Effects of the SGLT‐2 inhibitor dapagliflozin on glomerular and tubular injury markers
Bibliographic record
Abstract
The mechanisms by which SGLT‐2 inhibitors lower albuminuria are incompletely understood. We assessed in a post‐hoc analysis of a cross‐over trial the effects of the SGLT2 inhibitor dapagliflozin on glomerular markers (IgG to IgG4 and IgG to albumin), tubular markers (urinary KIM‐1, NGAL and LFABP) and inflammatory markers (urinary MCP‐1 and IL‐6) to provide more insight into kidney protective effects. Dapagliflozin decreased albuminuria by 43.9% (95% CI, 30.3%‐54.8%) and eGFR by 5.1 (2.0‐8.1) mL/min/1.73m 2 compared to placebo. Dapagliflozin did not change glomerular charge or size selectivity index compared to placebo. Dapagliflozin decreased urinary KIM‐1 excretion by 22.6% (0.3%‐39.8%; P = .05) and IL‐6 excretion by 23.5% (1.4%‐40.6%; P = .04) compared to placebo, whereas no changes in NGAL, LFABP and MCP‐1 were observed. During dapagliflozin treatment, changes in albuminuria correlated with changes in eGFR (r = 0.36; P = .05) and KIM‐1 (r = 0.39; P = .05). In conclusion, the albuminuria‐lowering effect of 6 weeks of dapagliflozin therapy may be the result of decreased intraglomerular pressure or reduced tubular cell injury.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".