Causal association of modifiable factors with cardiometabolic multimorbidity: an exposome-wide Mendelian randomization investigation.

BACKGROUND

Cardiometabolic multimorbidity (CMM), characterized by the co-existence of two or more cardiometabolic diseases (CMDs) including type 2 diabetes (T2D), coronary artery disease (CAD), and stroke, persists as a global health challenge. However, the causal associations of modifiable factors with CMDs and CMM remains to be systematically investigated.

METHODS

In this study, a three-stage design Mendelian randomization (MR) investigation was conducted, using two-sample MR with potential sample overlap correction and multiple testing, multivariable MR analysis, and multi-response MR, with modifiable factors covering domains of socioeconomic factors, behavioral factors, biochemical factors, and physical measures as exposures, and CMM and CMDs as outcomes. Updated large-scale genome-wide association study (GWAS) data based on systematic collection from GWAS Catalog were applied.

RESULTS

Our major findings suggested that, 13 of 23 modifiable factors across four domains, including educational attainment (odds ratio: 0.858, 95% confidence interval: 0.834-0.883), household income (0.794, 0.720-0.875), lifetime smoking behavior (1.201, 1.145-1.260), leisure screen time (1.255, 1.186-1.327), low-density lipoprotein cholesterol levels (1.062, 1.046-1.079), total cholesterol levels (1.045, 1.029-1.062), Apolipoprotein B (1.035, 1.019-1.051), fasting glucose (1.096, 1.038-1.157), glycated hemoglobin (HbA1c) (1.062, 1.037-1.089), systolic blood pressure (1.125, 1.104-1.146), diastolic blood pressure (1.104, 1.083-1.126), forced expiratory volume in 1 s (FEV) (0.953, 0.931-0.975), and body mass index (BMI) (1.243, 1.210-1.277) were evaluated with relatively robust effects on CMM. Furthermore, household income, lifetime smoking behavior, HbA1c, systolic blood pressure, and FEV with CMM were detected as independent associations within a single domain. Similar results were observed in each CMD. Moreover, the multi-response MR provided reinforcing evidence for the associations of educational attainment, serum urate, and BMI with CMM, and lifetime smoking behavior, moderate to vigorous intensity physical activity, and leisure screen time with diverse CMDs.

CONCLUSIONS

Promoting educational attainment, maintaining favorable serum urate, and controlling obesity are specifically prioritized for CMM prevention. Furthermore, avoiding smoking and sedentary behavior, and strengthening physical activity held prominent protective impacts on CMDs. Additionally, improving dyslipidemia and dysglycemia, maintaining favorable blood pressure, and enhancing lung function, would contribute to the co-management of CMDs and preventing the long-term CMM condition. Our investigation provided causality-oriented evidence to establish the risk profile of CMM.