THE EFFECT OF A LOW GLYCEMIC INDEX DIET ON DIABETIC NEPHROPATHY
Bibliographic record
Abstract
Background Microvascular complications, including renal disease, have been strongly associated with poor glycemic control. We have therefore assessed the effect of improved glycemic control after consumption of a low glycemic index diet on the degree of microalbuminuria. Methods 155 type 2 diabetic subjects treated with antihyperglycemic agents were randomized and completed either 6 months of high fiber or low glycemic index dietary advice in a parallel design. In 150 completers, 24 hour urine collections were obtained pre‐treatment and at the end of the 6 month study period. Results A total of 107 subjects had detectable albumin in their baseline 24 hour urine collections with 14 subjects having microalbuminuria (urinary albumin >30 mg/24 hours). The treatment difference in the change in microalbuminuria was not significant. However, with improved glycemic control on the low glycemic index diet microalbuminuria decreased by 63±24 mg/d (n=7, p=0.039) compared to a reduction of 15±13mg/d (n=7, p=0.308) on the high fiber diet. No between treatment difference was seen in serum creatinine or creatinine clearance. Conclusion These preliminary data suggest a possible benefit of low glycemic index diets on microalbuminuria in type 2 diabetes. Funding the Canadian Institutes of Health Research, Canada Research Chair Endowment of the Federal Government of Canada, and Barilla (Italy).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".