What do I need to know about liraglutide (Saxenda), the glucagon-like peptide 1 receptor agonist for weight management in children with obesity?
Bibliographic record
Abstract
Glucagon-like peptide 1 (GLP-1) is a gut hormone, or incretin, which is secreted into circulation by intestinal L cells in response to nutritional intake (1). Although incretin-based therapies, such as GLP-1 receptor agonists (GLP-1RA) and dipeptidyl peptidase 4 inhibitors (DPP-4i), are now well established as effective agents in the control of type 2 diabetes (T2D) and obesity in adults (2), their application in children is relatively novel. In early 2021, Health Canada approved a once daily subcutaneous injectable formulation of the GLP-1RA liraglutide (Saxenda) for use as a weight management therapy in adolescents with obesity (3). The same drug given at a lower dose of 1.8 mg daily (Victoza) was approved in 2020 for use as an add on therapy to metformin in children and adolescents with T2D (4). Once in circulation, GLP-1 acts by increasing insulin secretion and sensitivity, inhibiting glucagon secretion, slowing gastrointestinal motility, and inducing satiety through interaction with the central nervous system (5). Preloaded syringes of Saxenda deliver an adjustable dose of up to 3 mg of liraglutide (C172H265N43O51), a GLP-1 analogue with an amino acid substitution (arginine for lysine) and C-16 fatty acid addition. These alterations confer protection from its degrading exopeptidase counterpart, DPP-4, which typically limits the half-life of intrinsically produced GLP-1 to seconds or minutes in circulation (6). GLP-1RA are designed to mimic the effects of endogenous GLP-1 over a prolonged period, given their resistance to degradation.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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".