Adiponectin levels in individuals with type 2 diabetes on a high fiber or a low glycemic index diet.
Bibliographic record
Abstract
Objectives Adiponectin is considered to have anti‐diabetic, antiinflammatory and anti‐atherogenic effects. We investigated whether adiponectin levels correlated with cardiovascular risk factors in a cohort of people with type 2 diabetes and whether improving glycemic control through diet may increase adiponectin levels. Research Design and Methods Post‐hoc analysis of 156 men and women with type 2 diabetes who participated in a dietary study of low glycemic index (LGI) or a high cereal fiber diet (HCF) for 6 months with the aim of improving blood glucose control. Results Significant (p<0.05) negative correlations were seen at baseline with triglycerides (TG) in men and women and positive correlations with HDL‐cholesterol (HDL‐C) in women and with LDL‐C and total cholesterol in men. Adiponectin levels increased similarly in both diets: 9 percent (%) from baseline in the HCF diet and 14% in the LGI diet (p<0.01). A significant (p<0.01) negative correlation was observed between % baseline changes in adiponectin and HbA1c in women, after adjustment for body weight changes. Conclusions Improving glycemic control through diet may improve adiponectin levels in people with type 2 diabetes. Study supported by Barilla
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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) | 0.000 | 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".