Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases

Scott C. Ritchie*, Samuel A. Lambert, Matthew Arnold, Shu Mei Teo, Sol Lim, Petar Scepanovic, Jonathan Marten, Sohail Zahid, Mark Chaffin, Yingying Liu, Gad Abraham, Willem H. Ouwehand, David J. Roberts, Nicholas A. Watkins, Brian G. Drew, Anna C. Calkin, Emanuele Di Angelantonio, Nicole Soranzo, Stephen Burgess, Michael ChapmanSekar Kathiresan, Amit V. Khera, John Danesh, Adam S. Butterworth, Michael Inouye

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


Cardiometabolic diseases are frequently polygenic in architecture, comprising a large number of risk alleles with small effects spread across the genome1–3. Polygenic scores (PGS) aggregate these into a metric representing an individual’s genetic predisposition to disease. PGS have shown promise for early risk prediction4–7 and there is an open question as to whether PGS can also be used to understand disease biology8. Here, we demonstrate that cardiometabolic disease PGS can be used to elucidate the proteins underlying disease pathogenesis. In 3,087 healthy individuals, we found that PGS for coronary artery disease, type 2 diabetes, chronic kidney disease and ischaemic stroke are associated with the levels of 49 plasma proteins. Associations were polygenic in architecture, largely independent of cis and trans protein quantitative trait loci and present for proteins without quantitative trait loci. Over a follow-up of 7.7 years, 28 of these proteins associated with future myocardial infarction or type 2 diabetes events, 16 of which were mediators between polygenic risk and incident disease. Twelve of these were druggable targets with therapeutic potential. Our results demonstrate the potential for PGS to uncover causal disease biology and targets with therapeutic potential, including those that may be missed by approaches utilizing information at a single locus.

Original languageEnglish
Pages (from-to)1476-1483
Number of pages8
JournalNature Metabolism
Issue number11
Publication statusPublished - Nov 2021
Externally publishedYes

Bibliographical note

Funding Information:
Participants in the INTERVAL randomized controlled trial were recruited with the active collaboration of NHS Blood and Transplant (, which has supported fieldwork and other elements of the trial. DNA extraction and genotyping were co-funded by the National Institute for Health Research (NIHR), the NIHR BioResource ( and the NIHR Cambridge Biomedical Research Centre (BRC) (no. BRC-1215-20014). Olink Proteomics assays were funded by Biogen. SomaLogic assays were funded by Merck and the NIHR Cambridge BRC (no. BRC-1215-20014). The academic coordinating centre for INTERVAL was supported by core funding from the NIHR Blood and Transplant Research Unit in Donor Health and Genomics (no. NIHR BTRU-2014-10024), UK Medical Research Council (MRC) (no. MR/L003120/1), British Heart Foundation (nos SP/09/002, RG/13/13/30194 and RG/18/13/33946) and the NIHR Cambridge BRC (no. BRC-1215-20014). A complete list of the investigators and contributors to the INTERVAL trial is provided in ref. 25. The academic coordinating centre thanks blood donor centre staff and blood donors for participating in the INTERVAL trial. This work was supported by Health Data Research UK, which is funded by the UK MRC, Engineering and Physical Sciences Research Council (EPSRC), Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome. This study was also supported by the Victorian Government’s Operational Infrastructure Support programme. This work was performed using resources provided by the Cambridge Service for Data Driven Discovery operated by the University of Cambridge Research Computing Service ( high-performance-computing), provided by Dell EMC and Intel using tier-2 funding from the EPSRC (capital grant no. EP/P020259/1), and DiRAC funding from the Science and Technology Facilities Council ( This work uses data provided by patients and collected by the NHS and Public Health England as part of their care and support. Data on hospital episode statistics, mortality and cancer registration were obtained from NHS Digital (data sharing agreement reference no. DARS-NIC-156334-711SX). S.C.R. and J.M. were funded by the NIHR Cambridge BRC (no. BRC-1215-20014). S.A.L. is supported by a Canadian Institutes of Health Research postdoctoral fellowship (no. MFE-171279). G.A. was supported by a National Health and Medical Research Council of Australia Early Career Fellowship (no. 1090462). S.B. is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (no. 204623/Z/16/Z). A.V.K. was supported by grants from the National Human Genome Research Institute (award nos 1K08HG010155 and 5UM1HG008895), an institutional grant from the Broad Institute of MIT and Harvard (variant2function) and a Hassenfeld Scholar Award from Massachusetts General Hospital. J.D. holds a British Heart Foundation Professorship and an NIHR Senior Investigator Award. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The views expressed in this manuscript are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Nature Limited.


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