GLP‐1 agonists for obesity and type 2 diabetes in children: Systematic review and meta‐analysis
Bibliographic record
Abstract
Pharmacological options for management of obesity and type 2 diabetes mellitus (T2DM) in children are limited. We aimed to synthesize published randomized controlled trial (RCT) evidence on the efficacy of glucagon-like peptide-1 (GLP-1) agonists in T2DM, pre-diabetes, and obesity in children aged <18 years. Inclusion criteria were RCTs of any GLP-1 agonist, solely or in conjunction with other drugs, for the treatment of obesity, pre-diabetes, and/or T2DM in children aged <18 years old. Nine studies met the inclusion criteria (two for T2DM, one for pre-diabetes, and six for obesity without diabetes). In total, 286 children were allocated to GLP-1 agonist therapy. Compared with controls, GLP-1 agonist therapy reduced HbA1c by -0.30% (95% confidence interval [CI] -0.57, -0.04) with a larger effect in children with (pre-)diabetes (-0.72%; 95% CI -1.17, -0.28; three studies) than in children with obesity (-0.08%; 95% CI -0.13, -0.02; four studies). Conversely, GLP-1 agonist therapy reduced body weight more in children with obesity (-2.74 kg; 95% CI -3.77, -1.70; six studies) than in children with T2DM (-0.97 kg; 95% CI -2.01, 0.08; two studies). Adverse effects included gastrointestinal symptoms and minor hypoglycemic episodes, but not severe hypoglycemia. GLP-1 agonists are efficacious in treating children with obesity and/or T2DM. Effect sizes are comparable with those reported in adults.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".