Metabolomic biosignature differentiates melancholic depressive patients from healthy controls

Artificial Intelligence in Metabolomics: A Current Review

Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.

An Analysis of the Intestinal Microbiome Combined with Metabolomics to Explore the Mechanism of How Jasmine Tea Improves Depression in CUMS-Treated Rats

Recently, research has confirmed that jasmine tea may help improve the depressive symptoms that are associated with psychiatric disorders. Our team previously found that jasmine tea improved the depressive-like behavior that is induced by chronic unpredictable mild stress (CUMS) in Sprague Dawley (SD) rats. We hypothesized that the metabolic disorder component of depression may be related to the gut microbiota, which may be reflected in the metabolome in plasma. The influence of jasmine tea on gut microbiota composition and the association with depressive-related indexes were explored. Furthermore, the metabolites in plasma that are related to the gut microbiota were identified. SD rats were treated with control or CUMS and administrated jasmine tea for 8 weeks. The 16S rRNA gene amplicon sequencing was used to analyze the gut microbiota in feces samples, and untargeted metabolomics was used to analyze the metabolites in plasma. The results found that jasmine tea significantly ameliorated the depressive behavior induced by CUMS, significantly improved the neurotransmitter concentration (BDNF and 5-HT), and decreased the pro-inflammation levels (TNF-α and NF-κB). The intervention of jasmine tea also alleviated the dysbiosis caused by CUMS; increased the relative abundance of Bacteroides, Blautia, Clostridium, and Lactobacillus; and decreased Ruminococcus and Butyrivibrio in the CUMS-treated rats. Furthermore, the serum metabolites of the CUMS-treated rats were reversed after the jasmine tea intervention, i.e., 22 were up-regulated and 18 were down-regulated, which may have a close relationship with glycerophospholipid metabolism pathways, glycine serine and threonine metabolism pathways, and nicotinate and nicotinamide metabolism pathways. Finally, there were 30 genera of gut microbiota related to the depressive-related indexes, and 30 metabolites in the plasma had a strong predictive ability for depressive behavior. Potentially, our research implies that the intervention of jasmine tea can ameliorate the depression induced by CUMS via controlling the gut flora and the host's metabolism, which is an innovative approach for the prevention and management of depression.

Alterations in Vagal Tone Are Associated with Changes in the Gut Microbiota of Adults with Anxiety and Depression Symptoms: Analysis of Fecal Metabolite Profiles

Accumulating evidence suggests that interactions between the brain and gut microbiota significantly impact brain function and mental health. In the present study, we aimed to investigate whether young, healthy adults without psychiatric diagnoses exhibit differences in metabolic stool and microbiota profiles based on depression/anxiety scores and heart rate variability (HRV) parameters. Untargeted nuclear magnetic resonance-based metabolomics was used to identify fecal metabolic profiles. Results were subjected to multivariate analysis through principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), and the metabolites were identified through VIP score. Metabolites separating asymptomatic and symptomatic groups were acetate, valine, and glutamate, followed by sugar regions, glutamine, acetone, valerate, and acetoacetate. The main metabolites identified in high vagal tone (HVT) and low vagal tone (LVT) groups were acetate, valerate, and glutamate, followed by propionate and butyrate. In addition to the metabolites identified by the PLS-DA test, significant differences in aspartate, sarcosine, malate, and methionine were observed between the groups. Levels of acetoacetate were higher in both symptomatic and LVT groups. Valerate levels were significantly increased in the symptomatic group, while isovalerate, propionate, glutamate, and acetone levels were significantly increased in the LVT group. Furthermore, distinct abundance between groups was only confirmed for the Firmicutes phylum. Differences between participants with high and low vagal tone suggest that certain metabolites are involved in communication between the vagus nerve and the brain.

Using an Interpretable Amino Acid-Based Machine Learning Method to Enhance the Diagnosis of Major Depressive Disorder

Background: Major depressive disorder (MDD) is a leading cause of disability worldwide. At present, however, there are no established biomarkers that have been validated for diagnosing and treating MDD. This study sought to assess the diagnostic and predictive potential of the differences in serum amino acid concentration levels between MDD patients and healthy controls (HCs), integrating them into interpretable machine learning models. Methods: In total, 70 MDD patients and 70 HCs matched in age, gender, and ethnicity were recruited for the study. Serum amino acid profiling was conducted by means of chromatography-mass spectrometry. A total of 21 metabolites were analysed, with 17 from a preset amino acid panel and the remaining 4 from a preset kynurenine panel. Logistic regression was applied to differentiate MDD patients from HCs. Results: The best-performing model utilised both feature selection and hyperparameter optimisation and yielded a moderate area under the receiver operating curve (AUC) classification value of 0.76 on the testing data. The top five metabolites identified as potential biomarkers for MDD were 3-hydroxy-kynurenine, valine, kynurenine, glutamic acid, and xanthurenic acid. Conclusions: Our study highlights the potential of using an interpretable machine learning analysis model based on amino acids to aid and increase the diagnostic accuracy of MDD in clinical practice.

Assessing the risk of prenatal depressive symptoms in Chinese women: an integrated evaluation of serum metabolome, multivitamin supplement intake, and clinical blood indicators

Background

Prenatal depressive symptoms (PDS) is a serious public health problem. This study aimed to develop an integrated panel and nomogram to assess at-risk populations by examining the association of PDS with the serum metabolome, multivitamin supplement intake, and clinical blood indicators.

Methods

This study comprised 221 pregnant women, categorized into PDS and non-PDS groups based on the Edinburgh postnatal depression scale. The participants were divided into training and test sets according to their enrollment time. We conducted logistic regression analysis to identify risk factors, and employed liquid chromatography/high resolution mass spectrometry-based serum metabolome analysis to identify metabolic biomarkers. Multiple factor analysis was used to combine risk factors, clinical blood indicators and key metabolites, and then a nomogram was developed to estimate the probability of PDS.

Results

We identified 36 important differential serum metabolites as PDS biomarkers, mainly involved in amino acid metabolism and lipid metabolism. Multivitamin intake works as a protective factor for PDS. The nomogram model, including multivitamin intake, HDL-C and three key metabolites (histidine, estrone and valylasparagine), exhibited an AUC of 0.855 in the training set and 0.774 in the test set, and the calibration curves showed good agreement, indicating that the model had good stability.

Conclusion

Our approach integrates multiple models to identify metabolic biomarkers for PDS, ensuring their robustness. Furthermore, the inclusion of dietary factors and clinical blood indicators allows for a comprehensive characterization of each participant. The analysis culminated in an intuitive nomogram based on multimodal data, displaying potential performance in initial PDS risk assessment.

Circulating metabolites and depression: a bidirectional Mendelian randomization

Background

Studies have shown an association between depression and circulating metabolites, but the causal relationship between them has not been elucidated. The purpose of this study was to elucidate the causal relationship between circulating metabolites and depression and to explore the role of circulating metabolites in depression.

Methods

In this study, the top single-nucleotide polymorphisms (SNPs) associated with circulating metabolites (n = 24,925) and depression (n = 322,580) were obtained based on the publicly available genome-wide association study using two-sample Mendelian randomization (MR). SNP estimates were summarized through inverse variance weighted, MR Egger, weighted median, MR pleiotropy residual sum and outlier, and "leave-one-out" methods.

Results

Apolipoprotein A-I (OR 0.990, 95% CI 981-0.999) and glutamine (OR 0.985, 95% CI 0.972-0.997) had protective causal effects on depression, whereas acetoacetate (OR 1.021, 95% CI 1.009-1.034), glycoproteins (OR 1.005, 95% CI 1.000-1.009), isoleucine (OR 1.013, 95% CI 1.002-1.024), and urea (OR 1.020, 95% CI 1.000-1.039) had an anti-protective effect on depression. Reversed MR showed no effect of depression on the seven circulating metabolites.

Conclusion

In this study, MR analysis showed that apolipoprotein A-I and glutamine had a protective effect on depression, and acetoacetate, glycoprotein, isoleucine, glucose, and urea may be risk factors for depression. Therefore, further research must be conducted to translate the findings into practice.

A combination of neuroimaging and plasma metabolomic analysis suggests inflammation is associated with white matter structural connectivity in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a common mental disorder with unknown pathophysiology. The abnormality of white matter structural connectivity and dysregulation of metabolome in MDD had been widely reported previously. Exploration of the relationship between white matter structural connectivity and plasma metabolites would be helpful for explanation of molecular mechanism for the findings from neuroimaging researches in MDD.

METHODS

The diffusion spectrum imaging data were collected for identification of difference of white matter structural connectivity between MDD (n = 49) and HC (n = 68). The plasma metabolite profiles were acquired by liquid chromatography-mass spectrometry analysis and clustered as co-expression modules. The correlation analysis was performed to identify structural connectivity associated metabolite.

RESULTS

We identified two structural connectivity related metabolite modules. One module was correlated with fractional anisotropy (FA) value between left middle temporal gyrus and left inferior temporal gyrus, which were enriched in tryptophan metabolism pathway; another module was correlated with fiber numbers (FN) between right fusiform gyrus and right inferior temporal gyrus, which was enriched in lysophosphatidylcholine (LPC), lysophosphatidylinositol (LPI) and lysophosphatidylglycerol (LPG) lipid sets. l-Kynurenine in tryptophan metabolism pathway was negatively correlated with FN between right fusiform gyrus and right inferior temporal gyrus, and LPC was positively correlated with FA value between left middle temporal gyrus and left inferior temporal gyrus in MDD.

LIMITATIONS

First, the sample size was relatively small. Second, the long-term effects of antidepressants were not excluded.

CONCLUSION

The results suggested inflammation-related mechanism was associated with white matter structural connectivity in MDD.

Whole genome sequence analysis of two subspecies of Companilactobacillus Futsaii and experimental verification of drug resistance and effect on the exploratory behavior of mice based on unique gene

This study characterized the whole genome of Companilactobacillus futsaii subsp. chongqingii CQ16Z1 isolated from Chongqing of China, performed genome sequence analysis with Companilactobacillus futsaii subsp. futsaii YM0097 isolated from Taiwan of China, and experimentally verified drug resistance and effect on the exploratory behavior of male C57BL/6 mice and analysis of gut microbiota and metabolomic studies. The genome of CQ16Z1 is 2.6 Mb. Sequence analysis between genomes showed that the two strains are Companilactobacillus futsaii. The unique genes of CQ16Z1 and YM0097 are 217 and 267, which account for 9% and 11% of the whole genomes, respectively. According to unique gene annotation, the results showed that genes associated with carbohydrate metabolism, environmental information processing, metabolism of cofactors and vitamins, cell wall/membrane/envelope biogenesis, phage and drug resistance are significantly different. The results of the drug resistance experiment showed that YM0097 had different degrees of resistance to 13 antibiotics, while CQ16Z1 was sensitive to more than half of them. YM0097 contains 9 prophage regions and CQ16Z1 contains 3 prophage regions. The results of the open field test showed that the time (P = 0.005; P = 0.047) and distance (P < 0.010; P = 0.046) of the central area of Y97 group and CQ group are significantly different from the control group. The results of the elevated plus maze test showed that compared with the control group, Y97 group had significant differences in the number of entries to the open arms and the percentage of open arms entry times (P = 0.004; P = 0.025), while the difference between the CQ group and the control group was not significant. YM0097 has a more obvious effect on the exploratory behavior of mice. The effects of YM0097 and CQ16Z1 on the intestinal flora of mice are also different. YM0097 may be more beneficial to the intestinal flora of the host. And LC/MS also showed that the metabolic effects of the two strains on the host are different. Finally, we believe that YM0097 is more suitable for application research as a psychobiotics.

Metabolomics of Major Depressive Disorder: A Systematic Review of Clinical Studies

Although the understanding of the pathophysiology of major depressive disorder (MDD) has advanced greatly, this has not been translated into improved outcomes. To date, no biomarkers have been identified for the diagnosis, prognosis, and therapeutic management of MDD. Thus, we aim to review the biomarkers that are differentially expressed in MDD. A systematic review was conducted in January 2022 in the PubMed/MEDLINE, Scopus, Embase, PsycINFO, and Gale Academic OneFile databases for clinical studies published from January 2001 onward using the following terms: "Depression" OR "Depressive disorder" AND "Metabolomic." Multiple metabolites were found at altered levels in MDD, demonstrating the involvement of cellular signaling metabolites, components of the cell membrane, neurotransmitters, inflammatory and immunological mediators, hormone activators and precursors, and sleep controllers. Kynurenine and acylcarnitine were identified as consistent with depression and response to treatment. The most consistent evidence found was regarding kynurenine and acylcarnitine. Although the data obtained allow us to identify how metabolic pathways are affected in MDD, there is still not enough evidence to propose changes to current diagnostic and therapeutic actions. Some limitations are the heterogeneity of studies on metabolites, methods for detection, analyzed body fluids, and treatments used. The experiments contemplated in the review identified increased or reduced levels of metabolites, but not necessarily increased or reduced the activity of the associated pathways. The information acquired through metabolomic analyses does not specify whether the changes identified in the metabolites are a cause or a consequence of the pathology.

A pilot exploration of multi-omics research of gut microbiome in major depressive disorders

The pathophysiology of major depressive disorder (MDD) remains obscure. Recently, the microbiota-gut-brain (MGB) axis's role in MDD has an increasing attention. However, the specific mechanism of the multi-level effects of gut microbiota on host metabolism, immunity, and brain structure is unclear. Multi-omics approaches based on the analysis of different body fluids and tissues using a variety of analytical platforms have the potential to provide a deeper understanding of MGB axis disorders. Therefore, the data of metagenomics, metabolomic, inflammatory factors, and MRI scanning are collected from the two groups including 24 drug-naïve MDD patients and 26 healthy controls (HCs). Then, the correlation analysis is performed in all omics. The results confirmed that there are many markedly altered differences, such as elevated Actinobacteria abundance, plasma IL-1β concentration, lipid, vitamin, and carbohydrate metabolism disorder, and diminished grey matter volume (GMV) of inferior frontal gyrus (IFG) in the MDD patients. Notably, three kinds of discriminative bacteria, Ruminococcus bromii, Lactococcus chungangensis, and Streptococcus gallolyticus have an extensive correlation with metabolome, immunology, GMV, and clinical symptoms. All three microbiota are closely related to IL-1β and lipids (as an example, phosphoethanolamine (PEA)). Besides, Lactococcus chungangensis is negatively related to the GMV of left IFG. Overall, this study demonstrate that the effects of gut microbiome exert in MDD is multifactorial.

Discovery and validation of acetyl-L-carnitine in serum for diagnosis of major depressive disorder and remission status through metabolomic approach

Major depressive disorder (MDD) is one of the most common psychiatric disorders that accompany psychophysiological and mood changes. However, the pathophysiology-based disease mechanism of MDD is not yet fully understood, and diagnosis is also conducted through interviews with clinicians and patients. Diagnosis and treatment of MDD are limited due to the absence of biomarkers underlying the pathophysiological mechanisms of MDD. Although various attempts have been made to discover metabolite biomarkers for the diagnosis and treatment response of MDD, problems with sample size and consistency of results have limited clinical application. In addition, it was reported that future biomarker studies must consider exposure to antidepressants, which is the main cause of heterogeneity in depression subgroups. Therefore, the purpose of this study is to discover and validate biomarkers for the diagnosis of depression in consideration of exposure to drug treatment including antidepressants that contribute to the heterogeneity of the MDD subgroup. In the biomarker discovery and validation set, the disease group consisted of a mixture of patients exposed and unexposed to drug treatment including antidepressants for the treatment of MDD. The serum metabolites that differed between the MDD patients and the control group were profiled using mass spectrometry. The validation set including the remission group was used to verify the effectiveness as a biomarker for the diagnosis of depression and determination of remission status. The presence of different metabolites between the two groups was confirmed through serum metabolite profiling between the MDD patient group and the control group. Finally, Acetylcarnitine was selected as a biomarker. In validation, acetylcarnitine was significantly decreased in MDD and was distinguished from remission status. This study confirmed that the discovered acetylcarnitine has potential as a biomarker for diagnosing depression and determining remission status, regardless of exposure to drug treatment including antidepressants.

Plasma metabolomic profiles associated with chronic distress in women

Several forms of chronic distress including anxiety and depression are associated with adverse cardiometabolic outcomes. Metabolic alterations may underlie these associations. Whether these forms of distress are associated with metabolic alterations even after accounting for comorbid conditions and other factors remains unclear. Using an agnostic approach, this study examines a broad range of metabolites in relation to chronic distress among women. For this cross-sectional study of chronic distress and 577 plasma metabolites, data are from different substudies within the Women's Health Initiative (WHI) and Nurses' Health Studies (NHSI, NHSII). Chronic distress was characterized by depressive symptoms and other depression indicators in the WHI and NHSII substudies, and by combined indicators of anxiety and depressive symptoms in the NHSI substudy. We used a two-phase discovery-validation framework, with WHI (N = 1317) and NHSII (N = 218) substudies in the discovery phase (identifying metabolites associated with distress) and NHSI (N = 558) substudy in the validation phase. A differential network analysis provided a systems-level assessment of metabolomic alterations under chronic distress. Analyses adjusted for potential confounders and mediators (demographics, comorbidities, medications, lifestyle factors). In the discovery phase, 46 metabolites were significantly associated with depression measures. In validation, six of these metabolites demonstrated significant associations with chronic distress after adjustment for potential confounders. Among women with high distress, we found lower gamma-aminobutyric acid (GABA), threonine, biliverdin, and serotonin and higher C16:0 ceramide and 3-methylxanthine. Our findings suggest chronic distress is associated with metabolomic alterations and provide specific targets for future study of biological pathways in chronic diseases.

Potential biomarkers of major depression diagnosis and chronicity

BACKGROUND

Molecular biomarkers are promising tools to be routinely used in clinical psychiatry. Among psychiatric diseases, major depression disorder (MDD) has gotten attention due to its growing prevalence and morbidity.

METHODS

We tested some peripheral molecular parameters such as serum mature Brain-Derived Neurotrophic Factor (mBDNF), plasma C-Reactive Protein (CRP), serum cortisol (SC), and the salivary Cortisol Awakening Response (CAR), as well as the Pittsburgh sleep quality inventory (PSQI), as part of a multibiomarker panel for potential use in MDD diagnosis and evaluation of disease's chronicity using regression models, and ROC curve.

RESULTS

For diagnosis model, two groups were analyzed: patients in the first episode of major depression (MD: n = 30) and a healthy control (CG: n = 32). None of those diagnosis models tested had greater power than Hamilton Depression Rating Scale-6. For MDD chronicity, a group of patients with treatment-resistant major depression (TRD: n = 28) was tested across the MD group. The best chronicity model (p < 0.05) that discriminated between MD and TRD included four parameters, namely PSQI, CAR, SC, and mBDNF (AUC ROC = 0.99), with 96% of sensitivity and 93% of specificity.

CONCLUSION

These results indicate that changes in specific biomarkers (CAR, SC, mBDNF and PSQI) have potential on the evaluation of MDD chronicity, but not for its diagnosis. Therefore, these findings can contribute for further studies aiming the development of a stronger model to be commercially available and used in psychiatry clinical practice.

Metabolomics-based discrimination of patients with remitted depression from healthy controls using 1H-NMR spectroscopy

The aim of the study was to investigate differences in metabolic profiles between patients with major depressive disorder (MDD) with full remission (FR) and healthy controls (HCs). A total of 119 age-matched MDD patients with FR (n = 47) and HCs (n = 72) were enrolled and randomly split into training and testing sets. A 1H-nuclear magnetic resonance (NMR) spectroscopy-based metabolomics approach was used to identify differences in expressions of plasma metabolite biomarkers. Eight metabolites, including histidine, succinic acid, proline, acetic acid, creatine, glutamine, glycine, and pyruvic acid, were significantly differentially-expressed in the MDD patients with FR in comparison with the HCs. Metabolic pathway analysis revealed that pyruvate metabolism via the tricarboxylic acid cycle linked to amino acid metabolism was significantly associated with the MDD patients with FR. An algorithm based on these metabolites employing a linear support vector machine differentiated the MDD patients with FR from the HCs with a predictive accuracy, sensitivity, and specificity of nearly 0.85. A metabolomics-based approach could effectively differentiate MDD patients with FR from HCs. Metabolomic signatures might exist long-term in MDD patients, with metabolic impacts on physical health even in patients with FR.

Altered Fecal Metabolites and Colonic Glycerophospholipids Were Associated With Abnormal Composition of Gut Microbiota in a Depression Model of Mice

The microbiota–gut–brain axis has been considered to play an important role in the development of depression, but the underlying mechanism remains unclear. The gastrointestinal tract is home to trillions of microbiota and the colon is considered an important site for the interaction between microbiota and host, but few studies have been conducted to evaluate the alterations in the colon. Accordingly, in this study, we established a chronic social defeated stress (CSDS) mice model of depression. We applied 16S rRNA gene sequencing to assess the gut microbial composition and gas and liquid chromatography–mass spectroscopy to identify fecal metabolites and colonic lipids, respectively. Meanwhile, we used Spearman’s correlation analysis method to evaluate the associations between the gut microbiota, fecal metabolites, colonic lipids, and behavioral index. In total, there were 20 bacterial taxa and 18 bacterial taxa significantly increased and decreased, respectively, in the CSDS mice. Further, microbial functional prediction demonstrated a disturbance of lipid, carbohydrate, and amino acid metabolism in the CSDS mice. We also found 20 differential fecal metabolites and 36 differential colonic lipids (in the category of glycerolipids, glycerophospholipids, and sphingolipids) in the CSDS mice. Moreover, correlation analysis showed that fecal metabolomic signature was associated with the alterations in the gut microbiota composition and colonic lipidomic profile. Of note, three lipids [PC(16:0/20:4), PG(22:6/22:6), and PI(18:0/20:3), all in the category of glycerophospholipids] were significantly associated with anxiety- and depression-like phenotypes in mice. Taken together, our results indicated that the gut microbiota might be involved in the pathogenesis of depression via influencing fecal metabolites and colonic glycerophospholipid metabolism.

Comprehensive Analysis Strategy of Nervous-Endocrine-Immune-Related Metabolites to Evaluate Arachidonic Acid as a Novel Diagnostic Biomarker in Depression

Depression is one of the most complex multifactorial diseases affected by genetic and environmental factors. The molecular mechanism underlying depression remains largely unclear. To address this issue, a novel nervous-endocrine-immune (NEI) network module was used to find the metabolites and evaluate the diagnostic ability of patients with depression. During this process, metabolites were acquired from a professional depression metabolism database. Over-representation analysis was performed using IMPaLA. Then, the metabolite-metabolite interaction (MMI) network of the NEI system was used to select key metabolites. Finally, the receiver operating characteristic curve analysis was evaluated for the diagnostic ability of arachidonic acid. The results show that the numbers of the nervous system, endocrine system, and immune system pathways are 10, 19, and 12 and the numbers of metabolites are 38, 52, and 13, respectively. The selected shared metabolite-enriched pathways can be 97.56% of the NEI-related pathways. Arachidonic acid was extracted from the NEI system network by using an optimization formula and validated by in vivo experiments. It was indicated that the proposed model was good at screening arachidonic acid for the diagnosis of depression. This method provides reliable evidences and references for the diagnosis and mechanism research of other related diseases.

Peripheral Markers of Depression

Major Depressive Disorder (MDD) is a leading cause of disability worldwide, creating a high medical and socioeconomic burden. There is a growing interest in the biological underpinnings of depression, which are reflected by altered levels of biological markers. Among others, enhanced inflammation has been reported in MDD, as reflected by increased concentrations of inflammatory markers-C-reactive protein, interleukin-6, tumor necrosis factor-α and soluble interleukin-2 receptor. Oxidative and nitrosative stress also plays a role in the pathophysiology of MDD. Notably, increased levels of lipid peroxidation markers are characteristic of MDD. Dysregulation of the stress axis, along with increased cortisol levels, have also been reported in MDD. Alterations in growth factors, with a significant decrease in brain-derived neurotrophic factor and an increase in fibroblast growth factor-2 and insulin-like growth factor-1 concentrations have also been found in MDD. Finally, kynurenine metabolites, increased glutamate and decreased total cholesterol also hold promise as reliable biomarkers for MDD. Research in the field of MDD biomarkers is hindered by insufficient understanding of MDD etiopathogenesis, substantial heterogeneity of the disorder, common co-morbidities and low specificity of biomarkers. The construction of biomarker panels and their evaluation with use of new technologies may have the potential to overcome the above mentioned obstacles.

Metabonomic Profile and Signaling Pathway Prediction of Depression-Associated Suicidal Behavior

Suicide is the most severe consequence of depression which has become a leading cause of disability and a global disease burden. Recent evidence indicates a central role of small molecules in the pathogenesis of depression and associated suicidal behaviors. However, there lacks a systemic exploration of small molecules in the development of depression-associated suicide, and it remains unclear how they affect an individual's behavior. In order to compare the metabonomic profiles between drug-naïve patients with depression-associated suicidal behaviors and healthy individuals, we conducted a systemic database search for studies of metabolic characteristics in depression-associated suicidal behavior. Manual data curation and statistical analysis and integration were performed in Excel. We further performed an enrichment analysis of signaling pathway prediction using the Reactome Pathway Analysis tool. We have identified 17 metabolites that expressed differently between drug-naïve patients with depression-associated suicidal behaviors and healthy controls. We have integrated these metabolites into biological signaling pathways and provided a visualized signaling network in depressed suicidal patients. We have revealed that "transport of small molecules", "disease", "metabolism" and "metabolism of proteins" were the most relevant signaling sections, among which "transport of inorganic cations/anions and amino acids/oligopeptides", "SLC-mediated transmembrane transport", and "metabolism of amino acids and derivatives" should be further studied to elucidate their potential pathogenic mechanism in the development of depression and associated suicidal behavior. In conclusion, our findings of these 17 metabolites and associated signaling pathways could provide an insight into the molecular pathogenesis of depression-associated suicidal behavior and potential targets for new drug inventions.

The biological face of melancholia: Are there any reliable biomarkers for this depression subtype?

BACKGROUND

Melancholic depression (MD) is a subtype of Major Depression associated with more clinical severity and poorer prognosis that non-melancholic depression (NMD). The differentiation between depression subtypes is still clinical, although the identification of specific biomarkers could be useful for diagnosis and the development of new treatments. Purpose of the present manuscript is to review the biomarkers that have been associated with MD.

METHODS

We performed a bibliographic research on the main databases (PubMed, Embase, PsycInfo, Isi Web of Knowledge, Medscape, The Cochrane Library), in order to find studies that proposed biological markers for melancholic depression. A total of 14 studies met our inclusion criteria.

RESULTS

Most of studies focused on immune dysregulation. Subjects with MD show biological abnormalities than healthy controls (HC). MD might be characterized by specific biological changes and it could be associated to more severe abnormalities with respect to NMD; however especially about this latter point the available data are preliminary.

LIMITATIONS

Most available data have not been replicated; the studies focused on different biomarkers. In addition, many articles report results on a limited sample size.

CONCLUSIONS

Melancholic depression is a subtype of major depression that seems to be associated with specific alterations of different biological systems. Future studies with larger sample can confirm the results and hypothesis presented in this review.

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately, 22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among the methods considered, support vector machines (SVM), logistic regression (LR), clustering were the most commonly used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future.

Metabolomics in Psychiatric Disorders: What We Learn from Animal Models

Biomarkers are a recent research target within biological factors of psychiatric disorders. There is growing evidence for deriving biomarkers within psychiatric disorders in serum or urine samples in humans, however, few studies have investigated this differentiation in brain or cerebral fluid samples in psychiatric disorders. As brain samples from humans are only available at autopsy, animal models are commonly applied to determine the pathogenesis of psychiatric diseases and to test treatment strategies. The aim of this review is to summarize studies on biomarkers in animal models for psychiatric disorders. For depression, anxiety and addiction disorders studies, biomarkers in animal brains are available. Furthermore, several studies have investigated psychiatric medication, e.g., antipsychotics, antidepressants, or mood stabilizers, in animals. The most notable changes in biomarkers in depressed animal models were related to the glutamate-γ-aminobutyric acid-glutamine-cycle. In anxiety models, alterations in amino acid and energy metabolism (i.e., mitochondrial regulation) were observed. Addicted animals showed several biomarkers according to the induced drugs. In summary, animal models provide some direct insights into the cellular metabolites that are produced during psychiatric processes. In addition, the influence on biomarkers due to short- or long-term medication is a noticeable finding. Further studies should combine representative animal models and human studies on cerebral fluid to improve insight into mental disorders and advance the development of novel treatment strategies.

Metabolomic markers of antepartum depression and suicidal ideation

BACKGROUND

Recent analyses have described metabolomic markers for depression and suicidal ideation in non-pregnant adults. We examined the metabolomic profile of antepartum depression and suicidal ideation during mid-pregnancy, a time of high susceptibility to mood disorders.

METHODS

We collected fasting blood from 100 pregnant Peruvian women and profiled 307 plasma metabolites using liquid chromatography-mass spectrometry. We used the Patient Health Questionnaire 9 to define antepartum depression (score  ≥ 10) and suicidal ideation (having thoughts that you would be better off dead, or of hurting yourself). Logistic regression was used to calculate odds ratios (ORs).

RESULTS

Three triacylglycerol metabolites (C48:5 triacylglycerol [OR = =1.89; 95% confidence interval (CI): 1.14-3.14], C50:6 triacylglycerol [OR = =1.88; 95%CI: 1.13-3.14], C46:4 triacylglycerol [OR = =1.89; 95%CI: 1.11-3.21]) were associated with higher odds of antepartum depression and 4 metabolites (betaine [OR = =0.56; 95%CI:0.33-0.95], citrulline [OR = =0.58; 95%CI: 0.34-0.98], C5 carnitine [OR = =0.59; 95%CI: 0.36-0.99], C5:1 carnitine [OR = =0.59; 95%CI: 0.35-1.00]) with lower odds of antepartum depression. Twenty-six metabolites, including 5-hydroxytryptophan (OR = =0.52; 95%CI: 0.30-0.92), phenylalanine (OR = =0.41; 95%CI: 0.19-0.91), and betaine (OR = =0.53; 95%CI: 0.28-0.99) were associated with lower odds of suicidal ideation.

LIMITATIONS

Our cross-sectional study could not determine whether metabolites prospectively predict outcomes. No metabolites remained significant after multiple testing correction; these novel findings should be replicated in a larger sample.

CONCLUSIONS

Antepartum suicidal ideation metabolomic markers are similar to markers of depression among non-pregnant adults, and distinct from markers of antepartum depression. Findings suggest that mood disorder in pregnancy shares metabolomic similarities to mood disorder at other times and may further understanding of these conditions' pathophysiology.

Chronic Stress in a Rat Model of Depression Disturbs the Glutamine-Glutamate-GABA Cycle in the Striatum, Hippocampus, and Cerebellum

BACKGROUND

Major depressive disorder (MDD) is a complex psychiatric illness involving multiple brain regions. Increasing evidence indicates that the striatum is involved in depression, but the molecular mechanisms remain unclear.

METHODS

In this study, we performed a gas chromatography-mass spectrometer (GC/MS)-based metabolomic analysis in the striatum of depressed rats induced by chronic unpredictable mild stress (CUMS). We then compared striatal data with our previous data from the hippocampus and cerebellum to systematically investigate the potential pathogenesis of depression.

RESULTS

We identified 22 differential metabolites in the striatum between the CUMS and control groups; these altered metabolites were mainly involved in amino acid, carbohydrate, and nucleotide metabolism. Pathway analysis revealed that the shared metabolic pathways of the striatum, hippocampus, and cerebellum were mainly involved in the glutamine-glutamate metabolic system. Four genes in the striatum (GS, GLS2, GLT1, and SSADH), six genes in the hippocampus (GS, SNAT1, GAD1, SSADH, VGAT, and ABAT), and five genes in the cerebellum (GS, ABAT, SNAT1, VGAT, and GDH) were found to be significantly altered using RT-qPCR. Correlation analysis indicated that these differential genes were strongly correlated.

CONCLUSION

These results suggest that chronic stress might induce depressive behaviors by disturbing the glutamine-glutamate-GABA cycle in the striatum, hippocampus, and cerebellum, and that the glutamine-glutamate-GABA cycle among these three brain regions might generate cooperative action in response to chronic stress.

A functional variant of CB2 receptor gene interacts with childhood trauma and FAAH gene on anxious and depressive phenotypes

BACKGROUND

Accumulating data suggest that CB2 receptor plays a crucial role in development of anxiety via regulatory function of stress response and neuroimmune crosstalk. Although animal experiments confirm this relationship, relevant human genetic studies on CB2 receptor gene (CNR2) in association with affective phenotype are absent.

METHODS

CNR2 R63Q and FAAH C385A functional polymorphisms were genotyped of 921 volunteers from the general population. Phenotypic variables were measured by the Zung Self-related Depression Scale (ZSDS), The State-Trait Anxiety Inventory (Trait subscale, STAI-T) and the depressive and anxious subscales of the Brief Symptom Inventory (BSI-DEP and BSI-ANX). Early life trauma was assesssed by the Childhood Trauma Questionnaire (CHQ). Using general linear models we tested possible associations between phenotypic variance and genotype distribution.

RESULTS

There was a significant main effect of RR genotype of R63Q on ZSDS score (p = 0.007) and a remarkble interacting effect of CHQ and R63Q on scores of ZSDS, STAI-T and BSI-ANX scales (p = 0.009; p = 0.003; p = 0.001; respectively). R allele of R63Q and A allele of FAAH C385A were associated with significantly higher ZSDS, STAI-T and BSI-ANX scores compared to non-risk allele carriers (p = 0.009; p = 0.007; p = 0.007, respectively). The highest phenotypic scores were observed in GxGxE model (p_ZSDS = 0.04; p_BSI-DEP = 0.006; p_STAI-T = 0.001; p_BSI-ANX = 3.8 × 10^-5).

CONCLUSIONS

In this first human genetic study on CNR2 and childhood trauma we revealed that dysfunctional CB2 receptor and FAAH can contribute to greater sensitivity for childhood trauma possibly via weaker inhibiton of inflammatory and overactivated HPA axis.

Plasma metabolomic patterns in patients with exhaustion disorder

Exhaustion disorder (ED) is a stress-related disorder that often implies a great burden on the individual patient as well as on society. Previous studies have shown that ED is associated with metabolic deviations, such as lowered fasting glucose. Several mechanisms have been discussed as a plausible explanation of the lack of energy described by these patients. Metabolic processes and reduced ability to mobilize energy have been suggested as important factors. This study investigated metabolomics in 20 patients diagnosed with ED and compared them with 21 healthy controls. Plasma metabolic profiles were examined in both fasting and nonfasting (postprandial) conditions. Blood plasma samples were analyzed for metabolite content using gas chromatography mass spectrometry. A total of 62 different metabolites were simultaneously detected in each of the samples. Multivariate models indicated systematic differences between patients with ED and healthy controls in both their fasting and nonfasting plasma metabolite levels. Lysine and octadecenoic acid were more abundant and glutamine, glycine, serine and gluconic acid were less abundant in the patients across both conditions. In the present study, we comprehensively and simultaneously screen for changes in a large number of metabolites. Our results show a difference in systemic metabolites between patients with exhaustion disorder and healthy controls both in the fasting and in the postprandial states. Here, we present new potential biomarkers mirroring exhaustion disorder metabolism. Lay summary Exhaustion disorder (ED) patients suffer from stress-related symptoms including a reduced energy level. This study investigates the body's metabolism in patients with ED, both fasting and after a meal. New potential markers that may help future investigations on ED were identified.

The Metabolome and Osteoarthritis: Possible Contributions to Symptoms and Pathology

Osteoarthritis (OA) is a progressive, deteriorative disease of articular joints. Although traditionally viewed as a local pathology, biomarker exploration has shown that systemic changes can be observed. These include changes to cytokines, microRNAs, and more recently, metabolites. The metabolome is the set of metabolites within a biological sample and includes circulating amino acids, lipids, and sugar moieties. Recent studies suggest that metabolites in the synovial fluid and blood could be used as biomarkers for OA incidence, prognosis, and response to therapy. However, based on clinical, demographic, and anthropometric factors, the local synovial joint and circulating metabolomes may be patient specific, with select subsets of metabolites contributing to OA disease. This review explores the contribution of the local and systemic metabolite changes to OA, and their potential impact on OA symptoms and disease pathogenesis.

Blood and urinary metabolomic evidence validating traditional Chinese medicine diagnostic classification of major depressive disorder

Background

Major depressive disorder (MDD) is a highly heterogeneous disease. Further classification may characterize its heterogeneity. The purpose of this study was to examine whether metabolomic variables could differentiate traditional Chinese medicine (TCM) diagnostic subtypes of MDD.

Methods

Fifty medication-free patients who were experiencing a recurrent depressive episode were classified into Liver Qi Stagnation (LQS, n = 30) and Heart and Spleen Deficiency (HSD, n = 20) subtypes according to TCM diagnosis. Healthy volunteers (n = 28) were included as controls. Gas chromatography-mass spectrometry (GC–MS) was used to examine serum and urinary metabolomic profiles.

Results

Twenty-eight metabolites were identified for good separations between TCM subtypes and healthy controls in serum samples. Both TCM subtypes had similar profiles in proteinogenic branched-chain amino acids (BCAAs) (valine, leucine, and isoleucine) and energy metabolism-related metabolites that were differentiated from healthy controls. The LQS subtype additionally differed from healthy controls in multiple amino acid metabolites that are involved in biosynthesis of monoamine and amino acid neurotransmitters, including phenylalanine, 3-hydroxybutric acid, o-tyrosine, glycine, l-tryptophan, and N-acetyl-l-aspartic acid. Threonic acid, methionine, stearic acid, and isobutyric acid are differentially associated with the two subtypes.

Conclusions

While both TCM subtypes are associated with aberrant BCAA and energy metabolism, the LQS subtype may represent an MDD subpopulation characterized by abnormalities in the biosynthesis of monoamine and amino acid neurotransmitters and closer associations with stress-related pathophysiology. The metabolites differentially associated with the two subtypes are promising biomarkers for predicting TCM subtype-specific antidepressant response [registered at http://www.clinicaltrials.gov (NCT02346682) on January 27, 2015].

Electronic supplementary material

The online version of this article (10.1186/s13020-018-0211-z) contains supplementary material, which is available to authorized users.

Lipidomics in Major Depressive Disorder

Omic sciences coupled with novel computational approaches such as machine intelligence offer completely new approaches to major depressive disorder (MDD) research. The complexity of MDD's pathophysiology is being integrated into studies examining MDD's biology within the omic fields. Lipidomics, as a late-comer among other omic fields, is increasingly being recognized in psychiatric research because it has allowed the investigation of global lipid perturbations in patients suffering from MDD and indicated a crucial role of specific patterns of lipid alterations in the development and progression of MDD. Combinatorial lipid-markers with high classification power are being developed in order to assist MDD diagnosis, while rodent models of depression reveal lipidome changes and thereby unveil novel treatment targets for depression. In this systematic review, we provide an overview of current breakthroughs and future trends in the field of lipidomics in MDD research and thereby paving the way for precision medicine in MDD.

Atypical depression and non-atypical depression: Is HPA axis function a biomarker? A systematic review

BACKGROUND

The link between the abnormalities of the Hypothalamic-pituitary-adrenal (HPA) axis and depression has been one of the most consistently reported findings in psychiatry. At the same time, multiple studies have demonstrated a stronger association between the increased activation of HPA-axis and melancholic, or endogenous depression subtype. This association has not been confirmed for the atypical subtype, and some researchers have suggested that as an antinomic depressive subtype, it may be associated with the opposite type, i.e. hypo-function, of the HPA-axis, similarly to PTSD. The purpose of this systematic review is to summarise existing studies addressing the abnormalities of the HPA-axis in melancholic and/or atypical depression.

METHOD

We conducted a systematic review in the literature by searching MEDLINE, PsycINFO, OvidSP and Embase databases until June 2017. The following search items were used: "hypothalamic-pituitary-adrenal" OR "HPA" OR "cortisol" OR "corticotropin releasing hormone" OR "corticotropin releasing factor" OR "glucocorticoid*" OR "adrenocorticotropic hormone" OR "ACTH" AND "atypical depression" OR "non-atypical depression" OR "melancholic depression" OR "non-melancholic depression" OR "endogenous depression" OR "endogenomorphic depression" OR "non-endogenous depression". Search limits were set to include papers in English or German language published in peer-reviewed journals at any period. All studies were scrutinized to determine the main methodological characteristics, and particularly possible sources of bias influencing the results reported.

RESULTS

We selected 48 relevant studies. Detailed analysis of the methodologies used in the studies revealed significant variability especially regarding the samples' definition comparing the HPA axis activity of melancholic patients to atypical depression, including healthy controls. The results were subdivided into 4 sections: (1) 27 studies which compared melancholic OR endogenous depression vs. non-melancholic or non-endogenous depression or controls; (2) 9 studies which compared atypical depression or atypical traits vs. non-atypical depression or controls; (3) 7 studies which compared melancholic or endogenous and atypical depression subtypes and (4) 5 studies which used a longitudinal design, comparing the measures of HPA-axis across two or more time points. While the majority of studies did confirm the association between melancholic depression and increased post-challenge cortisol levels, the association with increases in basal cortisol and basal ACTH were less consistent. Some studies, particularly those focusing on reversed vegetative symptoms, demonstrated a decrease in the activity of the HPA axis in atypical depression compared to controls, but the majority did not distinguish it from healthy controls.

CONCLUSIONS

In conclusion, our findings indicate that there is a difference in the activity of the HPA-axis between melancholic and atypical depressive subtypes. However, these are more likely explained by hypercortisolism in melancholia; and most often normal than decreased function in atypical depression. Further research should seek to distinguish a particular subtype of depression linked to HPA-axis abnormalities, based on symptom profile, with a focus on vegetative symptoms, neuroendocrine probes, and the history of adverse childhood events. New insights into the dichotomy addressed in this review might be obtained from genetic and epigenetic studies of HPA-axis related genes in both subtypes, with an emphasis on the presence of vegetative symptoms.

Subtypes of depression and their overlap in a naturalistic inpatient sample of major depressive disorder

Subtyping depression is important in order to further delineate biological causes of depressive syndromes. The aim of this study was to evaluate clinical and outcome characteristics of distinct subtypes of depression and to assess proportion and features of patients fulfilling criteria for more than one subtype. Melancholic, atypical and anxious subtypes of depression were assessed in a naturalistic sample of 833 inpatients using DSM-IV specifiers based on operationalized criteria. Baseline characteristics and outcome criteria at discharge were compared between distinct subtypes and their overlap. A substantial proportion of patients (16%) were classified with more than one subtype of depression, 28% were of the distinct anxious, 7% of the distinct atypical and 5% of the distinct melancholic subtype. Distinct melancholic patients had shortest duration of episode, highest baseline depression severity, but were more often early improvers; distinct anxious patients had higher NEO-Five Factor Inventory (NEO-FFI) neuroticism scores compared with patients with unspecific subtype. Melancholic patients with overlap of anxious features had worse treatment outcome compared to distinct melancholic and distinct anxious subtype. Distinct subtypes differed in only few variables and patients with overlap of depression subtypes may have independent clinical and outcome characteristics. Studies investigating biological causes of subtypes of depression should take influence of features of other subtypes into account.

Replicable and Coupled Changes in Innate and Adaptive Immune Gene Expression in Two Case-Control Studies of Blood Microarrays in Major Depressive Disorder

BACKGROUND

Peripheral inflammation is often associated with major depressive disorder (MDD), and immunological biomarkers of depression remain a focus of investigation.

METHODS

We used microarray data on whole blood from two independent case-control studies of MDD: the GlaxoSmithKline-High-Throughput Disease-specific target Identification Program [GSK-HiTDiP] study (113 patients and 57 healthy control subjects) and the Janssen-Brain Resource Company study (94 patients and 100 control subjects). Genome-wide differential gene expression analysis (18,863 probes) resulted in a p value for each gene in each study. A Bayesian method identified the largest p-value threshold (q = .025) associated with twice the number of genes differentially expressed in both studies compared with the number of coincidental case-control differences expected by chance.

RESULTS

A total of 165 genes were differentially expressed in both studies with concordant direction of fold change. The 90 genes overexpressed (or UP genes) in MDD were significantly enriched for immune response to infection, were concentrated in a module of the gene coexpression network associated with innate immunity, and included clusters of genes with correlated expression in monocytes, monocyte-derived dendritic cells, and neutrophils. In contrast, the 75 genes underexpressed (or DOWN genes) in MDD were associated with the adaptive immune response and included clusters of genes with correlated expression in T cells, natural killer cells, and erythroblasts. Consistently, the MDD patients with overexpression of UP genes also had underexpression of DOWN genes (correlation > .70 in both studies).

CONCLUSIONS

MDD was replicably associated with proinflammatory activation of the peripheral innate immune system, coupled with relative inactivation of the adaptive immune system, indicating the potential of transcriptional biomarkers for immunological stratification of patients with depression.

Plasma Metabolites Predict Severity of Depression and Suicidal Ideation in Psychiatric Patients-A Multicenter Pilot Analysis

Evaluating the severity of depression (SOD), especially suicidal ideation (SI), is crucial in the treatment of not only patients with mood disorders but also psychiatric patients in general. SOD has been assessed on interviews such as the Hamilton Rating Scale for Depression (HAMD)-17, and/or self-administered questionnaires such as the Patient Health Questionnaire (PHQ)-9. However, these evaluation systems have relied on a person's subjective information, which sometimes lead to difficulties in clinical settings. To resolve this limitation, a more objective SOD evaluation system is needed. Herein, we collected clinical data including HAMD-17/PHQ-9 and blood plasma of psychiatric patients from three independent clinical centers. We performed metabolome analysis of blood plasma using liquid chromatography mass spectrometry (LC-MS), and 123 metabolites were detected. Interestingly, five plasma metabolites (3-hydroxybutyrate (3HB), betaine, citrate, creatinine, and gamma-aminobutyric acid (GABA)) are commonly associated with SOD in all three independent cohort sets regardless of the presence or absence of medication and diagnostic difference. In addition, we have shown several metabolites are independently associated with sub-symptoms of depression including SI. We successfully created a classification model to discriminate depressive patients with or without SI by machine learning technique. Finally, we produced a pilot algorithm to predict a grade of SI with citrate and kynurenine. The above metabolites may have strongly been associated with the underlying novel biological pathophysiology of SOD. We should explore the biological impact of these metabolites on depressive symptoms by utilizing a cross species study model with human and rodents. The present multicenter pilot study offers a potential utility for measuring blood metabolites as a novel objective tool for not only assessing SOD but also evaluating therapeutic efficacy in clinical practice. In addition, modification of these metabolites by diet and/or medications may be a novel therapeutic target for depression. To clarify these aspects, clinical trials measuring metabolites before/after interventions should be conducted. Larger cohort studies including non-clinical subjects are also warranted to clarify our pilot findings.