Abstract MP26: Dietary Patterns, Metabolome Profile and Risk of Type 2 Diabetes
Bibliographic record
Abstract
Background: Plasma metabolites have been associated with type 2 diabetes (T2D) risk and may reflect metabolic homeostasis as a result of the interplay among diet, genetics, and the gut microbiome. Hypothesis: We hypothesized that specific multi-metabolite signatures can characterize the adherence and metabolic response to various dietary patterns and are associated with incident T2D. Methods: We analyzed 20578 participants in the Nurses’ Health studies and Health Professional Follow-up Study (NHS/HPFS), Hispanic Community Health Study/Study of Latinos, and Women’s Health Initiative, whose blood metabolome were profiled by liquid chromatography-mass spectrometry. We applied elastic net regression to develop (n=1206 in a NHS/HPFS lifestyle sub-study) and validate (in the remaining samples) metabolic signatures for 3 dietary recommendation-based diets (a Mediterranean diet - AMED, a healthy eating index - AHEI, and an anti-hypertensive diet - DASH), 3 plant-based diets (PDIs), and 2 mechanism-based diets (proinflammatory [EDIP] and insulinemic [EDIH] diets). We tested associations between the dietary metabolic signatures and incident T2D in 14060 initially T2D-free participants (1832 cases in up to 22 years of follow-up). In sub-samples, we further examined genetic and microbial factors (shotgun sequencing) associated with metabolic signatures. Results: We identified 8 metabolic signatures, each consisted of 37-66 metabolites and was robustly correlated with the corresponding dietary pattern index in all validation cohorts ( r =0.11-0.38; P < 8.06 х10 -9 ). We noted shared and distinct metabolites cross metabolic signatures of various dietary patterns. In multivariable-analyses, metabolic signatures of healthful diets (i.e., AMED, AHEI, DASH, healthful PDI) were associated with a lower T2D risk (hazard ratio [HR]: 0.82-0.90; P < 3х10 -6 ), whereas metabolic signatures of unhealthful diets (e.g., EDIP and EDIH) were associated with a higher T2D risk (HR: 1.23-1.26; P < 2х10 -15 ). The metabolic signatures mediated 26%-56% of the associations between their corresponding dietary patterns and T2D risk ( P < 0.01). Further, a proportion of variation in the dietary metabolic signatures was explained by genetic variants (9.9% for EDIP to 34.5% for PDI) and gut microbial diversity (0.2% for PDI to 14.9% for EDIP). Dietary metabolic signatures were associated with 7 genetic loci including those involved in fatty acid and energy metabolism (e.g., FADS1/2 and CERS4 , P < 5х10 -8 ), and the abundance of 39 gut bacterial species (FDR <0.05). Conclusions: We identified metabolic signatures that characterized the adherence and metabolic responses (related to genetics and gut microbiome) to various dietary patterns, and were associated with T2D risk, in ethnically diverse populations. Metabolomic profiling may facilitate personalized nutritional interventions for T2D prevention.
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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) | 0.005 | 0.001 |
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".