Real-World Effectiveness Analysis of Switching From Liraglutide or Dulaglutide to Semaglutide in Patients With Type 2 Diabetes Mellitus: The Retrospective REALISE-DM Study
Bibliographic record
Abstract
Injectable semaglutide is a glucagon-like peptide-1 receptor agonist (GLP-1 RA) that was previously shown to be superior to liraglutide and dulaglutide in head-to-head comparisons in GLP-1 RA-naïve individuals. It is hypothesized that semaglutide will cause further reductions in glycated hemoglobin A1c (HbA1c) and weight in type 2 diabetes mellitus (T2DM) patients previously treated with liraglutide or dulaglutide. The REALISE-DM study provides the first real-world evidence of the effectiveness and tolerability of semaglutide in patients switching from another GLP-1 RA. This retrospective real-world effectiveness analysis included T2DM adults who were on a stable dose of liraglutide or dulaglutide prior to switching to semaglutide. The primary outcome was change in HbA1c. Secondary outcomes were the changes in weight and body mass index (BMI), the occurrence of gastrointestinal side effects (GSEs), and discontinuations. Linear mixed models were used to estimate changes in HbA1c, weight, and BMI, and logistic regression was employed to analyze GSEs and discontinuations. Six months after the 164 patients in this study had switched to semaglutide, their mean HbA1c had decreased by 0.65% (7.1 mmol/mol) (95% prediction interval [PI]: 0.48, 0.81% [5.2, 8.9 mmol/mol]) from a baseline of 7.9% (interquartile range [IQR]: 7.3, 8.8) (62.8 mmol/mol [IQR: 56.3, 72.7]), while their weight and BMI had reduced by 1.69 kg (95% PI: 1.01, 2.37) and 0.59 kg/m 2 (95% PI: 0.34, 0.84), respectively. Nineteen patients (11.6%) developed GSEs after switching. This study supports switching T2DM patients on liraglutide or dulaglutide to injectable semaglutide to achieve further reductions in HbA1c and weight. Although a small number of GSEs occurred, semaglutide was well tolerated by the majority of the patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".