Grape seed extract supplementation along with a restricted‐calorie diet improves cardiovascular risk factors in obese or overweight adult individuals: A randomized, placebo‐controlled trial
Bibliographic record
Abstract
Grape seed extract (GSE) is a flavonoid‐rich supplement, recently discussed as a potential moderator of inflammation and obesity. In this study, we aimed to investigate the effects of GSE supplementation along with a restricted‐calorie diet (RCD), on changes in blood lipid profile, visceral adiposity index (VAI), and atherogenic index of plasma (AIP). We designed a randomized, double‐blinded, placebo‐controlled clinical trial. Forty obese or overweight individuals (25 ≤ body mass index < 40 kg/m2) were randomly assigned to receive GSE (300 mg/day) or placebo, plus RCD, for 12 weeks. We studied the anthropometric measures, biochemical biomarkers and dietary intake within the study timelines. Levels of high‐density lipoprotein cholesterol (HDL‐C) and HDL‐C/low‐density lipoprotein cholesterol (LDL‐C) significantly increased in the GSE group as compared with the placebo group at week 12 (p = .03 and .008, respectively, adjusted for age, sex, energy and saturated fatty acid intake). We also observed a significant reduction in LDL‐C following GSE supplementation in comparison to placebo (adjusted for age, sex and energy intake, p = .04). VAI, AIP, total cholesterol and triglyceride significantly decreased in the GSE group compared with the baseline (p = .04, .02, .01, and .02, respectively). GSE supplementation may have a modulatory role in improving blood lipid profile in obese or overweight individuals, when accompanied by RCD.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".