Zhang Yue-Yang, Wang Bin-Lu, Chen Bing-Xue, Wan Qin
Department of Endocrinology and Metabolism, Affiliated Hospital of Southwest Medical University, Luzhou, China.
Metabolic Vascular Disease Key Laboratory of Sichuan Province, Luzhou, China.
Ther Adv Endocrinol Metab. 2025 May 31;16:20420188251343140. doi: 10.1177/20420188251343140. eCollection 2025.
In recent years, driven by the rapid advancement of proteomics research, numerous scholars have investigated the intricate associations between plasma proteins and various diseases. Thus, this study aimed to identify novel therapeutic targets for preventing and treating metabolic-related diseases through Mendelian randomization (MR).
This study primarily utilized the MR method, leveraging genetic data from multiple large-scale publicly available genome-wide association studies. We employed two-sample MR within this framework to assess the associations between 1001 plasma proteins and 5 metabolism-related diseases. Finally, we strengthen the robustness and reliability of the MR results by conducting a series of sensitivity analyses, including bidirectional MR, colocalization analysis, Cochran's test, and the MR-Egger intercept test.
The results from the inverse variance weighted method revealed that, following false discovery rate correction, many plasma proteins are significantly associated with metabolic-related diseases. Genetically predicted risks vary across diseases: for coronary artery disease, from 0.82 FGR proto-oncogene, Src family tyrosine kinase (FGR) to 1.13 (interleukin-6); for obesity, from 0.992 (POLR2F) to 1.005 (PRKAB1); for osteoporosis, from 0.998 (AIF1) to 1.001 (CLC); for stroke, from 0.71 (TNFRSF1A) to 1.47 (TGM2); and for type 2 diabetes, from 0.79 (KRT18) to 1.47 (RAB37).
Our findings reveal numerous plasma proteins linked to metabolic-related diseases. These findings offer fresh insights into the etiology, diagnostics, and treatment of these conditions.
近年来，在蛋白质组学研究快速发展的推动下，众多学者研究了血浆蛋白与各种疾病之间的复杂关联。因此，本研究旨在通过孟德尔随机化（MR）确定预防和治疗代谢相关疾病的新治疗靶点。
本研究主要采用MR方法，利用来自多个大规模公开可用的全基因组关联研究的遗传数据。我们在此框架内采用两样本MR来评估1001种血浆蛋白与5种代谢相关疾病之间的关联。最后，我们通过进行一系列敏感性分析来加强MR结果的稳健性和可靠性，包括双向MR、共定位分析、 Cochr an检验和MR-Egger截距检验。
逆方差加权法的结果显示，经过错误发现率校正后，许多血浆蛋白与代谢相关疾病显著相关。不同疾病的遗传预测风险各不相同：冠状动脉疾病方面，从0.82（FGR原癌基因，Src家族酪氨酸激酶（FGR））到1.13（白细胞介素-6）；肥胖方面，从0.992（POLR2F）到1.005（PRKAB1）；骨质疏松症方面，从0.998（AIF1）到1.001（CLC）；中风方面，从0.71（TNFRSF1A）到1.47（TGM2）；2型糖尿病方面，从0.79（KRT18）到1.47（RAB37）。
我们的研究结果揭示了许多与代谢相关疾病有关的血浆蛋白。这些发现为这些疾病的病因、诊断和治疗提供了新的见解。
