The effects of curcumin‐containing supplements on biomarkers of inflammation and oxidative stress: A systematic review and meta‐analysis of randomized controlled trials
Bibliographic record
Abstract
Besides other benefits, curcumin is getting more recognized for its antioxidant and anti-inflammatory properties, highlighting the importance of curcumin application for chronic disease prevention. This systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted to assess the influence of curcumin-containing supplements on biomarkers of inflammation and oxidative stress. MEDLINE, EMBASE, Web of Science, and Cochrane Central Register of Controlled Trials were searched till January 2018 for eligible studies. The selected studies were evaluated for their quality using the Cochrane risk of bias tool and relevant data were extracted from included studies. Data were pooled using the inverse variance method and expressed as standardized mean difference (SMD) with 95% confidence intervals (95% CI). Fifteen RCTs were included in the final analysis. The meta-analysis indicated that curcumin supplementation significantly decreased interleukin 6 (IL-6) (SMD -2.08; 95% CI [-3.90, -0.25]; p = 0.02), high-sensitivity C-reactive protein (hs-CRP) (SMD -0.65; 95% CI [-1.20, -0.10], p = 0.02), and malondialdehyde (MDA) concentrations (SMD -3.14; 95% CI [-4.76, -1.53], p < 0.001). Though, curcumin supplementation had no significant effect on tumor necrosis factor-alpha (SMD -1.62; 95% CI [-3.60, 0.36]; p = 0.10) and superoxide dismutase levels (SMD 0.34; 95% CI [-1.06, 1.74], p = 0.63). Overall, this meta-analysis suggests that taking curcumin-containing supplements may exert anti-inflammatory and antioxidant properties through a significant reduction in IL-6, hs-CRP, and MDA levels.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.041 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".