Center for Addiction Sciences and Therapeutics, University of Texas Medical Branch, Galveston, TX, USA.
Department of Psychiatry and Behavioral Sciences, University of Texas Medical Branch, Galveston, TX, USA.
Transl Psychiatry. 2023 Sep 14;13(1):296. doi: 10.1038/s41398-023-02591-3.
Significant trauma histories and post-traumatic stress disorder (PTSD) are common in persons with substance use disorders (SUD) and often associate with increased SUD severity and poorer response to SUD treatment. As such, this sub-population has been associated with unique risk factors and treatment needs. Understanding the distinct etiological profile of persons with co-occurring SUD and PTSD is therefore crucial for advancing our knowledge of underlying mechanisms and the development of precision treatments. To this end, we employed supervised machine learning algorithms to interrogate the responses of 160 participants with SUD on the multidimensional NIDA Phenotyping Assessment Battery. Significant PTSD symptomatology was correctly predicted in 75% of participants (sensitivity: 80%; specificity: 72.22%) using a classification-based model based on anxiety and depressive symptoms, perseverative thinking styles, and interoceptive awareness. A regression-based machine learning model also utilized similar predictors, but failed to accurately predict severity of PTSD symptoms. These data indicate that even in a population already characterized by elevated negative affect (individuals with SUD), especially severe negative affect was predictive of PTSD symptomatology. In a follow-up analysis of a subset of 102 participants who also completed neurocognitive tasks, comorbidity status was correctly predicted in 86.67% of participants (sensitivity: 91.67%; specificity: 66.67%) based on depressive symptoms and fear-related attentional bias. However, a regression-based analysis did not identify fear-related attentional bias as a splitting factor, but instead split and categorized the sample based on indices of aggression, metacognition, distress tolerance, and interoceptive awareness. These data indicate that within a population of individuals with SUD, aberrations in tolerating and regulating aversive internal experiences may also characterize those with significant trauma histories, akin to findings in persons with anxiety without SUD. The results also highlight the need for further research on PTSD-SUD comorbidity that includes additional comparison groups (i.e., persons with only PTSD), captures additional comorbid diagnoses that may influence the PTSD-SUD relationship, examines additional types of SUDs (e.g., alcohol use disorder), and differentiates between subtypes of PTSD.
创伤后应激障碍（PTSD）和显著的创伤史在物质使用障碍（SUD）患者中很常见，并且常常与 SUD 严重程度增加和对 SUD 治疗的反应较差相关。因此，这一亚群体具有独特的风险因素和治疗需求。因此，了解同时患有 SUD 和 PTSD 的个体的独特病因学特征对于深入了解潜在机制和开发精准治疗方法至关重要。为此，我们采用监督机器学习算法，对 160 名 SUD 患者的多维 NIDA 表型评估电池进行了检测。基于焦虑和抑郁症状、坚持思维模式和内感受意识的分类模型，正确预测了 75%的患者存在显著 PTSD 症状（敏感性：80%；特异性：72.22%）。基于类似预测因子的回归机器学习模型也未能准确预测 PTSD 症状的严重程度。这些数据表明，即使在已经存在较高负性情绪（SUD 患者）的人群中，特别是严重的负性情绪也可预测 PTSD 症状。在对完成神经认知任务的 102 名参与者的亚组进行的后续分析中，基于抑郁症状和与恐惧相关的注意力偏差，正确预测了 86.67%的参与者的共病状态（敏感性：91.67%；特异性：66.67%）。然而，基于回归的分析并未将与恐惧相关的注意力偏差确定为一个分裂因素，而是根据攻击性、元认知、痛苦耐受力和内感受意识的指标对样本进行了分割和分类。这些数据表明，在 SUD 患者人群中，对耐受和调节令人不快的内部体验的能力异常也可能是那些有显著创伤史的人的特征，这类似于无 SUD 的焦虑患者的发现。结果还强调了需要进一步研究 PTSD-SUD 共病，包括纳入其他对照组（即仅有 PTSD 的个体），纳入可能影响 PTSD-SUD 关系的其他共病诊断，研究其他类型的 SUD（如酒精使用障碍），以及区分 PTSD 的亚型。
