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精神病亚组的预后验证研究及遗传基础探索: PsyCourse 研究。

An Investigation of Psychosis Subgroups With Prognostic Validation and Exploration of Genetic Underpinnings: The PsyCourse Study.

机构信息

Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany.

Institute of Psychiatric Phenomics and Genomics, University Hospital, Ludwig Maximilian University of Munich, Munich, Germany.

出版信息

JAMA Psychiatry. 2020 May 1;77(5):523-533. doi: 10.1001/jamapsychiatry.2019.4910.

DOI:10.1001/jamapsychiatry.2019.4910
PMID:32049274
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7042925/
Abstract

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 (R2 = 0.41; 95% CI, 0.38-0.44), depression symptoms (R2 = 0.28; 95% CI, 0.25-0.32), global functioning (R2 = 0.16; 95% CI, 0.14-0.20), and quality of life (R2 = 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 η2 = 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.

摘要

重要性

识别精神病亚组可以提高临床和研究的准确性。研究集中在症状亚组上,但需要考虑更广泛的临床谱,厘清疾病轨迹,并研究与精神分裂症、双相情感障碍、重度抑郁症和教育成就相关的遗传因素。

目的

使用数据驱动的方法检测精神病亚组,并在 1.5 年内观察其疾病进程,以及与精神分裂症、双相情感障碍、重度抑郁症和教育成就相关的多基因评分。

设计、地点和参与者:这是一项正在进行的多地点、自然主义、纵向(6 个月间隔)队列研究,于 2012 年 1 月在 18 个地点开始。研究对象为来自二级和三级医疗机构的 1223 名 DSM-IV 诊断为精神分裂症、双相情感障碍(I/II)、分裂情感障碍、分裂样障碍和短暂精神病性障碍的个体(765 名在发现样本中,458 名在验证样本中)。2016 年 9 月提取发现数据,并于 2016 年 11 月至 2018 年 1 月进行分析,2018 年 10 月提取前瞻性验证数据,并于 2019 年 1 月至 5 月进行分析。

主要结局和测量

使用非负矩阵分解聚类方法对 188 个变量的临床电池进行分解,这些变量包括人口统计学特征、临床病史、症状、功能和认知。使用混合模型比较亚组特异性疾病进程,用协方差分析比较多基因评分。使用监督学习在验证数据中使用最可靠的 45 个变量进行复制结果。

结果

在发现样本的 765 名个体中,341 名(44.6%)为女性,平均(SD)年龄为 42.7(12.9)岁。发现了 5 个亚组,分别命名为情感性精神病(n=252)、自杀性精神病(n=44)、抑郁性精神病(n=131)、高功能精神病(n=252)和严重精神病(n=86)。精神病症状(R2=0.41;95%CI,0.38-0.44)、抑郁症状(R2=0.28;95%CI,0.25-0.32)、总体功能(R2=0.16;95%CI,0.14-0.20)和生活质量(R2=0.20;95%CI,0.17-0.23)存在显著的二次交互作用项。发现抑郁和严重精神病亚组的功能和疾病进程呈二次曲线,有部分恢复,随后再次出现严重疾病。在教育程度多基因评分方面存在差异(平均[SD]偏η2=0.014[0.003]),但在诊断性多基因风险方面没有差异。结果在验证队列中得到了很大程度的复制。

结论和相关性

通过独特的临床特征和疾病进程以及非诊断性遗传标志物的特异性,检测到精神病亚组。新的数据驱动临床方法对于未来的精神病分类学非常重要。研究结果表明,需要考虑提供短期至中期服务,以恢复抑郁和严重精神病亚组患者的功能。

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