Chromium supplementation in overweight and obesity: a systematic review and meta‐analysis of randomized clinical trials
Bibliographic record
Abstract
The increased prevalence of obesity has made the use of dietary supplements as weight reducing agents highly popular, but their efficacy has not been proven. One such supplement is chromium. The purpose of this review was to evaluate the evidence for or against the efficacy of chromium supplementation in overweight and obese individuals. Electronic searches were conducted in Medline, Embase, Amed and The Cochrane Library. The bibliographies of located articles were also searched. No age, gender or language restrictions were imposed. The reporting quality of identified randomized clinical trials (RCTs) was assessed using a methodological checklist adapted from the Consolidated Standard of Reporting Trials Statement and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Thirty-nine trials were identified and 20 were included. There were variations in reporting quality of included studies. A meta-analysis of 11 studies showed a statistically significant difference in weight loss favouring chromium over placebo (mean difference (MD): -0.50 kg; 95% confidence interval (CI): -0.97, -0.03). There was a high statistical heterogeneity. Adverse events included watery stools, vertigo, headaches and urticaria. The evidence from available RCTs shows that chromium supplementation generates statistically significant reductions in body weight. The magnitude of the effect is small, and the clinical relevance is uncertain. Future trials should last at least 16 weeks and greater uniformity in the measuring and assessment tools for body composition is recommended.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.026 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".