Identification of plasma proteomic markers underlying polygenic risk of type 2 diabetes and related comorbidities

Douglas P. Loesch (), Manik Garg, Dorota Matelska, Dimitrios Vitsios, Xiao Jiang, Scott C. Ritchie, Benjamin B. Sun, Heiko Runz, Christopher D. Whelan, Rury R. Holman, Robert J. Mentz, Filipe A. Moura, Stephen D. Wiviott, Marc S. Sabatine, Miriam S. Udler, Ingrid A. Gause-Nilsson, Slavé Petrovski, Jan Oscarsson, Abhishek Nag, Dirk S. Paul () and Michael Inouye
Additional contact information
Douglas P. Loesch: AstraZeneca
Manik Garg: AstraZeneca
Dorota Matelska: AstraZeneca
Dimitrios Vitsios: AstraZeneca
Xiao Jiang: AstraZeneca
Scott C. Ritchie: University of Cambridge
Benjamin B. Sun: Biogen Inc.
Heiko Runz: Biogen Inc.
Christopher D. Whelan: Janssen Pharmaceuticals
Rury R. Holman: University of Oxford
Robert J. Mentz: Duke University School of Medicine
Filipe A. Moura: Brigham and Women’s Hospital and Harvard Medical School
Stephen D. Wiviott: Brigham and Women’s Hospital and Harvard Medical School
Marc S. Sabatine: Brigham and Women’s Hospital and Harvard Medical School
Miriam S. Udler: Massachusetts General Hospital
Ingrid A. Gause-Nilsson: AstraZeneca
Slavé Petrovski: AstraZeneca
Jan Oscarsson: AstraZeneca
Abhishek Nag: AstraZeneca
Dirk S. Paul: AstraZeneca
Michael Inouye: University of Cambridge

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: Abstract Genomics can provide insight into the etiology of type 2 diabetes and its comorbidities, but assigning functionality to non-coding variants remains challenging. Polygenic scores, which aggregate variant effects, can uncover mechanisms when paired with molecular data. Here, we test polygenic scores for type 2 diabetes and cardiometabolic comorbidities for associations with 2,922 circulating proteins in the UK Biobank. The genome-wide type 2 diabetes polygenic score associates with 617 proteins, of which 75% also associate with another cardiometabolic score. Partitioned type 2 diabetes scores, which capture distinct disease biology, associate with 342 proteins (20% unique). In this work, we identify key pathways (e.g., complement cascade), potential therapeutic targets (e.g., FAM3D in type 2 diabetes), and biomarkers of diabetic comorbidities (e.g., EFEMP1 and IGFBP2) through causal inference, pathway enrichment, and Cox regression of clinical trial outcomes. Our results are available via an interactive portal ( https://public.cgr.astrazeneca.com/t2d-pgs/v1/ ).

Date: 2025
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DOI: 10.1038/s41467-025-56695-z

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