Circulating causal protein networks linked to future risk of myocardial infarction

Variations in blood protein levels have been linked to numerous complex diseases, including cardiovascular conditions. These associations highlight the intricate interplay between local and systemic factors in cardiovascular disease development, emphasizing the need for a comprehensive, systems-level understanding of its etiology. To address this, we develop a causal network inference framework using data from one of the largest serum proteomics studies to date, comprising measurements of 7523 serum proteins in the prospective, population-based Age, Gene/Environment Susceptibility-Reykjavik Study (AGES) cohort of 5376 older adults. Using cis-acting protein quantitative trait loci (pQTLs) as instrumental variables within a causal inference framework designed to mitigate hidden confounding, we identify 185 high-confidence causal serum protein subnetworks collectively interacting with 5611 targets. Several subnetworks, many forming hierarchical frameworks of directional relationships, are significantly associated with multiple cardiometabolic traits and with future risk of myocardial infarction and its long-term complication, heart failure.

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Journal
Nature Communications
Published
2025-12-18
DOI
https://doi.org/10.1038/s41467-025-67135-3
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
0.00

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article

Circulating causal protein networks linked to future risk of myocardial infarction

Vilmundur Guðnason, Valborg Guðmundsdóttir, Lori L. Jennings, Lenore J. Launer et al.
Nature Communications
Genetic Associations and Epidemiology
article

Circulating causal protein networks linked to future risk of myocardial infarction

Vilmundur Guðnason, Valborg Guðmundsdóttir, Lori L. Jennings, Lenore J. Launer, Sean Bankier, Joseph Loureiro, Anthony P. Orth, Þórarinn Jónmundsson, Tom Michoel, Johan Björkegren, Elísabet A. Frick, Valur Emilsson, Thor Aspelund, Nancy Finkel, Lingfei Wang, John R. Lamb, Heida Bjarnadottir
article en

Abstract

Variations in blood protein levels have been linked to numerous complex diseases, including cardiovascular conditions. These associations highlight the intricate interplay between local and systemic factors in cardiovascular disease development, emphasizing the need for a comprehensive, systems-level understanding of its etiology. To address this, we develop a causal network inference framework using data from one of the largest serum proteomics studies to date, comprising measurements of 7523 serum proteins in the prospective, population-based Age, Gene/Environment Susceptibility-Reykjavik Study (AGES) cohort of 5376 older adults. Using cis-acting protein quantitative trait loci (pQTLs) as instrumental variables within a causal inference framework designed to mitigate hidden confounding, we identify 185 high-confidence causal serum protein subnetworks collectively interacting with 5611 targets. Several subnetworks, many forming hierarchical frameworks of directional relationships, are significantly associated with multiple cardiometabolic traits and with future risk of myocardial infarction and its long-term complication, heart failure.

Nature CommunicationsVol. 17(1)
Genomics Institute of the Novartis Research Foundation (US), Karolinska University Hospital (SE), University of Iceland (IS), University of Massachusetts Chan Medical School (US), Hydrologic Research Center (US), Karolinska Institutet (SE), Novartis (United States) (US), National Institute on Aging (US), Icelandic Heart Association (IS), University of Bergen (NO)
European Commission, Norges Forskningsråd, L. Meltzers Høyskolefond, Hjartavernd, HORIZON EUROPE Framework Programme, National Institute on Aging, HORIZON EUROPE European Innovation Council
Openalex Percentile: Top 57%
Genetic Associations and Epidemiology
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