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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

基本信息

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
项目类别：
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

项目摘要

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.

确定预测因素和阐明精神疾病发病机制是治疗精神病的关键