Dapagliflozin in focal segmental glomerulosclerosis: a combined human-rodent pilot study
Bibliographic record
Abstract
Focal segmental glomerulosclerosis (FSGS) is an important cause of nondiabetic chronic kidney disease (CKD). Sodium-glucose cotransporter 2 inhibition (SGLT2i) therapy attenuates the progression of diabetic nephropathy, but it remains unclear whether SGLT2i provides renoprotection in nondiabetic CKD such as FSGS. The primary aim of this pilot study was to determine the effect of 8 wk of dapagliflozin on glomerular filtration rate (GFR) in humans and in experimental FSGS. Secondary end points were related to changes in renal hemodynamic function, proteinuria, and blood pressure (BP). GFR (inulin) and renal plasma flow (para-aminohippurate), proteinuria, and BP were measured in patients with FSGS ( n = 10), and similar parameters were measured in subtotally nephrectomized (SNx) rats. In response to dapagliflozin, changes in GFR, renal plasma flow, and 24-h urine protein excretion were not statistically significant in humans or rats. Systolic BP (SBP) decreased in SNx rats (196 ± 26 vs. 165 ± 33 mmHg; P < 0.001), whereas changes were not statistically significant in humans (SBP 112.7 ± 8.5 to 112.8 ± 11.2 mmHg, diastolic BP 71.8 ± 6.5 to 69.6 ± 8.4 mmHg; P = not significant), although hematocrit increased (0.40 ± 0.05 to 0.42 ± 0.05%; P = 0.03). In archival kidney tissue from a separate patient cohort, renal parenchymal SGLT2 mRNA expression was decreased in individuals with FSGS compared with controls. Short-term treatment with the SGLT2i dapagliflozin did not modify renal hemodynamic function or attenuate proteinuria in humans or in experimental FSGS. This may be related to downregulation of renal SGLT2 expression. Studies examining the impact of SGLT2i on markers of kidney disease in patients with other causes of nondiabetic CKD are needed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".