The Association of Mediterranean and DASH Diets with Mortality in Adults on Hemodialysis: The DIET-HD Multinational Cohort Study
Bibliographic record
Abstract
Background Mediterranean and Dietary Approaches to Stop Hypertension (DASH) diets associate with lower cardiovascular and all-cause mortality in the general population, but the benefits for patients on hemodialysis are uncertain. Methods Mediterranean and DASH diet scores were derived from the GA 2 LEN Food Frequency Questionnaire within the DIET-HD Study, a multinational cohort study of 9757 adults on hemodialysis. We conducted adjusted Cox regression analyses clustered by country to evaluate the association between diet score tertiles and all-cause and cardiovascular mortality (the lowest tertile was the reference category). Results During the median 2.7-year follow-up, 2087 deaths (829 cardiovascular deaths) occurred. The adjusted hazard ratios (95% confidence intervals) for the middle and highest Mediterranean diet score tertiles were 1.20 (1.01 to 1.41) and 1.14 (0.90 to 1.43), respectively, for cardiovascular mortality and 1.10 (0.99 to 1.22) and 1.01 (0.88 to 1.17), respectively, for all-cause mortality. Corresponding estimates for the same DASH diet score tertiles were 1.01 (0.85 to 1.21) and 1.19 (0.99 to 1.43), respectively, for cardiovascular mortality and 1.03 (0.92 to 1.15) and 1.00 (0.89 to 1.12), respectively, for all-cause mortality. The association between DASH diet score and all-cause death was modified by age ( P =0.03); adjusted hazard ratios for the middle and highest DASH diet score tertiles were 1.02 (0.81 to 1.29) and 0.70 (0.53 to 0.94), respectively, for younger patients (≤60 years old) and 1.05 (0.93 to 1.19) and 1.08 (0.95 to 1.23), respectively, for older patients. Conclusions Mediterranean and DASH diets did not associate with cardiovascular or total mortality in hemodialysis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".