Mood State Blood Biomarkers: A Discovery-Based Approach

情绪状态血液生物标志物：基于发现的方法

• 批准号: 7797060
• 负责人: Alexander B Niculescu
• 金额: --
• 依托单位: RLR VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-01 至 2012-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7797060
• 关键词: Adverse effects; Affect; Alcoholism; Algorithms; Animal Model; Animals; Area; Autopsy; Biological; Biological Markers; Bipolar Depression; Bipolar Disorder; Blood; Blood Tests; Blood specimen; Brain; Candidate Disease Gene; Caring; Cell Culture Techniques; Clinical; Clinical Treatment; Clinical assessments; Complex; Control Groups; Data; Data Set; Detection; Development; Diagnosis; Diagnostic and Statistical Manual; Disease; Early treatment; Environmental Exposure; Etiology; Family member; Foundations; Future; Gene Expression; Gene Expression Profiling; Genes; Genetic; Genetic Heterogeneity; Genetic Polymorphism; Goals; Growth Factor; Harvest; Health; Health Care Costs; Healthcare; Hospitalization; Human; Human Genetics; Human Herpesvirus 4; Impaired cognition; Individual; Inflammatory; Interview; Investigation; Knowledge; Laboratories; Lead; Linkage Disequilibrium Mapping; Lymphocyte; Measurement; Measures; Mental disorders; Meta-Analysis; Methodology; Methods; Modality; Modeling; Mood Disorders; Moods; Morphologic artifacts; Nature; Neurobiology; Paper; Pathway interactions; Patient Self-Report; Patients; Peripheral; Personal Satisfaction; Pharmaceutical Preparations; Pharmacogenomics; Pilot Projects; Post-Traumatic Stress Disorders; Predictive Value; Predictive Value of Tests; Probability; Public Domains; Publishing; Quality of life; Relapse; Reliance; Research; Resources; Risk; Safety; Sampling; Schizophrenia; Sensitivity and Specificity; Series; Severities; Signal Transduction; Source; Specificity; Study Section; Symptoms; Testing; Time; Transgenic Organisms; Validation; Veterans; Visit; Whole Blood; Work; abstracting; base; cohort; data modeling; drug development; functional genomics; genetic association; genetic linkage; genetic linkage analysis; genetic manipulation; genome wide association study; human disease; human tissue; immune activation; impression; improved; information gathering; interest; lymphoblastoid cell line; male; myelination; neuropsychiatry; peripheral blood; positional cloning; prevent; public health relevance; response; success; trait; translational approach; true biomarker

DESCRIPTION (provided by applicant): Abstract: There are to date no clinical laboratory blood tests for mood disorders. Given the complex nature of mood disorders, the current reliance on patient self-report of symptoms and the clinician's impression on interview of patient is a rate limiting step in delivering the best possible care with existing treatment modalities, as well as in developing new and improved treatment approaches, including new medications. We propose, and provide proof of principle for, an approach to help identify state blood biomarkers for mood state. Such biomarkers can serve as a basis for objective clinical laboratory tests. Our approach is based on the integration of human and animal model data, as a way of reducing the false-positives inherent in each approach and helping identify true biomarker molecules. We propose to measure gene expression differences in fresh blood samples from subjects with DIGS- diagnosed bipolar disorders that have low mood scores vs. those that have high mood scores at the time of the blood draw. We will then integrate our human blood gene expression data with human postmortem data, human genetic linkage/association data and animal model gene expression data (using data from two different animal models studied in our group, one pharmacogenomic and one transgenic), an approach called Convergent Functional Genomics, as a Bayesian strategy for cross-validating and prioritizing findings. In pilot studies for this project, we have identified a series of high probability blood candidate biomarker genes for mood state that deserve future scrutiny. Topping our list of candidate biomarker genes for mood state we have five genes involved in myelination (Mbp, Edg2, Mag, Pmp22 and Ugt8), and six genes involved in growth factor signaling (Fgfr1, Fzd3, Erbb3, Igfbp4, Igfbp6, and Ptprm). All of these genes have prior evidence of differential expression in human postmortem brains from mood disorder subjects. A predictive score developed based on a panel of ten top candidate biomarkers (five for high mood, five for low mood) shows sensitivity and specificity for high mood and low mood states, in two independent cohorts. Our preliminary studies suggest that blood biomarkers may offer an unexpectedly informative window into brain functioning and disease state, specifically in regards to mood disorders. This exciting preliminary work needs to be carefully extended and replicated in a larger independent cohort, as well as compared to normal controls to derive additional normative data, which is what we propose to do in this project. PUBLIC HEALTH RELEVANCE: Potential Impact on Veterans Health Care: A more objective assessment of mood state with biomarker profiling will lead to more targeted treatments for veterans affected by psychiatric disorders involving mood dysregulation primarily, such as in bipolar disorder or depression, and secondarily, such as in PTSD, with improved efficacy and decreased side-effects. This will have an impact on patient health, well- being, safety, quality of life, and independent functioning, as well as decrease hospitalizations and overall health-care costs. Moreover, biomarker profiling may help with assessing response to treatment, risk of relapse, and early intervention efforts to prevent the full-blown development of illness in susceptible individuals.

描述（由申请人提供）： 翻译后摘要：迄今为止，没有临床实验室血液检测情绪障碍。鉴于情绪障碍的复杂性质，目前依赖于患者自我报告的症状和临床医生对患者访谈的印象，这是用现有治疗方式提供最佳护理以及开发新的和改进的治疗方法（包括新药物）的速度限制步骤。我们提出了一种方法来帮助识别情绪状态的血液生物标志物，并提供了原理证明。这样的生物标志物可以作为客观的临床实验室测试的基础。 我们的方法是基于人类和动物模型数据的整合，作为减少每种方法固有的假阳性和帮助识别真正的生物标志物分子的一种方式。我们建议测量来自DIGS诊断的双相情感障碍受试者的新鲜血液样本中的基因表达差异，这些受试者在抽血时具有低情绪评分与具有高情绪评分的受试者。然后，我们将整合我们的人类血液基因表达数据与人类死后数据，人类遗传连锁/关联数据和动物模型基因表达数据（使用我们小组研究的两种不同动物模型的数据，一种药物基因组学和一种转基因），一种称为融合功能基因组学的方法，作为交叉验证和优先排序发现的贝叶斯策略。 在该项目的试点研究中，我们已经确定了一系列高概率的情绪状态血液候选生物标志物基因，值得未来仔细研究。在我们的情绪状态候选生物标志物基因列表中，我们有五个基因参与髓鞘形成（Mbp，Edg 2，Mag，Pmp 22和Ugt 8），六个基因参与生长因子信号传导（Fgfr 1，Fzd 3，Erbb 3，Igfbp 4，Igfbp 6和Ptprm）。所有这些基因都有先前的证据表明，在人类死后的大脑中，情绪障碍受试者的表达存在差异。基于一组10个顶级候选生物标志物（5个用于高情绪，5个用于低情绪）开发的预测评分在两个独立的队列中显示了高情绪和低情绪状态的灵敏度和特异性。我们的初步研究表明，血液生物标志物可能为大脑功能和疾病状态提供意想不到的信息窗口，特别是在情绪障碍方面。 这项令人兴奋的初步工作需要在更大的独立队列中仔细扩展和复制，并与正常对照进行比较，以获得额外的规范性数据，这也是我们在本项目中建议做的。 公共卫生相关性： 对退伍军人医疗保健的潜在影响：用生物标志物分析对情绪状态进行更客观的评估，将为受精神疾病影响的退伍军人提供更有针对性的治疗，这些精神疾病主要涉及情绪失调，如双相情感障碍或抑郁症，其次是创伤后应激障碍，疗效提高，副作用减少。这将对患者的健康、福祉、安全、生活质量和独立功能产生影响，并减少住院和总体医疗保健费用。此外，生物标志物分析可能有助于评估治疗反应、复发风险以及早期干预措施，以防止易感个体疾病的全面发展。