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.

项目摘要 背景：饮酒障碍（AUD）的终生患病率近30％，成年人为1440万 美国目前需要治疗。即使接受治疗，一年内有50-80％的个人中继。 恢复基础的机制仍然不太了解，特别是基本的个体差异 复发风险。初步数据：他人的工作和我们的初步数据至少支持参与 维护AUD中的三种神经行为机制：1）奖励反应性，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， 这个三域模型的加性因子。我们在慢性多毒物用户中对亚型亚型的初步分析 （n = 40; 75％男性）和过去AUD（n = 74; 32％男性）的个人证明了数据驱动的机器 学习方法可用于研究这些多因素障碍的个体差异。我们发现 AUD中的三种独特的“亚型”：“奖励饮酒者”类型（增加奖励反应性），一种“救济者”类型 （增加厌恶反应性）和“低功能饮酒者”类型（低执行控制）。目标和 假设：该项目在AUD中的直接目标是制定一个稀疏的个性化继电器预测 可以在治疗环境中使用的工具，以继续跟踪继电器风险随着时间的流逝。长期 目标是确定是否可以个性化救济预防干预措施。基本的假设是 独立因素的不同组合是每个人的AUD维护和救济。我们将 通过目标i：确定三个领域功能的个体差异（奖励反应性，厌恶性） 反应性，执行控制）和目标II：评估亚型或域特异性继电器的预测能力 预测模型与Aud-General模型，以确定其各自的临床实用性。具体目标：in AIM 1，我们将使用多个多数 方法方法（人格，神经认知，临床评估，任务/休息fMRI脑功能） 治疗队列（n = 200 AUD，治疗2-5周，女性> 40％； n = 100个对照）。在AIM 2中，我们将 在临床上遵循我们的样本（+6，+12个月）和员工机器学习方法，以评估模式是否 个体之间的障碍障碍风险的损害明显不同。创新：这项研究 提供a）神经行为中个体异质性的系统多方法评估 AUD的机制； b）大数据分析方法的应用，以救济预测 AUD数据集; c）开发稀疏但信息丰富的个性化继电器预测工具。 摘要：这项研究将为开发跟踪的个性化继电器预测工具铺平道路 随着时间的流逝，AUD的复发风险。这最终可能导致个性化治疗的发展 接近，有可能急剧改变当前的治疗景观。