Klein Robert J, Lekkas Damien, Nguyen Nhi D, Jacobson Nicholas C
Center for Technology and Behavioral Health, Dartmouth College, Hanover, USA.
The Well Living Lab, Rochester, USA.
Child Psychiatry Hum Dev. 2024 May 23. doi: 10.1007/s10578-024-01682-6.
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.
在一项针对低社会经济地位青少年(N = 856,年龄 = 15.98岁)的为期7年的11波研究中,我们比较了多个已确立的跨诊断风险因素,这些因素可预测从青春期到成年期显著抑郁、焦虑和药物滥用症状首次出现的情况。风险因素包括消极情绪、情绪调节能力、社会支持、性别、创伤史、父母药物滥用史、父母心理健康状况以及社会经济地位。机器学习模型显示,消极情绪是抑郁和焦虑的最重要预测因素,而情绪调节能力是未来显著药物滥用的最重要预测因素。这些发现凸显了情绪失调在一些最常见精神疾病发展过程中可能发挥的关键作用。