SGLT2 inhibitors and their role in reducing adiposopathy and inflammation in diabetes and non-diabetes CKD patients
Bibliographic record
Abstract
AIMS: This study aims to evaluate differences in adiposopathy and specific inflammatory biomarkers between type 2 diabetes mellitus (T2DM) and non-T2DM patients across various stages of chronic kidney disease (CKD). In addition, it explores potential pathways through which sodium-glucose cotransporter 2 inhibitors (SGLT2i) impact renal outcomes via adipose tissue. MATERIALS AND METHODS: An observational prospective study was conducted on 143 CKD patients divided into 2 groups: SGLT2i cohort (n = 31) and standard-of-care (SoC) cohort (n = 112). Clinical and analytical data were collected upon recruitment (T0) as well as after 8 months of follow-up (T8). RESULTS: At T0, patients under the SGLT2i group showed higher significance for cardiovascular upload versus those in SoC treatment, as well as higher values across several inflammation parameters (IL-6, TNF-α, ferritin). At T8, renal function improved in the SGLT2i group in relation to the SoC, accompanied by a decrease in most inflammatory and adiposopathy biomarkers, mainly leptin. Notably, dapagliflozin use (n = 20) was associated with significantly reduced leptin levels and stabilization of TNF-α concentrations vs. SoC at T8. CONCLUSION: SGLT2i treatment, and particularly dapagliflozin, modulates both adiposopathy and systemic inflammation while slowing down renal function loss, demonstrating benefits for CKD patients regardless of diabetes status.
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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".