2007-LB: Dapagliflozin Alters TCA Cycle Activation during Insulin Withdrawal in T1D Patients
Bibliographic record
Abstract
Introduction and Objective: SGLT2 inhibitors are efficacious in patients with T2D, renal dysfunction, and heart failure; yet their use in patients with T1D is limited due to increased risk of DKA despite demonstrating improvement in A1c and percent glucose time in range. To understand the molecular basis of SGLT2i-mediated DKA risk, we performed comprehensive plasma metabolomic analyses in patients with T1D treated with SGLT2i at baseline and during insulin withdrawal. Methods: Participants were randomized to usual care (UC) or usual care plus dapagliflozin (DAPA) 10 mg daily for 2 weeks followed by a day of supervised insulin withdrawal. Baseline blood samples were obtained and then hourly glucose and β-hydroxybutyrate (BOHB) until any stopping criteria were met (patient request, nausea/vomiting, 7 hours elapsed, BOHB ≥ 3 mmol/L, glucose ≥ 400 mg/dl) and final sample collected. Twenty persons with T1D, 11 males/9 females, age 48 ±18 years, baseline A1c 7.0 ± 0.9%, and time in range 61 ± 18% (all mean ± SD) completed the study. Baseline and insulin withdrawal samples were submitted for metabolomic analysis by UHPLC/MS. Results: DAPA increased TCA cycle metabolites citrate and aconitate at baseline, with a trend toward increased branched chain ketoacids and short chain acylcarnitines at baseline. During insulin withdrawal, glucose levels increased after UC but not DAPA. BOHB, acetoacetate, and acetylcarnitine were significantly higher during insulin withdrawal after DAPA compared to UC. TCA metabolites citrate, aconitate, alpha-ketoglutarate, and malate were increased during insulin withdrawal after UC. However, insulin withdrawal after DAPA resulted in unchanged alpha-ketoglutarate and malate and significantly decreased fumarate and succinate. Conclusion: SGLT2i adjunct therapy for T1D could improve glycemic control and mitigate complications. DAPA alters TCA cycle activation during insulin withdrawal. Future work will validate these findings and further investigate the molecular basis of SGLT2i-mediated DKA risk. Disclosure S.A. DiGruccio: None. K. Cho: None. K.E. Jones: None. M.C. Petersen: None. G.J. Patti: None. J.B. McGill: Advisory Panel; Bayer Pharmaceuticals, Inc. Consultant; Jaeb Center for Health Research. Advisory Panel; Boehringer-Ingelheim, Lilly Diabetes, Novo Nordisk, MannKind Corporation. Research Support; Diagnode, Lexicon Pharmaceuticals, Inc, Biomea Fusion. Funding JDRF (2-SRA-2022_1190-M-B/P22-03211); Washington University in St. Louis ICTS (CTRFP1712); NIH NIDDK (K12DK133995); NIH NIDDK (K08DK142012); NIH (UL1TR002345); NIH (KL2TR002346, NIH T32DK007120); Washington University DRC (P30DK020579)
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".