Unbiased lipidomic profiling reveals metabolomic changes during the onset and antipsychotics treatment of schizophrenia disease

Prediction of treatment response in drug-naïve schizophrenia patients from the perspective of targeted metabolomics

BACKGROUND

Schizophrenia (SZ) is a severe and chronic mental illness affecting approximately 1 % of the global population. Although antipsychotic medications can alleviate some symptoms, 20--30 % of patients exhibit resistance to available treatments. Therefore, identifying objective biomarkers related to treatment efficacy is crucial.

METHODS

A total of 56 drug-naïve SZ patients were recruited, and after 8 weeks of antipsychotic medication, they were classified as treatment responders (30) and non-responders (26) based on the improvement of their symptoms. Baseline plasma metabolites were measured by targeted metabolomics Biocrates MxP® Quant 500 Kit.

RESULTS

A total of 271 metabolites were identified, among which 31 exhibited significant differences between responders and non-responders, including phosphatidylcholine (PC) (14), sphingomyelin (8), ceramide (6), cholesteryl ester (2), and sarcosine (1), which were mainly concentrated in the sphingolipid metabolic pathway. Notably, key differential metabolites included phosphatidylcholine, sphingomyelin, and ceramide, which were predominantly enriched in the sphingolipid metabolism pathway. Through logistic regression analysis, sarcosine, PC aa C28:1, PC ae C34:2, and PC ae C36:3 emerged as significant predictors, yielding a combined area under the curve (AUC) of 0.877 for effectively distinguishing treatment responders from non-responders.

CONCLUSION

Our findings suggest that the combination of sarcosine, PC aa C28:1, PC ae C34:2, and PC ae C36:3 could serve as biomarkers for prediction of treatment response in patients with drug-naïve SZ.

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.

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.

Integrative metabolomics-genomics analysis identifies key networks in a stem cell-based model of schizophrenia

Schizophrenia (SCZ) is a neuropsychiatric disorder, caused by a combination of genetic and environmental factors. The etiology behind the disorder remains elusive although it is hypothesized to be associated with the aberrant response to neurotransmitters, such as dopamine and glutamate. Therefore, investigating the link between dysregulated metabolites and distorted neurodevelopment holds promise to offer valuable insights into the underlying mechanism of this complex disorder. In this study, we aimed to explore a presumed correlation between the transcriptome and the metabolome in a SCZ model based on patient-derived induced pluripotent stem cells (iPSCs). For this, iPSCs were differentiated towards cortical neurons and samples were collected longitudinally at various developmental stages, reflecting neuroepithelial-like cells, radial glia, young and mature neurons. The samples were analyzed by both RNA-sequencing and targeted metabolomics and the two modalities were used to construct integrative networks in silico. This multi-omics analysis revealed significant perturbations in the polyamine and gamma-aminobutyric acid (GABA) biosynthetic pathways during rosette maturation in SCZ lines. We particularly observed the downregulation of the glutamate decarboxylase encoding genes GAD1 and GAD2, as well as their protein product GAD65/67 and their biochemical product GABA in SCZ samples. Inhibition of ornithine decarboxylase resulted in further decrease of GABA levels suggesting a compensatory activation of the ornithine/putrescine pathway as an alternative route for GABA production. These findings indicate an imbalance of cortical excitatory/inhibitory dynamics occurring during early neurodevelopmental stages in SCZ. Our study supports the hypothesis of disruption of inhibitory circuits to be causative for SCZ and establishes a novel in silico approach that enables for integrative correlation of metabolic and transcriptomic data of psychiatric disease models.

LC-HRMS Lipidomic Fingerprints in Serbian Cohort of Schizophrenia Patients

Schizophrenia (SCH) is a major mental illness that causes impaired cognitive function and long-term disability, so the requirements for reliable biomarkers for early diagnosis and therapy of SCH are essential. The objective of this work was an untargeted lipidomic study of serum samples from a Serbian cohort including 30 schizophrenia (SCH) patients and 31 non-psychiatric control (C) individuals by applying liquid chromatography (LC) coupled with high-resolution mass spectrometry (HRMS) and chemometric analyses. Principal component analysis (PCA) of all samples indicated no clear separation between SCH and C groups but indicated clear gender separation in the C group. Multivariate statistical analyses (PCA and orthogonal partial least squares discriminant analysis (OPLS-DA)) of gender-differentiated SCH and C groups established forty-nine differential lipids in the differentiation of male SCH (SCH-M) patients and male controls (C-M), while sixty putative biomarkers were identified in the differentiation of female SCH patients (SCH-F) and female controls (C-F). Lipidomic study of gender-differentiated groups, between SCH-M and C-M and between SCH-F and C-F groups, confirmed that lipids metabolism was altered and the content of the majority of the most affected lipid classes, glycerophospholipids (GP), sphingolipids (SP), glycerolipids (GL) and fatty acids (FA), was decreased compared to controls. From differential lipid metabolites with higher content in both SCH-M and SCH-F patients groups compared to their non-psychiatric controls, there were four common lipid molecules: ceramides Cer 34:2, and Cer 34:1, lysophosphatidylcholine LPC 16:0 and triacylglycerol TG 48:2. Significant alteration of lipids metabolism confirmed the importance of metabolic pathways in the pathogenesis of schizophrenia.

Altered lipidomics biosignatures in schizophrenia: A systematic review

Multiomics approaches have significantly aided the identification of molecular signatures in complex neuropsychiatric disorders. Lipidomics, one of the newest additions in the -omics family, sheds light on lipid profiles and is an emerging methodological tool to study schizophrenia pathobiology, as lipid dysregulation has been repeatedly observed in schizophrenia. In this review, we performed a detailed literature search for lipidomics studies in schizophrenia. Following elaborate inclusion/exclusion criteria, we focused on human studies in schizophrenia and schizophrenia-related diagnoses in brain and blood specimens, including serum plasma, platelets and red blood cells. Eighteen studies fulfilled our inclusion criteria, of which five were conducted in the brain, 12 in peripheral material and one in both. Here, we first provide background on lipidomics and the main lipid categories addressed, review in detail the included literature and look for common lipidomics patterns in brain and the periphery that emerge from these studies. Furthermore, we highlight current limitations in schizophrenia lipidomics research and underline the need for following up on lipidomics results with complementary molecular approaches.

Alterations to sphingolipid metabolism from antipsychotic administration in healthy volunteers are restored following the use of cannabidiol

Randomized clinical trials substantiate cannabidiol (CBD) as a next-generation antipsychotic, effective in alleviating positive and negative symptoms associated with psychosis, while minimising the adverse effects seen with established treatments. Although the mechanisms remain debated, CBD is known to induce drug-responsive changes in lipid-based retrograde neurotransmitters. Lipid aberrations are also frequently observed with antipsychotics, which may contribute to their efficacy or increase the risk of undesirables, including metabolic dysfunction, obesity and dyslipidaemia. Our study investigated CBD's impact following lipid responses triggered by interaction with second-generation antipsychotics (SGA) in a randomized phase I safety study. Untargeted mass spectrometry assessed the lipidomic profiles of human sera, collected from 38 healthy volunteers. Serum samples were obtained prior to commencement of any medication (t = 0), 3 days after consecutive administration of one of the five, placebo-controlled, treatment arms designed to achieve steady-state concentrations of each SGA (amisulpride, 150 mg/day; quetiapine, 300 mg/day; olanzapine 10 mg/day; risperidone, 3 mg/day), and after six successive days of SGA treatment combined with CBD (800 mg/day). Receiver operating characteristics (ROC) refined 3712 features to a putative list of 15 lipids significantly altered (AUC > 0.7), classified into sphingolipids (53 %), glycerolipids (27 %) and glycerophospholipids (20 %). Targeted mass spectrometry confirmed reduced sphingomyelin and ceramide levels with antipsychotics, which mapped along their catabolic pathway and were restored by CBD. These sphingolipids inversely correlated with body weight after olanzapine, quetiapine, and risperidone treatment, where CBD appears to have arrested or attenuated these effects. Herein, we propose CBD may alleviate aberrant sphingolipid metabolism and that further investigation into sphingolipids as markers for monitoring side effects of SGAs and efficacy of CBD is warranted.

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.

CPT2 Deficiency Modeled in Zebrafish: Abnormal Neural Development, Electrical Activity, Behavior, and Schizophrenia-Related Gene Expression

Carnitine palmitoyltransferase 2 (CPT2) is an inner mitochondrial membrane protein of the carnitine shuttle and is involved in the beta-oxidation of long chain fatty acids. Beta-oxidation provides an alternative pathway of energy production during early development and starvation. CPT2 deficiency is a genetic disorder that we recently showed can be associated with schizophrenia. We hypothesize that CPT2 deficiency during early brain development causes transcriptional, structural, and functional abnormalities that may contribute to a CNS environment that is susceptible to the emergence of schizophrenia. To investigate the effect of CPT2 deficiency on early vertebrate development and brain function, CPT2 was knocked down in a zebrafish model system. CPT2 knockdown resulted in abnormal lipid utilization and deposition, reduction in body size, and abnormal brain development. Axonal projections, neurotransmitter synthesis, electrical hyperactivity, and swimming behavior were disrupted in CPT2 knockdown zebrafish. RT-qPCR analyses showed significant increases in the expression of schizophrenia-associated genes in CPT2 knockdown compared to control zebrafish. Taken together, these data demonstrate that zebrafish are a useful model for studying the importance of beta-oxidation for early vertebrate development and brain function. This study also presents novel findings linking CPT2 deficiency to the regulation of schizophrenia and neurodegenerative disease-associated genes.

The identification of novel schizophrenia-related metabolites using untargeted lipidomics

Human lipidome still remains largely unexplored among Chinese schizophrenia patients. We aimed to identify novel lipid molecules associated with schizophrenia and cognition among schizophrenia patients. The current study included 96 male schizophrenia patients and 96 gender-matched healthy controls. Untargeted lipidomics profiling was conducted among all participants. Logistic regression models were used to assess metabolite associations with schizophrenia. We further assessed the incremental predictive value of identified metabolites beyond conventional risk factors on schizophrenia status. In addition, identified metabolites were tested for association with cognitive function among schizophrenia patients using linear regression models. A total of 34 metabolites were associated with schizophrenia. Addition of these identified metabolites to age, body mass index, smoking, and education significantly increased the risk reclassification of schizophrenia. Among the schizophrenia-related metabolites, 10 were further associated with cognition in schizophrenia patients, including four metabolites associated with immediate memory, two metabolites associated with delayed memory, three metabolites associated with visuospatial, four metabolites associated with language, one metabolite associated with attention, and two metabolites associated with the total score. Our findings provide novel insights into the biological mechanisms of schizophrenia, suggesting that lipid metabolites may serve as potential diagnostic or therapeutic targets of schizophrenia.

The relationship between alterations in plasma metabolites and treatment responses in antipsychotic-naïve female patients with schizophrenia

This study aimed to explore the relationship between alterations in plasma metabolites and treatment responses amongst antipsychotic-naïve female patients with schizophrenia. A total of 38 antipsychotic-naïve female schizophrenia patients (ANS) and 19 healthy female controls (HC) were recruited. Plasma samples were obtained from all participants, and targeted metabolomics were measured with FIA-MS/MS and LC-MS/MS. The positive and negative syndrome scale (PANSS) was used to assess the severity of psychotic symptoms before and after eight weeks of treatment. Receiver operator characteristics (ROC) curves were used to predict diagnostic and therapeutic responses. A total of 186 metabolites passed quality control procedures and were used in statistical analysis to identify potential biomarkers. Before treatment, the ANS patients had lower levels of γ -Aminobutyric Acid (GABA) and higher levels of Cholesteryl esters (CE) (20:3), Cholic Acid (CA) and Glycocholic Acid (GCA) compared to the HCs. These four differential metabonomic markers were synthesised into a combinatorial biomarker panel. This panel significantly distinguished ANS from HC. Moreover, this biomarker panel was able to effectively predict therapeutic responses. Our results suggest that plasma CE (20:3), CA, GCA, and GABA levels may be useful for diagnosing and predicting antipsychotic efficacy amongst female schizophrenia patients.

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.

Metabolomics, Lipidomics, and Antipsychotics: A Systematic Review

Antipsychotics are an important pharmacotherapy option for the treatment of many mental illnesses. Unfortunately, selecting antipsychotics is often a trial-and-error process due to a lack of understanding as to which medications an individual patient will find most effective and best tolerated. Metabolomics, or the study of small molecules in a biosample, is an increasingly used omics platform that has the potential to identify biomarkers for medication efficacy and toxicity. This systematic review was conducted to identify metabolites and metabolomic pathways associated with antipsychotic use in humans. Ultimately, 42 studies were identified for inclusion in this review, with all but three studies being performed in blood sources such as plasma or serum. A total of 14 metabolite classes and 12 lipid classes were assessed across studies. Although the studies were highly heterogeneous in approach and mixed in their findings, increases in phosphatidylcholines, decreases in carboxylic acids, and decreases in acylcarnitines were most consistently noted as perturbed in patients exposed to antipsychotics. Furthermore, for the targeted metabolomic and lipidomic studies, seven metabolites and three lipid species had findings that were replicated. The most consistent finding for targeted studies was an identification of a decrease in aspartate with antipsychotic treatment. Studies varied in depth of detail provided for their study participants and in study design. For example, in some cases, there was a lack of detail on specific antipsychotics used or concomitant medications, and the depth of detail on sample handling and analysis varied widely. The conclusions here demonstrate that there is a large foundation of metabolomic work with antipsychotics that requires more complete reporting so that an objective synthesis such as a meta-analysis can take place. This will then allow for validation and clinical application of the most robust findings to move the field forward. Future studies should be carefully controlled to take advantage of the sensitivity of metabolomics while limiting potential confounders that may result from participant heterogeneity and varied analysis approaches.

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.

Metabolic detection of malignant brain gliomas through plasma lipidomic analysis and support vector machine-based machine learning

Background

Most malignant brain gliomas (MBGs) are associated with dismal outcomes, mainly due to their late diagnosis. Current diagnostic methods for MBGs are based on imaging and histological examination, which limits their early detection. Here, we aimed to identify reliable plasma lipid biomarkers for non-invasive diagnosis for MBGs.

Methods

Untargeted lipidomic analysis was firstly performed using a discovery cohort (n=107). The data were processed by a support vector machine (SVM)-based discriminating model to retrieve a panel of candidate biomarkers. Then, a targeted quantification method was developed, and the SVM-based diagnostic model was constructed using a training cohort (n=750) and tested using a test cohort (n=225). Finally, the performance of the diagnostic model was further evaluated in an independent validation cohort (n=920) enrolled from multiple medical centers.

Findings

A panel of 11 plasma lipids was identified as candidate biomarkers with an accuracy of 0.999. The diagnostic model developed achieved a high performance in distinguishing MBGs patients from normal controls with an area under the receiver-operating characteristic curve (AUC) of 0.9877 and 0.9869 in the training and test cohorts, respectively. In the validation cohort, the 11 lipid panel still achieved an accuracy of 0.9641 and an AUC of 0.9866.

Interpretation

The present study demonstrates the applicability and robustness of utilizing a machine learning algorithm to analyze lipidomic data for efficient and reliable biomarker screening. The 11 lipid biomarkers show great potential for the non-invasive diagnosis of MBGs with high throughput.

Funding

A full list of funding bodies that contributed to this study can be found in the Acknowledgments section.

Current State of Fluid Lipid Biomarkers for Personalized Diagnostics and Therapeutics in Schizophrenia Spectrum Disorders and Related Psychoses: A Narrative Review

Schizophrenia spectrum disorders (SSD) are traditionally diagnosed and categorized through clinical assessment, owing to their complex heterogeneity and an insufficient understanding of their underlying pathology. However, disease progression and accurate clinical diagnosis become problematic when differentiating shared aspects amongst mental health conditions. Hence, there is a need for widely accessible biomarkers to identify and track the neurobiological and pathophysiological development of mental health conditions, including SSD. High-throughput omics applications involving the use of liquid chromatography-mass spectrometry (LC-MS) are driving a surge in biological data generation, providing systems-level insight into physiological and pathogenic conditions. Lipidomics is an emerging subset of metabolomics, largely underexplored amongst the omics systems. Lipid profiles in the brain are highly enriched with well-established functions, including maintenance, support, and signal transduction of neuronal signaling pathways, making them a prospective and exciting source of biological material for neuropsychiatric research. Importantly, changes in the lipid composition of the brain appear to extend into the periphery, as there is evidence that circulating lipid alterations correlate with alterations of psychiatric condition(s). The relative accessibility of fluid lipids offers a unique source to acquire a lipidomic "footprint" of molecular changes, which may support reliable diagnostics even at early disease stages, prediction of treatment response and monitoring of treatment success (theranostics). Here, we summarize the latest fluid lipidomics discoveries in SSD-related research, examining the latest strategies to integrate information into multi-systems overviews that generate new perspectives of SSD-related psychosis identification, development, and treatment.

Ceramides: Shared Lipid Biomarkers of Cardiovascular Disease and Schizophrenia

INTRODUCTION

Schizophrenia, although a debilitating mental illness, greatly affects individuals' physical health as well. One of the leading somatic comorbidities associated with schizophrenia is cardiovascular disease, which has been estimated to be one of the leading causes of excess mortality in patients diagnosed with schizophrenia. Although the shared susceptibility to schizophrenia and cardiovascular disease is well established, the mechanisms linking these two disorders are not well understood. Genetic studies have hinted toward shared lipid metabolism abnormalities co-occurring in the two disorders, while lipid compounds have emerged as prognostic markers for cardiovascular disease. In particular, three ceramide species in the blood plasma, Cer(d18:1/16:0), Cer(d18:1/18:0), and Cer(d18:1/24:1), have been robustly linked to the latter disorder.

AIM

We aimed to assess the differences in abundances of Cer(d18:1/16:0), Cer(d18:1/18:0), and Cer(d18:1/24:1) in the blood plasma of schizophrenia patients compared to healthy controls.

METHODS

We measured the abundances of Cer(d18:1/16:0), Cer(d18:1/18:0), and Cer(d18:1/24:1) in a cohort of 82 patients with schizophrenia and 138 controls without a psychiatric diagnosis and validated the results using an independent cohort of 26 patients with schizophrenia, 55 control individuals, and 19 patients experiencing a first psychotic episode.

RESULTS

We found significant alterations for all three ceramide species Cer(d18:1/16:0), Cer(d18:1/18:0), and Cer(d18:1/24:1) and a particularly strong difference in concentrations between psychiatric patients and controls for the ceramide species Cer(d18:1/18:0).

CONCLUSIONS

The alteration of Cer(d18:1/16:0), Cer(d18:1/18:0), and Cer(d18:1/24:1) levels in the blood plasma might be a manifestation of metabolic abnormalities common to both schizophrenia and cardiovascular disease.

The Effects of Repetitive Transcranial Magnetic Stimulation on Cognitive Impairment and the Brain Lipidome in a Cuprizone-Induced Mouse Model of Demyelination

The protective effects of repetitive transcranial magnetic stimulation (rTMS) on myelin integrity have been extensively studied, and growing evidence suggests that rTMS is beneficial in improving cognitive functions and promoting myelin repair. However, the association between cognitive improvement due to rTMS and changes in brain lipids remains elusive. In this study, we used the Y-maze and 3-chamber tests, as well as a mass spectrometry-based lipidomic approach in a CPZ-induced demyelination model in mice to assess the protective effects of rTMS on cuprizone (CPZ)-induced cognitive impairment and evaluate changes in lipid composition in the hippocampus, prefrontal cortex, and striatum. We found that CPZ induced cognitive impairment and remarkable changes in brain lipids, specifically in glycerophospholipids. Moreover, the changes in lipids within the prefrontal cortex were more extensive, compared to those observed in the hippocampus and striatum. Notably, rTMS ameliorated CPZ-induced cognitive impairment and partially normalized CPZ-induced lipid changes. Taken together, our data suggest that rTMS may reverse cognitive behavioral changes caused by CPZ-induced demyelination by modulating the brain lipidome, providing new insights into the therapeutic mechanism of rTMS.

Shorter Chain Triglycerides Are Negatively Associated with Symptom Improvement in Schizophrenia

Schizophrenia is a serious mental disorder requiring lifelong treatment. While medications are available that are effective in treating some patients, individual treatment responses can vary, with some patients exhibiting resistance to one or multiple drugs. Currently, little is known about the causes of the difference in treatment response observed among individuals with schizophrenia, and satisfactory markers of poor response are not available for clinical practice. Here, we studied the changes in the levels of 322 blood plasma lipids between two time points assessed in 92 individuals diagnosed with schizophrenia during their inpatient treatment and their association with the extent of symptom improvement. We found 20 triglyceride species increased in individuals with the least improvement in Positive and Negative Syndrome Scale (PANSS) scores, but not in those with the largest reduction in PANSS scores. These triglyceride species were distinct from the rest of the triglyceride species present in blood plasma. They contained a relatively low number of carbons in their fatty acid residues and were relatively low in abundance compared to the principal triglyceride species of blood plasma.

The Antipsychotic Risperidone Alters Dihydroceramide and Ceramide Composition and Plasma Membrane Function in Leukocytes In Vitro and In Vivo

Atypical or second-generation antipsychotics are used in the treatment of psychosis and behavioral problems in older persons with dementia. However, these pharmaceutical drugs are associated with an increased risk of stroke in such patients. In this study, we evaluated the effects of risperidone treatment on phospholipid and sphingolipid composition and lipid raft function in peripheral blood mononuclear cells (PBMCs) of older patients (mean age >88 years). The results showed that the levels of dihydroceramides, very-long-chain ceramides, and lysophosphatidylcholines decreased in PBMCs of the risperidone-treated group compared with untreated controls. These findings were confirmed by in vitro assays using human THP-1 monocytes. The reduction in the levels of very-long-chain ceramides and dihydroceramides could be due to the decrease in the expression of fatty acid elongase 3, as observed in THP-1 monocytes. Moreover, risperidone disrupted lipid raft domains in the plasma membrane of PBMCs. These results indicated that risperidone alters phospholipid and sphingolipid composition and lipid raft domains in PBMCs of older patients, potentially affecting multiple signaling pathways associated with these membrane domains.

The impact of quetiapine on the brain lipidome in a cuprizone-induced mouse model of schizophrenia

The antipsychotic effect of Quetiapine (Que) has been extensively studied and growing evidence suggests that Que has a beneficial effect, improving cognitive functions and promoting myelin repair. However, the effects of Que on the brain lipidome and the association between Que-associated cognitive improvement and changes in lipids remain elusive. In the present study, we assessed the cognitive protective effects of Que treatment and used a mass spectrometry-based lipidomic approach to evaluated changes in lipid composition in the hippocampus, prefrontal cortex (PFC), and striatum in a mouse model of cuprizone (CPZ)-induced demyelination. CPZ induces cognitive impairment and remarkable lipid changes in the brain, specifically in lipid species of glycerophospholipids and sphingolipids. Moreover, the changes in lipid classes of the PFC were more extensive than those observed in the hippocampus and striatum. Notably, Que treatment ameliorated cuprizone-induced cognitive impairment and partly normalized CPZ-induced lipid changes. Taken together, our data suggest that Que may rescue cognitive behavioral changes from CPZ-induced demyelination through modulation of the brain lipidome, providing new insights into the pharmacological mechanism of Que for schizophrenia.

Metabolomic profiles associated with a mouse model of antipsychotic-induced food intake and weight gain

Antipsychotic drugs (AP) are used to treat a multitude of psychiatric conditions including schizophrenia and bipolar disorder. However, APs also have metabolic side effects including increased food intake and body weight, but the underlying mechanisms remain unknown. We previously reported that minocycline (MINO) co-treatment abrogates olanzapine (OLZ)-induced hyperphagia and weight gain in mice. Using this model, we investigated the changes in the pharmacometabolome in the plasma and hypothalamus associated with OLZ-induced hyperphagia and weight gain. Female C57BL/6 mice were divided into groups and fed either i) control, CON (45% fat diet) ii) CON + MINO, iii) OLZ (45% fat diet with OLZ), iv) OLZ + MINO. We identified one hypothalamic metabolite indoxylsulfuric acid and 389 plasma metabolites (including 19 known metabolites) that were specifically associated with AP-induced hyperphagia and weight gain in mice. We found that plasma citrulline, tricosenoic acid, docosadienoic acid and palmitoleic acid were increased while serine, asparagine and arachidonic acid and its derivatives were decreased in response to OLZ. These changes were specifically blocked by co-treatment with MINO. These pharmacometabolomic profiles associated with AP-induced hyperphagia and weight gain provide candidate biomarkers and mechanistic insights related to the metabolic side effects of these widely used drugs.

SWATH-MS for metabolomics and lipidomics: critical aspects of qualitative and quantitative analysis

INTRODUCTION

While liquid chromatography coupled to mass spectrometric detection in the selected reaction monitoring detection mode offers the best quantification sensitivity for omics, the number of target analytes is limited, must be predefined and specific methods developed. Data independent acquisition (DIA), including SWATH using quadrupole time of flight or orbitrap mass spectrometers and generic acquisition methods, has emerged as a powerful alternative technique for quantitative and qualitative analyses since it can cover a wide range of analytes without predefinition.

OBJECTIVES

Here we review the current state of DIA, SWATH-MS and highlight novel acquisition strategies for metabolomics and lipidomics and opportunities for data analysis tools.

METHOD

Different databases were searched for papers that report developments and applications of DIA and in particular SWATH-MS in metabolomics and lipidomics.

RESULTS

DIA methods generate digital sample records that can be mined retrospectively as further knowledge is gained and, with standardized acquisition schemes, used in multiple studies. The different chemical spaces of metabolites and lipids require different specificities, hence different acquisition and data processing approaches must be considered for their analysis.

CONCLUSIONS

Although the hardware and acquisition modes are well defined for SWATH-MS, a major challenge for routine use remains the lack of appropriate software tools capable of handling large datasets and large numbers of analytes.

Population Pharmacokinetics of Clozapine: A Systematic Review

BACKGROUND AND OBJECTIVE

Clozapine is a second-generation antipsychotic drug that is considered the most effective treatment for refractory schizophrenia. Several clozapine population pharmacokinetic models have been introduced in the last decades. Thus, a systematic review was performed (i) to compare published pharmacokinetics models and (ii) to summarize and explore identified covariates influencing the clozapine pharmacokinetics models.

METHODS

A search of publications for population pharmacokinetic analyses of clozapine either in healthy volunteers or patients from inception to April 2019 was conducted in PubMed and SCOPUS databases. Reviews, methodology articles, in vitro and animal studies, and noncompartmental analysis were excluded.

RESULTS

Twelve studies were included in this review. Clozapine pharmacokinetics was described as one-compartment with first-order absorption and elimination in most of the studies. Significant interindividual variations of clozapine pharmacokinetic parameters were found in most of the included studies. Age, sex, smoking status, and cytochrome P450 1A2 were found to be the most common identified covariates affecting these parameters. External validation was only performed in one study to determine the predictive performance of the models.

CONCLUSIONS

Large pharmacokinetic variability remains despite the inclusion of several covariates. This can be improved by including other potential factors such as genetic polymorphisms, metabolic factors, and significant drug-drug interactions in a well-designed population pharmacokinetic model in the future, taking into account the incorporation of larger sample size and more stringent sampling strategy. External validation should also be performed to the previously published models to compare their predictive performances.

Ion-Pair Selection Method for Pseudotargeted Metabolomics Based on SWATH MS Acquisition and Its Application in Differential Metabolite Discovery of Type 2 Diabetes

The pseudotargeted metabolomics method integrates advantages of nontargeted and targeted analysis because it can acquire data of metabolites in the multireaction monitoring (MRM) mode of mass spectrometry (MS) without needing standards. The key is the ion-pair information collection from samples to be analyzed. It is well-known that sequential windowed acquisition of all theoretical Fragment ion (SWATH) MS mode can acquire MS2 information to a maximum extent. To expediently acquire as many ion-pairs as possible with optimal collision energy (CE), an ion-pair selection approach based on SWATH MS acquisition with variable isolation windows was developed in this study. Initially, nontargeted acquisition of all metabolites information in plasma Standard Reference Material (SRM 1950) was performed by ultra high-performance liquid chromatography (UHPLC)-quadrupole time-of-flight (Q-TOF) MS platform with three CEs. With the help of software tool, the ion-pairs of unique metabolites were gained. Then they were validated in scheduled MRM coupled with UHPLC. After removing false positive, the ion-pairs with an optimal CE was integrated. A total of 1373 unique metabolite ion-pairs were obtained at positive ion mode. And repeatability of the established pseudotargeted approach was evaluated by intraday and interday precision. The results demonstrated the method was stable, reliable, and suitable for metabolomics study. As an application example, alterations of serum metabolites in Type 2 diabetes were investigated by using the established method. This work provides a pseudotargeted ion-pair selection method based on SWATH MS acquisition with the characters of increased metabolite coverage, suitable CE, and convenient processing.