Genetically predicted waist-to-hip circumference ratio and coronary artery disease: A sex-specific Mendelian randomization study

Coronary artery disease (CAD) affects millions of individuals worldwide and results in a substantial burden to healthcare systems. Although it is established that CAD affects females differently than males, differences between the sexes are not routinely accounted for. Body mass index is a known risk factor for CAD. However, more accurate metrics of body fat, including waist-to-hip circumference ratio (WHR), could be more meaningful clinically. WHR exhibits sex differences due to sex hormones, differing effects at genetic risk loci, and other factors. It is unclear if WHR is a causal factor for CAD in one or both sexes, but this information will be crucial for improving heart health. Causal inference, however, can be challenging. Large-scale cohorts with genetic data allow for Mendelian randomization, which, given certain assumptions, tests whether there is a causal relationship between an exposure and the outcome using genetic variants. We conducted sex-specific, one-sample MR analyses using two-stage least-squares regression in the UK Biobank with genetic variants robustly associated with WHR. We found evidence of a causal relationship between WHR and CAD risk in females (OR [95% CI] = 1.16 [1.06–1.26]; p value = 7.5E-4), whereas in males, we did not find evidence of a causal relationship (OR [95% CI] = 1.40 [0.98–2.01]; p value = 0.063). Results were supported by two additional MR approaches (using a genetic risk score and two-sample MR using the inverse variance weighted approach). We encourage future work assessing sex-specific effects using causal inference techniques to better understand factors contributing to complex disease risk. Coronary artery disease (CAD) is a global health concern that differs between females and males. We used sex-specific Mendelian randomization (MR) to test for a causal relationship between WHR and CAD. We found a potential link in females but not in males. Sex-specific analyses are essential for personalized medicine.