Disease Markers
Volume 2019 (2019), Article ID 4264803, 9 pages
https://doi.org/10.1155/2019/4264803

Research Article

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

Li Zhang, Wei Zou, Yi Huang, Xiaoke Wen, Jianxi Huang, Yichao Wang, and Xiaoqi Sheng

NHC Key Laboratory of Birth Defects Research, Prevention and Treatment, Hunan Provincial Maternal and Child Health Care Hospital, Changsha, China

Correspondence should be addressed to Wei Zou; zouwei3@foxmail.com and Xiaoqi Sheng; 76599219@qq.com

Received 13 September 2018; Revised 7 December 2018; Accepted 13 December 2018; Published 24 April 2019

Academic Editor: Hubertus Himmerich

Copyright © 2019 Li Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.

1. Introduction

Postpartum depression occurs in the first few weeks after delivery, as specified in the International Classification of Diseases, 10th edition (ICD-10). As a common disease, postpartum depression affects about 10-20% of newly delivered women [1, 2], which is deemed as a great threat as it is associated with low mood, sadness, decreased maternal sensitivity and attachment to infant, poor child development, and increased risk of suicide and infanticide [3–5]. The risk of suicide, owing to postpartum depression, is relatively high between all depressed women, and about 5% to 14% of postpartum depressed patients have self-harm ideation, which is more than prevalent attempts or actual death [3]. On the other hand, the mother is the first social environment for infant and introduces infants to the outside world, and hence, a bad mother-child relationship produced by postpartum depression will interfere with the healthy development of the emotion, behavior, and cognition of the infant [6]. A comprehensive study showed that children brought by postpartum depression mothers performed worse cognitive abilities at 18 months of age, comparing to the children with mentally healthy mothers [7].

The etiology of postpartum depression is complex. Clinical (premature births, intrauterine growth impairment, operative deliveries, and admission to neonatal care units), biological (sleep disruption, deregulation of neurotransmitters, and serotonin), hormonal (thyroid, cortisol, and oxytocin), and psychological variables (stressful life events, marital conflict, and low social support, attachment insecurity, and personality features) contribute to the development of postpartum depression [8–11]. A clinician subjectively diagnoses this disease according to the symptomatic clusters and scales, which is the sole diagnostic method. In this case, the accuracy of this diagnosis highly relies on the academic knowledge and clinical experience of the clinician. Currently, a certain part of depressed patients is not accurately diagnosed [12, 13]. Besides, the pronounced common accompanied physical symptoms, such as debilitation and dizziness, may lead to the negligence of the depression symptom for patients [14]. Therefore, biomarkers, obtained from a laboratory-based test, may be helpful for the diagnosis of postpartum depression.

Through quantification of the metabolites in biofluids, such as the urine and blood, metabolomic studies show the characteristic of metabolism in vivo and illuminate the physiology condition, which can find differentiating metabolites (biomarkers) for disease diagnosis. Nowadays, a large number of clinical metabolomic studies have been carried out to explore metabolic biomarkers [15]. Especially, metabolomic studies on the cerebrospinal fluid, blood, and urine of major depressive disorder patients have showed the metabolic alterations and found potential biomarkers [16–19]. As postpartum depression occurs in the immediate postpartum period, the patients are considered to be biochemically different from the other depressions patients [20] and hence may show different metabolomic alterations. Therefore, the metabolomic study on postpartum depression is necessary. However, reports on metabolomics of postpartum depression are rare. Recently, a gas chromatography-mass spectroscopy (GC-MS)-based uric metabolomic study on postpartum depression has been carried out and found several differentiating metabolites [21]. As the liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS), compared with GC-MS, can detect different metabolites, an LC-Q-TOF-MS-based uric metabolomic study was carried out to find out new metabolomic alterations associated with postpartum depression. The potential biomarkers found in this study may facilitate the diagnosis of this disease in the future.

2. Materials and Methods

2.1. Chemicals and Reagents

Acetonitrile and methanol (LC grade) were from Fisher Scientific (Fair Lawn, NJ, USA). Milli-Q (Bedford, MA, USA) pure water was used in this experiment. Formic acid and ammonium acetate (LC-grade), stearoylethanolamide, homocysteine, methylmalonic acid, 4-hydroxyhippuric acid, tyrosine, vanillylmandelic acid, glutaric acid, alanine, and naringin were obtained from Sigma-Aldrich (St. Louis, MO, USA).

2.2. Subjects

This study was approved by the Ethical Committee of Hunan Provincial Maternal and Child Health Care Hospital (No. EC201507), according to the Declaration of Helsinki. Informed consent was obtained from all individual participants included in the study. All participants, aged 20-40 years, were enrolled from the Hunan Provincial Maternal and Child Health Care Hospital from April to December in 2016. Postpartum depressed patients were enrolled from the Department of Clinical Psychology, who were at the sixth week after delivery. The patients were diagnosed with postpartum depression with a single depressed episode based on a psychiatric interview by two psychiatrists according to the affective module of ICD-10. Depressive symptoms were assessed by the 17-item Hamilton Depression Rating Scale (HDRS) [22], and only depressed subjects with HDRS scores greater than 17 were recruited. Healthy control subjects were recruited from the postnatal examination center when they received routine health examination at the sixth week after delivery. The 17-item HDRS was used to screen the women’s depressive symptoms. Women who scored below cut-off point 7 on HDRS were recruited into the healthy control group. The others whose scores were not less than 7 were transferred to the Department of Clinical Psychology for further assessment. All candidates with any previous lifetime history of neurological and/or ICD-10 classification of mental and behavioral disorders were excluded from this study. All patients and controls who suffered systemic illness or had received any kind of pharmacologic treatment in the last month were also excluded. All participants were randomly divided into the training group for multivariate statistical analysis and the validation group for T-prediction.

The urine sample was collected in the morning after fasting overnight and stored at −80°C.

2.3. Urine Sample Preparation

Urine samples were thawed at 4°C. A mixture of 50 μl urine and 50 μl water was added with 300 μl MeOH/acetonitrile solution (1 : 1, ). The internal standard naringin was added to the sample, and the sample was stored at 4°C overnight. Then, the sample was centrifuging for 10 min at 13,000 rpm. 5 μl supernatant was injected into the LC-Q-TOF-MS system. Quality control (QC) samples were prepared using mixed urine.

2.4. LC-Q-TOF-MS Analysis

The LC-Q-TOF-MS system used in this study was consisted by a 1290 LC and a 6540 mass system (Agilent Technology, USA). The chromatographic column was ZORBAX Eclipse XDB-C18 ( i.d., 3.5 μm) (Agilent Technology, USA). Acetonitrile (A) and 0.1% formic acid with 5 mmol/l ammonium acetate (B) were used as the mobile phase. The gradient was 5% A at  min, changed linearly to 100% A from  min to  min and hold for 4 min, with a flow rate of 0.4 ml/min. The column temperature was set at 35°C. The mass parameters in positive and negative ion modes were as follows: capillary voltage 3500 V, nozzle voltage (Expt) 500 V, fragmentor 120 V, skimmer 65 V, gas temperature 250°C, sheath gas temperature 350°C, collision energy 20 V (MS/MS mode), and scan range 20–1250. Both drying gas and collision gas were nitrogen.

2.5. Data Analysis

Both internal standards and QC sample were used to control the quality of sample analysis. Profinder B08.00 software (Agilent Technology, USA) was used for data extraction and alignment. Peak areas of metabolites were normalized by that of creatinine. Subsequently, to remove the average concentration difference between different metabolites which may lead to the bias in finding differentiating metabolites, the normalized peak area of each metabolite underwent mean centering. Then, a dataset including detected substances with corresponding normalized/mean-centered peak area was produced for further statistical analysis. To discriminate the metabolomic profiles between patients and controls, partial least square-discriminate analysis (PLS-DA) was performed by SIMCA-P 11.5 (Umetrics, Sweden) whose instruction can be obtained from https://umetrics.com/. With the SIMCA-P software, permutation analysis (200 times) and T-prediction were performed to validate the PLS-DA model. During the procedure of T-prediction, unknown samples in the prediction group were statistically analyzed under the built PLS-DA model for prediction or discrimination. The variable importance in the projection (VIP) values of metabolites was generated from the PLS-DA model. The values were obtained from a -test. To control the false discovery rate, the values were calculated by correcting values with Benjamini and Hochberg’s procedure [23]. Metabolites with and were deemed as potential biomarkers. Then, these metabolites were identified by their accurate molecular weights and fragment patterns referring to the Metlin database from Agilent, and the further identification resorted to the standards if commercially available. The volcano plot from Metaboanalysis 4.0 (http://www.metaboanalyst.ca) was to show the fold change (FC) and values of substances, and the heatmap was to visualize the level changes of potential biomarkers. The receiver operator characteristic (ROC) curve of each metabolite was plotted with the sensitivity on the -axis and the corresponding false-positive rates on the -axis, with a 95% confidence interval (CI). Unpaired Student’s -test was performed on SPSS 16.0 (SPSS Inc., Chicago, USA) for statistical analysis. was considered statistically significant.

3. Results

3.1. Information of Subjects

There are 49 postpartum depressed patients and 50 mentally healthy controls participated in this study. The metabolomic data of 40 patients and 40 controls were used for multivariate statistical analysis, and the data of the remaining 9 patients and 10 controls were employed for T-prediction. As presented in Table 1, demographics, age, race, and body mass index (BMI) of patients and controls showed no significant difference. The HDRS scores of patients were significantly different from that of controls.

Table 1: Demographic characteristics of the study population.

Parameters
Primary cohort
Validation cohort

PDPa
HCb
value
PDP
HC
value

Number of cases
40
40
—
9
10
—

Mean age (y), mean (SD)
27.0 (4.6)
27.2 (4.7)
0.831
24.7 (4.0)
27.7 (4.9)
0.161

Racial origin: Asian (Chinese) ( (%))
40 (100)
40 (100)
—
9 (100)
10 (100)
—

Body mass index
26.2 (3.8)
25.2 (3.3)
0.192
25.8 (3.2)
24.9 (2.1)
0.433

HDRS scores
29.5 (8.8)
4.3 (1.6)
<0.001
27.9 (7.1)
4.3 (1.4)
<0.001

aPostpartum depressed patients. bHealthy controls.

3.2. LC-Q-TOF-MS Analysis of Urine

Six QC samples were prepared and analyzed to test the repeatability of this method. The retention times and peak areas of selected 3 substances in each ion mode were statistically analyzed. Retention time shifts of these substances were less than 0.2 min, the relative standard deviations (RSDs) were less than 1.6%, and the RSDs of peak areas were less than 8.5% (data not presented). These results showed a good repeatability of this method. The internal standard naringin in samples was employed to monitor the sample preparation and instruction condition. The RSD of its peak areas in all urine samples was 9.2%, indicating a good availability of this metabolomic analysis. After data extraction by Profinder B08.00 software, 485 substances detected in each urine sample were used for multivariate statistical analysis.

3.3. Multivariate Statistical Analysis

The PLS-DA was carried out to discriminate the metabolomic profiles of patients and controls. As showed in Figure 1, the metabolomic profiles of patients and controls were distributed into two separated areas.

Figure 1: PLS-DA model (, , and ) discriminating the metabolic profiles of postpartum depressed patients and healthy controls.

A 200-time permutation test was performed to validate the PLS-DA model. As showed in Figure 2(a), all the left Q2 values by permutations were lower than the right original Q2 value, which meant that the model was valid. A T-prediction was carried out to further validate the PLS-DA model, within the further 9 patients and 10 controls. The results showed that the PLS-DA model could accurately predict these 19 samples (Figure 2(b)).

(a)

(b)

Figure 2: Validation of the PLS-DA model by the 200-time permutation test (a) and T-prediction (b).

The volcano plot of detected substances was presented in Figure 3, and 10 potential biomarkers whose and were marked in this plot. All these metabolites were identified by referring to the Metlin database, and 4-hydroxyhippuric acid, 4-hydroxybenzoic acid, alanine, glutaric acid, homocysteine, methylmalonic acid, tyrosine, and vanillylmandelic acid were further identified by commercial standards. The chromatography and mass spectra information, VIP, FC, , and values of these potential biomarkers are summarized in Table 2.

Figure 3: Volcano plot presenting the -log10() and log2(FC) of metabolites.

Table 2: The differentiating metabolites contributing to the discrimination of urine metabolomic profiles of postpartum depressed patients and healthy controls in the training group.

Metabolite
RT (min)
Ion mode
MS ()
MS/MS ()
VIP value
value
value
Trend
Fold changec
AUC (95% CI)
Sensitivity
Specificity

Alanine
1.7
[M+H]⁺
90.0557
44
2.97
<0.001
0.007
Upa
1.18
0.722 (0.621-0.827)
0.675
0.675

Methylmalonic acid
1.4
[M-H]⁻
117.0191
73
4.28
<0.001
<0.001
Up
1.52
0.848 (0.744-0.918)
0.725
0.825

Homocysteine
3.4
[M+H]⁺
136.0427
118, 90
4.30
<0.001
0.012
Up
1.62
0.852 (0.770-0.931)
0.775
0.800

Tyrosine
3.3
[M+H]⁺
182.0804
165, 136, 91
4.38
<0.001
0.048
Downb
-1.48
0.859 (0.761-0.920)
0.700
0.850

Glutaric acid
1.6
[M-H]⁻
131.0346
87, 59
2.77
<0.001
0.019
Up
1.31
0.711 (0.587-0.816)
0.625
0.775

Vanillylmandelic acid
1.9
[M-H]⁻
197.0450
137
4.33
<0.001
0.029
Up
1.42
0.851 (0.762-0.918)
0.750
0.750

4-Hydroxyhippuric acid
3.8
[M-H]⁻
194.0461
150, 93
4.71
<0.001
<0.001
Up
1.38
0.884 (0.806-0.946)
0.825
0.825

4-Hydroxybenzoic acid
3.6
[M-H]⁻
137.0231
93
2.64
<0.001
0.033
Up
1.11
0.714 (0.599-0.831)
0.650
0.725

5-Hydroxyindoleacetic acid
4.9
[M+H]⁺
192.0647
146, 118
3.39
<0.001
<0.001
Up
1.35
0.752 (0.650-0.836)
0.725
0.650

3-(3-Hydroxyphenyl)-3-hydroxypropionic acid
2.2
[M-H]⁻
181.0499
137, 119
4.16
<0.001
<0.001
Up
1.63
0.827 (0.725-0.907)
0.825
0.725

RT: retention time; VIP: variable importance in the projection; CI: confidence interval. aThe level in patients is higher than that in controls. bThe level in patients is lower than that in controls. cThe fold change was calculated by dividing the level of metabolite in patients by that in controls.

To visualize the level changes of these potential biomarkers in postpartum depression patients, a heatmap is plotted and presented in Figure 4. The concentration of tyrosine in urine of patients was significantly lower than that of controls, and the level of the remaining differentiating metabolites showed a reverse tendency.

Figure 4: Heatmap of the unsupervised hierarchical clustering of 10 differentiating metabolites across the postpartum depressed patients and healthy controls. The columns indicate the subjects, and the rows indicate the differentiating metabolites.

3.4. Potential Biomarker Evaluation

The ROC curves of potential biomarkers, obtained from biomarker analysis in MetaboAnalyst 3.0, were plotted to evaluate their potentials in serving as biomarkers for postpartum depression diagnosis. The AUCs of ROC curves in 95% CI, sensitivities, and specificities of these 10 potential biomarkers are summarized in Tables 2 and 3. Homocysteine, 4-hydroxyhippuric acid, and tyrosine showed relatively high sensitivities and specificities and were combined with logistic regression in SPSS software. The logistic regression model is logit  = –24.423 − 0.11 × tyrosine + 0.247 × 4-hydroxyhippuric acid + 0.102 × homocysteine, where logit () is the probability of postpartum depression. The AUC (95% CI), sensitivity, and specificity for the combined metabolites are 0.995 (0.981-1.000), 0.938, and 0.980, respectively.

Table 3: The ROC curve of the differentiating metabolites in the validation group.

Metabolite
AUC (95% CI)
Sensitivity
Specificity

Alanine
0.733 (0.478-0.944)
0.778
0.700

Methylmalonic acid
0.833 (0.600-0.989)
0.667
1.000

Homocysteine
0.856 (0.644-1.000)
0.778
0.900

Tyrosine
0.867 (0.689-0.989)
1.000
0.700

Glutaric acid
0.667 (0.367-0.889)
0.667
0.600

Vanillylmandelic acid
0.756 (0.467-0.933)
0.556
0.900

4-Hydroxyhippuric acid
0.889 (0.678-1.000)
0.778
0.900

4-Hydroxybenzoic acid
0.656 (0.311-0.900)
0.556
0.900

5-Hydroxyindoleacetic acid
0.700 (0.400-0.922)
0.667
0.800

3-(3-Hydroxyphenyl)-3-hydroxypropionic acid
0.722 (0.500-0.911)
0.667
0.800

4. Discussion

Postpartum depression is a serious psychiatric disorder, as it could be detrimental for both the mother and the child. Currently, the diagnosis of postpartum depression primarily depends on the subjective judgment of the clinician, according to the symptom of the patient. Besides, accompanied physical symptoms may lead to the negligence of the depression symptom for patients. Biomarker, as an objective index based on a laboratory test, may help the current diagnosis of postpartum depression. In this metabolomic study with an LC-Q-TOF-MS method, 10 differential metabolites in urine were found as potential biomarkers for facilitating the diagnosis. These metabolites indicated that postpartum depression could lead to alterations of amino acid metabolism, neurotransmitter metabolism, bacteria population, etc. Among them, alterations of neurotransmitter metabolism and bacterial metabolism in this study have not been found in the former GC-MS-based study [21].

In this study, the concentration of homocysteine in urine was significantly elevated in postpartum depressed patients, which was in concurrence with the previous findings in depressed patients [24, 25]. The restriction of homocysteine metabolism may lead to inhibition of methylation reaction in vivo which will affect the composition of phospholipids, the structure of cell membranes, and receptor function [26]. Inhibited methylation of myelin basic protein can lead to loosely compacted myelin and decreased function of nerve conduction [27]. Besides, homocysteine has been proved to interact with transition metals to show the abilities of oxidation toxicity and neuronal cell damage [28]. Vitamin B₁₂ is essential in the central nervous system, as it takes part in the methylation response which is essential for the production of monoamine, neurotransmitters, phospholipids, and nucleotides. Deficiency of vitamin B₁₂ may lead to an impaired methylation and the following psychiatric disease [29], such as depression [30]. Methylmalonic acid is a metabolic product of vitamin B₁₂, whose high level indicates a deficiency of vitamin B₁₂ [31]. In this study, we found the elevated level of methylmalonic acid in postpartum depressed patient, supporting the previous researches. Here, the levels of 4-hydroxyhippuric acid and 3-(3-hydroxyphenyl)-3-hydroxypropionic acid in postpartum depressed patients were higher than that in healthy women, which were observed in autism children [32, 33]. 3-(3-Hydroxyphenyl)-3-hydroxypropionic acid is a metabolic product from the action of the bacterium in vivo, whose level elevation may suggest the disturbance of microbial population balance. Tyrosine is a precursor of the catecholamine of the brain. The level of tyrosine was decreased in the urine of postpartum depressed patients in this study, which was consistent with the previous studies that showed the decrease in the blood of depressed patients [34] and supported the hypothesis that dopaminergic factors are involved in clinical depression [35]. 5-Hydroxyindoleacetic acid is a breakdown product of serotonin, whose increase in this study might indicate an extensive consumption of serotonin. The increase of 5-hydroxyindoleacetic acid was also observed in the cerebrospinal fluid of major depressed patients [36]. Vanillylmandelic acid is a major metabolic product of norepinephrine which is involved in the noradrenergic system and the hypothalamic-pituitary-adrenal axis, whose elevation is related to the depression symptom [37, 38] and was also found in this study. Glutaric acid is mainly produced from the metabolism of lysine, whose fluctuation in postpartum depressed patients indicates the change of lysine metabolism. Alanine, as a nonessential amino acid, is an inhibitory neurotransmitter in the brain. The concentration of alanine in postpartum depressed patients was significantly higher than that in controls in this study, which was consistent with that found in other depressed patients [39].

A meta-analysis pooling 50,371 patients across 41 studies to assess the diagnosis of depression showed that only 47.3% of cases were correctly identified by practitioners; across 19 studies, the sensitivity was 50.1% and the specificity was 81.3% at one-off assessment [12]. In this study, some of potential urine biomarkers, such as 4-hydroxyhippuric acid, homocysteine, and tyrosine showed relatively high sensitivities and specificities. The sensitivity and specificity for the combined metabolites are 0.938 and 0.980, respectively, which might be satisfactory for clinic use. Hence, these potential biomarkers might offer a novel objective diagnosis method of postpartum depression and help practitioners to improve diagnosis. Nevertheless, there are some limitations within this study. Firstly, only about 50 participants were recruited into the patient group and control group, respectively, and hence, this study is just a preliminary study. Secondly, the mothers mainly come from Hunan Province, China, and whether these potential biomarkers could be used in other human races is unknown. Moreover, these differentiating metabolites could discriminate the postpartum depressed patients from healthy mothers, but whether they could discriminate postpartum depression from other neuropsychiatric disorders, such as postpartum psychosis, postpartum bipolar, and baby blue, is unclear. Therefore, further cohort studies should be carried out to clarify these problems.

5. Conclusion

A postpartum depression urine metabolomic study was carried out by using the LC-Q-TOF-MS method. The urine metabolomic profile of postpartum depressed patients could be discriminated from that of healthy mothers. Ten differentiating metabolites were found as potential biomarkers to facilitate the diagnosis of postpartum depression, which are involved in amino acid metabolism, neurotransmitter metabolism, bacteria population, etc.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Ethical Approval

This study is conducted according to the principles expressed in the Declaration of Helsinki and was approved by the Ethical Committee of Hunan Provincial Maternal and Child Health Care Hospital (No. EC201507).

Consent

Informed consent was obtained from all individual participants included in the study.

Conflicts of Interest

There is no conflict of interest.

Acknowledgments

We are thankful for the help from Miss Qian Zhou and Yongchao Xie during the sample preparation. This work was supported by the National Natural Science Foundation of China (grant number 81501218), the Hunan Provincial Science and Technology Department (grant numbers 2019JJ80037 and 2017SK4006), and the Health and Family Planning Commission of Hunan Province (grant number B20180878).

References

N. I. Gavin, B. N. Gaynes, K. N. Lohr, S. Meltzerbrody, G. Gartlehner, and T. Swinson, “Perinatal depression: a systematic review of prevalence and incidence,” Obstetrics & Gynecology, vol. 106, no. 5, pp. 1071–1083, 2005.

D. T. Lee and T. K. Chung, “Postnatal depression: an update,” Best Practice & Research Clinical Obstetrics & Gynaecology, vol. 21, no. 2, pp. 183–191, 2007.

V. Lindahl, J. L. Pearson, and L. Colpe, “Prevalence of suicidality during pregnancy and the postpartum,” Archives of Womens Mental Health, vol. 8, no. 2, pp. 77–87, 2005.

N. R. Marmorstein, S. M. Malone, and W. G. Iacono, “Psychiatric disorders among offspring of depressed mothers: associations with paternal psychopathology,” The American Journal of Psychiatry, vol. 161, no. 9, pp. 1588–1594, 2004.

J. F. Paulson, S. Dauber, and J. A. Leiferman, “Individual and combined effects of postpartum depression in mothers and fathers on parenting behavior,” Pediatrics, vol. 118, no. 2, pp. 659–668, 2006.

S. L. Grace, A. Evindar, and D. E. Stewart, “The effect of postpartum depression on child cognitive development and behavior: a review and critical analysis of the literature,” Archives of Women’s Mental Health, vol. 6, no. 4, pp. 263–274, 2003.

L. Murray, “The impact of postnatal depression on infant development,” Journal of Child Psychology and Psychiatry, vol. 33, no. 3, pp. 543–561, 1992.

F. Aceti, V. Baglioni, P. Ciolli et al., “Maternal attachment patterns and personality in post partum depression,” Rivista di Psichiatria, vol. 47, no. 3, pp. 214–220, 2012.

M. Bloch, N. Rotenberg, D. Koren, and E. Klein, “Risk factors associated with the development of postpartum mood disorders,” Journal of Affective Disorders, vol. 88, no. 1, pp. 9–18, 2005.

S. K. Dørheim, G. T. Bondevik, M. Eberhard-Gran, and B. Bjorvatn, “Sleep and depression in postpartum women: a population-based study,” Sleep, vol. 32, no. 7, pp. 847–855, 2009.

M. Fiorelli, F. Aceti, I. Marini et al., “Magnetic resonance imaging studies of postpartum depression: an overview,” Behavioural Neurology, vol. 2015, Article ID 913843, 7 pages, 2015.

A. J. Mitchell, A. Vaze, and S. Rao, “Clinical diagnosis of depression in primary care: a meta-analysis,” The Lancet, vol. 374, no. 9690, pp. 609–619, 2009.

H. U. Wittchen, M. Höfler, and W. Meister, “Prevalence and recognition of depressive syndromes in German primary care settings: poorly recognized and treated?” International Clinical Psychopharmacology, vol. 16, no. 3, pp. 121–135, 2001.

H. Munitz, A. F. Valevski, A. Weizman et al., “Recognition and treatment of depression in primary care settings in 6 different countries: a retrospective file analysis by WHO,” European Journal of Psychiatry, vol. 14, no. 2, pp. 85–94, 2000.

R. Bujak, W. Struck-Lewicka, M. J. Markuszewski, and R. Kaliszan, “Metabolomics for laboratory diagnostics,” Journal of Pharmaceutical and Biomedical Analysis, vol. 113, pp. 108–120, 2015.

R. Kaddurah-Daouk, P. Yuan, S. H. Boyle et al., “Cerebrospinal fluid metabolome in mood disorders-remission state has a unique metabolic profile,” Scientific Reports, vol. 2, no. 1, p. 667, 2012.

B. S. Gadad, M. K. Jha, A. Czysz et al., “Peripheral biomarkers of major depression and antidepressant treatment response: current knowledge and future outlooks,” Journal of Affective Disorders, vol. 233, pp. 3–14, 2018.

J. J. Chen, C. J. Zhou, P. Zheng et al., “Differential urinary metabolites related with the severity of major depressive disorder,” Behavioural Brain Research, vol. 332, pp. 280–287, 2017.

J. X. Pan, J. J. Xia, F. L. Deng et al., “Diagnosis of major depressive disorder based on changes in multiple plasma neurotransmitters: a targeted metabolomics study,” Translational Psychiatry, vol. 8, no. 1, p. 130, 2018.

M. Bloch, D. R. Rubinow, P. J. Schmidt, A. Lotsikas, G. P. Chrousos, and G. Cizza, “Cortisol response to ovine corticotropin-releasing hormone in a model of pregnancy and parturition in euthymic women with and without a history of postpartum depression,” The Journal of Clinical Endocrinology & Metabolism, vol. 90, no. 2, pp. 695–699, 2005.

L. Lin, X. M. Chen, and R. H. Liu, “Novel urinary metabolite signature for diagnosing postpartum depression,” Neuropsychiatric Disease and Treatment, vol. 13, pp. 1263–1270, 2017.

M. Hamilton, “A rating scale for depression,” Journal of Neurology Neurosurgery & Psychiatry, vol. 23, no. 1, pp. 56–62, 1960.

Y. Benjamini and Y. Hochberg, “Controlling the false discovery rate: a practical and powerful approach to multiple testing,” Journal of the Royal Statistical Society Series B (Methodological), vol. 57, no. 1, pp. 289–300, 1995.

H. Tiemeier, H. R. van Tuijl, A. Hofman, J. Meijer, A. J. Kiliaan, and M. M. Breteler, “Vitamin B₁₂, folate, and homocysteine in depression: the Rotterdam Study,” The American Journal of Psychiatry, vol. 159, no. 12, pp. 2099–2101, 2002.

T. Tolmunen, J. Hintikka, S. Voutilainen et al., “Association between depressive symptoms and serum concentrations of homocysteine in men: a population study,” The American Journal of Clinical Nutrition, vol. 80, no. 6, pp. 1574–1578, 2004.

T. Bottiglieri, “Homocysteine and folate metabolism in depression,” Progress in Neuro-Psychopharmacology and Biological Psychiatry, vol. 29, no. 7, pp. 1103–1112, 2005.

S. G. Amur, G. Shanker, J. M. Cochran, H. S. Ved, and R. A. Pieringer, “Correlation between inhibition of myelin basic protein (arginine) methyltransferase by sinefungin and lack of compact myelin formation in cultures of cerebral cells from embryonic mice,” Journal of Neuroscience Research, vol. 16, no. 2, pp. 367–376, 1986.

A. R. White, X. Huang, M. F. Jobling et al., “Homocysteine potentiates copper- and amyloid beta peptide-mediated toxicity in primary neuronal cultures: possible risk factors in the Alzheimer’s-type neurodegenerative pathways,” Journal of Neurochemistry, vol. 76, no. 5, pp. 1509–1520, 2001.

T. Bottiglieri, “Folate, vitamin B12, and neuropsychiatric disorders,” Nutrition Reviews, vol. 54, no. 12, pp. 382–390, 1996.

I. R. Bell, J. S. Edman, F. D. Morrow et al., “B complex vitamin patterns in geriatric and young adult inpatients with major depression,” Journal of the American Geriatrics Society, vol. 39, no. 3, pp. 252–257, 1991.

D. G. Savage, J. Lindenbaum, S. P. Stabler, and R. H. Allen, “Sensitivity of serum methylmalonic acid and total homocysteine determinations for diagnosing cobalamin and folate deficiencies,” American Journal of Medicine, vol. 96, no. 3, pp. 239–246, 1994.

A. W. Lis, I. Mclaughlin, R. K. Mpclaughlin, E. W. Lis, and E. G. Stubbs, “Profiles of ultraviolet-absorbing components of urine from autistic children, as obtained by high-resolution ion-exchange chromatography,” Clinical Chemistry, vol. 22, no. 9, pp. 1528–1532, 1976.

A. Noto, V. Fanos, L. Barberini et al., “The urinary metabolomics profile of an Italian autistic children population and their unaffected siblings,” The Journal of Maternal-Fetal & Neonatal Medicine, vol. 27, Supplement 2, pp. 46–52, 2014.

O. Benkert, A. Renz, C. Marano, and N. Matussek, “Altered tyrosine daytime plasma levels in endogenous depressive patients,” Archives of General Psychiatry, vol. 25, no. 4, pp. 359–363, 1971.

A. Mclean, J. S. Rubinsztein, T. W. Robbins, and B. J. Sahakian, “The effects of tyrosine depletion in normal healthy volunteers: implications for unipolar depression,” Psychopharmacology, vol. 171, no. 3, pp. 286–297, 2004.

Y. Sheline, M. E. Bardgett, and J. G. Csernansky, “Correlated reductions in cerebrospinal fluid 5-HIAA and MHPG concentrations after treatment with selective serotonin reuptake inhibitors,” Journal of Clinical Psychopharmacology, vol. 17, no. 1, pp. 11–14, 1997.

A. Roy, D. Pickar, J. D. Jong, F. Karoum, and M. Linnoila, “Norepinephrine and its metabolites in cerebrospinal fluid, plasma, and urine: relationship to hypothalamic-pituitary-adrenal axis function in depression,” Archives of General Psychiatry, vol. 45, no. 9, pp. 849–857, 1988.

W. Wang, H. Guo, S. X. Zhang et al., “Targeted metabolomic pathway analysis and validation revealed glutamatergic disorder in the prefrontal cortex among the chronic social defeat stress mice model of depression,” Journal of Proteome Research, vol. 15, no. 10, pp. 3784–3792, 2016.

H. Mitani, Y. Shirayama, T. Yamada, K. Maeda, C. R. Ashby Jr, and R. Kawahara, “Correlation between plasma levels of glutamate, alanine and serine with severity of depression,” Progress in Neuro-Psychopharmacology and Biological Psychiatry, vol. 30, no. 6, pp. 1155–1158, 2006.