Identifying blood-based DNA methylation biomarkers of cannabis use

项目总结/摘要 这项拟议的研究将加速基于血液的DNA甲基化（DNAm）生物标志物的发现， 大麻使用表型（终生[曾经与从未]使用以及最近和使用频率）， 8个队列中9 878人的现有数据。大麻是世界上最常用的非法药物， 在美国，45%的美国人报告终身使用，15%的人报告2017年的使用情况。 两者都是不利的（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， 对于某些临床状况的治疗益处）的效果。要了解完整的 一系列相关的健康影响，迫切需要开发能够准确量化的工具 大麻使用模式在一生中，但目前可用的生物标志物，有限的窗口， 检测，缺乏这些属性。DNAm是生物标志物开发的优秀候选者，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。作为 美国国家药物滥用研究所所长诺拉·库洛博士及其同事强调， “迫切需要反映慢性药物暴露的生物标志物.";然而，生物标志物研究“需要[s] 表观基因组学和表观转录组学的优势尚处于起步阶段”。 我们建议收集八个队列的现有数据集， 表观全基因组关联研究（EWAS）分析迄今为止的任何大麻使用表型（N = 9，878）。在 目标1，我们将确定终身大麻使用的一般DNA生物标志物（即，观察到的DNAm差异 这可能是由遗传学和/或暴露驱动的）。为了实现目标1，我们将进行EWAS荟萃分析 我们将使用惩罚回归来训练和验证多CpG 预测模型（即，DNAm生物标志物）。在目标2中，我们将确定基因驱动与基因驱动 独立于目标1，通过采取多阶段方法来区分终生大麻使用的生物标志物 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 信息丰富，一般生物标志物可能提供最大的总体预测能力，遗传学 驱动的生物标志物作为遗传研究的精细表型，以及确定性驱动的生物标志物用于评估 行为矫正对相关健康影响的可能影响。在目标3中，我们将开发通用 持续性的DNA m生物标志物（即，在最近和以前的使用者中检测到的DNA m变化），一过性 (i.e.,仅在最近的使用者中检测到DNA m变化），以及大麻使用影响的严重性。这些 生物标志物可以使大麻相关结果的更具体的未来评估成为可能（例如，不利影响 与持续的DNAm变化有关）和潜在的治疗应用（例如，为了帮助监控依从性， 如瞬时和持续DNA m变化的组合所告知的）。

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).