Identification of Distinct Biotypes in Clinical High Risk for Psychosis State Using Objective Brain-Based Biomarkers

使用客观的脑基生物标志物识别临床高风险精神病状态的不同生物型

• 批准号: 10646765
• 负责人: Walid Yassine
• 金额: $ 19.11万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10646765
• 关键词: Address; Adopted; American; Biological; Biological Markers; Brain; Categories; Chronic; Clinical; Cluster Analysis; Cognitive; Confusion; Data; Data Set; Development; Diagnosis; Diagnostic; Disease remission; Early Intervention; Electrophysiology (science); Frequencies; Functional disorder; Future; Goals; Heterogeneity; Image; Individual; Intervention; Investigation; Longitudinal Studies; Measures; Modeling; Neurobiology; Outcome; Outcome Measure; Patients; Pattern; Phase; Population; Prevention; Psychiatry; Psychopathology; Psychoses; Reporting; Research; Risk Factors; Sampling; Schizophrenia; Stratification; Subgroup; Symptoms; Syndrome; Therapeutic Intervention; Validation; Variant; Work; affective psychoses; analytical method; biological heterogeneity; biotypes; brain based; clinical heterogeneity; clinical high risk for psychosis; clinical phenotype; clinically relevant; comparative; early psychosis; follow-up; functional disability; functional outcomes; high risk; high risk population; multimodal data; multimodality; neuroimaging; non-affective psychoses; outcome prediction; phenotypic data; prevent; prospective; psychosis risk; supervised learning; treatment response

Identifying predictors and elucidating mechanisms underlying psychosis onset are critical for the development of targeted interventions. The current symptom-based clinical high risk (CHR) syndrome has been validated as an indicator of future psychosis risk but does not provide accurate predictions regarding individual clinical trajectories or a stratification model that informs outcome or treatment response. The CHR phase is diverse in terms of risk factors and outcomes, with a significant proportion of CHR converters (<30%) developing non-affective psychosis (~73%) and a smaller group developing affective psychoses (~11%). In addition, CHR non- converters have variable outcomes, ranging from full remission to ongoing multifaceted sequelae. Furthermore, substantial neurobiological heterogeneity among CHR individuals is well- documented. Despite this, little research attempted to harness the rich multimodal data collected in large-scale CHR studies to form and validate neurobiologically driven CHR subgroups. Successful parsing of the underlying heterogeneity of CHR may yield more defined subgroups with distinct clinical trajectories and outcomes. Our group’s recent studies examining neurobiological heterogeneity in psychosis spectrum using the Bipolar-Schizophrenia Network for Intermediate Phenotypes dataset, adopted a data-driven cluster analytic method to define biological clusters (or biotypes) using cognitive, and electrophysiological biomarkers agnostic to traditional symptom-based diagnostic categories. The goal was to identify neurobiologically homogeneous biotypes with presumed distinct underlying pathophysiology. However, the sample consisted of chronic psychosis subjects and thus was unable to inform the development of early intervention and prevention applications, and lacked clinically relevant longitudinal outcome data. An approach focusing on CHR to define biotypes and assess them using longitudinal clinical, and functional outcome data has yet to be attempted. The availability of prospectively characterized and deeply phenotyped CHR samples from the North American Prodrome Longitudinal Study 2 and 3 is a unique opportunity to address this question using an objective approach. In the current proposal, we will identify distinct subgroups or ‘biotypes’ for CHR using cluster analysis and will compare, evaluate and validate the resulting biotypes. This proposal will have an important impact on our understanding of how biological heterogeneity contributes to clinical outcomes in CHR and elucidates a way to characterize biological heterogeneity in this population, providing biological targets for more effective diagnosis, and early therapeutic intervention.

识别预测因素并阐明精神病发作的机制对于 制定有针对性的干预措施。当前基于症状的临床高危（CHR） 综合征已被验证为未来精神病风险的指标，但并未提供 关于个体临床轨迹或提供信息的分层模型的准确预测 结果或治疗反应。 CHR 阶段的风险因素和结果各不相同， 很大比例的 CHR 转变者 (<30%) 发展为非情感性精神病 (~73%) 和一小部分人患有情感性精神病 (~11%)。此外，CHR 非 转化者的结果各不相同，从完全缓解到持续的多方面后遗症。 此外，CHR 个体之间存在显着的神经生物学异质性， 记录在案。尽管如此，很少有研究尝试利用收集到的丰富的多模式数据 在大规模 CHR 研究中形成和验证神经生物学驱动的 CHR 亚组。 成功解析 CHR 的潜在异质性可能会产生更明确的子组 具有独特的临床轨迹和结果。我们小组最近的研究检查 使用双相精神分裂症网络研究精神病谱系的神经生物学异质性 Intermediate Phenotypes数据集，采用数据驱动的聚类分析方法来定义 使用不可知的认知和电生理生物标记的生物簇（或生物型） 传统的基于症状的诊断类别。目标是从神经生物学角度识别 具有假定的不同潜在病理生理学的同质生物型。然而，样本 由慢性精神病受试者组成，因此无法告知早期精神病的发展 干预和预防应用，缺乏临床相关的纵向结果数据。 一种专注于 CHR 的方法，用于定义生物型并使用纵向临床对其进行评估，以及 功能结果数据尚未尝试。前瞻性特征的可用性 以及来自北美 Prodrome 纵向研究 2 的深度表型 CHR 样本 3 是使用客观方法解决这个问题的独特机会。在当前 根据提案，我们将使用聚类分析来识别 CHR 的不同亚组或“生物型”，并将 比较、评估和验证所得到的生物型。该提案将产生重要影响 基于我们对生物异质性如何影响 CHR 临床结果的理解 阐明了一种表征该群体生物异质性的方法，提供了生物 更有效的诊断和早期治疗干预的目标。