Effects of <scp><i>Nigella sativa</i></scp> on glycemic control, lipid profiles, and biomarkers of inflammatory and oxidative stress: A systematic review and meta‐analysis of randomized controlled clinical trials
Bibliographic record
Abstract
The aim of this systematic review and meta‐analysis was to evaluate the effects of Nigella sativa (N. sativa) on glycemic control, lipid profiles, and biomarkers of inflammatory and oxidative stress. Two independent authors systematically examined online databases consisting of, EMBASE, Scopus, PubMed, Cochrane Library, and Web of Science from inception until October 30, 2019. Cochrane Collaboration risk of bias tool was applied to assess the methodological quality of the studied trials. The heterogeneity among the included studies were assessed using the Cochrane's Q test and I‐square (I2) statistic. Data were pooled using a random‐effects model and weighted mean difference (WMD) was considered as the overall effect size. A total of 50 trials were included in this meta‐analysis. We found a significant reduction in total cholesterol (WMD: −16.80; 95% CI: −21.04, −12.55), triglycerides (WMD: −15.73; 95% CI: −20.77, −10.69), LDL‐cholesterol (WMD: −18.45; 95% CI: −22.44, −14.94) and VLDL‐cholesterol (WMD: −3.72; 95% CI: −7.27, −0.18) following supplementation with N. sativa. In addition, there was significant reductive effect observed with N. sativa on fasting glucose (WMD: −15.18; 95% CI: −19.82, −10.55) and HbA1C levels (WMD: −0.45; 95% CI: −0.66, −0.23). Effects of N. sativa on CRP (WMD: −3.61; 95% CI: −9.23, 2.01), TNF‐α (WMD: −1.18; 95% CI: −3.23, 0.86), TAC (WMD: 0.31; 95% CI: 0.00, 0.63), and MDA levels (WMD: −0.95; 95% CI: −2.18, 0.27) were insignificant. This meta‐analysis demonstrated the beneficial effects of N. sativa on fasting glucose, HbA1c, triglycerides, total‐, VLDL‐, LDL‐cholesterol 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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".