787-P: Glucose and Weight Outcomes Associated with Oral Semaglutide in the Real-World—Initial Results from the Association of British Clinical Diabetologists’ (ABCD) Audit
Bibliographic record
Abstract
Semaglutide is the first glucagon like peptide-1 receptor agonist (GLP1-RA) available in an oral preparation. Weight and HbA1c outcomes with injectable semaglutide in the real-world are well established. The aim of this analysis is to assess weight and HbA1c response to oral semaglutide. Methods: Data were extracted from the secure online ABCD audit tool. Individuals were included if baseline and follow-up weight and/or HbA1c data were available. Change in HbA1c, body mass index (BMI) and weight from baseline was assessed using a multivariate linear regression model and change in the numbers achieving an endpoint HbA1c≤7.5% [58mmol/mol] were assessed using Chi2 tests in Stata 16. Results: Data were available for 350 individuals with baseline mean±SD HbA1c 9.2%±1.7 [76.6mmol/mol±18.3], weight 101.8kg±21.9, BMI 34.3kg/m2±6.9, median diabetes duration 11years (IQR 6-16) and age 59 years (IQR 51-68); 63.0% were male and 79.7% were white. Median follow-up was 0.5years (IQR 0.3-0.8). Significant reductions in HbA1c of 0.7% (95%CI 0.4, 0.9; P<0.001) [7.4mmol/mol; 95%CI 4.7, 10.0; P<0.001] were observed. Weight decreased by 3.3kg (95%CI 2.3, 4.3; P<0.001) and BMI fell by 1.1kg/m2 (95%CI 0.6, 1.6; P<0.001). Twice as many people achieved a HbA1c≤7.5% at follow-up compared to baseline (28.6% [52/182] vs 14.3% [26/182]) - this change was statistically significant (P<0.001). Conclusion: In the real-world, oral semaglutide is associated with statistically significant and clinically meaningful reductions in HbA1c, weight and BMI. The numbers achieving a HbA1c≤7.5% also increased. In the light of this, further data collection and analysis should be undertaken, including comparisons between oral and injectable GLP1-RAs and analysis of switches between them Disclosure T.S.J.Crabtree: Speaker's Bureau; Abbott Diabetes, Novo Nordisk, Lilly, Sanofi, Insulet Corporation. K.Adamson: None. S.Krishnasamy: Other Relationship; Novo Nordisk, Sanofi, Eli Lilly and Company, AstraZeneca. M.Khine: None. P.De: Speaker's Bureau; Novo Nordisk, AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Takeda Canada, Daiichi Sankyo, Napp Pharmaceuticals Limited, Lilly Diabetes. R.Peter: None. R.E.Ryder: Other Relationship; Novo Nordisk, Speaker's Bureau; GI Dynamics, BioQuest. 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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".