Biomarkers for major depressive and bipolar disorders using metabolomics: A systematic review

The metabolome-wide signature of major depressive disorder

Major Depressive Disorder (MDD) is a common, frequently chronic condition characterized by substantial molecular alterations and pathway dysregulations. Single metabolite and targeted metabolomics platforms have revealed several metabolic alterations in depression, including energy metabolism, neurotransmission, and lipid metabolism. More comprehensive coverage of the metabolome is needed to further specify metabolic dysregulations in depression and reveal previously untargeted mechanisms. Here, we measured 820 metabolites using the metabolome-wide Metabolon platform in 2770 subjects from a large Dutch clinical cohort with extensive clinical phenotyping (1101 current MDD, 868 remitted MDD, 801 healthy controls) at baseline, which were repeated in 1805 subjects at 6-year follow up (327 current MDD, 1045 remitted MDD, 433 healthy controls). MDD diagnosis was based on DSM-IV psychiatric interviews. Depression severity was measured with the Inventory of Depressive Symptomatology Self-report. Associations between metabolites and MDD status and depression severity were assessed at baseline and at 6-year follow-up. At baseline, 139 and 126 metabolites were associated with current MDD status and depression severity, respectively, with 79 overlapping metabolites. Adding body mass index and lipid-lowering medication to the models changed results only marginally. Among the overlapping metabolites, 34 were confirmed in internal replication analyses using 6-year follow-up data. Downregulated metabolites were enriched with long-chain monounsaturated (P = 6.7e-07) and saturated (P = 3.2e-05) fatty acids; upregulated metabolites were enriched with lysophospholipids (P = 3.4e-4). Mendelian randomization analyses using genetic instruments for metabolites (N = 14,000) and MDD (N = 800,000) showed that genetically predicted higher levels of the lysophospholipid 1-linoleoyl-GPE (18:2) were associated with greater risk of depression. The identified metabolome-wide profile of depression indicated altered lipid metabolism with downregulation of long-chain fatty acids and upregulation of lysophospholipids, for which causal involvement was suggested using genetic tools. This metabolomics signature offers a window on depression pathophysiology and a potential access point for the development of novel therapeutic approaches.

The Potential Role of the Ketogenic Diet in Serious Mental Illness: Current Evidence, Safety, and Practical Advice

The ketogenic diet (KD) is a high-fat, low-carbohydrate diet that mimics the physiological state of fasting. The potential therapeutic effects in many chronic conditions have led to the gaining popularity of the KD. The KD has been demonstrated to alleviate inflammation and oxidative stress, modulate the gut microbiota community, and improve metabolic health markers. The modification of these factors has been a potential therapeutic target in serious mental illness (SMI): bipolar disorder, major depressive disorder, and schizophrenia. The number of clinical trials assessing the effect of the KD on SMI is still limited. Preliminary research, predominantly case studies, suggests potential therapeutic effects, including weight gain reduction, improved carbohydrate and lipid metabolism, decrease in disease-related symptoms, increased energy and quality of life, and, in some cases, changes in pharmacotherapy (reduction in number or dosage of medication). However, these findings necessitate further investigation through larger-scale clinical trials. Initiation of the KD should occur in a hospital setting and with strict care of a physician and dietitian due to potential side effects of the diet and the possibility of exacerbating adverse effects of pharmacotherapy. An increasing number of ongoing studies examining the KD's effect on mental disorders highlights its potential role in the adjunctive treatment of SMI.

The metabolic overdrive hypothesis: hyperglycolysis and glutaminolysis in bipolar mania

Evidence from diverse areas of research including chronobiology, metabolomics and magnetic resonance spectroscopy indicate that energy dysregulation is a central feature of bipolar disorder pathophysiology. In this paper, we propose that mania represents a condition of heightened cerebral energy metabolism facilitated by hyperglycolysis and glutaminolysis. When oxidative glucose metabolism becomes impaired in the brain, neurons can utilize glutamate as an alternative substrate to generate energy through oxidative phosphorylation. Glycolysis in astrocytes fuels the formation of denovo glutamate, which can be used as a mitochondrial fuel source in neurons via transamination to alpha-ketoglutarate and subsequent reductive carboxylation to replenish tricarboxylic acid cycle intermediates. Upregulation of glycolysis and glutaminolysis in this manner causes the brain to enter a state of heightened metabolism and excitatory activity which we propose to underlie the subjective experience of mania. Under normal conditions, this mechanism serves an adaptive function to transiently upregulate brain metabolism in response to acute energy demand. However, when recruited in the long term to counteract impaired oxidative metabolism it may become a pathological process. In this article, we develop these ideas in detail, present supporting evidence and propose this as a novel avenue of investigation to understand the biological basis for mania.

Biomarkers of bipolar disorder based on metabolomics: A systematic review

Bipolar disorder (BD) is a severe affective disorder characterized by recurrent episodes of depression or mania/hypomania, which significantly impair cognitive function, life skills, and social abilities of patients. There is little understanding of the neurobiological mechanisms of BD. The diagnosis of BD is primarily based on clinical assessment and psychiatric examination, highlighting the urgent need for objective markers to facilitate the diagnosis of BD. Metabolomics can be used as a diagnostic tool for disease identification and evaluation. This study summarized the altered metabolites in BD and analyzed aberrant metabolic pathways, which might contribute to the diagnosis of BD. Search of PubMed and Web of science for human BD studies related to metabolism to identify articles published up to November 19, 2022 yielded 987 articles. After screening and applying the inclusion and exclusion criteria, 16 untargeted and 11 targeted metabolomics studies were included. Pathway analysis of the potential differential biometabolic markers was performed using the Kyoto encyclopedia of genes and genomes (KEGG). There were 72 upregulated and 134 downregulated biomarkers in the untargeted metabolomics studies using blood samples. Untargeted metabolomics studies utilizing urine specimens revealed the presence of 78 upregulated and 54 downregulated metabolites. The targeted metabolomics studies revealed abnormalities in the metabolism of glutamate and tryptophan. Enrichment analysis revealed that the differential metabolic pathways were mainly involved in the metabolism of glucose, amino acid and fatty acid. These findings suggested that certain metabolic biomarkers or metabolic biomarker panels might serve as a reference for the diagnosis of BD.

Prefrontal Cortex Cytosolic Proteome and Machine Learning-Based Predictors of Resilience toward Chronic Social Isolation in Rats

Chronic social isolation (CSIS) generates two stress-related phenotypes: resilience and susceptibility. However, the molecular mechanisms underlying CSIS resilience remain unclear. We identified altered proteome components and biochemical pathways and processes in the prefrontal cortex cytosolic fraction in CSIS-resilient rats compared to CSIS-susceptible and control rats using liquid chromatography coupled with tandem mass spectrometry followed by label-free quantification and STRING bioinformatics. A sucrose preference test was performed to distinguish rat phenotypes. Potential predictive proteins discriminating between the CSIS-resilient and CSIS-susceptible groups were identified using machine learning (ML) algorithms: support vector machine-based sequential feature selection and random forest-based feature importance scores. Predominantly, decreased levels of some glycolytic enzymes, G protein-coupled receptor proteins, the Ras subfamily of GTPases proteins, and antioxidant proteins were found in the CSIS-resilient vs. CSIS-susceptible groups. Altered levels of Gapdh, microtubular, cytoskeletal, and calcium-binding proteins were identified between the two phenotypes. Increased levels of proteins involved in GABA synthesis, the proteasome system, nitrogen metabolism, and chaperone-mediated protein folding were identified. Predictive proteins make CSIS-resilient vs. CSIS-susceptible groups linearly separable, whereby a 100% validation accuracy was achieved by ML models. The overall ratio of significantly up- and downregulated cytosolic proteins suggests adaptive cellular alterations as part of the stress-coping process specific for the CSIS-resilient phenotype.

Stressed to the Core: Inflammation and Intestinal Permeability Link Stress-Related Gut Microbiota Shifts to Mental Health Outcomes

Stress levels are surging, alongside the incidence of stress-related psychiatric disorders. Perhaps a related phenomenon, especially in urban areas, the human gut contains fewer bacterial species than ever before. Although the functional implications of this absence are unclear, one consequence may be reduced stress resilience. Preclinical and clinical evidence has shown how stress exposure can alter the gut microbiota and their metabolites, affecting host physiology. Also, stress-related shifts in the gut microbiota jeopardize tight junctions of the gut barrier. In this context, bacteria and bacterial products can translocate from the gut to the bloodstream, lymph nodes, and other organs, thereby modifying systemic inflammatory responses. Heightened circulating inflammation can be an etiological factor in stress-related psychiatric disorders, including some cases of depression. In this review, we detail preclinical and clinical evidence that traces these brain-to-gut-to-brain pathways that underlie stress-related psychiatric disorders and potentially affect their responsivity to conventional psychiatric medications. We also review evidence for interventions that modulate the gut microbiota (e.g., antibiotics, probiotics, prebiotics) to reduce stress responses and psychiatric symptoms. Lastly, we discuss challenges to translation and opportunities for innovations that could impact future psychiatric clinical practice.

Metabolites and depressive symptoms: Network- and longitudinal analyses from the Finnish Depression and Metabolic Syndrome in Adults (FDMSA) Study

BACKGROUND

Depression is associated with metabolic abnormalities linked to metabolic syndrome and tissue inflammation, but the interplay between metabolic markers and their association with subsequent depression is unknown. Therefore, we aimed to describe the network of metabolites and their prospective association with depressive symptoms.

METHODS

The Finnish Depression and Metabolic Syndrome in Adults (FDMSA) cohort, originally a prospective case-control study, comprised a group with Beck Depression Inventory (BDI)-I scores ≥10 at baseline, and controls (n = 319, BDI-I < 10); mean (sd) follow-up time: 7.4 (0.7) years. Serum metabolic biomarkers were determined by proton nuclear magnetic resonance (NMR), and depressive symptoms sum-score by using the BDI-I. We examined the prospective associations between metabolites at baseline and BDI score at follow-up utilizing multivariate linear regression, parsimonious predictions models and network analysis.

RESULTS

Some metabolites tended to be either negatively (e.g. histidine) or positively associated (e.g. glycoprotein acetylation, creatinine and triglycerides in very large high density lipoproteins [XL-HDL-TG]) with depressive symptoms. None of the associations were significant after correction for multiple testing. The network analysis suggested high correlation among the metabolites, but that none of the metabolites directly influenced subsequent depressive symptoms.

LIMITATIONS

Although the sample size may be considered satisfactory in a prospective context, we cannot exclude the possibility that our study was underpowered.

CONCLUSIONS

Our results suggest that the investigated metabolic biomarkers are not a driving force in the development of depressive symptoms. These findings should be confirmed in studies with larger samples and studies that account for the heterogeneity of depressive disorders.

Brain metabolite levels in remitted psychotic depression with consideration of effects of antipsychotic medication

BACKGROUND

The neurobiology of psychotic depression is not well understood and can be confounded by antipsychotics. Magnetic resonance spectroscopy (MRS) is an ideal tool to measure brain metabolites non-invasively. We cross-sectionally assessed brain metabolites in patients with remitted psychotic depression and controls. We also longitudinally assessed the effects of olanzapine versus placebo on brain metabolites.

METHODS

Following remission, patients with psychotic depression were randomized to continue sertraline + olanzapine (n = 15) or switched to sertraline + placebo (n = 18), at which point they completed an MRS scan. Patients completed a second scan either 36 weeks later, relapse, or discontinuation. Where water-scaled metabolite levels were obtained and a Point-RESolved Spectroscopy sequence was utilized, choline, myo-inositol, glutamate + glutamine (Glx), N-acetylaspartate, and creatine were measured in the left dorsolateral prefrontal cortex (L-DLPFC) and dorsal anterior cingulate cortex (dACC). An ANCOVA was used to compare metabolites between patients (n = 40) and controls (n = 46). A linear mixed-model was used to compare olanzapine versus placebo groups.

RESULTS

Cross-sectionally, patients (compared to controls) had higher myo-inositol (standardized mean difference [SMD] = 0.84; 95%CI = 0.25-1.44; p = 0.005) in the dACC but not different Glx, choline, N-acetylaspartate, and creatine. Longitudinally, patients randomized to placebo (compared to olanzapine) showed a significantly greater change with a reduction of creatine (SMD = 1.51; 95%CI = 0.71-2.31; p = 0.0002) in the dACC but not glutamate + glutamine, choline, myo-inositol, and N-acetylaspartate.

CONCLUSIONS

Patients with remitted psychotic depression have higher myo-inositol than controls. Olanzapine may maintain creatine levels. Future studies are needed to further disentangle the mechanisms of action of olanzapine.

Combining Glial Fibrillary Acidic Protein and Neurofilament Light Chain for the Diagnosis of Major Depressive Disorder

Major depressive disorder (MDD) is a prevalent brain disorder affecting more than 2% of the world's population. Due to the lack of well-specific biomarkers, it is difficult to distinguish MDD from other diseases with similar clinical symptoms (such as Alzheimer's disease and cerebral thrombosis). In this work, we provided a strategy to address this issue by constructing a combinatorial biomarker of serum glial fibrillary acidic protein (GFAP) and neurofilament light chain (NFL). To achieve the convenient and sensitive detection of two proteins, we developed an electrochemical immunosandwich sensor using two metal-ion-doped carbon dots (Pb-CDs and Cu-CDs) as probes for signal output. Each probe contains approximately 300 Pb2+ or 200 Cu2+, providing excellent signal amplification. This method achieved detection limits of 0.3 pg mL-1 for GFAP and 0.2 pg mL-1 for NFL, lower than most of the reported detection limits. Analysis of real serum samples showed that the concentration ratio of GFAP to NFL, which is associated with the relative degree of brain inflammation and neurodegeneration, is suitable for not only distinguishing MDD from healthy individuals but also specifically distinguishing MDD from Alzheimer's disease and cerebral thrombosis. The good specificity gives the combinatorial GFAP/NFL biomarker broad application prospects in the screening, diagnosis, and treatment of MDD.

Metabolite Biomarkers of Prolonged and Intensified Pain and Distress in Head and Neck Cancer Patients Undergoing Radio- or Chemoradiotherapy by Means of NMR-Based Metabolomics-A Preliminary Study

Treatment of head and neck squamous cell carcinoma (HNSCC) has a detrimental impact on patient quality of life. The rate of recognized distress/depression among HNSCC patients ranges from 9.8% to 83.8%, and the estimated prevalence of depression among patients receiving radiotherapy is 63%. Shorter overall survival also occurs in preexisting depression or depressive conditions. The present study analyzes the nuclear magnetic resonance (NMR) blood serum metabolic profiles during radio-/chemoradiotherapy and correlates the detected alterations with pain and/or distress accumulated with the disease and its treatment. NMR spectra were acquired on a Bruker 400 MHz spectrometer and analyzed using multivariate methods. The results indicate that distress and/or pain primarily affect the serum lipids and metabolites of energy (glutamine, glucose, lactate, acetate) and one-carbon (glycine, choline, betaine, methanol, threonine, serine, histidine, formate) metabolism. Sparse disturbances in the branched-chain amino acids (BCAA) and in the metabolites involved in protein metabolism (lysine, tyrosine, phenylalanine) are also observed. Depending on the treatment modality-radiotherapy or concurrent chemoradiotherapy-there are some differences in the altered metabolites.

Biomarkers of Postpartum Depression: A Narrative Review

Postpartum depression (PPD) is a disorder that impairs the formation of the relationship between mother and child, and reduces the quality of life for affected women to a functionally significant degree. Studying markers associated with PPD can help in early detection, prevention, or monitoring treatment. The purpose of this paper is to review biomarkers linked to PPD and to present selected theories on the pathogenesis of the disease based on data from biomarker studies. The complex etiology of the disorder reduces the specificity and sensitivity of markers, but they remain a valuable source of information to help clinicians. The biggest challenge of the future will be to translate high-tech methods for detecting markers associated with postpartum depression into more readily available and less costly ones. Population-based studies are needed to test the utility of potential PPD markers.

Metabolomic epidemiology offers insights into disease aetiology

Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.

Association of the plasma complement system with brain volume deficits in bipolar and major depressive disorders

BACKGROUND

Inflammation plays a crucial role in the pathogenesis of major depressive disorder (MDD) and bipolar disorder (BD). This study aimed to examine whether the dysregulation of complement components contributes to brain structural defects in patients with mood disorders.

METHODS

A total of 52 BD patients, 35 MDD patients, and 53 controls were recruited. The human complement immunology assay was used to measure the levels of complement factors. Whole brain-based analysis was performed to investigate differences in gray matter volume (GMV) and cortical thickness (CT) among the BD, MDD, and control groups, and relationships were explored between neuroanatomical differences and levels of complement components.

RESULTS

GMV in the medial orbital frontal cortex (mOFC) and middle cingulum was lower in both patient groups than in controls, while the CT of the left precentral gyrus and left superior frontal gyrus were affected differently in the two disorders. Concentrations of C1q, C4, factor B, factor H, and properdin were higher in both patient groups than in controls, while concentrations of C3, C4 and factor H were significantly higher in BD than in MDD. Concentrations of C1q, factor H, and properdin showed a significant negative correlation with GMV in the mOFC at the voxel-wise level.

CONCLUSIONS

BD and MDD are associated with shared and different alterations in levels of complement factors and structural impairment in the brain. Structural defects in mOFC may be associated with elevated levels of certain complement factors, providing insight into the shared neuro-inflammatory pathogenesis of mood disorders.

Search for serum biomarkers in patients with bipolar disorder and major depressive disorder using metabolome analysis

Objective

Bipolar disorder (BD) and major depressive disorder (MDD) are two common psychiatric disorders. Due to the overlapping clinical symptoms and the lack of objective diagnostic biomarkers, bipolar disorder (BD) is easily misdiagnosed as major depressive disorder (MDD), which in turn affects treatment decisions and prognosis. This study aimed to investigate biomarkers that could be used to differentiate BD from MDD.

Methods

Nuclear magnetic resonance (NMR) spectroscopy was performed to assess serum metabolic profiles in depressed patients with BD (n = 59), patients with MDD (n = 14), and healthy controls (n = 10). Data was analyzed using partial least squares discriminant analysis, orthogonal partial least squares discriminant analysis and t-tests. Different metabolites (VIP > 1 and p < 0.05) were identified and further analyzed using Metabo Analyst 5.0 to identify relevant metabolic pathways.

Results

The metabolic phenotypes of the BD and MDD groups were significantly different from those of the healthy controls, and there were different metabolite differences between them. In the BD group, the levels of 3-hydroxybutyric acid, n-acetyl glycoprotein, β-glucose, pantothenic acid, mannose, glycerol, and lipids were significantly higher than those in the healthy control group, and the levels of lactate and acetoacetate were significantly lower than those in the healthy control group. In the MDD group, the levels of 3-hydroxybutyric acid, n-acetyl glycoprotein, pyruvate, choline, acetoacetic acid, and lipids were significantly higher than those of healthy controls, and the levels of acetic acid and glycerol were significantly lower than those of healthy controls.

Conclusion

Glycerolipid metabolism is significantly involved in BD and MDD. Pyruvate metabolism is significantly involved in MDD. Pyruvate, choline, and acetate may be potential biomarkers for MDD to distinguish from BD, and pantothenic acid may be a potential biomarker for BD to distinguish from MDD.

Venlafaxine's effect on resilience to stress is associated with a shift in the balance between glucose and fatty acid utilization

Brain metabolism is a fundamental process involved in the proper development of the central nervous system and in the maintenance of the main higher functions in humans. As consequence, energy metabolism imbalance has been commonly associated to several mental disorders, including depression. Here, by employing a metabolomic approach, we aimed to establish if differences in energy metabolite concentration may underlie the vulnerability and resilience in an animal model of mood disorder named chronic mild stress (CMS) paradigm. In addition, we have investigated the possibility that modulation of metabolite concentration may represent a pharmacological target for depression by testing whether repeated treatment with the antidepressant venlafaxine may normalize the pathological phenotype by acting at metabolic level. The analyses were conducted in the ventral hippocampus (vHip) for its key role in the modulation of anhedonia, a core symptom of patients affected by depression. Interestingly, we showed that a shift from glycolysis to beta oxidation seems to be responsible for the vulnerability to chronic stress and that vHip metabolism contributes to the ability of the antidepressant venlafaxine to normalize the pathological phenotype, as shown by the reversal of the changes observed in specific metabolites. These findings may provide novel perspectives on metabolic changes that could serve as diagnostic markers and preventive strategies for the early detection and treatment of depression as well as for the identification of potential drug targets.

Circulating metabolites modulated by diet are associated with depression

Metabolome reflects the interplay of genome and exposome at molecular level and thus can provide deep insights into the pathogenesis of a complex disease like major depression. To identify metabolites associated with depression we performed a metabolome-wide association analysis in 13,596 participants from five European population-based cohorts characterized for depression, and circulating metabolites using ultra high-performance liquid chromatography/tandem accurate mass spectrometry (UHPLC/MS/MS) based Metabolon platform. We tested 806 metabolites covering a wide range of biochemical processes including those involved in lipid, amino-acid, energy, carbohydrate, xenobiotic and vitamin metabolism for their association with depression. In a conservative model adjusting for life style factors and cardiovascular and antidepressant medication use we identified 8 metabolites, including 6 novel, significantly associated with depression. In individuals with depression, increased levels of retinol (vitamin A), 1-palmitoyl-2-palmitoleoyl-GPC (16:0/16:1) (lecithin) and mannitol/sorbitol and lower levels of hippurate, 4-hydroxycoumarin, 2-aminooctanoate (alpha-aminocaprylic acid), 10-undecenoate (11:1n1) (undecylenic acid), 1-linoleoyl-GPA (18:2) (lysophosphatidic acid; LPA 18:2) are observed. These metabolites are either directly food derived or are products of host and gut microbial metabolism of food-derived products. Our Mendelian randomization analysis suggests that low hippurate levels may be in the causal pathway leading towards depression. Our findings highlight putative actionable targets for depression prevention that are easily modifiable through diet interventions.

Metabolites for monitoring symptoms and predicting remission in patients with depression who received electroconvulsive therapy: a pilot study

The lack of biomarkers to monitor and predict the efficacy of electroconvulsive therapy (ECT) has hindered its optimal use. To establish metabolomic markers for monitoring and predicting the treatment efficacy of ECT, we comprehensively evaluated metabolite levels in patients with major depressive disorder (MDD) by performing targeted and non-targeted metabolomic analyses using plasma samples before and after the first, third, and final ECT sessions, and 3-7 days after the final session. We compared the plasma metabolomes of age- and sex-matched healthy controls (HCs). Thirteen hospitalized patients with MDD and their corresponding HCs were included in this study. We observed that patients with MDD exhibited lower levels of amino acids, including gamma-aminobutyric acid (GABA), and metabolites involved in tryptophan metabolism and the kynurenine pathway, and higher levels of cortisol at baseline. Furthermore, we investigated the relationship between metabolite levels and depression severity across seven measurement timepoints along with one correlation analysis and found that amino acids, including GABA and tryptophan catabolites, were significantly correlated with the severity of depression. Despite the exploratory nature of this study due to the limited sample size necessitating further validation, our findings suggest that the blood metabolic profile has potential as a biomarker for ECT.

A review of new insights into existing major depressive disorder biomarkers

As major depressive disorder (MDD) is such a diverse condition, there are currently no clear ways for determining its severity, endophenotype, or therapy response. The distinctive nature of depression, the variability of analysis in literature and the large number of conceptually complicated biomarkers are some of the many reasons for the lack of progress. Markers are involved in the process of neurotrophic, metabolic, and inflammation as well as neuroendocrine and neurotransmitter systems' components. Some clinical indicators are strong enough so that can be measured using assessments of proteomic, genetic, metabolomics, neuroimaging, epigenetic and transcriptomic. Markers of oxidative stress, endocrine, inflammatory, proteomic, and growth indicators are currently among the promising biologic systems/markers identified in this analysis. This narrative review examines succinct studies which investigated cytokines of inflammatory factors, peripheral factors of development, metabolic and endocrine markers as pathophysiological biomarkers of MDD, and treatment responses. Endocrine and metabolic alterations have also been linked to MDD in various studies. So, this study summarizes all of the numerous biomarkers that are significant in the detection or treatment of MDD patients. The paper also provides an overview of various biomarkers which are important for the regulation and its effects on MDD.

Effects of antipsychotic medication on functional connectivity in major depressive disorder with psychotic features

The effect of antipsychotic medication on resting state functional connectivity in major depressive disorder (MDD) is currently unknown. To address this gap, we examined patients with MDD with psychotic features (MDDPsy) participating in the Study of the Pharmacotherapy of Psychotic Depression II. All participants were treated with sertraline plus olanzapine and were subsequently randomized to continue sertraline plus olanzapine or be switched to sertraline plus placebo. Participants completed an MRI at randomization and at study endpoint (study completion at Week 36, relapse, or early termination). The primary outcome was change in functional connectivity measured within and between specified networks and the rest of the brain. The secondary outcome was change in network topology measured by graph metrics. Eighty-eight participants completed a baseline scan; 73 completed a follow-up scan, of which 58 were usable for analyses. There was a significant treatment X time interaction for functional connectivity between the secondary visual network and rest of the brain (t = -3.684; p = 0.0004; pFDR = 0.0111). There was no significant treatment X time interaction for graph metrics. Overall, functional connectivity between the secondary visual network and the rest of the brain did not change in participants who stayed on olanzapine but decreased in those switched to placebo. There were no differences in changes in network topology measures when patients stayed on olanzapine or switched to placebo. This suggests that olanzapine may stabilize functional connectivity, particularly between the secondary visual network and the rest of the brain.

Major Depressive Disorder and Oxidative Stress: A Review of Peripheral and Genetic Biomarkers According to Clinical Characteristics and Disease Stages

Major depressive disorder (MDD) is currently the main cause of disability worldwide, but its pathophysiology remains largely unknown, especially given its high heterogeneity in terms of clinical phenotypes and biological characteristics. Accordingly, its management is still poor. Increasing evidence suggests that oxidative stress, measured on various matrices such as serum, plasma or erythrocytes, has a critical role in MDD. The aim of this narrative review is to identify serum, plasma and erythrocyte biomarkers of oxidative stress in MDD patients according to disease stage and clinical features. Sixty-three articles referenced on PubMed and Embase between 1 January 1991, and 31 December 2022, were included. Modifications to antioxidant enzymes (mainly glutathione peroxidase and superoxide dismutase) in MDD were highlighted. Non-enzymatic antioxidants (mainly uric acid) were decreased in depressed patients compared to healthy controls. These changes were associated with an increase in reactive oxygen species. Therefore, increased oxidative damage products (principally malondialdehyde, protein carbonyl content and 8-hydroxy-2'-deoxyguanosine) were present in MDD patients. Specific modifications could be identified according to disease stages and clinical features. Interestingly, antidepressant treatment corrected these changes. Accordingly, in patients in remission from depression, oxidative stress markers were globally normalized. This narrative review suggests the particular interest of oxidative stress biomarkers for MDD care that may contribute to the heterogeneity of the disease and provide the opportunity to find new therapeutic targets.

Brain Inflammatory Marker Abnormalities in Major Psychiatric Diseases: a Systematic Review of Postmortem Brain Studies

Schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) are common neuropsychiatric disorders that lead to neuroinflammation in the pathogenesis. It is possible to further explore the connection between inflammation in the brain and SCZ, BD, and MDD. Therefore, we systematically reviewed PubMed and Web of Science on brain inflammatory markers measured in SCZ, BD, and MDD postmortem brains. Out of 2166 studies yielded by the search, 46 studies met the inclusion criteria in SCZ, BD, and MDD postmortem brains. The results were variable across inflammatory markers. For example, 26 studies were included to measure the differential expression between SCZ and control subjects. Similarly, seven of the included studies measured the differential expression of inflammatory markers in patients with BD. The heterogeneity from the included studies is not clear at present, which may be caused by several factors, including the measured brain region, disease stage, brain source, medication, and other factors.

Mechanisms of Maternal Diet-Induced Obesity Affecting the Offspring Brain and Development of Affective Disorders

Depression and metabolic disease are common disorders that share a bidirectional relationship and continue to increase in prevalence. Maternal diet and maternal behaviour both profoundly influence the developmental trajectory of offspring during the perinatal period. At an epidemiological level, both maternal depression and obesity during pregnancy have been shown to increase the risk of neuropsychiatric disease in the subsequent generation. Considerable progress has been made to understand the mechanisms by which maternal obesity disrupts the developing offspring gut-brain axis, priming offspring for the development of affective disorders. This review outlines such mechanisms in detail, including altered maternal care, the maternal microbiome, inflammation, breast milk composition, and maternal and placental metabolites. Subsequently, offspring may be prone to developing gut-brain interaction disorders with concomitant changes to brain energy metabolism, neurotransmission, and behaviour, alongside gut dysbiosis. The gut microbiome may act as a key modifiable, and therefore treatable, feature of the relationship between maternal obesity and the offspring brain function. Further studies examining the relationship between maternal nutrition, the maternal microbiome and metabolites, and offspring neurodevelopment are warranted to identify novel therapeutic targets.

Transcranial direct current stimulation for bipolar depression: systematic reviews of clinical evidence and biological underpinnings

Despite multiple available treatments for bipolar depression (BD), many patients face sub-optimal responses. Transcranial direct current stimulation (tDCS) has been advocated in the management of different conditions, including BD, especially in treatment-resistant cases. The optimal dose and timing of tDCS, the mutual influence with other concurrently administered interventions, long-term efficacy, overall safety, and biological underpinnings nonetheless deserve additional assessment. The present study appraised the existing clinical evidence about tDCS for bipolar depression, delving into the putative biological underpinnings with a special emphasis on cellular and molecular levels, with the ultimate goal of providing a translational perspective on the matter. Two separate systematic reviews across the PubMed database since inception up to August 8^th 2022 were performed, with fourteen clinical and nineteen neurobiological eligible studies. The included clinical studies encompass 207 bipolar depression patients overall and consistently document the efficacy of tDCS, with a reduction in depression scores after treatment ranging from 18% to 92%. The RCT with the largest sample clearly showed a significant superiority of active stimulation over sham. Mild-to-moderate and transient adverse effects are attributed to tDCS across these studies. The review of neurobiological literature indicates that several molecular mechanisms may account for the antidepressant effect of tDCS in BD patients, including the action on calcium homeostasis in glial cells, the enhancement of LTP, the regulation of neurotrophic factors and inflammatory mediators, and the modulation of the expression of plasticity-related genes. To the best of our knowledge, this is the first study on the matter to concurrently provide a synthesis of the clinical evidence and an in-depth appraisal of the putative biological underpinnings, providing consistent support for the efficacy, safety, and tolerability of tDCS.

Understanding ayahuasca effects in major depressive disorder treatment through in vitro metabolomics and bioinformatics

Emerging insights from metabolomic-based studies of major depression disorder (MDD) are mainly related to biochemical processes such as energy or oxidative stress, in addition to neurotransmission linked to specific metabolite intermediates. Hub metabolites represent nodes in the biochemical network playing a critical role in integrating the information flow in cells between metabolism and signaling pathways. Limited technical-scientific studies have been conducted to understand the effects of ayahuasca (Aya) administration in the metabolism considering MDD molecular context. Therefore, this work aims to investigate an in vitro primary astrocyte model by untargeted metabolomics of two cellular subfractions: secretome and intracellular content after pre-defined Aya treatments, based on DMT concentration. Mass spectrometry (MS)-based metabolomics data revealed significant hub metabolites, which were used to predict biochemical pathway alterations. Branched-chain amino acid (BCAA) metabolism, and vitamin B6 and B3 metabolism were associated to Aya treatment, as "housekeeping" pathways. Dopamine synthesis was overrepresented in the network results when considering the lowest tested DMT concentration (1 µmol L^-1). Building reaction networks containing significant and differential metabolites, such as nicotinamide, L-DOPA, and L-leucine, is a useful approach to guide on dose decision and pathway selection in further analytical and molecular studies.

Relationship between Urinary Metabolomic Profiles and Depressive Episode in Antarctica

Antarctic expeditions have a high risk of participant depression owing to long stays and isolated environments. By quantifying the stress state and changes in biomolecules over time before the onset of depressive symptoms, predictive markers of depression can be explored. Here, we evaluated the psychological changes in 30 participants in the Japanese Antarctic Research Expedition using the Patient Health Questionnaire-9 (PHQ-9). Urinary samples were collected every three months for a year, and comprehensive urinary metabolomic profiles were quantified using liquid chromatography time-of-flight mass spectrometry. Five participants showed major depressive episodes (PHQ-9 ≥ 10) at 12 months. The urinary metabolites between these participants and the 25 unaffected participants were compared at individual metabolite and pathway levels. The individual comparisons showed the most significant differences at 12 months in 14 metabolites, including ornithine and beta-alanine. Data from shorter stays showed less significant differences. In contrast, pathway and enrichment analyses showed the most significant difference at three months and a less significant difference at longer stays. These time transitions of urinary metabolites could help in the development of urinary biomarkers to detect subjects with depressive episodes at an early stage.

Psychological distress and metabolomic markers: A systematic review of posttraumatic stress disorder, anxiety, and subclinical distress

Psychological distress can be conceptualized as an umbrella term encompassing symptoms of depression, anxiety, posttraumatic stress disorder (PTSD), or stress more generally. A systematic review of metabolomic markers associated with distress has the potential to reveal underlying molecular mechanisms linking distress to adverse health outcomes. The current systematic review extends prior reviews of clinical depressive disorders by synthesizing 39 existing studies that examined metabolomic markers for PTSD, anxiety disorders, and subclinical psychological distress in biological specimens. Most studies were based on small sets of pre-selected candidate metabolites, with few metabolites overlapping between studies. Vast heterogeneity was observed in study design and inconsistent patterns of association emerged between distress and metabolites. To gain a more robust understanding of distress and its metabolomic signatures, future research should include 1) large, population-based samples and longitudinal assessments, 2) replication and validation in diverse populations, 3) and agnostic metabolomic strategies profiling hundreds of targeted and nontargeted metabolites. Addressing these research priorities will improve the scope and reproducibility of future metabolomic studies of psychological distress.

Understanding treatment-resistant depression using "omics" techniques: A systematic review

BACKGROUND

Treatment-resistant depression (TRD) results in huge healthcare costs and poor patient clinical outcomes. Most studies have adopted a "candidate mechanism" approach to investigate TRD pathogenesis, however this is made more challenging due to the complex and heterogeneous nature of this condition. High-throughput "omics" technologies can provide a more holistic view and further insight into the underlying mechanisms involved in TRD development, expanding knowledge beyond already-identified mechanisms. This systematic review assessed the information from studies that examined TRD using hypothesis-free omics techniques.

METHODS

PubMed, MEDLINE, Embase, APA PsycInfo, Scopus and Web of Science databases were searched on July 2022. 37 human studies met the eligibility criteria, totalling 17,518 TRD patients, 571,402 healthy controls and 62,279 non-TRD depressed patients (including antidepressant responders and untreated MDD patients).

RESULTS

Significant findings were reported that implicate the role in TRD of various molecules, including polymorphisms, genes, mRNAs and microRNAs. The pathways most commonly reported by the identified studies were involved in immune system and inflammation, neuroplasticity, calcium signalling and neurotransmitters.

LIMITATIONS

Small sample sizes, variability in defining TRD, and heterogeneity in study design and methodology.

CONCLUSIONS

These findings provide insight into TRD pathophysiology, proposing future research directions for novel drug targets and potential biomarkers for clinical staging and response to antidepressants (citalopram/escitalopram in particular) and electroconvulsive therapy (ECT). Further validation is warranted in large prospective studies using standardised TRD criteria. A multi-omics and systems biology strategy with a collaborative effort will likely deliver robust findings for translation into the clinic.

Clinical Utility of Fluid Biomarker in Depressive Disorder

Major depressive disorders are ranked as the single largest contributor to non-fatal health loss and biomarkers could largely improve our routine clinical activity by predicting disease course and guiding treatment. However there is still a dearth of valid biomarkers in the field of psychiatry. The initial assumption that a single biomarker can capture the myriad of complex processes proved to be naive. The purpose of this paper is to critically review the field and to illustrate the possible practical application for routine clinical care. Biomarkers derived from DNA analysis are the ones that have received the most attention. Other potential candidates include circulating transcription products, proteins, and inflammatory markers. DNA polygenic risk scores proved to be useful in other fields of medicine and preliminary results suggest that they could be useful both as risk and diagnostic biomarkers also in depression and for the choice of treatment. A number of other possible fluid biomarkers are currently under investigation for diagnosis, outcome prediction, staging, and stratification of interventions, however research is still needed before they can be used for routine clinical care. When available, clinicians may be able to receive a lab report with detailed information about disease risk, outcome prediction, and specific indications about preferred treatments.

Alterations of mitochondrial dynamics in serotonin transporter knockout rats: A possible role in the fear extinction recall mechanisms

Stress-related mental disorders encompass a plethora of pathologies that share the exposure to a negative environment as trigger for their development. The vulnerability to the effects of a negative environment is not equal to all but differs between individuals based on the genetic background makeup. Here, to study the molecular mechanisms potentially underlying increased threat anticipation, we employed an animal model showing this symptom (5-HTT knockout rats) which we exposed to Pavlovian fear conditioning (FC). We investigated the role of mitochondria, taking advantage of the recent evidence showing that the dynamic of these organelles is dysregulated after stress exposure. Behavioral experiments revealed that, during the second day of extinction of the FC paradigm, 5-HTT knockout (5-HTT-/-) animals showed a lack of fear extinction recall. From a mechanistic standpoint, we carried out our molecular analyses on the amygdala and prefrontal cortex, given their role in the management of the fear response due to their tight connection. We demonstrated that mitochondrial dynamics are impaired in the amygdala and prefrontal cortex of 5-HTT-/- rats. The dissection of the potential contributing factors revealed a critical role in the mechanisms regulating fission and fusion that are dysregulated in transgenic animals. Furthermore, mitochondrial oxidative phosphorylation, mitochondrial biogenesis, and the production of antioxidant enzymes were altered in these brain regions in 5-HTT-/- rats. In summary, our data suggest that increased extracellular 5-HT levels cause an unbalance of mitochondrial functionality that could contribute to the reduced extinction recall of 5-HTT-/- rats, pointing out the role of mitochondrial dynamics in the etiology of psychiatric disorders. Our findings, also, provide some interesting insights into the targeted development of drugs to treat such disorders.

Dopamine-Mediated Major Depressive Disorder in the Neural Circuit of Ventral Tegmental Area-Nucleus Accumbens-Medial Prefrontal Cortex: From Biological Evidence to Computational Models

Major depressive disorder (MDD) is a serious psychiatric disorder, with an increasing incidence in recent years. The abnormal dopaminergic pathways of the midbrain cortical and limbic system are the key pathological regions of MDD, particularly the ventral tegmental area- nucleus accumbens- medial prefrontal cortex (VTA-NAc-mPFC) neural circuit. MDD usually occurs with the dysfunction of dopaminergic neurons in VTA, which decreases the dopamine concentration and metabolic rate in NAc/mPFC brain regions. However, it has not been fully explained how abnormal dopamine concentration levels affect this neural circuit dynamically through the modulations of ion channels and synaptic activities. We used Hodgkin-Huxley and dynamical receptor binding model to establish this network, which can quantitatively explain neural activity patterns observed in MDD with different dopamine concentrations by changing the kinetics of some ion channels. The simulation replicated some important pathological patterns of MDD at the level of neurons and circuits with low dopamine concentration, such as the decreased action potential frequency in pyramidal neurons of mPFC with significantly reduced burst firing frequency. The calculation results also revealed that NaP and KS channels of mPFC pyramidal neurons played key roles in the functional regulation of this neural circuit. In addition, we analyzed the synaptic currents and local field potentials to explain the mechanism of MDD from the perspective of dysfunction of excitation-inhibition balance, especially the disinhibition effect in the network. The significance of this article is that we built the first computational model to illuminate the effect of dopamine concentrations for the NAc-mPFC-VTA circuit between MDD and normal groups, which can be used to quantitatively explain the results of existing physiological experiments, predict the results for unperformed experiments and screen possible drug targets.

The molecular pathophysiology of mood disorders: From the analysis of single molecular layers to multi-omic integration

Next-generation sequencing now enables the rapid and affordable production of reliable biological data at multiple molecular levels, collectively referred to as "omics". To maximize the potential for discovery, computational biologists have created and adapted integrative multi-omic analytical methods. When applied to diseases with traceable pathophysiology such as cancer, these new algorithms and statistical approaches have enabled the discovery of clinically relevant molecular mechanisms and biomarkers. In contrast, these methods have been much less applied to the field of molecular psychiatry, although diagnostic and prognostic biomarkers are similarly needed. In the present review, we first briefly summarize main findings from two decades of studies that investigated single molecular processes in relation to mood disorders. Then, we conduct a systematic review of multi-omic strategies that have been proposed and used more recently. We also list databases and types of data available to researchers for future work. Finally, we present the newest methodologies that have been employed for multi-omics integration in other medical fields, and discuss their potential for molecular psychiatry studies.

Peripheral blood and urine metabolites and biological functions in post-stroke depression

Post-stroke depression (PSD) is the most common and severe neuropsychiatric complication after stroke. However, the molecular mechanism of PSD is still unclear. Previous studies have identified peripheral blood and urine metabolites associated with PSD using metabolomics techniques. We searched and systematically summarized metabolites that may be involved in metabolic changes in peripheral blood and urine of patients with PSD from the Metabolite Network of Depression Database (MENDA) and other biomedical databases. MetaboAnalyst5.0 software was used for pathway analysis and enrichment analysis of differential metabolites, and subgroup analyses were performed according to tissue types and metabolomics techniques. We identified 47 metabolites that were differentially expressed between patients with and without PSD. Five differential metabolites were found in both plasma and urine, including L-glutamic acid, pyroglutamic acid, palmitic acid, L-phenylalanine, and L-tyrosine. We integrated these metabolites into metabolic pathways, and six pathways were significantly altered. These pathways could be roughly divided into three modules including amino acid metabolism, nucleotide metabolism, and glucose metabolism. Among them, the most significantly altered pathway was "phenylalanine metabolism" and the pathway containing the most associated metabolites was "aminoacyl-tRNA biosynthesis", which deserve further study to elucidate their role in the molecular mechanism of PSD. In summary, metabolic changes in peripheral blood and urine are associated with PSD, especially the disruption of "phenylalanine metabolism" and "aminoacyl-tRNA biosynthesis" pathways. This study provides clues to the metabolic characteristics of patients with PSD, which may help to elucidate the molecular pathogenesis of PSD.

The Plasma Levels of 3-Hydroxybutyrate, Dityrosine, and Other Markers of Oxidative Stress and Energy Metabolism in Major Depressive Disorder

Major depressive disorder (MDD) is a serious mental disease with a pathophysiology that is not yet fully clarified. An increasing number of studies show an association of MDD with energy metabolism alteration and the presence of oxidative stress. We aimed to evaluate plasma levels of 3-hydroxybutyrate (3HB), NADH, myeloperoxidase, and dityrosine (di-Tyr) in adolescent and adult patients with MDD, compare them with healthy age-matched controls, and assess the effect of antidepressant treatment during hospitalisation on these levels. In our study, plasmatic levels of 3HB were elevated in both adolescents (by 55%; p = 0.0004) and adults (by 88%; p < 0.0001) with MDD compared to controls. Levels of dityrosine were increased in MDD adults (by 19%; p = 0.0092) but not adolescents. We have not found any significant effect of antidepressants on the selected parameters during the short observation period. Our study supports the findings suggesting altered energy metabolism in MDD and demonstrates its presence independently of the age of the patients.

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.

Modifying the maternal microbiota alters the gut-brain metabolome and prevents emotional dysfunction in the adult offspring of obese dams

Maternal obesity disturbs brain-gut-microbiota interactions and induces negative affect in the offspring, but its impact on gut and brain metabolism in the offspring (F1) are unknown. Here, we tested whether perinatal intake of a multispecies probiotic could mitigate the abnormal emotional behavior in the juvenile and adult offspring of obese dams. Untargeted NMR-based metabolomic profiling and gene-expression analysis throughout the gut-brain axis were then used to investigate the biology underpinning behavioral changes in the dams and their offspring. Prolonged high-fat diet feeding reduced maternal gut short-chain fatty acid abundance, increased markers of peripheral inflammation, and decreased the abundance of neuroactive metabolites in maternal milk during nursing. Both juvenile (postnatal day [PND] 21) and adult (PND112) offspring of obese dams exhibited increased anxiety-like behavior, which were prevented by perinatal probiotic exposure. Maternal probiotic treatment increased gut butyrate and brain lactate in the juvenile and adult offspring and increased the expression of prefrontal cortex PFKFB3, a marker of glycolytic metabolism in astrocytes. PFKFB3 expression correlated with the increase in gut butyrate in the juvenile and adult offspring. Maternal obesity reduced synaptophysin expression in the adult offspring, while perinatal probiotic exposure increased expression of brain-derived neurotrophic factor. Finally, we showed that the resilience of juvenile and adult offspring to anxiety-like behavior was most prominently associated with increased brain lactate abundance, independent of maternal group. Taken together, we show that maternal probiotic supplementation exerts a long-lasting effect on offspring neuroplasticity and the offspring gut-liver-brain metabolome, increasing resilience to emotional dysfunction induced by maternal obesity.

Metabolomic signature and mitochondrial dynamics outline the difference between vulnerability and resilience to chronic stress

Stress is the foremost environmental factor involved in the pathophysiology of major depressive disorder (MDD). However, individual differences among people are critical as some people exhibit vulnerability while other are resilient to repeated exposure to stress. Among the others, a recent theory postulates that alterations of energy metabolism might contribute to the development of psychopathologies. Here we show that the bioenergetic status in the ventral hippocampus (vHip), a brain subregion tightly involved in the regulation of MDD, defined the development of vulnerability or resilience following two weeks of chronic mild stress. Among the different metabolomic signatures observed, the glycolysis and tricarboxylic acid cycle may be specifically involved in defining vulnerability, revealing a previously unappreciated mechanism of sensitivity to stress. These findings point to mitochondrial morphology and recycling as critical in the ability to cope with stress. We show that vulnerable rats favor mitochondrial fusion to counteract the overproduction of reactive oxidative species whereas resilient rats activate fission to guarantee metabolic efficiency. Our results indicate that the modulation of the energetic metabolite profile in vHip under chronic stress exposure may represent a mechanism to explain the difference between vulnerable and resilient rats, unraveling novel and promising targets for specific therapeutic interventions.

The Application of Metabolomics in Forensic Science with Focus on Forensic Toxicology and Time-of-Death Estimation

Recently, the diagnostic methods used by scientists in forensic examinations have enormously expanded. Metabolomics provides an important contribution to analytical method development. The main purpose of this review was to investigate and summarize the most recent applications of metabolomics in forensic science. The primary research method was an extensive review of available international literature in PubMed. The keywords “forensic” and “metabolomics” were used as search criteria for the PubMed database scan. Most authors emphasized the analysis of different biological sample types using chromatography methods. The presented review is a summary of recently published implementations of metabolomics in forensic science and types of biological material used and techniques applied. Possible opportunities for valuable metabolomics’ applications are discussed to emphasize the essential necessities resulting in numerous nontargeted metabolomics’ assays.

Depressive and Neurocognitive Disorders in the Context of the Inflammatory Background of COVID-19

The dysfunctional effects of the coronavirus disease 2019 (COVID-19) infection on the nervous system are established. The manifestation of neuropsychiatric symptoms during and after infection is influenced by the neuroinvasive and neurotrophic properties of SARS-CoV-2 as well as strong inflammation characterised by a specific "cytokine storm". Research suggests that a strong immune response to a SARS-CoV-2 infection and psychological stressors related to the pandemic may cause chronic inflammatory processes in the body with elevated levels of inflammatory markers contributing to the intensification of neurodegenerative processes. It is suggested that neuroinflammation and associated central nervous system changes may significantly contribute to the etiopathogenesis of depressive disorders. In addition, symptoms after a COVID-19 infection may persist for up to several weeks after an acute infection as a post-COVID-19 syndrome. Moreover, previous knowledge indicates that among SSRI (selective serotonin reuptake inhibitor) group antidepressants, fluoxetine is a promising drug against COVID-19. In conclusion, further research, observation and broadening of the knowledge of the pathomechanism of a SARS-CoV-2 infection and the impact on potential complications are necessary. It is essential to continue research in order to assess the long-term neuropsychiatric effects in COVID-19 patients and to find new therapeutic strategies.

Brain Metabolic Profile after Intranasal vs. Intraperitoneal Clomipramine Treatment in Rats with Ultrasound Model of Depression

BACKGROUND

Molecular mechanisms of depression remain unclear. The brain metabolome after antidepressant therapy is poorly understood and had not been performed for different routes of drug administration before the present study. Rats were exposed to chronic ultrasound stress and treated with intranasal and intraperitoneal clomipramine. We then analyzed 28 metabolites in the frontal cortex and hippocampus.

METHODS

Rats' behavior was identified in such tests: social interaction, sucrose preference, forced swim, and Morris water maze. Metabolic analysis was performed with liquid chromatography.

RESULTS

After ultrasound stress pronounced depressive-like behavior, clomipramine had an equally antidepressant effect after intranasal and intraperitoneal administration on behavior. Ultrasound stress contributed to changes of the metabolomic pathways associated with pathophysiology of depression. Clomipramine affected global metabolome in frontal cortex and hippocampus in a different way that depended on the route of administration. Intranasal route was associated with more significant changes of metabolites composition in the frontal cortex compared to the control and ultrasound groups while the intraperitoneal route corresponded with more profound changes in hippocampal metabolome compared to other groups. Since far metabolic processes in the brain can change in many ways depending on different routes of administration, the antidepressant therapy should also be evaluated from this point of view.

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.

Resting-state brain metabolic fingerprinting clusters (biomarkers) and predictive models for major depression in multiple myeloma patients

Background

Major depression is a common comorbidity in cancer patients. Oncology clinics lack practical, objective tools for simultaneous evaluation of cancer and major depression. Fludeoxyglucose F-18 positron emission tomography–computed tomography (FDG PET/CT) is universally applied in modern medicine.

Methods

We used a retrospective analysis of whole-body FDG PET/CT images to identify brain regional metabolic patterns of major depression in multiple myeloma patients. The study included 134 multiple myeloma (MM) patients, 38 with major depression (group 1) and 96 without major depression (group 2).

Results

In the current study, Statistic Parameter Mapping (SPM) demonstrated that the major depression patient group (n = 38) had significant regional metabolic differences (clusters of continuous voxels) as compared to the non-major depression group (n = 96) with the criteria of height threshold T = 4.38 and extent threshold > 100 voxels. The five significant hypo- and three hyper-metabolic clusters from the computed T contrast maps were localized on the glass-brain view, consistent with published brain metabolic changes in major depression patients. Subsequently, using these clusters as features for classification learner, the fine tree and medium tree algorithms from 25 classification algorithms best fitted our data (accuracy 0.85%; AUC 0.88; sensitivity 79%; and specificity 88%).

Conclusion

This study demonstrated that whole-body FDG PET/CT scans could provide added value for screening for major depression in cancer patients in addition to staging and evaluating response to chemoradiation therapies.

Depression: Biological markers and treatment

Nowadays depression is considered as a systemic illness with different biological mechanisms involved in its etiology, including inflammatory response, hypothalamic-pituitary-adrenal (HPA) axis dysregulation and neurotransmitter and neurotrophic systems imbalance. Novel "omics" approaches, such as metabolomics and glycomics provide information about altered metabolic pathways and metabolites, as well as disturbances in glycosylation processes affected by or causing the development of depression. The clinical diagnosis of depression continues to be established based on the presence of the specific symptoms, but due to its heterogeneous underlying biological background, that differs according to the disease stage, there is an unmet need for treatment response biomarkers which would facilitate the process of appropriate treatment selection. This paper provides an overview of the role of major stress response system, the HPA axis, and its dysregulation in depression, possible involvement of neurotrophins, especially brain-derived neurotrophic factor, glial cell line-derived neurotrophic factor and insulin-like growth factor-1, in the development of depression. Article discusses how activated inflammation processes and increased cytokine levels, as well as disturbed neurotransmitter systems can contribute to different stages of depression and could specific metabolomic and glycomic species be considered as potential biomarkers of depression. The second part of the paper includes the most recent findings about available medical treatment of depression. The described biological factors impose an optimistic conclusion that they could represent easy obtainable biomarkers potentially predicting more personalized treatment and diagnostic options.

Alterations in acylcarnitines, amines, and lipids inform about the mechanism of action of citalopram/escitalopram in major depression

Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for major depressive disorder (MDD), yet their mechanisms of action are not fully understood and their therapeutic benefit varies among individuals. We used a targeted metabolomics approach utilizing a panel of 180 metabolites to gain insights into mechanisms of action and response to citalopram/escitalopram. Plasma samples from 136 participants with MDD enrolled into the Mayo Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) were profiled at baseline and after 8 weeks of treatment. After treatment, we saw increased levels of short-chain acylcarnitines and decreased levels of medium-chain and long-chain acylcarnitines, suggesting an SSRI effect on β-oxidation and mitochondrial function. Amines-including arginine, proline, and methionine sulfoxide-were upregulated while serotonin and sarcosine were downregulated, suggesting an SSRI effect on urea cycle, one-carbon metabolism, and serotonin uptake. Eighteen lipids within the phosphatidylcholine (PC aa and ae) classes were upregulated. Changes in several lipid and amine levels correlated with changes in 17-item Hamilton Rating Scale for Depression scores (HRSD₁₇). Differences in metabolic profiles at baseline and post-treatment were noted between participants who remitted (HRSD₁₇≤ 7) and those who gained no meaningful benefits (<30% reduction in HRSD₁₇). Remitters exhibited (a) higher baseline levels of C3, C5, alpha-aminoadipic acid, sarcosine, and serotonin; and (b) higher week-8 levels of PC aa C34:1, PC aa C34:2, PC aa C36:2, and PC aa C36:4. These findings suggest that mitochondrial energetics-including acylcarnitine metabolism, transport, and its link to β-oxidation-and lipid membrane remodeling may play roles in SSRI treatment response.

Plasma metabolites were associated with spatial working memory in major depressive disorder

Major depressive disorder (MDD) is a common disease with both affective and cognitive disorders. Alterations in metabolic systems of MDD patients have been reported, but the underlying mechanisms still remains unclear. We sought to identify abnormal metabolites in MDD by metabolomics and to explore the association between differential metabolites and neurocognitive dysfunction.Plasma samples from 53 MDD patients and 83 sex-, gender-, BMI-matched healthy controls (HCs) were collected. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) system was then used to detect metabolites in those samples. Two different algorithms were applied to identify differential metabolites in 2 groups. Of the 136 participants, 35 MDD patients and 48 HCs had completed spatial working memory test. Spearman rank correlation coefficient was applied to explore the relationship between differential metabolites and working memory in these 2 groups.The top 5 metabolites which were found in sparse partial least squares-discriminant analysis (sPLS-DA) model and random forest (RF) model were the same, and significant difference was found in 3 metabolites between MDD and HCs, namely, gamma-glutamyl leucine, leucine-enkephalin, and valeric acid. In addition, MDD patients had higher scores in spatial working memory (SWM) between errors and total errors than HCs. Valeric acid was positively correlated with working memory in MDD group.Gamma-glutamyl leucine, leucine-enkephalin, and valeric acid were preliminarily proven to be decreased in MDD patients. In addition, MDD patients performed worse in working memory than HCs. Dysfunction in working memory of MDD individuals was associated with valeric acid.

Altered Fractional Amplitude of Low-Frequency Fluctuation in Major Depressive Disorder and Bipolar Disorder

Background: Discriminating between major depressive disorder (MDD) and bipolar disorder (BD) remains challenging and cognitive deficits in MDD and BD are generally recognized. In this study, the fractional amplitude of low-frequency fluctuation (fALFF) approach was performed to explore neural activity and cognition in first-episode, drug-naïve BD and MDD patients, as well as the relationship between altered fALFF values and clinical or psychometric variables. Methods: A total of 21 BD patients, 25 MDD patients, and 41 healthy controls (HCs) completed clinical assessments and resting-state functional magnetic resonance imaging (rs-fMRI) scans in this study. The rs-fMRI data were analyzed by fALFF method and Pearson correlation analyses were performed between altered fALFF values and clinical variables or cognition. Support vector machine (SVM) was adopted to identify the three groups from each other with abnormal fALFF values in the brain regions obtained by group comparisons. Results: (1) The fALFF values were significantly different in the frontal lobe, temporal lobe, and left precuneus among three groups. In comparison to HCs, BD showed increased fALFF values in the right inferior temporal gyrus (ITG) and decreased fALFF values in the right middle temporal gyrus, while MDD showed decreased fALFF values in the right cerebellar lobule IV/V. In comparison to MDD, BD showed decreased fALFF values in bilateral posterior cingulate gyrus and the right cerebellar lobule VIII/IX. (2) In the BD group, a negative correlation was found between increased fALFF values in the right ITG and years of education, and a positive correlation was found between decreased fALFF values in the right cerebellar lobule VIII/IX and visuospatial abilities. (3) The fALFF values in the right cerebellar lobule VIII/IX may have the ability to discriminate BD patients from MDD patients, with sensitivity, specificity, and accuracy all over 0.70. Conclusions: Abnormal brain activities were observed in BD and MDD and were related with cognition in BD patients. The abnormality in the cerebellum can be potentially used to identify BD from MDD patients.

Metabolomics Analysis on Mice With Depression Ameliorated by Acupoint Catgut Embedding

Depression is a prevalent mental disease characterized by persistent low mood, lack of pleasure, and exhaustion. Acupoint catgut embedding (ACE) is a kind of modern acupuncture treatment, which has been widely used for the treatment of a variety of neuropsychiatric diseases. To investigate the effects and underlying mechanism of ACE on depression, in this study, we applied ACE treatment at the Baihui (GV20) and Dazhui (GV14) acupoints of corticosterone (CORT)-induced depression model mice. The results showed that ACE treatment significantly attenuated the behavioral deficits of depression model mice in the open field test (OFT), elevated-plus-maze test (EPMT), tail suspension test (TST), and forced swimming test (FST). Moreover, ACE treatment reduced the serum level of adreno-cortico-tropic-hormone (ACTH), enhanced the serum levels of 5-hydroxytryptamine (5-HT), and noradrenaline (NE). Furthermore, metabolomics analysis revealed that 23 differential metabolites in the brain of depression model mice were regulated by ACE treatment for its protective effect. These findings suggested that ACE treatment ameliorated depression-related manifestations in mice with depression through the attenuation of metabolic dysfunction in brain.

Plasma Metabolomics Profiling of Metabolic Pathways Affected by Major Depressive Disorder

Background: Major depressive disorder (MDD) is a common disease which is complicated by metabolic disorder. Although MDD has been studied relatively intensively, its metabolism is yet to be elucidated. Methods: To profile the global pathophysiological processes of MDD patients, we used metabolomics to identify differential metabolites and applied a new database Metabolite set enrichment analysis (MSEA) to discover dysfunctions of metabolic pathways of this disease. Hydrophilic metabolomics were applied to identify metabolites by profiling the plasma from 55 MDD patients and 100 sex-, gender-, BMI-matched healthy controls. The metabolites were then analyzed in MSEA in an attempt to discover different metabolic pathways. To investigate dysregulated pathways, we further divided MDD patients into two cohorts: (1) MDD patients with anxiety symptoms and (2) MDD patients without anxiety symptoms. Results: Metabolites which were hit in those pathways correlated with depressive and anxiety symptoms. Altogether, 17 metabolic pathways were enriched in MDD patients, and 23 metabolites were hit in those pathways. Three metabolic pathways were enriched in MDD patients without anxiety, including glycine and serine metabolism, arginine and proline metabolism, and phenylalanine and tyrosine metabolism. In addition, L-glutamic acid was positively correlated with the severity of depression and retardation if hit in MDD patients without anxiety symptoms. Conclusions: Different kinds of metabolic pathophysiological processes were found in MDD patients. Disorder of glycine and serine metabolism was observed in both MDD patients with anxiety and those without.

Individual prediction of symptomatic converters in youth offspring of bipolar parents using proton magnetic resonance spectroscopy

Children of individuals with bipolar disorder (bipolar offspring) are at increased risk for developing mood disorders, but strategies to predict mood episodes are unavailable. In this study, we used support vector machine (SVM) to characterize the potential of proton magnetic resonance spectroscopy (¹H-MRS) in predicting the first mood episode in youth bipolar offspring. From a longitudinal neuroimaging study, 19 at-risk youth who developed their first mood episode (converters), and 19 without mood episodes during follow-up (non-converters) were selected and matched for age, sex and follow-up time. Baseline ¹H-MRS data were obtained from anterior cingulate cortex (ACC) and bilateral ventrolateral prefrontal cortex (VLPFC). Glutamate (Glu), myo-inositol (mI), choline (Cho), N-acetyl aspartate (NAA), and phosphocreatine plus creatine (PCr + Cr) levels were calculated. SVM with a linear kernel was adopted to classify converters and non-converters based on their baseline metabolites. SVM allowed the significant classification of converters and non-converters across all regions for Cho (accuracy = 76.0%), but not for other metabolites. Considering all metabolites within each region, SVM allowed the significant classification of converters and non-converters for left VLPFC (accuracy = 76.5%), but not for right VLPFC or ACC. The combined mI, PCr + Cr, and Cho from left VLPFC achieved the highest accuracy differentiating converters from non-converters (79.0%). Our findings from this exploratory study suggested that ¹H-MRS levels of mI, Cho, and PCr + Cr from left VLPFC might be useful to predict the development of first mood episode in youth bipolar offspring using machine learning. Future studies that prospectively examine and validate these metabolites as predictors of mood episodes in high-risk individuals are necessary.