The Efficacy of Glucomannan Supplementation in Overweight and Obesity: A Systematic Review and Meta-Analysis of Randomized Clinical Trials
Bibliographic record
Abstract
OBJECTIVE: The increased prevalence of obesity has resulted in the current high popularity of dietary supplements marketed as weight reducing agents. The efficacy of most of these supplements is not established. The soluble fiber, glucomannan, is often recommended for weight loss. The aim of this systematic review is to evaluate the evidence for or against the efficacy of glucomannan in body weight reduction. METHODS: Electronic searches were conducted in Medline, Embase, Amed, and The Cochrane Library. Hand searches of bibliography were also conducted. Outcomes of interest were body weight and body mass index. Studies involving only overweight and/or obese participants were included. Two reviewers independently determined the eligibility of studies and assessed the reporting quality of included randomized controlled trials (RCTs), using the CONSORT and PRISMA guidelines. RESULTS: Eighteen trials were identified, and 9 were included. There was a variation in the reporting quality of the included RCTs. A meta-analysis (random effect model) of 8 RCTs revealed a nonstatistically significant difference in weight loss between glucomannan and placebo (mean difference [MD]: -0.22 kg; 95% confidence interval [CI], -0.62, 0.19; I(2) = 65%). Adverse events included abdominal discomfort, diarrhea, and constipation. CONCLUSION: The evidence from available RCTs does not show that glucomannan intake generates statistically significant weight loss. Future trials 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 | 0.033 | 0.074 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.035 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 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".