Glucagon‐like peptide‐1 receptor agonists for antipsychotic‐associated cardio‐metabolic risk factors: A systematic review and individual participant data meta‐analysis
Bibliographic record
Abstract
AIMS: To evaluate if glucagon-like peptide-1 receptor agonists (GLP-1RAs) reduce antipsychotic-associated body weight gain in patients with schizophrenia, when compared to controls. MATERIALS AND METHODS: We systematically searched PubMed/EMBASE/PsycINFO/Cochrane using the search terms '(antipsychotic and GLP-1RA)'. Individual participant data from studies randomizing patients to GLP-1RA or control were meta-analysed. The primary outcome was difference in body weight between GLP-1RA and control; secondary outcomes included cardio-metabolic variables and adverse drug reactions (ADRs). Multiple linear regression was conducted including sex, age, psychosis severity, metabolic variable, ADRs, and GLP-1RA agent. RESULTS: Three studies (exenatide once-weekly = 2; liraglutide once-daily = 1) provided participant-level data (n = 164, age = 40.0 ± 11.1 years, body weight = 105.8 ± 20.8 kg). After 16.2 ± 4.0 weeks of treatment, body weight loss was 3.71 kg (95% CI = 2.44-4.99 kg) greater for GLP-1RA versus control (p < 0.001), number-needed-to-treat ≥5% body weight loss = 3.8 (95% CI = 2.6-7.2). Waist circumference, body mass index, HbA1c, fasting glucose and visceral adiposity were each significantly lower with GLP-1RA. Sex, age, psychosis severity, nausea, any ADR, and GLP-1RA agent did not significantly impact outcomes. Body weight loss with GLP-1RAs was greater for clozapine/olanzapine-treated patients (n = 141) than other antipsychotics (n = 27) (4.70 kg, 95% CI = 3.13-6.27 vs. 1.5 kg, 95% CI = -1.47-4.47) (p < 0.001). Nausea was more common with GLP-1RAs than control (53.6% vs. 27.5%, p = 0.002, number-needed-to-harm = 3.8). CONCLUSION: GLP-1RAs are effective and tolerable for antipsychotic-associated body weight gain, particularly clozapine/olanzapine-treated patients. With few included patients, further studies are required before making routine use recommendations for GLP-1RAs.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.034 |
| 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) | 0.004 | 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".