Treatment-related weight gain and metabolic complications in children with mental health disorders: potential role for lifestyle interventions
Bibliographic record
Abstract
Over 1 million Canadian children are estimated to have a mental health disorder, which are commonly treated with medications, such as second-generation antipsychotics (SGAs). Estimates suggest that SGA prescriptions to children are increasing in Canada. Although these medications are important and lifesaving components of psychiatric treatment, they are not without side effects. For some children, SGA treatment is associated with adverse metabolic complications including rapid weight gain, dyslipidemia, elevated blood pressure, and risk for type 2 diabetes. It is not clear why these complications develop, but it is assumed that SGAs stimulate appetite and food intake, and reduce resting energy expenditure leading to weight gain and that the metabolic complications occur secondary to the weight gain. Understanding the mechanisms underlying these complications is key to being able to identify children at risk and prevent and optimize treatment. In this narrative review, we provide an overview of the literature pertaining to the weight gain and metabolic complications in children treated with SGAs, highlighting the scope of the problem and the current limited research on how diet and physical activity can be used to prevent or lessen the severity of the metabolic complications and improve the long-term health trajectories of SGA-treated children. Novelty: Children are increasingly being treated with second-generation antipsychotics for mental health disorders. Dietary and physical activity assessments are not commonly considered in clinical settings. Randomized controlled trials of lifestyle interventions are needed to determine the effectiveness of mitigating the cardiometabolic complications in second-generation antipsychotic-treated children.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".