Markers of Kidney Injury, Inflammation, and Fibrosis Associated With Ertugliflozin in Patients With CKD and Diabetes
Bibliographic record
Abstract
Introduction Sodium-glucose cotransporter-2 (SGLT2) inhibitors improve cardiovascular and kidney outcomes through mechanisms that are incompletely understood. In this exploratory post-hoc analysis of the VERTIS RENAL trial, we report the association between the SGLT2 inhibitor, ertugliflozin, and markers of kidney injury, inflammation, and fibrosis in participants with type 2 diabetes (T2D) and stage 3 chronic kidney disease (CKD). Methods Participants were randomized to ertugliflozin (5 or 15 mg/d) or placebo, and plasma samples for biomarker analysis were collected at baseline, 26 weeks, and 52 weeks. Results Ertugliflozin-treated participants had lower plasma levels of kidney injury molecule-1 (KIM-1) at 26 weeks ( P = 0.044) and 52 weeks ( P = 0.007) and higher eotaxin-1 at 52 weeks ( P = 0.007) postrandomization compared with placebo. The change in KIM-1 was not associated with the baseline urine albumin to creatinine ratio (UACR) or the estimated glomerular filtration rate (eGFR, P interaction > 0.05). Additionally, the change in KIM-1 was positively correlated with the change in UACR in participants treated with ertugliflozin ( P = 0.0071). No other significant associations between ertugliflozin and changes in the markers of tubular injury, inflammation, fibrosis, oxidative stress, and endothelial dysfunction were observed. Conclusion In conclusion, in participants with T2D and stage 3 CKD, ertugliflozin was associated with a sustained lowering of the tubular injury marker KIM-1 regardless of baseline kidney function.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".