Pyruvate Supplementation for Weight Loss: A Systematic Review and Meta-Analysis of Randomized Clinical Trials
Bibliographic record
Abstract
BACKGROUND: Several slimming aids being sold as food supplements are widely available. One of them is pyruvate. Its efficacy in causing weight reduction in humans has not been fully established. The objective of this systematic review was to examine the efficacy of pyruvate in reducing body weight. METHODS: Electronic and nonelectronic searches were conducted to identify all relevant human randomized clinical trials. The bibliographies of all located articles were also searched. No restrictions in language or time were applied. Two independent reviewers extracted the data according to predefined criteria. A fixed-effect model was used to calculate mean differences (MD) and 95% confidence interval (CI). RESULTS: Nine trials were identified and 6 were included. All had methodological weaknesses. The meta-analysis revealed a statistically significant difference in body weight with pyruvate compared to placebo (MD: -0.72 kg; 95% CI: -1.24 to -0.20). The magnitude of the effect is small, and its clinical relevance is uncertain. Adverse events included gas, bloating, diarrhea, and increase in low-density lipoprotein (LDL) cholesterol. CONCLUSION: The evidence from randomized clinical trials does not convincingly show that pyruvate is efficacious in reducing body weight. Limited evidence exists about the safety of pyruvate. Future trials involving the use of this supplement should be more rigorous and better reported.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.021 |
| 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) | 0.006 | 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".