Department of Anesthesiology, the Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China.
PLoS One. 2024 Sep 30;19(9):e0310898. doi: 10.1371/journal.pone.0310898. eCollection 2024.
The objective of this study was to explore the potential causalities of fat mass, nonfat mass and height (henceforth, 'anthropometric measures') with sepsis risk and mortality. We conducted the Mendelian randomization (MR) investigation using genome-wide association study (GWAS) summary statistics of anthropometric measures, sepsis, and sepsis mortality. The GWAS summary data from the UK Biobank was used. Firstly, MR analysis was performed to estimate the causal effect of anthropometric measures on the risk of sepsis. The inverse-variance weighted (IVW) method was utilized as the primary analytical approach, together with weighted median-based method. Cochrane's Q test and MR-Egger intercept test were performed to assess heterogeneity and pleiotropy, respectively. Finally, we performed a series of sensitivity analyses to enhance the precision and veracity of our findings. The IVW method showed that genetically predicted weight-related measures were suggestively linked to an increased risk of sepsis. However, height displayed no causal association with sepsis risk and mortality. Furthermore, weight-related measures also displayed significant MR association with the sepsis mortality, except body nonfat mass and right leg nonfat mass. However, MVMR analysis indicated the observed effects for weight-related measures in the univariable MR analyses are more likely a bias caused by the interrelationship between anthropometric measures. According to the MR-Egger intercept assessment, our MR examination was not influenced by horizontal pleiotropy (all p>0.05). Moreover, the reliability of the estimated causal association was confirmed by the sensitivity analyses. In conclusion, these findings provided vital new knowledge on the role of anthropometric-related measures in the sepsis etiology.
本研究旨在探讨体脂肪量、非脂肪量和身高(以下简称“人体测量指标”)与脓毒症风险和死亡率之间的潜在因果关系。我们使用人体测量指标、脓毒症和脓毒症死亡率的全基因组关联研究(GWAS)汇总统计数据进行孟德尔随机化(MR)研究。GWAS 汇总数据来自英国生物银行。首先,我们进行了 MR 分析,以估计人体测量指标对脓毒症风险的因果影响。我们使用逆方差加权(IVW)方法作为主要分析方法,并结合加权中位数法。我们进行了 Cochrane's Q 检验和 MR-Egger 截距检验,分别评估异质性和多效性。最后,我们进行了一系列敏感性分析,以提高我们发现的准确性和可信度。IVW 方法表明,遗传预测的体重相关指标与脓毒症风险增加有提示性关联。然而,身高与脓毒症风险和死亡率没有因果关系。此外,体重相关指标与脓毒症死亡率也有显著的 MR 关联,除了体非脂肪量和右腿非脂肪量。然而,MVMR 分析表明,在单变量 MR 分析中观察到的体重相关指标的效应更有可能是由人体测量指标之间的相互关系引起的偏差。根据 MR-Egger 截距评估,我们的 MR 检查不受水平多效性的影响(所有 p>0.05)。此外,敏感性分析证实了估计因果关系的可靠性。总之,这些发现为人体测量相关指标在脓毒症病因学中的作用提供了重要的新知识。