Effects of garlic supplementation on oxidative stress and antioxidative capacity biomarkers: A systematic review and meta‐analysis of randomized controlled trials
Bibliographic record
Abstract
Evidence suggests that garlic supplementation may have an effect on oxidative stress by augmenting the rate of enzymatic and non‐enzymatic antioxidants and diminishing pro‐oxidant enzymes. Given inconsistencies across studies, we aimed to systematically review the current literature and quantify the effects of garlic supplementation on oxidative stress. We conducted a systematic search with multiple databases (Scopus, PubMed, and Web of Science) to find relevant articles published prior to October 2020. Results were reported as bias‐corrected standardized mean difference (Hedges' g) with 95% confidence intervals (CI) using random‐effects models. Cochrane's Q and I squared (I2) tests were used to determine heterogeneity among the studies included. Twelve randomized controlled trials (RCTs) were included. Garlic doses ranged from 80 to 4,000 mg/day, and intervention duration varied between 2 and 24 weeks. Garlic supplementation increased serum level of total antioxidant capacity (TAC) (Hedges' g: 2.77, 95% CI: 1.37 to 4.17, p < 0.001) and superoxide dismutase (SOD) (Hedges' g: 13.76, 95% CI: 4.24 to 23.29, p = 0.004), while it reduced the malondialdehyde serum level (MDA) (Hedges' g: ‐1.94, 95% CI: −3.17 to −0.70, p = 0.002). Due to limited data available, glutathione (GSH) was not considered for the current meta‐analysis. The nonlinear dose‐response effect of garlic supplementation was not observed with regard to serum TAC and MDA levels (TAC: p‐nonlinearity = 0.398; MDA: p‐nonlinearity = 0.488). Garlic supplementation appears to improve serum levels of TAC, MDA, and SOD. Garlic supplementation may be useful to reduce oxidative stress and related diseases. Future studies with large sample sizes and longer duration are required to confirm these findings.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.034 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".