Can pulses play a role in improving cardiometabolic health? Evidence from systematic reviews and meta‐analyses
Bibliographic record
Abstract
Obesity, diabetes, and cardiovascular disease (CVD) present important unmet prevention and treatment challenges. Dietary pulses are sustainable, affordable, and nutrient-dense foods that have shown a wide range of health benefits in the prevention and management of these conditions. Despite these findings, recommendations for pulse intake continue to vary across chronic disease guidelines, and intake levels continue to remain low. Here, we summarize findings from recent systematic reviews and meta-analyses assessing the relationship between dietary pulse consumption and cardiometabolic health and assess the overall strength of the evidence using the Grading of Recommendations, Assessment, Development, and Evaluation tool. We conclude that systematic reviews and meta-analyses of prospective cohort studies assessing the relationship between legumes and the risk of coronary heart disease appear to provide moderate-quality evidence of a benefit, and several systematic reviews and meta-analyses of randomized controlled trials assessing the effect of pulses on cardiometabolic risk factors provide low- to moderate-quality evidence of a benefit. There remains an urgent need, however, for more high-quality prospective cohort studies and large, high-quality, randomized trials to clarify the benefits of dietary pulses in the prevention and management of overweight/obesity, diabetes, and CVD.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.021 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".