SP415EMPAGLIFLOZIN AND PROGRESSION OF CHRONIC KIDNEY DISEASE IN TYPE 2 DIABETES COMPLICATED BY NEPHROTIC-RANGE PROTEINURIA: INSIGHTS FROM THE EMPA-REG OUTCOME® TRIAL
Bibliographic record
Abstract
INTRODUCTION AND AIMS: In patients with diabetic kidney disease, nephrotic-range proteinuria is a major risk factor for accelerated glomerular filtration rate (GFR) loss, cardiovascular disease and all-cause mortality. In this post-hoc analysis of the EMPA-REG OUTCOME® trial, we evaluated the effects of the SGLT2 inhibitor empagliflozin (EMPA) in patients with type 2 diabetes, established cardiovascular disease and nephrotic-range proteinuria at study inclusion. METHODS: Patients were randomised to receive EMPA 10 or 25 mg/day, or placebo (PBO), in addition to standard of care. Median observation time was 3.1 years. According to Kidney Disease: Improving Global Outcomes (KDIGO) criteria, nephrotic-range proteinuria was defined as urine albumin:creatinine ratio (UACR) ≥2200 mg/g. A mixed-model repeated measures analysis was used to evaluate changes in estimated GFR (eGFR) over time. Treatment differences in the average rate of annual loss of eGFR were assessed using a random coefficient model. A Cox proportional hazards model was used to investigate the risk of all-cause hospitalisation as ascertained by investigator ‘serious adverse event’ reporting. RESULTS: We identified 112 patients with nephrotic-range proteinuria (PBO, n=42; pooled EMPA, n=70). At baseline, mean [SD] eGFR (PBO, 63.6 [23.5]; EMPA, 60.3 [19.5] mL/min/1.73m²) and median UACR [interquartile range] (PBO, 3676 [2713-4865]; EMPA, 3532 [2701-4879] mg/g creatinine) were balanced between groups. After an acute fall in eGFR during the first 4 weeks in both groups, the PBO group experienced a steeper decline in eGFR than the EMPA group (Figure). Between week 4 to last value on treatment the annual loss of eGFR was 10.7 mL/min/1.73m² with PBO and 4.5 mL/min/1.73m² with EMPA; thus, yearly eGFR loss was 6.1 mL/min/1.73m² slower with EMPA than PBO (p=0.0098). Moreover, EMPA significantly reduced the risk of all-cause hospitalisation by 47% versus PBO (hazard ratio 0.53 [0.30-0.93]; p=0.0263). CONCLUSIONS: EMPA could be a new treatment option to slow GFR decline and reduce all-cause hospitalisations in patients with type 2 diabetes and cardiovascular disease at high risk for rapid loss of renal function due to nephrotic-range proteinuria.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.001 | 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".