An Investigation of Psychosis Subgroups With Prognostic Validation and Exploration of Genetic Underpinnings The PsyCourse Study
An Investigation of Psychosis Subgroups With Prognostic Validation and Exploration of Genetic Underpinnings The PsyCourse Study
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DOI：
10.1001/jamapsychiatry.2019.4910
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发表时间：
2020-05-01
期刊：
影响因子：
25.8
通讯作者：
Koutsouleris, Nikolaos
中科院分区：
文献类型：
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作者：
Dwyer, Dominic B.;Kalman, Janos L.;Koutsouleris, Nikolaos
这项队列研究旨在检测精神病亚组，并检查他们在1.5年以上的病程以及他们在精神分裂症、双相情感障碍、重度抑郁症和教育成就方面的多基因评分。使用高维临床数据的数据驱动聚类能否揭示与预后和多基因风险相关的精神病亚群？在这项包括1223名个体的队列研究中，在765名主要患有双相情感障碍和精神分裂症的发现样本中，发现了5个亚组，它们具有不同的临床特征、疾病轨迹和受教育程度的遗传评分。结果在458个人的样本中得到了验证。新的数据驱动的聚类与严格的验证相结合，可能提供一种方法，将基于症状的精神病分类扩展到功能结果、遗传标记和基于轨迹的分层。意义确定精神病亚群可以提高临床和研究的准确性。研究主要集中在症状亚群上，但有必要考虑更广泛的临床谱，理清疾病轨迹，并调查遗传关联。目的采用数据驱动的方法检测精神病亚群，并对其1.5年以上病程、精神分裂症、双相情感障碍、重度抑郁症和学业成就的多基因评分进行分析。设计、环境和参与者本研究于2012年1月开始，在18个地点进行多地点、自然、纵向（间隔6个月）队列研究。从二级和三级医疗机构收集了1223例（发现样本765例，验证样本458例）诊断为DSM-IV精神分裂症、双相情感障碍（I/II）、分裂情感障碍、精神分裂样障碍和短暂性精神障碍的参考样本数据。发现数据提取于2016年9月，分析时间为2016年11月至2018年1月；前瞻性验证数据提取于2018年10月，分析时间为2019年1月至5月。采用非负矩阵分解聚类法对188个测量人口学特征、临床病史、症状、功能和认知的临床变量进行分解。将亚型特异性病程与混合模型和多基因评分进行比较，并进行协方差分析。使用监督学习在验证数据中复制具有最可靠判别的45个变量的结果。结果发现样本765例中，女性341例（44.6%），平均（SD）年龄42.7（12.9）岁。共发现5个亚组，分别为情感性精神病（n = 252）、自杀性精神病（n = 44）、抑制性精神病（n = 131）、高功能精神病（n = 252）和重度精神病（n = 86）。在精神病症状（R-2 = 0.41; 95% CI, 0.38-0.44）、抑郁症状（R-2 = 0.28; 95% CI, 0.25-0.32）、整体功能（R-2 = 0.16; 95% CI, 0.14-0.20）和生活质量（R-2 = 0.20; 95% CI, 0.17-0.23）中发现了具有显著二次交互项的病程。抑郁症和严重精神病亚组表现出最低的功能和二次病程，部分恢复后再次出现严重疾病。在教育程度多基因得分方面存在差异（mean [SD] partial eta(2) = 0.014[0.003]），但在诊断多基因风险方面没有差异。结果在验证队列中基本重复。结论和相关性精神病亚组检测具有独特的临床特征和病程和特异性的非诊断性遗传标记。新的数据驱动的临床方法对未来的精神病分类很重要。研究结果表明，有必要考虑短期到中期的服务提供，以恢复抑郁症和严重精神病亚组患者的功能。
This cohort study aims to detect psychosis subgroups and examine their illness courses over 1.5 years and their polygenic scores for schizophrenia, bipolar disorder, major depression disorder, and educational achievement.Question Will data-driven clustering using high-dimensional clinical data reveal psychosis subgroups with relevance to prognoses and polygenic risk? Findings In this cohort study including 1223 individuals, in the discovery sample of 765 individuals with predominantly bipolar and schizophrenia diagnoses, 5 subgroups were detected with different clinical signatures, illness trajectories, and genetic scores for educational attainment. Results were validated in a sample of 458 individuals. Meaning New data-driven clustering paired with rigorous validation may offer a means to extend symptom-based psychosis taxonomies toward functional outcomes, genetic markers, and trajectory-based stratifications.Importance Identifying psychosis subgroups could improve clinical and research precision. Research has focused on symptom subgroups, but there is a need to consider a broader clinical spectrum, disentangle illness trajectories, and investigate genetic associations. Objective To detect psychosis subgroups using data-driven methods and examine their illness courses over 1.5 years and polygenic scores for schizophrenia, bipolar disorder, major depression disorder, and educational achievement. Design, Setting, and Participants This ongoing multisite, naturalistic, longitudinal (6-month intervals) cohort study began in January 2012 across 18 sites. Data from a referred sample of 1223 individuals (765 in the discovery sample and 458 in the validation sample) with DSM-IV diagnoses of schizophrenia, bipolar affective disorder (I/II), schizoaffective disorder, schizophreniform disorder, and brief psychotic disorder were collected from secondary and tertiary care sites. Discovery data were extracted in September 2016 and analyzed from November 2016 to January 2018, and prospective validation data were extracted in October 2018 and analyzed from January to May 2019. Main Outcomes and Measures A clinical battery of 188 variables measuring demographic characteristics, clinical history, symptoms, functioning, and cognition was decomposed using nonnegative matrix factorization clustering. Subtype-specific illness courses were compared with mixed models and polygenic scores with analysis of covariance. Supervised learning was used to replicate results in validation data with the most reliably discriminative 45 variables. Results Of the 765 individuals in the discovery sample, 341 (44.6%) were women, and the mean (SD) age was 42.7 (12.9) years. Five subgroups were found and labeled as affective psychosis (n = 252), suicidal psychosis (n = 44), depressive psychosis (n = 131), high-functioning psychosis (n = 252), and severe psychosis (n = 86). Illness courses with significant quadratic interaction terms were found for psychosis symptoms (R-2 = 0.41; 95% CI, 0.38-0.44), depression symptoms (R-2 = 0.28; 95% CI, 0.25-0.32), global functioning (R-2 = 0.16; 95% CI, 0.14-0.20), and quality of life (R-2 = 0.20; 95% CI, 0.17-0.23). The depressive and severe psychosis subgroups exhibited the lowest functioning and quadratic illness courses with partial recovery followed by reoccurrence of severe illness. Differences were found for educational attainment polygenic scores (mean [SD] partial eta(2) = 0.014 [0.003]) but not for diagnostic polygenic risk. Results were largely replicated in the validation cohort. Conclusions and Relevance Psychosis subgroups were detected with distinctive clinical signatures and illness courses and specificity for a nondiagnostic genetic marker. New data-driven clinical approaches are important for future psychosis taxonomies. The findings suggest a need to consider short-term to medium-term service provision to restore functioning in patients stratified into the depressive and severe psychosis subgroups.