Switching between GLP‐1 receptor agonists in clinical practice: Expert consensus and practical guidance
Bibliographic record
Abstract
BACKGROUND: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are an established treatment for patients with type 2 diabetes (T2D). Differences between GLP-1RAs in pharmacokinetics, dosing regimens and clinical effects, including cardiovascular (CV) outcomes, mean there may be benefits to switching from one to another. However, clinical guidance on switching is lacking and data from clinical trials are limited. This article provides a clinical perspective and consensus on the benefits of switching between GLP-1RAs, the triggers for switching and how best to manage this in clinical practice. Once weekly (OW) semaglutide is used as an example to illustrate how the authors might switch to a different GLP-1RA in clinical practice. METHODS: Literature was searched and perspectives from 10 healthcare professionals with experience in switching patients with T2D to OW semaglutide from another GLP-1RA were collated. RESULTS: targets not being met, a desire for additional weight loss, poor adherence, patients moving to increased CV risk status and adverse effects with the current GLP-1RA. Non-medical triggers for switching included patient preference, cost, formulary changes and insurance mandates. Once the decision to switch is made, an individualised approach is recommended, based on considerations that include reimbursement requirements, treatment duration with (and dose of) previous GLP-1RA, the patient's experience initiating the prior GLP-1RA, any concomitant treatment and clinical characteristics. When switching, it is important to emphasise that treatment burden will not increase and that if gastrointestinal adverse effects occur, they are typically transient. Any transient gastrointestinal adverse effects that may occur (or recur) when switching to another GLP-1RA can be reduced by slow up-titration and advising patients to reduce food portion sizes and fat intake. CONCLUSION: Switching from one GLP-1RA to another, such as OW semaglutide, can provide clinical benefits and may delay the need for treatment intensification.
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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 | 0.109 | 0.242 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".