2/5 Biomarkers/Biotypes, Course of Early Psychosis and Specialty Services (BICEPS)

2/5 生物标志物/生物型，早期精神病课程和专业服务 (BICEPS)

• 批准号: 10681376
• 负责人: Elliot S Gershon
• 金额: $ 27.56万
• 依托单位: UNIVERSITY OF CHICAGO
• 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/10681376
• 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.

项目概要 越来越多的证据表明，在协调专科护理 (CSC) 服务中对精神病进行早期干预 改善结果和生活。早期精神病（EP）的结果是多种多样的，从早期到 从疾病发作开始完全恢复治疗抵抗和功能下降。这种异质性限制了 我们有能力预测治疗计划和定制类型、持续时间所需的个人水平结果 和治疗干预的强度。早期的生物标志物以及临床和人口特征 疾病可以预测结果，但单独来看，其预后价值有限。我们的双相-精神分裂症 中间表型网络 (BSNIP) 联盟最近开发、复制和验证了一个 基于生物标记（脑电图、眼动测试和神经认知）的分类（生物型 1、2 和 3） 特发性精神病（精神分裂症、分裂情感性障碍或双相情感障碍）病例的跨诊断样本 精神障碍），年龄范围为 18-35 岁。在本研究中，我们将利用这种分类， 结合临床和生物标志物数据来预测 1、6 和 12 点随访期间的疾病轨迹和结果 在五个 B-SNIP 站点的 CSC 诊所的 320 名 EP 患者中进行了几个月的研究。首先，我们将描述结果 EP 中的轨迹和生物型结构。我们现有的数据表明 EP 中的 Biotype 结构将相同 就像我们的大样本一样。其次，我们将研究九个生物因素和三个因素的预测价值。 B-SNIP 识别的症状和功能结果的生物型。我们预测 EP 人口将 表现出不同的结果临床轨迹（良好、中等和较差）并且将具有生物型结构 类似于在慢性精神病受试者中观察到的情况，即生物型 1、2 和 3）（假设 1）。生物型3，和 Biotye-2 病例将获得最佳结果（使用症状、分类和维度来定义） 认知和功能测量）；在所有目标中，Biotype-1 的 CSC 治疗结果将是最差的 时间点（假设2）。值得注意的是，Biotype-1和Biotype-2病例将具有相同水平的认知功能 在基线。最后，我们将研究临床的预测价值（如诊断、病程、 药物滥用和治疗依从性）以及多变量中的生物标志物（包括神经影像学）特征 模型并将开发可行的生物标志物电池和预测算法以应用于社区 CSC 站点遍布全国。因此，我们将为该领域提供一种预测 CSC 治疗中 EP 病例成功的方法 改善临床实践并提高现有治疗资源的有效利用。