thousands of cases.54,59 This approach has been successful for many diseases and phenotypes, including traits such as schizophrenia and hypertension, for which findings from the initial rounds of single-study GWAS had, as with DKD, appeared unrewarding. The sample size at which the discovery of statistically significant genome-wide associ ations begins to increase varies by at least an order of magnitude between traits (Figure 2). The reasons behind these differences are poorly described, but probably include between-trait differences in heritability, selective
pressure, aetiological heterogeneity and the extent of case-control misclassification. The overall impact of these factors on the outcome of GWAS is difficult to predict in advance for any given trait. The largest GWAS meta-analysis of DKD published to date involved the 6,691 patients with T1DM assembled by GENIE; variants reaching a P
Introduction
Department of Clinical Sciences, Diabetes and Endocrinology, Clinical Research Centre, Entrance 72, University Hospital in Malmö, Lund University, 205 02 Malmö, Sweden (E.A., L.C.G.). Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK (N.R.v.Z.). Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington, Oxford OX3 7LJ, UK (M.I.M.). Correspondence to: M.I.M. mark.mccarthy@ drl.ox.ac.uk
Diabetes mellitus is associated with a range of microvascular, macrovascular and metabolic complications, which together have a sizeable and increasing impact on the global burden of morbidity and mortality. 1,2 Diabetes is the leading global cause of end-stage renal disease (ESRD), and a major contributor to blindness, lower limb amputation, and cardiac disease.3,4 Despite substantial research efforts, there remains considerable uncertainty over the underlying mechanisms whereby prolonged hyperglycaemia, in concert with the other metabolic features of diabetes, results in such a charac teristic pattern of damage. Although the pathology and epidemiology of diabetic kidney disease (DKD) are well described,5 in particular for type 1 diabetes mellitus (T1DM),5 the processes underlying its development and the factors that influence individual predisposition are poorly understood.6 The extent to which the mechanisms underlying DKD among patients with T1DM or type 2 diabetes mellitus (T2DM) are shared is a matter of debate: whereas the overall clinical presentation is similar for both T1DM and T2DM, the histological findings in T2DM are notably more variable as compared with T1DM. This difference in part reflects the comorbidity and age-associated effects of hypertension and coexisting atherosclerosis.7–10 The degree to which florid cardiovascular disease in patients with poorly controlled diabetes reflects qualitatively distinct processes, Competing interests E.A. and N.R.v.Z. have received salary support, and L.C.G. and M.I.M. have received research support, from the InnovativeMedicines Initiative and the Juvenile Diabetes Research Foundation for research on the genetic basis of diabetic complications.
rather than acceleration of the mechanisms that operate in normoglycaemic individuals, is also far from clear. Uncertainties over the causal mechanisms underlying diabetic complications represent serious obstacles to the development of strategies to address currently unmet medical needs, impeding efforts to identify validated therapeutic targets that can be subsequently modulated for remedial benefit. These challenges are illustrated by aliskiren, a direct inhibitor of renin. Despite considerable prior data indicating that this drug would be a useful adjunct in the management of DKD, the data obtained from clinical trials suggested otherwise.11 The ALTITUDE study found no significant difference in renal and cardiovascular outcomes between patients treated with aliskiren and those who received placebo.11 Absence of a definitive mechanistic framework for disease pathogenesis further limits the capacity to stratify individual risk, for example, through the use of surrogate biomarkers to monitor the development and progression of diabetic complications. The absence of a mechanistic framework also negatively influences individual clinical management and contributes to the excessive costs associ ated with developing novel therapies.12,13 The identification of robust surrogate biomarkers for DKD, approved by the relevant regulatory authorities, enable clinical trials to be more efficient and involve smaller numbers of patients at high risk of complications (selected on the basis of genetic or non-genetic biomarker status) to be followed for shorter intervals than would otherwise be the case for clinical trials of renal outcomes.14 This Review explores the role that human genetics can play in delivering novel insights into the mechanistic basis of diabetic complications, including cardiovascular
NATURE REVIEWS | NEPHROLOGY
ADVANCE ONLINE PUBLICATION | 1 © 2015 Macmillan Publishers Limited. All rights reserved
REVIEWS Key points ■■ The majority of the morbidity and mortality associated with diabetes mellitus is due to complications, such as diabetic kidney disease ■■ A limited understanding of the underlying mechanisms responsible for the development of diabetic complications compromises efforts to develop novel strategies for treatment and prevention ■■ Studies of human genetics offer powerful tools for delivering robust mechanistic insights into complex traits, such as diabetic complications, which have a substantial genetic component ■■ Scientific progress to date has been limited by small sample sizes and/or phenotypic imprecision; however, several major discovery efforts are underway that have been designed to address these issues ■■ The identification of genetic variants with robust effects on the risk of developing diabetic complications will accelerate efforts to develop more effective strategies for treatment and prevention
disease, diabetic retinopathy, diabetic neuropathy and DKD. Compelling evidence has indicated that individual predisposition to DKD in patients with T1DM has a substantial genetic component.15–22 Identification of the specific genetic variants responsible for such predisposition offers a powerful strategy to define key patho genetic mechanisms and generate therapeutic targets with a causal link to human disease. In addition, charac terization of the wider phenotypic impact of a variant of interest provides clues to the potential consequences of therapeutic perturbation of the cognate pathway— information that can help to prioritize among a set of potential therapeutic targets.23
Microvascular complications Diabetic kidney disease DKD affects ~30% of all patients with T1DM or T2DM.24,25 The early phase of DKD is typically charac terized by glomerular hyperfiltration, followed by progressive morphological and ultrastructural changes, including expansion of the extracellular matrix (ECM) and loss of the charge barrier in the glomerular basement membrane.26 The overall result of such changes is protein leakage, characterized initially as microalbuminuria (urinary albumin excretion rate [AER] 20–199 μg/min) that progresses to macroalbuminuria (AER ≥200 μg/min). The estimated glomerular filtration rate (eGFR) decreases, leading to chronic kidney disease (CKD; eGFR 10 million variants across the genome.51 The requirement to make due allowance for extensive multiple testing has led to the adoption of rigorous thresholds for the declaration of statistical significance and a strong impetus towards replication, both of which have limited false claims of association. 52,53 GWAS have proven highly effective in identif ying robustly associated loci for numerous complex traits and diseases, such as T2DM and coronary artery disease (CAD).54,55 In the case of T2DM, >80 disease regions have been identified to date.54 The majority of these disease regions map to intergenic loci,
NATURE REVIEWS | NEPHROLOGY
ADVANCE ONLINE PUBLICATION | 3 © 2015 Macmillan Publishers Limited. All rights reserved
REVIEWS Table 1 | Major consortia addressing the genetic basis of diabetes complications and associated traits Acronym
Full name
Trait of Interest
URL
CARDIoGRAMplusC4D133
Coronary ARtery DIsease Genome wide Replication and Meta-analysis plus The Coronary Artery Disease Genetics consortium
Coronary artery disease
http://www.cardiogramplusc4d.org/
CARe consortium77
Candidate-gene Association REsource Study
Multiple phenotypes
http://www.nhlbi.nih.gov/research/ resources/genetics-genomics/care
DCCT/EDIC134
The Diabetes Control and Complications Trial and the Epidemiology of Diabetes Interventions and Complications study
Complications of diabetes
http://diabetes.niddk.nih.gov/dm/pubs/ control/
DIAGRAM109
DIAbetes Genetics Replication And Meta-analysis consortium
Type 2 diabetes
http://diagram-consortium.org/about.html
FIND
Family Investigation of Nephropathy and Diabetes
Diabetic kidney disease
https://www.niddkrepository.org/studies/ find
GENIE60
GEnetics of Nephropathy —an International Effort
Diabetic kidney disease
http://www.ncbi.nlm.nih.gov/projects/gap/ cgi-bin/study.cgi?study_id=phs000389.v1.p1
GIANT135
The Genetic Investigation of Anthropometric Traits consortium
Anthropometric traits
http://www.broadinstitute.org/collaboration/ giant/index.php/GIANT_consortium
GoKIND136
Genetics of Kidneys in Diabetes study
Diabetic kidney disease
https://www.niddkrepository.org/studies/ gokind/
JDRF–DNCRI
Diabetes Research Foundation– Diabetic Nephropathy Collaborative Research Initiative
Diabetic kidney disease
http://jdrf.org/press-releases/jdrf-formslargest-ever-international-effort-to-researchgenetics-of-diabetic-kidney-disease/
MAGIC137
The Meta-Analyses of Glucose and Insulin-related traits consortium
Glycaemic traits
http://www.magicinvestigators.org/
SUMMIT76
SUrrogate markers for Micro- and Macro-vascular hard endpoints for Innovative diabetes Tools
Complications of diabetes
http://www.imi-summit.eu/
and few of them involve genes previously implicated in disease, an observation that casts further doubt on the value of the candidate gene approach.54 The first application of GWAS to DKD was published in 2009.56 This study, which used pooled DNA from 547 patients with T1DM and ESRD and 549 controls with T1D and no evidence of DKD, identified no significant associations.56 A further study from the GoKinD combined a GWAS of >1,700 individually typed patients with T1DM (820 of whom had DKD) with a replication analysis in 1,304 patients from the DCCT/EDIC trial (Table 1).57 Promising associations were identified in the FRMD3 and CARS loci; however, neither signal reached genome-wide significance nor were the results widely replicated.58 Over the past 5 years, discovery efforts for complex traits have moved towards the development of inter national consortia to support large-scale meta-analysis of multiple individual GWAS; for some diseases, these efforts include many tens of thousands of cases.54,59 This approach has been successful for many diseases and phenotypes, including traits such as schizophrenia and hypertension, for which findings from the initial rounds of single-study GWAS had, as with DKD, appeared unrewarding. The sample size at which the discovery of statistically significant genome-wide associ ations begins to increase varies by at least an order of magnitude between traits (Figure 2). The reasons behind these differences are poorly described, but probably include between-trait differences in heritability, selective
pressure, aetiological heterogeneity and the extent of case-control misclassification. The overall impact of these factors on the outcome of GWAS is difficult to predict in advance for any given trait. The largest GWAS meta-analysis of DKD published to date involved the 6,691 patients with T1DM assembled by GENIE; variants reaching a P
