The Effect of Isolated Dietary Fiber Supplements on Markers of Metabolic Diseases in Human Intervention Studies: A Systematic Review (P08-077-19)
Bibliographic record
Abstract
While epidemiological research provides strong evidence for the health benefits of increased dietary fiber (DF) intake, human intervention studies that supplement isolated DFs have shown inconsistent results. Therefore, the objective of this systematic review was to assess the effect of DF supplementation and the role of DF dose, DF physiochemical properties, intervention duration, and placebo type on immunometabolic markers of disease in intervention studies in healthy adults. We searched several databases for intervention studies published from 1990–2018 that assessed the effect of DF on a range of well-established immunometabolic markers. Eligible studies were those that supplemented isolated DF for >2 weeks as the only intervention in at least one study arm, and reported baseline data to assess the effect of the placebo. In total, 77 publications were included. DF supplementation significantly reduced total cholesterol (TC), low density lipoprotein (LDL), homeostatic model assessment of insulin resistance (HOMA-IR), and insulin area under the curve (AUC) in 36–49% of interventions, while less than 20% reported significant effects on C-reactive protein (CRP), interleukin-6 (IL-6), fasting glucose, glucose AUC, fasting insulin, high density lipoprotein (HDL), and triglycerides (TG). Higher DF doses were more successful in reducing CRP, TC, and LDL (40–63%), higher-viscosity and insoluble DFs for TC and LDL (≥50%), and longer interventions for CRP and glucose (50%). Placebos containing digestible carbohydrates were used in more than half of the studies, and confounded significant results for IL-6, glucose AUC, and insulin AUC. The efficacy of isolated DF consumption differs vastly among immunometabolic markers assessed. Our findings suggest that DF supplementation interventions, as currently used, should focus on improving fasting cholesterol levels. However, although more research is needed to draw definitive Conclusions, higher doses, specific DF types, longer interventions, and the use of DF to replace digestible carbohydrates may improve the success of DF interventions. J.W. is a Campus Alberta Innovation Program chair for Nutrition, Microbes, and Gastrointestinal Health and recipient of grants from the Canadian Institutes of Health Research and JPI (healthy diet for a healthy life).
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".