EFFECT OF LOW GLYCEMIC INDEX DIET ON APOLIPOPROTEIN B AND LDL PARTICLE SIZE
Bibliographic record
Abstract
Background Apolipoprotein B (Apo B) and small LDL particles have been strongly associated with increased coronary artery disease while low glycemic index diets (GI) may reduce risk. We have therefore assessed the effect of consumption of a low GI diet on Apo B and LDL particle size. 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. Serum samples were obtained pre‐treatment and at the end of the 6 month study period. Results There was no significant treatment difference in Apo B but a significant reduction in estimated small dense LDL cholesterol (sdLDL‐C) on the low GI diet by ‐0.15 ± 0.08 mmol/L compared to the high fiber diet (p=0.048). No significant treatment differences were seen in LDL particle size distribution. Dietary glycemic load, not glycemic index, correlated positively to serum levels of sdLDL‐c (Rho=0.17, p=0.036), while correlated negatively with serum levels of medium LDL particles (Rho=‐0.17, p=0.042) and large LDL particles (Rho=‐0.18, p=0.026). Conclusion These findings suggest a benefit of low glycemic index diets in reduction of sdLDL‐c levels in type 2 diabetes. Funding 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".