Identifying blood-based DNA methylation biomarkers of cannabis use

识别大麻使用的血液 DNA 甲基化生物标志物

• 批准号: 10669580
• 负责人: Fang Fang
• 金额: $ 49.07万
• 依托单位: RESEARCH TRIANGLE INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10669580
• 关键词: Acceleration; Acute; Address; Adherence; Adverse effects; Age; American; Animal Model; Automobile Driving; Behavior Therapy; Biological Markers; Blood; Blood specimen; Bronchitis; Candidate Disease Gene; Cannabis; Chronic; Clinical; Collection; DNA Methylation; Data; Data Set; Detection; Drug Exposure; Evaluation; Frequencies; Future; Genes; Genetic; Genetic Risk; Genetic study; Health; Human; Impaired cognition; Individual; Longevity; Mediator; Mendelian randomization; Meta-Analysis; Methods; Methylation; Modeling; Modification; Monitor; National Institute of Drug Abuse; National Institute of Environmental Health Sciences; Outcome; Phenotype; Prevalence; Psychoses; Quantitative Trait Loci; Recording of previous events; Reporting; Research; Research Design; Research Personnel; Sample Size; Sampling; Sister; Sperm Count Procedure; Stress; Testing; Therapeutic; Therapeutic Effect; Time; Training; United States; Validation; Variant; biomarker development; biomarker discovery; biomarker driven; biomarker validation; candidate marker; cannabis use behavior; cohort; epigenome-wide association studies; epigenomics; epitranscriptomics; genome-wide; illicit drug use; infancy; marijuana use; marijuana use disorder; methylation biomarker; multidisciplinary; novel; predictive modeling; secondary analysis; sex; tool; urinary

PROJECT SUMMARY/ABSTRACT The proposed study will accelerate the discovery of blood-based DNA methylation (DNAm) biomarkers for cannabis use phenotypes (lifetime [ever vs. never] use and recency and frequency of use), by leveraging existing data on 9,878 individuals across eight cohorts. Cannabis is the most commonly used illicit drug in the United States, with 45% of Americans reporting lifetime use and 15% reporting past-year use in 2017. Both adverse (e.g., cannabis use disorder, cognitive impairment, bronchitis, psychosis) and beneficial (e.g., therapeutic benefits for certain clinical conditions) effects have been reported. To understand the full spectrum of associated health effects, there is an urgent need to develop tools that can accurately quantify patterns of cannabis use across a lifetime, yet currently available biomarkers, with limited windows of detection, lack these attributes. DNAm is an excellent candidate for biomarker development, as it has the potential to differentiate acute from chronic exposure and timing, duration, and frequency of exposure. As stressed by the National Institute on Drug Abuse director, Dr. Nora Volkow, and colleagues, there is an “urgent need for biomarkers that reflect chronic drug exposure ...”; yet, biomarker research that “take[s] advantage of epigenomics and epitranscriptomics is in its infancy”. We propose to assemble a collection of existing datasets across eight cohorts, enabling the largest epigenome-wide association study (EWAS) analyses of any cannabis use phenotype to date (N = 9,878). In Aim 1, we will identify general DNAm biomarkers of lifetime cannabis use (i.e., observed DNAm differences that can be driven by genetics and/or exposure). To achieve Aim 1, we will conduct an EWAS meta-analysis of lifetime cannabis use, from which we will use penalized regression to train and validate multi-CpG predictive models (i.e., DNAm biomarkers). In Aim 2, we will identify genetically- vs exposure-driven biomarkers of lifetime cannabis use, independently of Aim 1, by taking a multi-stage approach to tease apart the underlying mechanisms driving the DNAm differences. Each type of biomarker can be uniquely informative, with general biomarkers possibly providing the greatest overall predictive ability, genetically driven biomarkers as a refined phenotype for genetic studies, and exposure-driven biomarkers for evaluating the possible impact of behavior modification on related health effects. In Aim 3, we will develop general DNAm biomarkers of persistent (i.e., DNAm changes detected in both recent and former users), transient (i.e., DNAm changes detected in only recent users), and heaviness of cannabis use effects. These biomarkers can enable more specific future evaluations of cannabis-related outcomes (e.g., adverse effects related to persistent DNAm changes) and potential treatment applications (e.g., to help monitor adherence, as informed by a combination of transient and persistent DNAm changes).

项目摘要/摘要 拟议的研究将加速发现基于血液的DNA甲基化(DNaM)生物标记物 大麻使用表型(终身[从未]使用和最近使用以及使用频率)，通过利用 8个队列中9878人的现有数据。大麻是世界上最常用的非法药物 美国，45%的美国人报告终身使用，15%的人报告过去一年使用。 有害的(例如，大麻使用障碍、认知障碍、支气管炎、精神病)和有益的(例如， 某些临床情况下的治疗益处)的效果已有报道。了解完整的 因此，迫切需要开发出能够准确量化的工具 一生中大麻使用的模式，但目前可用的生物标记物，窗口有限 检测，缺乏这些属性。DNaM是开发生物标记物的极佳候选者，因为它具有 区分急性和慢性暴露的可能性以及暴露的时间、持续时间和频率。AS 国家药物滥用研究所所长诺拉·沃尔科夫博士和他的同事们强调，有一个 “对反映慢性药物暴露的生物标记物的迫切需求……”；然而，生物标记物的研究“[S] 表观基因组学和表观转录组学的优势还处于初级阶段。 我们建议将八个队列的现有数据集集合在一起，从而实现最大的 表观基因组关联研究(Ewas)分析了迄今为止使用的任何大麻的表型(N=9,878)。在……里面 目标1，我们将确定终生大麻使用的一般dNaM生物标志物(即观察到的dNaM差异 这可能是由遗传和/或暴露造成的)。为了实现目标1，我们将进行Ewas元分析 ，我们将使用惩罚回归来训练和验证多CpG 预测模型(即dNaM生物标记物)。在目标2中，我们将确定遗传驱动与暴露驱动 终身大麻生物标记物的使用，独立于目标1，通过采取多阶段方法梳理分开 驱动dNaM差异的潜在机制。每种类型的生物标志物可以是唯一的 信息性，通用生物标志物可能提供最大的总体预测能力，从基因上 驱动生物标记物作为遗传学研究的改进表型，以及暴露驱动的生物标记物用于评估 行为改变对相关健康影响的可能影响。在目标3中，我们将开发通用 DNaM生物标志物的持久性(即，在最近和以前的用户中都检测到dNaM变化)、暂时性 (即，仅在最近的使用者中检测到dNaM的变化)，以及大麻使用影响的严重性。这些 生物标志物可以使今后能够更具体地评估与大麻有关的结果(例如，不良影响 与持续的dNaM改变相关)和潜在的治疗应用(例如，帮助监测依从性， 通过瞬时和持久的dNaM变化的组合来通知)。