Metformin for the prevention of clozapine‐induced weight gain: A retrospective naturalistic cohort study
Bibliographic record
Abstract
OBJECTIVE: Clozapine is presently the sole antipsychotic with an indication for treatment-resistant Schizophrenia, but is associated with significant weight gain and other metabolic aberrations. This retrospective chart review aimed to evaluate the effectiveness of adjunctive metformin in preventing clozapine-induced weight gain. METHODS: We conducted a retrospective chart review of patients newly initiated on clozapine at the Centre for Addiction and Mental Health in Canada, from November 2014 to April 2021. Our primary outcome was body weight at 6 and 12 months after clozapine initiation. Other metabolic parameters served as secondary outcomes. RESULTS: Among 396 patients (males: 71.5%, mean age: 42.8 years) initiated on clozapine, 69 were on metformin or prescribed it ≤3 months after clozapine initiation. The clozapine+metformin group demonstrated less weight gain compared with the clozapine-only group at 6 months (clozapine+metformin: -0.15 kg [SE = 1.08] vs. clozapine-only: 2.99 kg, SE = 0.54) and 12 months after clozapine initiation (clozapine+metformin: -0.67 kg, SE = 1.22 vs. clozapine-only: 4.72 kg, SE = 0.67). Adaptive changes were also observed for fasting glucose (F = 3.10, p = 0.046) and triglycerides (F = 8.56, p < 0.001) in the clozapine+metformin group compared with clozapine only. CONCLUSION: In this large retrospective naturalistic cohort study, co-prescription of clozapine and metformin was associated with less weight gain and related metabolic dysfunction at 6 and 12 months after initiation versus clozapine alone. These findings provide evidence for the effectiveness of metformin in preventing clozapine-induced weight gain; larger randomized controlled trials are needed to confirm these results.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".