930-P: Dapagliflozin Improves Glucose Metrics and Decreases Insulin Requirements in Adults with Type 1 Diabetes but Adds Ketosis Risk
Bibliographic record
Abstract
Introduction: SGLT2 inhibitors slow CKD progression but none are approved in T1D due to risk of ketosis-related adverse events, including DKA. We evaluated the impact of dapagliflozin on glucose metrics, insulin requirements and BHOB. Methods: After informed consent, 20 participants with T1D completed two weeks of outpatient care while monitoring capillary BOHB, both with and without open label dapagliflozin (10 mg) in a randomized crossover design. Glycemic metrics were monitored via CGM, and insulin use was recorded from pump download or self-reports in MDI users. BOHB (Precision Xtra®, Abbott) measurements were obtained up to 3X daily during the study periods. Results: Participant age was 48 ± 18 years, 45% female, A1c 7.0 ± 0.9%, baseline TIR 61 ± 18% (mean ± SD). The baseline median insulin TDD was 57 (38 - 88). Sixteen patients used CSII and 4 MDI. SGTL2i use led to a 6.9% reduction in median TDD, 26.3% in basal doses. Participants averaged 36 cBHOB measurements in both usual care and SGLT-2 treatment phases. During usual care, there were 3 ketosis events (cBOHB > 1.5 mmol/L) versus 10 during SGLT-2 treatment (P=0.11). Most ketosis events occurred in one individual over a 24hr period. No DKA occurred. Conclusion: In adults with T1D, dapagliflozin use was associated with reduction in insulin doses and improvement in GMI. Ketosis events were concentrated in 1 participant. Disclosure A.M. Markov: None. K.E. Jones: None. M.C. Petersen: None. P. Krutilova: None. A.M. McKee: Advisory Panel; Medtronic, Novo Nordisk. Employee; Novo Nordisk. K.L. Bohnert: None. S.E. Adamson: None. M. Salam: None. J.B. McGill: Advisory Panel; Bayer Inc., Boehringer-Ingelheim, ClearNote Health, Lilly Diabetes, MannKind Corporation, Novo Nordisk. Research Support; Novo Nordisk.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".