Keyser-Marcus Lori, Ramey Tatiana, Bjork James M, Martin Caitlin E, Sabo Roy, Moeller F Gerard
Department of Psychiatry, Division of Addictions, Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA 23219, USA.
Division of Therapeutics and Medical Consequences, National Institute on Drug Abuse (NIDA), Gaithersburg, MD 20877, USA.
Int J Environ Res Public Health. 2023 Dec 21;21(1):14. doi: 10.3390/ijerph21010014.
Standard nosological systems, such as DSM-5 or ICD-10, are relied upon as the diagnostic basis when developing treatments for individuals with substance use disorder (SUD). Unfortunately, the vast heterogeneity of individuals within a given SUD diagnosis results in a variable treatment response and/or difficulties ascertaining the efficacy signal in clinical trials of drug development. Emerging precision medicine methods focusing on targeted treatments based on phenotypic subtypes rather than diagnosis are being explored as alternatives. The goal of the present study was to provide initial validation of emergent subtypes identified by an addiction-focused phenotyping battery. Secondary data collected as part of a feasibility study of the NIDA phenotyping battery were utilized. Participants completed self-report measures and behavioral tasks across six neurofunctional domains. Exploratory and confirmatory factor analysis (EFA/CFA) were conducted. A three-factor model consisting of negative emotionality, attention/concentration, and interoception and mindfulness, as well as a four-factor model adding a second negative emotion domain, emerged from the EFA as candidate models. The CFA of these models did not result in a good fit, possibly resulting from small sample sizes that hindered statistical power.
在为患有物质使用障碍（SUD）的个体制定治疗方案时，标准的疾病分类系统，如《精神疾病诊断与统计手册》第5版（DSM - 5）或《国际疾病分类》第10版（ICD - 10），被用作诊断依据。不幸的是，在给定的SUD诊断中，个体的巨大异质性导致治疗反应各不相同，和/或在药物开发的临床试验中难以确定疗效信号。目前正在探索以表型亚型而非诊断为基础的靶向治疗的新兴精准医学方法作为替代方案。本研究的目的是对由专注于成瘾的表型检测组合所确定的新兴亚型进行初步验证。利用了作为美国国立药物滥用研究所（NIDA）表型检测组合可行性研究一部分收集的二次数据。参与者完成了六个神经功能领域的自我报告测量和行为任务。进行了探索性和验证性因素分析（EFA/CFA）。探索性因素分析得出了一个由负性情绪、注意力/专注力以及内感受和正念组成的三因素模型，以及一个增加了第二个负性情绪领域的四因素模型作为候选模型。这些模型的验证性因素分析结果拟合不佳，可能是由于样本量小阻碍了统计效力所致。
