Liu Jingwen, Pan Renbing
Department of Psychiatry, Longyou People's Hospital Affiliated with Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Quzhou, Zhejiang, China.
Department of Urology, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, Zhejiang, China.
Medicine (Baltimore). 2025 Aug 22;104(34):e44089. doi: 10.1097/MD.0000000000044089.
Coronavirus disease 2019 (COVID-19) brings heavy burden to patients and society globally. Observational research suggest inflammatory regulators are related to COVID-19. Nevertheless, their causal effects are still unclear. Herein, we performed a Mendelian randomization (MR) analysis to estimate the causality between systemic inflammatory regulators and COVID-19 diverse phenotypes. Genetic instrumental variables associated with systemic inflammatory regulators were extracted from genome-wide association study (GWAS) data involving 8293 European participants. Summary statistics for COVID-19 diverse subtypes were obtained from the COVID-19 host genetic initiative (54,071 cases and 4,905,697 controls). We performed multi-omics approach and MR study to detect the causal links through integrating GWAS and protein quantity trait loci data. Inverse variance weighted method was applied as the main analysis approach. Additionally, MR-Egger intercept regression and Cochran Q test were employed to verify pleiotropy and heterogeneity. Lastly, single-cell RNA sequencing analysis was performed to reveal the expression of significant genes. The MR analysis of the integrated GWAS and protein quantity trait loci data demonstrated that CCL27 and CXCL9 were causally associated with a higher risk of COVID-19 susceptibility (CCL27: odds ratio [OR]: 1.06, 95% confidence interval [CI]: 1.01-1.10, P = .015; CXCL9: OR: 1.04, 95% CI: 1.01-1.08, P = .023), while hepatocyte growth factor was causally linked to a lower risk of COVID-19 susceptibility (OR: 0.89, 95% CI: 0.83-0.96, P = .002). In terms of hospitalization, the inverse variance weighted approach provided evidence to support that genetically predicted CXCL9 and interleukin-16 had negative associations with the risk of COVID-19 hospitalization (interleukin-16: OR: 0.92, 95% CI: 0.85-0.99, P = .034; CXCL9: OR: 0.85, 95% CI: 0.74-0.98, P = .043). No heterogeneity and directional pleiotropy were detected through sensitivity analysis. Our results supported the causal associations between specific inflammatory regulators and COVID-19 diverse phenotypes, thereby providing promising biomarkers of severity stratification and new insights for the therapeutic targets of COVID-19.
2019冠状病毒病(COVID-19)给全球患者和社会带来了沉重负担。观察性研究表明,炎症调节因子与COVID-19有关。然而,它们的因果效应仍不清楚。在此,我们进行了一项孟德尔随机化(MR)分析,以估计全身炎症调节因子与COVID-19不同表型之间的因果关系。从涉及8293名欧洲参与者的全基因组关联研究(GWAS)数据中提取与全身炎症调节因子相关的基因工具变量。COVID-19不同亚型的汇总统计数据来自COVID-19宿主遗传倡议(54071例病例和4905697例对照)。我们采用多组学方法和MR研究,通过整合GWAS和蛋白质数量性状位点数据来检测因果联系。采用逆方差加权法作为主要分析方法。此外,采用MR-Egger截距回归和Cochran Q检验来验证多效性和异质性。最后,进行单细胞RNA测序分析以揭示重要基因的表达。整合GWAS和蛋白质数量性状位点数据的MR分析表明,CCL27和CXCL9与COVID-19易感性风险较高存在因果关系(CCL27:比值比[OR]:1.06,95%置信区间[CI]:1.01-1.10,P = 0.015;CXCL9:OR:1.04,95% CI:1.01-1.08,P = 0.023),而肝细胞生长因子与COVID-19易感性风险较低存在因果联系(OR:0.89,95% CI:0.83-0.96,P = 0.002)。在住院方面,逆方差加权法提供了证据支持基因预测的CXCL9和白细胞介素-16与COVID-19住院风险呈负相关(白细胞介素-16:OR:0.92,95% CI:0.85-0.99,P = 0.034;CXCL9:OR:0.85,95% CI:0.74-0.98,P = 0.043)。通过敏感性分析未检测到异质性和方向性多效性。我们的结果支持特定炎症调节因子与COVID-19不同表型之间的因果关联,从而为严重程度分层提供了有前景的生物标志物,并为COVID-19的治疗靶点提供了新的见解。