RESUMO
Metabolic syndrome (MetS) is common among schizophrenia patients, and one of MetS's causes may be an imbalance in nitric oxide regulation. In this study, we examined associations of three polymorphic variants of the nitric oxide synthase 1 adapter protein (NOS1AP) gene with MetS in schizophrenia. NOS1AP regulates neuronal nitric oxide synthase, which controls intracellular calcium levels and may influence insulin secretion. The aim of the investigation was to study polymorphic variants of the NOS1AP gene as possible markers of MetS in patients with schizophrenia. A total of 489 Caucasian patients with schizophrenia (ICD-10) from Siberia (Russia) were included in the study, and 131 (26.8%) patients had MetS (IDF classification, 2007). The participants were genotyped for three single-nucleotide polymorphisms in NOS1AP (rs12143842, rs10494366, and rs12029454). Logistic regression was used for association analysis. Single-nucleotide polymorphisms, sex, and age served as covariates; the dependent variable was the coded parameter of the presence/absence of MetS. Polymorphisms rs12143842 and rs10494366 showed a stable association even after Bonferroni's correction for multiple comparisons (p = 0.005 and 0.002, respectively), indicating a statistically significant contribution of these polymorphic variants to the pathogenesis of MetS. Our results suggest that in patients with schizophrenia, NOS1AP may be involved in MetS pathophysiology.
RESUMO
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-17ß-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.