| Title: | Plasma proteomic signatures for type 2 diabetes and related traits in the UK Biobank cohort |
| Journal: | Diabetes Research and Clinical Practice |
| Published: | 22 Apr 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40274105/ |
| DOI: | https://doi.org/10.1016/j.diabres.2025.112194 |
Publication 15789
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Abstract
OBJECTIVE: The plasma proteome holds promise as a diagnostic and prognostic tool that can accurately reflect complex human traits and disease processes. We assessed the ability of proteins to predict type 2 diabetes and related traits.</p>
STUDY DESIGN: We analyzed clinical, genetic, and proteomic data from three UK Biobank subcohorts for associations with truncal fat, estimated maximum oxygen consumption (VO2max), and type 2 diabetes. Using least absolute shrinkage and selection operator (LASSO) regression, we compared predictive performance of each trait between data types. The benefit of proteomic signatures (PSs) over the type 2 diabetes clinical risk score, QDiabetes was evaluated. Two-sample Mendelian randomization (MR) identified potentially causal proteins for each trait.</p>
RESULTS: LASSO-derived PSs improved prediction of truncal fat and VO2max over clinical and genetic factors. We observed a modest improvement in type 2 diabetes prediction over the QDiabetes score when combining a PS derived for type 2 diabetes that was further augmented with fat- and fitness-associated PSs. Two-sample MR identified a few proteins as potentially causal for each trait.</p>
CONCLUSION: Plasma PSs modestly improve type 2 diabetes prediction beyond clinical and genetic factors. Candidate causally associated proteins deserve further study as potential novel therapeutic targets for type 2 diabetes.</p>
15 Keywords
- Aged
- Biological Specimen Banks
- Blood Proteins
- Cohort Studies
- Diabetes Mellitus, Type 2
- Female
- Humans
- Male
- Mendelian Randomization Analysis
- Middle Aged
- Oxygen Consumption
- Proteome
- Proteomics
- UK Biobank
- United Kingdom
15 Authors
- Trisha P Gupte
- Zahra Azizi
- Pik Fang Kho
- Jiayan Zhou
- Kevin Nzenkue
- Ming-Li Chen
- Daniel J Panyard
- Rodrigo Guarischi-Sousa
- Austin T Hilliard
- Disha Sharma
- Kathleen Watson
- Fahim Abbasi
- Philip S Tsao
- Shoa L Clarke
- Themistocles L Assimes
1 Application
| Application ID | Title |
|---|---|
| 52374 | Genetic architecture of human phenotypes |
Enabling scientific discoveries that improve human health