A two-sample Mendelian randomization study on the relationship of body weight, body mass index, and waist circumference with cardiac arrest.

Abstract

Background: This study aims to explore the causal relationship of body weight, body mass index (BMI), and waist circumference (WC) with the risk of cardiac arrest (CA) using two-sample Mendelian randomization (MR).

Methods: Data were summarized using genome-wide association studies (GWAS). Two-sample MR analyses were performed using the inverse variance weighting (IVW) method, the weighted median method, and the MR-Egger analysis. Heterogeneity test and sensitivity analysis were performed using Cochran's Q test and the leave-one-out method, respectively. The Steiger test was used to detect reverse causality. Bayesian model-averaged MR was used to identify the most influential risk factors.

Results: A total of 13 GWAS data were collected for BMI, body weight and WC. IVW analyses showed a positive correlation of body weight, BMI, and WC with CA (all OR>1 and P<0.05), with MR-Egger and weighted median methods confirming the IVW findings. No horizontal pleiotropy or heterogeneity was observed. Sensitivity analysis indicated that no single nucleotide polymorphism (SNP) caused significant changes in overall causality. Bayesian model-averaged MR was also used to rank causality based on marginal inclusion probability (MIP), and the corresponding model-averaged causal estimate (MACE) were confirmed, which indicated that WC (GWAS ID: ukb-b-9405) was the highest-ranked risk factor (MIP=0.119, MACE=0.011); its posterior probability was 0.057. A total of 14 sex-specific GWAS data on weight, BMI, and WC were analyzed in relationship with CA, and the MR results showed no significant effects of sex-specific factors.

Conclusion: Body weight, BMI, and WC are causally associated with an increased risk of CA, with WC identified as the most important risk factor.