Vegetable and Fruit Intake Variety and Cardiovascular Health and Mortality: A Systematic Review and Meta-Analysis of Observational Studies
Bibliographic record
Abstract
INTRODUCTION: A multitude of evidence supports the consumption of a higher quantity of vegetables and fruits for their cardiovascular benefits. Nonetheless, the extent to which variety is associated with cardiovascular health remains unclear. OBJECTIVE: To conduct a systematic review and meta-analysis of observational studies (prospective cohort and cross-sectional studies) assessing the role of a variety of vegetable and fruit consumption in cardiovascular morbidity and mortality in adults. DATA SOURCES: MEDLINE-PubMed, Cochrane databases, and reference lists were searched through March 2023. DATA EXTRACTION: Two independent reviewers extracted data and assessed the risk of bias (National Heart, Lung, and Blood Institute Tool and Newcastle-Ottawa Scale). DATA ANALYSIS: statistic). The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) was used to assess the overall certainty of the evidence. Five cross-sectional (n = 45,761) and seven prospective studies (n = 253,422) met the eligibility criteria. Greater variety of vegetable and fruit consumption was prospectively related to decreased all-cause mortality (risk ratio, 0.89 [95% CI, 0.82-0.97], seven study comparisons, n = 196,925), while no significant associations were observed with assessed cardiovascular-related mortality or morbidity. For all outcomes, the certainty of the evidence was graded as "low" or "very low" owing to inconsistency and/or imprecision. CONCLUSIONS: Overall, this study shows that greater variety in vegetable and fruit consumption may reduce all-cause mortality and highlights the need for additional studies with a higher degree of evidence to better understand its role in cardiovascular health.
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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 | 0.031 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.008 | 0.010 |
| 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) | 0.002 | 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".