Effect of konjac glucomannan soluble fiber, on clinical lipid targets: A systematic review and meta‐analysis of RCTs
Bibliographic record
Abstract
Background Current evidence suggests that the consumption of konjac glucomannan (KJM), a viscous soluble fiber, may significantly reduce LDL cholesterol. However, it is less known whether KJM has an effect on two newer clinical lipid targets in cardiovascular disease risk management. Objective To quantify the effect of KJM fiber on LDL cholesterol, non‐HDL cholesterol and apolipoprotein B reduction in a systematic review and meta‐analysis of randomized controlled trials (RCTs) in the general population. Design MEDLINE, Embase, CINAHL, and Cochrane CENTRAL was searched. Data were extracted by two independent reviews. Eligible studies included RCTs of ≥ 3 weeks follow‐up duration assessing the effect of KJM on LDL cholesterol, non‐HDL cholesterol, or apolipoprotein B. Using the statistical software RevMan (v5.3), the data were pooled using the generic inverse variance method with random effects models and expressed as mean differences (MD) with 95% confidence intervals (CI). Heterogeneity was assessed by the Cochran Q statistic and quantified by the I 2 statistic. Results A total of 12 studies (n = 370), 8 in adults and 4 in children, were included in the meta‐analysis. KJM significantly reduced LDL cholesterol (MD = −0.35 [−0.46, −0.25] mmol/L) and non‐HDL cholesterol (MD = −0.32 [−0.46, −0.19] mmol/L). It did not appear to affect apoB, based on the results from 6 trials. Conclusion Consumption of approximately 3 g/d of KJM reduced significantly LDL cholesterol and non‐HDL cholesterol by 10% and 7%, respectively. The information may be of interest to health agencies in crafting future dietary recommendations related to cardiovascular disease risk management.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".