A diagnostic plasma protein panel for alcohol abuse

描述（由申请人提供）：酗酒和酒精中毒仍然是严重的社会问题。不幸的是，由于缺乏对饮酒行为的高度准确的诊断测试，对有害酒精消费的治疗和监测受到阻碍。我们这个研究项目的长期目标是为高危人群开发酒精滥用的生物标志物。特别是，我们已经通过一个控制良好的非人类酒精滥用灵长类动物模型确定了高度敏感和特异性的血浆蛋白生物标志物。在这项研究中，我们建议将这些来自非人类灵长类动物的发现转化为监测人类酒精滥用的潜在诊断工具。这项工作将与世界各地的三个临床地点合作进行。具体来说，我们将从科茨维尔退伍军人事务医学中心、耶鲁大学医学院和芬兰卫生与福利研究所（芬兰赫尔辛基）获得鉴定的临床样本。与本研究计划相关的研究将分为两个具体目标。特异性目标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、受试者内部的纵向样本，这些样本来自正在戒断和早期戒酒（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个生物标志物的列表，以确定最能诊断人类饮酒表型和戒断/戒酒的一组。这些实验将采用为人类分析物设计的多重定量液相免疫分析（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确定具有高灵敏度和特异性的血浆蛋白分析物面板。然后，具体目标2将改进血浆生物标志物面板，以便在来自多个地点的横断面人群中诊断有害饮酒。受试者内部对饮酒行为的评估（目标1）要容易得多，因为测试不比较不同的基线生理。一般人群筛查的要求要严格得多，并将在三个独立地点（耶鲁大学、宾夕法尼亚州科茨维尔退伍军人管理局医学中心和芬兰国家健康与福利研究所）的样本中进行评估。这些地点提供了大量的样本和饮酒行为，从中抽取实验群体。总之，我们相信这项工作是创新的，因为它通过识别新的靶点，利用了生物标志物开发的新概念。具体的目标将建立在先前成功的非人灵长类动物模型上，通过人类样本的表征。所有拟议的研究都将涉及与正常的临床和心理社会评估的相关性（审计自我报告；cdt百分比，AST/ALT， GGT等）。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.