Metabolomics strategy assisted by transcriptomics analysis to identify biomarkers associated with schizophrenia

Discovery of biological markers for schizophrenia based on metabolomics: a systematic review

Introduction and methods

To discover biomarkers for schizophrenia (SCZ) at the metabolomics level, we registered this systematic review (CRD42024572133 (https://www.crd.york.ac.uk/PROSPERO/home)) including 56 qualified articles, and we identified the characteristics of metabolites, metabolite combinations, and metabolic pathways associated with SCZ.

Results

Our findings showed that decreased arachidonic acid, arginine, and aspartate levels, and the increased levels of glucose 6-phosphate and glycylglycine were associated with the onset of SCZ. Metabolites such as carnitine and methionine sulfoxide not only helped to identify SCZ in Miao patients, but also were different between Miao patients and Han patients. The decrease in benzoic acid and betaine and the increase in creatine were the notable metabolic characteristics of first-episode schizophrenia (FESCZ). The metabolite combination formed by metabolites such as methylamine, dimethylamine and other metabolites had the best diagnostic effect. Arginine and proline metabolism and arginine biosynthesis had a clear advantage in identifying SCZ and acute SCZ. Butanoate metabolism played an important role in identifying SCZ, toxoplasma infection and SCZ comorbidity. Biosynthesis of unsaturated fatty acids was also significantly enriched in the diagnosis and treatment of SCZ.

Discussion

This study summarizes the current progress in clinical metabolomic research related to SCZ, deepens understanding of the pathogenesis of SCZ, and lays a foundation for subsequent research on SCZ-related metabolites.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/home, identifier CRD42024572133.

Serum metabolic profile evidence for relationship between schizophrenia and depression: An untargeted metabolomics

Given the genetic and clinical overlap observed between schizophrenia and depression, the present study was to identify the similarities and differences in serum metabolic profiles between patients with schizophrenia and depression. Global metabolomics research methods based on UHPLC-QTOF-MS/MS were performed. A total of 113 and 118 differential metabolites were screened and identified in depression and schizophrenia groups, respectively, as compared to health control; among those, 94 differential metabolites were shared by both. Pathway analysis indicated arginine and proline metabolism, alanine, aspartate, and glutamate metabolism were two significant metabolic pathways both in depression and schizophrenia groups as compared with health control groups, respectively. Similarly, 77 differential metabolites were identified between depression and schizophrenia groups, in which, serum N-acetylglutamine and isovalerylglycine levels showed significant differences between patients with depression and schizophrenia with p values less than 0.001 and without significant outliers. Sphingolipid metabolism was identified as a significant metabolic pathway distinguishing between depression and schizophrenia groups based on pathway analysis. Conclusively, common alterations in arginine and proline metabolism, alanine, aspartate, and glutamate metabolism were observed in patients with schizophrenia and depression; whereas differences in serum N-acetylglutamine and isovalerylglycine levels as well as sphingolipid metabolism were discovered between the two categories of patients.

Peripheral Lipid Signatures, Metabolic Dysfunction, and Pathophysiology in Schizophrenia Spectrum Disorders

Metabolic dysfunction is commonly observed in schizophrenia spectrum disorders (SSDs). The causes of metabolic comorbidity in SSDs are complex and include intrinsic or biological factors linked to the disorder, which are compounded by antipsychotic (AP) medications. The exact mechanisms underlying SSD pathophysiology and AP-induced metabolic dysfunction are unknown, but dysregulated lipid metabolism may play a role. Lipidomics, which detects lipid metabolites in a biological sample, represents an analytical tool to examine lipid metabolism. This systematic review aims to determine peripheral lipid signatures that are dysregulated among individuals with SSDs (1) with minimal exposure to APs and (2) during AP treatment. To accomplish this goal, we searched MEDLINE, Embase, and PsychINFO databases in February 2024 to identify all full-text articles written in English where the authors conducted lipidomics in SSDs. Lipid signatures reported to significantly differ in SSDs compared to controls or in relation to AP treatment and the direction of dysregulation were extracted as outcomes. We identified 46 studies that met our inclusion criteria. Most of the lipid metabolites that significantly differed in minimally AP-treated patients vs. controls comprised glycerophospholipids, which were mostly downregulated. In the AP-treated group vs. controls, the significantly different metabolites were primarily fatty acyls, which were dysregulated in conflicting directions between studies. In the pre-to-post AP-treated patients, the most impacted metabolites were glycerophospholipids and fatty acyls, which were found to be primarily upregulated and conflicting, respectively. These lipid metabolites may contribute to SSD pathophysiology and metabolic dysfunction through various mechanisms, including the modulation of inflammation, cellular membrane permeability, and metabolic signaling pathways.

Asparagine reduces the risk of schizophrenia: a bidirectional two-sample mendelian randomization study of aspartate, asparagine and schizophrenia

BACKGROUND

Despite ongoing research, the underlying causes of schizophrenia remain unclear. Aspartate and asparagine, essential amino acids, have been linked to schizophrenia in recent studies, but their causal relationship is still unclear. This study used a bidirectional two-sample Mendelian randomization (MR) method to explore the causal relationship between aspartate and asparagine with schizophrenia.

METHODS

This study employed summary data from genome-wide association studies (GWAS) conducted on European populations to examine the correlation between aspartate and asparagine with schizophrenia. In order to investigate the causal effects of aspartate and asparagine on schizophrenia, this study conducted a two-sample bidirectional MR analysis using genetic factors as instrumental variables.

RESULTS

No causal relationship was found between aspartate and schizophrenia, with an odds ratio (OR) of 1.221 (95%CI: 0.483-3.088, P-value = 0.674). Reverse MR analysis also indicated that no causal effects were found between schizophrenia and aspartate, with an OR of 0.999 (95%CI: 0.987-1.010, P-value = 0.841). There is a negative causal relationship between asparagine and schizophrenia, with an OR of 0.485 (95%CI: 0.262-0.900, P-value = 0.020). Reverse MR analysis indicates that there is no causal effect between schizophrenia and asparagine, with an OR of 1.005(95%CI: 0.999-1.011, P-value = 0.132).

CONCLUSION

This study suggests that there may be a potential risk reduction for schizophrenia with increased levels of asparagine, while also indicating the absence of a causal link between elevated or diminished levels of asparagine in individuals diagnosed with schizophrenia. There is no potential causal relationship between aspartate and schizophrenia, whether prospective or reverse MR. However, it is important to note that these associations necessitate additional research for further validation.

Lactate levels in the brain and blood of schizophrenia patients: A systematic review and meta-analysis

BACKGROUND

The pathophysiological mechanisms of schizophrenia are still unclear. Converging evidence suggests that energy metabolism abnormalities are involved in schizophrenia, and support its role in the pathophysiology of this disease. Lactate plays an important role in energy metabolism. Many studies have reported changes in the levels of lactate in the brain and serum of schizophrenia patients; however, the results from these studies are not consistent. To overcome this limitation, the goal of the present meta-analysis is to analyze the changes in lactate levels in the brain and blood of schizophrenia patients.

METHODS

For this systematic review and meta-analysis, we performed a thorough search of relevant literature in the English language, using the MEDLINE, Cochrane, and Embase databases.

RESULTS

In the present meta-analysis, 20 studies were scrutinized, including 13 studies on brain lactate levels, which involved 322 schizophrenia patients and 324 healthy individuals as controls. 7 studies on blood lactate levels, involving 234 schizophrenia patients and 238 healthy individuals, were also included. Brain lactate levels were elevated in schizophrenia patients, both in vivo and in post-mortem studies. Nevertheless, blood lactate levels in schizophrenia patients have revealed no statistically significant difference, as compared with control individuals.

CONCLUSIONS

In comparison with healthy individuals, schizophrenia patients had higher lactate levels in the brain, rather than in the blood. These findings suggest independent regulatory mechanisms of lactate levels in the brain and peripheral tissues. Abnormal lactate metabolism in the brain may be an important pathological mechanism in schizophrenia.

Metabolome subtyping reveals multi-omics characteristics and biological heterogeneity in major psychiatric disorders

Growing evidence suggests that major psychiatric disorders (MPDs) share common etiologies and pathological processes. However, the diagnosis is currently based on descriptive symptoms, which ignores the underlying pathogenesis and hinders the development of clinical treatments. This highlights the urgency of characterizing molecular biomarkers and establishing objective diagnoses of MPDs. Here, we collected untargeted metabolomics, proteomics and DNA methylation data of 327 patients with MPDs, 131 individuals with genetic high risk and 146 healthy controls to explore the multi-omics characteristics of MPDs. First, differential metabolites (DMs) were identified and we classified MPD patients into 3 subtypes based on DMs. The subtypes showed distinct metabolomics, proteomics and DNA methylation signatures. Specifically, one subtype showed dysregulation of complement and coagulation proteins, while the DNA methylation showed abnormalities in chemical synapses and autophagy. Integrative analysis in metabolic pathways identified the important roles of the citrate cycle, sphingolipid metabolism and amino acid metabolism. Finally, we constructed prediction models based on the metabolites and proteomics that successfully captured the risks of MPD patients. Our study established molecular subtypes of MPDs and elucidated their biological heterogeneity through a multi-omics investigation. These results facilitate the understanding of pathological mechanisms and promote the diagnosis and prevention of MPDs.

Identification of a Hypoxia-Related Signature as Candidate Detector for Schizophrenia Based on Genome-Wide Gene Expression

INTRODUCTION

Schizophrenia (SCZ), a severe neuropsychiatric disorder with high genetic susceptibility, has high rates of misdiagnosis due to the unavoidably subjective factors and heterogeneous clinical presentations. Hypoxia has been identified as an importantly risk factor that participates in the development of SCZ. Therefore, development of a hypoxia-related biomarker for SCZ diagnosis is promising. Therefore, we dedicated to develop a biomarker that could contribute to distinguishing healthy controls and SCZ patients.

METHODS

GSE17612, GSE21935, and GSE53987 datasets, consisting of 97 control samples and 99 SCZ samples, were involved in our study. The hypoxia score was calculated based on the single-sample gene-set enrichment analysis using the hypoxia-related differentially expressed genes to quantify the expression levels of these genes for each SCZ patient. Patients in high-score groups were defined if their hypoxia score was in the upper half of all hypoxia scores and patients in low-score groups if their hypoxia score was in the lower half. GSEA was applied to detect the functional pathway of these differently expressed genes. CIBERSORT algorithm was utilized to evaluate the tumor-infiltrating immune cells of SCZ patients.

RESULTS

In this study, we developed and validated a biomarker consisting of 12 hypoxia-related genes that could distinguish healthy controls and SCZ patients robustly. We found that the metabolism reprogramming might be activated in the patient with high hypoxia score. Finally, CIBERSORT analysis illustrated that lower composition of naive B cells and higher composition of memory B cells might be observed in low-score groups of SCZ patients.

CONCLUSION

These findings revealed that the hypoxia-related signature was acceptable as a detector for SCZ, providing further insight into effective diagnosis and treatment strategies for SCZ.

Exploring the key factors of schizophrenia relapse by integrating LC-MS/1H NMR metabolomics and weighted correlation network analysis

BACKGROUND

Lack of comprehending key factors of schizophrenia relapse has impeded its effective treatment, indicating that the mechanism clarification and available intervention of schizophrenia relapse required further amelioration.

METHOD

Based on the integration of LC-MS and ¹H NMR metabolomics, a weighted correlation network was established to screen pivotal factors of accelerating schizophrenia relapse. Then, the cluster most correlated with schizophrenia relapse was explored, and the biological function of cluster was investigated. Next, the key biomarker related to schizophrenia relapse was obtained through multiple algorithms. Moreover, the Lilikoi algorithm and correlation analysis were implemented to reveal the association between key biomarker and schizophrenia relapse.

RESULT

Results showed that 458 different forms of metabolites were identified for structuring the weighted correlation network. The module-trait correlation indicated that the turquoise module was the most highly correlated with schizophrenia relapse. Further, network analysis revealed that, in turquoise module, cluster 1 composed of 139 metabolites (involved in lipid metabolism and energy metabolism) was the most important subnetwork relevant to schizophrenia relapse. Finally, phenylalanylphenylalanine was recommended as the key biomarker related to schizophrenia relapse. Moreover, the correlation analysis indicated that phenylalanylphenylalanine might affect the progression of schizophrenia by intervening in energy metabolism.

CONCLUSION

In summary, critical factors of schizophrenia relapse have been revealed in our research, expounding the schizophrenia progression more systemically, which could shed some light on improving the intervention of schizophrenia relapse.

Exploring the role of ketone bodies in the diagnosis and treatment of psychiatric disorders

In recent times, advances in the field of metabolomics have shed greater light on the role of metabolic disturbances in neuropsychiatric conditions. The following review explores the role of ketone bodies and ketosis in both the diagnosis and treatment of three major psychiatric disorders: major depressive disorder, anxiety disorders, and schizophrenia. Distinction is made between the potential therapeutic effects of the ketogenic diet and exogenous ketone preparations, as exogenous ketones in particular offer a standardized, reproducible manner for inducing ketosis. Compelling associations between symptoms of mental distress and dysregulation in central nervous system ketone metabolism have been demonstrated in preclinical studies with putative neuroprotective effects of ketone bodies being elucidated, including effects on inflammasomes and the promotion of neurogenesis in the central nervous system. Despite emerging pre-clinical data, clinical research on ketone body effectiveness as a treatment option for psychiatric disorders remains lacking. This gap in understanding warrants further investigating, especially considering that safe and acceptable ways of inducing ketosis are readily available.

Probing Folate-Responsive and Stage-Sensitive Metabolomics and Transcriptional Co-Expression Network Markers to Predict Prognosis of Non-Small Cell Lung Cancer Patients

Tumour metabolomics and transcriptomics co-expression network as related to biological folate alteration and cancer malignancy remains unexplored in human non-small cell lung cancers (NSCLC). To probe the diagnostic biomarkers, tumour and pair lung tissue samples (n = 56) from 97 NSCLC patients were profiled for ultra-performance liquid chromatography tandem mass spectrometry (UPLC/MS/MS)-analysed metabolomics, targeted transcriptionomics, and clinical folate traits. Weighted Gene Co-expression Network Analysis (WGCNA) was performed. Tumour lactate was identified as the top VIP marker to predict advance NSCLC (AUC = 0.765, Sig = 0.017, CI 0.58-0.95). Low folate (LF)-tumours vs. adjacent lungs displayed higher glycolytic index of lactate and glutamine-associated amino acids in enriched biological pathways of amino sugar and glutathione metabolism specific to advance NSCLCs. WGCNA classified the green module for hub serine-navigated glutamine metabolites inversely associated with tumour and RBC folate, which module metabolites co-expressed with a predominant up-regulation of LF-responsive metabolic genes in glucose transport (GLUT1), de no serine synthesis (PHGDH, PSPH, and PSAT1), folate cycle (SHMT1/2 and PCFR), and down-regulation in glutaminolysis (SLC1A5, SLC7A5, GLS, and GLUD1). The LF-responsive WGCNA markers predicted poor survival rates in lung cancer patients, which could aid in optimizing folate intervention for better prognosis of NSCLCs susceptible to folate malnutrition.

NMR Metabolomics in Serum Fingerprinting of Schizophrenia Patients in a Serbian Cohort

Schizophrenia is a widespread mental disorder that leads to significant functional impairments and premature death. The state of the art indicates gaps in the understanding and diagnosis of this disease, but also the need for personalized and precise approaches to patients through customized medical treatment and reliable monitoring of treatment response. In order to fulfill existing gaps, the establishment of a universal set of disorder biomarkers is a necessary step. Metabolomic investigations of serum samples of Serbian patients with schizophrenia (51) and healthy controls (39), based on NMR analyses associated with chemometrics, led to the identification of 26 metabolites/biomarkers for this disorder. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models with prediction accuracies of 0.9718 and higher were accomplished during chemometric analysis. The established biomarker set includes aspartate/aspartic acid, lysine, 2-hydroxybutyric acid, and acylglycerols, which are identified for the first time in schizophrenia serum samples by NMR experiments. The other 22 identified metabolites in the Serbian samples are in accordance with the previously established NMR-based serum biomarker sets of Brazilian and/or Chinese patient samples. Thirteen metabolites (lactate/lactic acid, threonine, leucine, isoleucine, valine, glutamine, asparagine, alanine, gamma-aminobutyric acid, choline, glucose, glycine and tyrosine) that are common for three different ethnic and geographic origins (Serbia, Brazil and China) could be a good start point for the setup of a universal NMR serum biomarker set for schizophrenia.

Plasma metabolomics of schizophrenia with cognitive impairment: A pilot study

Schizophrenia (SCZ) acts as a complex and burdensome disease, in which the functional outcome can be validly predicted by cognitive impairment, as one of the core features. However, there still lack considerable markers of cognitive deficits in SCZ. Based on metabolomics, it is expected to identify different metabolic characteristics of SCZ with cognitive impairment. In the present study, 17 SCZ patients with cognitive impairment (CI), 17 matched SCZ patients with cognitive normal (CN), and 20 healthy control subjects (HC) were recruited, whose plasma metabolites were measured using ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The result of metabolic profiling indicated the identification of 46 differentially expressed metabolites between HC, CN, and CI groups, with 7 differentially expressed metabolites between CN and CI groups. Four differential metabolites (imidazolepropionic acid, Homoserine, and Aspartic acid) were repeatedly found in both screenings, by which the formed biomarker panel could discriminate SCZ with cognitive impairment from matched patients (AUC = 0.974) and health control (AUC = 0.841), respectively. Several significant metabolic pathways were highlighted in pathway analysis, involving Alanine, aspartate and glutamate metabolism, D-glutamine and D-glutamate metabolism, and Citrate cycle (TCA cycle). In this study, several differentially expressed metabolites were identified in SCZ with cognitive impairment, providing novel insights into clinical treatment strategies.

MiR-574-5P, miR-1827, and miR-4429 as Potential Biomarkers for Schizophrenia

Schizophrenia is a severe chronic debilitating disorder with millions of affected individuals. Diagnosis is based on clinical presentations, which are made when the progressive disease has appeared. Early diagnosis may help improve the clinical outcomes and response to treatments. Lack of a reliable molecular diagnostic invokes the identification of novel biomarkers. To elucidate the molecular basis of the disease, in this study we used two mRNA expression arrays, including GSE93987 and GSE38485, and one miRNA array, GSE54914, and meta-analysis was conducted for evaluation of mRNA expression arrays via metaDE package. Using WGCNA package, we performed network analysis for both mRNA expression arrays separately. Then, we constructed protein-protein interaction network for significant modules. Limma package was employed to analyze the miRNA array for identification of dysregulated miRNAs (DEMs). Using genes of significant modules and DEMs, a mRNA-miRNA network was constructed and hub genes and miRNAs were identified. To confirm the dysregulated genes, expression values were evaluated through available datasets including GSE62333, GSE93987, and GSE38485. The ability of the detected hub miRNAs to discriminate schizophrenia from healthy controls was evaluated by assessing the receiver-operating curve. Finally, the expression levels of genes and miRNAs were evaluated in 40 schizophrenia patients compared with healthy controls via Real-Time PCR. The results confirmed dysregulation of hsa-miR-574-5P, hsa-miR-1827, hsa-miR-4429, CREBRF, ARPP19, TGFBR2, and YWHAZ in blood samples of schizophrenia patients. In conclusion, three miRNAs including hsa-miR-574-5P, hsa-miR-1827, and hsa-miR-4429 are suggested as potential biomarkers for diagnosis of schizophrenia.

Metabolomic connections between schizophrenia, antipsychotic drugs and metabolic syndrome: A variety of players

BACKGROUND

Diagnosis of schizophrenia lacks of reliable medical diagnostic tests and robust biomarkers applied to clinical practice. Schizophrenic patients undergoing treatment with antipsychotics suffer a reduced life expectancy due to metabolic disarrangements that co-exist with their mental illness and predispose them to develop metabolic syndrome, also exacerbated by medication. Metabolomics is an emerging and potent technology able to accelerate this biomedical research. <P> Aim: This review focus on a detailed vision of the molecular mechanisms involved both in schizophrenia and antipsychotic-induced metabolic syndrome, based on innovative metabolites that consistently change in nascent metabolic syndrome, drug-naïve, first episode psychosis and/or schizophrenic patients compared to healthy subjects. <P> Main lines: Supported by metabolomic approaches, although not exclusively, noteworthy variations are reported mainly through serum samples of patients and controls in several scenes: 1) alterations in fatty acids, inflammatory response indicators, amino acids and biogenic amines, biometals and gut microbiota metabolites (schizophrenia); 2) alterations in metabolites involved in carbohydrate and gut microbiota metabolism, inflammation and oxidative stress (metabolic syndrome), some of them shared with the schizophrenia scene; 3) alterations of cytokines secreted by adipose tissue, phosphatidylcholines, acylcarnitines, Sirtuin 1, orexin-A and changes in microbiota composition (antipsychotic-induced metabolic syndrome). <P> Conclusion: Novel insights into the pathogenesis of schizophrenia and metabolic side-effects associated to its antipsychotic treatment, represent an urgent request for scientifics and clinicians. Leptin, carnitines, adiponectin, insulin or interleukin-6 represent some examples of candidate biomarkers. Cutting-edge technologies like metabolomics have the power of strengthen research for achieving preventive, diagnostic and therapeutical solutions for schizophrenia.

Design of Multi-Shelled Hollow Cr2 O3 Spheres for Metabolic Fingerprinting

Schizophrenia (SZ) detection enables effective treatment to improve the clinical outcome, but objective and reliable SZ diagnostics are still limited. An ideal diagnosis of SZ suited for robust clinical screening must address detection throughput, low invasiveness, and diagnosis accuracy. Herein, we built a multi-shelled hollow Cr₂ O₃ spheres (MHCSs) assisted laser desorption/ionization mass spectrometry (LDI MS) platform for the direct metabolic profiling of biofluids towards SZ diagnostics. The MHCSs displayed strong light absorption for enhanced ionization and microscale surface roughness with stability for the effective LDI of metabolites. We profiled urine and serum metabolites (≈1 μL) with the enhanced LDI efficacy in seconds. We discriminated SZ patients (SZs) from healthy controls (HCs) with the highest area under the curve (AUC) value of 1.000 for the blind test. We identified four compounds with optimal diagnostic power as a simplified metabolite panel for SZ and demonstrated the metabolite quantification for clinic use. Our approach accelerates the growth of new platforms toward a precision diagnosis in the near future.