Lin Lin, Chen Xiao-Mei, Liu Rong-Hua
Department of Obstetrics and Gynecology, Linyi People's Hospital, Shandong, People's Republic of China.
Neuropsychiatr Dis Treat. 2017 May 10;13:1263-1270. doi: 10.2147/NDT.S135190. eCollection 2017.
Postpartum depression (PPD) could affect ~10% of women and impair the quality of mother-infant interactions. Currently, there are no objective methods to diagnose PPD. Therefore, this study was conducted to identify potential biomarkers for diagnosing PPD.
Morning urine samples of PPD subjects, postpartum women without depression (PPWD) and healthy controls (HCs) were collected. The gas chromatography-mass spectroscopy (GC-MS)-based urinary metabolomic approach was performed to characterize the urinary metabolic profiling. The orthogonal partial least-squares-discriminant analysis (OPLS-DA) was used to identify the differential metabolites. The logistic regression analysis and Bayesian information criterion rule were further used to identify the potential biomarker panel. The receiver operating characteristic curve analysis was conducted to evaluate the diagnostic performance of the identified potential biomarker panel.
Totally, 73 PPD subjects, 73 PPWD and 74 HCs were included, and 68 metabolites were identified using GC-MS. The OPLS-DA model showed that there were 22 differential metabolites (14 upregulated and 8 downregulated) responsible for separating PPD subjects from HCs and PPWD. Meanwhile, a panel of five potential biomarkers - formate, succinate, 1-methylhistidine, α-glucose and dimethylamine - was identified. This panel could effectively distinguish PPD subjects from HCs and PPWD with an area under the curve (AUC) curve of 0.948 in the training set and 0.944 in the testing set.
These results demonstrated that the potential biomarker panel could aid in the future development of an objective diagnostic method for PPD.
产后抑郁症（PPD）可能影响约10%的女性，并损害母婴互动质量。目前，尚无诊断PPD的客观方法。因此，本研究旨在确定诊断PPD的潜在生物标志物。
收集PPD患者、无抑郁的产后女性（PPWD）和健康对照者（HCs）的晨尿样本。采用基于气相色谱-质谱联用（GC-MS）的尿液代谢组学方法来表征尿液代谢谱。使用正交偏最小二乘判别分析（OPLS-DA）来识别差异代谢物。进一步采用逻辑回归分析和贝叶斯信息准则规则来确定潜在的生物标志物组合。进行受试者工作特征曲线分析以评估所确定的潜在生物标志物组合的诊断性能。
共纳入73例PPD患者、73例PPWD和74例HCs，使用GC-MS鉴定出68种代谢物。OPLS-DA模型显示，有22种差异代谢物（14种上调和8种下调）可将PPD患者与HCs和PPWD区分开来。同时，确定了一组由五种潜在生物标志物组成的组合——甲酸、琥珀酸、1-甲基组氨酸、α-葡萄糖和二甲胺。该组合在训练集中曲线下面积（AUC）为0.948，在测试集中为0.944，能够有效区分PPD患者与HCs和PPWD。
这些结果表明，潜在的生物标志物组合有助于未来开发一种用于PPD的客观诊断方法。
