| Title: | A Metabolomics-Based EAT-Lancet Diet Score and Type 2 Diabetes: Interaction with Genetic, Lifestyle, and Environmental Factors |
| Journal: | Nutrition Metabolism and Cardiovascular Diseases |
| Published: | 16 Jul 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42552140/ |
| DOI: | https://doi.org/10.1016/j.numecd.2026.104874 |
Publication 21342
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Abstract
BACKGROUND AND AIMS: Diet is a major modifiable determinant of type 2 diabetes (T2D), but conventional dietary assessments may inadequately capture interindividual metabolic responses to diet. We aimed to derive a metabolomics-based EAT-Lancet diet score, compare its association with T2D with that of a questionnaire-based score, and evaluate the effect modification by major T2D risk factors.</p>
METHODS AND RESULTS: We included 96,716 participants without T2D at baseline from the UK Biobank with circulating metabolomics data and at least two valid 24-h dietary assessments. A metabolomics-based EAT-Lancet diet was derived using elastic net regression linking dietary intake with circulating metabolites. Cox proportional hazards models assessed associations with incident T2D and compared results with a questionnaire-based diet. Interactions with key T2D risk factors were assessed on the multiplicative and additive scales. During a median follow-up of 14.2 years, 2798 participants developed T2D. The metabolomics-based EAT-Lancet diet showed a stronger inverse association with incident T2D (hazard ratio [HR] 0.75; 95% confidence interval [CI] [0.72, 0.77]) than the questionnaire-based (HR 0.90; 95% CI [0.87, 0.94]). Metabolites characterizing the diet were enriched in pathways related to carbohydrate, energy metabolism and amino acid metabolism. Additive interactions between metabolomics-based diet and genetic risk, BMI, smoking status, and air pollution exposure were observed (RERI 0.31-4.77; AP 13%-61%).</p>
CONCLUSIONS: A metabolomics-based EAT-Lancet diet was inversely associated with T2D risk and modified associations between major T2D risk factors and incident T2D. Integrating metabolomics into dietary assessment may enhance risk stratification and support precision nutrition strategies for T2D prevention.</p>
7 Authors
- Zixuan Zhang
- Guangrui Yang
- Shuxiao Shi
- Jingxuan Wang
- Victor W Zhong
- Yi Chen
- Xihao Du
1 Application
| Application ID | Title |
|---|---|
| 101169 | Lifestyle, biological factors, genetic predisposition, and age-related diseases: associations and mechanisms |
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