Evidence mapPaperPMID 41430683Full record

ArticleCardiovascular diabetology2025

Very long-chain saturated fatty acids in plasma lipids: association with cardiometabolic risk influenced by lipid interactions.

Inés Domínguez-López, Fabian Eichelmann, Marcela Prada, Matthias B Schulze

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Article in Cardiovascular diabetology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 citing paper in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Inés Domínguez-LópezDepartment of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany. Ines.Dominguez-Lopez@dife.de.
Fabian EichelmannDepartment of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
Marcela PradaDepartment of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
Matthias B SchulzeDepartment of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundVery long-chain saturated fatty acids (VLCSFA) may influence cardiometabolic health differently from other, often detrimental, saturated fatty acids (SFA). Evidence remains inconclusive, partly because VLCSFA are metabolically derived from SFA, making it difficult to disentangle their individual effects due to potential confounding of correlated lipids. Prior studies rarely accounted for correlations with other lipids or do not consider VLCSFA-specific lipid classes. We investigated prospective associations of circulating VLCSFA (C20:0, C22:0, C24:0) across multiple plasma lipid classes with type 2 diabetes (T2D) and cardiovascular disease (CVD), accounting for confounding by correlated lipids.

methodsWe constructed two nested case-cohort studies within the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort: 1911 in the T2D case-cohort (774 cases); 1704 in the CVD case-cohort (547 cases). Plasma concentrations of VLCSFA were measured in 12 lipid classes. A data-driven network including SFA across all lipid classes was used to identify precursors and downstream lipid metabolites for each lipid class of VLCSFA. The correlated lipids were gradually incorporated in multivariable-adjusted Cox regression models between individual lipids and disease risk.

resultsC20:0 was distributed across more lipid classes than C22:0 and C24:0. After including all correlated precursors and downstream lipid metabolites in the model, we observed that higher C22:0 levels were linked to higher T2D risk, while associations for C20:0 and C24:0 varied by class. Ceramides C20:0 (hazard ratio [HR] per SD: 0.52, 95% CI 0.35-0.79) and C24:0 (0.46, 0.27-0.79) were inversely associated with T2D, whereas dihydroceramides C20:0 (1.36, 1.07-1.72) and sphingomyelin C24:0 (1.61, 1.15-2.26) showed positive associations. Monoglycerides and cholesteryl esters containing VLCSFA were associated to higher risk of both outcomes. Most of these relationships were not observed when the confounding or mediation by correlated lipids was not considered.

conclusionsVLCSFA show different metabolic roles in cardiometabolic diseases and highlight the importance of adjusting for confounding by correlated lipids. These findings challenge the traditional view that SFA exert uniform negative effects and suggest class-specific VLCSFA profiles may improve risk prediction of cardiometabolic diseases, guiding more precise prevention strategies.

Indexed as

Cardiovascular DiseasesDiabetes Mellitus, Type 2Fatty AcidsLipidsAdultAgedBiomarkersCardiometabolic Risk FactorsCase-Control StudiesFemaleGermanyHumansMaleMiddle AgedPrognosisProspective StudiesBiomarkersFatty AcidsLipidsCardiometabolicCardiovascular diseaseFatty acidsLipid networkLipidomicsType 2 diabetes

Identifiers

PMID41430683
PMCPMC12838474

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.