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.

将醉酒的患者从急诊室放置到住院医院设置中以监测 对于大多数城市医院来说，/治疗可能的戒酒综合征（AWS）是常见的。 尽管导致这一结果的情况有所不同，但许多此类住院的贡献是 缺乏可靠的临床信息来预测患者是否可能遭受医学上严重的AWS。 因此，临床医生经常被迫不必要地违背他们的意愿住院。一个 预测谁会体验AWS的方法可以解决这一困境，改善结果，避免 不必要的患者疏远并降低医疗费用。 新开发的表观遗传技术可能能够预测AWS的可能性。在过去的5个 使用基因组广泛的方法，我们和其他人表明，大量饮酒是 与DNA甲基化状态的深刻变化有关。此外，我们最近改进了 使用这些昂贵的耗时甲基化阵列获得的签名，易于执行 潜在的临床可用数字PCR面板，高度敏感且特定于大酒精 消耗。这些面板现在正在商业上用于保险承销。但是，是否 该DNA甲基化面板或任何其他DNA甲基化面板也可能有助于确定可能性 AWS，单独或与复合自我报告/生物标志物工具（例如酒精预测）一起 戒断量表（爪子）尚不清楚。 在此高风险R21应用中，我们将测试DNA甲基化是否可以帮助当前方案 预测戒酒。具体来说，我们将征求爱荷华大学的150名科目 酒精解毒。然后，我们将使用一系列工具（包括 PAWSS，将它们静脉浮动以为甲基化研究提供生物材料，然后遵循其中的每一个 受试者确定其中哪个进入了开发AWS。最后，我们将确定DNA甲基化 这些主题的状态。我们假设DNA甲基化将预测AWS，并且 爪子和DNA甲基化预测AWS比单独测量更好。这是创新的，因为 不存在评估饮酒风险的普遍接受的生物标志物，并且使用DNA 出于这些目的的甲基化尚未测试。团队已准备好进行研究， 包括经过董事会认证的临床医生，统计学家和DNA甲基化领先的专家。机构在 该检查将基于该检查每年接纳数千名陶醉的患者。作为直接结果 这项研究我们将确定DNA甲基化的可行性，以预测AWS并收集数据以设计 一项功能强大的R01调查，专门研究了这种新方法 措施。