Risperidone-Induced Obesity in Children and Adolescents With Autism Spectrum Disorder: Genetic and Clinical Risk Factors

Abstract

Aims: Obesity is a significant problem for patients taking atypical antipsychotics. There were two aims of our study. The first aim was to compare the prevalence of overweight and obesity between children and adolescents with autism spectrum disorder (ASD) treated with risperidone with the general pediatric population. The second aim was to investigate the association of the HTR2C-759C>T, ABCB1 1236C>T, ABCB1 2677G>T/A, and ABCB1 3435C>T polymorphisms with risperidone-induced overweight and obesity in children and adolescents with ASD. Methods: Body weight and height were measured in 134 subjects. Overweight and obesity in children and adolescents were classified using the International Obesity Task Force (IOTF) criteria. Genotyping was performed by TaqMan real-time polymerase chain reaction (PCR). Results: Our study found that the prevalence of overweight and obesity was significantly higher in children and adolescents with ASD treated with risperidone compared with healthy individuals (p 0.01 and p 0.002). The genetic polymorphisms of HTR2C-759C>T, ABCB1 1236C>T, ABCB1 2677G>T/A, and ABCB1 3435C>T were not associated with overweight/obesity in children and adolescents with ASD treated with risperidone after adjustment for multiple comparisons by the method of Bonferroni. Additionally, haplotype analysis revealed that there was no significant association between ABCB1 3435T-2677T/A-1236T haplotype and overweight/obesity. In multivariate logistic regression, after adjustment by the Bonferroni correction, there was only the duration of risperidone treatment that was significantly associated with overweight/obesity in children and adolescents with ASD. Conclusions: The findings suggest that children and adolescents with ASD treated with risperidone are at a higher risk of obesity, especially patients with extended treatment with risperidone. For the pharmacogenetic factors,-759C>T polymorphism of HTR2C gene

Fatores associados ao excesso de peso em crianças e adolescentes com Transtorno do Espectro Autista (TEA): uma revisão integrativa

“…Para o diagnóstico de excesso de peso, o principal indicador avaliado é o índice de massa corporal (IMC) para a idade, e os pontos de corte variam de acordo com a curva empregada e a faixa etária (Oliveira et al, 2022) (Granich et al, 2016;Vanwong et al, 2020), sendo que esta última só foi empregada isoladamente em um estudo. Em uma revisão sistemática (Oliveira et al, 2022) foi observado que, de forma geral, as curvas de IMC para a idade do CDC são menos acuradas para o diagnóstico de rastreios nutricionais quando comparadas com as curvas da OMS e do IOTF, mas há que há controvérsias sobre qual delas seria mais apropriada para uso internacional, principalmente quando se trata de crianças maiores de 5 anos e adolescentes (Oliveira et al, 2022).…”

“…Apesar da grande variabilidade nas prevalências de excesso de peso encontradas nos estudos, aqueles que compararam a prevalência de excesso de peso entre com TEA e um grupo controle (Broder-Fingert et al, 2014;Corbett et al, 2021;Criado et al, 2018;Hill et al, 2015;Kummer et al, 2016;Levy et al, 2019;Raspini et al, 2021;Shedlock et al, 2016;Vanwong et al, 2020;Vinck-Baroody et al, 2015), observaram que as crianças e/ou adolescentes com diagnóstico de TEA apresentavam uma chance significativamente maior de sobrepeso e/ou obesidade do que aqueles sem TEA. Este resultado corrobora com o resultado de outros estudos, que observaram que crianças e adolescentes com TEA apresentam uma chance superior de estarem com sobrepeso e/ou obesas quando comparados aos seus pares com desenvolvimento típico (Balogun, 2016;Li et al, 2020;Zheng et al, 2017).…”

“…Muitos estudos encontraram associação entre o excesso de peso e variáveis sociodemográficas. Dentre elas, a mais frequente foi a idade, sendo que a idade mais avançada foi frequentemente associada a maiores chances de ter sobrepeso e/ou obesidade em crianças e adolescentes com TEA (Broder-Fingert et al, 2014;Dempsey et al, 2017;Hill et al, 2015;Nor et al, 2019;Vanwong et al, 2020;Xiong et al, 2009).…”

External Evaluation of Risperidone Population Pharmacokinetic Models Using Opportunistic Pediatric Data

“…Risperidone has also been demonstrated to be an efficacious option for the management of attention-deficit/hyperactivity disorder and various other psychiatric disorders associated with anxiety and irritability in children ( Eapen and Gururaj, 2005 ; Jensen et al, 2007 ; Biederman et al, 2008 ; Arnold et al, 2015 ; Lee et al, 2018 ). Risperidone use is associated with dose and duration-dependent adverse effects, including weight gain, extrapyramidal symptoms, prolactin elevation, sedation, and QTc interval prolongation ( Vanwong et al, 2020 ; Kloosterboer et al, 2021 ).…”

A Machine Learning Model to Predict Risperidone Active Moiety Concentration Based on Initial Therapeutic Drug Monitoring

“…Short-term usage of risperidone could lead to some rapid occurrence of ADRs, such as nausea, vomiting, and excessive sedation. In addition, some long-term ADRs induced by risperidone were also received with concerns, such as obesity, insulin resistance, leptin resistance, and hyperuricemia, depending on risperidone dosage or treating period (12)(13)(14)(15). Therapeutic drug monitoring (TDM) is a vital method to ensure individualized antipsychotic regimen with low ADR risks and higher treatment efficacy (8,16).…”