| Title: | Blood Plasma Proteome-Wide Association Study Implicates New Proteins in Type 2 Diabetes Mellitus Pathogenesis |
| Journal: | The Journal of Clinical Endocrinology & Metabolism |
| Published: | 12 Aug 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40796333/ |
| DOI: | https://doi.org/10.1210/clinem/dgaf462 |
Publication 19749
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Abstract
The pathophysiological mechanisms underlying type 2 diabetes mellitus (T2DM) remain incompletely understood, and the disease continues to impose a substantial burden on global health. In this study, we integrated the data from the largest genome-wide association study (GWAS; N = 898 130) of T2DM with human plasma protein quantitative trait locus (pQTL; N = 53 022) data to conduct the first proteome-wide association study (PWAS) of T2DM. Following Mendelian randomization and colocalization analyses, we identified 9 independent putatively causal proteins. Among these, 3 were successfully replicated in other independent pQTL datasets, including 2 (HYOU1 and FLT3) that were novel and not identified in the original GWAS. Further integration with expression quantitative trait locus (eQTL) data from 3 diabetes-related tissues (blood, adipose tissue, and pancreas) revealed that 5 of the causal proteins also showed significant associations with T2DM at their cis-regulatory mRNA levels. Subsequent functional annotation supported potential pathogenic roles of the causal proteins. Notably, drug repurposing analysis identified 29 candidate drugs for T2DM treatment by targeting 4 causal proteins. In conclusion, our findings provide new insights into the pathogenesis of T2DM and highlight promising targets for future mechanistic and therapeutic investigations.</p>
10 Keywords
- Blood Proteins
- Diabetes Mellitus, Type 2
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Humans
- Male
- Mendelian Randomization Analysis
- Polymorphism, Single Nucleotide
- Proteome
- Quantitative Trait Loci
10 Authors
- Yang Yang
- Jia-Hao Wang
- Hao-An Wang
- Tian-Pei Zhang
- Guanchen Liu
- Shan-Shan Dong
- Tie-Lin Yang
- Wei Chen
- Ruo-Han Hao
- Yan Guo
1 Application
| Application ID | Title |
|---|---|
| 46387 | Integration of regulatory elements and multi-trait genomic and phenomic data to improve the accuracy of risk prediction of human complex diseases/traits |
Enabling scientific discoveries that improve human health