Protein diversity, type 2 diabetes, and effect modifiers: a multi-country prospective study
Bibliographic record
Abstract
BACKGROUND: Dietary diversity may affect type 2 diabetes (T2D) but no studies have examined protein diversity by source. We examined five diversity scores and the 10-year risk of T2D and effect modification. METHODS: A prospective study of 10 363 incident T2D cases and a representative sub-cohort of 13 937 individuals sampled from a cohort of 340 234 participants in eight European countries (1993-2007). Five diversity scores were derived from self-reported diet data (gr/day): diversity of food groups (range: 0-5); and diversity within subtype of vegetables (0-4); meat/alternatives (0-6); animal-protein (0-8); and plant-protein sources (0-5). Country-specific hazard ratios (HRs) and 95% confidence intervals (CIs) were obtained by using Prentice-weighted Cox regression and combined by using mixed-effects models. Models were stratified by sex (male/female) and obesity status (body mass index ≥ 30 kg/m2; waist circumference ≥ 88 cm for females and ≥102 cm for males). RESULTS: Daily intake of five food groups (versus up to three) was linked to lower T2D incidence overall [HR 0.86 (95% CI 0.75, 0.98)], in females [0.86 (0.77, 0.96)], and in people without central obesity [0.79 (0.70, 0.89)]. Three or more subtypes of plant protein were inversely associated with T2D overall [0.78 (0.65, 0.98)], in females [0.75 (0.62, 0.90)] and people without central obesity [0.82 (0.68, 1.00)]. Additionally, consuming three subtypes of vegetables was inversely associated with T2D overall [0.90 (0.83, 0.98)] and in males [0.85 (0.73, 0.99)]. CONCLUSION: Diabetes prevention may benefit not only from a diet consisting of five different food groups, but also from a diet that is diverse in plant-protein sources, with specific benefits for female Europeans and those without central obesity.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".