Modulation of the Gastrointestinal Microbiome with Nondigestible Fermentable Carbohydrates To Improve Human Health
Bibliographic record
Abstract
There is a clear association between the gastrointestinal (GI) microbiome and the development of chronic noncommunicable diseases, providing a rationale for the development of strategies that target the GI microbiota to improve human health. In this article, we discuss the potential of supplementing the human diet with nondigestible fermentable carbohydrates (NDFCs) to modulate the composition, structure, diversity, and metabolic potential of the GI microbiome in an attempt to prevent or treat human disease. The current concepts by which NDFCs can be administered to humans, including prebiotics, fermentable dietary fibers, and microbiota-accessible carbohydrates, as well as the mechanisms by which these carbohydrates exert their health benefits, are discussed. Epidemiological research presents compelling evidence for the health effects of NDFCs, with clinical studies providing further support for some of these benefits. However, rigorously designed human intervention studies with well-established clinical markers and microbial endpoints are still essential to establish (i) the clinical efficiency of specific NDFCs, (ii) the causal role of the GI microbiota in these effects, (iii) the underlying mechanisms involved, and (iv) the degree by which inter-individual differences between GI microbiomes influence these effects. Such studies would provide the mechanistic understanding needed for a systematic application of NDFCs to improve human health via GI microbiota modulation while also allowing the personalization of these dietary strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".