Jinwala Zeal, Green ReJoyce, Khan Yousef, Gelernter Joel, Kember Rachel L, Hartwell Emily E
Crescenz VA Medical Center, Philadelphia, PA 19104, United States; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, United States.
Medical University of South Carolina, Charleston, SC 29425, United States.
Drug Alcohol Depend. 2025 Sep 1;274:112797. doi: 10.1016/j.drugalcdep.2025.112797. Epub 2025 Jul 16.
Few individuals with alcohol use disorder (AUD) receive treatment. Previous studies have shown drinking behavior, psychological problems, and substance dependence to predict treatment seeking. However, to date, no studies have incorporated polygenic scores (PGS), a measure of genetic risk for AUD.
In a deeply-phenotyped sample, we identified 9103 individuals diagnosed with DSM-IV AUD and indicated treatment-seeking status. We implemented a random forest (RF) model to predict treatment-seeking based on 91 clinically relevant phenotypes. We calculated AUD PGS for those with genetic data (African ancestry [AFR] n = 3192, European ancestry [EUR] n = 3553) and generated RF models for each ancestry group, first without and then with PGS. Lastly, we developed models stratified by age (< and ≥40 years old).
66.6 % reported treatment seeking (M=40.0, 62.4 % male). Across models, top predictors included years of alcohol use and related psychological problems, psychiatric diagnoses, and heart disease. In the models without PGS, we found 77.6 % accuracy and 0.829 AUC for EUR and 75.1 % and 0.770 for AFR; the addition of PGS did not substantially change these metrics. PGS was the 9th most important predictor for EUR and 28th for AFR. In the age-stratified analysis, PGS ranked 8th for < 40 and 34th for ≥ 40 in EUR ancestry, and it ranked 70th for < 40 and 78th for ≥ 40 in the AFR sample.
Alcohol use, psychiatric issues, and comorbid medical disorders were predictors of treatment seeking. Incorporating PGS did not substantially alter performance, but was a more important predictor in younger individuals with AUD.
很少有酒精使用障碍（AUD）患者接受治疗。先前的研究表明，饮酒行为、心理问题和物质依赖可预测治疗寻求情况。然而，迄今为止，尚无研究纳入多基因评分（PGS），这是一种衡量AUD遗传风险的指标。
在一个深度表型样本中，我们识别出9103名被诊断为DSM-IV AUD并表明治疗寻求状态的个体。我们实施了一个随机森林（RF）模型，以基于91种临床相关表型预测治疗寻求情况。我们为有遗传数据的个体（非洲血统[AFR] n = 3192，欧洲血统[EUR] n = 3553）计算了AUD PGS，并为每个血统组生成了RF模型，首先不使用PGS，然后使用PGS。最后，我们开发了按年龄分层（<40岁和≥40岁）的模型。
66.6%的人报告寻求治疗（M = 40.0，62.4%为男性）。在所有模型中，最重要的预测因素包括饮酒年限和相关心理问题、精神疾病诊断以及心脏病。在不使用PGS的模型中，我们发现欧洲血统组的准确率为77.6%，AUC为0.829，非洲血统组的准确率为75.1%，AUC为0.770；添加PGS并没有显著改变这些指标。PGS在欧洲血统组中是第9重要的预测因素，在非洲血统组中是第28重要的预测因素。在年龄分层分析中，在欧洲血统中，PGS在<40岁组中排名第8，在≥40岁组中排名第34；在非洲血统样本中，它在<40岁组中排名第70，在≥40岁组中排名第78。
饮酒、精神问题和合并的医学疾病是治疗寻求的预测因素。纳入PGS并没有显著改变模型性能，但在年轻的AUD患者中是一个更重要的预测因素。
