Dietary pulses as a means to improve the gut microbiome, inflammation, and appetite control in obesity
Bibliographic record
Abstract
A dysbiotic intestinal microbiome has been linked to chronic diseases such as obesity, which may suggest that interventions that target the microbiome may be useful in treating obesity and its complications. Appetite dysregulation and chronic systemic low-grade inflammation, such as that observed in obesity, are possibly linked with the intestinal microbiome and are potential therapeutic targets for the treatment of obesity via the microbiome. Dietary pulses (e.g., common beans) are composed of nutrients and compounds that possess the potential to modulate the gut microbiota composition and function which can in turn improve appetite regulation and chronic inflammation in obesity. This narrative review summarizes the current state of knowledge regarding the connection between the gut microbiome and obesity, appetite regulation, and systemic and adipose tissue inflammation. More specifically, it highlights the efficacy of interventions employing dietary common beans as a means to improve gut microbiota composition and/or function, appetite regulation, and inflammation in both rodent obesity and in humans. Collectively, results presented and discussed herein provide insight on the gaps in knowledge necessary for a comprehensive understanding of the potential of beans as a treatment for obesity while highlighting what further research is required to gain this understanding.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".