Cardiovascular and Metabolic Risk

Sex Differences in the Risk of Coronary Heart Disease Associated With Type 2 Diabetes: A Mendelian Randomization Analysis

  1. Tricia M. Peters1,2⇑,
  2. Michael V. Holmes3,4,5,6,
  3. J. Brent Richards1,2,
  4. Tom Palmer6,7,
  5. Vincenzo Forgetta1,
  6. Cecilia M. Lindgren8,9,10,
  7. Folkert W. Asselbergs11,12,13,
  8. Christopher P. Nelson14,15,
  9. Nilesh J. Samani14,15,
  10. Mark I. McCarthy9,16,17,
  11. Anubha Mahajan9,16,
  12. George Davey Smith6,18,
  13. Mark Woodward19,20,21,
  14. Linda M. O’Keeffe6,22 and
  15. Sanne A.E. Peters19,20,23
  1. 1Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Montreal, Quebec, Canada
  2. 2Division of Endocrinology, Department of Medicine, Jewish General Hospital, McGill University, Montreal, Quebec, Canada
  3. 3Medical Research Council Population Health Research Unit, University of Oxford, Oxford, U.K.
  4. 4Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K.
  5. 5National Institute for Health Research, Oxford Biomedical Research Centre, Oxford University Hospital, Oxford, U.K.
  6. 6Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, U.K.
  7. 7Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, U.K.
  8. 8Big Data Institute, Li Ka Shing Center for Health Information and Discovery, Oxford University, Oxford, U.K.
  9. 9Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, U.K.
  10. 10Program in Medical and Population Genetics, Broad Institute, Boston, MA
  11. 11Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
  12. 12Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, U.K.
  13. 13Health Data Research UK and Institute of Health Informatics, University College London, London, U.K.
  14. 14Department of Cardiovascular Sciences, University of Leicester, Leicester, U.K.
  15. 15National Institute for Health Research Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, U.K.
  16. 16Oxford Centre for Diabetes, Endocrinology, and Metabolism, Churchill Hospital, University of Oxford, Oxford, U.K.
  17. 17Oxford National Institute for Health Research Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, U.K.
  18. 18School of Social and Community Medicine, University of Bristol, Bristol, U.K.
  19. 19George Institute for Global Health, University of Oxford, Oxford, U.K.
  20. 20George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia
  21. 21Department of Epidemiology, Johns Hopkins University, Baltimore, MD
  22. 22School of Public Health, University College Cork, Cork, Ireland
  23. 23Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
  1. Corresponding author: Tricia M. Peters, tricia.peters{at}mcgill.ca

  1. L.M.O. and S.A.E.P. contributed equally to this work.

Diabetes Care 2021 Feb; 44(2): 556-562. https://doi.org/10.2337/dc20-1137

Abstract

OBJECTIVE Observational studies have demonstrated that type 2 diabetes is a stronger risk factor for coronary heart disease (CHD) in women compared with men. However, it is not clear whether this reflects a sex differential in the causal effect of diabetes on CHD risk or results from sex-specific residual confounding.

RESEARCH DESIGN AND METHODS Using 270 single nucleotide polymorphisms (SNPs) for type 2 diabetes identified in a type 2 diabetes genome-wide association study, we performed a sex-stratified Mendelian randomization (MR) study of type 2 diabetes and CHD using individual participant data in UK Biobank (251,420 women and 212,049 men). Weighted median, MR-Egger, MR-pleiotropy residual sum and outlier, and radial MR from summary-level analyses were used for pleiotropy assessment.

RESULTS MR analyses showed that genetic risk of type 2 diabetes increased the odds of CHD for women (odds ratio 1.13 [95% CI 1.08–1.18] per 1-log unit increase in odds of type 2 diabetes) and men (1.21 [1.17–1.26] per 1-log unit increase in odds of type 2 diabetes). Sensitivity analyses showed some evidence of directional pleiotropy; however, results were similar after correction for outlier SNPs.

CONCLUSIONS This MR analysis supports a causal effect of genetic liability to type 2 diabetes on risk of CHD that is not stronger for women than men. Assuming a lack of bias, these findings suggest that the prevention and management of type 2 diabetes for CHD risk reduction is of equal priority in both sexes.

Introduction

Type 2 diabetes is a major risk factor for coronary heart disease (CHD) (1). A meta-analysis of observational studies demonstrated that type 2 diabetes is associated with a 44% greater relative risk of CHD in women compared with men (2). However, whether this reflects sex differences in the causal effect of type 2 diabetes on CHD or arises from confounding in observational studies is not well understood. Most observational studies adjust for traditional cardiovascular risk factors, yet novel biomarkers, social and behavioral factors, or women-specific risk factors, such as gestational diabetes mellitus, are not generally adjusted for and may explain some of the sex difference (3–5). Sex differences in screening for and treatment of type 2 diabetes might also contribute to the greater excess risk of CHD conferred by type 2 diabetes among women relative to men (6).

Mendelian randomization (MR) analysis exploits the natural random allocation of genetic variants at conception and is an increasingly used approach that can limit potential confounding in human research (7). Under the assumption that differences in the risk of disease arising from genotype mimic changes in the risk of disease acquired during life, MR can be used to detect causal effects. MR studies have supported a causal relationship between genetic predisposition to type 2 diabetes and CHD (8,9). However, these studies did not evaluate sex differences in the causal role of type 2 diabetes in CHD risk. If type 2 diabetes has a stronger causal effect on CHD risk in women compared with men, randomly allocated genetic variants that are risk alleles for type 2 diabetes should also be more strongly associated with the risk of CHD in women than in men. Therefore, in this study, we conducted an MR analysis to examine the sex-specific causal effect of the genetic risk of type 2 diabetes on CHD.

Research Design and Methods

Data Sources and Study Participants

Data from the UK Biobank and a consortium of genome-wide association studies (GWAS) for type 2 diabetes were used. The UK Biobank is a large prospective study of >500,000 individuals (10). Baseline data collection in the UK Biobank was conducted between 2006 and 2010 across 22 assessment centers. Participants aged 37–73 years completed touchscreen questionnaires, were interviewed by trained research nurses, and had physical measurements taken and blood samples extracted and frozen. The presence of type 2 diabetes and CHD was self-reported at study baseline and confirmed by a trained nurse. Genotyping was performed using the Affymetrix UK BiLEVE Axiom array or the Affymetrix UK Biobank Axiom array. A combined reference panel including UK10K samples was used for imputation (11). In accordance with the National Research Ethics Service and the governing research ethics committee of UK Biobank, generic Research Tissue Bank approval was obtained, and study participants provided written informed consent (10).

For the current study, we included individual participant data on 463,469 UK Biobank participants who had concordant genetic and self-reported sex, who clustered with the Great Britain population in 1000 Genomes (12), whose genetic data were of sufficient quality (13), and who provided data on type 2 diabetes and CHD at baseline. Individuals with self-reported type 1 diabetes, gestational diabetes mellitus only, or a diabetes diagnosis before the age of 18 years were excluded. CHD was defined as a self-reported history of angina or myocardial infarction, and linkage with hospital admissions data and the national death register was used to also identify incident diagnoses of CHD after the baseline visit through ICD-9 or ICD-10 codes (410–414 and I20–I25, respectively) using follow-up data from recruitment through the end of February 2016 (mean 5.3 [SD 2.4] years), with 3,453 incident cases of CHD for women and 7,420 incident cases for men. Myocardial infarction was also defined using the UK Biobank algorithm (https://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/alg_outcome_mi.pdf).

Sex-specific summary-level data (β-coefficients and SEs) for the genetic contribution of type 2 diabetes risk were obtained from the European Diabetes Meta-Analysis of Trans-Ethnic Association Studies (DIAMANTE) GWAS of individuals with type 2 diabetes (30,053 women and 41,846 men) and control participants (434,336 women and 383,767 men) of European descent (14). The UK Biobank was excluded from GWAS estimates used in our analyses to avoid sample overlap.

MR and Selection of Single Nucleotide Polymorphisms for Analyses

MR studies exploit the random assortment and independent inheritance of genetic variants in the population, which removes bias that is due to reverse causation and, if conducted appropriately, greatly reduces bias from residual or unmeasured confounding (15). However, three key assumptions must be met for genetic variants to serve as instrumental variables of an exposure in MR analyses (16) (Supplementary Fig. 1). First, the variants must be associated with the exposure of interest; second, they must not be associated with confounders of the relationship between the exposure and the outcome; and third, they must be independent of the outcome except for their association through the exposure. This third assumption relates to the issue of horizontal pleiotropy in which one or more variants used in the instrumental variable influences the outcome through a pathway other than the exposure of interest. When horizontal pleiotropy has a net effect to bias the properties of the genetic instrument, the summary MR estimate can be biased either toward or away from the null. In this situation, horizontal pleiotropy leads to bias of the underlying “true” causal effect, and it is termed unbalanced horizontal, or directional, pleiotropy.

In this study, we used data from the UK Biobank for individual participant MR analysis. Single nucleotide polymorphisms (SNPs) with significant associations (P < 5 × 10−8) with type 2 diabetes from the sex-combined European DIAMANTE GWAS were selected (Supplementary Table 1). We assessed linkage disequilibrium (r2 > 0.2) using PLINK (17) on a reference panel consisting of a random selection of 50,000 individuals from UK Biobank. Of 291 genome-wide significant SNPs from the European DIAMANTE GWAS, 270 were found in UK Biobank that were biallelic, were not in linkage disequilibrium, and were not derived from GWAS that adjusted for BMI. The SNPs were aligned to the same effect allele, and effect allele frequencies were checked for concordance. These 270 SNPs were used to generate sex-specific weighted genetic risk scores as the instrumental variable for analyses (18). Individual SNPs were coded as 0, 1, or 2, depending on the number of type 2 diabetes risk alleles. Each SNP was weighted by the corresponding sex-specific β-coefficient obtained from the European DIAMANTE GWAS and then summed for all SNPs. This method reduces the risk of false-positive results and bias toward the confounded observational association that may occur when all data (SNPs, exposure, outcome) are obtained from a single sample (19).

Statistical Analysis

The strength of the genetic risk score as an instrument for type 2 diabetes was assessed using the F-statistic, where an F-statistic >10 provides evidence against the possibility of bias arising because of a weak instrument (20). The association of sex-specific genetic risk scores with potential confounders was evaluated to assess the validity of the second assumption of MR (i.e., the genetic instrument is not associated with potential confounders) and was also compared with the observational association of type 2 diabetes status with potential confounders.

Two-stage residual inclusion (TSRI) estimation using logistic regression at the second stage (21) and Terza SEs (22) were used to evaluate the association of the genetic risk scores for type 2 diabetes with CHD to estimate the odds of CHD per 1-log unit increase in the odds of type 2 diabetes. This method includes first-stage residuals to correct for endogeneity (21) since application of traditional instrumental variable estimation approaches can be problematic for models that include a binary exposure and a binary outcome (23). Models were adjusted for age, genotype array, and the first four principal components of ancestry.

To assess and account for potential directional horizontal pleiotropy, we also performed summary-level MR analyses using SNP to type 2 diabetes estimates from DIAMANTE and SNP to CHD estimates in UK Biobank. For summary-level analyses, we obtained odds ratios (ORs) and 95% CIs for the causal effect of a 1-log unit increase in the odds of genetic liability to type 2 diabetes on the odds of CHD using the weighted median, MR-Egger, MR-pleiotropy residual sum and outlier (PRESSO), and radial MR methods (24–27). The weighted median method calculates a median of the SNP-specific causal estimates from the ratio method for each SNP (25). It has been shown to yield consistent estimates when the weights of up to half the instruments are not valid. The MR-Egger method is equivalent to an inverse variance–weighted method but does not constrain the intercept to zero, and as such, the MR-Egger estimate is the slope of the modified linear regression equation, and the intercept represents the average pleiotropic effect across SNPs (24). A nonzero intercept provides evidence of unbalanced horizontal pleiotropy, and the slope of the regression coefficient should provide an estimate that is free from bias induced by unbalanced horizontal pleiotropy. Analyses were conducted using the MendelianRandomization package in R Studio version 1.2.1206. The MR-PRESSO test detects and corrects for horizontal pleiotropy and was performed using the MRPRESSO package in R (26). The first part of the test (MR-PRESSO global test) identifies the presence of horizontal pleiotropy, the second part corrects the causal estimate for identified pleiotropy through outlier removal, and the third part (MR-PRESSO distortion test) tests whether the causal estimate significantly differs before and after correction. Additional analyses for pleiotropy assessment used radial MR-Egger models to identify outliers in the UK Biobank analysis using the WSpiller/RadialMR package in R with modified second-order weights (27), and analyses were repeated after exclusion of sex-specific outliers. P values for the test of interaction for estimates from separate analyses were used to assess interaction by sex for each analysis (28).

Results

Characteristics of the UK Biobank participants are presented in Table 1 and Supplementary Table 3. The mean age was 57 (SD 8) years, and 46% of participants were men. The prevalence of type 2 diabetes was 4% in women and 8% in men. CHD was documented among 5% of women (n = 12,716) and 12% of men (n = 26,344), with myocardial infarction diagnosed in 1.5% of women (n = 3,807) and 6% of men (n = 12,871). Both women and men with CHD were more likely to have traditional CHD risk factors (older age, type 2 diabetes, and history of smoking, dyslipidemia, and hypertension) (Supplementary Table 3).

View this table:
Table 1

Population characteristics, UK Biobank (N = 463,469)

The sex-specific 270-SNP genetic risk score showed a strong association with type 2 diabetes in both sexes (F-statistic 683 for women and 1,005 for men) (Supplementary Table 2), thus satisfying the first assumption of MR that the genetic instrument is associated with the exposure. We evaluated whether the apparent difference in instrument strength by sex was due to sex differences in the prevalence of type 2 diabetes. In a random subset of UK Biobank participants with 750 cases of type 2 diabetes for both women (n = 18,493) and men (n = 9,100), the adjusted F-statistic of 47 (R2 = 0.02) for women and adjusted F-statistic of 45 (R2 = 0.03) for men were similar (data not shown). Thus, because the difference in instrument strength by sex is a product of greater prevalence of type 2 diabetes in men, it is not likely to appreciably affect the comparative validity of estimates derived from MR analyses.

Potential confounders were similarly distributed across quartiles of the genetic risk score for both women and men (Table 2). Conversely, conventional observational analyses showed that type 2 diabetes status was strongly associated with all potential confounders assessed (Table 2), highlighting the need for instrumental variables in this setting.

View this table:
Table 2

Association of sex-specific genetic risk scores (270 SNPs) for type 2 diabetes, by quartile, with potential confounders, and association of observational type 2 diabetes with potential confounders in the UK Biobank

Individual participant results from TSRI analyses in UK Biobank showed similar effects of genetic risk of type 2 diabetes on CHD for each sex (OR 1.13 [95% CI 1.08–1.18] for women, 1.21 [1.17–1.26] for men) (Table 3). Sensitivity analyses using the weighted median method showed attenuated results (1.04 [1.00–1.08] for women, 1.06 [1.03–1.09] for men) (Table 3). Using MR Egger, evidence of directional pleiotropy was observed in women (OR 1.01 [95% CI 0.96–1.06], intercept 0.004 [95% CI 0.000–0.008]) (Table 3) and men (OR 1.00 [0.96–1.04], intercept 0.008 [0.004–0.011]) (Table 3). Results from MR-PRESSO after outlier correction were slightly attenuated compared with those from TSRI analyses for both women (three outliers removed, OR 1.08 [1.05–1.13]) and men (five outliers removed, 1.13 [1.10–1.17]) (Table 3). Analyses excluding SNPs from the genetic instrument that were identified as outliers by radial MR showed similar effect estimates as the TSRI results (OR 1.09 [1.05–1.14] for women, 1.24 [1.20–1.29] for men) (Table 3). We used additional measures to assess for heterogeneity on the basis of MR-Egger regression, including the Cochran Q test and I2 statistic. The Q test showed evidence of heterogeneity in the effect of type 2 diabetes SNPs on CHD for both women (Q-statistic 395.8) and men (666.0). The I2 statistic measures heterogeneity in the genetic associations with the exposure, and results (I2 = 84.7% for women and 87.1% men) showed some evidence of heterogeneity in the associations of SNPs with type 2 diabetes. Such heterogeneity could be reflective of multiple causal pathways between type 2 diabetes and risk of CHD.

View this table:
Table 3

MR analysis of type 2 diabetes and risk of CHD, by sex, in UK Biobank

Conclusions

In this MR study of the sex-specific effect of type 2 diabetes on CHD, we found that genetic predisposition to type 2 diabetes does not confer a greater excess risk of CHD for women than for men. While our results are consistent with previous sex-combined MR studies providing support for a causal role of type 2 diabetes in CHD risk (8,9), the finding that the causal effect of genetic liability to type 2 diabetes on CHD risk is not stronger for women than for men is novel and differs from sex-specific estimates from the accumulated observational evidence (2). This includes a recent analysis in the UK Biobank, which showed a stronger association of type 2 diabetes with CHD for women than for men (29).

There are several potential explanations for the differences between the findings of our MR study and the observational evidence. As with any observational study, studies of sex differences in the association of type 2 diabetes with CHD may not have controlled for all relevant confounders or may have controlled for confounders that were poorly measured, leading to residual confounding. If this residual confounding differs between the sexes, a sex difference in the observational association of type 2 diabetes with CHD could arise. For example, men are typically at higher absolute risk of CHD, and the prevalence of many cardiovascular risk factors is higher for men than for women (1). However, cardiovascular risk factors, including type 2 diabetes, appear to confer a greater relative CHD risk for women than for men in observational analyses (29). Furthermore, among individuals with type 2 diabetes compared with those without type 2 diabetes, several studies have shown that the differences in cardiovascular risk factors, including blood pressure, dyslipidemia, and particularly anthropometric variables, are greater among women than among men (3,6). Although women generally display a more favorable cardiometabolic risk profile than men, this favorable risk profile declines and ultimately reverses as glycemic control deteriorates (30).

Yet observational evidence of sex differences in the association of other major risk factors with CHD is not universally observed, suggesting that mechanisms other than confounding alone may be involved. An alternative explanation is that sex differences in the effect of diabetes on CHD risk seen in observational studies reflect the more adverse deterioration in cardiovascular risk profile along the glucose intolerance spectrum in women compared with men. A recent MR study showed that the association of BMI with the risk of diabetes was stronger for women than for men (31). Accordingly, a pathway of type 2 diabetes progression and glycemic dysregulation that leads to more adverse complications of diabetes for women than for men may underpin the observational findings rather than a direct sex difference in the effect of diabetes on CHD risk.

Furthermore, women may be perceived as having lower cardiovascular risk, and consequently, type 2 diabetes and comorbid cardiovascular risk factors may be treated less aggressively (32,33). Guidelines for the diagnosis and treatment of type 2 diabetes and CHD are not sex specific; our results of a similar causal association of type 2 diabetes with CHD by sex would support the notion that for a given state of glycemic dysregulation and burden of cardiovascular risk factors, prevention and management of type 2 diabetes for the reduction of CHD risk should be of equal priority for both women and men. In addition, sex-specific confounders, such as reproductive factors including gestational diabetes mellitus, are rarely adjusted for in observational studies that include both sexes; this could inflate the association of type 2 diabetes with CHD in women if the cumulative duration of the exposure to diabetes is greater, on average, among women than among men. Sex-specific residual confounding may therefore explain some of the discrepancy between the MR and observational evidence. Alternatively, the discrepancy might arise if the MR analysis does not account for genetic variation in the risk of type 2 diabetes that derives from sex chromosomes, as the GWAS data includes only autosomal SNPs. For example, a recent MR study observed a causal association of genetically determined testosterone (X chromosome) with increased type 2 diabetes risk for women but not for men (34). Multiple other mechanisms could also play a role in conferring higher CHD risks for women with type 2 diabetes compared with men independent of glucose dysregulation or diabetes, including sex differences in microvasculature such as vascular responsivity to aldosterone (35).

The diagnosis of type 2 diabetes is defined by a cut point along a continuum of glycemia that is based on the risk of associated complications, such as retinopathy (36). Accordingly, an individual with borderline glycemia who is not yet diagnosed with type 2 diabetes may display phenotypic and genetic similarity compared with an individual with diagnosed diabetes. Exposure misclassification of this type would tend to bias individual participant MR estimates toward the null, leading to an underestimation of the MR results. In our individual participant MR, this scenario would only affect our conclusion when prediabetes affected a differential proportion of women and men in the study population. Of note, this should not influence summary-level MR results because the exposure is fully defined by genotype.

There are several strengths of our study, including the use of MR, which under specific assumptions can be used to test the hypothesis that a particular risk factor is causal for an outcome (16). In accordance with the first assumption of MR, the sex-specific genetic risk scores were very strong instruments for type 2 diabetes for both women and men. Meeting the second and third assumptions of MR, the genetic risk scores were shown to be broadly independent of measured potential confounding factors. Furthermore, for both women and men, results of sensitivity analyses after correction for outliers were similar to initial results. However, there are also limitations of our study. Although the genetic risk scores were strong instruments for type 2 diabetes, our instruments may have been underpowered to detect modest differences in sex-specific causal effects. Furthermore, our analysis used genetic risk scores derived from 270 genome-wide significant type 2 diabetes SNPs in the sex-combined European DIAMANTE GWAS (14). Genetic instruments obtained from the SNPs that are associated with type 2 diabetes in sex-specific GWASs could also have been constructed. However, the European DIAMANTE GWAS observed only one significant sex-differentiated SNP (14), and thus, the impact of the use of a sex-combined instrument is unlikely to have changed our results substantially. Moreover, such an instrument would not permit direct comparison of sex differences in the overall genetic predisposition to type 2 diabetes but, instead, compares the causal effect of two distinct sex-specific instruments on CHD risk.

SNPs included in the genetic instruments for type 2 diabetes may affect CHD risk through pathways separate from their effect on type 2 diabetes risk, and these pathways could differ by sex. For example, there was some evidence of directional pleiotropy using MR-Egger. However, the intercept for both men and women neared zero, and MR-Egger generally lacks power. Moreover, results from outlier-robust sensitivity analyses were more similar to the overall results. This suggests that our primary results are in fact robust and that MR-Egger results may have been influenced by sensitivity of this method to extreme outliers (37).

These results might reflect multiple different scenarios (38), some of which may have downstream effects on type 2 diabetes risk and may differentially affect CHD risk by sex. Taken together, we cannot exclude a sex-specific causal effect through other pathways not captured in our genetic instrument. Of note, our instrumental variables for type 2 diabetes were derived from the DIAMANTE GWAS effect estimates without adjustment for BMI since the influence of BMI on type 2 diabetes risk may be sex differential (31). Considering the important role of BMI in type 2 diabetes risk, adjusting for measures of adiposity in the type 2 diabetes genetic risk score could bias a true differential effect of type 2 diabetes on CHD to the null. In addition, the UK Biobank and the European DIAMANTE GWAS used for our analyses included primarily European populations, and therefore, we cannot assess sex differences in the causal effect of type 2 diabetes with CHD across ethnicities. Furthermore, despite the large sample size of the UK Biobank, a low overall response rate of ∼5.5% limits the generalizability of our results. Considering that the participating population is unlikely representative of the general U.K. population, as recently demonstrated (39), it is possible that our findings might be biased if there is a sex-specific selection bias that is associated with both the exposure and the outcome. Finally, a recent study demonstrated an association of autosomal loci with sex, which may introduce bias as a result of sex differences in study participation (40). If risk alleles for type 2 diabetes were associated with study participation in a sex-specific manner, this may have resulted in an inability to consistently detect a sex difference in the causal effect of type 2 diabetes with CHD in our MR analyses.

In conclusion, the present MR analysis supports a causal effect of type 2 diabetes on the risk of CHD, with similar effects seen between women and men. In the absence of bias, these findings suggest that the prevention and management of type 2 diabetes for the reduction of CHD risk should be of equal priority for both women and men.

Article Information

Acknowledgments. The authors thank the European DIAMANTE investigators for making data available. This research has been conducted using the UK Biobank resource under application number 24281.

Funding. This research is supported by the Lady Davis Institute for Medical Research and the Department of Medicine, Jewish General Hospital (T.M.P.); the UK Medical Research Council; British Heart Foundation Intermediate Clinical Research Fellowship (FS/18/23/33512); and the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (M.V.H.). J.B.R. is supported by the Canadian Institutes of Health Research (CIHR), the Lady Davis Institute of the Jewish General Hospital, the Canadian Foundation for Innovation, the NIH Foundation, Cancer Research UK, the Fonds de Recherche Québec Santé (FRQS), and an FRQS Clinical Research Scholarship. C.M.L. is supported by the Li Ka Shing Foundation, the NIHR Oxford Biomedical Research Centre, National Institutes of Health grants CRR00070 CR00.01 and 5P50-HD-028138-27, WT-SSI/John Fell funds, and Widenlife. F.W.A. is supported by University College London Hospitals NIHR Biomedical Research Centre. C.P.N. and N.J.S. are supported by the British Heart Foundation. M.I.M. is supported by Wellcome Trust grants 090532, 098381, 203141, and 212259 and National Institute of Diabetes and Digestive and Kidney Diseases grant U01-DK-105535 and was a Wellcome investigator and an NIHR senior investigator. L.M.O. is supported by a UK Medical Research Council Population Health Scientist fellowship (MR/M014509/1). S.A.E.P. is supported by a UK Medical Research Council Skills Development Fellowship (MR/P014550/1).

The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health.

Duality of Interest. M.V.H. has collaborated with Boehringer Ingelheim in research and in accordance with the policy of The Clinical Trial Service Unit and Epidemiological Studies Unit (University of Oxford), did not accept any personal payment. J.B.R. has served as an advisor to GlaxoSmithKline. M.I.M. has served on advisory panels for Pfizer, Novo Nordisk, and Zoe Global; has received honoraria from Merck, Pfizer, Novo Nordisk, and Eli Lilly; and has received research funding from AbbVie, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, Novo Nordisk, Pfizer, Roche, Sanofi Aventis, SERVIER, and Takeda. As of June 2019, M.I.M. is an employee of Genentech and a holder of Roche stock. As of January 2020, A.M. is an employee of Genentech and a holder of Roche stock. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. T.M.P. analyzed the data, wrote the manuscript, and contributed to the study design and conception. M.V.H., J.B.R., M.W., and L.M.O. contributed to the study design and reviewed/edited the manuscript. T.P. contributed to the study design and data analysis and reviewed the manuscript. V.F. contributed to the data analysis and reviewed the manuscript. C.M.L., F.W.A., C.P.N., N.J.S., and G.D.S reviewed/edited the manuscript. M.I.M. and A.M. contributed data and reviewed/edited the manuscript. S.A.E.P. contributed to the study design and data analysis and wrote the manuscript. T.M.P. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Footnotes

  • M.I.M. is currently affiliated with Genentech, South San Francisco, CA.

  • A.M. is currently affiliated with Genentech, South San Francisco, CA.

  • This article contains supplementary material online at https://doi.org/10.2337/figshare.13135781.

  • Received May 14, 2020.
  • Accepted October 21, 2020.
https://www.diabetesjournals.org/content/license

Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/content/license.

References

  1. ↵
    1. Mozaffarian D,
    2. Benjamin EJ,
    3. Go AS, et al.; Writing Group Members; American Heart Association Statistics Committee; Stroke Statistics Subcommittee
    . Executive summary: heart disease and stroke statistics--2016 update: a report from the American Heart Association. Circulation 2016;133:447–454
    OpenUrlFREE Full Text
  2. ↵
    1. Peters SAE,
    2. Huxley RR,
    3. Woodward M
    . Diabetes as risk factor for incident coronary heart disease in women compared with men: a systematic review and meta-analysis of 64 cohorts including 858,507 individuals and 28,203 coronary events. Diabetologia 2014;57:1542–1551
    OpenUrlCrossRefPubMedWeb of Science
  3. ↵
    1. Peters SAE,
    2. Bots SH,
    3. Woodward M
    . Sex differences in the association between measures of general and central adiposity and the risk of myocardial infarction: results from the UK Biobank. J Am Heart Assoc 2018;7:e008507
    OpenUrlAbstract/FREE Full Text
    1. Li J,
    2. Song C,
    3. Li C,
    4. Liu P,
    5. Sun Z,
    6. Yang X
    . Increased risk of cardiovascular disease in women with prior gestational diabetes: a systematic review and meta-analysis. Diabetes Res Clin Pract 2018;140:324–338
    OpenUrlCrossRefPubMed
  5. ↵
    1. Peters TM,
    2. Pelletier R,
    3. Behlouli H,
    4. Rossi AM,
    5. Pilote L
    . Excess psychosocial burden in women with diabetes and premature acute coronary syndrome. Diabet Med 2017;34:1568–1574
    OpenUrlPubMed
  6. ↵
    1. Juutilainen A,
    2. Kortelainen S,
    3. Lehto S,
    4. Rönnemaa T,
    5. Pyörälä K,
    6. Laakso M
    . Gender difference in the impact of type 2 diabetes on coronary heart disease risk. Diabetes Care 2004;27:2898–2904
    OpenUrlAbstract/FREE Full Text
  7. ↵
    1. Davey Smith G,
    2. Hemani G
    . Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet 2014;23:R89–R98
    OpenUrlCrossRefPubMedWeb of Science
  8. ↵
    1. Ross S,
    2. Gerstein HC,
    3. Eikelboom J,
    4. Anand SS,
    5. Yusuf S,
    6. Paré G
    . Mendelian randomization analysis supports the causal role of dysglycaemia and diabetes in the risk of coronary artery disease. Eur Heart J 2015;36:1454–1462
    OpenUrlCrossRefPubMed
  9. ↵
    1. Ahmad OS,
    2. Morris JA,
    3. Mujammami M, et al
    . A Mendelian randomization study of the effect of type-2 diabetes on coronary heart disease. Nat Commun 2015;6:7060
    OpenUrlCrossRefPubMed
  10. ↵
    1. Sudlow C,
    2. Gallacher J,
    3. Allen N, et al
    . UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 2015;12:e1001779
    OpenUrlCrossRefPubMed
  11. ↵
    1. Walter K,
    2. Min JL,
    3. Huang J, et al.; UK10K Consortium
    . The UK10K project identifies rare variants in health and disease. Nature 2015;526:82–90
    OpenUrlCrossRefPubMed
  12. ↵
    1. Kemp JP,
    2. Morris JA,
    3. Medina-Gomez C, et al
    . Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis. Nat Genet 2017;49:1468–1475
    OpenUrlCrossRefPubMed
  13. ↵
    1. UK Biobank
    . Genotyping and quality control of UK Biobank, a large-scale, extensively phenotyped prospective resource, 2015. Accessed 17 July 2019. Available from https://www.ukbiobank.ac.uk/wp-content/uploads/2014/04/UKBiobank_genotyping_QC_documentation-web.pdf
  14. ↵
    1. Mahajan A,
    2. Taliun D,
    3. Thurner M, et al
    . Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet 2018;50:1505–1513
    OpenUrlCrossRefPubMed
  15. ↵
    1. Evans DM,
    2. Davey Smith G
    . Mendelian randomization: new applications in the coming age of hypothesis-free causality. Annu Rev Genomics Hum Genet 2015;16:327–350
    OpenUrlCrossRefPubMed
  16. ↵
    1. Lawlor DA,
    2. Harbord RM,
    3. Sterne JAC,
    4. Timpson N,
    5. Davey Smith G
    . Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 2008;27:1133–1163
    OpenUrlCrossRefPubMed
  17. ↵
    1. Chang CC,
    2. Chow CC,
    3. Tellier LC,
    4. Vattikuti S,
    5. Purcell SM,
    6. Lee JJ
    . Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 2015;4:7
    OpenUrlCrossRefPubMed
  18. ↵
    1. Burgess S,
    2. Thompson SG
    . Use of allele scores as instrumental variables for Mendelian randomization. Int J Epidemiol 2013;42:1134–1144
    OpenUrlCrossRefPubMedWeb of Science
  19. ↵
    1. Burgess S,
    2. Davies NM,
    3. Thompson SG
    . Bias due to participant overlap in two-sample Mendelian randomization. Genet Epidemiol 2016;40:597–608
    OpenUrlCrossRefPubMed
  20. ↵
    1. Stock J,
    2. Yogo M,
    3. Wright J
    . A survey of weak instruments and weak identification in generalized method of moments. J Bus Econ Stat 2002;20:518–529
    OpenUrlCrossRefWeb of Science
  21. ↵
    1. Palmer TM,
    2. Holmes MV,
    3. Keating BJ,
    4. Sheehan NA
    . Correcting the standard errors of 2-stage residual inclusion estimators for Mendelian randomization studies. Am J Epidemiol 2017;186:1104–1114
    OpenUrl
  22. ↵
    1. Terza JV
    . Simpler standard errors for two-stage optimization estimators. Stata J Promot Commun Stat Stata 2016;16:368–385
    OpenUrl
  23. ↵
    1. Burgess S,
    2. Thompson SG; CRP CHD Genetics Collaboration
    . Methods for meta-analysis of individual participant data from Mendelian randomisation studies with binary outcomes. Stat Methods Med Res 2016;25:272–293
    OpenUrlCrossRefPubMed
  24. ↵
    1. Bowden J,
    2. Davey Smith G,
    3. Burgess S
    . Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 2015;44:512–525
    OpenUrlCrossRefPubMed
  25. ↵
    1. Bowden J,
    2. Davey Smith G,
    3. Haycock PC,
    4. Burgess S
    . Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol 2016;40:304–314
    OpenUrlCrossRefPubMed
  26. ↵
    1. Verbanck M,
    2. Chen C-Y,
    3. Neale B,
    4. Do R
    . Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 2018;50:693–698
    OpenUrlCrossRefPubMed
  27. ↵
    1. Bowden J,
    2. Spiller W,
    3. Del Greco M F, et al
    . Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int J Epidemiol 2018;47:1264–1278
    OpenUrl
  28. ↵
    1. Altman DG,
    2. Bland JM
    . Interaction revisited: the difference between two estimates. BMJ 2003;326:219
    OpenUrlFREE Full Text
  29. ↵
    1. Millett ERC,
    2. Peters SAE,
    3. Woodward M
    . Sex differences in risk factors for myocardial infarction: cohort study of UK Biobank participants. BMJ 2018;363:k4247
    OpenUrlAbstract/FREE Full Text
  30. ↵
    1. Peters SAE,
    2. Huxley RR,
    3. Sattar N,
    4. Woodward M
    . Sex differences in the excess risk of cardiovascular diseases associated with type 2 diabetes: potential explanations and clinical implications. Curr Cardiovasc Risk Rep 2015;9:36
    OpenUrlCrossRefPubMed
  31. ↵
    1. Censin JC,
    2. Peters SAE,
    3. Bovijn J, et al
    . Causal relationships between obesity and the leading causes of death in women and men. PLoS Genet 2019;15:e1008405
    OpenUrlCrossRefPubMed
  32. ↵
    1. Eapen ZJ,
    2. Liang L,
    3. Shubrook JH, et al
    . Current quality of cardiovascular prevention for Million Hearts: an analysis of 147,038 outpatients from The Guideline Advantage. Am Heart J 2014;168:398–404
    OpenUrlCrossRefPubMed
  33. ↵
    1. Zhao M,
    2. Vaartjes I,
    3. Graham I, et al
    . Sex differences in risk factor management of coronary heart disease across three regions. Heart 2017;103:1587–1594
    OpenUrlAbstract/FREE Full Text
  34. ↵
    1. Ruth KS,
    2. Day FR,
    3. Tyrrell J, et al.; Endometrial Cancer Association Consortium
    . Using human genetics to understand the disease impacts of testosterone in men and women. Nat Med 2020;26:252–258
    OpenUrlPubMed
  35. ↵
    1. Haas AV,
    2. Rosner BA,
    3. Kwong RY, et al
    . Sex differences in coronary microvascular function in individuals with type 2 diabetes. Diabetes 2019;68:631–636
    OpenUrlAbstract/FREE Full Text
  36. ↵
    1. Colagiuri S,
    2. Lee CMY,
    3. Wong TY,
    4. Balkau B,
    5. Shaw JE,
    6. Borch-Johnsen K; DETECT-2 Collaboration Writing Group
    . Glycemic thresholds for diabetes-specific retinopathy: implications for diagnostic criteria for diabetes [published correction appears in Diabetes Care 2011;34:1888]. Diabetes Care 2011;34:145–150
    OpenUrlAbstract/FREE Full Text
  37. ↵
    1. Burgess S,
    2. Thompson SG
    . Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol 2017;32:377–389
    OpenUrlCrossRefPubMed
  38. ↵
    1. Holmes MV,
    2. Davey Smith G
    . Can Mendelian randomization shift into reverse gear? Clin Chem 2019;65:363–366
    OpenUrlFREE Full Text
  39. ↵
    1. Batty GD,
    2. Gale CR,
    3. Kivimäki M,
    4. Deary IJ,
    5. Bell S
    . Comparison of risk factor associations in UK Biobank against representative, general population based studies with conventional response rates: prospective cohort study and individual participant meta-analysis. BMJ 2020;368:m131
    OpenUrlAbstract/FREE Full Text
  40. ↵
    1. Pirastu N,
    2. Cordioli M,
    3. Nandakumar P, et al
    . Genetic analyses identify widespread sex-differential participation bias. bioRxiv 2020. Accessed 26 March 2020. Available from https://biorxiv.org/content/early/2020/03/23/2020.03.22.001453.abstract
Back to top
View Selected Citations (0)
Print
Download PDF
Article Alerts
Email Article
Citation Tools
Add to Selected Citations

Sex Differences in the Risk of Coronary Heart Disease Associated With Type 2 Diabetes: A Mendelian Randomization Analysis
Tricia M. Peters, Michael V. Holmes, J. Brent Richards, Tom Palmer, Vincenzo Forgetta, Cecilia M. Lindgren, Folkert W. Asselbergs, Christopher P. Nelson, Nilesh J. Samani, Mark I. McCarthy, Anubha Mahajan, George Davey Smith, Mark Woodward, Linda M. O’Keeffe, Sanne A.E. Peters
Diabetes Care Feb 2021, 44 (2) 556-562; DOI: 10.2337/dc20-1137
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo

Jump to section

Subjects