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Cluster analysis reveals subclinical subgroups with shared autistic and schizotypal traits

机译:集群分析显示了具有共享自闭症和斯派比尔特征的亚临床亚组

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摘要

Autism and schizophrenia spectrum research is typically based on coarse diagnostic classification, which overlooks individual variation within clinical groups. This method limits the identification of underlying cognitive, genetic and neural correlates of specific symptom dimensions. This study, therefore, aimed to identify homogenous subclinical subgroups of specific autistic and schizotypal traits dimensions, that may be utilised to establish more effective diagnostic and treatment practices. Latent profile analysis of subscale scores derived from an autism-schizotypy questionnaire, completed by 1678 subclinical adults aged 18-40 years (1250 females), identified a local optimum of eight population clusters: High, Moderate and Low Psychosocial Difficulties; High, Moderate and Low Autism-Schizotypy; High Psychosis-Proneness; and Moderate Schizotypy. These subgroups represent the convergent and discriminant dimensions of autism and schizotypy in the subclinical population, and highlight the importance of examining subgroups of specific symptom characteristics across these spectra in order to identify the underlying genetic and neural correlates that can be utilised to advance diagnostic and treatment practices.
机译:自闭症和精神分裂症谱研究通常基于粗诊断分类,其忽略了临床组内的个体变化。该方法限制了特定症状尺寸的潜在认知,遗传和神经相关性的鉴定。因此,该研究旨在鉴定特定自闭症和斯派比尔特征尺寸的均匀亚临床亚组,其可用于建立更有效的诊断和治疗方法。潜伏期分析源自自闭症 - 斯派比问卷的分数,由18-40岁的1678名课程成人完成(1250名女性),确定了八个人群集群的局部最优:高度,中度和低的心理社会困难;高,中度和低自闭症 - 斯派比;高精神病度 - 至上;和适度的斯派比。这些亚组代表了亚临床群体中自闭症和酶痉挛的会聚和判别尺寸,并突出了在这些光谱上检查特定症状特征的子组的重要性,以确定可用于推进诊断和治疗的潜在遗传和神经相关性实践。

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