Is Eating a Greater Diversity of Foods Associated with Lower Incidence of Type 2 Diabetes?: Prospective Analysis of the EPIC-InterAct Case-Cohort Study
Bibliographic record
Abstract
Healthy eating includes a diverse diet as this ensures adequate amounts of various nutrients for different metabolic pathways. Dietary diversity may also improve survival and promote metabolic health. However, the impact of dietary diversity on type 2 diabetes (T2D) in heterogenous populations is unknown. We aimed to examine whether greater diversity in total diet and within food groups was related to T2D incidence in multi-country populations. We evaluated baseline (1991-1998) self-reported diet data from 23,649 participants (including 10,363 incident events of T2D) from 8 European countries in the EPIC-InterAct study. Incident T2D by 2007 was verified with multiple sources, including objective records in each participating center. We constructed multiple scores for dietary diversity to assess: diversity between food groups (DDS-total5) (range: 0 to 5), and diversity of subtypes of vegetables (DDS-veg) (0 to 4), meats (DDS-meat) (0 to 6), animal protein (DDS-proA) (0 to 8) and plant protein (DDS-proP) (0 to 5). We fitted Prentice-weighted Cox regression to estimate hazard ratios (HR) and 95% confidence intervals (95% CI) by country and meta-analyzed the country-specific estimates. In multivariable-adjusted models, we found a significant association between total dietary diversity (all 5 food groups versus 3 or fewer) and incident T2D (HR 0.86 [95% CI: 0.75–0.98]). Similarly, within the vegetable group and within plant protein group, highest diversity scores were significantly inversely associated with incident T2D (0.90 [0.83–0.98] and 0.78 [0.65–0.93], respectively) compared to lowest scores. Both vegetable diversity and plant-protein diversity were robust to additional statistical adjustment, including food quantity, ratio of saturated to unsaturated fatty acids, prevalent non-communicable diseases and reproductive history. Conversely, greater diversity of animal-protein subtypes was associated with higher T2D incidence, in particular after adjusting for prevalent diseases (1.39 [1.21-1.59]). This multi-country case-cohort study supports the consumption of diets comprising all 5 food groups as well as diversity of vegetable and plant-protein as a strategy to help prevent diabetes among a European population. HM is funded by a Canadian Institutes of Health Research Vanier Scholarship.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".