Plasma proteomic signatures for type 2 diabetes and related traits in the UK Biobank cohort

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

doi:10.1016/j.diabres.2025.112194

Plasma proteomic signatures for type 2 diabetes and related traits in the UK Biobank cohort

Diabetes Res Clin Pract. 2025 Jun:224:112194. doi: 10.1016/j.diabres.2025.112194. Epub 2025 Apr 22.

Authors

Affiliations

¹ Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: gtrisha@stanford.edu.
² Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: zazizi@stanford.edu.
³ Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: pkho@stanford.edu.
⁴ Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: jyzhou@stanford.edu.
⁵ Meharry Medical College, Nashville, TN, USA. Electronic address: knzenkue23@email.mmc.edu.
⁶ Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: mlichen@stanford.edu.
⁷ Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: dpanyard@stanford.edu.
⁸ Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA; Palo Alto Veterans Institute for Research (PAVIR), Stanford, CA, USA. Electronic address: rgsousa@stanford.edu.
⁹ Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA; Palo Alto Veterans Institute for Research (PAVIR), Stanford, CA, USA. Electronic address: ahilliar@stanford.edu.
¹⁰ Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: dishas@stanford.edu.
¹¹ Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: ktwatson@stanford.edu.
¹² Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: fahim@stanford.edu.
¹³ Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: ptsao@stanford.edu.
¹⁴ Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: shoa@stanford.edu.
¹⁵ Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA; Department of Epidemiology and Population Health,Stanford University School of Medicine, Stanford, CA, USA. Electronic address: tassimes@stanford.edu.

PMID: 40274105
PMCID: PMC12094890 (available on 2026-06-01)
DOI: 10.1016/j.diabres.2025.112194

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.

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.

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.

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.

Keywords: Adiposity; Fitness; Genetics; Proteomics; Risk prediction mode; Type 2 diabetes.

MeSH terms

Aged
Biological Specimen Banks
Blood Proteins* / analysis
Blood Proteins* / metabolism
Cohort Studies
Diabetes Mellitus, Type 2* / blood
Diabetes Mellitus, Type 2* / diagnosis
Diabetes Mellitus, Type 2* / epidemiology
Diabetes Mellitus, Type 2* / genetics
Female
Humans
Male
Mendelian Randomization Analysis
Middle Aged
Oxygen Consumption
Proteome* / metabolism
Proteomics* / methods
UK Biobank
United Kingdom / epidemiology

Substances

Proteome
Blood Proteins

Abstract

MeSH terms

Substances

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