To What Extent Does Greater Diversity of the Diet Prevent Cardiovascular Diseases and Related Mortality? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Dietary diversity is linked to factors associated with the development of cardiovascular diseases (CVDs), such as improved dyslipidemia and insulin resistance. However, the role of dietary diversity in the risk of CVDs has remained controversial. This meta-analysis assessed whether greater diversity across the diet and within food groups can protect against CVDs and related mortality. A systematic search was done using bibliographic databases of PubMed/Medline, Scopus, and Web of Science for longitudinal studies published between 2008–2020 from developed countries. Random-effects models pooled risk ratios (RR) and 95% confidence intervals (CI); the Cochrane Q test and subgroup analysis assessed heterogeneity and its potential sources. Sensitivity analysis checked for robustness of findings and Egger test assessed publication bias. From the 6713 results, 4,950 titles and abstracts were screened for eligibility to provide, respectively, 8 included CVD studies (437,244 participants and 18,820 cases) and 7 included CVD-mortality studies (160,877 participants and 5,631 cases). All studies were assessed as high or moderate quality. There was a small inverse but non-significant association between total dietary diversity and CVDs (RR: 0.93 [95% CI: 0.86, 1.00], I² = 31.7%), which was driven by the effect in non-European populations and the diversity measured by food groups not foods. Moreover, total dietary diversity was inversely but non-significantly associated with mortality from CVDs (RR: 0.83 [95% CI: 0.70, 1.00], I² = 46.1%). This relationship was significant when studies used >3 categories of exposure, FFQ assessment tool, were conducted on non-Europeans, had ≥10,000 participants, and adjusted for energy intake. These subgroup characteristics were sources of heterogeneity. Studies reporting diversity within food groups could only be pooled for the association between vegetables diversity and CVD mortality, which was null. We found no evidence of publication bias or a single study driving the observed associations. Findings indicated greater total dietary diversity may benefit CVDs and related mortality in non-European populations. More research is needed regarding within-group diversity. None.
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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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.046 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".