No abstract

The majority of studies on psoriasis have focused on explaining the genetic background and its associations with the immune system’s response. The aim of this study was to identify the low-molecular weight compounds contributing to the metabolomic profile of psoriasis and to provide computational models that help with the classification and monitoring of the severity of the disease. We compared the results from targeted and untargeted analyses of patients’ serums with plaque psoriasis to controls. The main differences were found in the concentrations of acylcarnitines, phosphatidylcholines, amino acids, urea, phytol, and 1,11-undecanedicarboxylic acid. The data from the targeted analysis were used to build classification models for psoriasis. The results from this study provide an overview of the metabolomic serum profile of psoriasis along with promising statistical models for the monitoring of the disease.Electronic supplementary materialThe online version of this article (doi:10.1007/s00403-017-1760-1) contains supplementary material, which is available to authorized users.

Materials and Methods ethics approval. This study was approved by the Research Ethics Committee of the University of Tartu. Permission number 245/M-18. The Declaration of Helsinki protocols were followed and patients gave their informed, written consent.

Acylcarnitines (ACs) have been shown to have a potential to activate pro-inflammatory signaling pathways and to foster the development of insulin resistance. The first task of the current study was to study the full list of ACs (from C2 to C18) in first episode psychosis (FEP) patients before and after antipsychotic treatment. The second task was to relate ACs to inflammatory and metabolic biomarkers established in the same patient cohort as in our previous studies. Serum levels of ACs were determined with the AbsoluteIDQ p180 kit (BIOCRATES Life Sciences AG, Innsbruck, Austria) using the flow injection analysis tandem mass spectrometry ([FIA]-MS/MS) as well as liquid chromatography ([LC]-MS/MS) technique. Identification and quantification of the metabolites was achieved using multiple reactions monitoring along with internal standards. The comparison of ACs in antipsychotic-naïve first-episode psychosis (FEP) patients (N = 38) and control subjects (CSs, N = 37) revealed significantly increased levels of long-chain ACs (LCACs) C14:1 (p = 0.0001), C16 (p = 0.00002), and C18:1 (p = 0.000001) in the patient group. These changes of LCACs were associated with augmented levels of CARN palmitoyltransferase 1 (CPT-1) (p = 0.006). By contrast, the level of short-chain AC (SCAC) C3 was significantly reduced (p = 0.00003) in FEP patients. Seven months of antipsychotic drug treatment ameliorated clinical symptoms in patients (N = 36) but increased significantly their body mass index (BMI, p = 0.001). These changes were accompanied by significantly reduced levels of C18:1 (p = 0.00003) and C18:2 (p = 0.0008) as well as increased level of C3 (p = 0.01). General linear model revealed the relation of LCACs (C16, C16:1, and C18:1) to the inflammatory markers (epidermal growth factor, IL-2, IL-4, IL-6), whereas SCAC C3 was linked to the metabolic markers (leptin, C-peptide) and BMI. FEP was associated with an imbalance of ACs in patients because the levels of several LCACs were significantly higher and the levels of several SCACs were significantly reduced compared with CSs. This imbalance was modified by 7 months of antipsychotic drug treatment, reversing the levels of both LCACs and SCACs to that established for CSs. This study supports the view that ACs have an impact on both inflammatory and metabolic alterations inherent for FEP.

Apart from the refined management-oriented clinical stratification of chronic obstructive pulmonary disease (COPD), the molecular pathologies behind this highly prevalent disease have remained obscure. The aim of this study was the characterization of patients with COPD, based on the metabolomic profiling of peripheral blood and exhaled breath condensate (EBC) within the context of defined clinical and demographic variables. Mass-spectrometry-based targeted analysis of serum metabolites (mainly amino acids and lipid species), untargeted profiles of serum and EBC of patients with COPD of different clinical characteristics (n = 25) and control individuals (n = 21) were performed. From the combined clinical/demographic and metabolomics data, associations between clinical/demographic and metabolic parameters were searched and a de novo phenotyping for COPD was attempted. Adjoining the clinical parameters, sphingomyelins were the best to differentiate COPD patients from controls. Unsaturated fatty acid-containing lipids, ornithine metabolism and plasma protein composition-associated signals from the untargeted analysis differentiated the Global Initiative for COPD (GOLD) categories. Hierarchical clustering did not reveal a clinical-metabolomic stratification superior to the strata set by the GOLD consensus. We conclude that while metabolomics approaches are good for finding biomarkers and clarifying the mechanism of the disease, there are no distinct co-variate independent clinical-metabolic phenotypes.