Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.
Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, The Netherlands.
Eur Psychiatry. 2023 Feb 21;66(1):e27. doi: 10.1192/j.eurpsy.2023.13.
Current categorical classification systems of psychiatric diagnoses lead to heterogeneity of symptoms within disorders and common co-occurrence of disorders. We investigated the heterogeneous and overlapping nature of symptom endorsement in a population-based sample across three of the most common categories of psychiatric disorders: depressive disorders, anxiety disorders, and sleep-wake disorders using unsupervised machine learning approaches.
We assessed a total of 43 symptoms in a discovery sample of 6,602 participants of the population-based Rotterdam Study between 2009 and 2013, and in a replication sample of 3,005 participants between 2016 and 2020. Symptoms were assessed using the Center for Epidemiologic Studies Depression Scale, the Hospital Anxiety and Depression Scale, and the Pittsburgh Sleep Quality Index. Hierarchical clustering analysis was applied on test items and participants to investigate common patterns of symptoms co-occurrence, and further quantitatively investigated with clustering methods to find groups that may represent similar psychiatric phenotypes.
First, clustering analyses of the questionnaire items suggested a three-cluster solution representing clusters of "mixed" symptoms, "depressed affect and nervousness", and "troubled sleep and interpersonal problems". A highly similar clustering solution was independently established in the replication sample. Second, four groups of participants could be separated, and these groups scored differently on the item clusters.
We identified three clusters of psychiatric symptoms that most commonly co-occur in a population-based sample. These symptoms clustered stable over samples, but across the topics of depression, anxiety, and poor sleep. We identified four groups of participants that share (sub)clinical symptoms and might benefit from similar prevention or treatment strategies, despite potentially diverging, or lack of, diagnoses.
当前的精神科诊断类别分类系统导致了疾病内部症状的异质性和疾病的共同共病。我们使用无监督机器学习方法研究了基于人群的样本中三种最常见的精神障碍类别：抑郁障碍、焦虑障碍和睡眠-觉醒障碍中症状的异质性和重叠性质。
我们评估了 2009 年至 2013 年在基于人群的鹿特丹研究中，共有 6602 名参与者的发现样本中的 43 种症状，以及 2016 年至 2020 年的 3005 名参与者的复制样本中的 43 种症状。使用流行病学研究抑郁量表、医院焦虑和抑郁量表和匹兹堡睡眠质量指数评估症状。对测试项目和参与者进行层次聚类分析，以调查症状共现的常见模式，并进一步使用聚类方法进行定量研究，以找到可能代表相似精神表型的组。
首先，问卷项目的聚类分析表明，三聚类解决方案代表了“混合”症状、“抑郁情绪和神经质”和“睡眠障碍和人际关系问题”的聚类。在复制样本中独立建立了一个高度相似的聚类解决方案。其次，可以分离出四个参与者组，这些组在项目聚类上的得分不同。
我们确定了三个最常见的基于人群的样本中共同出现的精神科症状聚类。这些症状在样本中稳定聚类，但跨越了抑郁、焦虑和睡眠不佳的主题。我们确定了四个共享（亚）临床症状的参与者组，尽管可能存在不同或缺乏诊断，但可能受益于类似的预防或治疗策略。
