Modeling Cardiorenal Protection with SGLT2 Inhibition in Type 1 Diabetes: An Analysis of EASE-2 and -3
Bibliographic record
Abstract
Background: Sodium-glucose cotransporter 2 (SGLT2) inhibitors significantly reduce cardiorenal risk in people with type 2 diabetes. It is unknown if these protective effects extend to individuals with type 1 diabetes (T1D). To better understand the potential benefits of SGLT2 inhibition in T1D, we applied the Steno T1 risk engine (SRE) and Scottish Diabetes Research Network (SDRN) risk prediction models to estimate risk of cardiovascular disease (CVD) and end-stage kidney disease (ESKD) in two large T1D cohorts. Methods: Medical history, demographic and biomarker data were extracted from 730 and 960 participants with T1D from the EASE-2 and 3 trials, respectively. The SRE and SDRN risk prediction models were employed at baseline, week 26 (EASE-3) or 52 (EASE-2), and 3 weeks post drug washout to estimate the 10-year CVD and 5-year ESKD risk. Risk reduction was calculated as the percent change in estimated CVD and ESKD risk from baseline between empagliflozin (pooled 10 and 25mg) vs. placebo groups, and compared using a two-way repeated measures ANOVA. Results: Empagliflozin significantly reduced the estimated 10-year CVD risk following 26 weeks (SRE: -9.6% [-12.1, -7.1] & SDRN: -22.4% [-28.1, -16.6]; p<0.01) and 52 weeks (SRE: -9.2% [-11.6, -6.9] & SDRN: -16.7% [-20.7, -12.7]; p<0.01) of treatment compared with placebo. No significant reduction in 5-year ESKD risk was observed while on treatment. The ESKD risk was significantly lower after a 3-week drug washout (SRE: -8.4% [-15.1, -1.7] (EASE-3) & -9.7% [-13.5, -6.0] (EASE-2); p<0.01), in keeping with the expected rise in eGFR. Conclusion: Empagliflozin improves predicted CVD and ESKD risk in T1D participants, thus demonstrating the need for dedicated outcome trials in patients with T1D and established kidney or cardiovascular disease. ESKD risk estimation using GFR-based models may be best implemented after drug washout as to avoid the reversible hemodynamic eGFR “dip” with SGLT2 inhibition.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".