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. 2024 May 23:10.1007/s10578-024-01682-6.
doi: 10.1007/s10578-024-01682-6. Online ahead of print.

Comparing Transdiagnostic Risk Factors: Predicting Emergence of Significant Depressive, Anxiety, and Substance Abuse Symptoms Among Juvenile Delinquents

Robert J Klein  1   2   3   4 , Damien Lekkas  5   6 , Nhi D Nguyen  5 , Nicholas C Jacobson  5   7   8
Affiliations

Comparing Transdiagnostic Risk Factors: Predicting Emergence of Significant Depressive, Anxiety, and Substance Abuse Symptoms Among Juvenile Delinquents

Robert J Klein et al. Child Psychiatry Hum Dev. .

Abstract

In a 7-year 11-wave study of low-SES adolescents (N = 856, age = 15.98), we compared multiple well-established transdiagnostic risk factors as predictors of first incidence of significant depressive, anxiety, and substance abuse symptoms across the transition from adolescence to adulthood. Risk factors included negative emotionality, emotion regulation ability, social support, gender, history of trauma, parental histories of substance abuse, parental mental health, and socioeconomic status. Machine learning models revealed that negative emotionality was the most important predictor of both depression and anxiety, and emotion regulation ability was the most important predictor of future significant substance abuse. These findings highlight the critical role that dysregulated emotion may play in the development of some of the most prevalent forms of mental illness.

Keywords: Anxiety; Depression; First incidence; Machine learning; Negative emotionality; Risk factor; Substance abuse; Transdiagnostic.

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Conflict of interest statement

Conflict of interest The authors declare no conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Fig. 1
Fig. 1
Clinically significant depression means for each follow-up wave
Fig. 2
Fig. 2
Clinically Significant anxiety means for each follow-up wave
Fig. 3
Fig. 3
Clinically significant substance abuse means for each follow-up wave
Fig. 4
Fig. 4
SHAP plots showing individual participants’ impact on depression model. A higher (positive) SHAP value reflects that the corresponding individual’s predictor value influences the model to predict the presence of the corresponding outcome. A lower (negative) SHAP value indicates that the corresponding individual’s predictor value influences the model to predict the absence of the corresponding outcome. A SHAP value closer to zero indicates little to no influence on the model’s prediction
Fig. 5
Fig. 5
SHAP plot of individual participants’ impact on model predicting anxiety
Fig. 6
Fig. 6
SHAP plot of individual participants’ impact on model predicting substance abuse
See this image and copyright information in PMC

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