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Immune activation plays a major role in the pathogenesis of schizophrenia, as confirmed by many studies, systematic reviews, and meta-analyses. The important role of neuroinflammation in the formation of the relation between impaired neurobiological processes and schizophrenia psychopathology is being actively discussed. We quantified serum concentrations of 22 cytokines in 236 patients with schizophrenia and 103 mentally and somatically healthy individuals by a multiplex assay. We found higher TGF-α (p = 0.014), IFN-γ (p = 0.036), IL-5 (p < 0.001), IL-6 (p = 0.047), IL-8 (p = 0.005), IL-10 (p <0.001), IL-15 (p = 0.007), IL-1RA (p = 0.007), and TNF-α (p < 0.001) levels in patients with schizophrenia than in healthy individuals. Subgroup analysis revealed a much greater number of statistically significant differences in cytokine levels among females than among males. Patients with a continuous course of schizophrenia showed statistically significantly higher levels of IL-12p70 (p = 0.019), IL-1α (p = 0.046), and IL-1β (p = 0.035) compared with patients with an episodic course. Most cytokines were positively correlated with positive, general, and total PANSS scores. In patients with a duration of schizophrenia of 10 years or more, the level of IL-10 was higher than that in patients with a disease duration of 5 years or less (p = 0.042). Thus, an imbalance in cytokines was revealed in patients with schizophrenia, depending on sex and clinical characteristics of the disease.

We present first evidence showing that some electrophoretically homogeneous IgGs from the sera of patients with schizophrenia (36.4%) and their Fab and F(ab)2 fragments as well as from healthy donors (33.3%) possess catalase activity. The relative catalase activity of IgGs from the sera of individual schizophrenia patients (and healthy donors) significantly varied from patient to patient, but the activity of IgGs from healthy donors is on average 15.8-fold lower than that for schizophrenia patients. After extensive dialysis of purified IgGs against EDTA chelating metal ions, the relative catalase activity of IgGs decreases on average approximately 2.5-3.7-fold; all IgGs possess metal-dependent and independent catalase activity. The addition of external Me2+ ions to dialyzed and non-dialyzed IgGs leads to a significant increase in their activity. The best activator of dialyzed and non-dialyzed IgGs is Co2+, the activation by Cu2+, Mn2+, and Ni2+ ions were rare and always lower than by Co2+. Every IgG preparation demonstrates several individual sets of very well expressed pH optima in the pH range from 4.0 to 9.5. These data speak for the individual repertoire of catalase IgGs in every person and an extreme diversity of abzymes in their pH optima and activation by different metal ions. It is known that antioxidant enzymes such as superoxide dismutases, catalases, and glutathione peroxidases represent critical defense mechanisms preventing oxidative modifications of DNA, proteins, and lipids. Catalase activity of human IgGs could probably also play a major role in the protection of organisms from oxidative stress and toxic compounds.

The life expectancy of patients with psychotic disorders is significantly shorter than that of the general population; antipsychotic-induced metabolic disorders play a significant role in reducing life expectancy. Both metabolic syndrome (MetS) and schizophrenia are multifactorial conditions. One area where the two conditions overlap is oxidative stress, which is present in both diseases. The glutathione-S-transferase (GST) system is a major line of defense against exogenous toxicants and oxidative damage to cells. The aim of our study was to perform an association analysis of gene polymorphisms with metabolic disorders in patients with schizophrenia treated with antipsychotic therapy. Methods: A total of 639 white patients with schizophrenia (ICD-10) from Siberia (Russia) were included in the study. Genotyping was carried out using real-time polymerase chain reaction for two single-nucleotide polymorphisms (SNPs) in the GSTP1 (rs614080 and rs1695) and one SNP in the GSTO1 (rs49252). Results: We found that rs1695*GG genotype of GSTP1 is a risk factor for the development of overweight (OR 2.36; 95% CI: 1.3–4.29; p = 0.0054). In the subgroup of patients receiving first-generation antipsychotics as basic therapy, the risk of overweight was associated with carriage of the rs1695*GG (OR 5.43; 95% CI: 2.24–13.16; p < 0.001) genotype of GSTP1 in a recessive model of inheritance. In contrast, an association of rs1695*G GSTP1 with obesity (OR: 0.42; 95% CI: 0.20–0.87; p = 0.018) was shown in the dominant model of inheritance in patients receiving second-generation antipsychotics. Conclusions: The pilot results obtained confirm the hypothesis of a violation of the antioxidant status, in particular the involvement of GSTP1, in the development of antipsychotic-induced metabolic disorders in schizophrenia. Further studies with larger samples and different ethnic groups are needed to confirm the obtained results.

The present study aims at comparing the change in cytokine levels in schizophrenia patients treated with atypical antipsychotics, with or without metabolic syndrome (MetS). The study included 101 patients with schizophrenia, 38 with and 63 without MetS, who received risperidone, quetiapine, olanzapine or aripiprazole for six weeks. We analyzed the concentration of 21 cytokines in the serum patients. The treatment with atypical antipsychotics changed some proinflammatory cytokine levels. It led to increased IFN-α2 (p = 0.010), IL-1α (p = 0.024) and IL-7 (p = 0.017) levels in patients with MetS, whereas the same treatment led to decreased levels of IFN-γ (p = 0.011), IL-1β (p = 0.035), IL-12р40 (p = 0.011), IL-17A (p = 0.031), IL-6 (p = 0.043) and TNF-α (p = 0.012) in individuals without MetS. Our results demonstrated the effects of atypical antipsychotics on the immune–inflammatory parameters, depending on the metabolic disturbances in schizophrenia patients.

Background: Schizophrenia is a complex mental disorder with a high heritability. Dysfunction of the N-methyl-D-aspartate (NMDA)-type glutamate receptors may be involved in the pathogenesis of schizophrenia. In this study, we examined the contribution of GRIN2A and GRIN2B (Glutamate Ionotropic Receptor NMDA Type Subunit 2A/2B) polymorphisms to the clinical features of schizophrenia, such as the leading symptoms, the type of course, and the age of onset. Methods: A population of 402 Russian patients with schizophrenia from the Siberian region was investigated. Genotyping of seventeen single-nucleotide polymorphisms (SNPs) in GRIN2A and GRIN2B was performed using QuantStudio™ 3D Digital PCR System Life Technologies amplifier using TaqMan Validated SNP Genotyping Assay kits (Applied Biosystems). The results were analyzed using Chi-square and the Fisher’s exact tests. Results: We found an association of GRIN2A rs7206256 and rs11644461 and GRIN2B rs7313149 with the early onset (before the age of 18 years old) schizophrenia. We did not reveal any associations of GRIN2A and GRIN2B polymorphisms with leading (positive vs. negative) symptoms or type of course (continuous vs. episodic) of schizophrenia. Conclusions: In the study, we confirmed the involvement of the GRIN2A and GRIN2B genes in the early onset of schizophrenia in a Russian population of the Siberian region.

Machine learning and artificial intelligence technologies are known to be a convenient tool for analyzing multi-domain data in precision psychiatry. In the case of schizophrenia, the most commonly used data sources for such purposes are neuroimaging, voice and language patterns, and mobile phone data. Data on peripheral markers can also be useful for building predictive models. Here, we have developed five predictive models for the binary classification of schizophrenia patients and healthy individuals. Data on serum concentrations of cytokines, chemokines, growth factors, and age were among 38 parameters used to build these models. The sample consisted of 217 schizophrenia patients and 90 healthy individuals. The models architecture was involved logistic regression, deep neural networks, decision trees, support vector machine, and k-nearest neighbors algorithms. It was shown that the algorithm based on a deep neural network (consisting of five layers) showed a slightly higher sensitivity (0.87 ± 0.04) and specificity (0.52 ± 0.06) than other algorithms. Combining all variables into a single classifier showed a cumulative effect that exceeded the effectiveness of individual variables, indicating the need to use multiple biomarkers to diagnose schizophrenia. Thus, the data obtained showed the promise of using data on peripheral biomarkers and machine learning methods for diagnosing schizophrenia.

Several studies have shown that patients with schizophrenia are at high risk for metabolic syndrome (MetS) and bioenergetic dysfunction. Because acylcarnitines are involved in bioenergetic pathways and reflect the functioning of mitochondria, we hypothesized that these compounds are biomarkers of MetS in schizophrenia. The aim of this work was to quantify acylcarnitines and branched-chain amino acids in patients with schizophrenia comorbid with MetS. The study included 112 patients with paranoid schizophrenia treated with antipsychotics. Among them, 39 subjects met criteria of MetS. Concentrations of 30 acylcarnitines and three amino acids in dry serum spots were measured by liquid chromatography coupled with tandem mass spectrometry. MetS patients were found to have higher levels of valeryl carnitine (C5), leucine/isoleucine, and alanine as compared with patients without MetS, indicating possible participation of these compounds in the pathogenesis of metabolic disorders in schizophrenia. In patients with paranoid schizophrenia with or without MetS, lower levels of carnitines C10, C10:1, C12, and C18 were recorded as compared with the healthy individuals (n = 70), implying deterioration of energy metabolism. We believe that this finding can be explained by effects of antipsychotic medication on an enzyme called carnitine-palmitoyl transferase I.

The adipokines leptin, adiponectin, tumor necrosis factor-alpha (TNF-α), and interleukin 6 (IL-6) might be associated with metabolic syndrome (MetS) in patients with schizophrenia. In the present study, we attempted to confirm the results of previous reports and assessed their MetS-related correlation with body fat composition and biochemical parameters. We measured in 46 patients with schizophrenia and MetS serum levels of adiponectin insulin, leptin, TNF-α and IL-6 and compared these levels to those of patients with schizophrenia without MetS. The MetS patients had significantly increased leptin levels and leptin/adiponectin ratios, as well as decreased adiponectin levels. Leptin levels correlated with several metabolic parameters, both in patients with and without MetS, including body fat percentage, total fat fold, and body mass index (BMI). Patients without abnormal MetS components had lower levels of leptin and leptin/adiponectin ratios compared with patients who had one or two MetS components. Leptin/adiponectin ratios were higher in patients who had four rather than three MetS components. Multiple regression analysis revealed multiple associations for leptin but only one for adiponectin, TNF-α, and IL-6. Our results support an important pathophysiological role for leptin more than adiponectin in patients with schizophrenia with MetS.

Metabolic syndrome (MetS) is a common comorbidity of schizophrenia and significantly shortens life expectancy of the patients. Intercellular (ICAM), vascular (VCAM), and neural (NCAM) cell adhesion molecules (CAMs) mediate neuroinflammatory processes, and their soluble forms (e.g., sICAM) in plasma are present in parallel with their cell-bound forms. In this study, their serum levels were examined in 211 white Siberian patients with paranoid schizophrenia (82 patients with and 129 without MetS according to the 2005 International Diabetes Federation criteria). Serum levels of CAMs were determined with Magpix and Luminex 200 (Luminex, Austin, TX, USA) using xMAP Technology. The level of sICAM-1 was significantly higher and that of sVCAM-1 significantly lower in patients with MetS compared to patients without MetS. Levels of NCAM did not differ between the groups. More pronounced Spearman’s correlations between CAMs, age, duration of schizophrenia, and body–mass index were observed among patients without MetS than among patients with MetS. Our results are consistent with MetS’s being associated with endothelial dysfunction along with other components of inflammation. Through these endothelial components of peripheral inflammatory processes, MetS might induce intracerebral neuroinflammatory changes, but further investigation is needed to confirm this.

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.