Chen Shuhui, Lin Hao, Liu Bin, Pan Hejing, Xu Yaling, Mao Yingying, Huang Lin
School of Public Health, Zhejiang Chinese Medical University, Hangzhou, 310053, China.
College of Basic Medical Science, Zhejiang Chinese Medical University, Hangzhou, 310053, China.
BMC Immunol. 2024 Dec 23;25(1):86. doi: 10.1186/s12865-024-00677-1.
Frailty is an emerging global burden of disease, characterized as an age-related clinical syndrome. Recent studies have suggested a potential link of circulating protein levels with the onset of frailty. This study aims to analyze the potential causal relationships of plasma proteins with frailty using a Mendelian Randomization (MR) study design.
Associations of plasma proteins with frailty were assessed using inverse variance weighted (IVW), MR-Egger regression, weighted median, maximum-likelihood method, and MR-PRESSO test. Protein-protein interaction network construction and gene ontology functional enrichment analysis were conducted based on MR-identified target proteins.
After false discovery rate (FDR) correction, MR analysis identified five plasma proteins, including BIRC2 [OR = 0.978, 95%CI (0.967-0.990)] and PSME1 [OR = 0.936, 95%CI (0.909-0.965)], as protective factors against frailty, and 49 proteins, including APOB [OR = 1.053, 95%CI (1.037-1.069)] and CYP3A4 [OR = 1.098, 95%CI (1.068-1.128)], as risk factors. Network analysis suggested BIRC2, PSME1, APOE, and CTNNB1 as key intervention targets.
This study employed MR design to investigate the association of circulating plasma proteins with frailty, identified five proteins negatively associated with frailty risk and 49 proteins positively associated with frailty.
衰弱是一种新出现的全球性疾病负担，其特征为与年龄相关的临床综合征。最近的研究表明循环蛋白水平与衰弱的发生之间可能存在联系。本研究旨在使用孟德尔随机化（MR）研究设计分析血浆蛋白与衰弱之间的潜在因果关系。
使用逆方差加权（IVW）、MR-Egger回归、加权中位数、最大似然法和MR-PRESSO检验评估血浆蛋白与衰弱之间的关联。基于MR识别的靶蛋白构建蛋白质-蛋白质相互作用网络并进行基因本体功能富集分析。
经过错误发现率（FDR）校正后，MR分析确定了五种血浆蛋白，包括BIRC2[比值比（OR）=0.978，95%置信区间（CI）（0.967-0.990）]和PSME1[OR=0.936，95%CI（0.909-0.965）]，作为预防衰弱的保护因素，以及49种蛋白，包括APOB[OR=1.053，95%CI（1.037-1.069）]和CYP3A4[OR=1.098，95%CI（1.068-1.128）]，作为危险因素。网络分析表明BIRC2、PSME1、APOE和CTNNB1是关键干预靶点。
本研究采用MR设计研究循环血浆蛋白与衰弱的关联，确定了五种与衰弱风险呈负相关的蛋白和49种与衰弱呈正相关的蛋白。
