1867-LB: A Real-World Study Assessing the Efficacy and Safety of Switching from Basal–Bolus Insulin Therapy to Once-Daily iGlarLixi in People with Type 2 Diabetes—Soli De-escalation
Bibliographic record
Abstract
Introduction and Objective: Basal bolus insulin (BBI) therapy is associated with body weight (BW) gain and an increased risk of hypoglycemia, further increasing the treatment burden for people with T2D. This real-world study assessed the efficacy and safety of switching from BBI to iGlarLixi, a once-daily fixed-ratio combination of insulin glargine 100 U/mL plus lixisenatide, in people with T2D. Methods: This real-world retrospective study included people with T2D (≥18 years) who received ≥1 iGlarLixi prescription during the identification (ID) period following prior treatment with BBI using the Optum Market Clarity® electronic medical record dataset of people with T2D from the United States. The primary endpoint was HbA1c change at 6 months. Secondary endpoints were HbA1c change at 3 months, BW change and hypoglycemic event rate at 6 months. Results: In total, 11,887 people received ≥1 iGlarLixi prescription during the ID period, and of these, 372 had switched from a BBI regimen to iGlarLixi (mean age, 59 years; mean baseline HbA1c, 9.55%; mean baseline BW, 101.9 kg). From baseline to Month 6, switching to iGlarLixi resulted in a significant mean HbA1c reduction of −0.93% (95% confidence interval [CI]: −1.13, −0.74; p<0.0001) to 8.61% (n=372). At Month 3, HbA1c was 8.68% (n=204). The mean change in BW at 6 months was −0.89 kg (95% CI: −1.61, −0.18; n=359). The hypoglycemia event rate was 46.24 per 100 person-years of follow-up (P100PYFU) at baseline and 40.32 P100PYFU (event rate ratio: 0.77; 95% CI: 0.57, 1.05) after 6 months of iGlarLixi treatment. Conclusion: In a real-world setting, people with T2D and elevated HbA1c switching from a BBI regimen to a once-daily injection of iGlarLixi achieved lower HbA1c, weight benefit and numerically lower hypoglycemic events. Disclosure F. Giorgino: Consultant; AstraZeneca, Boehringer-Ingelheim, Eli Lilly and Company, LifeScan Diabetes Institute, Merck Sharp & Dohme Corp., Medtronic, Novo Nordisk, Roche Diabetes Care, Sanofi. Research Support; Eli Lilly and Company, Roche Diabetes Care. F. Lauand: Employee; Sanofi. Stock/Shareholder; Sanofi. S. Servera: Employee; Sanofi. X. Li: Employee; Sanofi. Z. Taybani: Advisory Panel; Boehringer-Ingelheim. Consultant; Novo Nordisk, AstraZeneca. Speaker's Bureau; Sanofi. A.Y.Y. Cheng: Advisory Panel; Abbott. Speaker's Bureau; Amgen Inc., AstraZeneca, Bausch Health. Advisory Panel; Bayer Inc. Speaker's Bureau; Abbott, Bayer Inc. Research Support; Applied Therapeutics Inc. Advisory Panel; Boehringer-Ingelheim. Speaker's Bureau; Boehringer-Ingelheim, Dexcom, Inc. Advisory Panel; Dexcom, Inc., Eli Lilly and Company. Speaker's Bureau; Eli Lilly and Company. Advisory Panel; Eisai Inc. Speaker's Bureau; GlaxoSmithKline plc. Advisory Panel; HLS Therapeutics Inc. Speaker's Bureau; HLS Therapeutics Inc., Insulet Corporation. Advisory Panel; Insulet Corporation, Janssen Pharmaceuticals, Inc. Speaker's Bureau; Janssen Pharmaceuticals, Inc., Medtronic. Advisory Panel; Novo Nordisk. Speaker's Bureau; Novo Nordisk. Research Support; Novo Nordisk. Speaker's Bureau; Pfizer Inc. Advisory Panel; Sanofi. Speaker's Bureau; Sanofi. Research Support; Sanofi. Advisory Panel; Takeda Canada, AstraZeneca, Sanofi. Consultant; Abbott, AstraZeneca, Bayer Inc., Boehringer-Ingelheim, Dexcom, Inc., Eisai Inc., Eli Lilly and Company, Insulet Corporation, HLS Therapeutics Inc., Janssen Pharmaceuticals, Inc., Novo Nordisk, Sanofi, Takeda Pharmaceutical Company Limited. V. Fonseca: Consultant; Abbott, Bayer Inc. Stock/Shareholder; BRAVO4HEALTH, LLC. Consultant; Corcept Therapeutics. Speaker's Bureau; Eli Lilly and Company. Research Support; Fractyl Health, Inc. Consultant; Sun Pharmaceutical Industries Ltd. Stock/Shareholder; Amgen Inc. A. Tebaibia: None. I. Anaforoglu: None. Funding Sanofi
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".