Age-specific urinary metabolite signatures and functions in patients with major depressive disorder

Metabolomics on depression: A comparison of clinical and animal research

BACKGROUND

Depression is a major cause of suicide and mortality worldwide. This study aims to conduct a systematic review to identify metabolic biomarkers and pathways for major depressive disorder (MDD), a prevalent subtype of clinical depression.

METHODS

We searched for metabolomics studies on depression published between January 2000 and January 2023 in the PubMed and Web of Science databases. The reported metabolic biomarkers were systematically evaluated and compared. Pathway analysis was implemented using MetaboAnalyst 5.0.

RESULTS

We included 26 clinical studies on MDD and 78 metabolomics studies on depressive-like animal models. A total of 55 and 77 high-frequency metabolites were reported consistently in two-thirds of clinical and murine studies, respectively. In the comparison between murine and clinical studies, we identified 9 consistently changed metabolites (tryptophan, tyrosine, phenylalanine, methionine, fumarate, valine, deoxycholic acid, pyruvate, kynurenic acid) in the blood, 1 consistently altered metabolite (indoxyl sulfate) in the urine and 14 disturbed metabolic pathways in both types of studies. These metabolic dysregulations and pathways are mainly implicated in enhanced inflammation, impaired neuroprotection, reduced energy metabolism, increased oxidative stress damage and disturbed apoptosis, laying solid molecular foundations for MDD.

LIMITATIONS

Due to unavailability of original data like effect-size results in many metabolomics studies, a meta-analysis cannot be conducted, and confounding factors cannot be fully ruled out.

CONCLUSIONS

This systematic review delineated metabolic biomarkers and pathways related to depression in the murine and clinical samples, providing opportunities for early diagnosis of MDD and the development of novel diagnostic targets.

Targeting PDK2 rescues stress-induced impaired brain energy metabolism

Depression is a mental illness frequently accompanied by disordered energy metabolism. A dysregulated hypothalamus pituitary adrenal axis response with aberrant glucocorticoids (GCs) release is often observed in patients with depression. However, the associated etiology between GCs and brain energy metabolism remains poorly understood. Here, using metabolomic analysis, we showed that the tricarboxylic acid (TCA) cycle was inhibited in chronic social defeat stress (CSDS)-exposed mice and patients with first-episode depression. Decreased mitochondrial oxidative phosphorylation was concomitant with the impairment of the TCA cycle. In parallel, the activity of pyruvate dehydrogenase (PDH), the gatekeeper of mitochondrial TCA flux, was suppressed, which is associated with the CSDS-induced neuronal pyruvate dehydrogenase kinase 2 (PDK2) expression and consequently enhanced PDH phosphorylation. Considering the well-acknowledged role of GCs in energy metabolism, we further demonstrated that glucocorticoid receptors (GR) stimulated PDK2 expression by directly binding to its promoter region. Meanwhile, silencing PDK2 abrogated glucocorticoid-induced PDH inhibition, restored the neuronal oxidative phosphorylation, and improved the flux of isotope-labeled carbon (U-13C] glucose) into the TCA cycle. Additionally, in vivo, pharmacological inhibition and neuron-specific silencing of GR or PDK2 restored CSDS-induced PDH phosphorylation and exerted antidepressant activities against chronic stress exposure. Taken together, our findings reveal a novel mechanism of depression manifestation, whereby elevated GCs levels regulate PDK2 transcription via GR, thereby impairing brain energy metabolism and contributing to the onset of this condition.

Astrocyte metabolism and signaling pathways in the CNS

Astrocytes comprise half of the cells in the central nervous system and play a critical role in maintaining metabolic homeostasis. Metabolic dysfunction in astrocytes has been indicated as the primary cause of neurological diseases, such as depression, Alzheimer's disease, and epilepsy. Although the metabolic functionalities of astrocytes are well known, their relationship to neurological disorders is poorly understood. The ways in which astrocytes regulate the metabolism of glucose, amino acids, and lipids have all been implicated in neurological diseases. Metabolism in astrocytes has also exhibited a significant influence on neuron functionality and the brain's neuro-network. In this review, we focused on metabolic processes present in astrocytes, most notably the glucose metabolic pathway, the fatty acid metabolic pathway, and the amino-acid metabolic pathway. For glucose metabolism, we focused on the glycolysis pathway, pentose-phosphate pathway, and oxidative phosphorylation pathway. In fatty acid metabolism, we followed fatty acid oxidation, ketone body metabolism, and sphingolipid metabolism. For amino acid metabolism, we summarized neurotransmitter metabolism and the serine and kynurenine metabolic pathways. This review will provide an overview of functional changes in astrocyte metabolism and provide an overall perspective of current treatment and therapy for neurological disorders.

Effects of Exercise Training on Executive Functioning in Adults with Depression: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

BACKGROUND

Exercise is a promising nonpharmacological intervention to improve executive function (EF). However, results from randomized trials and meta-analyses examining the effects of exercise on working memory in adults with depression are mixed, and the influence of exercise on EF, as well as the key moderators of the relationship, remain inconclusive.

OBJECTIVE

The present systematic review with meta-analysis examined the influence of exercise interventions on EF in adults with depression, and the influence of key moderating variables.

METHODS

Electronic searches were conducted using Embase, Cochrane Central, Scopus, Ovid MEDLINE, PubMed, Web of Science, China National Knowledge Infrastructure, Wanfang Database, and Weipu Database up to 25 June 2022, and updated on 16 January 2023. Randomized controlled trials (RCTs) examining the effects of exercise training on EF in adults with depression were included. A three-level meta-analysis based on a random-effects model was applied in R. Study quality was assessed using the Physiotherapy Evidence Database (PEDro) scale.

RESULTS

A total of 14 RCTs that evaluated 1201 adults with depression were included. The results indicated that exercise significantly improved global EF [g = 0.180; 95% confidence intervals (CI) = 0.038, 0.323], and the subdomains of working memory (g = 0.182; 95% CI = 0.015, 0.350), cognitive flexibility (g = 0.222; 95% CI = 0.048, 0.395), and reasoning/planning (g = 0.889; 95% CI = 0.571, 1.206). In subgroup analyses, significant improvements in EF were only observed for aerobic exercise (g = 0.203; 95% CI = 0.023, 0.382), moderate-to-vigorous intensity exercise (g = 0.200; 95% CI = 0.022, 0.379), exercise performed three or more times per week (g = 0.207; 95% CI = 0.026, 0.388), in sessions ≤ 60 min (g = 0.173; 95% CI = 0.003, 0.343), and in program durations lasting at least 13 weeks (g = 0. 248; 95% CI = 0.034, 0.462).

CONCLUSIONS

This meta-analysis demonstrates the benefits of exercise training for improving EF and the subdomains of working memory, cognitive flexibility, and reasoning/planning in adults with depression. Future randomized clinical trials are warranted to determine the therapeutic effects of exercise training on EF and cognitive symptoms in depressed patients.

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.

Exercise and Interorgan Communication: Short-Term Exercise Training Blunts Differences in Consecutive Daily Urine 1H-NMR Metabolomic Signatures between Physically Active and Inactive Individuals

Physical inactivity is a worldwide health problem, an important risk for global mortality and is associated with chronic noncommunicable diseases. The aim of this study was to explore the differences in systemic urine ¹H-NMR metabolomes between physically active and inactive healthy young males enrolled in the X-Adapt project in response to controlled exercise (before and after the 3-day exercise testing and 10-day training protocol) in normoxic (21% O₂), normobaric (~1000 hPa) and normal-temperature (23 °C) conditions at 1 h of 50% maximal pedaling power output (W_peak) per day. Interrogation of the exercise database established from past X-Adapt results showed that significant multivariate differences existed in physiological traits between trained and untrained groups before and after training sessions and were mirrored in significant differences in urine pH, salinity, total dissolved solids and conductivity. Cholate, tartrate, cadaverine, lysine and N6-acetyllisine were the most important metabolites distinguishing trained and untrained groups. The relatively little effort of 1 h 50% W_peak per day invested by the untrained effectively modified their resting urine metabolome into one indistinguishable from the trained group, which hence provides a good basis for the planning of future recommendations for health maintenance in adults, irrespective of the starting fitness value. Finally, the 3-day sessions of morning urine samples represent a good candidate biological matrix for future delineations of active and inactive lifestyles detecting differences unobservable by single-day sampling due to day-to-day variability.

Brain Functional Network and Amino Acid Metabolism Association in Females with Subclinical Depression

This study aimed to investigate the association between complex brain functional networks and the metabolites in urine in subclinical depression. Electroencephalography (EEG) signals were recorded from 78 female college students, including 40 with subclinical depression (ScD) and 38 healthy controls (HC). The phase delay index was utilized to construct functional connectivity networks and quantify the topological properties of brain networks using graph theory. Meanwhile, the urine of all participants was collected for non-targeted LC-MS metabolic analysis to screen differential metabolites. The global efficiency was significantly increased in the α-2, β-1, and β-2 bands, while the characteristic path length of β-1 and β-2 and the clustering coefficient of β-2 were decreased in the ScD group. The severity of depression was negatively correlated with the level of cortisone (p = 0.016, r = −0.40). The metabolic pathways, including phenylalanine metabolism, phenylalanine tyrosine tryptophan biosynthesis, and nitrogen metabolism, were disturbed in the ScD group. The three metabolic pathways were negatively correlated (p = 0.014, r = −0.493) with the global efficiency of the brain network of the β-2 band, whereas they were positively correlated (p = 0.014, r = 0.493) with the characteristic path length of the β-2 band. They were mainly associated with low levels of L-phenylalanine, and the highest correlation sparsity was 0.11. The disturbance of phenylalanine metabolism and the phenylalanine, tryptophan, tyrosine biosynthesis pathways cause depressive symptoms and changes in functional brain networks. The decrease in the L-phenylalanine level may be related to the randomization trend of the β-1 frequency brain functional network.

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.

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.

Irisin Acts via the PGC-1α and BDNF Pathway to Improve Depression-like Behavior

Depression is the most prevalent psychiatric disorder experienced by the world's population. Mechanisms associated with depression-like behavior have not been fully investigated. Among the therapeutic solution for depression, exercise is considered an important regulator attenuating depressive neuropathology. Exercise has been reported to boost the secretion of myokines such as irisin and myostatin in skeletal muscles. Myokines secreted during exercise are involved in various cellular responses including the endocrine and autocrine systems. Especially, irisin as a cleaved version of fibronectin domain-containing protein 5 has multiple functions such as white fat-browning, energy expenditure increase, anti-inflammatory effects, and mitochondrial function improvement in both systemic circulation and central nervous system. Furthermore, irisin activates energy metabolism-related signaling peroxisome proliferator-activated receptor-gamma coactivator-1 alpha and memory formation-related signaling brain-derived neurotrophic factor involved in depression. However, the role and mechanism of irisin in depression disorder is not obvious until now. Here, we review recent evidences regarding the therapeutic effect of irisin in depression disorder. We suggest that irisin is a key molecule that suppresses several neuropathological mechanisms involved in depression.

Changes in metabolomics and lipidomics in brain tissue and their correlations with the gut microbiome after chronic food-derived arsenic exposure in mice

Arsenic can cause neurodegenerative diseases of the brain, but the definite mechanism is still unknown. In this study, to discuss the disturbances on brain metabolome and lipidome under subchronic arsenic exposure, we treated mice with the arsenic-containing feed (concentration of total arsenic = 30 mg/kg) prepared in accordance with the proportion of rice arsenicals for 16 weeks and performed metabolomics and lipidomics studies respectively using UHPLC-Triple-TOF-MS/MS and UHPLC-Q Exactive Focus MS/MS on mice brain. In addition, the distributions of arsenical metabolites along the feed-gut-blood-brain chain were analyzed by ICP-MS and HPLC-ICP-MS, and fecal microbial variations were investigated by 16 s sequencing. The data showed that although only a tiny amount of arsenic (DMA=0.101 mg/kg, uAs=0.071 mg/kg) enters the brain through the blood-brain barrier, there were significant changes in brain metabolism, including 118 metabolites and 17 lipids. These different metabolites were involved in 30 distinct pathways, including glycometabolism, and metabolisms of lipid, nucleic acid, and amino acid were previously reported to be correlated with neurodegenerative diseases. Additionally, these different metabolites were significantly correlated with 12 gut bacterial OTUs, among which Lachnospiraceae, Muribaculaceae, Ruminococcaceae, and Erysipelotrichaceae were also previously reported to be related to the distortion of metabolism, indicating that the disturbance of metabolism in the brain may be associated with the disturbance of gut microbes induced by arsenic. Thus, the current study demonstrated that the brain metabolome and lipidome were significantly disturbed under subchronic arsenic exposure, and the disturbances also significantly correlated with some gut microbiome and may be associated with neurodegenerative diseases. Although preliminary, the results shed some light on the pathophysiology of arsenic-caused neurodegenerative diseases.

Agomelatine might be more appropriate for elderly, depressed, type 2 diabetes mellitus patients than paroxetine/fluoxetine

Agomelatine was a novel and melatonergic antidepressant. The present study was conducted to find out whether age was an important factor for agomelatine in treating depressed type 2 diabetes mellitus (T2DM) patients. In total, 193 depressed T2DM patients were included. There were 84 patients ranged from 27 years old to 49 years old (age phase I) (n = 44 receiving agomelatine, n = 40 receiving paroxetine or fluoxetine), and 109 patients ranged from 50 years old to 70 years old (age phase II) (n = 56 receiving agomelatine, n = 53 receiving paroxetine or fluoxetine). The Hamilton Depression Rating Scale (HDRS) score, Hamilton Anxiety Rating Scale (HARS) score, fasting plasma glucose (FPG), hemoglobin A1c (HbA1c) level and body mass index (BMI) were assessed after 12 weeks treatment. After treatment, we found that among patients in age phase I, there were no significant differences in final average HDRS score, HARS score, FPG, HbA1c level, BMI, response rate and remission rate between the two groups. However, among patients in age phase II, compared to patients receiving paroxetine or fluoxetine, patients receiving agomelatine had the significantly lower average HDRS score, HARS score, HbA1c level and BMI, and significantly higher response rate and remission rate. The incidence of treatment-related adverse events was similar between the two groups in both age phases. These results suggested that age was an important factor for agomelatine in treating depressed T2DM patients. Compared to paroxetine/fluoxetine, agomelatine might be more appropriate for elderly depressed T2DM patients.

Energy metabolism in major depressive disorder: Recent advances from omics technologies and imaging

Major depressive disorder (MDD) is a serious psychiatric disorder that associated with high rate of disability and increasing suicide rate, and the pathogenesis is still unclear. Many researches showed that the energy metabolism of patients with depression is impaired, which may be the direction of depression treatment. In this review, we focus on the "omics" technologies such as genomics, proteomics, transcriptomics and metabolomics, as well as imaging, and the progress on energy metabolism of MDD. These findings indicate that abnormal energy metabolism is one of the important mechanisms for the occurrence and development of depression. Although the research on various mechanisms of depression is still ongoing, the rapid development of new technologies and the joint use of various technologies will help to clarify the pathogenesis of depression and explore efficient diagnosis and treatment methods.

Potential Biomarkers for Diagnosing Major Depressive Disorder Patients with Suicidal Ideation

Background

Major depressive disorder (MDD) and suicide are two major health problems, but there are still no objective methods to diagnose MDD or suicidal ideation (SI). This study was conducted to identify potential biomarkers for diagnosing MDD patients with SI.

Methods

First-episode drug-naïve MDD patients with SI and demographics-matched healthy controls (HCs) were recruited. First-episode drug-naïve MDD patients without SI were also included. The serum lipids, C-reactive protein (CRP), transferring (TRSF), homocysteine (HCY) and alpha 1-antitrypsin (AAT) in serum were detected. The univariate and multivariate statistical analyses were used to identify and validate the potential biomarkers.

Results

The 86 HCs, 53 MDD patients with SI and 20 MDD patients without SI were included in this study. Four potential biomarkers were identified: AAT, TRSF, high-density lipoprotein cholesterol (HDLC), and apolipoprotein A1 (APOA1). After one month treatment, the levels of AAT and APOA1 were significantly improved. The panel consisting of these potential biomarkers had an excellent diagnostic performance, yielding an area under the ROC curve (AUC) of 0.994 and 0.990 in the training and testing set, respectively. Moreover, this panel could effectively distinguish MDD patients with SI from MDD patients without SI (AUC=0.928).

Conclusion

These results showed that these potential biomarkers could facilitate the development of an objective method for diagnosing MDD patients with SI, and the decreased AAT levels in MDD patients might lead to the appearance of SI by resulting in the elevated inflammation.

Urinary Metabolite Signatures for Predicting Elderly Stroke Survivors with Depression

BACKGROUND

Post-stroke depression (PSD) is a major complication in stroke survivors, especially in elderly stroke survivors. But there are still no objective methods to diagnose depression in elderly stroke survivors. Thus, this study was conducted to identify potential biomarkers for diagnosing elderly PSD subjects.

METHODS

Elderly (60 years or older) stroke survivors with depression were assigned into the PSD group, and elderly stroke survivors without depression and elderly healthy controls (HCs) were assigned into the non-depressed group. Urinary metabolite signatures obtained from gas chromatography-mass spectrometry (GC-MS)-based metabolomic platform were collected. Both univariate and multivariate statistical analysis were used to find the differential urinary metabolites between the two groups.

RESULTS

The 78 elderly HCs, 122 elderly stroke survivors without depression and 124 elderly PSD subjects were included. A set of 13 differential urinary metabolites responsible for distinguishing PSD subjects from non-depressed subjects were found. The Phenylalanine, tyrosine and tryptophan biosynthesis, Phenylalanine metabolism and Galactose metabolism were found to be significantly changed in elderly PSD subjects. The phenylalanine was significantly negatively correlated with age and depressive symptoms. Meanwhile, a biomarker panel consisting of 3-hydroxyphenylacetic acid, tyrosine, phenylalanine, sucrose, palmitic acid, glyceric acid, azelaic acid and α-aminobutyric acid was identified.

CONCLUSION

These results provided candidate molecules for developing objective methods to diagnose depression in elderly stroke survivors, suggested that taking supplements of phenylalanine might be an effective method to prevent depression in elderly stroke survivors, and would be helpful for future revealing the pathophysiological mechanism of PSD.

Metabolic Signatures of Gestational Weight Gain and Postpartum Weight Loss in a Lifestyle Intervention Study of Overweight and Obese Women

Background: Overweight and obesity amongst women of reproductive age are increasingly common in developed economies and are shown to adversely affect birth outcomes and both childhood and adulthood health risks in the offspring. Metabolic profiling in conditions of overweight and obesity in pregnancy could potentially be applied to elucidate the molecular basis of the adverse effects of gestational weight gain (GWG) and postpartum weight loss (WL) on future risks for cardiovascular disease (CVD) and other chronic diseases. Methods: Biofluid samples were collected from 114 ethnically diverse pregnant women with body mass index (BMI) between 25 and 40 kg/m² from Chicago (US), as part of a randomized lifestyle intervention trial (Maternal Offspring Metabolics: Family Intervention Trial; NCT01631747). At 15 weeks, 35 weeks of gestation, and at 1 year postpartum, the blood plasma lipidome and metabolic profile of urine samples were analyzed by liquid chromatography mass spectrometry (LC-MS) and ¹H nuclear magnetic resonance spectroscopy (¹H NMR) respectively. Results: Urinary 4-deoxyerythronic acid and 4-deoxythreonic acid were found to be positively correlated to BMI. Seventeen plasma lipids were found to be associated with GWG and 16 lipids were found to be associated with WL, which included phosphatidylinositols (PI), phosphatidylcholines (PC), lysophospholipids (lyso-), sphingomyelins (SM) and ether phosphatidylcholine (PC-O). Three phospholipids found to be positively associated with GWG all contained palmitate side-chains, and amongst the 14 lipids that were negatively associated with GWG, seven were PC-O. Six of eight lipids found to be negatively associated with WL contained an 18:2 fatty acid side-chain. Conclusions: Maternal obesity was associated with characteristic urine and plasma metabolic phenotypes, and phospholipid profile was found to be associated with both GWG and postpartum WL in metabolically healthy pregnant women with overweight/obesity. Postpartum WL may be linked to the reduction in the intake of linoleic acid/conjugated linoleic acid food sources in our study population.

Bacterial Metabolites of Human Gut Microbiota Correlating with Depression

Depression is a global threat to mental health that affects around 264 million people worldwide. Despite the considerable evolution in our understanding of the pathophysiology of depression, no reliable biomarkers that have contributed to objective diagnoses and clinical therapy currently exist. The discovery of the microbiota-gut-brain axis induced scientists to study the role of gut microbiota (GM) in the pathogenesis of depression. Over the last decade, many of studies were conducted in this field. The productions of metabolites and compounds with neuroactive and immunomodulatory properties among mechanisms such as the mediating effects of the GM on the brain, have been identified. This comprehensive review was focused on low molecular weight compounds implicated in depression as potential products of the GM. The other possible mechanisms of GM involvement in depression were presented, as well as changes in the composition of the microbiota of patients with depression. In conclusion, the therapeutic potential of functional foods and psychobiotics in relieving depression were considered. The described biomarkers associated with GM could potentially enhance the diagnostic criteria for depressive disorders in clinical practice and represent a potential future diagnostic tool based on metagenomic technologies for assessing the development of depressive disorders.

Untargeted metabolomics analysis on Cicer arietinium L.-Induced Amelioration in T2D rats by UPLC-Q-TOF-MS/MS

ETHNOPHARMACOLOGICAL RELEVANCE

Cicer arietinium L., which belongs to Cicer genus, was not only a kind of traditional Chinese medicines (TCM) recorded in Pharmacopoeia of the People's Republic of China (version 2015), but also a kind of Uighur antidiabetic medicines. It has been used as an adjuvant drug or functional food for thousand years in Xinjiang province, China. However, the mechanisms of C. arietinium treatment in T2D have not been fully understood especially on the perspective of metabolomics.

AIM OF THE STUDY

To clarify the potential mechanisms of C. arietinium treatment in T2D from the perspective of metabolomics since T2D is indeed a kind of metabolic syndromes.

MATERIALS AND METHODS

T2D rat model was built by HFD for 4 weeks, combining with STZ administration. T2D rats were administrated C. arietinium extraction or metformin (positive control) for 4 weeks. UPLC-Q-TOF-MS was applied to screen and identify differential metabolites among groups.

RESULTS

After 4 weeks of treatments, IR and inflammation were greatly ameliorated in C. arietinium group. And the therapeutic efficiency of C. arietinium treatment was comparable to metformin treatment. Differential metabolites related to C. arietinium treatment, including acylcarnitines, amino acid related metabolites and organic acids, were further used to indicate relevant pathways in T2D rats, including glyoxylate and dicarboxylate metabolism, tricarboxylic acid cycle, vitamin B6 metabolism and energy metabolism.

CONCLUSIONS

In summary, C. arietinium treatment could effectively alleviate diabetic symptoms and regulate metabolic disorders in T2D rats.

The effect of acupuncture on depression and its correlation with metabolic alterations: A randomized controlled trial

BACKGROUND

Electroacupuncture (EA) treatment has antidepressant effect and when patients were treated with EA and antidepressants, the effect could be maintained for a longer time. However, the effect of EA combined with antidepressants based on metabolism is still in the initial observation stage, which requires further research.

METHODS

A total of 60 patients with moderate depression were assigned into 2 groups at a ratio of 1:1, the EA group (receiving EA and antidepressants) and the control group (taking antidepressants only) in this randomized controlled pilot trial. The EA treatment was performed 3 times a week for 8 consecutive weeks and then follow up for 4 weeks. The patients' depressive mood was measured by the Hamilton Depression scale (HAMD) at baseline, week 4, week 8 and week 12. Before and after 8-week treatment, morning urine samples from all patients were analyzed by the gas chromatography-mass spectrometry (GC-MS) to find possible metabolic markers of depression and of EA treatment related changes.

RESULTS

Compared with the control group, the EA group showed more significant improvements in depressive symptoms measured by HAMD at week 4 (16.89 ± 5.74 vs 25.58 ± 7.03, P < .001), week 8 (9.59 ± 5.13 vs 25.04 ± 7.49, P < .001) and week 12 (11.07 ± 6.85 vs 27.25 ± 7.14, P < .001). The significant differences in urinary specific metabolites before and after EA treatment were malonic acid (fatty acid biosynthesis), cysteine (glutamate metabolism), glutathione (glutamate metabolism), tryptophan (tryptophan metabolism), proline (glutamate metabolism), and N-acetyl-5-hydroxytryptamine. These metabolites are involved in tryptophan metabolism, glutamate metabolism, and fatty acid biosynthesis.

CONCLUSION

EA treatment combined with antidepressants is more effective in improving depressive symptoms than antidepressants alone. EA may treat depression by acting on tryptophan metabolism, glutamate metabolism, and fatty acid biosynthesis.

TRIAL REGISTRATION

Chinese Clinical Trial Registry: ChiCTR-2000030786.

Circulating microRNA 134 sheds light on the diagnosis of major depressive disorder

Major depressive disorder (MDD) is a prevalent and debilitating psychiatric mood disorder that lacks objective laboratory-based tests to support its diagnosis. A class of microRNAs (miRNAs) has been found to be centrally involved in regulating many molecular processes fundamental to central nervous system function. Among these miRNAs, miRNA-134 (miR-134) has been reported to be related to neurogenesis and synaptic plasticity. In this study, the hypothesis that plasma miR-134 can be used to diagnose MDD was tested. Perturbation of peripheral and central miR-134 in a depressive-like rat model was also examined. By reverse-transcription quantitative PCR, miR-134 was comparatively measured in a small set of plasma samples from MDD and healthy control (HC) subjects. To determine its diagnostic efficacy, plasma miR-134 levels were assessed in 100 MDD, 50 bipolar disorder (BD), 50 schizophrenic (SCZ), and 100 HC subjects. A chronic unpredictable mild stress (CUMS) rat model was also developed to evaluate miR-134 expression in plasma, hippocampus (HIP), prefrontal cortex (PFC), and olfactory bulb. We found that plasma miR-134 was significantly downregulated in MDD subjects. Diagnostically, plasma miR-134 levels could effectively distinguish MDD from HC with 79% sensitivity and 84% specificity, while distinguishing MDD from HC, BD, and SCZ subjects with 79% sensitivity and 76.5% specificity. Congruent with these clinical findings, CUMS significantly reduced miR-134 levels in the rat plasma, HIP, and PFC. Although limited by the relatively small sample size, these results demonstrated that plasma miR-134 displays potential ability as a biomarker for MDD.

Age-specific differential changes on gut microbiota composition in patients with major depressive disorder

Emerging evidence has shown the age-related changes in gut microbiota, but few studies were conducted to explore the effects of age on the gut microbiota in patients with major depressive disorder (MDD). This study was performed to identify the age-specific differential gut microbiota in MDD patients. In total, 70 MDD patients and 71 healthy controls (HCs) were recruited and divided into two groups: young group (age 18-29 years) and middle-aged group (age 30-59 years). The 16S rRNA gene sequences were extracted from the collected fecal samples. Finally, we found that the relative abundances of Firmicutes and Bacteroidetes were significantly decreased and increased, respectively, in young MDD patients as compared with young HCs, and the relative abundances of Bacteroidetes and Actinobacteria were significantly decreased and increased, respectively, in middle-aged MDD patients as compared with middle-aged HCs. Meanwhile, six and 25 differentially abundant bacterial taxa responsible for the differences between MDD patients (young and middle-aged, respectively) and their respective HCs were identified. Our results demonstrated that there were age-specific differential changes on gut microbiota composition in patients with MDD. Our findings would provide a novel perspective to uncover the pathogenesis underlying MDD.

Identification of Potential Metabolite Markers for Middle-Aged Patients with Post-Stroke Depression Using Urine Metabolomics

BACKGROUND

Post-stroke depression (PSD) is one of the most common complications in stroke survivors. But, there are still no objective methods to diagnose PSD. This study aims to identify potential biomarkers for diagnosing PSD in middle-aged stroke survivors.

METHODS

Middle-aged subjects aged 30 to 59 years (92 PSD patients and 89 stroke survivors without depression) were included in this study. Urinary metabolites were detected by gas chromatography-mass spectrometry (GC-MS). Differential urinary metabolites and potential biomarkers were screened by applying statistical analysis.

RESULTS

The different urinary metabolic phenotypes between PSD patients and stroke survivors without depression were identified. A total of 12 differential urinary metabolites were accurately identified by using orthogonal partial least-squares-discriminant analysis. After analyzing those 12 differential urinary metabolites by step-wise logistic regression analysis, only seven metabolites (palmitic acid, hydroxylamine, myristic acid, glyceric acid, lactic acid, tyrosine and azelaic acid) were finally selected as potential biomarkers for diagnosing PSD in middle-aged stroke survivors. A panel consisting of these potential biomarkers could effectively diagnose middle-aged PSD patients.

CONCLUSION

Urinary metabolic profiles were different between middle-aged PSD patients and stroke survivors without depression. Our results would be helpful in future for developing an objective method to diagnose PSD in middle-aged stroke survivors.

Dual Metabolomic Platforms Identified a Novel Urinary Metabolite Signature for Hepatitis B Virus-Infected Patients with Depression

OBJECTIVE

Depression could make the treatment outcome worse. However, up to now, no objective methods were developed to diagnose depression in hepatitis B virus (HBV)-infected patients. Therefore, the dual metabolomic platforms were used here to identify potential biomarkers for diagnosing HBV-infected patients with depression (dHB).

METHODS

Both gas chromatography-mass spectrometry-based and nuclear magnetic resonance-based metabolomic platforms were used to conduct urine metabolic profiling of dHB subjects and HBV-infected patients without depression (HB). Orthogonal partial least-squares discriminant analysis was used to identify the differential metabolites between dHB subjects and HB subjects, and the step-wise logistic regression analysis was used to identify potential biomarkers.

RESULTS

In total, 21 important metabolites responsible for distinguishing dHB subjects from HB subjects were identified. Meanwhile, seven potential biomarkers (α-ydroxyisobutyric acid, hippuric acid, azelaic acid, isobutyric acid, malonic acid, levulinic acid, and phenylacetylglycine) were viewed as potential biomarkers. The simplified biomarker panel consisting of these seven metabolites had an excellent diagnostic performance in discriminating dHB subjects from HB subjects. Moreover, this panel could yield a higher accuracy in separating dHB subjects from HB subjects than our previous panels (identified by single metabolomic platform) did.

CONCLUSION

These results suggested that the dual metabolomic platforms could yield a better urinary biomarker panel for dHB subjects than any single metabolomic platform did, and our results could be helpful for developing an objective method in future to diagnose depression in HBV-infected patients.

Absence of gut microbiota affects lipid metabolism in the prefrontal cortex of mice

Objectives: Lipid metabolism is closely associated with many important biological functions. Here, we conducted this study to explore the effects of gut microbiota on the lipid metabolism in the prefrontal cortex of mice. Methods: Germ-free (GF) mice, specific pathogen-free (SPF) and colonized GF (CGF) mice were used in this study. The open field test (OFT), forced swimming test (FST) and novelty suppressed feeding test (NSFT) were conducted to assess the changes in general behavioral activity. The liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) was used to obtain the lipid metabolites. Both one-way analysis of variance (one-way ANOVA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used to obtain the key differential lipid metabolites. Results: The behavioral tests showed that compared to SPF mice, GF mice had more center distance, more center time, less immobility time and less latency to familiar food. Meanwhile, 142 key differential lipid metabolites between SPF mice and GF mice were identified. These lipid metabolites mainly belonged to glycerophospholipids, glycerolipids, sphingolipids, and saccharolipids. The gut microbiota colonization did not reverse these changed behavioral phenotypes, but could restore 25 key differential lipid metabolites. Discussion: These results showed that the absence of gut microbiota could influence host behaviors and lipid metabolism. Our findings could provide original and valuable data for future studies to further investigate the microbiota-gut-brain axis.