Sotagliflozin and Kidney and Cardiorenal Outcomes in SCORED
Bibliographic record
Abstract
Background: SGLT2 inhibitors reduce kidney and cardiovascular (CV) outcomes in patients with and without type 2 diabetes (T2D). The aim of this exploratory analysis was to evaluate the effect of sotagliflozin (SOTA), a dual SGLT1 and 2 inhibitor, on kidney and cardiorenal outcomes in patients with T2D and chronic kidney disease (CKD). Methods: SCORED, a Phase 3, double-blind, placebo-controlled study, randomized 10,584 patients with T2D, CKD, and CV risk factors to SOTA or placebo (1:1). Kidney criteria for inclusion were an eGFR 25 to 60 mL/min/1.73m2 regardless of UACR. The outcomes in this analysis included kidney and cardiorenal composites derived using laboratory values, with treatment comparisons by proportional hazards models. Results: At baseline, median eGFR was 45 mL/min/1.73m2 and 35, 34, and 31% of patients were categorized as having normo-, micro-, and macroalbuminuria, respectively. Over a median follow up of 16 months, SOTA reduced the primary CV endpoint by 26% (p<0.001). SOTA reduced the risk of the composite of first event of 50% decline in eGFR, eGFR<15 mL/min/1.73m2, chronic dialysis, renal transplant, or renal or CV death (p=0.0023, Figure 1). Results were generally consistent when using different eGFR decline thresholds and/or only renal death (all p<0.01, Figure 2). Conclusions: SOTA reduced the risk of kidney and cardiorenal endpoints in patients with T2D and CKD. Funding: Commercial Support - Lexicon Pharmaceuticals, Inc.Figure 1.: First event within cardiorenal compositeFigure 2.: Forest plot of various cardiorenal composites
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".