Curcumin on Human Health: A Comprehensive Systematic Review and Meta‐Analysis of 103 Randomized Controlled Trials
Why this work is in the frame
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Bibliographic record
Abstract
The aim of this meta-analysis was to determine the effect of curcumin on a range of health outcomes. PubMed, EMBASE, Scopus, and Web of Science were searched from inception until September 2023. Randomized clinical trials (RCTs) that compared the effect of Curcuma longa L. with placebo were considered eligible. The risk of bias and overall certainty of evidence were assessed using the Newcastle-Ottawa Scale and Grading of Recommendations Assessment, Development, and Evaluation (GRADE), respectively. We meta-analyzed the effect sizes across eligible studies using the random-effects model. In total, 103 RCTs on 42 outcomes were included, incorporating a total population of 7216 participants. Overall, 23 out of 42 (55%) outcomes reported statistically significant effect sizes. The credibility of the evidence was rated as high for fasting blood sugar (FBS), C-reactive protein (CRP), high-density lipoprotein (HDL), and weight. The remaining outcomes presented moderate (waist circumference [WC], hip circumference [HC], body mass index [BMI], insulin, Homeostatic Model Assessment for Insulin Resistance [HOMA-IR], quantitative insulin-sensitivity check index [QUICKI], leptin, gamma-glutamyl transferase [GGT], glutathione [GSH], and superoxide dismutase [SOD]), low (14 outcomes), or very low (14 outcomes) evidence. In conclusion, curcumin supplementation can modify FBS and some glycemic indices, lipid parameters, as well as inflammatory and oxidative parameters. This updated summary of the accumulated evidence may help inform clinicians and future guidelines regarding medical and scientific interest in curcumin. However, due to limitations in the methodological quality of the included studies, well-designed and long-term RCTs with large sample sizes are needed. Trial registration: PROSPERO: CRD42021251969.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.034 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it