Personalized relapse prediction in Alcohol Use Disorder

酒精使用障碍的个性化复发预测

• 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
• 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

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.

项目总结 背景：酒精使用障碍(AUD)的终生患病率接近30%，美国有1440万成年人 美国目前需要治疗。即使接受了治疗，50%-80%的人也会在一年内复发。 复苏背后的机制仍不清楚，特别是潜在的个体差异 复发风险。初步数据：其他人的工作和我们的初步数据支持至少 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)执行控制。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)，我们证明了酒精滥用的最高预测因素是独立的， 这三个领域模型的附加因素。慢性多药依赖者亚型的初步分析 (N=40；75%男性)和过去有AUD的个人(N=74；32%男性)，证明了数据驱动的机器 学习方法可以用来研究这些多因素损伤的个体差异。我们发现 AUD中有三种不同的“亚型”：“奖赏饮酒者”类型(奖赏反应性增强)、“解脱饮酒者”类型 (更多的厌恶反应)和“低功能饮酒者”类型(低执行控制力)。目标和 假设：该项目在澳大利亚的近期目标是开发一种稀疏的个性化复发预测 可在治疗环境中使用的工具，用于持续跟踪随时间推移的复发风险。长期的 目标是确定复发预防干预措施是否可以个性化。基本的假设是 独立因素的不同组合是每个人维持和复发的基础。我们会 通过目标一来测试这一点：确定三个领域(奖励反应性、厌恶性)的功能个体差异 反应性、执行控制)和目标II：评估特定亚型或特定领域复发的预测能力 预测模型与AUD通用模型的对比，以确定它们各自的临床实用性。具体目标：在 目标1，我们将评估三个感兴趣领域的澳元基础上的个体差异 方法(人格、神经认知、临床评估、任务/静息fMRI脑功能) 治疗队列(N=200 AUD，治疗后2-5周，女性占40%；N=100名对照)。在目标2中，我们将 临床跟踪我们的样本(+6个月、+12个月)，并使用机器学习方法来评估这些模式 复发风险的潜在损害程度在不同的人之间有明显的不同。创新：这项研究 提供了一个系统的，多方法的评估个体的异质性在神经行为 AUD背后的机制；b)将大数据分析方法用于复发预测 AUD数据集；以及c)开发稀疏但信息量很大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 随着时间的推移，澳元的复发风险。这最终会导致个性化治疗的发展。 方法，有可能极大地改变目前的治疗格局。