Metabolic Profiling Indicates Diversity in the Metabolic Physiologies Associated With Maternal Postpartum Depressive Symptoms

Unveiling chiral amino acid alterations and glycine dysregulation in late-life depression through targeted metabolomics

Background

Late-life depression (LLD) is a major depressive disorder that is highly prevalent among older people, and there are currently no validated biomarkers for the diagnosis and treatment of LLD. Although dysregulated amino acid metabolism has been increasingly implicated in neuropsychiatric disorders, including LLD, most existing studies overlook the chiral nature of amino acids, potentially leading to inaccurate or incomplete findings. To address this gap, this study aimed to precisely characterize the serum chiral amino acid profiles in patients with LLD and identify potential biomarkers.

Methods

Using liquid chromatography tandem mass spectrometry combined with a chiral derivatization technique, the serum levels of 34 amino acids were analyzed in 53 LLD patients and 37 healthy controls (HCs).

Results

Significant alterations in both D- and L-enantiomers were observed, including reduced levels of D-methionine, D-glutamic acid, D-threonine, and L-threonine, alongside elevated glycine levels in LLD compared to HCs. The combination of D-methionine and glycine demonstrated moderate discriminatory power for distinguishing LLD from HCs, with an area under the curve of 0.71. Notably, glycine levels were significantly lower in antidepressant treatment responders than in non-responders. Additionally, D- and L-glutamic acid levels were differentially associated with specific cognitive function indicators.

Discussion

These findings underscore the importance of accounting for amino acid chirality in biomarker research and highlight chiral amino acids as promising candidates for the diagnosis of LLD and the prediction of treatment response.

Genetic prediction of blood metabolites mediating the relationship between gut microbiota and postpartum depression: A mendelian randomization study

BACKGROUND

Observational studies have suggested an association between gut microbiota(GM) and postpartum depression (PPD). However, the causal relationship remains unclear, and the role of blood metabolites in this association remains elusive.

METHODS

This study firstly elucidated the causal relationship among 196 GM taxa, 224 blood metabolites, and PPD from a genetic perspective, employing two-sample Mendelian randomization (MR). Subsequently, a two-step mediation MR approach was employed to explore the role of blood metabolites as potential mediators. To validate the relevant findings, we further selected other data (GM and blood metabolites) from the IEU Open GWAS and GWAS Catalog for analysis. Our primary analysis utilized the inverse variance weighted method. To enhance the robustness of our results, we also applied MR-Egger method, weighted median method, Cochran's Q test, MR-Egger regression, and MR-PRESSO.

RESULTS

MR analysis results revealed a nominal association (p < 0.05) between 13 GM taxa, 6 blood metabolites, and PPD. After multiple-testing correction (PFDR < 0.1), Bifidobacteriales (PFDR = 0.034), Bifidobacteriaceae (PFDR = 0.055) and Guanosine (PFDR = 0.081) showed significant causal relationships with PPD. In our validation results, the higher level of Alphaproteobacteria (OR: 1.057, 95% CI: 1.024-1.091; p = 0.0006) retained a causal relationship with a higher risk of PPD. Finally, mediation analysis revealed that the impact of Odoribacter on PPD was mediated indirectly through Hyodeoxycholate, with a mediation proportion of 16.8%.

CONCLUSION

Our findings elucidated the underlying mechanisms between the GM, blood metabolites, and PPD. These findings contribute to the prevention and diagnosis of PPD, offering novel insights into microbiome-based therapies and metabolite-targeted interventions for the treatment of PPD.

Perinatal depression and risk of maternal cardiovascular disease: a Swedish nationwide study

BACKGROUND AND AIMS

Increasing evidence suggests that some reproductive factors/hazards are associated with a future risk of cardiovascular disease (CVD) in women. While major (non-perinatal) depression has consistently been associated with CVD, the long-term risk of CVD after perinatal depression (PND) is largely unknown.

METHODS

A nationwide population-based matched cohort study involving 55 539 women diagnosed with PND during 2001-14 in Sweden and 545 567 unaffected women individually matched on age and year of conception/delivery was conducted. All women were followed up to 2020. Perinatal depression and CVD were identified from Swedish national health registers. Using multivariable Cox models, hazard ratios (HR) of any and type-specific CVD according to PND were estimated.

RESULTS

The mean age at the PND diagnosis was 30.8 [standard deviation (SD) 5.6] years. During the follow-up of up to 20 years (mean 10.4, SD 3.6), 3533 (6.4%) women with PND (expected number 2077) and 20 202 (3.7%) unaffected women developed CVD. Compared with matched unaffected women, women with PND had a 36% higher risk of developing CVD [adjusted HR = 1.36, 95% confidence interval (CI): 1.31-1.42], while compared with their sisters, women with PND had a 20% higher risk of CVD (adjusted HR = 1.20, 95% CI 1.07-1.34). The results were most pronounced in women without a history of psychiatric disorder (P for interaction < .001). The association was observed for all CVD subtypes, with the highest HR in the case of hypertensive disease (HR = 1.50, 95% CI: 1.41-1.60), ischaemic heart disease (HR = 1.37, 95% CI: 1.13-1.65), and heart failure (HR 1.36, 95% CI: 1.06-1.74).

CONCLUSIONS

Women with PND are at higher risk of CVD in middle adulthood. Reproductive history, including PND, should be considered in CVD risk assessments of women.

Blood metabolomic and postpartum depression: a mendelian randomization study

BACKGROUND

Postpartum depression is a complex mental health condition that often occurs after childbirth and is characterized by persistent sadness, anxiety, and fatigue. Recent research suggests a metabolic component to the disorder. This study aims to investigate the causal relationship between blood metabolites and postpartum depression using mendelian randomization (MR).

METHODS

This study used a bi-directional MR framework to investigate the causal relationship between 1,400 metabolic biomarkers and postpartum depression. We used two specific genome-wide association studies datasets: one with single nucleotide polymorphisms data from mothers diagnosed with postpartum depression and another with blood metabolite data, both of which focused on people of European ancestry. Genetic variants were chosen as instrumental variables from both datasets using strict criteria to improve the robustness of the MR analysis. The combination of these datasets enabled a thorough examination of genetic influences on metabolic profiles associated with postpartum depression. Statistical analyses were conducted using techniques such as inverse variance weighting, weighted median, and model-based estimation, which enabled rigorous causal inference from the observed associations. postpartum depression was defined using endpoint definitions approved by the FinnGen study's clinical expert groups, which included leading experts in their respective medical fields.

RESULTS

The MR analysis identified seven metabolites that could be linked to postpartum depression. Out of these, one metabolite was found to be protective, while six were associated with an increased risk of developing the condition. The results were consistent across multiple MR methods, indicating a significant correlation.

CONCLUSIONS

This study emphasizes the potential of metabolomics for understanding postpartum depression. The discovery of specific metabolites associated with the condition sheds new insights on its pathophysiology and opens up possibilities for future research into targeted treatment strategies.

Inflammatory biomarkers and perinatal depression: A systematic review

BACKGROUND

Approximately 10 to 20% of pregnant women worldwide experience perinatal depression (PND), a depressive episode with onset during pregnancy or after childbirth. We performed a systematic review to identify, summarize and discuss studies on inflammatory biomarkers described in relation to PND.

METHOD

Inclusion criteria defined the selection of observational studies written in English, French, Spanish or Portuguese, that evaluate analytical levels of inflammatory molecules (protein levels) in biological fluids in women, with a diagnosis of depression using ICD/DSM diagnostic criteria or depressive symptoms assessed by standardized psychometric instruments, during pregnancy and/or postpartum. Case reports, experimental studies, reviews, qualitative analysis, meta-analysis, gray literature or replicated data were excluded. Three electronic databases were used for search (Pubmed, Web of Science and PsychInfo) and quality assessment of selected studies were performed using the Newcastle-Ottawa Scale. Data extraction included study design; number of subjects; obstetric information; tools and timepoints of depression and inflammatory markers assessment.

RESULTS

56 studies (sample size for cross-sectional and case-control studies ranging from 10 to 469; sample size for longitudinal studies ranging from 26 to 467), where the major aim was to analyze the association between depression and inflammatory biomarkers during pregnancy and postpartum period were included in this systematic review. Overall, the findings of our systematic review lend support to the hypothesis that several inflammatory markers may be associated with peripartum depressive symptoms. The associations were somewhat different looking at pregnancy compared to the delivery time-point and postpartum, and mainly referred to increased levels of IL-6, IL-8, CRP and TNF-α among depressed.

DISCUSSION

In summary, our systematic review findings provide evidence supporting the hypothesis that several inflammatory markers may correlate with peripartum depressive symptoms. However, our work also highlighted notable differences in the timing of biological sampling for inflammatory markers and in the methodologies used to assess depression during the perinatal period. Additionally, variations were observed in how inflammatory biomarkers and depression were approached, including their classification as exposure or outcome variables, and the timing of assessments. It is essential for future research to investigate the influence of biological fluids and the timing of assessments for both inflammatory biomarkers and depression to gain a deeper understanding of their association. This comprehensive exploration is pivotal for elucidating the intricate relationship between inflammation and perinatal depression.

Study on plasma metabolomics profiling of depression in Chinese community-dwelling older adults based on untargeted LC/GC‒MS

Depression is a serious psychiatric illness that causes great inconvenience to the lives of elderly individuals. However, the diagnosis of depression is somewhat subjective. Nontargeted gas chromatography (GC)/liquid chromatography (LC)-mass spectrometry (MS) was used to study the plasma metabolic profile and identify objective markers for depression and metabolic pathway variation. We recruited 379 Chinese community-dwelling individuals aged ≥ 65. Plasma samples were collected and detected by GC/LC‒MS. Orthogonal partial least squares discriminant analysis and a heatmap were utilized to distinguish the metabolites. Receiver operating characteristic curves were constructed to evaluate the diagnostic value of these differential metabolites. Additionally, metabolic pathway enrichment was performed to reveal metabolic pathway variation. According to our standard, 49 people were included in the depression cohort (DC), and 49 people age- and sex-matched individuals were included in the non-depression cohort (NDC). 64 metabolites identified via GC‒MS and 73 metabolites identified via LC‒MS had significant contributions to the differentiation between the DC and NDC, with VIP values > 1 and p values < 0.05. Three substances were detected by both methods: hypoxanthine, phytosphingosine, and xanthine. Furthermore, 1-(sn-glycero-3-phospho)-1D-myo-inositol had the largest area under the curve (AUC) value (AUC = 0.842). The purine metabolic pathway is the most important change in metabolic pathways. These findings show that there were differences in plasma metabolites between the depression cohort and the non-depression cohort. These identified differential metabolites may be markers of depression and can be used to study the changes in depression metabolic pathways.

Fast Label-Free Metabolic Profile Recognition Identifies Phenylketonuria and Subtypes

Phenylketonuria (PKU) is the most common inherited metabolic disease in humans. Clinical screening of newborn heel blood samples for PKU is costly and time-consuming because it requires multiple procedures, like isotope labeling and derivatization, and PKU subtype identification requires an additional urine sample. Delayed diagnosis of PKU, or subtype identification can result in mental disability. Here, plasmonic silver nanoshells are used for laser desorption/ionization mass spectrometry (MS) detection of PKU with label-free assay by recognizing metabolic profile in dried blood spot (DBS) samples. A total of 1100 subjects are recruited and each DBS sample can be processed in seconds. This platform achieves PKU screening with a sensitivity of 0.985 and specificity of 0.995, which is comparable to existing clinical liquid chromatography MS (LC-MS) methods. This method can process 360 samples per hour, compared with the LC-MS method which processes only 30 samples per hour. Moreover, this assay enables precise identification of PKU subtypes without the need for a urine sample. It is demonstrated that this platform enables high-performance and fast, low-cost PKU screening and subtype identification. This approach might be suitable for the detection of other clinically relevant biomarkers in blood or other clinical samples.

Bilirubin and postpartum depression: an observational and Mendelian randomization study

Background

Postpartum depression (PPD) is one of the most common complications of delivery and is usually disregarded. Several risk factors of PPD have been identified, but its pathogenesis has not been completely understood. Serum bilirubin has been found to be a predictor of depression, whose relationship with PPD has not been investigated.

Methods

Observational research was performed followed by a two-sample Mendelian randomization (MR) analysis. From 2017 to 2020, the clinical data of pregnant women were retrospectively extracted. Logistic regression and random forest algorithm were employed to assess the risk factors of PPD, including the serum levels of total bilirubin and direct bilirubin. To further explore their potential causality, univariable and multivariable Mendelian randomization (MVMR) were conducted. Sensitivity analyses for MR were performed to test the robustness of causal inference.

Results

A total of 1,810 patients were included in the PPD cohort, of which 631 (34.87%) were diagnosed with PPD. Compared with the control group, PPD patients had a significantly lower level of total bilirubin (9.2 μmol/L, IQR 7.7, 11.0 in PPD; 9.7 μmol/L, IQR 8.0, 12.0 in control, P < 0.001) and direct bilirubin (2.0 μmol/L, IQR 1.6, 2.6 in PPD; 2.2 μmol/L, IQR 1.7, 2.9 in control, P < 0.003). The prediction model identified eight independent predictive factors of PPD, in which elevated total bilirubin served as a protective factor (OR = 0.94, 95% CI 0.90-0.99, P = 0.024). In the MR analyses, genetically predicted total bilirubin was associated with decreased risk of PPD (IVW: OR = 0.86, 95% CI 0.76-0.97, P = 0.006), which remained consistent after adjusting educational attainment, income, and gestational diabetes mellitus. Conversely, there is a lack of solid evidence to support the causal relationship between PPD and bilirubin.

Conclusion

Our results suggested that decreased total bilirubin was associated with the incidence of PPD. Future studies are warranted to investigate its potential mechanisms and illuminate the pathogenesis of PPD.

Association of Diabetes Mellitus in Pregnancy and Perinatal Depression

OBJECTIVE

Diabetes is frequently linked with depression, and both conditions are common complications during pregnancy. However, research findings exploring the relationship between diabetes mellitus in pregnancy (DMP) and perinatal depression (PND) have been inconsistent. Thus, this study seeks to examine the association between DMP and PND in a prospective population-based cohort.

METHODS

Women aged 18 to 48 years ( n = 4459) were identified from the Biology, Affect, Stress, Imaging and Cognition study. The diagnosis of DMP was based on International Classification of Diseases code O24 from medical records and was classified as pregestational, gestational, or unspecified diabetes. PND was assessed using psychometric instruments, clinical interviews, and/or register data and categorized into antepartum or postpartum depression. Multivariable logistic regressions were used to study the associations of DMP with antepartum and postpartum depression. The association between DMP and continuous depression scores, antepartum and postpartum, was investigated with multivariable linear regressions.

RESULTS

Of 4459 pregnancies, 949 women had antepartum depression (21.2%) and 1123 had postpartum depression (25%). DMP had a prevalence of 1.2%. Women with DMP had twofold higher odds for postpartum depression compared with women without DMP. Although no association was observed between DMP and antepartum depression, DMP was associated with higher antepartum depression scores.

CONCLUSIONS

Our study shows an association between DMP and PND, which might be considered a risk factor when screening for high-risk groups.

Overview of metabolomic aspects in postpartum depression

Along with the typical biochemical alterations that occur during pregnancy, certain metabolic changes might be associated with the development of several psychiatric disorders, including postpartum depression (PPD), which is the most common type of psychiatric disorder during pregnancy or first postpartum year, and it develops in about 15% of women. Metabolomics is a rapidly developing discipline that deals with the metabolites as the final products of all genetically controlled biochemical pathways, highly influenced by external and internal changes. The aim of this paper was to review the published studies whose results suggest or deny a possible association between the fine regulation of the metabolome and PPD, enabling conclusions about whether metabolomics could be a useful tool in defining the biochemical pathways directly involved in the etiology, diagnosis and course of PPD. Beside numerous hormonal changes, a lot of different metabolic pathways have been discovered to be affected in women with PPD or associated with its development, including alterations in the energy metabolism, tryptophan and amino acid metabolism, steroid metabolism, purine cycle, as well as neurotransmitter metabolism. Additionally, metabolomics helped in defining the association between PPD and the exposure to various endocrine disrupting metabolites during pregnancy. Finally, metabolome reflects different PPD therapies and exposure of fetus or breastfed infants to pharmacotherapy prescribed to a mother suffering from PPD. This review can help in creating the picture about metabolomics' broad application in PPD studies, but it also implies that its potential is still not completely used.

Potential CSF biomarkers of postpartum depression following delivery via caesarian section

BACKGROUND

Postpartum depression (PPD), the depressive episodes following delivery, is a serious and frequent psychiatric disorder. While numerous screening tools existed for depressive episodes, e.g., the Edinburgh Postnatal Depression Scale (EPDS), there are no objective biological measures for predicting PPD. Despite several studies done to identify biomarkers in PPD, there has been limited exploration into cerebrospinal fluid (CSF) which directly interfaces with the brain. Consequently, novel potential biomarkers of CSF are required to predict PPD, so as to target specific preventive interventions.

METHODS

Seventy-five parturients undergoing caesarean delivery were enrolled for CSF collection at delivery. Of the twenty-eight subjects who didn't meet any exclusion criteria, the number of the healthy parturients whose score of EPDS 6-weeks postpartum (6-wpp) < 5 and PPD patients whose EPDS 6-wpp ≥ 13 was ten respectively. Gas chromatography-mass spectrometry (GC-MS) analysis of CSF was used for metabolomic assessments.

RESULTS

We found that capric acid, dodecanoic acid, arachidic acid and behenic acid in CSF were significantly negatively correlated with PPD symptoms, meanwhile L-tryptophan had an obvious positive correlation. Moreover, these five biomarkers can be used as effective predictive biomarkers for PPD.

LIMITATIONS

The main limitations are the inclusion of only parturients who underwent caesarean sections and a small sample size.

CONCLUSIONS

This study innovatively investigated potential predictive biomarkers of PPD before the onset through intrapartum maternal CSF metabolomics, which offered a more objective approach to predict and diagnose PPD, leading to help identify high-risk parturients for early initiation of secondary prevention to reduce global PPD burden.

Cohort profile: the U-BIRTH study on peripartum depression and child development in Sweden

PURPOSE

The current U-BIRTH cohort (Uppsala Birth Cohort) extends our previous cohort Biology, Affect, Stress, Imaging and Cognition (BASIC), assessing the development of children up to 11 years after birth. The U-BIRTH study aims to (1) assess the impact of exposure to peripartum mental illness on the children's development taking into account biological and environmental factors during intrauterine life and childhood; (2) identify early predictors of child neurodevelopmental and psychological problems using biophysiological, psychosocial and environmental variables available during pregnancy and early post partum.

PARTICIPANTS

All mothers participating in the previous BASIC cohort are invited, and mother-child dyads recruited in the U-BIRTH study are consecutively invited to questionnaire assessments and biological sampling when the child is 18 months, 6 years and 11 years old. Data collection at 18 months (n=2882) has been completed. Consent for participation has been obtained from 1946 families of children having reached age 6 and from 698 families of children having reached age 11 years.

FINDINGS TO DATE

Based on the complete data from pregnancy to 18 months post partum, peripartum mental health was significantly associated with the development of attentional control and gaze-following behaviours, which are critical to cognitive and social learning later in life. Moreover, infants of depressed mothers had an elevated risk of difficult temperament and behavioural problems compared with infants of non-depressed mothers. Analyses of biological samples showed that peripartum depression and anxiety were related to DNA methylation differences in infants. However, there were no methylation differences in relation to infants' behavioural problems at 18 months of age.

FUTURE PLANS

Given that the data collection at 18 months is complete, analyses are now being undertaken. Currently, assessments for children reaching 6 and 11 years are ongoing.

Exploration for biomarkers of postpartum depression based on metabolomics: A systematic review

BACKGROUND

Postpartum depression (PPD) is the most frequent psychiatric complication during the postnatal period and its mechanisms are not fully understood. Metabolomics, can quantitatively measure metabolites in a high-throughput method, and thus uncover the underlying pathophysiology of disease.

OBJECTIVES

In this study, we reviewed metabolomics studies conducted on PPD, aiming to understand the changes of metabolites in PPD patients and analyze the potential application of metabolomics in PPD prediction and diagnosis.

METHODS

Relevant articles were searched in PubMed, Google scholar, and Web of Science databases from January 2011 to July 2022. The metabolites involved were systematically examined and compared. MetaboAnalyst online software was applied to analyze metabolic pathways.

RESULTS

A total of 14 papers were included in this study. There were several highly reported metabolites, such as kynurenine, kynurenic acid, and eicosapentaenoic acid. Dysregulation of metabolic pathways involved amino acids metabolism, fatty acids metabolism, and steroids metabolism.

LIMITATIONS

The included studies are relatively inadequate, and further work is needed.

CONCLUSIONS

This study summarized significant metabolic alterations that provided clues for the prediction, diagnosis, and pathogenesis of PPD.

Integrated network pharmacology and hepatic metabolomics to reveal the mechanism of Acanthopanax senticosus against major depressive disorder

Objective:Acanthopanax senticosus (Rupr. et Maxim.) Harms (ASH) is a traditional herbal medicine widely known for its antifatigue and antistress effects, as well as tonifying qi, invigorating spleen and kidney, and tranquilizing the mind. Recent evidence suggests that ASH has a therapeutic effect on major depressive disorder (MDD), but its mechanism is still unclear. The current study aimed to investigate the effect of ASH on MDD and potential therapeutic mechanisms.

Materials and Methods: The chemical compound potential target network was predicted based on network pharmacology. Simultaneously, chronic unpredictable mild stress (CUMS) model mice were orally administrated ASH with three dosages (400, 200, and 100 mg/kg) for 6 weeks, and hepatic metabolomics based on gas chromatography–mass spectrometry (GC–MS) was carried out to identify differential metabolites and related metabolic pathways. Next, the integrated analysis of metabolomics and network pharmacology was applied to find the key target. Finally, molecular docking technology was employed to define the combination of the key target and the corresponding compounds.

Results: A total of 13 metabolites and four related metabolic pathways were found in metabolomics analysis. From the combined analysis of network pharmacology and metabolomics, six targets (DAO, MAOA, MAOB, GAA, HK1, and PYGM) are the overlapping targets and two metabolic pathways (glycine, serine, and threonine metabolism and starch and sucrose metabolism) are the most related pathways. Finally, DAO, MAOA, MAOB, GAA, HK1, and PYGM were verified bounding well to their corresponding compounds including isofraxidin, eleutheroside B1, eleutheroside C, quercetin, kaempferol, and acacetin.

Conclusion: Based on these results, it was implied that the potential mechanism of ASH on MDD was related to the regulation of metabolism of several excitatory amino acids and carbohydrates, as well as the expression of DAO, MAOA, MAOB, GAA, HK1, and PYGM.