A systematic review of the effect of dietary pulses on microbial populations inhabiting the human gut
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it