Battista Jillian T, Vidrascu Elena, Robertson Madeline M, Robinson Donita L, Boettiger Charlotte A
Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Bowles Center for Alcohol Studies, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Alcohol Clin Exp Res (Hoboken). 2025 Mar;49(3):564-572. doi: 10.1111/acer.15534. Epub 2025 Feb 22.
Hazardous use of alcohol is associated with cognitive-behavioral impairments and accelerated aging. To date, however, accelerated brain aging has not been tested as a mediating factor between alcohol use and associated task-based behavioral deficits, such as behavioral inflexibility. Here, we evaluated hazardous alcohol use as a predictor of machine learning-derived brain aging and tested if this measure accounted for the relationship between hazardous alcohol use and a task-based measure of behavioral flexibility.
In this secondary analysis, we applied brainageR, a machine learning algorithm, to anatomical T1-weighted magnetic resonance imaging (MRI) images to estimate brain age for a sample of healthy adults (ages 22-40) who self-reported alcohol use with the alcohol use disorder identification test (AUDIT) and performed the hidden association between images task (HABIT), a behavioral flexibility task. Behavioral inflexibility was quantified as the proportion of perseverative errors performed on the HABIT as a measure of habitual action selection. We then analyzed AUDIT score as a predictor of brain aging, and brain aging as a predictor of behavioral inflexibility. Lastly, we conducted a mediation analysis to evaluate brain aging as a mediator between alcohol use and behavioral inflexibility.
Controlling for chronological age and sex, a higher AUDIT score predicted significantly more accelerated brain aging, which was further associated with more perseverative errors on the HABIT. Moreover, brain aging significantly mediated the association between AUDIT scores and behavioral inflexibility.
Our findings demonstrate that alcohol use is a significant predictor of accelerated brain aging, even in young adulthood. In addition, our findings suggest that such brain changes may mechanistically link more hazardous alcohol use to impaired behavioral flexibility. Future studies should also explore factors, such as other lifestyle behaviors, that may mitigate alcohol- and age-related processes.
有害饮酒与认知行为障碍及加速衰老有关。然而，迄今为止，加速脑衰老尚未作为饮酒与相关基于任务的行为缺陷（如行为不灵活性）之间的中介因素进行测试。在此，我们评估了有害饮酒作为机器学习得出的脑衰老预测指标，并测试了该指标是否能解释有害饮酒与基于任务的行为灵活性指标之间的关系。
在这项二次分析中，我们将机器学习算法brainageR应用于解剖学T1加权磁共振成像（MRI）图像，以估计一组健康成年人（年龄在22 - 40岁之间）的脑龄，这些成年人通过酒精使用障碍识别测试（AUDIT）自我报告饮酒情况，并完成了图像隐藏关联任务（HABIT），这是一项行为灵活性任务。行为不灵活性被量化为在HABIT任务中持续性错误的比例，作为习惯性动作选择的一种度量。然后，我们分析AUDIT分数作为脑衰老的预测指标，以及脑衰老作为行为不灵活性的预测指标。最后，我们进行了中介分析，以评估脑衰老作为饮酒与行为不灵活性之间的中介因素。
在控制了实际年龄和性别后，较高的AUDIT分数显著预测了更严重的脑加速衰老，这进一步与HABIT任务中更多的持续性错误相关。此外，脑衰老显著中介了AUDIT分数与行为不灵活性之间的关联。
我们的研究结果表明，即使在年轻成年期，饮酒也是脑加速衰老的一个重要预测指标。此外，我们的研究结果表明，这种脑变化可能在机制上将更多有害饮酒与行为灵活性受损联系起来。未来的研究还应探索其他因素，如其他生活方式行为，这些因素可能减轻与酒精和年龄相关的过程。
