Featured Article
Article Title
Comparative physiological effects of antipsychotic drugs in children and young people: A network meta-analysis
Authors
Maria Rogdaki, Robert A McCutcheon, Enrico D’Ambrosio, Valentina Mancini, Cameron J Watson, Jack B Fanshawe, Richard Carr, Laurence Telesia, Maria Giulia Martini, Aaron Philip, Barnabas J Gilbert, Gonzalo Salazar-de-Pablo, Marinos Kyriakopoulos, Dan Siskind, Christoph U Correll, Andrea Cipriani, Orestis Efthimiou, Oliver D Howes, Toby Pillinger
Abstract
Background: The degree of physiological responses to individual antipsychotic drugs is unclear in children and adolescents. With network meta-analysis, we aimed to investigate the effects of various antipsychotic medications on physiological variables in children and adolescents with neuropsychiatric and neurodevelopmental conditions.
Methods: For this network meta-analysis, we searched Medline, EMBASE, PsycINFO, Web of Science, and Scopus from database inception until Dec 22, 2023, and included randomized controlled trials comparing antipsychotics with placebo in children or adolescents younger than 18 years with any neuropsychiatric and neurodevelopmental condition. Primary outcomes were mean change from baseline to end of acute treatment in body weight, BMI, fasting glucose, total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides, prolactin, heart rate, systolic blood pressure (SBP), and QT interval corrected for heart rate (QTc) for patients receiving either active treatment or placebo. For multigroup trials reporting several doses, we calculated a summary value for each physiological variable for all doses. After transitivity assessment, we fitted frequentist random-effects network meta-analyses for all comparisons in the network. A Kilim plot was used to summarise the results for all treatments and outcomes, providing information regarding the strength of the statistical evidence of treatment effects, using p values. Network heterogeneity was assessed with τ, risk of bias of individual trials was assessed with the Cochrane Collaboration’s Tool for Assessing Risk of Bias, and the credibility of findings from each network meta-analysis was assessed with the Confidence in Network Meta-Analysis (CINEMA) app. This study is registered on PROSPERO (CRD42021274393).
Findings: Of 6676 studies screened, 47 randomized controlled trials were included, which included 6500 children (mean age 13·29 years, SD 2·14) who received treatment for a median of 7 weeks (IQR 6–8) with either placebo (n=2134) or one of aripiprazole, asenapine, blonanserin, clozapine, haloperidol, lurasidone, molindone, olanzapine, paliperidone, pimozide, quetiapine, risperidone, or ziprasidone (n=4366). Mean differences for bodyweight change gain compared with placebo ranged from –2·00 kg (95% CI –3·61 to –0·39) with molindone to 5·60 kg (0·27 to 10·94) with haloperidol; BMI –0·70 kg/m² (–1·21 to –0·19) with molindone to 2·03 kg/m² (0·51 to 3·55) with quetiapine; total cholesterol –0·04 mmol/L (–0·39 to 0·31) with blonanserin to 0·35 mmol/L (0·17 to 0·53) with quetiapine; LDL cholesterol –0·12 mmol/L (–0·31 to 0·07) with risperidone or paliperidone to 0·17 mmol/L (–0·06 to 0·40) with olanzapine; HDL cholesterol 0·05 mmol/L (–0·19 to 0·30) with quetiapine to 0·48 mmol/L (0·18 to 0·78) with risperidone or paliperidone; triglycerides –0·03 mmol/L (–0·12 to 0·06) with lurasidone to 0·29 mmol/L (0·14 to 0·44) with olanzapine; fasting glucose from –0·09 mmol/L (–1·45 to 1·28) with blonanserin to 0·74 mmol/L (0·04 to 1·43) with quetiapine; prolactin from –2·83 ng/mL (–8·42 to 2·75) with aripiprazole to 26·40 ng/mL (21·13 to 31·67) with risperidone or paliperidone; heart rate from –0·20 bpm (–8·11 to 7·71) with ziprasidone to 12·42 bpm (3·83 to 21·01) with quetiapine; SBP from –3·40 mm Hg (–6·25 to –0·55) with ziprasidone to 10·04 mm Hg (5·56 to 14·51) with quetiapine; QTc from –0·61 ms (–1·47 to 0·26) with pimozide to 0·30 ms (–0·05 to 0·65) with ziprasidone.
Summary of Research
“During the past 30 years, there has been an increase in rates of antipsychotic drug prescriptions for children and adolescents younger than 18 years in high-income countries, with an estimated prescription prevalence of 0.5% in 2019. Compared with individuals older than 18 years, antipsychotic treatment in children and adolescents is associated with an increased risk of side effects, including large and rapid weight gain and metabolic dysregulation. Childhood weight gain and metabolic dysregulation can lead to adult obesity, metabolic syndrome, and cardiovascular disease. Moreover, long-term exposure to prolactin-increasing antipsychotics is associated with reduced bone mineral density and increased risk of breast cancer. Thus, there is an association between the physiological side effects of antipsychotic drugs and long-term physical health conditions. As most children and adolescents continue treatment into adulthood, reducing the antipsychotic side-effect burden in this population would improve their short-term and long-term physical health outcomes” (p. 511).
“We aimed to investigate the effects of different antipsychotic drugs on physiological variables; explore whether age, sex, ethnicity, baseline body weight, or a diagnosis of schizophrenia altered vulnerability to antipsychotic-induced physiological alterations; and, in children and adolescents with schizophrenia, examine the relationship between improvement in psychotic symptoms and severity of antipsychotic-induced metabolic disturbance… This network meta-analysis is reported in line with PRISMA extension statement guidelines. We searched Medline, EMBASE, PsycINFO, Web of Science, and Scopus from database inception until Dec 22, 2023… We included randomized controlled trials published in English comparing antipsychotics with placebo in children and adolescents (i.e., aged <18 years) with any neuropsychiatric or neurodevelopmental condition” (p. 511 -512).
“We found substantial variation in the effect of antipsychotics on body weight, BMI, triglycerides, total cholesterol, prolactin, heart rate, and SBP. Antipsychotic Induced effects were large and considered clinically relevant for body weight, BMI, fasting glucose, triglycerides, heart rate, and SBP” (p. 518).
“In agreement with previous studies, weight gain was more common with olanzapine, quetiapine, clozapine, and risperidone or paliperidone than with other treatments... Furthermore, similar to studies in adults, we estimated that fasting glucose and lipid alterations were greater with quetiapine and olanzapine than with other antipsychotics. One previous meta-analysis of observational data of acute antipsychotic treatment of children and adolescents with schizophrenia spectrum disorders suggested that quetiapine increased triglycerides… Quetiapine was associated with the largest increase in heart rate and SBP. Risperidone or paliperidone ranked highest in terms of associated change in prolactin, which is consistent with the conclusion of a previous, smaller meta-analysis in pediatric patients with schizophrenia and schizophrenia spectrum disorders. Molindone ranked highest in terms of associated body weight loss, which is consistent with previous data from adults with schizophrenia” (p. 518 - 519).
“Aripiprazole, blonanserin, lurasidone, and ziprasidone showed relatively benign physiological side-effect profiles. The most prominent antipsychotic-induced increases in prolactin were seen among White participants. However, this association might have been a chance finding as multiple analyses were done, so the finding warrants further study. By contrast to a similar network meta-analysis in adults with schizophrenia, we found no evidence that the severity of antipsychotic induced physiological change was associated with bodyweight, sex, or ethnicity” (p. 519).
“Furthermore, similar results for children with neuropsychiatric and neurodevelopmental conditions compared with children and adolescents with a diagnosis of schizophrenia suggest uniform susceptibility to antipsychotic physiological effects across the pediatric population. We included all randomized controlled trials of children and adolescents with a range of neuro psychiatric and neurodevelopmental disorders to improve the generalisability of our findings. No inconsistency was found in our network meta-analyses of changes in weight, BMI, fasting glucose, prolactin, SBP, heart rate, or QTc, supporting the robustness of our results. However, global inconsistency was seen in the network meta-analyses of changes in weight, fasting glucose, and prolactin (although minimal evidence suggested local inconsistency). Local and global inconsistency was also seen in the network meta-analyses of changes in total cholesterol, HDL cholesterol, and LDL cholesterol” (p. 519).
Translating Research into Practice
“The probability and severity of alterations of physiological parameters associated with particular antipsychotic drugs can now form a part of evidence-based prescribing decisions for children and adolescents. Transparency when communicating the benefits and risks of antipsychotic treatment will inform the best choice of antipsychotic drug that aligns with the preferences of young people. These findings must be considered in future updates of antipsychotic treatment guidelines for pediatric patients. Future meta-analyses of alterations of physiological parameters during maintenance antipsychotic therapy in young people are indicated to further improve prescribing practice, and by incorporating them into digital tools, they have the potential to facilitate personalized antipsychotic treatment options” (p. 511).
Other Interesting Tidbits for Researchers and Clinicians
“An important limitation of our network meta-analyses is that—except for analyses of changes in bodyweight, BMI, triglycerides, and QTc—confidence in the evidence was generally very low for more than 50% of treatment comparisons, largely resulting from large imprecision in estimated effects. A similar issue was seen in the network meta-analysis of acute comparative metabolic effects of antipsychotics in adults with schizophrenia, with confidence of outcomes for up to 100% of treatment comparisons regarded as either low or very low. Although we did not identify demographic or physiological predictors of antipsychotic-induced physiological alterations, such analyses should ideally be done as network meta-analyses of individual participant data— an important focus for future research. Furthermore, insufficient trial numbers meant we were unable to explore the relationship between physiological changes and improvements in psychopathology in children and adolescents with schizophrenia, so this topic should also be examined in future studies. Future meta-analyses could also focus on specific clinical outcomes such as rates of emergent diabetes rather than simply mean change in metabolic variables. As metabolic outcomes were invariably not available in trials older than 30 years, our data were mainly generated by trials of antipsychotics licensed in the past 35 years. To improve prescribing practice, further work is needed to define the metabolic profile of older drugs in children and adolescents with neuropsychiatric and neurodevelopmental disorders. Trials also did not typically provide information on lifestyle factors (eg, diet and exercise), physical comorbidities, puberty stages, or co-prescribed medication, all of which could have influenced physiological variables. Nevertheless, because of randomisation, we expect no systematic imbalances in such confounding factors between groups in our network meta-analyses. However, we could not check their distributions across treatment comparisons in the network, in which imbalances might jeopardise network transitivity. Finally, we focused on short-term trials and effects. However, as physiological alterations accumulate with time and antipsychotic drugs are often prescribed for long-term treatment, a network meta-analysis of physiological side-effects of antipsychotic maintenance therapy in children and adolescents is indicated” (p. 519).
