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Cardiovascular Diabetology

Metabotype identification via fasting and postprandial metabolomics and its association with type 2 diabetes incidence

Published in:
Authors
Keyong Deng
David A. Hughes
Renée de Mutsert
Astrid van Hylckama Vlieg
Saskia le Cessie
Frits R. Rosendaal
Dennis O. Mook-Kanamori
Ko Willems van Dijk
Nicholas J. Timpson
Ruifang Li-Gao
Published in
Cardiovascular Diabetology | Issue 1/2025

Abstract

Background

Metabotypes 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.

Methods

Using 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.

Results

Four 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.

Conclusion

We 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.
Title
Metabotype identification via fasting and postprandial metabolomics and its association with type 2 diabetes incidence
Authors
Keyong Deng
David A. Hughes
Renée de Mutsert
Astrid van Hylckama Vlieg
Saskia le Cessie
Frits R. Rosendaal
Dennis O. Mook-Kanamori
Ko Willems van Dijk
Nicholas J. Timpson
Ruifang Li-Gao
Publication date
01-12-2025
Publisher
BioMed Central
Keyword
Type 2 Diabetes
Published in
Cardiovascular Diabetology / Issue 1/2025
Electronic ISSN: 1475-2840
DOI
https://doi.org/10.1186/s12933-025-02821-6
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