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Background <p>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.</p> Methods <p>Using fasting and postprandial metabolomic data from the Netherlands Epidemiology of Obesity (NEO) study (N = 5320), we applied <i>k</i>-means clustering to identify individual’s metabotypes for three prandial states (fasting, postprandial [150&#xa0;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.</p> Results <p>Four metabotypes characterized by different metabolic profiles in each state were identified. During a median follow-up of 6.7&#xa0;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 4<sub>fasting</sub> and metabotype 4<sub>postprandial</sub> 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.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月8日

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  • 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

摘要

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.