European Heart Journal Advance Access published March 29, 2015
CLINICAL RESEARCH
European Heart Journal
doi:10.1093/eurheartj/ehv083
Coronary artery disease
Mendelian randomization analysis supports the
causal role of dysglycaemia and diabetes in the
risk of coronary artery disease
Stephanie Ross 1,2,3, Hertzel C. Gerstein 1,4, John Eikelboom 1,4, Sonia S. Anand 1,2,3,4,
Salim Yusuf 1,2,4, and Guillaume Paré 1,2,3,5,6*
1
Population Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton General Hospital Campus, Hamilton, ON, Canada; 2Department of Clinical
Epidemiology & Biostatistics, Population Genomics Program, McMaster University, Hamilton, ON, Canada; 3Population Genomics Program, Chanchlani Research Centre, McMaster
University, Hamilton, ON, Canada; 4Department of Medicine, McMaster University, Hamilton, ON, Canada; 5Department of Pathology and Molecular Medicine, McMaster University,
Hamilton, ON, Canada; and 6Thrombosis and Atherosclerosis Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
Received 6 January 2015; revised 19 February 2015; accepted 3 March 2015
Introduction
----------------------------------------------------------------------------------------------------------------------------------------------------------Keywords
Genetic variants † Dysglycaemia † Diabetes † Coronary artery disease
Introduction
Large prospective observational studies have reported that type 2
diabetes increases the risk of cardiovascular events by 2-fold following the adjustment for other risk factors.1 These and other
studies have also reported a progressive relationship between
various measures of glycaemia, including fasting glucose (FG) and glycated haemoglobin (HbA1c), and cardiovascular outcomes, both in
people with diabetes and in people without a history of diabetes or
cardiovascular events.1,2 Conversely, large randomized controlled
trials assessing the effect of glucose lowering have also yielded
mixed results.3 – 7 For instance, meta-analyses have demonstrated a
modest 9% reduction in the composite cardiovascular outcome
and a 15% reduction in coronary artery disease (CAD).8 – 10 Moreover, at least one analysis suggests that this effect on CAD is due to
the effect of the intervention on HbA1c.11
* Corresponding author. Tel: +1 905 527 4322, Fax: +1 905 296 5806, Email: [email protected]
Published on behalf of the European Society of Cardiology. All rights reserved. & The Author 2015. For permissions please email: [email protected].
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Type 2 diabetes is a strong risk factor for coronary artery disease (CAD). However, the absence of a clear reduction in
CAD by intensive glucose lowering in randomized controlled trials has fuelled uncertainty regarding the causal role of
dysglycaemia and CAD.
.....................................................................................................................................................................................
Objective
To assess whether Mendelian randomization supports a causal role of dysglycaemia and diabetes for risk of CAD.
.....................................................................................................................................................................................
Methods
Effect size estimates of common genetic variants associated with fasting glucose (FG), glycated haemoglobin (HbA1c), and
diabetes were obtained from the Meta-Analyses of Glucose and Insulin-Related Traits Consortium and Diabetes
Genetics Replication and Meta-Analysis consortia. The corresponding effect estimates of these single nucleotide polymorphisms (SNPs) on the risk of CAD were then evaluated in CARDIOGRAMplusC4D.
.....................................................................................................................................................................................
Results
SNPs associated with HbA1c and diabetes were associated with an increased risk of CAD. Using information from 59
genetic variants associated with diabetes, the causal effect of diabetes on the risk of CAD was estimated at an odds
ratio (OR) of 1.63 (95% Confidence Interval (CI): 1.23–2.07; P ¼ 0.002). On the other hand, nine genetic variants associated with HbA1c were associated with an OR of 1.53 per 1% HbA1c increase (95% CI: 1.14–2.05; P ¼ 0.023) in the risk of
CAD while this effect was non-significant among 30 genetic variants associated with FG per mmol/L (OR: 1.18, 95% CI:
0.97 –1.42; P ¼ 0.102). No significant differences were observed when categorizing genetic loci according to their effect
on either b-cell dysfunction or insulin resistance.
.....................................................................................................................................................................................
Conclusions
These Mendelian randomization analyses support a causal role for diabetes and its associated high glucose levels on CAD,
and suggest that long-term glucose lowering may reduce CAD events.
Page 2 of 9
Methods
Data sources
All single nucleotide polymorphism (SNP) associations were taken from
public repositories of genome-wide databases, where each of .3 million
SNPs are tested for their relationship with a given outcome using data
from .80 000 individuals. Effect size estimates for single SNPs associated
with glucose traits (FG and HbA1C) were obtained from the MetaAnalyses of Glucose and Insulin-Related Traits Consortium (MAGIC)
study, a genome-wide association study (GWAS) consisting of .133
010 of European descent without diabetes.15 – 17 Genetic data for the association of diabetes was obtained from the Diabetes Genetics Replication and Meta-Analysis (DIAGRAM) Consortium study, a GWAS of
34 840 cases and 114 981 controls of European descent.18 Genetic
data for the association of the risk of CAD was obtained from the Coronary Artery Disease Genome-wide Replication and Meta-Analysis (CARDIoGRAMplusC4D) Consortium study, a two-stage GWAS of 63 746
cases of CAD and 130 681 controls.19 When not available in CARDIoGRAMplusC4D, effect estimates were obtained from the CARDIoGRAM, which is a meta-analysis of 22 GWAS studies of 22 233 cases
and 64 762 controls.20 Coronary artery disease outcomes were
defined as one of the following: myocardial infarction (MI), .50% stenosis in at least one coronary vessel at angiography, history of percutaneous transluminal coronary angioplasty or coronary artery bypass graft
surgery, angina or death due to CAD.19 Genetic data on the association
of low-density lipoprotein (LDL), high-density lipoprotein (HDL), total
cholesterol (TC), and triglycerides (TG) were obtained from the
Global Lipids Genetics Consortium study, a GWAS of 188 577 individuals
from 60 studies.21 Genetic data for the association of body mass index
(BMI) was obtained from the Genetic Investigation of Anthropometric
Traits (GIANT) GWAS in .133 154 European individuals.22 Further
details of each consortium are included in the Supplementary material
online, Methods.
SNP selection
SNPs were selected if they were associated with at least one of the two
glucose traits (FG and HbA1C) or diabetes at genome-wide-level significance
of P , 5 × 1028. For duplicate SNPs and SNPs associated with glucose
trait or diabetes that were ,500 kb apart, we obtained the linkage disequilibrium estimates using data from the 1000 Genomes Pilot 123 and then
assigned a lead SNP based on the strength of association with either
glucose traits or diabetes (Supplementary material online, Figure S1).
Statistical analysis
We first tested FG, HbA1c, and diabetes separately for the risk of CAD.
This was done by using the effect estimates of SNPs associated with FG,
HbA1c, or diabetes with their corresponding genetic effect estimates on
CAD using data from the MAGIC, DIAGRAM, and CARDIOGRAMplusC4D consortia, respectively. Figure 1 represents the schematic
representation of the Mendelian Randomization design. Linear regressions (without intercept) were performed using the effect estimates of
SNPs on FG, HbA1C, and diabetes as the independent variables and the
genetic effect sizes on CAD as the dependent variable. Since SNPs associated with dysglycaemia may not only be directly associated with the risk
of CAD but also with other CAD risk factors, we adjusted the causal
effect of dysglycaemia on the risk of CAD for other CAD risk factors.
This was done by developing multivariate models adjusting the effect of
each SNP on CAD for its genetic effect on LDL, HDL, TC, TG, and
BMI. Throughout the manuscript, the effect of the SNPs associated
with diabetes on the risk of CAD was expressed as the relative risk of
CAD among diabetic individuals when compared with individuals
without diabetes. Here, literature estimates of the prevalence of diabetes
and the prevalence of CAD in non-diabetic individuals were
obtained,1,2,24 and these estimates were applied to derive the relative
risk of CAD associated with diabetes (further details in Supplementary
material online, Methods). Estimates of FG available in units of mg/dL
were converted to mmol/L using a multiplication factor of 0.055.
Genetic effects of fasting glucose and HbA1c on risk of CAD were
expressed per 1 mmol/L and 1% genetic increase in fasting glucose and
HbA1c, respectively. Next, the causal effects of glucose traits and diabetes
on the risk of CAD derived from Mendelian randomization were compared with estimates obtained from observational studies to determine
if there was a similar magnitude of effect. We compared the magnitude
of the genetic effect for the risk of CAD to the effect obtained from observational studies using a z-test based on the Altman and Bland
method.25 We also derived power estimates of the causal effect of
SNPs associated with glucose traits and diabetes on the risk of CAD
using simulations. To do so, we first calculated the predicted effect of
each SNP on CAD by matching their effect on glycaemia or diabetes
with expected effect on CAD based on estimates from observational
studies. We then performed 10 000 random simulations, regressing the
predicted effect of each SNP on CAD on their known effect on
glucose traits and diabetes. Finally, loci were categorized according to
their effect on ‘b-cell dysfunction’ or ‘insulin resistance’ to determine if
association with CAD could be ascribed to either hypo- or hyperinsulinaemia, respectively. Only SNPs with an unequivocal effect on
‘b-cell dysfunction’ or ‘insulin resistance’ based on literature reviews
were included in this analysis, with all SNPs of unknown function or
with conflicting reports excluded. All statistical analyses were performed
using R.
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The conflicting reports from epidemiological studies and clinical
trials regarding the potential effects of dysglycaemia on cardiovascular outcomes have fuelled uncertainty regarding the etiologic relationship between dysglycaemia and CAD. However, Mendelian
randomization analyses may help to clarify the relationship
between glucose traits, diabetes, and risk of CAD. This approach
uses genetic associations to explore the effects of modifiable exposures on outcomes. It is based on the principle that genetic variants
are randomly allocated at birth and this distribution is independent
of many factors that may bias observational associations,12 such as
confounding factors and reverse causation.13 However, this approach does not rule out the possibility that genetic variants associated with dysglycaemia may also be correlated with other CAD
risk factors such as dyslipidaemia, blood pressure elevation, or
weight gain.12 This limitation can be circumvented by adopting the
method proposed by Do et al. 14 to adjust Mendelian randomization
analyses for genetic effects on these other risk factors. This approach
allows for the dissection of causal influences for the risk of CAD
among sets of correlated glucose traits and CAD risk factors.
To explore the relationship between dysglycaemia-related indices
(i.e. FG, HbA1C and diabetes) and the risk of CAD, we identified
genetic variants associated with these three indices and then confirmed whether their genetic effect supports a causal association
with CAD. We also explored whether genetic variants that modify
b-cell function have a different relationship to CAD than variants
that modify insulin resistance.
S. Ross et al.
Mendelian randomization analysis supports the causal role of dysglycaemia and diabetes
Page 3 of 9
Figure 1 Schematic representation of the Mendelian randomization design.
Association of glucose levels and diabetes
with risk of coronary artery disease
Thirty SNPs were associated with FG,15 nine associated with
HbA1C,17 and 59 associated with diabetes.18 Further details on the
risk alleles and minor allele frequencies for the trait-specific SNPs
are presented in Supplementary material online, Table S1. To investigate the consistency and directional effect of SNPs association with
glucose traits and CAD, we plotted the effect of SNPs on FG,
HbA1c, and diabetes with their corresponding effect on risk of
CAD (Figure 2). Next, to explore whether SNPs associated with
glucose traits and diabetes predict the risk of CAD, we performed
linear regression analyses for each trait using the respective effect
sizes of SNPs on FG, HbA1c, and diabetes as the independent variables with the corresponding effects sizes for CAD as the dependent
variables (Figure 3). SNPs associated with HbA1C and diabetes were
significantly associated with an increased risk of CAD (odds ratio, OR:
1.53 per % increase in HbA1c, 95% Confidence Interval (CI): 1.14–
2.05; P ¼ 0.023, and OR: 1.57, 95% CI: 1.16–2.05; P ¼ 0.008, respectively) while SNPs associated with FG were not associated
with risk of CAD (P . 0.05; Figure 3). When regression models for
HbA1C and diabetes were adjusted for the effects of SNPs on
other CAD risk factors (i.e. LDL, HDL, TC, TG, and BMI), only
SNPs associated with diabetes remained significantly associated
with CAD (OR: 1.63, 95% CI: 1.23 –2.07; P ¼ 0.002) while association with HbA1c (OR:1.66, 95% CI: 0.44–6.35; P ¼ 0.510) and FG
(OR: 1.16, 95% CI: 0.94–1.42; P ¼ 0.185) were non-significant.
Comparison with literature estimates
from observational studies
To date, the largest prospective meta-analysis to assess the effects of
glucose traits and diabetes on the risk of CVD is the Emerging Risk
Factor Collaboration (ERFC).1,2 The authors reported that diabetes
was associated with an increased risk of CAD (hazard ratio (HR):
2.00, 95% CI: 1.83–2.19) in 698 782 individuals from 102 prospective
studies. They also observed similar trends for FG (HR: 1.02 per
mmol/L, 95% CI: 1.02 –1.03), and HbA1C (HR: 1.43 per %, 95% CI:
1.07 –1.91) in 294 998 individuals without diabetes or CAD from
73 prospective studies. We sought to determine whether the
causal effects of glycaemia and diabetes on the risk of CAD estimated
using Mendelian randomization were consistent with the aforementioned estimates obtained from the ERFC (Figure 4). We observed
that the causal effects of diabetes on the risk of CAD derived from
our Mendelian randomization was similar to that of the risk estimates
obtained from the ERFC (P for difference ¼ 0.161). There were also
no statistical differences for glucose traits SNPs and the corresponding literature estimates (P for difference .0.05 for all). Using
reported risk estimates from observational studies, we estimated
power to detect a genetic association between CAD and diabetes
at 100%, 72.2% for HbA1C, and 5.6% for FG.
Effect of gene function on coronary artery
disease risk estimates
We also explored whether the causal association of diabetes with
CAD differed between sets of genes known to influence either
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Results
Page 4 of 9
S. Ross et al.
Figure 3 Genetic estimates of association of diabetes, glycated haemoglobin, and fasting glucose with risk of coronary artery disease. Analyses
were adjusted for the potential pleiotropic effects of low-density lipoprotein, high-density lipoprotein, total cholesterol, triglyceride, and body
mass index.
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Figure 2 Effect of SNPs associated with levels of fasting glucose, glycated haemoglobin, and diabetes on the risk of coronary artery disease. Each
black dot represents an SNP associated with a glycaemic trait (fasting glucose, glycated hemoglobin or diabetes) with a P , 5 × 1028. The association of each SNP with coronary artery disease (b-value) is represented by the y-axis while association with glycaemic trait is represented by the
x-axis. The blue line illustrates regression of coronary artery disease effects on glycaemic effects. The P-value for the association of fasting
glucose SNPs with the risk of coronary artery disease was 0.102, glycated haemoglobin SNPs with the risk of coronary artery disease was 0.023,
diabetes SNPs with the risk of coronary artery disease was 0.008.
Mendelian randomization analysis supports the causal role of dysglycaemia and diabetes
Page 5 of 9
Figure 4 Comparison of estimated effects of glycaemia and diabetes on coronary artery disease derived from genetic analysis with estimates from
observational studies. Observational estimates represent estimates obtained from the Emerging Risk Factor Collaboration.1,2
b-cell function or insulin resistance. We thus stratified diabetes SNPs
according to their known biological function, namely: ‘b-cell dysfunction’ or ‘insulin resistance’ (Figure 5). Among the 59 SNPs associated
with diabetes, there were 26 loci associated with b-cell dysfunction
and 11 loci associated with insulin resistance. Loci influencing b-cell
dysfunction and insulin resistance were both associated with an
increased risk in CAD (OR 1.83, 95% CI: 1.19–2.62;
P ¼ 0.015 and OR: 2.35, 95% CI: 1.46–3.53; P ¼ 0.01, respectively).
Discussion
Using genetic information from 59 SNPs with known association with
diabetes, our Mendelian randomization analysis supports a causal
role of diabetes for CAD. We demonstrated that SNPs associated
with HbA1C and diabetes were associated with an increased risk of
CAD, which is consistent with findings from large observational
studies.1,2 Furthermore, consistent results were obtained when
restricting the analysis to genes affecting either b-cell dysfunction
or insulin resistance, suggesting that the therapeutic interventions
that act through these different pathways have the potential to
reduce CAD irrespective of their mechanism of action. Although
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Figure 5 Subgroup analysis of loci influencing b-cell dysfunction
or insulin resistance on risk of coronary artery disease.
our estimates of the effect of diabetes on CAD appeared to be
more modest in comparison with observational studies1,2 this may
be explained by residual confounding or bias among these studies.
SNPs associated with HbA1C were also associated with an increased
risk of CAD but the effect was attenuated after adjustment for other
CAD risk factors. SNPs associated with FG were not associated
with the risk of CAD but we had limited power to detect an effect
for FG (5.6%).
These analyses have several strengths. First, the random allocation
of genetic variants acts to reduce the potential effects of confounding
and reverse-causation observed in epidemiological studies. Furthermore, unlike other Mendelian randomization analyses that have
assessed the effect of glucose traits on the risk of CVD outcomes,26
we were able to control for genetic effects on other CAD risk factors
such as blood lipids, blood pressure, and obesity. We also had very
robust estimates from the CARDIOGRAMplusC4D, DIAGRAM,
GIANT, GLGC, and MAGIC consortia which collectively included
a total of 681 875 individuals. The differences we observed
between carriers and non-carriers of genetic variants represent lifelong effects on HbA1C and diabetes. Indeed, in the UKPDS trial,
the authors reported a non-significant reduction in the risk of MI
among patients randomized to intensive or conventional glucoselowering strategies.27 In addition, after 8.5 years of post-trial observations, those originally randomized to the active arm experienced a
15% reduction in MI (P ¼ 0.01) and 13% reduction in all-cause mortality (P ¼ 0.007).28 Similar trends were also observed in the DCCT/
EDIC trial.29 Thus, the genetic properties of our analysis provide
further support that long-term treatment with glucose-lowering
agents may be beneficial. Also, loci involved in b-cell dysfunction
and insulin resistance were both associated with CAD. While the
causal effect of diabetes on CAD using all SNPs was nominally
lower than in each individual subgroup, these differences may be
due to fewer SNPs in each subgroup with wider confidence intervals
and the exclusion of SNPs with unknown function. Taken together,
these results suggest that long-term treatment with glucose-lowering
agents, regardless of the mechanism of action, may be required
Page 6 of 9
Supplementary material
Supplementary material is available at European Heart Journal online.
Acknowledgements
We are thankful to all the participants having agreed to contribute
to this project, and to the DIAGRAM, CARDIoGRAMplusC4D,
MAGIC, GIANT, and GLCC consortia for making their data available.
Funding
This work was supported by the following grants: J.E., receiving support
from Canada Research Chair in Cardiovascular Medicine; S.S.A. received
support from Canada Research Chair in Ethnic Diversity and Cardiovascular Disease, Eli Lilly Canada/May Cohen Chair in Women’s Health, and
Heart and Stroke Foundation of Ontario/Michael G. DeGroote Chair in
Population Health Research; H.C.G. received support from Population
Health Institute Chair in Diabetes Research and Care; G.P. received
support from Canada Research Chair in Genetic and Molecular Epidemiology, CISCOProfessorship in Integrated Health Systems.
Conflict of interest: J.E. received consulting fees, grant support, and
lecture fees from Sanofi-Aventis, Bristol-Myers Squibb, and AstraZeneca,
consulting fees and grant support from Novartis, and consulting fees and
lecture fees from Eli Lilly; S.S.A. received lecture fees from Bristol-Myers
Squibb; H.G.C. received honoraria for consulting and academic presentations from Sanofi-Aventis, Novo Nordisk, and Eli Lilly; and G.P. received
consulting fees from Sanofi-Aventis and Bristol-Myers Squibb.
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before the effects of glycaemic intervention on CVD events may be
observed.
There are several limitations in our study. First, the definition of
diabetes used in the DIAGRAM consortium was specific to each
cohort, which might introduce heterogeneity into results. Second,
estimates obtained from the DIAGRAM and CARDIOGRAMplusC4D consortia consist of both incident and prevalent cases
from prospective and case –control studies. Third, owing to a lack
of genetic data, we were only able to explore the genetic effect of
glucose traits and diabetes in predominantly European populations
and we were also unable to account for other confounders that
might influence the effect of SNPs associated with glycaemia traits
and diabetes on the risk of CAD, such as smoking and waist-to-hip
ratio. Fourth, due to the lack of statistical power, we were limited
in our ability to evaluate associations with FG. Fifth, we were
unable to assess whether there was a non-linear trend for glycaemia
traits and the risk of CAD using Mendelian randomization since we
did not have individual level data.30 Finally, we were not able to
assess the mechanism of action for all SNPs included in this analysis
owing to a lack of functional data in the literature.
In summary, our genetic analysis provides further insight into the
causal role of glucose levels, diabetes, and the risk of CAD. Our
results support that diabetes has an independent and causal effect
on the risk of major cardiovascular events. Thus improved glycaemic
control among diabetic patients and prevention of diabetes may
reduce the risk of CAD outcomes. Our results emphasize the need
to further explore the benefits of long-term glucose lowering on
CAD.
S. Ross et al.
Mendelian randomization analysis supports the causal role of dysglycaemia and diabetes
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