A Preliminary Study of Uric Metabolomic Alteration for Postpartum Depression Based on Liquid Chromatography Coupled to Quadrupole Time-of-Flight Mass Spectrometry

Abstract

Postpartum depression affects about 10-20% of newly delivered women, which is harmful for both mothers and infants. However, the current diagnosis of postpartum depression depends on the subjective judgment of a practitioner, which may lead to misdiagnosis. Hence, an appended objective diagnosis index may help the practitioner to improve diagnosis. A metabolomic study can find biomarkers as an objective index to facilitate disease diagnosis. Forty-nine postpartum depressed patients and 50 healthy controls were recruited into this study. The metabolites in urine were scanned with LC-Q-TOF-MS. The metabolomic data were analyzed with a multivariate statistical analysis method. Data from 40 patients and 40 controls were used for partial least square-discriminate analysis (PLS-DA). The urine metabolomic profiles of patients were different from those of controls. The PLS-DA model was validated by a permutation test, and the model could accurately classify the other 9 patients and 10 controls in T-prediction. Ten differentiating metabolites were found as main contributors to this difference, which are involved in amino acid metabolism, neurotransmitter metabolism, bacteria population, etc. Some of these potential biomarkers, such as 4-hydroxyhippuric acid, homocysteine, and tyrosine, showed relatively high sensitivities and specificities. The metabolic profile alteration induced by postpartum depression was found, and some of the differentiating metabolites may serve as biomarkers to facilitate the diagnosis of postpartum depression.