1870-LB: Evaluating Remission of Type 2 Diabetes Using a Metabolic Intervention Including Fixed-Ratio Insulin Degludec and Liraglutide—A Randomized Controlled Trial
Bibliographic record
Abstract
Introduction & Objective: Remission is an emerging therapeutic target in type 2 diabetes. The aim was to evaluate the effect on type 2 diabetes remission of a short-term intensive metabolic intervention consisting of frequent diet, exercise and diabetes management coaching, together with metformin and fixed-ratio insulin degludec/liraglutide. Methods: In a multicenter open-label randomized controlled trial, insulin-naïve participants within 5 years of diabetes diagnosis were assigned to a 16-week remission intervention regimen or standard care and were followed for relapse of diabetes and sustained remission for an additional year after stopping glucose-lowering drugs. Results: A total of 159 participants aged 57 ± 10 years, with diabetes duration 2.6 ± 1.5 years, BMI 33.5 ± 6.5 kg/m2 and A1C 7.0 ± 0.5 % were randomized (79 intervention, 80 control) and analyzed. At the end of the 16-week intervention period, compared to controls, intervention participants achieved lower A1C (5.8 ± 0.4% vs 6.8 ± 0.6%; p <0.0001), and lost more weight (3.3 ± 4.4% vs 1.9 ± 3.0%; p=0.02). There was a lower hazard of diabetes relapse overall in the intervention group compared to controls (HR 0.63; 95%CI 0.45, 0.88; p=0.007) but this was not sustained over time. Remission rates in the intervention group were not significantly higher at 12 weeks (17.7% vs 12.5%; RR 1.42, 95%CI 0.67, 3.00; p=0.36) or 52 weeks (6.3% vs 3.8%; RR 1.69, 95%CI 0.42, 6.82) following the intervention period. Conclusion: An intensive remission-induction intervention including fixed-ratio insulin degludec/liraglutide reduced the risk of type 2 diabetes relapse without sustained remission. Disclosure Z. Punthakee: Advisory Panel; Dexcom, Inc., Sanofi. Research Support; Novo Nordisk. Advisory Panel; Eli Lilly and Company. S. Hall: None. N. McInnes: Other Relationship; Sanofi. D. Sherifali: None. K. Tsiplova: None. M. Ghosh: Advisory Panel; Viatris Inc. S.B. Harris: Consultant; Abbott. Research Support; Boehringer-Ingelheim. Consultant; Dexcom, Inc. Advisory Panel; Eli Lilly and Company. Consultant; Eli Lilly and Company, Novo Nordisk, Sanofi. Research Support; Novartis AG. Consultant; Bayer Inc. H.A. Lochnan: Research Support; Novo Nordisk. Advisory Panel; Ipsen. Research Support; Spruce Biosciences. T.P.P. Ransom: Advisory Panel; Abbott. Speaker's Bureau; Abbott. Advisory Panel; Aegerion Pharmaceuticals. Speaker's Bureau; Amgen Inc. Other Relationship; Boehringer-Ingelheim, Eli Lilly and Company. Speaker's Bureau; Eli Lilly and Company, GlaxoSmithKline plc. Other Relationship; Medtronic. Advisory Panel; Novo Nordisk. Speaker's Bureau; Novo Nordisk. Other Relationship; Novo Nordisk, Sanofi. R.J. Sigal: Research Support; Lexicon Pharmaceuticals, Inc., Bayer Inc., Novo Nordisk. T. Spaic: Research Support; Novo Nordisk Canada Inc., Lilly Diabetes. Advisory Panel; Sanofi, Dexcom, Inc. Research Support; Tandem Diabetes Care, Inc. H.C. Gerstein: Advisory Panel; Sanofi. Research Support; Sanofi. Advisory Panel; Abbott, Eli Lilly and Company. Research Support; Eli Lilly and Company. Other Relationship; Eli Lilly and Company. Advisory Panel; Novo Nordisk. Research Support; Novo Nordisk. Other Relationship; Novo Nordisk, AstraZeneca. Research Support; Hanmi Pharm. Co., Ltd. Advisory Panel; Hanmi Pharm. Co., Ltd. Other Relationship; Zuellig Pharma Holdings Pte. Ltd., Jiangsu Hengrui Pharmaceuticals Co., Ltd., Carbon Brand. Consultant; Boehringer-Ingelheim, Kowa Company, Ltd., Pfizer Inc., Shionogi & Co., Ltd. Funding 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".