Predicting the First Onset of Suicidal Thoughts and Behaviors in Adolescents Using Multimodal Risk Factors: A 4-Year Longitudinal Study
Date
2025
Authors
Nguyen, J.
Dwyer, D.
Toenders, Y.J.
Tagliaferri, S.D.
van Velzen, L.S.
Clark, S.R.
Scott, I.
Hartmann, S.
Wigman, J.T.W.
Lin, A.
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Journal article
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Journal of the American Academy of Child and Adolescent Psychiatry, 2025; 1-15
Statement of Responsibility
Josh Nguyen, Dominic B. Dwyer, Yara J. Toenders, Scott D. Tagliaferri, Laura S. van Velzen, Scott R. Clark, Isabelle Scott, Simon Hartmann, Johanna T.W. Wigman, Ashleigh Lin, Andrew D. Thompson, Cassandra M.J. Wannan, Caroline X. Gao, Stephen J. Wood, G. Paul Amminger, Alison R. Yung, Nikolaos Koutsouleris, Jessica A. Hartmann, Hok Pan Yuen, Christopher G. Davey, Angelica Ronald, Patrick D. McGorry, Christel Middeldorp, Barnaby Nelson, Lianne Schmaal
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Abstract
Objective Suicide is one of the leading causes of death among youth worldwide, yet existing studies that aimed to predict the first onset of suicidal thoughts and behaviors (STB) included a limited number of data modalities and/or focused on adult populations. This study aimed to prospectively predict first-onset STB across 4-year follow-ups in adolescents using an existing STB history classification model that was previously applied to baseline data and a new machine learning model with 195 biopsychosocial features. Method Participants were 7,503 unrelated adolescents (54.5% female, ages 9-11 years at baseline) from the multisite, longitudinal Adolescent Brain Cognitive Development (ABCD) Study. An existing baseline STB history classification model was applied to predict longitudinal first-onset STB in adolescents compared with healthy controls and clinical controls (individuals with a mental health disorder but no STB). A new elastic net logistic regression model with 195 features was trained on data from 14 sites (n = 5,220), and the resulting top 15 features were validated at 7 independent sites (n = 2,283). Results The previously developed model to classify STB lifetime history also prospectively predicted first-onset STB in adolescents with an area under the curve (AUC) [95% CI] of 0.73 [0.70, 0.75], p < .001, compared with healthy controls and AUC [95% CI] of 0.63 [0.60, 0.66], p < .001, compared with clinical controls. The newly trained model with top 15 features performed similarly with AUC [95% CI] of 0.73 [0.71, 0.76], p < .001, and AUC [95% CI] of 0.64 [0.60, 0.66], p < .001, for the same comparison groups. The most consistent predictors across models included female sex, sleep disturbances, and maladaptive home and school environments. Conclusion The models predicted first-onset STB in adolescents with moderate accuracy. This study also confirmed the roles of well-established psychological risk factors for STB and identified several novel neurocognitive and brain imaging risk factors. Future studies should validate these models in large-scale diverse samples before clinical translation.
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©2025 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).