Investigating the causal relationships between excess adiposity and cardiometabolic health in men and women

Abstract

Aims/hypothesis: Excess adiposity is differentially associated with increased risk of cardiometabolic disease in men and women, according to observational studies. Causal inference studies largely assume a linear relationship between BMI and cardiometabolic outcomes, which may not be the case. In this study, we investigated the shapes of the causal relationships between BMI and cardiometabolic diseases and risk factors. We further investigated sex differences within the causal framework.

Methods: To assess causal relationships between BMI and the outcomes, we used two-stage least-squares Mendelian randomisation (MR), with a polygenic risk score for BMI as the instrumental variable. To elucidate the shapes of the causal relationships, we used a non-linear MR fractional polynomial method, and used piecewise MR to investigate threshold relationships and confirm the shapes.

Results: BMI was associated with type 2 diabetes (OR 3.10; 95% CI 2.73, 3.53), hypertension (OR 1.53; 95% CI 1.44, 1.62) and coronary artery disease (OR 1.20; 95% CI 1.08, 1.33), but not chronic kidney disease (OR 1.08; 95% CI 0.67, 1.72) or stroke (OR 1.08; 95% CI 0.92, 1.28). The data suggest that these relationships are non-linear. For cardiometabolic risk factors, BMI was positively associated with glucose, HbA_1c, triacylglycerol levels and both systolic and diastolic BP. BMI had an inverse causal relationship with total cholesterol, LDL-cholesterol and HDL-cholesterol. The data suggest a non-linear causal relationship between BMI and BP and other biomarkers (p<0.001) except lipoprotein A. The piecewise MR results were consistent with the fractional polynomial results. The causal effect of BMI on coronary artery disease, total cholesterol and LDL-cholesterol was different in men and women, but this sex difference was only significant for LDL-cholesterol after controlling for multiple testing (p<0.001). Further, the causal effect of BMI on coronary artery disease varied by menopause status in women.

Conclusions/interpretation: We describe the shapes of causal effects of BMI on cardiometabolic diseases and risk factors, and report sex differences in the causal effects of BMI on LDL-cholesterol. We found evidence of non-linearity in the causal effect of BMI on diseases and risk factor biomarkers. Reducing excess adiposity is highly beneficial for health, but there is greater need to consider biological sex in the management of adiposity.

Keywords: Cardiometabolic; Causal; Mendelian randomisation; Obesity.

Fig. 1

Forest plots of a summary meta-analysis combining the causal effect estimates of BMI on CMDs in (a) men, (b) women, and (c) all participants. The common outcome in both fixed and random effect lines represents any CMD. T2D, type 2 diabetes

Fig. 2

Plots showing the estimated shapes of the causal relationships between BMI and CMDs in combined and sex-specific analyses. Shape estimates are derived from the function of fractional polynomials based on the doubly ranked method that best fits the data. The solid black line represents the function curve, the blue band represents the 95% CI, the red dot represents the reference BMI of 25 kg/m², and the dashed red line represents the null effect size. The plots have been cropped to depict estimated causal associations up to an OR of 3.0 for ease of comparison. HTN, hypertension; T2D, type 2 diabetes

Fig. 3

Plots showing the estimated shapes of the causal relationships between BMI and selected cardiometabolic biomarkers in combined and sex-specific analyses. Shape estimates are derived from the function of fractional polynomials based on the doubly ranked method that best fits the data. The solid black line represents the function curve, the blue band represents the 95% CI, the red dot represents the reference BMI of 25 kg/m², and the red dashed line represents the null effect size. HDL-c, HDL-cholesterol; LDL-c, LDL-cholesterol; TChol, total cholesterol; TG, triacylglycerol