Modeling Cardiorenal Protection with Sodium-Glucose Cotransporter 2 Inhibition in Type 1 Diabetes: An Analysis of DEPICT-1 and DEPICT-2
Bibliographic record
Abstract
Key Points Risk modelling analysis of DEPICT trials show that dapagliflozin reduced estimated cardiovascular and kidney disease risk in T1D persons. Greatest reduction in estimated ESKD risk was accompanied by an expected rise in eGFR, after 4 weeks post drug discontinuation. Dedicated outcome trials with SGLT2 inhibitors are warranted in T1D persons with CKD or CVD for best determination of efficacy and risks. Background Sodium-glucose cotransporter-2 (SGLT2) inhibitors improve glycemia and reduce insulin requirements in type 1 diabetes (T1D) and type 2 diabetes. Although SGLT2 inhibitors lower cardiovascular disease (CVD) and ESKD risk in type 2 diabetes, no dedicated cardiorenal outcome trials in T1D have been conducted to date. Using validated risk prediction models, this study evaluated the effect of SGLT2 inhibition on estimated CVD and ESKD risk in a T1D cohort. Methods Demographics, medical history, and biomarkers were extracted from 1473 participants with T1D enrolled in the Dapagliflozin Evaluation in Patients with Inadequately Controlled Type -1 and -2 trials. Data at baseline, 24, 52, and 56 weeks (4 weeks after drug cessation) were used to estimate 10-year CVD and 5-year ESKD risk using the Steno T1 Risk Engine (SRE) and Scottish Diabetes Research Network (SDRN) risk prediction models. Risk reduction was determined on the basis of relative change in risk from baseline between participants receiving dapagliflozin (pooled 5 and 10 mg) versus placebo. Subgroup analyses were conducted by age, sex, diabetes duration, CVD risk, and CKD status at baseline. Results The relative change in 10-year estimated CVD risk (SRE: –6.50% [–8.04% to –4.95%] and SDRN: –6.77% [–8.40% to –5.13%]; all P < 0.001) and 5-year ESKD risk (SRE: –4.48% [–7.68% to –1.28%]; P = 0.006) were lower at the end of 24 weeks of dapagliflozin treatment compared with placebo. Furthermore, the greatest relative change in 5-year ESKD risk was observed at week 56 (SRE: –12.84% [–16.65% to –9.03%]; P < 0.001), in conjunction with an expected rise in eGFR after drug washout. Subgroup analysis revealed larger relative lowering in 10-year CVD risk in those with CKD compared with those without (SRE: –11.3% versus –5.9%, and SDRN: –11.9% versus –6.1%, respectively; all P interaction < 0.02). Conclusions Dapagliflozin improves estimated CVD and ESKD risk in participants with T1D, emphasizing the need for cardiorenal outcome trials in people living with T1D.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 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".