Prebiotic fiber modulation of the gut microbiota improves risk factors for obesity and the metabolic syndrome
Bibliographic record
Abstract
Prebiotic fibers are non-digestible carbohydrates that promote the growth of beneficial bacteria in the gut. Prebiotic consumption may benefit obesity and associated co-morbidities by improving or normalizing the dysbiosis of the gut microbiota. We evaluated the dose response to a prebiotic diet on the gut microbiota, body composition and obesity associated risk factors in lean and genetically obese rats. Prebiotic fibers increased Firmicutes and decreased Bacteroidetes, a profile often associated with a leaner phenotype. Bifidobacteria and Lactobacillus numbers also increased. Changes in the gut microbiota correlated with energy intake, glucose, insulin, satiety hormones, and hepatic cholesterol and triglyceride accumulation. Here we provide a comprehensive analysis evaluating the results through the lens of the gut microbiota. Salient, new developments impacting the interpretation and significance of our data are discussed. We propose that prebiotic fibers have promise as a safe and cost-effective means of modulating the gut microbiota to promote improved host:bacterial interactions in obesity and insulin resistance. Human clinical trials should be undertaken to confirm these effects.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".