Nut consumption and total and cause-specific mortality: results from the Golestan Cohort Study
Bibliographic record
Abstract
Background: A number of prospective studies have observed inverse associations between nut consumption and chronic diseases. However, these studies have predominantly been conducted in Western countries, where nut consumption tends to be more common among individuals with healthier lifestyles. It is important to examine the association in other parts of the world, and particularly among populations with different patterns of disease, socioeconomic status, lifestyles and disease risk factors. Our objective was to examine the association between nut consumption and mortality in a population whose nut consumption does not track with a healthy lifestyle. Methods: We examined the association between nut consumption and all-cause and cause-specific mortality in the 50 045 participants of the Golestan Cohort Study. Participants were aged 40 and older at baseline in 2004, and have been actively followed since that time. Dietary data were collected using a validated semi-quantitative food frequency questionnaire that was administered at baseline. Results: During 349 677 person-years of follow-up, 3981 cohort participants died, including 1732 women and 2249 men. Nut consumption was associated inversely with all-cause mortality. The pooled multivariate adjusted hazard ratios for death among participants who ate nuts, as compared with those who did not, were 0.89 [95% confidence interval (CI), 0.82-0.95] for the consumption of less than one serving of nuts per week, 0.75 (95% CI, 0.67-0.85) for one to less than three servings per week and 0.71 (95% CI, 0.58-0.86) for three or more servings per week ( P < 0.001 for trend). Among specific causes, significant inverse associations were observed between nut consumption and deaths due to cardiovascular disease, all cancers and gastrointestinal cancers. Conclusions: This study provides evidence for an inverse association between nut consumption and mortality in a developing country, where nut consumption does not track with a healthy lifestyle. Further work is needed to establish the underlying mechanisms responsible for this association.
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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 | 0.002 |
| 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) | 0.001 | 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".