Modeling Cardiorenal Protection with SGLT2 Inhibition in Type 1 Diabetes: An Analysis of DEPICT-1 and -2
Bibliographic record
Abstract
Background: Sodium-glucose co-transporter-2 (SGLT2) inhibitors improve glycemic control and lower insulin requirements in type 1 (T1D) and type 2 diabetes (T2D). While SGLT2 inhibitors lower cardiovascular disease (CVD) and end-stage kidney disease (ESKD) risk in T2D, dedicated cardiorenal outcome trials have not been performed in T1D. Using validated risk prediction models, the current analysis evaluated the effect of SGLT2 inhibition on estimated CVD and ESKD risk in a T1D cohort. Methods: Demographics, medical history, biomarkers, and blood pressure were extracted from 1,473 participants with T1D enrolled in the DEPICT-1 and -2 trials. Data at baseline, week -24, -52, and -56 (4 weeks off-treatment) were used to estimate 10-year CVD and 5-year ESKD risk using the Steno T1 Risk Engine (SRE) and the Scottish Diabetes Research Network (SDRN) risk prediction models. Risk reduction was determined based on the percent change in risk from baseline between participants receiving dapagliflozin (pooled 5 and 10mg) vs. placebo, and evaluated statistically using a two-factor repeated measures ANOVA. Results: The relative 10-year estimated CVD risk was significantly lower following 52 weeks of dapagliflozin treatment (SRE: -5.8% [-8.4, -3.3%] & SDRN: -11.9% [-16.1, -7.8%]; ; P<0.01). The 5-year ESKD risk was also significantly lower following 52 weeks of dapagliflozin treatment (SRE: -8.4% [-12.4, -4.4%]; P<0.01). The greatest improvement in ESKD risk was observed at week 56 (-12.8% [-16.6, -9.0%]; P<0.01), in conjunction with an expected rise in eGFR post drug cessation. Conclusion: Dapagliflozin improves estimated CVD and ESKD risk in T1D participants, emphasizing the need for cardiorenal outcome trials in people living with T1D. By avoiding the acute, reversible GFR “dip” with SGLT2 inhibition, estimation of ESKD risk using models that incorporate GFR may be best performed when drug is discontinued.Figure 1.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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".