Increased Risk of Obesity and Metabolic Dysregulation following 12 Months of Second-Generation Antipsychotic Treatment in Children: A Prospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To determine the risk of developing obesity and related metabolic complications in children following long-term treatment with risperidone or quetiapine. METHODS: This was a 1-year naturalistic longitudinal study conducted between February 2009 and March 2012. A total of 130 children aged 2 to 18 years without prior exposure to second-generation antipsychotics (SGAs) were enrolled at initiation of treatment with either risperidone or quetiapine. Metabolic parameters were measured at baseline and months 6 and 12. Data of 37 participants (20 treated with risperidone and 17 treated with quetiapine) who completed 12-month monitoring were used in the analysis. RESULTS: After 1 year of SGA treatment, mean weight increased significantly by 10.8 kg (95% CI 7.9 kg to 13.7 kg) for risperidone and 9.7 kg (95% CI 6.5 kg to 12.8 kg) for quetiapine. Body mass index z score also increased significantly in both groups (P < 0.001). There was a high incidence of children becoming overweight or obese (6/15 [40.0%] for risperidone-treated and 7/14 [50.0%] for quetiapine-treated). The mean levels of fasting glucose (for risperidone-treated) and ratio of total cholesterol to high-density lipoprotein cholesterol (for quetiapine-treated) increased significantly by 0.23 mmol/L (95% CI 0.03 mmol/L to 0.42 mmol/L) and 0.48 mmol/L (95% CI 0.15 mmol/L to 0.80 mmol/L), respectively. CONCLUSION: Children treated with risperidone or quetiapine are at a significant risk for developing obesity, elevated waist circumference, and dyslipidemia during 12 months of treatment. These data emphasize the importance of regular monitoring for early identification and treatment of metabolic side effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".