Resistant starches for the management of metabolic diseases
Bibliographic record
Abstract
PURPOSE OF REVIEW: Recent clinical trials and animal studies indicate that resistant starches may be beneficial therapeutic tools for the management of metabolic diseases. The purpose of this review is to summarize these findings and discuss the established and proposed mechanisms by which resistant starches exert their benefits. We also examine open questions regarding how resistant starches improve metabolism and propose future research directions for the field. RECENT FINDINGS: Data from both humans and animal models clearly support a role for resistant starches in improving a variety of metabolic features; however, discrepancies do exist regarding specific effects. Concomitant improvements in both insulin levels and body fat depots are often reported in rodents fed resistant starches, whereas resistant starch feeding in humans improves insulin sensitivity without having a major impact on fat mass. These differences could be explained by the coexistence of several mechanisms (both gut microbiota-dependent and gut microbiota-independent) underpinning the metabolic benefits of resistant starches. SUMMARY: Together, the studies presented in this review offer new insights into the potential pathways by which resistant starches enhance metabolic health, including modulation of the gut microbiota, gut peptides, circulating inflammatory mediators, innate immune cells, and the bile acid cycle.
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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 | 0.001 |
| 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) | 0.004 | 0.002 |
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".