A systematic review and meta-analysis of nut consumption and incident risk of CVD and all-cause mortality
Bibliographic record
Abstract
Dietary patterns containing nuts are associated with a lower risk of CVD mortality, and increased nut consumption has been shown to have beneficial effects on CVD risk factors including serum lipid levels. Recent studies have reported on the relationship between nut intake and CVD outcomes and mortality. Our objective was to systematically review the literature and quantify associations between nut consumption and CVD outcomes and all-cause mortality. Five electronic databases (through July 2015), previous reviews and bibliographies of qualifying articles were searched. In the twenty included prospective cohort studies (n 467 389), nut consumption was significantly associated with a lower risk of all-cause mortality (ten studies; risk ratio (RR) 0·81; 95 % CI 0·77, 0·85 for highest v. lowest quantile of intake, P het=0·04, I 2=43 %), CVD mortality (five studies; RR 0·73; 95 % CI 0·68, 0·78; P het=0·31, I 2=16 %), all CHD (three studies; RR 0·66; 95 % CI 0·48, 0·91; P het=0·0002, I 2=88 %) and CHD mortality (seven studies; RR 0·70; 95 % CI 0·64, 0·76; P het=0·65, I 2=0 %), as well as a statistically non-significant reduction in the risk of non-fatal CHD (three studies; RR 0·71; 95 % CI 0·49, 1·03; P het=0·03, I 2=72 %) and stroke mortality (three studies; RR 0·83; 95 % CI 0·69, 1·00; P het=0·54, I 2=0 %). No evidence of association was found for total stroke (two studies; RR 1·05; 95 % CI 0·69, 1·61; P het=0·04, I 2=77 %). Data on total CVD and sudden cardiac death were available from one cohort study, and they were significantly inversely associated with nut consumption. In conclusion, we found that higher nut consumption is associated with a lower risk of all-cause mortality, total CVD, CVD mortality, total CHD, CHD mortality and sudden cardiac death.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".