Shi Yinli, Guan Shuang, Liu Xi, Zhai Hongjun, Zhang Yingying, Liu Jun, Yang Weibin, Wang Zhong
Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Chengdu University of Traditional Chinese Medicine Key Laboratory of Systematic Research of Distinctive Chinese Medicine Resources in Southwest China, Chengdu, China.
J Cell Mol Med. 2025 Jan;29(1):e70329. doi: 10.1111/jcmm.70329.
This study aims to elucidate the potential genetic commonalities between metabolic syndrome (MetS) and rheumatic diseases through a disease interactome network, according to publicly available large-scale genome-wide association studies (GWAS). The analysis included linkage disequilibrium score regression analysis, cross trait meta-analysis and colocalisation analysis to identify common genetic overlap. Using modular partitioning, the network-based association between the two disease proteins in the protein-protein interaction set was divided and quantified. Clinical samples from public databases were used to confirm the mapped genes. Mendelian randomisation analyses were conducted using genetic instrumental variables for causal inference. MetS and rheumatoid arthritis (RA), ankylosing spondylitis (AS), systemic lupus erythematosus (SLE), Sjogren's syndrome (SS) and their primary module networks shared topological overlap and genetic correlation. Functional analysis highlighted the significance of these shared targets in processes such as a diverse array of metabolic pathways involving glucose, lipids, energy, protein transport, inflammatory response, autophagy and cytokine regulation, elucidating the pathways through which MetS intersects with rheumatic diseases. Causal associations were determined between MetS phenotypes and rheumatic diseases. The persistence of MetS effects on rheumatic diseases remained evident even after adjusting for alcohol consumption and smoking. We have highlighted specific genetic associations between MetS and rheumatic diseases. Several genes (e.g., PRRC2A, PSMB8, BAG6, GPSM3, PBX2, etc.) have been identified with molecular commonalities in MetS and RA, AS, SLE and SS, which may serve as potential targets for shared treatments.
本研究旨在根据公开可用的大规模全基因组关联研究(GWAS),通过疾病相互作用组网络阐明代谢综合征(MetS)与风湿性疾病之间潜在的遗传共性。分析包括连锁不平衡评分回归分析、跨性状荟萃分析和共定位分析,以确定共同的遗传重叠。使用模块划分,对蛋白质-蛋白质相互作用集中两种疾病蛋白质之间基于网络的关联进行划分和量化。利用公共数据库中的临床样本对映射基因进行验证。使用遗传工具变量进行孟德尔随机化分析以进行因果推断。MetS与类风湿关节炎(RA)、强直性脊柱炎(AS)、系统性红斑狼疮(SLE)、干燥综合征(SS)及其主要模块网络存在拓扑重叠和遗传相关性。功能分析突出了这些共享靶点在涉及葡萄糖、脂质、能量、蛋白质转运、炎症反应、自噬和细胞因子调节等多种代谢途径等过程中的重要性,阐明了MetS与风湿性疾病相交的途径。确定了MetS表型与风湿性疾病之间的因果关联。即使在调整饮酒和吸烟因素后,MetS对风湿性疾病的影响仍然明显。我们突出了MetS与风湿性疾病之间的特定遗传关联。已在MetS与RA、AS、SLE和SS中鉴定出几个具有分子共性的基因(如PRRC2A、PSMB8、BAG6、GPSM3、PBX2等),它们可能作为联合治疗的潜在靶点。