Dietary Patterns, Circulating Metabolome, and Risk of Type 2 Diabetes
Bibliographic record
Abstract
Circulating metabolites may reflect biological homeostasis and have been linked to dietary intakes and human health, and may hold the promises to facilitate objective assessments of intakes and metabolic response to diets. Here, we integrated metabolomic, genetic, and metagenomic data from five longitudinal cohorts comprising 21,474 participants of diverse ethnic backgrounds, to develop metabolomic signatures for popular dietary patterns (i.e., three guideline-based diets, three plant-based diets, and two mechanism-based diets) and systematically investigated their clinical relevance. Applying machine-learning models in two deeply-phenotyped lifestyle validation studies, we identified eight metabolomic signatures (each included 37 to 66 metabolites) significantly correlated with their respective dietary pattern indices, consistently across multiple independent validation cohorts (r = 0.11–0.38; P < 8.06×10⁻⁹). These signatures included shared metabolites between diets (e.g., up to 67% among guideline-based diets, including hippuric and 3-indolepropionic acid), and metabolites unique to specific diets (e.g., N6,N6,N6-trimethyllysine to proinflammatory diet). In multivariable-adjusted analyses of 5 prospective cohorts (1,832 incident cases during up to 27 years of follow-up), the metabolomic signatures of healthful diets (i.e., Mediterranean and healthful plant-based diets) were associated with lower T2D risk (HR: 0.82–0.90; P < 3×10⁻⁶), while signatures for unhealthy diets (e.g., proinflammatory and hyperinsulinemia diets) were associated with higher T2D risk (HR: 1.23–1.26; P < 2×10⁻¹⁵); these associations were further supported by Mendelian randomization analysis incorporating genetic data. Finally, through genome-wide and taxa-wide associating analyses, we identified 15 genetic loci – including those involved in fatty acid and energy metabolism (e.g., FADS1/2 and CERS4 ; P < 5×10 -8 ), and 39 gut microbial species – including those relevant to butyric acid metabolism (e.g., E. eligens and F. pranusnitzii ; FDR < 0.05), significantly associated with the metabolomic signatures of diets. Genetic variants and gut microbial diversity explained up to 19.1% and 10.6% of the variation in these signatures, respectively, underscoring a potential role of host genetics and gut microbiota in dietary metabolism. In conclusion, our study identified metabolomic signatures reflecting both intakes and individual metabolic response to various diets and are associated with future T2D risk. These signatures may facilitate individualized dietary assessments and risk stratification in future nutritional research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".