Healthy food diversity and the risk of major chronic diseases in the EPIC-Potsdam study
Bibliographic record
Abstract
Practicing a diverse diet may reduce chronic disease risk, but clear evidence is scarce and previous diet diversity measures rarely captured diet quality. We investigated the effect of the Healthy Food Diversity (HFD)-Index on incident type 2 diabetes (T2D), myocardial infarction (MI) and stroke among a middle-aged German population. The EPIC-Potsdam study recruited 27,548 participants from 1994 to 1998. Semiquantitative food frequency questionnaire was used to calculate the HFD-Index. Longitudinal associations of HFD-Index and verified incident diseases were investigated by multiple-adjusted Cox proportional hazards regression models. Among 26,591 participants (mean age 50.5 years, 60% women), 1537, 376 and 412 developed T2D, MI and stroke, respectively, over an average follow-up of 10.6 years. There was no association between HFD-Index and incident T2D or MI. Higher compared to lower HFD-Index was inversely associated with incident stroke in men [HR (95% CI): 0.80 (0.70, 0.92)], but positively associated with incident stroke in women [1.20 (1.01, 1.42)]. Although there was no clear association between HFD-Index and T2D or MI incidence, we found a beneficial association in men and a harmful association in women for incident stroke. We emphasised the need for further investigations on combining diet diversity and diet quality in relation to health outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".