Xu Tingting, Wang Shaokun, Zhao Liqiang, Wang Jiawen, Xing Jihong
Department of Emergency Medicine, the First Hospital of Jilin University, Changchun 130000, China.
Department of Emergency Medicine, Liaoyuan Municipal Central Hospital, Liaoyuan 136200, China.
World J Emerg Med. 2025;16(2):129-135. doi: 10.5847/wjem.j.1920-8642.2025.035.
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).
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.
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 <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.
Body weight, BMI, and WC are causally associated with an increased risk of CA, with WC identified as the most important risk factor.
本研究旨在利用两样本孟德尔随机化（MR）方法探讨体重、体重指数（BMI）和腰围（WC）与心脏骤停（CA）风险之间的因果关系。
使用全基因组关联研究（GWAS）汇总数据。采用逆方差加权（IVW）法、加权中位数法和MR-Egger分析进行两样本MR分析。分别使用Cochran's Q检验和留一法进行异质性检验和敏感性分析。使用Steiger检验来检测反向因果关系。采用贝叶斯模型平均MR方法来确定最具影响力的危险因素。
共收集了13项关于BMI、体重和WC的GWAS数据。IVW分析显示体重、BMI和WC与CA呈正相关（所有OR>1且P<0.05），MR-Egger法和加权中位数法证实了IVW的结果。未观察到水平多效性或异质性。敏感性分析表明，没有单核苷酸多态性（SNP）导致总体因果关系发生显著变化。还使用贝叶斯模型平均MR根据边际包含概率（MIP）对因果关系进行排序，并确认了相应的模型平均因果估计（MACE），这表明WC（GWAS ID：ukb-b-9405）是排名最高的危险因素（MIP=0.119，MACE=0.011）；其后验概率为0.057。共分析了14项关于体重、BMI和WC的性别特异性GWAS数据与CA的关系，MR结果显示性别特异性因素无显著影响。
体重、BMI和WC与CA风险增加存在因果关联，其中WC被确定为最重要的危险因素。
