Diet-Quality Scores and the Risk of Type 2 Diabetes in Men
Bibliographic record
Abstract
OBJECTIVE: To 1) compare associations of diet-quality scores, which were inversely associated with cardiovascular disease, with incident type 2 diabetes and 2) test for differences in absolute-risk reduction across various strata. RESEARCH DESIGN AND METHODS: Men from the Health Professionals Follow-Up Study, who were initially free of type 2 diabetes, cardiovascular disease, or cancer (n = 41,615), were followed for ≤ 20 years. The Healthy Eating Index (HEI) 2005, the alternative HEI (aHEI) the Recommended Food Score, the alternative Mediterranean Diet (aMED) Score, and the Dietary Approaches to Stop Hypertension (DASH) Score were calculated from food-frequency questionnaires. Cox proportional hazard models with time-varying covariates were used to assess risk by quintiles and continuous intervals. RESULTS: There were 2,795 incident cases of type 2 diabetes. After multivariate adjustment, the aHEI, aMED, and DASH scores were significantly associated with reduced risk. A 1-SD increase was associated with 9-13% reduced risk (P < 0.01), and the DASH score was associated with lower risk independent of other scores. These scores were associated with lower absolute risk among those who were overweight or obese compared with normal weight (P for interaction < 0.01). CONCLUSIONS: Several diet-quality scores were associated with a lower risk of type 2 diabetes and reflect a common dietary pattern characterized by high intakes of plant-based foods such as whole grains; moderate alcohol; and low intakes of red and processed meat, sodium, sugar-sweetened beverages, and trans fat. High-quality diets may yield the greatest reduction in diabetes cases when followed by those with a high BMI.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".