Gowin Joshua L, Sloan Matthew E, Morris James K, Schwandt Melanie L, Diazgranados Nancy, Ramchandani Vijay A
Department of Radiology, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.
Laboratory on Human Psychopharmacology, National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, United States.
Front Psychol. 2021 Oct 20;12:750395. doi: 10.3389/fpsyg.2021.750395. eCollection 2021.
High-intensity binge drinking, defined as consuming 2-3 times the level of a binge (4 or 5 drinks for women or men), increases the risks of overdose and alcohol-related cancer relative to lower levels of drinking. This study examined the relationship between high-intensity binge drinking and three domains hypothesized to contribute to alcohol use disorder (AUD): incentive salience, negative emotionality, and executive function. This cross-sectional study at the National Institute on Alcohol Abuse and Alcoholism examined 429 adults with AUD and 413 adults without a history of AUD. Drinking was assessed using the 90-day Timeline Followback interview. The AUD sample was divided into training and testing sets, and a machine learning model was generated in the training set and then applied to the testing set, to classify individuals based on if they had engaged in high-intensity binge drinking. We also conducted regression models for the following dependent variables: the presence of high-intensity binge drinking, frequency of high-intensity binge drinking, and number of drinks per of binge. Independent variables in these regression models were determined by variable selection from the machine learning algorithm and included time thinking about alcohol, depression rating, and positive urgency as representative variables for the three domains. These variables were assessed using self-report measures. The models were applied to the adults without a history of AUD to determine generalizability. The machine learning algorithm displayed reasonable accuracy when classifying individuals as high-intensity binge drinkers (area under ROC=0.74, 95% CI 0.67, 0.80). In adults with AUD, greater depression rating (OR=1.04, 95% CI 1.01, 1.070) and amount of time thinking about alcohol (OR=1.48, 95% CI 1.20, 1.91) were associated with greater likelihood of high-intensity binge drinking. They were also associated with greater frequency of high-intensity binge drinking days and greater number of drinks on binge occasions. Our findings suggest that incentive salience may contribute to high-intensity binge drinking in both controls and individuals with AUD. Negative emotionality was only associated with high-intensity binge drinking in individuals diagnosed with AUD, suggesting that it may be a consequence rather than a cause of high-intensity binge drinking.
高强度狂饮被定义为饮酒量达到狂饮水平(女性或男性一次饮用4或5杯酒)的2至3倍,与较低饮酒水平相比,会增加过量饮酒和酒精相关癌症的风险。本研究考察了高强度狂饮与假设会导致酒精使用障碍(AUD)的三个方面之间的关系:动机显著性、负性情绪和执行功能。美国国立酒精滥用与酒精中毒研究所开展的这项横断面研究,对429名患有酒精使用障碍的成年人和413名无酒精使用障碍病史的成年人进行了调查。饮酒情况通过90天时间线追溯访谈进行评估。将患有酒精使用障碍的样本分为训练集和测试集,在训练集中生成一个机器学习模型,然后应用于测试集,以便根据个体是否有高强度狂饮行为进行分类。我们还针对以下因变量进行了回归模型分析:高强度狂饮行为的存在情况、高强度狂饮的频率以及每次狂饮的饮酒量。这些回归模型中的自变量通过机器学习算法进行变量选择确定,包括思考酒精的时间、抑郁评分以及积极紧迫感,作为这三个方面的代表性变量。这些变量通过自我报告测量方法进行评估。将这些模型应用于无酒精使用障碍病史的成年人,以确定其普遍性。在将个体分类为高强度狂饮者时,机器学习算法显示出合理的准确性(ROC曲线下面积=0.74,95%置信区间0.67,0.80)。在患有酒精使用障碍的成年人中,更高的抑郁评分(比值比=1.04,95%置信区间1.01,1.070)和思考酒精的时间(比值比=1.48,95%置信区间1.20,1.91)与高强度狂饮的可能性增加相关。它们还与高强度狂饮天数的增加频率以及狂饮场合饮酒量的增加相关。我们的研究结果表明,动机显著性可能在对照组和患有酒精使用障碍的个体中都对高强度狂饮有影响。负性情绪仅与被诊断患有酒精使用障碍的个体的高强度狂饮相关,这表明它可能是高强度狂饮的结果而非原因。