Predicting Alcohol Withdrawal using DNA Methylation

将醉酒患者从急诊室安置到住院环境中进行监测 和/或治疗可能的酒精戒断综合征(AWS)在大多数城市医院是常见的。 尽管导致这一结果的情况各不相同，但许多住院治疗的原因之一是 缺乏可靠的临床信息来预测患者是否可能患有严重的AWS。 正因为如此，临床医生经常被迫让病人住院，往往是违背他们的意愿，不必要的。一个 预测谁将体验AWS的方法可以解决这一困境，改善结果，避免 不必要的疏远病人，降低医疗费用。 新开发的表观遗传学技术可能能够预测AWS的可能性。在过去5年中 多年来，我们和其他人使用全基因组方法表明，大量饮酒 与DNA甲基化状态的深刻变化有关。此外，我们最近改进了 使用这些昂贵、耗时的甲基化阵列获得的签名易于执行， 具有潜在临床应用价值的高灵敏度、高特异性的重酒数字化聚合酶链式反应仪板 消费。这些面板现在正被用于商业保险承保。然而，无论是 该DNA甲基化小组或任何其他DNA甲基化小组也可用于确定可能性 单独或与酒精预测等复合自我报告/生物标记物工具一起使用 戒断规模(PAWSS)未知。 在这个高风险的R21应用中，我们将测试DNA甲基化是否可以帮助当前的方案 预测酒精戒断。具体来说，我们将征集150名被爱荷华大学录取的受试者 戒酒。然后，我们将使用一系列工具来描述每个主题的特征，包括 PAWSS，对它们进行抽血，为甲基化研究提供生物材料，然后遵循每个 受试者，以确定他们中的哪些人去开发AWS。最后，我们将确定DNA甲基化 在这些科目中的每一个中的地位。我们假设DNA甲基化将预测AWS，并且 PAWS和DNA甲基化对AWS的预测比单独使用任何一项指标都要好。它的创新之处在于 普遍接受的用于评估戒酒风险的生物标记物并不存在，DNA的使用 用于这些目的的甲基化还没有经过测试。该团队已做好充分准备进行研究和 包括董事会认证的临床医生、统计学家和DNA甲基化方面的领先专家。该机构位于 这项检查将以此为基础，每年接纳数以千计的醉酒患者。作为直接的结果 本研究将建立DNA甲基化预测AWS的可行性，并收集数据进行设计 一项强大的R01调查，专门检查这一新方法与现有方法的比较 措施。

The placement of intoxicated patients from the emergency room into inpatient hospital settings to monitor and/treat possible alcohol withdrawal syndrome (AWS) is common occurrence for most urban hospitals. Although the circumstances that lead to this outcome vary, a contributor to many of these hospitalizations is the lack of reliable clinical information to predict whether a patient is likely to suffer medically severe AWS. Because of this, clinicians are often forced to hospitalize patients, often against their will, unnecessarily. A method predicting who will experience AWS could address this predicament, improve outcomes, avoid unnecessary alienation of patients and decrease healthcare costs. Newly developed epigenetic techniques may be able to predict the likelihood of AWS. Over the past 5 years using genome wide approaches, we and other have shown that heavy alcohol consumption is associated with profound changes in DNA methylation status. Furthermore, we have recently refined the signatures obtained using these expensive time-consuming methylation arrays to an easy to perform, potentially clinically employable digital PCR panel that is highly sensitive and specific for heavy alcohol consumption. These panels are now being used commercially for insurance underwriting. However, whether this DNA methylation panel or any other DNA methylation panel could also be useful for determining likelihood of AWS, alone or together with composite self-report/biomarker tools such as the Prediction of Alcohol Withdrawal Scale (PAWSS) is unknown. In this high risk R21 application, we will test whether DNA methylation can aid current schemes for predicting alcohol withdrawal. Specifically, we will solicit 150 subjects admitted to the University of Iowa for alcohol detoxification. We will then characterize each of these subjects with a battery of tools including the PAWSS, phlebotomize them to provide biomaterial for the methylation studies, then follow each of these subjects to determine which of them went onto develop AWS. Finally, we will determine DNA methylation status in each of these subjects. We hypothesize that the DNA methylation will predict AWS and that the PAWS and DNA methylation predict AWS better than either measure alone. It is innovative because generally accepted biomarkers for assessing risk for alcohol withdrawal do not exist and the use of DNA methylation for these purposes has not been tested. The team is well prepared to conduct the research and includes board-certified clinicians, statisticians and a leading expert on DNA methylation. The institution at which the examination will be based admits thousands of intoxicated patients annually. As a direct result of this research we will establish the feasibility of DNA methylation to predict AWS and gather the data to design a well powered R01 investigation that specifically examines this new approach as compared to existing measures.