New insights of minimum requirement on legumes (Fabaceae sp.) daily intake in Malaysia
Bibliographic record
Abstract
Abstract Background Despite their low energy density and high nutrient content, legumes may be under-recognised as a beneficial food for the prevention and management of obesity and related diseases, such as cardiovascular disease (CVD). This study aims to analyse the moderation effect of legume intake on the relationship between BMI and the prevalence of CVD among the Malaysian adult population. Methods This study addressed communities of urban and rural Malaysia, including adults aged between 35 and 70 years old at the baseline recruitment. A series of standardised questionnaires were used to assess legume intake, history of comorbidities and socio-demographic information. Resting blood pressure measurements and physical examinations were performed to collect blood pressure and anthropometric data. Bivariate analysis was completed to determine the association between legume intake, socio-demographic characteristics and CVD prevalence. Moderation analysis was used to quantify the moderation effect of minimum daily legume intake on the relationship between BMI and CVD prevalence. Results This study found that those who consume less than 3 servings of legumes per day benefit from protective effects against CVD risk (POR = 0.56, 95% CI = 0.37 – 0.85). Moderation analysis of a minimum of three servings/day for the relationship between BMI and CVD prevalence showed significant effects. The group that benefited the most from this effect was those with a BMI in the range of 26 to 34 kg/m2. Conclusions This study provides new insights into the recommendation for legume intake according to the relationship between BMI and the prevalence of CVD in Malaysian adults. This study recommends that those with a BMI of 26 to 34 kg/m2 should consume at least 3 servings of legumes per day to reduce the risk of CVD. Further prospective research is warranted to affirm these findings throughout the Malaysian population.
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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 | 0.001 | 0.001 |
| 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".