Yang Xiaojun, Zhang Bowen, Wen Fuyuan, Qi Han, Zhang Fengxu, Xie Yunyi, Peng Wenjuan, Li Boya, Qu Aibin, Yao Xinyue, Zhang Ling
Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University and Beijing Key Laboratory of Environment and Aging, Beijing 100069, China.
Int J Mol Sci. 2025 May 9;26(10):4538. doi: 10.3390/ijms26104538.
This study aims to identify genetically influenced metabolites (GIMs) associated with SSBP and elucidate their regulatory pathways through metabolome genome-wide association studies (mGWASs). Untargeted metabolomics and genome-wide genotyping were performed on 54 participants from the Systematic Epidemiological Study of Salt Sensitivity (EpiSS). The mGWAS was conducted on 970 plasma metabolites, and their potential biological mechanisms were explored. The multivariable logistic regression model and mendelian randomization (MR) were employed to investigate the association and causal relationship between GIMs and SSBP. Metabolomic analysis was performed on 100 subjects in the replication analysis to validate the GIMs identified in the discovery set and their causal association with SSBP. The mGWAS revealed associations between 1485 loci and 18 metabolites. After performing linkage disequilibrium analysis, 368 independent mQTLs were identified and annotated to 141 genes. These functional genes were primarily implicated in the signal transduction of sinoatrial node and atrial cardiac muscle cells. Five key genes were identified using CytoHubba, including , , , , and . One-sample MR analysis revealed 14 GIMs with causal associations to SSBP, with LysoPC (0:0/22:5-3) positively associated with SSBP ( < 0.05). The causal relationship between Phe-lle and SSBP was validated in the replication analysis. This study elucidates the genetic regulatory mechanisms underlying metabolites and identifies GIMs that are causally associated with SSBP. These findings provide insights into identifying metabolic biomarkers of SSBP and characterizing its genetic and metabolic regulation mechanisms.
本研究旨在识别与盐敏感性血压(SSBP)相关的遗传影响代谢物(GIMs),并通过代谢组全基因组关联研究(mGWASs)阐明其调控途径。对来自盐敏感性系统流行病学研究(EpiSS)的54名参与者进行了非靶向代谢组学和全基因组基因分型。对970种血浆代谢物进行了mGWAS,并探索了它们潜在的生物学机制。采用多变量逻辑回归模型和孟德尔随机化(MR)来研究GIMs与SSBP之间的关联和因果关系。在重复分析中对100名受试者进行了代谢组学分析,以验证在发现集中鉴定出的GIMs及其与SSBP的因果关联。mGWAS揭示了1485个位点与18种代谢物之间的关联。在进行连锁不平衡分析后,鉴定出368个独立的代谢物数量性状基因座(mQTLs),并注释到141个基因。这些功能基因主要参与窦房结和心房心肌细胞的信号转导。使用CytoHubba鉴定出五个关键基因,包括 、 、 、 和 。单样本MR分析揭示了14种与SSBP有因果关联的GIMs,其中溶血磷脂酰胆碱(0:0/22:5-3)与SSBP呈正相关( < 0.05)。苯丙氨酸-异亮氨酸与SSBP之间的因果关系在重复分析中得到验证。本研究阐明了代谢物潜在的遗传调控机制,并鉴定出与SSBP有因果关联的GIMs。这些发现为识别SSBP的代谢生物标志物以及表征其遗传和代谢调控机制提供了见解。