Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Duke-National University of Singapore, Singapore, Singapore.
Depress Anxiety. 2017 Dec;34(12):1116-1122. doi: 10.1002/da.22670. Epub 2017 Jul 3.
Only one-third of patients with major depressive disorder (MDD) achieve remission with initial treatment. Consequently, current clinical practice relies on a "trial-and-error" approach to identify an effective treatment for each patient. The purpose of this report was to determine whether we could identify a set of clinical and biological parameters with potential clinical utility for prescription of exercise for treatment of MDD in a secondary analysis of the Treatment with Exercise Augmentation in Depression (TREAD) trial.
Participants with nonremitted MDD were randomized to one of two exercise doses for 12 weeks. Participants were categorized as "remitters" (≤12 on the IDS-C), nonresponders (<30% drop in IDS-C), or neither. The least absolute shrinkage and selection operator (LASSO) and random forests were used to evaluate 30 variables as predictors of both remission and nonresponse. Predictors were used to model treatment outcomes using logistic regression.
Of the 122 participants, 36 were categorized as remitters (29.5%), 56 as nonresponders (45.9%), and 30 as neither (24.6%). Predictors of remission were higher levels of brain-derived neurotrophic factor (BDNF) and IL-1B, greater depressive symptom severity, and higher postexercise positive affect. Predictors of treatment nonresponse were low cardiorespiratory fitness, lower levels of IL-6 and BDNF, and lower postexercise positive affect. Models including these predictors resulted in predictive values greater than 70% (true predicted remitters/all predicted remitters) with specificities greater than 25% (true predicted remitters/all remitters).
Results indicate feasibility in identifying patients who will either remit or not respond to exercise as a treatment for MDD utilizing a clinical decision model that incorporates multiple patient characteristics.
仅有三分之一的重度抑郁症（MDD）患者在初始治疗后达到缓解。因此，当前的临床实践依赖于“试错”方法来为每位患者确定有效的治疗方法。本报告的目的是确定我们是否可以在治疗增强抑郁（TREAD）试验的二次分析中，确定一组具有潜在临床应用价值的临床和生物学参数，以用于开出运动治疗 MDD 的处方。
非缓解性 MDD 患者被随机分配到 12 周的两种运动剂量之一。将患者分为“缓解者”（IDS-C 评分≤12）、非应答者（IDS-C 评分下降<30%）或两者都不是。最小绝对收缩和选择算子（LASSO）和随机森林用于评估 30 个变量作为缓解和无反应的预测因子。使用逻辑回归使用预测因子对治疗结果进行建模。
在 122 名参与者中，36 名被归类为缓解者（29.5%），56 名被归类为非应答者（45.9%），30 名既不是缓解者也不是非应答者（24.6%）。缓解的预测因子是较高的脑源性神经营养因子（BDNF）和白细胞介素-1B 水平、更严重的抑郁症状和较高的运动后积极情绪。治疗无反应的预测因子是低心肺适能、较低的白细胞介素-6 和 BDNF 水平以及较低的运动后积极情绪。包括这些预测因子的模型导致预测值大于 70%（真正预测缓解者/所有预测缓解者），特异性大于 25%（真正预测缓解者/所有缓解者）。
结果表明，使用包含多个患者特征的临床决策模型，识别将对运动治疗 MDD 缓解或无反应的患者是可行的。
