Abstract P1116: The DASH Dietary Pattern and Risk of Cardiovascular Disease Mortality During 1988-2019 in US Adults: A Population-Based Cohort Study
Bibliographic record
Abstract
Introduction: The Dietary Approaches to Stop Hypertension (DASH) diet reduces cardiovascular disease (CVD) risk factors in clinical trials and has been associated with reduced CVD risk. However, evidence on reduced CVD mortality outcomes has yet to be observed in the National Health and Nutrition Examination Survey (NHANES), a representative sample of the US population, possibly due to an insufficient follow-up in early analyses and a limited dietary exposure assessment. Objective: To examine the association between the DASH dietary pattern and CVD mortality with a longer follow-up using a food-based score and a more robust dietary exposure assessment. Methods: A total of 13,586 nonpregnant adults without a history of CVD were included from NHANES (1988-1994). Diet was assessed by a 24-hour dietary recall supplemented with a food frequency questionnaire at baseline using the DASH-style diet adherence score (DASH score) with positive points for servings of fruits, vegetables, nuts and legumes, whole grains, and low-fat dairy products, and negative points for total dietary sodium, servings of red and processed meats, and sweetened beverages (range, 8-40 points). Mortality data were obtained from National Death Index records until 31 December 2019. Weighted Cox proportional hazards regression models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs). The primary outcome was CVD-mortality. Other mortality outcomes included coronary heart disease (CHD), stroke, and all-cause mortality. Results: During a mean follow-up of 23-years, 1,751 CVD deaths, including 1,416 CHD, 335 stroke deaths and 5,202 all-cause deaths were documented over 311,826 person-years. After multivariable adjustments comparing the highest to lowest tertiles of the DASH score, participants with higher DASH scores were associated with 24% (HR: 0.76 [95% CI: 0.61, 0.94]), 25% (0.75 [0.60, 0.95]) and 19% (0.82 [0.71, 0.94]) lower risk of CVD, CHD, and all-cause mortality, respectively. There was no association with stroke mortality (0.80 [0.48, 1.38]). An increase in DASH score by 10-points was associated with a 20% (0.80 [0.70, 0.93]), 23% (0.77 [0.65, 0.90]), and 18% (0.82 [0.75, 0.90]) lower risk of CVD, CHD, and all-cause mortality, respectively. Conclusion: Among US adults, greater adherence to a food-based DASH score was inversely associated with CVD, CHD, and all-cause mortality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
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".