Impact of Psychoactive Drug Use on Developing Obesity among Children and Adolescents with Autism Spectrum Diagnosis: A Nested Case–Control Study
Bibliographic record
Abstract
BACKGROUND: Obesity in children on the autism spectrum (AS) is becoming a significant health concern. The purpose of this study was to identify the predictors of obesity in a cohort of AS youth and to assess the impact of psychoactive medication use while exploring the second-generation antipsychotics (SGAs) dose-response curve. STUDY DESIGN: A nested case-control study was conducted using Quebec public administrative databases. Subjects with AS <18 years [≥2 diagnoses International Classification of Diseases: 9th revision (ICD-9): 299.X] were identified (January 1993 to May 2011). Cases were defined as subjects with an obesity diagnosis (ICD-9: 278.X) during the coverage period and matched to 10 controls for age, gender, and follow-up duration. Potential risk factors for obesity (sociodemographic characteristics, other neuropsychiatric conditions, and psychoactive drug use) were evaluated and analyzed using conditional logistic regression. RESULTS: From a cohort of 5369 AS subjects, we identified 135 obesity cases. Among the different risk factors, only SGAs [rate ratio (RR): 1.04, 95% confidence interval (CI): 1.01-1.07] increased the probability of obesity in multivariate analysis. Exposure for ≥12 months increased significantly the likelihood of obesity (RR: 2.01, 95% CI: 1.18-3.42). Higher risk was observed with chlorpromazine-equivalent daily doses ≥100 mg (RR: 2.20, 95% CI: 1.00-4.84). Among SGA users, concomitant antidepressants (per 30-day exposure) slightly increased the probability (RR: 1.08, 95% CI: 1.01-1.15). CONCLUSIONS: Longer and higher SGA exposure increased the risk of obesity, which has to be considered in relation to the paucity of evidence supporting long-term psychoactive medication use in AS children. Results highlight the need to promote optimal use and interventions to mitigate metabolic side effects of SGAs in this population.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".