5/5 - Biomarkers/Biotypes, Course of Early Psychosis and Specialty Services (BICEPS)

5/5 - 生物标志物/生物型、早期精神病课程和专业服务 (BICEPS)

• 批准号: 10683289
• 负责人: BRETT A CLEMENTZ
• 金额: $ 30.2万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10683289
• 关键词: Accounting; Age Years; Biological; Biological Markers; Bipolar Disorder; Boston; Categories; Chronic; Clinic; Clinical; Cognition; Cognitive; Communities; Coordinated Specialty Care; Data; Data Collection; Diagnosis; Diagnostic; Dimensions; Disease remission; Early Intervention; Electroencephalography; Enrollment; Eye Movements; Funding; Goals; Grant; Heterogeneity; Image; Impaired cognition; Impairment; Individual; Intervention; Measures; Modeling; Multivariate Analysis; Neurobiology; Neurocognition; Neurocognitive; Neurosciences Research; Onset of illness; Outcome; Participant; Patients; Phenotype; Population; Population Characteristics; Predictive Value; Procedures; Psychoses; Psychotic Disorders; Recovery; Resources; Sampling; Schizoaffective Disorders; Schizophrenia; Schizophreniform Disorder; Services; Site; Stimulus; Structure; Subgroup; Substance abuse problem; Testing; Therapeutic Intervention; Time; Variant; biomarker development; biomarker identification; biotypes; care outcomes; care systems; clinical practice; clinical predictors; cognitive function; community setting; cost; design; early detection biomarkers; early psychosis; early satiety; follow-up; functional decline; functional outcomes; imaging biomarker; improved; improved outcome; individual variation; medical specialties; meetings; neuroimaging; outcome prediction; pharmacologic; phenotypic biomarker; prediction algorithm; prognostic value; programs; psychosocial; psychotic; recruit; response; success; therapy resistant; treatment adherence; treatment planning; treatment program

PROJECT SUMMARY There is increasing evidence that early intervention for psychosis in coordinated specialty care (CSC) services improves outcomes and lives. The outcome of early course psychosis (EP) is heterogeneous, ranging from early full recovery to treatment resistance and functional decline from the onset of illness. This heterogeneity limits our ability to predict individual level outcomes needed for treatment planning and for tailoring the type, duration and intensity of therapeutic interventions. Biomarkers as well as clinical and demographic features, early in the illness can predict outcome, but taken individually, their prognostic value is limited. Our Bipolar- Schizophrenia Network for Intermediate Phenotypes (BSNIP) consortium has recently developed, replicated and validated a biomarker (EEG, eye movement testing, and neurocognition) based categorization (Biotypes 1, 2 and 3) in a trans-diagnostic sample of cases with idiopathic psychosis (schizophrenia, schizoaffective disorder, or bipolar disorder with psychosis), ranging from 18-35 years of age. In this study, we will leverage this categorization, along with clinical and biomarker data to predict illness trajectory and outcome during follow-up at 1, 6 and 12 months in 320 EP patients across CSC clinics at the five B-SNIP sites. First, we will characterize outcome trajectories and Biotype structure in EP. Our available data indicate the Biotype structure will be the same in EP as in our large sample. Second, we will investigate the predictive value of the nine bio-factors and the three Biotypes identified by B-SNIP for symptomatic and functional outcome. We predict that the EP population will manifest distinct outcome clinical trajectories (good, intermediate and poor) and will have a Biotype structure similar to that seen in chronic psychosis subjects, i.e., Biotypes 1, 2 and 3) (hypothesis 1). Biotype-3, and Biotye-2 cases, will have the best outcomes (defined both categorically, and dimensionally, using symptomatic, cognitive and functional measures); Biotype-1 will have the worst outcomes to CSC treatment, across all target time points (hypothesis 2). Notably, Biotype-1 and Biotype-2 cases will have the same level of cognition function at baseline. Finally, we will investigate the predictive value of clinical (such as diagnosis, illness duration, substance abuse, and treatment adherence), and biomarker (including neuroimaging) features in a multi-variate model and will develop a feasible biomarker battery and predictive algorithm for application in community CSC sites nation-wide. We will thus provide to the field a means for predicting success of EP cases in CSC treatment to improve clinical practice and to enhance efficient use of available treatment resources.

项目摘要 越来越多的证据表明，在协调的专业护理中， (CSC)服务改善成果和生活。早期精神病（EP）的结局是 异质性，从早期完全恢复到治疗抵抗和功能下降 从发病开始。这种异质性限制了我们预测个体水平结果的能力 治疗计划以及调整治疗类型、持续时间和强度所需的 干预措施。生物标志物以及临床和人口统计学特征，在疾病的早期， 预测结果，但单独考虑，其预后价值有限。我们的双极- 中间表型精神分裂症网络（BSNIP）财团最近开发了， 复制并验证了基于生物标志物（EEG、眼动测试和神经认知）的 分类（生物型1，2和3）的情况下，特发性 精神病（精神分裂症、情感障碍或双相情感障碍伴精神病），范围 18-35岁。在这项研究中，我们将利用这种分类，沿着临床和 生物标志物数据，以预测疾病轨迹和结果在随访期间，在1，6和12个月， 5家B-SNIP研究中心CSC诊所的320例EP患者。首先，我们将描述结果 在EP中的轨迹和生物型结构。我们现有的数据表明，生物型结构将是 在EP中与我们的大样本中相同。第二，我们将调查九个预测值 生物因子和通过B-SNIP鉴定的三种生物型的症状和功能结果。 我们预测EP人群将表现出不同的临床结局轨迹（良好， 中度和较差），并将具有类似于慢性精神病中所见的生物型结构 受试者，即，生物型1、2和3）（假设1）。生物型-3和Biotye-2病例将具有 最佳结局（使用症状、认知和 功能指标）;在所有目标中，生物型-1将具有CSC治疗的最差结局 时间点（假设2）。值得注意的是，生物型-1和生物型-2病例将具有相同水平的 认知功能基线。最后，我们将研究临床（如 诊断、病程、药物滥用和治疗依从性）和生物标志物（包括 神经影像学）特征，并将开发一个可行的生物标志物电池， 预测算法在全国社区CSC站点中的应用。因此，我们将提供 该字段是预测CSC治疗中EP病例成功的一种手段，以改善临床实践 以及提高可用处理资源的有效利用。