Effects of resistant starch on the indicators of glucose regulation in persons diagnosed with type 2 diabetes and those at risk: A meta‐analysis
Bibliographic record
Abstract
Resistant starch (RS) has significant effects on patients with type 2 diabetes (T2DM), but there are no published studies of its effect on persons at risk for T2DM. Randomized controlled trials were searched using the PubMed, Embase, and Cochrane Library databases. This meta-analysis compared the regulatory effects of the RS and placebo dietary interventions on fasting plasma glucose (FPG), fasting insulin (FIN), insulin resistance (HOMA-IR), and glycosylated hemoglobin (HbA1c) in persons diagnosed with T2DM and those at risk. Of the 1,144 studies retrieved, 15 met the inclusion criteria and the sample size was 772. The FIN and HbA1c were significantly decreased in the intervention group, but not in the placebo group. Both FPG and HOMA-IR were improved, but there was no significant difference. Our results suggest that the RS dietary intervention can be used as an auxiliary tool for managing T2DM in diagnosed persons and those at risk. Practical applications RS has drawn extensive attentions in recent years. Studies have found that RS has certain regulatory effects on human blood glucose, insulin, body weight, etc. The meta-analysis on the regulatory effects of RS on the population with dysglycemia can provide a certain reference value for RS as a meaningful regulatory method.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.045 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".