在深度表型样本中使用多基因评分和机器学习预测酒精使用障碍的寻求治疗状态

IF 3.6 2区 医学 Q1 PSYCHIATRY
Zeal Jinwala , ReJoyce Green , Yousef Khan , Joel Gelernter , Rachel L. Kember , Emily E. Hartwell
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引用次数: 0

摘要

背景: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以预测寻求治疗的可能性。然而,到目前为止,还没有研究纳入多基因评分(PGS),这是AUD遗传风险的衡量标准。方法在一个深度表型样本中,我们确定了9103名诊断为DSM-IV AUD并表明寻求治疗状态的个体。我们实施了一个随机森林(RF)模型来预测基于91临床相关表型的治疗寻求。我们计算了具有遗传数据的人(非洲血统[AFR] n = 3192,欧洲血统[EUR] n = 3553)的AUD PGS,并为每个血统组生成了RF模型,首先没有PGS,然后有PGS。最后,我们建立了按年龄分层的模型(<;结果66.6%的患者报告求医问药(男性62.4%)。在所有模型中,最重要的预测因素包括多年的酒精使用和相关的心理问题、精神诊断和心脏病。在没有PGS的模型中,我们发现EUR的准确率为77.6%,AUC为0.829,AFR的准确率为75.1%,AUC为0.770;PGS的加入并没有实质性地改变这些指标。PGS是预测EUR的第九大重要指标,预测AFR的第28位。在年龄分层分析中,在欧洲血统中,PGS在40岁和40岁以上人群中排名第8,在40岁和40岁以上人群中排名第34,在AFR样本中,PGS在40岁和40岁以上人群中排名第70,在40岁和40岁以上人群中排名第78。结论酒精使用、精神问题和合并症是寻求治疗的预测因素。合并PGS并没有实质性地改变表现,但在年轻AUD患者中是更重要的预测因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting treatment-seeking status for alcohol use disorder using polygenic scores and machine learning in a deeply-phenotyped sample

Background

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.

Methods

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).

Results

66.6 % reported treatment seeking (Mage=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.

Conclusion

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.
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来源期刊
Drug and alcohol dependence
Drug and alcohol dependence 医学-精神病学
CiteScore
7.40
自引率
7.10%
发文量
409
审稿时长
41 days
期刊介绍: Drug and Alcohol Dependence is an international journal devoted to publishing original research, scholarly reviews, commentaries, and policy analyses in the area of drug, alcohol and tobacco use and dependence. Articles range from studies of the chemistry of substances of abuse, their actions at molecular and cellular sites, in vitro and in vivo investigations of their biochemical, pharmacological and behavioural actions, laboratory-based and clinical research in humans, substance abuse treatment and prevention research, and studies employing methods from epidemiology, sociology, and economics.
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