The Effect of Dapagliflozin on Albuminuria in DECLARE-TIMI 58
Bibliographic record
Abstract
Objective: Sodium glucose co-transporter 2 inhibitors (SGLT2i) improve albuminuria in patients with high cardiorenal risk. We report albuminuria change in the DECLARE-TIMI 58 cardiovascular outcome trial, which included populations with lower cardiorenal risk. Methods: DECLARE-TIMI 58 randomized 17,160 patients with type-2 diabetes, Creatinine Clearance>60 ml/min, and either atherosclerotic cardiovascular disease (CVD); (40.6%) or risk-factors for CVD (59.4%) to dapagliflozin or placebo. Urinary albumin-creatinine ratio (UACR) was tested at baseline, 6-months, 12-months and yearly thereafter. Change in UACR over time was measured as a continuous and categorical variable (≤15; >15 to<30; ≥30-≤300; >300 mg/g), by treatment arm. Composite cardiorenal outcome was ≥40% sustained-decline in eGFR to <60 mL/min/1.73m², end-stage kidney disease, cardiovascular- or renal-death; specific renal outcome included all except cardiovascular-death. Results: Baseline UACR was available for 16,843 (98.15%) participants; 9,067 (53.83%) with ≤15 mg/g; 2,577 (15.30%) with <15->30 mg/g; 4,030 (23.93%) with 30-300 mg/g; and 1,169 (6.94%) with >300 mg/g. Measured as continuous variable, UACR improved from baseline to 4.0 years with dapagliflozin, compared to placebo, across all UACR and eGFR categories (all p<0.0001). Sustained-confirmed ≥1 category improvement in UACR was more common in dapagliflozin vs. placebo [HR=1.45 (95%CI 1.35-1.56 p<0.0001)]. Cardiorenal outcome was reduced with dapagliflozin for subgroups of UACR ≥30 mg/g (p<0.0125, Pint=0.033); renal-specific outcome was reduced for all UACR subgroups (p<0.05, Pint=0.480). Conclusion: In DECLARE-TIMI 58, dapagliflozin demonstrated a favourable effect on UACR and renal specific outcome across baseline UACR categories, including patients with normal albumin excretion. The results suggest a role for SGLT2i also in the primary prevention of diabetic kidney disease.
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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 | 0.001 | 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) | 0.003 | 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".