Predicting Alcohol Withdrawal using DNA Methylation

利用 DNA 甲基化预测酒精戒断情况

• 批准号: 10620314
• 负责人: Allan M Andersen
• 金额: $ 22.21万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-10 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10620314
• 关键词: Address; Admission activity; Alcohol consumption; Alcohol withdrawal syndrome; Alcohols; Algorithms; Biocompatible Materials; Biological Assay; Biological Markers; Board Certification; Caring; Clinical; Clinical Markers; Complex; Consumption; DNA Methylation; Data; Disease; Drug Metabolic Detoxication; Emergency Department patient; Epigenetic Process; Evaluation; Health Care Costs; Hospitalization; Hospitals; Inpatients; Institution; Insurance; Intake; Interview; Intoxication; Investigation; Iowa; Laboratories; Legal; Measurement; Measures; Medical; Methods; Methylation; Monitor; Outcome; Patient Monitoring; Patient Self-Report; Patients; Phosphatidylethanolamine; Recording of previous events; Research; Risk; Risk Assessment; Scheme; Screening for cancer; Series; Severities; Signal Transduction; Site; Structure; Techniques; Testing; Time; Triage; Universities; Urban Hospitals; Venous blood sampling; Withdrawal; alcohol prevention; alcohol use disorder; cancer type; carbohydrate-deficient transferrin; colon cancer screening; cost; design; digital; epigenetic marker; experience; genome-wide; head-to-head comparison; high risk; improved; improved outcome; innovation; instrument; medical attention; methylation testing; novel strategies; tool

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.

将中毒患者从急诊室转移到住院医院进行监测 和/治疗可能的酒精戒断综合症 (AWS) 在大多数城市医院中很常见。 尽管导致这种结果的情况各不相同，但导致许多住院治疗的一个因素是 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，临床医生常常被迫让患者住院，而且常常违背患者的意愿，而且是不必要的。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 不必要地疏远患者并降低医疗费用。 新开发的表观遗传学技术或许能够预测 AWS 发生的可能性。过去 5 多年来，我们和其他人使用全基因组方法证明，大量饮酒会导致 与DNA甲基化状态的深刻变化有关。此外，我们最近还完善了 使用这些昂贵且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数字 PCR 检测板，对重度酒精具有高度敏感性和特异性 消耗。这些面板现已用于商业保险承保。然而，无论 该 DNA 甲基化组或任何其他 DNA 甲基化组也可用于确定可能性 AWS，单独或与复合自我报告/生物标记工具（例如酒精预测）一起使用 戒断量表 (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 个被爱荷华大学录取的科目 酒精解毒。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，对它们进行静脉切开术，为甲基化研究提供生物材料，然后按照这些进行 以确定哪些人继续开发 AWS。最后，我们将确定DNA甲基化 在每个科目中的地位。我们假设 DNA 甲基化将预测 AWS，并且 PAWS 和 DNA 甲基化对 AWS 的预测效果比单独使用任何一个指标都要好。它之所以具有创新性是因为 用于评估酒精戒断风险的普遍接受的生物标志物并不存在，并且使用 DNA 用于这些目的的甲基化尚未经过测试。该团队已做好充分准备进行研究和 包括经过委员会认证的临床医生、统计学家和 DNA 甲基化方面的领先专家。该机构位于 该检查所依据的检查每年都会收治数千名醉酒患者。作为直接结果 这项研究我们将建立 DNA 甲基化来预测 AWS 的可行性并收集数据进行设计 一项强有力的 R01 调查，专门检查了这种新方法与现有方法的比较 措施。