Ferguson Laura B, Mayfield R Dayne, Messing Robert O
Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin, Austin, TX, United States.
Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, TX, United States.
Front Mol Neurosci. 2022 Nov 4;15:1032362. doi: 10.3389/fnmol.2022.1032362. eCollection 2022.
Alcohol use disorder (AUD) is highly prevalent and one of the leading causes of disability in the US and around the world. There are some molecular biomarkers of heavy alcohol use and liver damage which can suggest AUD, but these are lacking in sensitivity and specificity. AUD treatment involves psychosocial interventions and medications for managing alcohol withdrawal, assisting in abstinence and reduced drinking (naltrexone, acamprosate, disulfiram, and some off-label medications), and treating comorbid psychiatric conditions (e.g., depression and anxiety). It has been suggested that various patient groups within the heterogeneous AUD population would respond more favorably to specific treatment approaches. For example, there is some evidence that so-called reward-drinkers respond better to naltrexone than acamprosate. However, there are currently no objective molecular markers to separate patients into optimal treatment groups or any markers of treatment response. Objective molecular biomarkers could aid in AUD diagnosis and patient stratification, which could personalize treatment and improve outcomes through more targeted interventions. Biomarkers of treatment response could also improve AUD management and treatment development. Systems biology considers complex diseases and emergent behaviors as the outcome of interactions and crosstalk between biomolecular networks. A systems approach that uses transcriptomic (or other -omic data, e.g., methylome, proteome, metabolome) can capture genetic and environmental factors associated with AUD and potentially provide sensitive, specific, and objective biomarkers to guide patient stratification, prognosis of treatment response or relapse, and predict optimal treatments. This Review describes and highlights state-of-the-art research on employing transcriptomic data and artificial intelligence (AI) methods to serve as molecular biomarkers with the goal of improving the clinical management of AUD. Considerations about future directions are also discussed.
酒精使用障碍(AUD)在美国乃至全球都极为普遍,是导致残疾的主要原因之一。目前存在一些重度饮酒和肝损伤的分子生物标志物,可提示AUD,但这些标志物在敏感性和特异性方面存在不足。AUD的治疗包括心理社会干预以及用于管理酒精戒断、协助戒酒和减少饮酒量的药物(纳曲酮、阿坎酸、双硫仑以及一些未按说明书用药的药物),还有治疗共病的精神疾病(如抑郁症和焦虑症)。有人提出,在异质性AUD人群中的不同患者群体可能对特定治疗方法反应更佳。例如,有证据表明,所谓的奖励性饮酒者对纳曲酮的反应比对阿坎酸更好。然而,目前尚无客观的分子标志物将患者分为最佳治疗组,也没有治疗反应的标志物。客观的分子生物标志物有助于AUD的诊断和患者分层,通过更具针对性的干预实现治疗个体化并改善治疗效果。治疗反应的生物标志物还可改善AUD的管理和治疗研发。系统生物学将复杂疾病和突发行为视为生物分子网络之间相互作用和串扰的结果。使用转录组学(或其他组学数据,如甲基化组、蛋白质组、代谢组)的系统方法能够捕捉与AUD相关的遗传和环境因素,并有可能提供敏感、特异且客观的生物标志物,以指导患者分层、治疗反应或复发的预后评估以及预测最佳治疗方案。本综述描述并重点介绍了利用转录组学数据和人工智能(AI)方法作为分子生物标志物的前沿研究,旨在改善AUD的临床管理。同时也讨论了对未来方向的思考。