Decreases in Dietary Glycemic Index Are Related to Weight Loss among Individuals following Therapeutic Diets for Type 2 Diabetes
Bibliographic record
Abstract
This study assessed the effect of changes in glycemic index (GI) and load (GL) on weight loss and glycated hemoglobin (HbA1c) among individuals with type 2 diabetes beginning a vegan diet or diet following the 2003 American Diabetes Association (ADA) recommendations. The study was a 22-wk, randomized trial of 99 participants with type 2 diabetes who were counseled to follow 1 of 2 diet treatments. GI and GL changes were assessed based on 3-d dietary records. The relationships between GI/GL and changes in weight and HbA1C were calculated. In an intention-to-treat analysis (n = 99), the vegan group reduced GI to a greater extent than the ADA group (P < 0.05), but GL was reduced further in the ADA than the vegan group (P < 0.001). GI predicted changes in weight (P = 0.001), adjusting for changes in fiber, carbohydrate, fat, alcohol, energy intake, steps per day, group, and demographics, such that for every point decrease in GI, participants lost ~0.2 kg (0.44 lb). GI was not a predictor for changes in HbA1C after controlling for weight loss (P = 0.33). Weight loss was a predictor of changes in HbA1C (P = 0.047). GL was not related to weight loss or changes in HbA1C. A low-GI diet appears to be one of the determinants of success of a vegan or ADA diet in reducing body weight among people with type 2 diabetes. The reduction of body weight, in turn, was predictive of decreasing HbA1C.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".