Should Viscous Fiber Supplements Be Considered in Diabetes Control? Results From a Systematic Review and Meta-analysis of Randomized Controlled Trials
Bibliographic record
Abstract
OBJECTIVE Evidence from randomized controlled trials (RCTs) suggests that viscous dietary fiber may offer beneficial effects on glycemic control and, thus, an improved cardiovascular disease risk profile. Our purpose was to conduct a systematic review and meta-analysis of RCTs to synthesize the therapeutic effect of viscous fiber supplementation on glycemic control in type 2 diabetes. RESEARCH DESIGN AND METHODS MEDLINE, Embase, and Cochrane Central Register of Controlled Trials were searched through 15 June 2018. We included RCTs ≥3 weeks in duration that assessed the effects of viscous fiber on markers of glycemic control in type 2 diabetes. Two independent reviewers extracted data. Data were pooled using the generic inverse variance method and expressed as mean differences (MD) with 95% CIs. Heterogeneity was assessed (Cochran Q statistic) and quantified (I2 statistic). The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach was used to evaluate the overall certainty of the evidence. RESULTS We identified 28 eligible trial comparisons (n = 1,394). Viscous fiber at a median dose of ∼13.1 g/day significantly reduced HbA1c (MD −0.58% [95% CI −0.88, −0.28]; P = 0.0002), fasting blood glucose (MD −0.82 mmol/L [95% CI −1.32, −0.31]; P = 0.001), and HOMA-insulin resistance (IR) (MD −1.89 [95% CI −3.45, −0.33]; P = 0.02) compared with control and in addition to standard of care. The certainty of evidence was graded moderate for HbA1c, fasting glucose, fasting insulin, and HOMA-IR and low for fructosamine. CONCLUSIONS Viscous fiber supplements improve conventional markers of glycemic control beyond usual care and should be considered in the management of type 2 diabetes.
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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 | 0.059 | 0.144 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.035 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".