Background Most 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).

Methods Participants 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%).

Results Models 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.

Conclusions Models 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.

The authors have declared no competing interest.

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), held in the National Institute of Mental Health Data Archive (NDA). The ABCD Study is supported by the National Institutes of Health (NIH) and additional federal partners under awards numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. SL was supported by a Chinese Scholarship Council PhD studentship. TW was supported by a Wellcome Trust Career Development Award (225945/Z/22/Z). GM was part-funded by a Klingenstein Third Generation Foundation Fellowship (20212999).

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org) Researchers can access the ABCD data via their data-sharing procedures.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

Researchers can access the ABCD data via their data sharing procedures. The code for analyzing the data is available in OSF.

https://osf.io/d3hv8/overview