Cardiovascular Risk Prediction Modelling with Sotagliflozin in Type 1 Diabetes: Analysis of inTandem 1-3 Trials
Bibliographic record
Abstract
Background: Sodium-glucose cotransporter (SGLT) inhibitors reduce cardiovascular risk in people with type 2 diabetes. It is unknown if this extends to individuals with type 1 diabetes (T1D). To understand the potential benefits of SGLT inhibition in T1D, we applied the Scottish Diabetes Research Network (SDRN) risk prediction models to estimate cardiovascular disease (CVD) risk in people with T1D treated with sotagliflozin, a dual SGLT1&2 inhibitor. Methods: In a patient-level analysis, medical history, demographic and biochemical data were extracted from 2829 participants with T1D pooled from the inTandem1-3 trials. The SDRN risk prediction model was employed at baseline and week 24 to estimate the 10-year CVD. Risk reduction was calculated as the percent and absolute change in estimated CVD risk from baseline and compared between sotagliflozin (pooled 200 and 400mg) and placebo groups using a two-way repeated measures ANOVA. The log-transformed change in relative (absolute) risk from baseline was the dependent variable, treatment and visit as fixed effect, and log-transformed baseline value as covariate. Subgroup analyses were based on baseline CVD risk (<10%, 10 to 20%, ≥20%) and baseline chronic kidney disease (CKD) risk defined by KDIGO. Results: Baseline SDRN risk score was approximately 11%. Sotagliflozin significantly reduced the estimated 10-year CVD risk following 24 weeks of treatment compared with placebo. Placebo adjusted relative change from baseline was -7.0% (95% CI -8.4, -5.5; p<0.0001). Placebo adjusted absolute change from baseline was -0.9% (95% CI -1.2, -0.7; p<0.0001). Results were consistent across subgroups based on baseline CVD and CKD risk, with evidence of greater risk reduction in those at higher CVD and CKD risk. Conclusion: Sotagliflozin improves predicted CVD risk in T1D participants at low cardiorenal risk. By highlighting the predicted cardiovascular risk benefit in a large low-risk T1D cohort, our findings underline the need to investigate the effect of SGLT inhibitors on hard cardiovascular endpoints in T1D, especially in high-risk cohorts. Funding: Commercial Support - Lexicon pharmaceuticals
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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 | 0.053 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.013 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".