青少年心理健康中多症状集群的纵向动态和多能性。

Michelle Frances Kennedy, Paul Schwenn, Amanda Boyes, Lia Mills, Taliah Prince, Marcella Parker, Daniel F Hermens
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引用次数: 0

摘要

背景:青春期是一个敏感的发育期,其特点是新出现的精神健康症状和正式诊断发病率增加。这些症状可能终生造成重大负担。青少年脑纵向研究(LABS)于 2018 年启动,旨在追踪普通人群参与者中精神健康症状的发病情况和轨迹。这项研究旨在确定青少年新出现的精神健康症状的多症状群,并研究这些群如何随年龄而变化,以及随着时间的推移而变化,从而深入了解失调发展的多能性:12-17岁的LABS参与者(166人)大约每4个月完成一次迷你国际神经精神访谈(MINI Kid),时间点多达15个。由于数据集维度较高,因此首先使用降维步骤(统一流形逼近和投影;UMAP)对数据进行处理。然后,使用贝叶斯平均模型、高斯混合模型和分层聚类对数据进行聚类,以识别不同的症状群。使用单独的 XGBoost 分类器模型,根据原始神经精神病学访谈回答对症状群进行描述。使用马尔可夫链转换概率矩阵和纵向分析对症状群的动态进行了分析。为了探究症状群与心理困扰和幸福感之间的关系,使用凯斯勒心理困扰量表(K10)和 COMPAS-W 幸福感量表的得分进行了相关分析:确定了六个基于症状的群组(状态):注意力、焦虑、抑郁、躁狂发作-遗传性、失乐症和健康。抑郁和焦虑群组具有最大的多能性。对心理困扰和健康状况的分析表明,这些状态之间存在反比关系:心理困扰越严重的人症状越多,反之,健康状况越好的人症状越少:解读:绘制青少年心理健康症状的多症状群及其多能性和过渡性轨迹图,可以更有效地确定预防干预措施的目标。这种方法超越了分类的范畴,可减轻早期症状发展为持久性精神障碍的情况。
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Longitudinal Dynamics and Pluripotentiality of Polysymptomatic Clustering in Adolescent Mental Health.
Background: Adolescence represents a sensitive developmental period characterised by an increased incidence of emerging mental health symptoms and formal diagnostic onset. These conditions can remain a significant burden throughout life. The Longitudinal Adolescent Brain Study (LABS) commenced in 2018 to track the onset and trajectory of mental health symptoms among general population participants. This research aims to identify polysymptomatic clusters of emerging mental health symptoms in adolescents and examine how these clusters vary by age and change over time, providing insights into the pluripotentiality of disorder development. Methods: LABS participants aged 12-17 years (n=166) completed the Mini International Neuropsychiatric Interview (MINI Kid) approximately every 4 months, with up to 15 timepoints. Due to this high dimensional dataset, the data was first processed using a dimensionality reduction step (uniform manifold approximation and projection; UMAP). Following this, the data was clustered using Bayesian model averaging of k-means, gaussian mixture model and hierarchical clustering to identify distinct symptom clusters. Symptom clusters were described in terms of the original neuropsychiatric interview responses using separate XGBoost classifier models. Symptom cluster dynamics were analysed using Markov chain transition probability matrices and longitudinal analysis. To explore the relationship between symptom clusters and psychological distress and wellbeing, correlational analyses were conducted using scores from the Kessler Psychological Distress Scale (K10) and the COMPAS-W Wellbeing Scale. Outcomes: Six symptom-based clusters (states) were identified: attention, anxiety, depression, manic episode - heritability, anhedonia, and well. Depression and anxiety clusters had the greatest pluripotentiality. Analysis of psychological distress and wellbeing demonstrated an inverse relationship between the states: those with greater psychological distress had more symptoms, conversely those with greater wellbeing had fewer symptoms. Interpretations: Mapping polysymptomatic clusters of mental health symptoms and their pluripotential and transitory trajectories in adolescents enables more effective targeting of preventive interventions. This approach moves beyond categorical classifications to mitigate the progression of early symptoms into enduring psychiatric disorders.
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