Evidence map›Paper›PMID 40453312›Full record

ArticleTherapeutic advances in endocrinology and metabolism2025

Novel therapeutic targets for metabolism-related diseases: proteomic Mendelian randomization and colocalization analyses.

Yue-Yang Zhang, Bin-Lu Wang, Bing-Xue Chen, Qin Wan

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Article in Therapeutic advances in endocrinology and metabolism, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yue-Yang ZhangDepartment of Endocrinology and Metabolism, Affiliated Hospital of Southwest Medical University, Luzhou, China.ORCID https://orcid.org/0009-0006-7751-4888
Bin-Lu WangDepartment of Endocrinology and Metabolism, Affiliated Hospital of Southwest Medical University, Luzhou, China.
Bing-Xue ChenDepartment of Ultrasound Medicine, Affiliated Hospital of Southwest Medical University, Luzhou, China.
Qin WanDepartment of Endocrinology and Metabolism, Affiliated Hospital of Southwest Medical University, No. 23 Taiping Street, Jiangyang District, Luzhou, Sichuan 646000, China.ORCID https://orcid.org/0000-0001-7765-1416

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: 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). Methods: 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 Results: 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). Conclusion: 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.

Indexed as

Mendelian randomizationmetabolic-related diseasesplasma proteinproteomicstype 2 diabetes

Identifiers

PMID40453312
PMCPMC12126668

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