The effects of curcumin supplementation on endothelial function: A systematic review and meta‐analysis of randomized controlled trials
Bibliographic record
Abstract
Impaired endothelial function is an important risk factor for cardiovascular disease (CVD). Curcumin supplementation might be an appropriate approach to decrease the complications of CVD. Randomized controlled trials assessing the effects of curcumin supplementation on endothelial function were included. Two independent authors systematically searched online database including EMBASE, Scopus, PubMed, Cochrane Library, and Web of Science with no time restriction. Cochrane Collaboration risk of bias tool was applied to assess the methodological quality of included trials. Between‐study heterogeneities were estimated using the Cochran's Q test and I‐square (I2) statistic. Data were pooled using a random‐effects model, and weighted mean differences (WMDs) were considered as the overall effect sizes. Ten studies with 11 effect sizes were included. We found a significant increase in flow‐mediated dilation (FMD) following curcumin supplementation (WMD: 1.49; 95% CI [0.16, 2.82]). There was no effect of curcumin supplement on pulse wave velocity (PWV; WMD: −41.59; 95% CI [−86.59, 3.42]), augmentation index (Aix; WMD: 0.71; 95% CI [−1.37, 2.79]), endothelin‐1 (ET‐1; WMD: −0.30; 95% CI [−0.96, 0.37]), and soluble intercellular adhesion molecule‐1 (sICAM‐1; WMD: −10.11; 95% CI [−33.67, 13.46]). This meta‐analysis demonstrated the beneficial effects of curcumin supplementation on improving FMD, though it did not influence PWV, Aix, Et‐1, and sICAM‐1.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.028 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".