PS-B07-8: LEAFY VEGETABLE CONSUMPTION AND RISK OF METABOLIC SYNDROME AND ITS SYMPTOMS: A SYSTEMATIC REVIEW OF COHORT AND RANDOMISED CONTROL TRIALS
Bibliographic record
Abstract
Objective: Metabolic syndrome (MetS) is a cluster of cardiovascular risk factors, including dyslipidemia, hypertension, hyperglycemia, and obesity. It increases the risk of type 2 diabetes, cardiovascular disease, and all cause mortality and exerts an enormous global burden on health systems. A better understanding of dietary approaches that can reduce the risk of MetS or its symptoms is crucial. Leafy vegetables contain bioactive compounds such as nitrates, dietary fibre and flavonoids that are protective against illnesses. However, investigations of whether leafy vegetable consumption can reduce MetS and its symptoms are unclear. This systematic review investigates the effect of leafy vegetable consumption on the risk of MetS and its symptoms. Design and method: Randomised control trials (RCT), and observational studies were searched in the Web of Science, Scopus, and MEDLINE databases until October 2020. Inclusion criteria were: studies in adults aged ≧ 18 years without MetS symptoms, the intervention of leafy vegetables, the outcome of a change in or incidence of MetS or any of its symptoms. The Cochrane tool and Newcastle Ottawa Scale were used to assess the risk of bias in RCTs and cohort studies, respectively. Results: A total of eight studies (six RCTs and two cohort studies) were included in the qualitative analysis. There were no trials assessing obesity or MetS. The results are shown in Table 1. Conclusion: Few studies evaluated the impact of leafy vegetable consumption on MetS and its symptoms. Beneficial effects of leafy vegetables are reported for blood glucose and blood pressure regulation, but the evidence is limited. More studies are needed to build a robust body of evidence.
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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.015 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".