ArticleCardiovascular diabetology2025
Metabotype identification via fasting and postprandial metabolomics and its association with type 2 diabetes incidence.
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 2 papers.
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Who cites it
2 citing papers in PubMed.
- Leveraging Multiomic Signatures to Predict Body Composition.Advances in nutrition (Bethesda, Md.) · 2026Review
- Nutrigenomics meets multi-omics: integrating genetic, metabolic, and microbiome data for personalized nutrition strategies.Genes & nutrition · 2025Review
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Authors and funding
10 authors.
Funding
Abstract
backgroundMetabotypes represent distinct metabolic profiles that groups of individuals share, facilitating disease risk stratification. We aimed to apply metabolomics to identify metabotypes in different prandial states and examine its associations with type 2 diabetes mellitus (T2DM) risk.
methodsUsing fasting and postprandial metabolomic data from the Netherlands Epidemiology of Obesity (NEO) study (N = 5320), we applied k-means clustering to identify individual’s metabotypes for three prandial states (fasting, postprandial [150 min after meal], and postprandial minus fasting, i.e., delta state) separately. Cox proportional hazard models were used to estimate risk of T2DM with metabotypes in each state. Random forest models were used to identify core metabolites contributing to metabotype assignments.
resultsFour metabotypes characterized by different metabolic profiles in each state were identified. During a median follow-up of 6.7 years, comparing to metabotype 1, metabotype 4 in both fasting and postprandial states had a higher risk of developing T2DM, with adjusted hazard ratios and 95% confidence intervals of 3.4 (2.2, 5.3) and 2.4 (1.6, 3.8), respectively. However, metabotypes identified in the delta state did not demonstrate the ability to stratify T2DM risk. The core metabolites contributing to metabotype 4fasting and metabotype 4postprandial were lipoproteins (e.g., MVLDLTG, SHDLC), and these metabotypes associated with a higher T2DM risk exhibited an unhealthier habitual diet. The association between fasting metabotypes and incident T2DM was further validated in the UK Biobank.
conclusionWe provided a comprehensive overview of associations between metabolomics-based metabotypes and T2DM risk across different prandial states. The distinct metabolic profiles offer opportunities for metabotype-tailored intervention studies to prevent T2DM.
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