Sotagliflozin added to optimized insulin therapy leads to <scp>HbA1c</scp> reduction without weight gain in adults with type 1 diabetes: A pooled analysis of <scp>inTandem1</scp> and <scp>inTandem2</scp>
Bibliographic record
Abstract
AIM: To evaluate whether the addition of sotagliflozin to optimized insulin significantly increases the proportion of adults with type 1 diabetes who achieve HbA1c goals without weight gain. MATERIALS AND METHODS: In a patient-level pooled analysis (n = 1575) of data from two phase 3, 52-week clinical trials (inTandem1 and inTandem2), the change from baseline in HbA1c and weight as well as the proportion of participants achieving an HbA1c of less than 7% without weight gain were compared between groups treated with placebo, sotagliflozin 200 mg and sotagliflozin 400 mg. RESULTS: From a mean baseline HbA1c of 7.7%, mean HbA1c changes at week 24 were -0.36% (95% CI -0.44% to -0.29%) and -0.38% (-0.45% to -0.31%) with sotagliflozin 200 and 400 mg versus placebo (P = .001 for both), respectively, with sustained effects through week 52. Weight significantly decreased at weeks 24 and 52 in both sotagliflozin groups compared with placebo. At week 52, the proportion of patients who achieved an HbA1c of less than 7% without weight gain was 21.8% with sotagliflozin 200 mg, 26.1% with sotagliflozin 400 mg and 9.1% with placebo (P < .001). Other HbA1c, weight and safety composite variables showed similar significant trends. CONCLUSION: When added to optimized insulin therapy, sotagliflozin improved glycaemic control and body weight and enabled more adults with type 1 diabetes to achieve HbA1c goals without weight gain over 52 weeks, although there was more diabetic ketoacidosis relative to placebo.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".