A diagnostic plasma protein panel for alcohol abuse

描述（由申请人提供）：酗酒和酗酒仍然是重大的社会问题。不幸的是，有害酒精消费的治疗和监测受到缺乏高度准确的饮酒行为诊断测试的阻碍。我们这个研究项目的长期目标是开发用于高危人群的酒精滥用生物标志物。特别是，我们已经确定了高度敏感和特异性的血浆蛋白生物标志物使用良好控制的非人灵长类动物模型的酒精滥用。我们建议，在这个应用程序中，将这些发现从非人灵长类动物转化为潜在的诊断工具，用于监测人类的酒精滥用。这将与世界各地的三个临床研究中心合作进行。具体而言，我们将从科茨维尔退伍军人事务医疗中心、耶鲁大学医学院和芬兰健康与福利研究所（赫尔辛基，芬兰）获得去识别临床样本。与本研究计划相关的研究将分为两个具体目标。具体目标1将验证一种新的血浆生物标志物面板在恢复酗酒者。为此，我们的临床合作者（来自耶鲁大学和Coatesville VAMC）将提供匿名的受试者内纵向样本，这些样本来自戒断和早期戒酒（21至28天）的受试者以及普通人群。这些注释良好的样本将用于完善27种生物标志物的列表，以确定对人类饮酒表型和戒断/禁欲最具诊断性的集合。这些实验将采用针对人分析物设计的多重定量液相免疫测定法（Myriad RBM DiscoveryMAP v1.0）。与所有生物标志物发现项目一样，我们的主要重点将是鉴定具有高灵敏度和特异性的血浆蛋白分析物组。然后，具体目标2将完善血浆生物标志物组，以允许在来自多个地点的横截面人群中诊断危险饮酒。饮酒行为的受试者内评估（目标#1）要容易得多，因为该测试不比较不同的基线生理学。对一般人群筛查的要求要严格得多，将在三个独立地点（耶鲁大学、科茨维尔宾夕法尼亚州退伍军人管理局医疗中心和芬兰国家卫生和福利研究所）的样本中进行评估。这些网站提供了大量的样本和饮酒行为，从中可以得出实验组。总之，我们认为这项工作是创新的，因为它通过识别新的靶标来利用生物标志物开发中的新概念。具体的目标将建立在先前的成功，在非人灵长类动物模型的特征人类样本。所有拟议的研究都将涉及与饮酒的正常临床和心理社会评估（AUDIT自我报告; CDT、AST/ALT、GGT等）的相关性。此外，由于我们没有使用单一的生物标志物测量，我们将应用复杂的生物统计方法（随机森林，支持向量机，主成分分析）进行数据分析。

DESCRIPTION (provided by applicant): Alcohol abuse and alcoholism remain significant societal problems. Unfortunately, treatment for and monitoring of harmful alcohol consumption is hampered by the lack of a highly accurate diagnostic test of alcohol drinking behavior. Our long-term goal for this research project is to develop biomarkers of alcohol abuse for use in at- risk populations. In particular, we have identified highly sensitive and specific plasma protein biomarkers using a well-controlled nonhuman primate model of alcohol abuse. We propose, in this application to translate these findings from the nonhuman primate into potential diagnostic tools for monitoring alcohol abuse in humans. This will be undertaken in collaboration with three clinical sites around the world. Specifically, we will obtain de- identified clinical samples from he Coatesville Veterans Affairs Medical Center, the Yale University School of Medicine, and the Institute for Health and Welfare of Finland (Helsinki, Finland). Studies associated with this research program will be divided into two specific aims. Specific aim 1 will validate a novel plasma biomarker panel in recovering alcoholics. In this aim, our clinical collaborators (from Yale and the Coatesville VAMC) will provide anonymous, within-subject longitudinal samples from subjects undergoing withdrawal and early abstinence (21 to 28 days) from abusive drinking as well as from the general population. These well-annotated samples will be used to refine the list of 27 biomarkers to identify the set that is most diagnostic of the human drinking phenotype and withdrawal/ abstinence. These experiments will employ a multiplex quantitative solution-phase immunoassay designed for human analytes (Myriad RBM DiscoveryMAP v1.0). As with all biomarker discovery projects, our primary emphasis will be on identifying a plasma protein analyte panel with high sensitivity and specificity. Specific aim 2 will then refine the plasma biomarker panel to permit diagnosis of hazardous drinking in a cross-sectional population from multiple sites. Within- subject assessment of drinking behavior (Aim #1) is much easier because the test does not compare differing baseline physiologies. Requirements for general population screening are much more stringent and will be assessed in samples from three independent sites (Yale University, Coatesville PA Veterans Administration Medical Center and the Finland National Institute for Health and Welfare). These sites provide a rich number of samples and drinking behaviors from which to draw experimental cohorts. In summary, we believe this work is innovative in that it capitalizes on new concepts in biomarker development through the identification of novel targets. The specific aims will build on preceding successes in the nonhuman primate model by characterization of human samples. All of the proposed studies will involve correlation with normal clinical and psychosocial assessments of alcohol consumption (AUDIT self-reports; percentCDT, AST/ALT, GGT, etc.). In addition, because we are not using unitary biomarker measures, we will apply sophisticated biostatistical approaches (random forest, support vector machine, principle component analysis) to the data analysis.