Department of Neurology, The Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu 212001, China; Department of Neuroimaging laboratory, School of Medicine, Jiangsu University, Zhenjiang, Jiangsu 212013, China.
Department of Radiology, The Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu 212001, China; Department of Neuroimaging laboratory, School of Medicine, Jiangsu University, Zhenjiang, Jiangsu 212013, China.
J Affect Disord. 2019 May 1;250:307-312. doi: 10.1016/j.jad.2019.03.008. Epub 2019 Mar 7.
Although several pharmacological treatment options for depression are currently available, a large proportion of patients still do not achieve a complete remission or respond adequately to the initial antidepressant prescribed for reasons that remain relatively unknown. This study explored the application of serum biomarkers to the predict the efficacy of escitalopram for treating depression, to guide clinical drug selection.
In this study, 306 patients suffering from depression were treated with escitalopram (10 mg) for 6 weeks. After 6 weeks of treatment, the patients were divided into an escitalopram-sensitive group (ES, n = 172) and an escitalopram-insensitive group (EIS, n = 134) according their HAMD-24 scores after 6 weeks of treatment. Serum samples from all participants were collected on the first day, and 10 different serum biomarkers were analysed. Data from 100 patients in the ES group and 100 patients in the EIS group were then used to build a logistic regression model, and a receiver operating characteristic (ROC) curve was drawn. To validate the accuracy of our model, another 72 patients in the ES group and 34 patients in the EIS group were studied.
Of the 10 selected serum biomarkers, 4 were screened to build the regression model. BDNF, FGF-2, TNF-α and 5-HT. The regression equation was Z = 1/[1 + e], and the 4 biomarkers-combined detection achieved an AUC (area under the ROC curve) of 0.929 and a predictive accuracy of 88.70%.
Decision support tools based on our combined biomarker prediction models hold comparatively great promises; however, they need to be validated on a much larger scales than current studies provide.
The logistic regression model and ROC curves based of the serum biomarkers used in this study provide a more reliable means to predict the efficacy of escitalopram in patients with depression, and provide clinical evidence for drug selection.
尽管目前有几种治疗抑郁症的药物选择，但由于未知的原因，仍有很大一部分患者无法完全缓解或对最初开的抗抑郁药反应不足。本研究探讨了血清生物标志物在预测依西酞普兰治疗抑郁症疗效中的应用，以指导临床药物选择。
本研究对 306 例抑郁症患者进行依西酞普兰（10mg）治疗 6 周。治疗 6 周后，根据患者治疗 6 周后的 HAMD-24 评分，将患者分为依西酞普兰敏感组（ES 组，n=172）和依西酞普兰不敏感组（EIS 组，n=134）。所有患者均于第 1 天采集血清样本，检测 10 种不同的血清生物标志物。然后使用 ES 组的 100 例患者和 EIS 组的 100 例患者的数据构建逻辑回归模型，并绘制受试者工作特征（ROC）曲线。为了验证我们模型的准确性，还对 ES 组的 72 例患者和 EIS 组的 34 例患者进行了研究。
从 10 种筛选出的血清生物标志物中，筛选出 4 个构建回归模型。BDNF、FGF-2、TNF-α和 5-HT。回归方程为 Z=1/[1+e]，4 种生物标志物联合检测的 AUC（ROC 曲线下面积）为 0.929，预测准确率为 88.70%。
基于我们联合生物标志物预测模型的决策支持工具具有很大的应用前景；然而，它们需要在比目前研究更大的范围内进行验证。
本研究基于血清生物标志物的逻辑回归模型和 ROC 曲线为预测依西酞普兰治疗抑郁症患者的疗效提供了更可靠的方法，并为药物选择提供了临床证据。
