A systematic review of the effect of dietary pulses on microbial populations inhabiting the human gut
Bibliographic record
Abstract
Pulses are dry leguminous crops consisting of beans, lentils, chickpeas, and peas. They are a broad category of food that are often aggregated when their contribution to healthy dietary patterns are disseminated. However, the different genera and varieties of pulses vary in composition and are consumed in different amounts, largely dictated by geographic region and ethnicity. Given the number of pulse-derived components, including fibre, that have the capacity to alter the composition of the gut microbiome, the objective of this study was to systematically review dietary pulses and pulse-derived ingredients as a broader food group, to determine their effect on gut microbiota in humans. Major scientific databases were used to conduct the search, which spanned from 1990 until February 2019. The search strategy identified 2,444 articles and five studies were included in this analysis. Two studies used whole pulses (chickpeas and pinto beans), one study used cooked navy bean powder, and the two remaining studies used pulse-derived fibre (lupin or yellow pea hulls). Although inconsistent, some studies demonstrated that whole pulses (pinto beans and chickpeas), cooked navy bean powder, and pulse-derived fibre (lupin kernel fibre), did impose changes to the microbiota that inhabit the human large intestine. However, there was considerable variability concerning the methodologies and endpoints used to decipher the observed effects on the abundance, diversity, and/or richness of specific microbiota or the microbiome. More extensive human studies that directly link the effects of specific types of pulses on the gastrointestinal microbial environment to health outcomes in the host are required.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".