Predicting first-onset depression in adolescents: Do general population models generalize to youth with ADHD?

BackgroundMost studies seeking to identify youth at increased risk for depression have developed prediction models using a limited set of risk factors in general population samples. It is unclear whether these models generalize to high-risk youth. Here, we developed machine learning algorithms to predict first-onset depression in youth from the general population and high-risk youth with attention-deficit/hyperactivity disorder (ADHD). MethodsParticipants were 4803 unrelated children from the ABCD study with no prior mood disorder and complete data at baseline (age 9-10 years) and 2-year follow-up. Support Vector Machine, Random Forest, and Elastic Net models were used to predict first-onsets from clinically-relevant risk factors spanning mental and physical health, cognitive, dispositional, interpersonal, and socio-environmental domains. Predictive performance was evaluated in the full sample and separately in participants with ADHD (N=584, 12.16%). ResultsModels trained on the full sample achieved good discriminative predictive power (area under the curve [AUC]=0.70 and accuracy=0.70-0.82). Predictors that replicated across models included earlier pubertal development, higher behavioral inhibition and aggression, and more time spent passively watching media content. In the ADHD subsample, model performance declined (AUC=0.46-0.61) and predictors only partly overlapped with those identified in the full sample. ConclusionsModels effectively predicted depression in the general population but showed poor generalization to high-risk youth with ADHD, suggesting different risk factors in this group. These findings highlight that models trained in general population samples may not generalize to high-risk groups, pointing to the need for more tailored efforts to predict depression in youth at increased risk.