MDDScore: Confirmation of a Blood Test to Aid in the Diagnosis of Major Depressive Disorder
MDDScore: Confirmation of a Blood Test to Aid in the Diagnosis of Major Depressive Disorder
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DOI：
10.4088/jcp.14m09029
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发表时间：
2015-02-01
影响因子：
5.3
通讯作者：
Papakostas, George I.
中科院分区：
文献类型：
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作者：
Bilello, John A.;Thurmond, Linda M.;Papakostas, George I.
背景：此前，开发了一种生物标志物组合来辅助重度抑郁症（MDD）的诊断；它由 9 个与神经营养、代谢、炎症和下丘脑-垂体-肾上腺轴通路相关的生物标志物组成。该面板和相关算法在区分 MDD 患者与非 MDD 个体方面产生了良好的临床敏感性和特异性（分别为 92% 和 81%）。为了进一步验证该小组，我们使用更多的新的前瞻性 MDD 患者和类似收集的非抑郁受试者群体进行了一项前瞻性研究。还评估了算法中添加的性别和体重指数 (BMI) 影响。 方法：从 2011 年和 2012 年使用标准精神病学评估工具和根据 DSM-IV 标准进行结构化临床访谈在多个地点进行临床评估的 MDD 患者 (n = 68) 中获取血样。从非抑郁受试者中获取血液样本（n = 86）作为对照。 MDD 和非抑郁样本被随机分为独立训练集 (n = 102) 和验证集 (n = 52)。通过免疫分析对血清中的分析物进行定量。结果：训练集生物标志物数据用于开发逻辑回归模型，其中包括性别和体重指数，以允许它们与生化分析物相互作用。对于训练集，测试的敏感性和特异性（95% CI）分别为 93% (0.80-0.98) 和 95% (0.85-0.99)。然后将该方法（称为 MDDScore）应用于独立验证集，其敏感性和特异性分别为 96% (0.77-0.98) 和 86% (0.66-0.95)。训练集的总体准确率为 94%；验证集准确度为 91%。结论：对随机独立样本集的检查证实了先前建立的生物标志物组识别 MDD 患者的能力；准确率超过90%。改进后的模型将性别和 BMI 添加到先前建立的 9 个生物标志物组中，该模型稳健且简单；它为 MDD 提供了迄今为止最严格、最客观的诊断测试。 (C) 版权所有 2015 Physicians Postgraduate Press, Inc.
Background: Previously, a biomarker panel was developed for use as an aid to major depressive disorder (MDD) diagnosis; it consisted of 9 biomarkers associated with the neurotrophic, metabolic, inflammatory, and hypothalamic-pituitary-adrenal axis pathways. This panel and associated algorithm produced good clinical sensitivity and specificity (92% and 81%, respectively) in differentiating MDD patients from individuals without MDD. To further validate the panel, we performed a prospective study using a larger set of new prospectively acquired MDD patients and a similarly collected population of nondepressed subjects. The addition of gender and body mass index (BMI) effects to the algorithm was also evaluated.Method: Blood samples were obtained from MDD patients (n = 68) clinically evaluated at multiple sites in 2011 and 2012 using standard psychiatric assessment tools and structured clinical interviews according to DSM-IV criteria. Blood samples (n = 86) from nondepressed subjects were obtained as controls. MDD and nondepressed samples were randomized into independent training (n = 102) and validation sets (n = 52). Analytes in sera were quantified by immunoassay.Results: Training set biomarker data were used to develop a logistic regression model that included gender and BMI in a manner that allowed for their interaction with the biochemical analytes. For the training set, the sensitivity and specificity of the test (with 95% CI) were 93% (0.80-0.98) and 95% (0.85-0.99), respectively. This method (designated the MDDScore) was then applied to the independent validation set and had a sensitivity and specificity of 96% (0.77-0.98) and 86% (0.66-0.95), respectively. The overall accuracy for the training set was 94%; the validation set accuracy was 91%.Conclusion: Examination of a randomized independent set of samples confirms the ability of the previously established biomarker panel to identify persons with MDD; the accuracy was over 90%. The improved model that adds gender and BMI to the previously established panel of 9 biomarkers is robust and simple; it provides the most rigorously tested, objective diagnostic test for MDD to date. (C) Copyright 2015 Physicians Postgraduate Press, Inc.