Effects of canagliflozin versus glimepiride on adipokines and inflammatory biomarkers in type 2 diabetes
Bibliographic record
Abstract
OBJECTIVE: Type 2 diabetes and obesity are pro-inflammatory states associated with increased risk of cardiovascular disease. Canagliflozin, an SGLT2 inhibitor, demonstrated superiority in lowering HbA1c versus glimepiride with less hypoglycemia and greater weight reduction via loss of fat mass in a 52-week trial of type 2 diabetes patients. This post hoc, exploratory analysis assessed the effects of canagliflozin versus glimepiride on select adipokines, inflammatory biomarkers, and chemokines. METHODS: Changes from baseline to Week 52 in serum leptin, adiponectin, IL-6, TNFα, CRP, PAI-1, VCAM-1, and MCP-1 were measured in a randomly selected subset of type 2 diabetes patients on metformin receiving canagliflozin 300 mg (n = 100) or glimepiride (n = 100) in the overall study. Correlations between change in biomarkers and change in select metabolic and anthropometric variables were assessed. RESULTS: At Week 52, canagliflozin decreased median serum leptin by 25% (95% CI: -34%, -15%) and increased median serum adiponectin by 17% (95% CI: 11%, 23%) compared with glimepiride. There was a 22% reduction in median serum IL-6 (95% CI: -34%, -10%) and a 7% increase in median serum TNFα (95% CI: 1%, 12%) with canagliflozin versus glimepiride. No between-group differences were observed with the other biomarkers. The decrease in serum leptin with canagliflozin was correlated with change in weight (r ≥ 0.3) only; the increase in adiponectin and decrease in IL-6 with canagliflozin occurred independently of changes in HbA1c, weight, or lipids. CONCLUSIONS: These results indicate that canagliflozin may improve adipose tissue function and induce changes in serum leptin, adiponectin, and IL-6 that favorably impact insulin sensitivity and cardiovascular disease risk.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".