2355-PUB: Discontinuation of Dapagliflozin in DEPICT-1 and DEPICT-2 Led to Clinically Meaningful Increases in HbA1c and Body Weight
Bibliographic record
Abstract
Concerns about hypoglycaemia and weight gain are barriers to treatment adherence, and consequently glucose control, in type 1 diabetes (T1D). In adults inadequately controlled by insulin alone, DEPICT-1 and DEPICT-2 phase 3 trials showed statistically significant decreases in HbA1c, glycaemic variability and body weight for adjunct dapagliflozin (DAPA), along with moderate reduction in insulin requirements. As adherence to T1D treatment can be a daily challenge, understanding the implications of discontinuation is important. This study evaluated changes in HbA1c, weight and insulin dose following discontinuation of DAPA in the DEPICT trials. Data were analyzed from patients discontinuing DAPA (5 mg or 10 mg, added to insulin) at any point in DEPICT-1 or DEPICT-2 (unplanned or end of study). Annualized rates of change in HbA1c (%) and weight (kg) following discontinuation of DAPA were estimated via linear regression and trajectories derived from mean rates of change. Insulin doses were summarized before and after discontinuation and correlated to analyzes of weight and HbA1c. The main reasons for unplanned discontinuation of DAPA were adverse events and patient withdrawal of consent. Following DAPA discontinuation, annualized changes in HbA1c and weight were +0.99% (95% CI: 0.39, 1.59) and +3.75Kg (1.65, 5.86), respectively (N=91). Mean insulin dose two weeks post-discontinuation was higher than two weeks prior to discontinuation (5mg: +4.0 IU, 10mg: +4.4 IU). These estimates directly contrast to observed reductions in HbA1c, weight and insulin dose associated with DAPA treatment. Discontinuation of DAPA led to significant increases in HbA1c and weight, in addition to higher insulin doses soon after discontinuation. Enabling ongoing DAPA treatment may benefit patients due to the detrimental impact of poor glycaemic control and weight gain on retinopathy, nephropathy and cardiovascular disease shown in long-term T1D studies, such as DCCT and EDIC. Disclosure J. Gordon: Research Support; Self; AstraZeneca, Bristol-Myers Squibb Company, GlaxoSmithKline plc., Novartis AG, Novo Nordisk A/S, Pfizer Inc., Takeda Canada, Takeda Pharmaceutical Company Limited. T. Danne: Advisory Panel; Self; Abbott, AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Lilly Diabetes, Medtronic, Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis. Speaker's Bureau; Self; Dexcom, Inc. L.M. Beresford-Hulme: None. H. Bennett: Consultant; Self; AstraZeneca. Employee; Self; Health Economics and Outcomes Research Ltd. A. Tank: Employee; Self; AstraZeneca. C. Edmonds: Employee; Self; AstraZeneca. F.A. Thoren: Employee; Self; AstraZeneca. M.F. Scheerer: Employee; Self; AstraZeneca. P. McEwan: Consultant; Self; AstraZeneca. Employee; Self; Health Economics and Outcomes Research Ltd.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.215 | 0.030 |
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".