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安置到住院医院进行监测 和/或治疗可能的酒精戒断综合征（AWS）是大多数城市医院的常见事件。 虽然导致这种结果的情况各不相同，但许多住院治疗的原因是 缺乏可靠的临床信息来预测患者是否可能患有医学上严重的AWS。 正因为如此，临床医生往往被迫住院病人，往往违背他们的意愿，不必要的。一 预测谁将体验AWS的方法可以解决这一困境，改善结果，避免 减少病人的不必要的疏远，降低医疗费用。 新开发的表观遗传技术可能能够预测AWS的可能性。过去5 多年来使用全基因组方法，我们和其他人已经表明，大量饮酒是 与DNA甲基化状态的深刻变化有关。此外，我们最近还改进了 使用这些昂贵耗时的甲基化阵列获得的特征易于执行， 对重酒精高度敏感和特异的潜在的临床上可使用的数字PCR面板 消费这些面板目前正用于商业保险承保。但无论 该DNA甲基化组或任何其他DNA甲基化组也可用于确定 单独或与复合自我报告/生物标志物工具（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目前的方案， 预测酒精戒断。具体来说，我们将征集150名被爱荷华州大学录取的受试者， 酒精解毒然后，我们将用一系列工具来描述这些主题中的每一个，包括 PAWSS，采血，为甲基化研究提供生物材料，然后跟踪每一个 以确定他们中的谁去开发AWS。最后，我们将确定DNA甲基化 在每个主题中。我们假设DNA甲基化可以预测AWS， PAWS和DNA甲基化预测AWS比单独测量更好。创新是因为 普遍接受的生物标志物评估酒精戒断的风险并不存在，使用DNA 尚未测试用于这些目的的甲基化。该小组已为开展研究做好充分准备， 包括委员会认证的临床医生，统计学家和DNA甲基化的领先专家。该机构在 每年都有成千上万的中毒病人入院接受检查。直接因 本研究将建立DNA甲基化预测AWS的可行性，并收集数据设计 一项有力的R 01调查，专门研究这种新方法与现有方法的比较 措施