Discovery and Validation of Plasma Biomarkers for Major Depressive Disorder Classification Based on Liquid Chromatography–Mass Spectrometry

Discovery of potential female-specific biomarkers for major depressive disorder by LC-MS-based metabolomics

The prevalence of major depressive disorder (MDD) is higher in females than males, emphasizing the need to identify gender-specific biomarkers to improve diagnosis accuracy. In this study, a cross-sectional investigation with 258 samples was conducted to evaluate the discriminative power of potential gender-specific biomarkers for MDD. Eighteen MDD-related differential metabolites have been identified, involving pathways of phospholipids, glycerolipids, fatty acids, sphingolipids, cholesterol, vitamin E, and heme. A potential biomarker combination consisting of palmitelaidic acid, gamma carboxyethyl hydroxychroman (gamma-CEHC), and lysoPE(16:0) was confirmed for predicting depression in women using binary logistic regression analysis. To evaluate the panel's specificity, nine generalized anxiety disorder (GAD) samples, which share highly similar clinical symptoms with MDD, were included in the validation set. The discovery and validation sets yielded an area under the receiver operating characteristic curve of 0.86 and 0.83, respectively. All nine female GAD samples were correctly predicted as non-MDD, demonstrating the panel's specificity in diagnosing female MDD. Remarkably, this composite panel achieved a 75 % prediction accuracy in female samples in both the discovery and validation sets, but it did not reach 60 % prediction accuracy in male samples in either set. Our findings highlight the importance of gender-specific molecular diagnostics in developing practical and accurate diagnostic methods for MDD.

Screening for depression in the general population through lipid biomarkers

BACKGROUND

Anxiety and depression significantly contribute to the overall burden of mental disorders, with depression being one of the leading causes of disability. Despite this, no biochemical test has been implemented for the diagnosis of these mental disorders, while recent studies have highlighted lipids as potential biomarkers.

METHODS

Using a streamlined high-throughput lipidome analysis method, direct-infusion mass spectrometry, we evaluated blood plasma lipid levels in 604 individuals from a general urban population and analysed their association with self-reported anxiety and depression symptoms. We also assessed lipidome profiles in 32 patients with clinical depression, matched to 21 healthy controls.

FINDINGS

We found a significant correlation between lipid abundances and the severity of self-reported depression symptoms. Moreover, lipid alterations detected in high scoring volunteers mirrored the lipidome profiles identified in patients with clinical depression included in our study. Based on these findings, we developed a lipid-based predictive model distinguishing individuals reporting severe depressive symptoms from non-depressed subjects with high accuracy.

INTERPRETATION

This study demonstrates the possibility of generalizing lipid alterations from a clinical cohort to the general population and underscores the potential of lipid-based biomarkers in assessing depressive states.

FUNDING

This study was sponsored by the Moscow Center for Innovative Technologies in Healthcare, №2707-2, №2102-11.

St. John's Wort Extract Ze 117 and Escitalopram Alter Plasma and Hippocampal Lipidome in a Rat Model of Chronic-Stress-Induced Depression

Chronic stress is a key factor in the development of depression. It leads to hyperactivation of the hypothalamic-pituitary-adrenal (HPA) axis, which in turn increases the formation of glucocorticoids (GCs). Chronically elevated GC levels disrupt neuroplasticity and affect brain lipid metabolism, which may, ultimately, contribute to the development of depression. This study aimed to investigate the effects of the antidepressants St. John's Wort extract and escitalopram on lipid metabolism in vivo. Therefore, repeated corticosterone injections were used to induce depression-like behavior in rats. Male Sprague-Dawley rats were stressed with corticosterone injections (40 mg/kg, s.c.) over 22 consecutive days and were concomitantly treated with varying doses of the St. John's wort extract Ze 117 (30, 90 or 180 mg/kg, p.o.) or escitalopram (10 mg/kg, p.o.) and behavioral changes were evaluated using a modified forced swim test. The results indicate that repeated corticosterone injections significantly decreased the latency to first immobility. Furthermore, co-treatment of corticosterone with Ze 117 increased latency to first immobility significantly compared to rats treated with corticosterone alone. To further investigate the biochemical effects of corticosterone-induced stress, as well as the possible counter-regulation by antidepressants, the lipidomes of the plasma and hippocampus samples were analyzed by shotgun mass spectrometry. Corticosterone-induced stress significantly altered key lipid metabolites in the plasma but not in the hippocampal samples. In the hippocampus, however, specific glycerophospholipids such as lysophosphatidylethanolamines (LPEs) increased with escitalopram treatment and with Ze 117, both showing significant correlations with behavioral parameters. In summary, our study shows significant behavioral- and lipidome-altering processes with Ze 117 and escitalopram in rat plasma and hippocampal samples, thereby providing new targets and biomarker ideas for clinical diagnosis and antidepressant intervention.

Cognitive improvements linked to lysophosphatidylethanolamine after olanzapine treatment in drug-naïve first-episode schizophrenia

BACKGROUND

Cognitive impairments are a hallmark symptom of schizophrenia (SCZ). Phosphatidylethanolamine (PE) is the second most abundant phospholipid in mammalian cells, yet its role in cognitive deficits remains unexplored. The aim of this study was to investigate the association between plasma LysoPE and cognitive improvements following olanzapine monotherapy in drug-naïve first-episode (DNFE) SCZ patients.

METHODS

Twenty-five female DNFE SCZ patients were treated with olanzapine for four weeks, and cognitive function was assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) at baseline and after the 4-week follow-up. Utilizing an untargeted ultra-performance liquid chromatography-mass spectrometry (UPLC-MS)-based metabolomics approach, we measured LysoPE concentrations.

RESULTS

Significant improvements in immediate and delayed memory domains were observed post-treatment. We identified nine differential LysoPE species after olanzapine monotherapy, with increased concentrations for all LysoPE except LysoPE (22:6). Elevated LysoPE (22:1) concentration positively correlated with cognitive improvement in patients. Baseline LysoPE (16:1) emerged as a predictive factor for cognitive improvement following olanzapine monotherapy.

CONCLUSIONS

This study offers preliminary evidence for the involvement of LysoPE in cognitive improvements observed in drug-naïve first-episode SCZ patients after olanzapine treatment.

Metabolomics signatures of sweetened beverages and added sugar are related to anthropometric measures of adiposity in young individuals: results from a cohort study

BACKGROUND

The associations of sweetened beverages (SBs) and added sugar (AS) intake with adiposity are still debated. Metabolomics could provide insights into the mechanisms linking their intake to adiposity.

OBJECTIVES

We aimed to identify metabolomics biomarkers of intake of low- and no-calorie sweetened beverages (LNCSBs), sugar-sweetened beverages (SSBs), and ASs and to investigate their associations with body mass index, body fat percentage, and waist circumference.

METHODS

We analyzed 3 data sets from the Dortmund Nutritional and Anthropometric Longitudinally Designed (DONALD) cohort study, of children who provided 2 urine samples (n = 297), adolescents who provided a single urine sample (n = 339), and young adults who provided a single plasma sample (n = 195). Urine and plasma were analyzed using untargeted metabolomics. Dietary intakes were assessed using 3-d weighed dietary records. The random forest, partial least squares, and least absolute shrinkage and selection operator were jointly used for metabolite selection. We examined associations of intakes with metabolites and anthropometric measures using linear and mixed-effects regression.

RESULTS

In adolescents, LNCSB were positively associated with acesulfame (β: 0.0012; 95% confidence interval [CI]: 0.0006, 0.0019) and saccharin (β: 0.0009; 95% CI: 0.0002, 0.0015). In children, the association was observed with saccharin (β: 0.0016; 95% CI: 0.0005, 0.0027). In urine and plasma, SSBs were positively associated with 1-methylxanthine (β: 0.0005; 95% CI: 0.0003, 0.0008; and β: 0.0010, 95% CI 0.0004, 0.0015, respectively) and 5-acetylamino-6-amino-3-methyluracil (β: 0.0005; 95% CI: 0.0002, 0.0008; and β: 0.0009; 95% CI: 0.0003, 0.0014, respectively). AS was associated with urinary sucrose (β: 0.0095; 95% CI: 0.0069, 0.0121) in adolescents. Some of the food-related metabolomics profiles were also associated with adiposity measures.

CONCLUSIONS

We identified SBs- and AS-related metabolites, which may be important for understanding the interplay between these intakes and adiposity in young individuals.

Metabolic features of adolescent major depressive disorder: A comparative study between treatment-resistant depression and first-episode drug-naive depression

Major depressive disorder (MDD) is a psychiatric illness that can jeopardize the normal growth and development of adolescents. Approximately 40% of adolescent patients with MDD exhibit resistance to conventional antidepressants, leading to the development of Treatment-Resistant Depression (TRD). TRD is associated with severe impairments in social functioning and learning ability and an elevated risk of suicide, thereby imposing an additional societal burden. In this study, we conducted plasma metabolomic analysis on 53 adolescents diagnosed with first-episode drug-naïve MDD (FEDN-MDD), 53 adolescents with TRD, and 56 healthy controls (HCs) using hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS) and reversed-phase liquid chromatography-mass spectrometry (RPLC-MS). We established a diagnostic model by identifying differentially expressed metabolites and applying cluster analysis, metabolic pathway analysis, and multivariate linear support vector machine (SVM) algorithms. Our findings suggest that adolescent TRD shares similarities with FEDN-MDD in five amino acid metabolic pathways and exhibits distinct metabolic characteristics, particularly tyrosine and glycerophospholipid metabolism. Furthermore, through multivariate receiver operating characteristic (ROC) analysis, we optimized the area under the curve (AUC) and achieved the highest predictive accuracy, obtaining an AUC of 0.903 when comparing FEDN-MDD patients with HCs and an AUC of 0.968 when comparing TRD patients with HCs. This study provides new evidence for the identification of adolescent TRD and sheds light on different pathophysiologies by delineating the distinct plasma metabolic profiles of adolescent TRD and FEDN-MDD.

Dysregulations of amino acid metabolism and lipid metabolism in urine of children and adolescents with major depressive disorder: a case-control study

RATIONALE

The mechanisms underlying major depressive disorder (MDD) in children and adolescents are unclear. Metabolomics has been utilized to capture metabolic signatures of various psychiatric disorders; however, urinary metabolic profile of MDD in children and adolescents has not been studied.

OBJECTIVES

We analyzed urinary metabolites in children and adolescents with MDD to identify potential biomarkers and metabolic signatures.

METHODS

Here, liquid chromatography-mass spectrometry was used to profile metabolites in urine samples from 192 subjects, comprising 80 individuals with antidepressant-naïve MDD (AN-MDD), 37 with antidepressant-treated MDD (AT-MDD) and 75 healthy controls (HC). We performed orthogonal partial least squares discriminant analysis to identify differential metabolites and employed logistic regression and receiver operating characteristic analysis to establish a diagnostic panel.

RESULTS

In total, 143 and 71 differential metabolites were identified in AN-MDD and AT-MDD, respectively. These were primarily linked to lipid metabolism, molecular transport, and small molecule biochemistry. AN-MDD additionally exhibited dysregulated amino acid metabolism. Compared to HC, a diagnostic panel of seven metabolites displayed area under the receiver operating characteristic curves of 0.792 for AN-MDD, 0.828 for AT-MDD, and 0.799 for all MDD. Furthermore, the urinary metabolic profiles of children and adolescents with MDD significantly differed from those of adult MDD.

CONCLUSIONS

Our research suggests dysregulated amino acid metabolism and lipid metabolism in the urine of children and adolescents with MDD, similar to results in plasma metabolomics studies. This contributes to the comprehension of mechanisms underlying children and adolescents with MDD.

Lipid variability in drug-naïve individuals affected with Major Depressive Disorder: a systematic review and meta-analysis

BACKGROUND

This study aimed to provide a comprehensive synthesis of the evidence examining lipid profiles in drug-naïve MDD patients.

MATERIALS AND METHODS

We searched PubMed, Scopus, and ISI Web of Science up to August 2023 for total cholesterol, HDL-C, LDL-C, and triglyceride levels in drug-naïve MDD patients.

RESULTS

A total of 17 articles comprising 2174 individuals including drug-naïve MDD subjects and controls were included. Our results showed that concentrations of total cholesterol were lower in drug-naïve MDD patients compared with healthy controls (SMD -0.49, 95% CI -0.881 to -0.105; p = 0.015; I2 = 90.6%). However, comparison of other lipid levels between MDD patients and healthy controls demonstrated no significant difference. The results revealed that the association of total cholesterol levels with MDD is more prominent in male-dominant studies (SMD -1.20, 95% CI -2.23 to -0.18, I2 = 87.9%) than in female-dominant studies (SMD -0.25, 95% CI -0.63-0.13, I2 = 89.0%). In meta-regression, none of the factors including year of publication, Newcastle-Ottawa Scale score, sample size, BMI, and mean age of participants had a significant influence on the association between cholesterol levels and MDD.

CONCLUSIONS

Lower levels of total cholesterol, especially in males, are associated with MDD, so early lipid monitoring and targeted interventions are necessary.

Differences in metabolomic profiles between Black and White women in the U.S.: Analyses from two prospective cohorts

There is growing interest in incorporating metabolomics into public health practice. However, Black women are under-represented in many metabolomics studies. If metabolomic profiles differ between Black and White women, this under-representation may exacerbate existing Black-White health disparities. We therefore aimed to estimate metabolomic differences between Black and White women in the U.S. We leveraged data from two prospective cohorts: the Nurses' Health Study (NHS; n = 2077) and Women's Health Initiative (WHI; n = 2128). The WHI served as the replication cohort. Plasma metabolites (n = 334) were measured via liquid chromatography-tandem mass spectrometry. Observed metabolomic differences were estimated using linear regression and metabolite set enrichment analyses. Residual metabolomic differences in a hypothetical population in which the distributions of 14 risk factors were equalized across racial groups were estimated using inverse odds ratio weighting. In the NHS, Black-White differences were observed for most metabolites (75 metabolites with observed differences ≥ |0.50| standard deviations). Black women had lower average levels than White women for most metabolites (e.g., for N6, N6-dimethlylysine, mean Black-White difference = - 0.98 standard deviations; 95% CI: - 1.11, - 0.84). In metabolite set enrichment analyses, Black women had lower levels of triglycerides, phosphatidylcholines, lysophosphatidylethanolamines, phosphatidylethanolamines, and organoheterocyclic compounds, but higher levels of phosphatidylethanolamine plasmalogens, phosphatidylcholine plasmalogens, cholesteryl esters, and carnitines. In a hypothetical population in which distributions of 14 risk factors were equalized, Black-White metabolomic differences persisted. Most results replicated in the WHI (88% of 272 metabolites available for replication). Substantial differences in metabolomic profiles exist between Black and White women. Future studies should prioritize racial representation.

Two Omics Methods Expose Anti-Depression Mechanism of Raw and Vinegar-Baked Bupleurum Scorzonerifolium Willd

Bupleurum scorzonerifolium willd. (BS) and its vinegar-baked product (VBS) has been frequently utilized for depression management in clinical Chinese medicine. This paper aims to elucidate the antidepressant mechanism of BS and VBS from the perspectives of metabonomics and gut microbiota. A rat model of depression was established by CUMS combined with feeding alone to evaluate the antidepressant effects of BS and VBS. UPLC-Q-TOF-MS/MS-based metabolomics and 16S rRNA sequencing of rat feces were applied and the correlation of differential metabolic markers and intestinal floras was analyzed. The result revealed that BS and VBS significantly improved depression-like behaviors and the levels of monoamine neurotransmitters in CUMS rats. There were 27 differential endogenous metabolites between CUMS and normal rats, which were involved in 8 metabolic pathways. Whereas, BS and VBS could regulate 18 and 20 metabolites respectively, wherein fifteen of them were shared metabolites. On the genus level, BS and VBS could regulate twenty-five kinds of intestinal floras in CUMS rats, that is, they increased the abundance of beneficial bacteria and decreased the abundance of harmful bacteria. In conclusion, both BS and VBS exert excellent antidepressant effects by regulating various metabolic pathways and ameliorating intestinal microflora dysfunction.

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

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

Non-Targeted Metabolomics Investigation of a Sub-Chronic Variable Stress Model Unveils Sex-Dependent Metabolic Differences Induced by Stress

Depression is twice as prevalent in women as in men, however, most preclinical studies of depression have used male rodent models. This study aimed to examine how stress affects metabolic profiles depending on sex using a rodent depression model: sub-chronic variable stress (SCVS). The SCVS model of male and female mice was established in discovery and validation sets. The stress-induced behavioral phenotypic changes were similar in both sexes, however, the metabolic profiles of female plasma and brain became substantially different after stress, whereas those of males did not. Four stress-differential plasma metabolites-β-hydroxybutyric acid (BHB), L-serine, glycerol, and myo-inositol-could yield biomarker panels with excellent performance to discern the stressed individuals only for females. Disturbances in BHB, glucose, 1,5-anhydrosorbitol, lactic acid, and several fatty acids in the plasma of stressed females implied a systemic metabolic shift to β-oxidation in females. The plasma levels of BHB and corticosterone only in stressed females were observed not only in SCVS but also in an acute stress model. These results collectively suggest a sex difference in the metabolic responses by stress, possibly involving the energy metabolism shift to β-oxidation and the HPA axis dysregulation in females.

Major depressive disorder: hypothesis, mechanism, prevention and treatment

Worldwide, the incidence of major depressive disorder (MDD) is increasing annually, resulting in greater economic and social burdens. Moreover, the pathological mechanisms of MDD and the mechanisms underlying the effects of pharmacological treatments for MDD are complex and unclear, and additional diagnostic and therapeutic strategies for MDD still are needed. The currently widely accepted theories of MDD pathogenesis include the neurotransmitter and receptor hypothesis, hypothalamic-pituitary-adrenal (HPA) axis hypothesis, cytokine hypothesis, neuroplasticity hypothesis and systemic influence hypothesis, but these hypothesis cannot completely explain the pathological mechanism of MDD. Even it is still hard to adopt only one hypothesis to completely reveal the pathogenesis of MDD, thus in recent years, great progress has been made in elucidating the roles of multiple organ interactions in the pathogenesis MDD and identifying novel therapeutic approaches and multitarget modulatory strategies, further revealing the disease features of MDD. Furthermore, some newly discovered potential pharmacological targets and newly studied antidepressants have attracted widespread attention, some reagents have even been approved for clinical treatment and some novel therapeutic methods such as phototherapy and acupuncture have been discovered to have effective improvement for the depressive symptoms. In this work, we comprehensively summarize the latest research on the pathogenesis and diagnosis of MDD, preventive approaches and therapeutic medicines, as well as the related clinical trials.

Untargeted Plasma Metabolomic Profiling in Patients with Depressive Disorders: A Preliminary Study

Depressive disorder is a multifactorial disease that is based on dysfunctions in mental and biological processes. The search for biomarkers can improve its diagnosis, personalize therapy, and lead to a deep understanding of the biochemical processes underlying depression. The purpose of this work was a metabolomic analysis of blood serum to classify patients with depressive disorders and healthy individuals using Compound Discoverer software. Using high-resolution mass spectrometry, blood plasma samples from 60 people were analyzed, of which 30 were included in a comparison group (healthy donors), and 30 were patients with a depressive episode (F32.11) and recurrent depressive disorder (F33.11). Differences between patient and control groups were identified using the built-in utilities in Compound Discoverer software. Compounds were identified by their accurate mass and fragment patterns using the mzCloud database and tentatively identified by their exact mass using the ChemSpider search engine and the KEGG, ChEBI, FDA UNII-NLM, Human Metabolome and LipidMAPS databases. We identified 18 metabolites that could divide patients with depressive disorders from healthy donors. Of these, only two compounds were tentatively identified using the mzCloud database (betaine and piperine) based on their fragmentation spectra. For three compounds ((4S,5S,8S,10R)-4,5,8-trihydroxy-10-methyl-3,4,5,8,9,10-hexahydro-2H-oxecin-2-one, (2E,4E)-N-(2-hydroxy-2-methylpropyl)-2,4-tetradecadienamide and 17α-methyl-androstan-3-hydroxyimine-17β-ol), matches were found in the mzCloud database but with low score, which could not serve as reliable evidence of their structure. Another 13 compounds were identified by their exact mass in the ChemSpider database, 9 (g-butyrobetaine, 6-diazonio-5-oxo-L-norleucine, 11-aminoundecanoic acid, methyl N-acetyl-2-diazonionorleucinate, glycyl-glycyl-argininal, dilaurylmethylamine, 12-ketodeoxycholic acid, dicetylamine, 1-linoleoyl-2-hydroxy-sn-glycero-3-PC) had only molecular formulas proposed, and 4 were unidentified. Thus, the use of Compound Discoverer software alone was not sufficient to identify all revealed metabolites. Nevertheless, the combination of the found metabolites made it possible to divide patients with depressive disorders from healthy donors.

Lipids in Psychiatric Disorders: Functional and Potential Diagnostic Role as Blood Biomarkers

Lipids are a crucial component of the human brain, serving important structural and functional roles. They are involved in cell function, myelination of neuronal projections, neurotransmission, neural plasticity, energy metabolism, and neuroinflammation. Despite their significance, the role of lipids in the development of mental disorders has not been well understood. This review focused on the potential use of lipids as blood biomarkers for common mental illnesses, such as major depressive disorder, anxiety disorders, bipolar disorder, and schizophrenia. This review also discussed the impact of commonly used psychiatric medications, such as neuroleptics and antidepressants, on lipid metabolism. The obtained data suggested that lipid biomarkers could be useful for diagnosing psychiatric diseases, but further research is needed to better understand the associations between blood lipids and mental disorders and to identify specific biomarker combinations for each disease.

Metabolomic profiles of 38 acylcarnitines in major depressive episodes before and after treatment

BACKGROUND

Major depression is associated with changes in plasma L-carnitine and acetyl-L-carnitine. But its association with acylcarnitines remains unclear. The aim of this study was to assess metabolomic profiles of 38 acylcarnitines in patients with major depression before and after treatment compared to healthy controls (HCs).

METHODS

Metabolomic profiles of 38 plasma short-, medium-, and long-chain acylcarnitines were performed by liquid chromatography-mass spectrometry in 893 HCs from the VARIETE cohort and 460 depressed patients from the METADAP cohort before and after 6 months of antidepressant treatment.

RESULTS

As compared to HCs, depressed patients had lower levels of medium- and long-chain acylcarnitines. After 6 months of treatment, increased levels of medium- and long-chain acyl-carnitines were observed that no longer differed from those of controls. Accordingly, several medium- and long-chain acylcarnitines were negatively correlated with depression severity.

CONCLUSIONS

These medium- and long-chain acylcarnitine dysregulations argue for mitochondrial dysfunction through fatty acid β-oxidation impairment during major depression.

Assessment of biomarker stability and assay performance parameters for medical diagnosis: a case study of diagnosis of major depressive disorder

Aim: Assessing the stability profiles and assay performance of 24 biomarker assays in 32 biomarker/body fluid combinations identified as relevant for prediction of major depressive disorder. Materials & methods: Combinations were tested for stability and assay performance with ELISA at different storage and freeze-thaw conditions in pooled samples of 40 patients. Results: Stability and assay performance issues were found in almost all cases except three biomarkers in urine and three in serum. Conclusion: This study shows that, to produce reliable measurement data, assessments of stability and assay performance are essential. In development, other quality assurance parameters might be implemented to increase the level of measurement reliability by increasing assay performance control.

Metabolomic profiles of chronic distress are associated with cardiovascular disease risk and inflammation-related risk factors

BACKGROUND

Chronic psychological distress is associated with increased risk of cardiovascular disease (CVD) and investigators have posited inflammatory factors may be centrally involved in these relationships. However, mechanistic evidence and molecular underpinnings of these processes remain unclear, and data are particularly sparse among women. This study examined if a metabolite profile linked with distress was associated with increased CVD risk and inflammation-related risk factors.

METHODS

A plasma metabolite-based distress score (MDS) of twenty chronic psychological distress-related metabolites was developed in cross-sectional, 1:1 matched case-control data comprised of 558 women from the Nurses' Health Study (NHS; 279 women with distress, 279 controls). This MDS was then evaluated in two other cohorts: the Women's Health Initiative Observational Cohort (WHI-OS) and the Prevención con Dieta Mediterránea (PREDIMED) trial. We tested the MDS's association with risk of future CVD in each sample and with levels of C-reactive protein (CRP) in the WHI-OS. The WHI-OS subsample included 944 postmenopausal women (472 CHD cases; mean time to event = 5.8 years); the PREDIMED subsample included 980 men and women (224 CVD cases, mean time to event = 3.1 years).

RESULTS

In the WHI-OS, a 1-SD increase in the plasma MDS was associated with a 20% increased incident CHD risk (odds ratio [OR] = 1.20, 95% CI: 1.04 - 1.38), adjusting for known CVD risk factors excluding total and HDL cholesterol. This association was attenuated after including total and HDL cholesterol. CRP mediated an average 12.9% (95% CI: 4.9% - 28%, p < 10-15) of the total effect of MDS on CHD risk when adjusting for matching factors. This effect was attenuated after adjusting for known CVD risk factors. Of the metabolites in the MDS, tryptophan and threonine were inversely associated with incident CHD risk in univariate models. In PREDIMED, each one SD increase in the MDS was associated with an OR of 1.19 (95% CI: 1.00 - 1.41) for incident CVD risk, after adjusting all risk factors. Similar associations were observed in men and women. Four metabolites in the MDS were associated with incident CVD risk in PREDIMED in univariate models. Biliverdin and C36:5 phosphatidylcholine (PC) plasmalogen had inverse associations; C16:0 ceramide and C18:0 lysophosphatidylethanolamine(LPE) each had positive associations with CVD risk.

CONCLUSIONS

Our study points to molecular alterations that may underlie the association between chronic distress and subsequent risk of cardiovascular disease in adults.

Recent advances in LC-MS-based metabolomics for clinical biomarker discovery

The employment of liquid chromatography-mass spectrometry (LC-MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC-MS-untargeted and targeted metabolomics. To improve the sensitivity of low-abundance or poor-ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC-MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC-MS metabolomics to accelerate metabolite identification and data-processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC-MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.

The effect of prenatal stress on offspring depression susceptibility in relation to the gut microbiome and metabolome

Prenatal stress (PS) increases offspring susceptibility to depression, but the underlying mechanism remains unclear. Our previous results showed that PS can affect depression-like behavior in offspring through neurotransmitters and neuroinflammatory substances in the hippocampus and frontal cortex. In recent years there has been increasing evidence for a role of the gut microbiome in depression. The brain-gut axis theory suggests there is a need to further explore the mechanism involving the gut microbiome in the susceptibility of offspring to depression caused by PS. In the present study we used a stress model relevant to depression in which pregnant female rats undergo prenatal restraint stress and the offspring show susceptibility to depression. High-resolution gene sequencing for 16S ribosomal RNA markers and non-targeted metabolomic analysis were used to evaluate the fecal microbiome and the availability of metabolites, respectively. PS was found to induce depressive-like behavior in susceptible offspring (PS-S), as detected by the sucrose preference and forced swimming tests, as well as altering Alpha and Beta diversity. The different microbiota between the PS-S and control groups were mainly involved in membrane transport, carbohydrate metabolism, amino acid metabolism, and replication and repair pathways. In total, 237 and 136 important differential metabolites with significant influence on modeling analysis were obtained under positive and negative modes, respectively. The main canonical pathways found to be altered were glycerophospholipid metabolism and glycerolipid metabolism. These results suggest that gut microbiota might contribute to the onset of PS-induced depression-like behavior by affecting the glycerophospholipid and glycerolipid metabolic pathways.

Chronic inflammation, neuroglial dysfunction, and plasmalogen deficiency as a new pathobiological hypothesis addressing the overlap between post-COVID-19 symptoms and myalgic encephalomyelitis/chronic fatigue syndrome

After five waves of coronavirus disease 2019 (COVID-19) outbreaks, it has been recognized that a significant portion of the affected individuals developed long-term debilitating symptoms marked by chronic fatigue, cognitive difficulties ("brain fog"), post-exertional malaise, and autonomic dysfunction. The onset, progression, and clinical presentation of this condition, generically named post-COVID-19 syndrome, overlap significantly with another enigmatic condition, referred to as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Several pathobiological mechanisms have been proposed for ME/CFS, including redox imbalance, systemic and central nervous system inflammation, and mitochondrial dysfunction. Chronic inflammation and glial pathological reactivity are common hallmarks of several neurodegenerative and neuropsychiatric disorders and have been consistently associated with reduced central and peripheral levels of plasmalogens, one of the major phospholipid components of cell membranes with several homeostatic functions. Of great interest, recent evidence revealed a significant reduction of plasmalogen contents, biosynthesis, and metabolism in ME/CFS and acute COVID-19, with a strong association to symptom severity and other relevant clinical outcomes. These bioactive lipids have increasingly attracted attention due to their reduced levels representing a common pathophysiological manifestation between several disorders associated with aging and chronic inflammation. However, alterations in plasmalogen levels or their lipidic metabolism have not yet been examined in individuals suffering from post-COVID-19 symptoms. Here, we proposed a pathobiological model for post-COVID-19 and ME/CFS based on their common inflammation and dysfunctional glial reactivity, and highlighted the emerging implications of plasmalogen deficiency in the underlying mechanisms. Along with the promising outcomes of plasmalogen replacement therapy (PRT) for various neurodegenerative/neuropsychiatric disorders, we sought to propose PRT as a simple, effective, and safe strategy for the potential relief of the debilitating symptoms associated with ME/CFS and post-COVID-19 syndrome.

Plasma lipidomic profile of depressive symptoms: a longitudinal study in a large sample of community-dwelling American Indians in the strong heart study

Dyslipidemia has been associated with depression, but individual lipid species associated with depression remain largely unknown. The temporal relationship between lipid metabolism and the development of depression also remains to be determined. We studied 3721 fasting plasma samples from 1978 American Indians attending two exams (2001-2003, 2006-2009, mean ~5.5 years apart) in the Strong Heart Family Study. Plasma lipids were repeatedly measured by untargeted liquid chromatography-mass spectrometry (LC-MS). Depressive symptoms were assessed using the 20-item Center for Epidemiologic Studies for Depression (CES-D). Participants at risk for depression were defined as total CES-D score ≥16. Generalized estimating equation (GEE) was used to examine the associations of lipid species with incident or prevalent depression, adjusting for covariates. The associations between changes in lipids and changes in depressive symptoms were additionally adjusted for baseline lipids. We found that lower levels of sphingomyelins and glycerophospholipids and higher level of lysophospholipids were significantly associated with incident and/or prevalent depression. Changes in sphingomyelins, glycerophospholipids, acylcarnitines, fatty acids and triacylglycerols were associated with changes in depressive symptoms and other psychosomatic traits. We also identified differential lipid networks associated with risk of depression. The observed alterations in lipid metabolism may affect depression through increasing the activities of acid sphingomyelinase and phospholipase A2, disturbing neurotransmitters and membrane signaling, enhancing inflammation, oxidative stress, and lipid peroxidation, and/or affecting energy storage in lipid droplets or membrane formation. These findings illuminate the mechanisms through which dyslipidemia may contribute to depression and provide initial evidence for targeting lipid metabolism in developing preventive and therapeutic interventions for depression.

Instrumental Drift in Untargeted Metabolomics: Optimizing Data Quality with Intrastudy QC Samples

Untargeted metabolomics is an important tool in studying health and disease and is employed in fields such as biomarker discovery and drug development, as well as precision medicine. Although significant technical advances were made in the field of mass-spectrometry driven metabolomics, instrumental drifts, such as fluctuations in retention time and signal intensity, remain a challenge, particularly in large untargeted metabolomics studies. Therefore, it is crucial to consider these variations during data processing to ensure high-quality data. Here, we will provide recommendations for an optimal data processing workflow using intrastudy quality control (QC) samples that identifies errors resulting from instrumental drifts, such as shifts in retention time and metabolite intensities. Furthermore, we provide an in-depth comparison of the performance of three popular batch-effect correction methods of different complexity. By using different evaluation metrics based on QC samples and a machine learning approach based on biological samples, the performance of the batch-effect correction methods were evaluated. Here, the method TIGER demonstrated the overall best performance by reducing the relative standard deviation of the QCs and dispersion-ratio the most, as well as demonstrating the highest area under the receiver operating characteristic with three different probabilistic classifiers (Logistic regression, Random Forest, and Support Vector Machine). In summary, our recommendations will help to generate high-quality data that are suitable for further downstream processing, leading to more accurate and meaningful insights into the underlying biological processes.

Identification of potential biomarkers for diagnosis of syphilis from the cerebrospinal fluid based on untargeted metabolomic analysis

The infection rate of syphilis continues to rise globally, and the difficulty in diagnosis of neurosyphilis promptly needs to be resolved. More specific and sensitive diagnostic markers for latent syphilis and neurosyphilis should be found. Here the metabolic profiles of 88 cerebrospinal fluid samples from syphilis patients and controls were analyzed by LC/MS-based untargeted metabolomics. In total, 272 metabolites based on 3937 features obtained in ESI- mode and 252 metabolites based on 3799 features in ESI+ mode were identified. The experimental process was evaluated by principal component analysis, partial least squares discriminant analysis, and hierarchical cluster analysis. A clear separation between latent syphilis and neurosyphilis was found. Levels of lipid and linoleic acid metabolites, such as 9-oxo-octadecadienoic acid and 9,10,13-trihydroxyoctadecenoic acid, were increased in syphilis patients. In patients with neurosyphilis, significant changes in levels of 5-hydroxy-L-tryptophan (5-HTP) and acetyl-N-formyl-5-methoxykynurenamine (AFMK) in the tryptophan-kynurenine pathway were also detected. Only one metabolite, theophylline, differed significantly between symptomatic and asymptomatic neurosyphilis patients. Additionally, KEGG analysis revealed significant enrichment of tryptophan metabolism pathways, indicating a high correlation between tryptophan metabolism and syphilis symptoms. Levels of linoleic acid metabolites, 5-HTP, AFMK and theophylline were significantly altered in different patients. The role of these differential metabolites in the development of syphilis is worthy of further exploration. Our results may promote the development of biomarkers for diagnosis of latent syphilis from neurosyphilis, and for that of asymptomatic neurosyphilis from symptomatic neurosyphilis in the future.

Lipid Alteration Signature in the Blood Plasma of Individuals With Schizophrenia, Depression, and Bipolar Disorder

Importance

No clinically applicable diagnostic test exists for severe mental disorders. Lipids harbor potential as disease markers.

Objective

To define a reproducible profile of lipid alterations in the blood plasma of patients with schizophrenia (SCZ) independent of demographic and environmental variables and to investigate its specificity in association with other psychiatric disorders, ie, major depressive disorder (MDD) and bipolar disorder (BPD).

Design, Setting, and Participants

This was a multicohort case-control diagnostic analysis involving plasma samples from psychiatric patients and control individuals collected between July 17, 2009, and May 18, 2018. Study participants were recruited as consecutive and volunteer samples at multiple inpatient and outpatient mental health hospitals in Western Europe (Germany and Austria [DE-AT]), China (CN), and Russia (RU). Individuals with DSM-IV or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnoses of SCZ, MDD, BPD, or a first psychotic episode, as well as age- and sex-matched healthy controls without a mental health-related diagnosis were included in the study. Samples and data were analyzed from January 2018 to September 2020.

Main Outcomes and Measures

Plasma lipidome composition was assessed using liquid chromatography coupled with untargeted mass spectrometry.

Results

Blood lipid levels were assessed in 980 individuals (mean [SD] age, 36 [13] years; 510 male individuals [52%]) diagnosed with SCZ, BPD, MDD, or those with a first psychotic episode and in 572 controls (mean [SD] age, 34 [13] years; 323 male individuals [56%]). A total of 77 lipids were found to be significantly altered between those with SCZ (n = 436) and controls (n = 478) in all 3 sample cohorts. Alterations were consistent between cohorts (CN and RU: [Pearson correlation] r = 0.75; DE-AT and CN: r = 0.78; DE-AT and RU: r = 0.82; P < 10-38). A lipid-based predictive model separated patients with SCZ from controls with high diagnostic ability (area under the receiver operating characteristic curve = 0.86-0.95). Lipidome alterations in BPD and MDD, assessed in 184 and 256 individuals, respectively, were found to be similar to those of SCZ (BPD: r = 0.89; MDD: r = 0.92; P < 10-79). Assessment of detected alterations in individuals with a first psychotic episode, as well as patients with SCZ not receiving medication, demonstrated only limited association with medication restricted to particular lipids.

Conclusions and Relevance

In this study, SCZ was accompanied by a reproducible profile of plasma lipidome alterations, not associated with symptom severity, medication, and demographic and environmental variables, and largely shared with BPD and MDD. This lipid alteration signature may represent a trait marker of severe psychiatric disorders, indicating its potential to be transformed into a clinically applicable testing procedure.

Multi-omics data reveals the important role of glycerophospholipid metabolism in the crosstalk between gut and brain in depression

BACKGROUND

Gut microbiota plays a critical role in the onset and development of depression, but the underlying molecular mechanisms are unclear. This study was conducted to observe the characteristics of gut microbiota, lipid metabolism and neurotransmitters in Gut-Liver-Brain axis in depressed mice (DM), and identify some novel perceptions on relationships between gut microbiota and depression.

METHODS

A mouse model of depression was built used chronic unpredictable mild stress (CUMS). Fecal samples (measuring gut microbiota compositions, microbial genes and lipid metabolites), liver samples (measuring lipid metabolites), and hippocampus (measuring neurotransmitters) were collected. Both univariate and multivariate statistical analyses were used to identify the differential gut microbiota, metabolic signatures and neurotransmitters in DM.

RESULTS

There were significant differences on both microbial and metabolic signatures between DM and control mice (CM): 71 significantly changed operational taxonomic units (OTUs) (60.56% belonged to phylum Firmicutes) and 405 differential lipid metabolites (51.11% belonged to Glycerophospholipid (GP) metabolism) were identified. Functional analysis showed that depressive-like behaviors (DLB)-related differential microbial genes were mainly enriched in GP metabolism. Weighted correlation network analysis (WGCNA) showed that DLB-related differential metabolites mainly belonged to GPs. Meanwhile, seven differential neurotransmitters were identified. Comprehensive analysis found that Lachnospiraceae and gamma-aminobutyric acid (GABA) were significantly correlated with 94.20% and 53.14% differential GPs, respectively, and GABA was significantly correlated with three main DLB phenotypes.

CONCLUSION

Our results provided novel perceptions on the role of Gut-Liver-Brain axis in the onset of depression, and showed that GP metabolism might be the bridge between gut microbiota and depression. "Lachnospiraceae-GP metabolism-GABA" held the promise as a potential way between gut microbiota and brain functions in DM.

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSION

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

Mechanism of lily bulb and Rehmannia decoction in the treatment of lipopolysaccharide-induced depression-like rats based on metabolomics study and network pharmacology

CONTEXT

Lily bulb and Rehmannia decoction (LBRD), consisting of Lilium henryi Baker (Liliaceae) and Rehmannia glutinosa (Gaertn) DC (Plantaginaceae), is a specialized traditional Chinese medicine formula for treating depression. However, the underlying mechanisms, especially the relationship between LBRD efficacy and metabolomics, remains unclear.

OBJECTIVE

This study was aimed to investigate the metabolic mechanism of LBRD in treating depression.

MATERIALS AND METHODS

Network pharmacology was conducted using SwissTargetPrediction, DisGeNET, DrugBank, Metascape, etc., to construct component-target-pathway networks. The depression-like model was induced by intraperitoneal injection with lipopolysaccharide (LPS) (0.3 mg/kg) for 14 consecutive days. After the administration of LBRD (90 g/kg) and fluoxetine (2 mg/kg) for 14 days, we assessed behaviour and the levels of neurotransmitter, inflammatory cytokine and circulating stress hormone. Prefrontal metabolites of rats were detected by using liquid chromatography-mass spectrometry metabolomics method.

RESULTS

The results of network pharmacology showed that LBRD mainly acted on neurotransmitter and second messenger pathways. Compared to the model group, LBRD significantly ameliorated depressive phenotypes and increased the level of 5-HT (13.4%) and GABA (24.8%), as well as decreased IL-1β (30.7%), IL-6 (32.8%) and TNF-α (26.6%). Followed by LBRD treatment, the main metabolites in prefrontal tissue were contributed to retrograde endocannabinoid signalling, glycerophospholipid metabolism, glycosylphosphatidylinositol-anchor biosynthesis, autophagy signal pathway, etc.

DISCUSSION AND CONCLUSIONS

LBRD were effective at increasing neurotransmitter, attenuating proinflammatory cytokine and regulating glycerophospholipid metabolism and glutamatergic synapse, thereby ameliorating depressive phenotypes. This research will offer reference for elucidating the metabolomic mechanism underlying novel antidepressant agents contained LBRD formula.

Network science approach elucidates integrative genomic-metabolomic signature of antidepressant response and lifetime history of attempted suicide in adults with major depressive disorder

Background: Individuals with major depressive disorder (MDD) and a lifetime history of attempted suicide demonstrate lower antidepressant response rates than those without a prior suicide attempt. Identifying biomarkers of antidepressant response and lifetime history of attempted suicide may help augment pharmacotherapy selection and improve the objectivity of suicide risk assessments. Towards this goal, this study sought to use network science approaches to establish a multi-omics (genomic and metabolomic) signature of antidepressant response and lifetime history of attempted suicide in adults with MDD. Methods: Single nucleotide variants (SNVs) which associated with suicide attempt(s) in the literature were identified and then integrated with a) p180-assayed metabolites collected prior to antidepressant pharmacotherapy and b) a binary measure of antidepressant response at 8 weeks of treatment using penalized regression-based networks in 245 'Pharmacogenomics Research Network Antidepressant Medication Study (PGRN-AMPS)' and 103 'Combining Medications to Enhance Depression Outcomes (CO-MED)' patients with major depressive disorder. This approach enabled characterization and comparison of biological profiles and associated antidepressant treatment outcomes of those with (N = 46) and without (N = 302) a self-reported lifetime history of suicide attempt. Results: 351 SNVs were associated with suicide attempt(s) in the literature. Intronic SNVs in the circadian genes CLOCK and ARNTL (encoding the CLOCK:BMAL1 heterodimer) were amongst the top network analysis features to differentiate patients with and without a prior suicide attempt. CLOCK and ARNTL differed in their correlations with plasma phosphatidylcholines, kynurenine, amino acids, and carnitines between groups. CLOCK and ARNTL-associated phosphatidylcholines showed a positive correlation with antidepressant response in individuals without a prior suicide attempt which was not observed in the group with a prior suicide attempt. Conclusion: Results provide evidence for a disturbance between CLOCK:BMAL1 circadian processes and circulating phosphatidylcholines, kynurenine, amino acids, and carnitines in individuals with MDD who have attempted suicide. This disturbance may provide mechanistic insights for differential antidepressant pharmacotherapy outcomes between patients with MDD with versus without a lifetime history of attempted suicide. Future investigations of CLOCK:BMAL1 metabolic regulation in the context of suicide attempts may help move towards biologically-augmented pharmacotherapy selection and stratification of suicide risk for subgroups of patients with MDD and a lifetime history of attempted suicide.

Metabolomic transition trajectory and potential mechanisms of N-nitrosomethylbenzylamine induced esophageal squamous cell carcinoma in rats

Esophageal squamous cell carcinoma (ESCC) is an environment-relevant malignancy with a high mortality. Nitrosamines, a class of nitrogen-containing environmental carcinogens, are widely suggested as a risk factor for ESCC. However, how nitrosamines affect metabolic regulation to promote ESCC tumorigenesis is largely unknown. In this study, the transition trajectory of serum metabolism in the course of ESCC induced by N-nitrosomethylbenzylamine (NMBA) in rats was depicted by an untargeted metabolomic analysis, and the potential molecular mechanisms were revealed. The results showed that the metabolic alteration in rats was slight at the basal cell hyperplasia (BCH) stage, while it became apparent when the esophageal lesion developed into dysplasia (DYS) or more serious conditions. Moreover, serum metabolism of severe dysplasia (S-DYS) showed more similar characteristics to that of carcinoma in situ (CIS) and invasive cancer (IC). Aberrant nicotinate (NA) and nicotinamide (NAM) metabolism, tryptophan (TRP) metabolism, and sphingolipid metabolism could be the key players favoring the malignant transformation of esophageal epithelium induced by NMBA. More particularly, NA and NAM metabolism in the precancerous stages and TRP metabolism in the cancerous stages were demonstrated to replenish NAD⁺ in different patterns. Furthermore, both the IDO1-KYN-AHR axis mediated by TRP metabolism and the SPHK1-S1P-S1PR1 axis by sphingolipid metabolism provided an impetus to create the pro-inflammatory yet immune-suppressive microenvironment to facilitate the esophageal tumorigenesis and progression. Together, these suggested that NMBA exerted its carcinogenicity via more than one pathway, which may act together to produce combination effects. Targeting these pathways may open up the possibility to attenuate NMBA-induced esophageal carcinogenesis. However, the interconnection between different metabolic pathways needs to be specified further. And the integrative and multi-level systematic research will be conducive to fully understanding the mechanisms of NMBA-induced ESCC.

Differential Gut Microbiota Compositions Related With the Severity of Major Depressive Disorder

Objective

Increasing evidence shows a close relationship between gut microbiota and major depressive disorder (MDD), but the specific mechanisms remain unknown. This study was conducted to explore differential gut microbiota compositions related to the severity of MDD.

Methods

Healthy controls (HC) (n = 131) and MDD patients (n = 130) were included. MDD patients with Hamilton Depression Rating Scale (HDRS) score <25 and ≥25 were assigned into moderate (n = 72) and severe (n = 58) MDD groups, respectively. Univariate and multivariate analyses were used to analyze the gut microbiota compositions at the genus level.

Results

Thirty-six and 27 differential genera were identified in moderate and severe MDD patients, respectively. The differential genera in moderate and severe MDD patients mainly belonged to three (Firmicutes, Actinobacteriota, and Bacteroidota) and two phyla (Firmicutes and Bacteroidota), respectively. One specific covarying network from phylum Actinobacteriota was identified in moderate MDD patients. In addition, five genera (Collinsella, Eggerthella, Alistipes, Faecalibacterium, and Flavonifractor) from the shared differential genera by two MDD groups had a fair efficacy in diagnosing MDD from HC (AUC = 0.786).

Conclusions

Our results were helpful for further exploring the role of gut microbiota in the pathogenesis of depression and developing objective diagnostic methods for MDD.

Plasma Metabolomic Signature of Early Abuse in Middle-Aged Women

OBJECTIVE

Metabolomic profiling may provide insights into biological mechanisms underlying the strong epidemiologic links observed between early abuse and cardiometabolic disorders in later life.

METHODS

We examined the associations between early abuse and midlife plasma metabolites in two nonoverlapping subsamples from the Nurses' Health Study II, comprising 803 (mean age = 40 years) and 211 women (mean age = 61 years). Liquid chromatography-tandem mass spectrometry assays were used to measure metabolomic profiles, with 283 metabolites consistently measured in both subsamples. Physical and sexual abuse before age 18 years was retrospectively assessed by validated questions integrating type/frequency of abuse. Analyses were conducted in each sample and pooled using meta-analysis, with multiple testing adjustment using the q value approach for controlling the positive false discovery rate.

RESULTS

After adjusting for age, race, menopausal status, body size at age 5 years, and childhood socioeconomic indicators, more severe early abuse was consistently associated with five metabolites at midlife (q value < 0.20 in both samples), including lower levels of serotonin and C38:3 phosphatidylethanolamine plasmalogen and higher levels of alanine, proline, and C40:6 phosphatidylethanolamine. Other metabolites potentially associated with early abuse (q value < 0.05 in the meta-analysis) included triglycerides, phosphatidylcholine plasmalogens, bile acids, tyrosine, glutamate, and cotinine. The association between early abuse and midlife metabolomic profiles was partly mediated by adulthood body mass index (32% mediated) and psychosocial distress (13%-26% mediated), but not by other life-style factors.

CONCLUSIONS

Early abuse was associated with distinct metabolomic profiles of multiple amino acids and lipids in middle-aged women. Body mass index and psychosocial factors in adulthood may be important intermediates for the observed association.

Altered Fecal Metabolomics and Potential Biomarkers of Psoriatic Arthritis Differing From Rheumatoid Arthritis

Psoriatic arthritis (PsA) is a chronic inflammatory joint disease, and the diagnosis is quite difficult due to the unavailability of reliable clinical markers. This study aimed to investigate the fecal metabolites in PsA by comparison with rheumatoid arthritis (RA), and to identify potential diagnostic biomarkers for PsA. The metabolic profiles of the fecal samples from 27 PsA and 29 RA patients and also 36 healthy controls (HCs) were performed on ultra-high-performance liquid chromatography coupled with hybrid triple quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS). And differentially altered metabolites were screened and assessed using multivariate analysis for exploring the potential biomarkers of PsA. The results showed that 154 fecal metabolites were significantly altered in PsA patients when compared with HCs, and 45 metabolites were different when compared with RA patients. A total of 14 common differential metabolites could be defined as candidate biomarkers. Furthermore, a support vector machines (SVM) model was performed to distinguish PsA from RA patients and HCs, and 5 fecal metabolites, namely, α/β-turmerone, glycerol 1-hexadecanoate, dihydrosphingosine, pantothenic acid and glutamine, were determined as biomarkers for PsA. Through the metabolic pathways analysis, we found that the abnormality of amino acid metabolism, bile acid metabolism and lipid metabolism might contribute to the occurrence and development of PsA. In summary, our research provided ideas for the early diagnosis and treatment of PsA by identifying fecal biomarkers and analyzing metabolic pathways.

Carnitine and Depression

Depression has become one of the most common mental diseases in the world, but the understanding of its pathogenesis, diagnosis and treatments remains insufficient. Carnitine is a natural substance that exists in organisms, which can be synthesized in vivo or supplemented by intake. Relationships of carnitine with depression, bipolar disorder and other mental diseases have been reported in different studies. Several studies show that the level of acylcarnitines (ACs) changes significantly in patients with depression compared with healthy controls while the supplementation of acetyl-L-carnitine is beneficial to the treatment of depression. In this review, we aimed to clarify the effects of ACs in depressive patients and to explore whether ACs might be the biomarkers for the diagnosis of depression and provide new ideas to treat depression.

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.

Changes in gut viral and bacterial species correlate with altered 1,2-diacylglyceride levels and structure in the prefrontal cortex in a depression-like non-human primate model

Major depressive disorder (MDD) is a debilitating mental disease, but its underlying molecular mechanisms remain obscure. Our previously established model of naturally occurring depression-like (DL) behaviors in Macaca fascicularis, which is characterized by microbiota-gut-brain (MGB) axis disturbances, can be used to interrogate how a disturbed gut ecosystem may impact the molecular pathology of MDD. Here, gut metagenomics were used to characterize how gut virus and bacterial species, and associated metabolites, change in depression-like monkey model. We identified a panel of 33 gut virus and 14 bacterial species that could discriminate the depression-like from control macaques. In addition, using lipidomic analyses of central and peripheral samples obtained from these animals, we found that the DL macaque were characterized by alterations in the relative abundance, carbon-chain length, and unsaturation degree of 1,2-diacylglyceride (DG) in the prefrontal cortex (PFC), in a brain region-specific manner. In addition, lipid-reaction analysis identified more active and inactive lipid pathways in PFC than in amygdala or hippocampus, with DG being a key nodal player in these lipid pathways. Significantly, co-occurrence network analysis showed that the DG levels may be relevant to the onset of negative emotions behaviors in PFC. Together our findings suggest that altered DG levels and structure in the PFC are hallmarks of the DL macaque, thus providing a new framework for understanding the gut microbiome's role in depression.

MicroRNA-Messenger RNA Regulatory Network Mediates Disrupted TH17 Cell Differentiation in Depression

Accumulating evidence indicates an important role for microRNA (miRNA)-messenger RNA (mRNA) regulatory networks in human depression. However, the mechanisms by which these networks act are complex and remain poorly understood. We used data mining to identify differentially expressed miRNAs from GSE81152 and GSE152267 datasets, and differentially expressed mRNAs were identified from the Netherlands Study of Depression and Anxiety, the GlaxoSmithKline-High-Throughput Disease-specific target Identification Program, and the Janssen-Brain Resource Company study. We constructed a miRNA-mRNA regulatory network based on differentially expressed mRNAs that intersected with target genes of differentially expressed miRNAs, and then performed bioinformatics analysis of the network. The key candidate genes were assessed in the prefrontal cortex of chronic social defeat stress (CSDS) depression mice by quantitative real-time polymerase chain reaction (qRT-PCR). Three differentially expressed miRNAs were commonly identified across the two datasets, and 119 intersecting differentially expressed mRNAs were identified. A miRNA-mRNA regulatory network including these three key differentially expressed miRNAs and 119 intersecting differentially expressed mRNAs was constructed. Functional analysis of the intersecting differentially expressed mRNAs revealed that an abnormal inflammatory response characterized by disturbed T-helper cell 17 (Th17) differentiation was the primary altered biological function. qRT-PCR validated the decreased expression of Th17 cell differentiation-related genes, including interleukin (IL)17A, IL21, IL22, and IL1β, and the increased expression of retinoic acid receptor-related orphan receptor gamma-t (RORγt) in CSDS mice, which showed significant depressive- and anxiety-like behaviors. This study indicates that an abnormal inflammatory response characterized by disturbed Th17 cell differentiation is the primary altered biological process in major depressive disorder. Our findings indicate possible biomarkers and treatment targets and provide novel clues to understand the pathogenesis of major depressive disorder.

Simultaneous Analysis of the Metabolome and Lipidome Using Polarity Partition Two-Dimensional Liquid Chromatography-Mass Spectrometry

Comprehensive metabolic profiling is a considerable challenge for systems biology since the metabolites in biological samples have significant polarity differences. A heart-cutting two-dimensional liquid chromatography-mass spectrometry (2D-LC-MS) method-based polarity partition was established to analyze both the metabolome and lipidome in a single run. Based on the polarity partition strategy, metabolites with high polarity were retained and separated by one-dimensional hydrophilic chromatography, while low- and medium-polarity lipids were collected into a sample loop and injected into two-dimensional reversed-phase chromatography for separation. A simple online dilution strategy realized the online coupling of the 2D-LC-MS, which effectively solved band broadening and peak distortion caused by solvent incompatibility. Moreover, a dual gradient elution procedure was introduced to further broaden the coverage of low-polarity lipids. The metabolites' log P values, which this 2D-LC-MS method could analyze, ranged from -8.79 to 26.86. The feasibility of the 2D-LC-MS system was demonstrated by simultaneous analysis of the metabolome and lipidome in rat plasma related to depression. A total of 319 metabolites were determined within 40 min, including organic acids, nucleosides, carbohydrate derivatives, amino acids, lipids, and other organic compounds. Finally, 44 depression-related differential metabolites were screened. Compared with conventional LC-MS-based methods, the 2D-LC method covered over 99% of features obtained by two conventional methods. In addition, the selectivity and resolution of the hydrophilic metabolites were improved, and the matrix effects of the hydrophobic metabolites were reduced in the developed method. The results indicated that the established 2D-LC system is a powerful tool for comprehensive metabolomics studies.

Plasma metabolomic profiles associated with chronic distress in women

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

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.

Biomarkers of Post-COVID Depression

The COVID-19 pandemic is spreading around the world and 187 million people have already been affected. One of its after-effects is post-COVID depression, which, according to the latest data, affects up to 40% of people who have had SARS-CoV-2 infection. A very important issue for the mental health of the general population is to look for the causes of this complication and its biomarkers. This will help in faster diagnosis and effective treatment of the affected patients. In our work, we focused on the search for major depressive disorder (MDD) biomarkers, which are also present in COVID-19 patients and may influence the development of post-COVID depression. For this purpose, we searched PubMed, Scopus and Google Scholar scientific literature databases using keywords such as 'COVID-19', 'SARS-CoV-2', 'depression', 'post-COVID', 'biomarkers' and others. Among the biomarkers found, the most important that were frequently described are increased levels of interleukin 6 (IL-6), soluble interleukin 6 receptor (sIL-6R), interleukin 1 β (IL-1β), tumor necrosis factor α (TNF-α), interferon gamma (IFN-γ), interleukin 10 (IL-10), interleukin 2 (IL-2), soluble interleukin 2 receptor (sIL-2R), C-reactive protein (CRP), Monocyte Chemoattractant Protein-1 (MCP-1), serum amyloid a (SAA1) and metabolites of the kynurenine pathway, as well as decreased brain derived neurotrophic factor (BDNF) and tryptophan (TRP). The biomarkers identified by us indicate the etiopathogenesis of post-COVID depression analogous to the leading inflammatory hypothesis of MDD.

Novel metabolomic profile of subjects with non-classic apparent mineralocorticoid excess

Nonclassic apparent mineralocorticoid excess (NC-AME) is proposed as a novel clinical condition with a mild phenotypic spectrum that ranges from normotension to severe hypertension. This condition is mainly characterized by a high serum cortisol to cortisone ratio (F/E) and concomitant low cortisone (E), however further metabolic changes in NC-AME have not been studied. A cross-sectional study was performed in a primary-care cohort of 396 Chilean subjects, which were classified in two groups: NC-AME (n = 28) and healthy controls (n = 27). A discovery study based in untargeted metabolomics assay in serum samples from both groups was performed by UPLC-Q-TOF/MS. Global metabolomic variations were assayed by principal component analysis and further compared by orthogonal partial least-squares discriminant analysis (OPLS-DA). NC-AME subjects exhibited higher values of blood pressure, fractional excretion of potassium, and lower plasma renin activity and urinary sodium to potassium ratio. Metabolomic analyses showed 36 differentially regulated metabolites between NC-AME and control subjects. A ROC curve analyses identified eight metabolites with high discriminatory capacity between NC-AME and control subjects. Moreover, gamma-l-glutamyl-l-methionine sulfoxide and 5-sulfoxymethylfurfural, exhibited significant association with cortisone, which are potential biomarkers of NC-AME, however further assays should elucidate its biological role in setup and progression of this phenotype.

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.

Endogenous n-3 PUFAs attenuated olfactory bulbectomy-induced behavioral and metabolomic abnormalities in Fat-1 mice

Depression is associated with abnormal lipid metabolism, and omega (n)-3 polyunsaturated fatty acids (PUFAs) can effectively treat depression. However, mechanism of lipid metabolism involved in the depressive attenuation remains poorly understood. Olfactory bulbectomy (OB)-induced changes in animal behavior and physiological functions are similar to those observed in depressed patients. Therefore, the present study used wild type (WT) and Fat-1 mice with or without OB to explore whether endogenous n-3 PUFA treatment of depression was through rectifying lipid metabolism, and to discover the possible lipid metabolic pathways. In WT mice, OB enhanced locomotor activity associated with up-regulation of lipid metabolites in the serum, such as phosphatidylcholines, L-a-glutamyl-L-Lysine and coproporphyrinogen III (Cop), which were involved in anti-inflammatory lipid metabolic pathways. OB also increased microglia activation marker CD11b and pro-inflammatory cytokines in the hippocampus. In one of the lipid pathways, increased Cop was significantly correlated with the hyper-activity of the OB mice. These OB-induced changes were markedly attenuated by endogenous n-3 PUFAs in Fat-1 mice. Additionally, increased expressions of anti-inflammatory lipid genes, such as fatty acid desaturase (Fads) and phospholipase A2 group VI (Pla2g6), were found in the hippocampus of Fat-1 mice compared with WT mice. Furthermore, Cop administration increased the production of pro-inflammatory cytokines and nitric oxide in a microglial cell line BV2. In conclusion, endogenous n-3 PUFAs in Fat-1 mice attenuated abnormal behavior in the depression model through restoration of lipid metabolism and suppression of inflammatory response.

Characterization of Potential Protein Biomarkers for Major Depressive Disorder Using Matrix-Assisted Laser Desorption Ionization/Time-of-Flight Mass Spectrometry

Matrix-assisted laser desorption ionization/time-of-flight (MALDI-TOF) mass spectrometry is a sensitive analytical tool for characterizing various biomolecules in biofluids. In this study, MALDI-TOF was used to characterize potential plasma biomarkers for distinguishing patients with major depressive disorder (MDD) from patients with schizophrenia and healthy controls. To avoid interference from albumin—the predominant protein in plasma—the plasma samples were pretreated using acid hydrolysis. The results obtained by MALDI-TOF were also validated by electrospray ionization-quadrupole time-of-flight (ESI-QTOF) mass spectrometry. The analytical results were further treated with principal component analysis (PCA), hierarchical clustering analysis (HCA), and receiver operating characteristic (ROC) curve analysis. The statistical analyses showed that MDD patients could be distinguished from schizophrenia patients and healthy controls by the lack of apolipoprotein C1 (Apo C1), which, in fact, was detected in healthy controls and schizophrenia patients. This protein is suggested to be a potential plasma biomarker for distinguishing MDD patients from healthy controls and schizophrenia patients. Since sample preparation for MALDI-TOF is very simple, high-throughput plasma apolipoprotein analysis for clinical purposes is feasible.

A systematic review and meta-analysis of lipid metabolomic signatures of Major Depressive Disorder

The aim of this meta-analysis was to provide a comprehensive synthesis of the evidence examining biomarker signatures in MDD patients including lipids, lipid regulatory proteins (LRP), and polyunsaturated fatty acid (PUFA) as compared to healthy individuals. We performed meta-analyses and meta-regression of the studies comparing lipid, LRP, and PUFA levels between MDD patients and healthy individuals by searching Embase, Ovid Medline, Scopus, PsycINFO, PubMed, and Cochrane databases. Search was performed in these databases up to September 2019 and 29 studies were included. Levels of lipid parameter triglyceride (TG) (SMD 0.55, 95% CI 0.30-0.80, p < 0.0001) were higher while total cholesterol (TC) (SMD = -0.46, 95%CI -0.93 to -0.001, p = 0.04) and very low-density lipoprotein (VLDL) (SMD = -0.46, 95%CI -0.71 to -0.20, p = 0.02) were lower in MDD patients than controls. Subgroup analysis for age showed that the levels of high-density lipoprotein (HDL) were lower in ≥40-year age group (SMD = -0.38, 95%CI -0.70 to -0.06, p = 0.01) and levels of TC was lower in MDD patients in studies from Asian countries (SMD = -0.74, 95%CI -1.37 to -0.10, p = 0.02). TG levels were found to be high all subgroups in MDD patients than controls. A negative association between TC levels and use of lipid lowering medications and a positive association between smoking and LDL levels was found using meta-regression analysis. This study will be useful for physicians when considering the assessment of lipidand LRP profiles in MDD patients to reduce the cardiovascular morbidity and mortality.

Associations of depression status with plasma levels of candidate lipid and amino acid metabolites: a meta-analysis of individual data from three independent samples of US postmenopausal women

Recent animal and small clinical studies have suggested depression is related to altered lipid and amino acid profiles. However, this has not been examined in a population-based sample, particularly in women. We identified multiple metabolites associated with depression as potential candidates from prior studies. Cross-sectional data from three independent samples of postmenopausal women were analyzed, including women from the Women's Health Initiative-Observational Study (WHI-OS, n = 926), the WHI-Hormone Trials (WHI-HT; n = 1,325), and the Nurses' Health Study II Mind-Body Study (NHSII-MBS; n = 218). Positive depression status was defined as having any of the following: elevated depressive symptoms, antidepressant use, or depression history. Plasma metabolites were measured using liquid chromatography-tandem mass spectrometry (21 phosphatidylcholines (PCs), 7 lysophosphatidylethanolamines, 5 ceramides, 3 branched chain amino acids, and 9 neurotransmitters). Associations between depression status and metabolites were evaluated using multivariable linear regression; results were pooled by random-effects meta-analysis with multiple testing adjustment using the false discovery rate (FDR). Prevalence rates of positive depression status were 24.4% (WHI-OS), 25.7% (WHI-HT), and 44.7% (NHSII-MBS). After multivariable adjustment, positive depression status was associated with higher levels of glutamate and PC 36 : 1/38 : 3, and lower levels of tryptophan and GABA-to-glutamate and GABA-to-glutamine ratio (FDR-p < 0.05). Positive associations with LPE 18 : 0/18 : 1 and inverse associations with valine and serotonin were also observed, although these associations did not survive FDR adjustment. Associations of positive depression status with several candidate metabolites including PC 36 : 1/38 : 3 and amino acids involved in neurotransmission suggest potential depression-related metabolic alterations in postmenopausal women, with possible implications for later chronic disease.

Metabolomic Characterization of Pediatric Acute-Onset Neuropsychiatric Syndrome (PANS)

Introduction

PANS is a controversial clinical entity, consisting of a complex constellation of psychiatric symptoms, adventitious changes, and expression of various serological alterations, likely sustained by an autoimmune/inflammatory disease. Detection of novel biomarkers of PANS is highly desirable for both diagnostic and therapeutic management of affected patients. Analysis of metabolites has proven useful in detecting biomarkers for other neuroimmune-psychiatric diseases. Here, we utilize the metabolomics approach to determine whether it is possible to define a specific metabolic pattern in patients affected by PANS compared to healthy subjects.

Design

This observational case-control study tested consecutive patients referred for PANS between June 2019 to May 2020. A PANS diagnosis was confirmed according to the PANS working criteria (National Institute of Mental Health [NIMH], 2010). Healthy age and sex-matched subjects were recruited as controls.

Methods

Thirty-four outpatients referred for PANS (mean age 9.5 years; SD 2.9, 71% male) and 25 neurotypical subjects matched for age and gender, were subjected to metabolite analysis. Serum samples were obtained from each participant and were analyzed using Nuclear Magnetic Resonance (NMR) spectroscopy. Subsequently, multivariate and univariate statistical analyses and Receiver Operator Curves (ROC) were performed.

Results

Separation of the samples, in line with the presence of PANS diagnosis, was observed by applying a supervised model (R²X = 0.44, R²Y = 0.54, Q² = 0.44, p-value < 0.0001). The significantly altered variables were 2-Hydroxybutyrate, glycine, glutamine, histidine, tryptophan. Pathway analysis indicated that phenylalanine, tyrosine, and tryptophan metabolism, as well as glutamine and glutamate metabolism, exhibited the largest deviations from neurotypical controls.

Conclusion

We found a unique plasma metabolic profile in PANS patients, significantly differing from that of healthy children, that suggests the involvement of specific patterns of neurotransmission (tryptophan, glycine, histamine/histidine) as well as a more general state of neuroinflammation and oxidative stress (glutamine, 2-Hydroxybutyrate, and tryptophan-kynurenine pathway) in the disorder. This metabolomics study offers new insights into biological mechanisms underpinning the disorder and supports research of other potential biomarkers implicated in PANS.

The Study on the Pathogenesis of Pediatric Lymphoma Based on the Combination of Pseudotargeted and Targeted Metabolomics

Pediatric lymphoma is a kind of malignant tumor with high mortality. The complexity of pediatric lymphoma shows a great challenge for effective diagnosis and treatment. In order to meet the challenge, the combination of pseudotargeted and targeted metabolomics was used to analyze the serum metabolites in pediatric lymphoma patients and healthy controls for discovering the metabolites related to pediatric lymphoma. The serum samples were obtained from the treatment group (n = 43), the control group (n = 26), and the patients group (n = 18). A total of 17 serum metabolites, including carnitine, leucine, creatine, urea, (6Z,9Z,12Z)-octadecatrienoic acid, linoleate, octadecenoic acid, L-palmitoylcarnitine, hexadecanoic acid, tetradecanoic acid, (9Z)-hexadecenoic acid, uric acid, glucose, 1-methylnicotinamide, hypoxanthine, L-glutamine, and taurine, were found to be related to pediatric lymphoma. They could provide a scientific diagnostic basis and therapeutic target for pediatric lymphoma and elucidate the mechanism of pediatric lymphoma.