Etkin Amit, Patenaude Brian, Song Yun Ju C, Usherwood Timothy, Rekshan William, Schatzberg Alan F, Rush A John, Williams Leanne M
1] Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA [2] Sierra-Pacific Mental Illness Research, Education, and Clinical Center (MIRECC) Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.
Brain Dynamics Center, University of Sydney Medical School and Westmead Millennium Institute for Medical Research at Westmead Hospital, Sydney, NSW, Australia.
Neuropsychopharmacology. 2015 May;40(6):1332-42. doi: 10.1038/npp.2014.333. Epub 2014 Dec 30.
Depression involves impairments in a range of cognitive and emotional capacities. It is unknown whether these functions can inform medication choice when considered as a composite predictive biomarker. We tested whether behavioral tests, grounded in the neurobiology of cognitive and emotional functions, predict outcome with common antidepressants. Medication-free outpatients with nonpsychotic major depressive disorder (N=1008; 665 completers) were assessed before treatment using 13 computerized tests of psychomotor, executive, memory-attention, processing speed, inhibitory, and emotional functions. Matched healthy controls (N=336) provided a normative reference sample for test performance. Depressed participants were then randomized to escitalopram, sertraline, or venlafaxine-extended release, and were assessed using the 16-item Quick Inventory of Depressive Symptomatology (QIDS-SR16) and the 17-item Hamilton Rating Scale for Depression. Given the heterogeneity of depression, analyses were furthermore stratified by pretreatment performance. We then used pattern classification with cross-validation to determine individual patient-level composite predictive biomarkers of antidepressant outcome based on test performance. A subgroup of depressed participants (approximately one-quarter of patients) were found to be impaired across most cognitive tests relative to the healthy norm, from which they could be discriminated with 91% accuracy. These patients with generally impaired cognitive task performance had poorer treatment outcomes. For this impaired subgroup, task performance furthermore predicted remission on the QIDS-SR16 at 72% accuracy specifically following treatment with escitalopram but not the other medications. Therefore, tests of cognitive and emotional functions can form a clinically meaningful composite biomarker that may help drive general treatment outcome prediction for optimal treatment selection in depression, particularly for escitalopram.
抑郁症涉及一系列认知和情感能力的损害。当被视为一种综合预测生物标志物时，这些功能是否能为药物选择提供依据尚不清楚。我们测试了基于认知和情感功能神经生物学的行为测试是否能预测常用抗抑郁药的疗效。对1008名无精神病性重度抑郁症的未服药门诊患者（665名完成者）在治疗前进行了评估，使用了13项关于精神运动、执行、记忆注意力、处理速度、抑制和情感功能的计算机化测试。336名匹配的健康对照者提供了测试表现的规范参考样本。然后将抑郁参与者随机分为艾司西酞普兰组、舍曲林组或文拉法辛缓释组，并使用16项抑郁症状快速量表（QIDS-SR16）和17项汉密尔顿抑郁评定量表进行评估。鉴于抑郁症的异质性，分析还根据治疗前的表现进行了分层。然后我们使用交叉验证的模式分类法，根据测试表现确定个体患者水平的抗抑郁药疗效综合预测生物标志物。相对于健康标准，发现一小部分抑郁参与者（约四分之一的患者）在大多数认知测试中存在损害，能够以91%的准确率将他们区分出来。这些认知任务表现普遍受损的患者治疗效果较差。对于这个受损亚组，任务表现还能以72%的准确率预测在使用艾司西酞普兰治疗后而非其他药物治疗后的QIDS-SR16缓解情况。因此，认知和情感功能测试可以形成一种具有临床意义的综合生物标志物，可能有助于推动抑郁症最佳治疗选择的总体治疗效果预测，特别是对于艾司西酞普兰。
