High-carbohydrate–low-glycaemic index dietary advice improves glucose disposition index in subjects with impaired glucose tolerance
Bibliographic record
Abstract
Controversy exists about the optimal amount and source of dietary carbohydrate for managing insulin resistance. Therefore, we compared the effects on insulin sensitivity (SI), pancreatic responsivity (AIRglu) and glucose disposition index of dietary advice aimed at reducing the amount or altering the source of dietary carbohydrate in subjects with impaired glucose tolerance (IGT). Subjects were randomized to high-carbohydrate-high-glycaemic index (GI) (high-GI, n 11), high-carbohydrate-low-GI (low-GI, n 13), or low-carbohydrate-high-monounsaturated fat (MUFA, n 11) dietary advice, with SI, AIRglu and DI measured using a frequently sampled, intravenous glucose tolerance test before and after 4 months treatment. Carbohydrate and fat intakes and diet GI, respectively, were: high-GI, 53 %, 28 %, 83; low-GI, 55 %, 25 %, 76; MUFA, 47 %, 35 %, 82. Weight changes on each diet differed significantly from each other: high-GI, -0.49 (sem 0.29) kg; low-GI, -0.19 (sem 0.40) kg; MUFA +0.27 (sem 0.45) kg. Blood lipids did not change, but glycated haemoglobin increased significantly on MUFA, 0.02 (sem 0.11) %, relative to low-GI, -0.19 (sem 0.08) %, and high-GI, -0.13 (sem 0.14) %. Diastolic blood pressure fell by 8 mmHg on low-GI relative to MUFA (P=0.038). Although SI and AIRglu did not change significantly, DI, a measure of the ability of beta-cells to overcome insulin resistance by increasing insulin secretion, increased on low-GI by >50 % (P=0.02). After adjusting for baseline values, the increase in DI on low-GI, 0.17 (sem 0.07), was significantly greater than those on MUFA, -0.09 (sem 0.08) and high-GI, -0.03 (sem 0.02) (P=0.019). Thus, the long-term effects of altering the source of dietary carbohydrate differ from those of altering the amount. High-carbohydrate-low-GI dietary advice improved beta-cell function in subjects with IGT, and may, therefore, be useful in the management of IGT.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".