Group-Based Trajectory Modeling to Identify Patterns of Antipsychotic-Induced Weight Gain Among Children and Adolescents

OBJECTIVES: Antipsychotic-induced weight gain (AIWG) is the most common side effect associated the use of second-generation antipsychotic (SGA) medications among children and adolescents. Group-based trajectory model (GBTM) can graphically display the longitudinal weight change overtime. The objective of this study was to model the development of AIWG among children and adolescents taking SGAs.

METHODS: This study has used IQVIA National Electronic Medical Record database from 2016 to 2021. Eligible children and adolescents 6-17 years of age who had at least one prescription of SGA, more than 90 days of continuous SGA treatment, and had BMI z-score at baseline and at least one BMI z-score during SGA treatment. Absolute weight gain was difference between follow-up BMI z-score and baseline BMI z-score. A significant weight gain was defined as at least 0.5-unit absolute BMI z-score increase. GBTM was used to model development of AIWG during a 24-month period since SGA initiation, and survival analysis was conducted to further examine the time to significant weight gain among SGA recipients who were obese and nonobese at baseline.

RESULTS: Of the 16,262 patients meeting inclusion criteria, 4,572 (28%) experienced significant weight gain during SGA treatment. The median time to significant weight gain is 10 weeks for nonobese and 24 weeks for obese. The GBTM identified 5 distinctive trajectories for AIWG: rapid weight gain (1.9%); gradual weight gain (9.1%); slightly weight gain (20.1%); stable weight (63.1%), and gradual weight loss (5.7%). Survival analysis showed that nonobese patients were at higher risk for significant weight gain in the first two years of SGA treatment compared to those with obesity at baseline (HR, 95%CI: 7.52, 6.75-8.38).

CONCLUSIONS: Based on current findings, warranting more intense analysis with more data points across time. Understanding the potential patterns of AIWG using GBTM can enable targeted monitoring and interventions to reduce AIWG.