Effects of exenatide and metformin in combination on some adipocytokine levels: a comparison with metformin monotherapy
Bibliographic record
Abstract
The aim of this study was to evaluate the effects of exenatide on levels of serum adipocytokines and on β-cell function. The study was conducted between 2008 and 2012. After a run-in period with metformin, 174 patients with type-2 diabetes were randomly distributed to either a group receiving exenatide at 10 μg twice daily, or a group receiving the placebo, for 12 months. We evaluated body mass index (BMI), blood pressure, glycemic control, lipid profile, fasting plasma insulin (FPI), HOMA-IR, HOMA-β, fasting plasma proinsulin (FPPr), proinsulin : fasting plasma insulin ratio (Pr/FPI ratio), C-peptide, glucagon, retinol binding protein-4 (RBP-4), visfatin, omentin-1, and microalbuminuria. We used ELISA methods to assess the various parameters. Patients also underwent a combined euglycemic-hyperinsulinemic and hyperglycemic clamp, with subsequent arginine stimulation. After 12 months, a combination of exenatide and metformin produced a better decrease in body mass, BMI, glycemic control, FPI, FPPr, FPPr/FPI ratio, HOMA-IR, and glucagon level. Treatment with exenatide + metformin was superior to the placebo + metformin in increasing HOMA-β, C-peptide, and β-cell function. Significant negative correlations were found between M value, an index of insulin sensitivity, and measured adipocytokines. In conclusion, the combination of exenatide + metformin plays a role in improving some adipocytokine levels, and is better than metformin alone. The significant negative correlation between M value and measured adipocytokines is another confirmation of the positive effects linked to the improvement in insulin sensitivity.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".