Department of Psychology, University of California Los Angeles, Los Angeles, California, USA.
Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA.
Addict Biol. 2021 May;26(3):e12949. doi: 10.1111/adb.12949. Epub 2020 Jul 29.
Given the significant cost of alcohol use disorder (AUD), identifying risk factors for alcohol seeking represents a research priority. Prominent addiction theories emphasize the role of motivation in the alcohol seeking process, which has largely been studied using preclinical models. In order to bridge the gap between preclinical and clinical studies, this study examined predictors of motivation for alcohol self-administration using a novel paradigm. Heavy drinkers (n = 67) completed an alcohol infusion consisting of an alcohol challenge (target breath alcohol = 60 mg%) and a progressive-ratio alcohol self-administration paradigm (maximum breath alcohol 120 mg%; ratio requirements range = 20-3 139 response). Growth curve modeling was used to predict breath alcohol trajectories during alcohol self-administration. K-means clustering was used to identify motivated (n = 41) and unmotivated (n = 26) self-administration trajectories. The data were analyzed using two approaches: a theory-driven test of a-priori predictors and a data-driven, machine learning model. In both approaches, steeper delay discounting, indicating a preference for smaller, sooner rewards, predicted motivated alcohol seeking. The data-driven approach further identified phasic alcohol craving as a predictor of motivated alcohol self-administration. Additional application of this model to AUD translational science and treatment development appear warranted.
鉴于酒精使用障碍（AUD）的巨大成本，确定酒精寻求的风险因素代表了研究重点。突出的成瘾理论强调了动机在酒精寻求过程中的作用，这在很大程度上是通过临床前模型研究的。为了弥合临床前和临床研究之间的差距，本研究使用新的范式检查了酒精自我给药动机的预测因素。大量饮酒者（n = 67）完成了酒精输注，包括酒精挑战（目标呼气酒精 = 60 mg%）和递增比率酒精自我给药范式（最大呼气酒精 120 mg%；比率要求范围 = 20-3 139 次反应）。增长曲线模型用于预测酒精自我给药期间的呼气酒精轨迹。K-均值聚类用于识别有动机（n = 41）和无动机（n = 26）自我给药轨迹。使用两种方法分析数据：预先预测因素的理论驱动测试和数据驱动的机器学习模型。在这两种方法中，延迟折扣越陡峭，表明对较小、较早的奖励的偏好越大，这表明有动机的酒精寻求。数据驱动的方法进一步确定了阵发性酒精渴望是有动机的酒精自我给药的预测因素。该模型在 AUD 转化科学和治疗开发中的进一步应用似乎是合理的。
