Personalized relapse prediction in Alcohol Use Disorder

酒精使用障碍的个性化复发预测

基本信息

批准号：
10440767
负责人：
Anna Zilverstand
金额：
$ 52.64万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-05-01 至 2028-02-29
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10440767
关键词：
Adult Alcohol abuse Alcohol dependence Alcohols Automobile Driving Behavior Big Data Methods Brain Chronic Clinical Clinical assessments Communities Data Data Set Development Drug user Emotional Enrollment Female Functional Magnetic Resonance Imaging Goals Haemophilus influenzae type b Heterogeneity Impairment Individual Individual Differences Investigation Link Machine Learning Maintenance Measures Methods Modeling Neurocognition Normal Range Pattern Personality Prevalence Recovery Relapse Rest Rewards Sampling Seminal Structure Testing Time Work addiction alcohol use disorder artificial neural network behavior prediction behavioral impairment cohort design disorder subtype drug of abuse executive function high reward high risk drinking impaired driving performance individual variation innovation interest machine learning method male neurobehavioral neuroimaging novel personalized medicine predictive modeling predictive tools preventive intervention relapse prediction relapse prevention relapse risk

项目摘要

PROJECT SUMMARY Background: Alcohol use disorder (AUD) has a lifetime prevalence of nearly 30%, with 14.4 million adults in the US currently in need of treatment. Even with treatment, 50-80% of individuals relapse within a year. Mechanisms underlying recovery are still not well understood, specifically individual differences underlying relapse risk. Preliminary data: The work of others and our preliminary data support the involvement of at least three neuro-behavioral mechanisms in the maintenance of AUD: 1) reward reactivity, 2) aversive reactivity and 3) executive control. Using a data-driven machine learning approach in a non-clinical community sample (N=1204; 46% male), we demonstrated that the top predictors of alcohol abuse constituted independent, additive factors of this three-domain model. Our preliminary analyses on subtyping in chronic poly-drug users (N=40; 75% male) and individuals with past AUD (N=74; 32% male), demonstrated that data-driven machine learning approaches can be used to study individual differences in these multi-factorial impairments. We found three distinct ‘subtypes’ in AUD: a “reward drinker” type (increased reward reactivity), a “relief drinker” type (increased aversive reactivity) and a “low functioning drinker” type (low executive control). Goals and Hypothesis: The immediate goal of this project in AUD is to develop a sparse personalized relapse prediction tool that can be employed in a treatment setting to continuously track relapse risk over time. The long-term goal is to determine if relapse prevention interventions can be personalized. The underlying hypothesis is that different combinations of independent factors underlie AUD maintenance and relapse per individual. We will test this by Aim I: determining individual differences in function on three domains (reward reactivity, aversive reactivity, executive control), and Aim II: evaluating the predictive power of subtype- or domain-specific relapse prediction models versus an AUD-general model, to determine their respective clinical utility. Specific Aims: In Aim 1, we will assess individual differences underlying AUD across the three domains of interest using a multi- method approach (personality, neurocognition, clinical assessments, task/resting fMRI brain function) in a large treatment cohort (N=200 AUD, 2-5 weeks into treatment, >40% female; N=100 controls). In Aim 2, we will follow our sample clinically (+6, +12 months) and employ machine learning methods to evaluate if the patterns of impairments underlying relapse risk are distinctly different between individuals. Innovation: This study provides a) a systematic, multi-method assessment of the individual heterogeneity in the neuro-behavioral mechanisms underlying AUD; b) an application of big data analytical approaches for relapse prediction to an AUD dataset; and c) the development of sparse yet highly informative personalized relapse prediction tools. Summary: This study will pave the way for the development of personalized relapse prediction tools that track relapse risk in AUD over time. This can ultimately lead to the development of personalized treatment approaches, with the potential to dramatically transform the current treatment landscape.

项目概要背景：酒精使用障碍 (AUD) 的终生患病率接近 30%，有 1,440 万名成年人患有酒精使用障碍 (AUD)。美国目前需要治疗，即使接受治疗，50-80%的人也会在一年内复发。恢复的机制仍不清楚，特别是个体的潜在差异复发风险：其他人的工作和我们的初步数据至少支持参与。维持 AUD 的三种神经行为机制：1）奖赏反应性，2）厌恶反应性和 3）在非临床社区样本中使用数据驱动的机器学习方法。（N=1204；46% 男性），我们证明酒精滥用的首要预测因素是独立的、这个三域模型的附加因素。我们对慢性多种吸毒者亚型的初步分析。（N=40；75% 男性）和过去持有 AUD 的个人（N=74；32% 男性）证明，数据驱动机器我们发现，学习方法可用于研究这些多因素损伤的个体差异。澳元的三种不同“亚型”：“奖励饮酒者”类型（奖励反应性增加）、“缓解饮酒者”类型（厌恶反应增加）和“低功能饮酒者”类型（低执行控制）。假设：该项目的近期目标（澳元）是开发稀疏个性化复发预测可在治疗环境中使用的工具，以随着时间的推移持续跟踪复发风险。目标是确定复发预防干预措施是否可以个性化。基本假设是：每个人的 AUD 维持和复发都有不同的独立因素组合。通过目标 I 进行测试：确定三个领域（奖励反应性、厌恶性）功能的个体差异反应性、执行控制）和目标 II：评估亚型或域特异性复发的预测能力预测模型与 AUD 一般模型，以确定其各自的临床效用：目标 1，我们将使用多因素评估三个感兴趣领域中 AUD 的个体差异方法方法（人格、神经认知、临床评估、任务/静息功能磁共振成像脑功能）治疗队列（N = 200 AUD，治疗 2-5 周，> 40% 女性；N = 100 名对照组）。遵循我们的临床样本（+6、+12 个月）并采用机器学习方法来评估模式是否复发风险背后的损伤在个体之间明显不同。创新：这项研究。提供a）对神经行为个体异质性进行系统的、多方法的评估 AUD 的基础机制；b) 应用大数据分析方法进行复发预测 AUD 数据集；c) 开发稀疏但信息丰富的个性化复发预测工具。摘要：这项研究将为个性化复发预测工具的开发铺平道路，该工具可跟踪随着时间的推移，澳元的复发风险最终会导致个性化治疗的发展。方法，有可能极大改变当前的治疗格局。