The metabolic adverse effects of antipsychotic use in individuals with intellectual and/or developmental disability: A systematic review and <scp>meta‐analysis</scp>
Bibliographic record
Abstract
Abstract Objective Individuals with intellectual and/or developmental disability (IDD) are often prescribed antipsychotics (APs). However, despite their known propensity to cause metabolic adverse effects, including weight gain, diabetes, and increased risk of cardiovascular events, there is currently a limited body of literature describing the metabolic consequences of AP use in this population. Methods We searched MEDLINE, EMBASE, PsychINFO, CENTRAL, and CINAHL databases to identify all randomized trials that reported on the metabolic effects of APs in individuals with IDD. Random effects meta‐analyses were used to examine weight gain as both a continuous and dichotomous outcome. Results Eighteen randomized trials met our inclusion criteria with a total of 1376 patients across a variety of IDDs. AP use was associated with significantly greater weight gain compared with placebo (Continuous: mean difference = 1.10 kg, [0.79, 1.40], p < 0.00001, I 2 = 54%; Dichotomous: odds ratio = 3.94, [2.15, 7.23], p < 0.00001, I 2 = 0). Sub‐group analysis revealed no significant effect of AP type. Data regarding the effects of APs on other metabolic outcomes were limited. Conclusion This review (PROSPERO # CRD42021255558) demonstrates that AP use is associated with significant weight gain among patients with IDD. Concerningly, most reported studies were in children and adolescents, which sets up an already vulnerable population for adverse medical sequalae at an early age. There was also a lack of long‐term studies in adults with IDD. Further studies are required to better understand how AP use affects metabolic parameters in this group of individuals.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.020 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".