Effects of the SGLT2 Inhibitor Dapagliflozin on Proteinuria in Non-Diabetic Patients with CKD (DIAMOND): A Randomized Double-Blind Cross-Over Trial
Bibliographic record
Abstract
Background: Sodium glucose co-transporter 2 (SGLT2) inhibition decreases albuminuria and reduces the risk of kidney disease progression in patients with type 2 diabetes. These benefits are unlikely mediated by improvements in glycemic control alone. We therefore examined the renal effects of the SGLT2 inhibitor dapagliflozin in patients with proteinuric kidney disease without diabetes. Methods: A multicenter double-blind placebo controlled 6-week crossover study was performed in six hospitals in the Netherlands, Canada, and Malaysia. Patients (18-75 years old), without diagnosis of diabetes, 24-h urinary protein excretion >500 and ≤3500 mg/24h and estimated glomerular filtration rate (eGFR) ≥25 ml/min/1.73m2 on stable renin angiotensin system blockade were included. Participants were randomly assigned to one of the two consecutive treatment periods of first placebo and then dapagliflozin 10 mg/day or vice versa. The primary outcome was percentage change from baseline in 24-h proteinuria. The main secondary outcome was change in iohexol measured GFR (mGFR). Results: Fifty-eight patients were screened of whom 53 patients were randomized. Median baseline proteinuria was 1110 mg/24h (IQR 730, 1560) mg/24h; mean mGFR was 58.3 ml/min/1.73m2 (SD 23). The difference in mean proteinuria change from baseline between dapagliflozin and placebo was 0.9% (95% CI: -16.6, 22.1; p=0.93). Compared to placebo, mGFR changed with dapagliflozin treatment by -6.6 ml/min/1.73m2 (95% CI: -9.0, -4.2; p<0.0001) at week 6, which was completely reversible within 6 weeks after dapagliflozin discontinuation. Differences between dapagliflozin and placebo in body weight, systolic blood pressure and hematocrit were -1.5 kg (95% CI: -3.0, -0.03; p=0.0455), -3.6 mmHg (95% CI: -7.6, 0.4; p=0.0775) and 0.02 L/L (95% CI: 0.01, 0.03; p<0.0001). HbA1c did not change. The number of patients with adverse events during dapagliflozin treatment (n=17; 32.1%) and during placebo treatment (n=13; 25.0%) was similar. No hypoglycemic events were reported. Conclusions: Six week treatment with dapagliflozin does not affect proteinuria in patients with chronic kidney disease without diabetes, but did induce acute and reversible decline in mGFR, body weight reduction, and increased hemoconcentration. Funding: Commercial Support - AstraZeneca
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".