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 有越来越多的证据表明，早期干预精神病协调专业护理 （CSC）服务改善了成果和生活。早期精神病（EP）的结果是 异构性，从早期完全恢复到治疗耐药性和功能下降 从疾病开始。这种异质性限制了我们预测个人水平结果的能力 需要进行治疗计划和剪裁治疗的类型，持续时间和强度 干预措施。生物标志物以及临床和人群特征，在疾病的早期可以 预测结果，但单独进行，其预后价值是有限的。我们的双极性 用于中间表型（BSNIP）联盟的精神分裂症网络最近已开发 基于复制并验证了生物标志物（EEG，眼动测试和神经认知） 在特发性病例的反诊断样本中分类（生物型1、2和3） 精神病（精神分裂症，精神分裂症或精神病的躁郁症），范围 从18-35岁开始。在这项研究中，我们将利用这种分类以及临床和 生物标志物数据以预测1、6和12个月的随访期间的疾病轨迹和结果 五个B-SNIP部位的CSC诊所中的320名EP患者。首先，我们将表征结果 EP中的轨迹和生物型结构。我们的可用数据表明生物型结构将是 EP在我们的大样本中一样。其次，我们将研究九个的预测值 B-SNIP鉴定出有症状和功能性结果的生物因子和三种生物型。 我们预测，EP人群将表现出不同的结果临床轨迹（良好， 中级和差），并且具有类似于慢性精神病的生物型结构 受试者，即生物型1、2和3）（假设1）。 Biotype-3和Biotye-2病例将具有 最佳结果（使用症状，认知和尺寸定义，并在尺寸上定义 功能度量）；生物型1将在所有目标中都具有最差的CSC处理结果 时间点（假设2）。值得注意的是，生物型-1和生物型2病例的水平相同 基线的认知功能。最后，我们将研究临床的预测价值（例如 诊断，疾病持续时间，药物滥用和治疗依从性）和生物标志物（包括 神经影像学）在多变量模型中的功能，并将开发可行的生物标志物电池和 用于在全国范围内应用社区CSC网站应用的预测算法。因此，我们将提供 该领域是预测EP病例在CSC治疗中成功以改善临床实践的手段 并增强有效利用可用的治疗资源。