Arage G, Dekkers KF, Rašo LM, Hammar U, Ericson U, Larsson SC, Engel H, Baldanzi G, Pertiwi K, Sayols-Baixeras S, Landberg R, Sundström J, Smith JG, Engström G, Ärnlöv J, Orho-Melander M, Lind L, Fall T, Ahmad S
Metabolism 168 (-) 156188 [2025-07-00; online 2025-03-11]
Higher meat intake has been associated with adverse health outcomes, including cardiovascular disease (CVD). This study investigated plasma metabolites associated with meat intake and their relation with cardiometabolic biomarkers, subclinical CVD markers, and incident CVD. Associations between self-reported meat intake and 1272 plasma metabolites were investigated in the SCAPIS cohort (n = 8,819; ages 50-64). Meat-associated metabolites were further examined for relation with subclinical CVD markers in the POEM cohort (n = 502; age 50) and incident CVD in the EpiHealth cohort (n = 2,278; ages 45-75; 107 incident cases over 9.6 years follow-up). Meat intake was categorized into white, unprocessed red, and processed red meat. Linear regression analyzed associations between meat intake, metabolites and cardiometabolic biomarkers, and subclinical CVD markers, while Cox models evaluated association between meat-associated metabolites and incident CVD. After correction for multiple testing, 458, 368, and 403 metabolites were associated with white, unprocessed red, and processed red meat, respectively. Processed red meat-associated metabolites were associated with higher levels of fasting insulin, hemoglobin A1c, and lipoprotein(a), and were inversely associated with maximal oxygen consumption. Two metabolites, 1-palmitoyl-2-linoleoyl-GPE (16:0/18:2) (hazard ratios (HR: 1.32; 95 % CI: 1.08, 1.62)) and glutamine degradant (HR: 1.35; 95 % CI: 1.07, 1.72), that were inversely associated with intake of all meat types, were also associated with a higher risk of incident CVD. This study provides comprehensive analysis of self-reported meat intake and plasma metabolites. The findings may enhance our understanding of the relationship between meat intake and CVD, and provide insights into underlying mechanisms.
Bioinformatics Support for Computational Resources [Service]
PubMed 40081615
DOI 10.1016/j.metabol.2025.156188
Crossref 10.1016/j.metabol.2025.156188
pii: S0026-0495(25)00057-5