Clinical image of sepsis-associated encephalopathy midst E. coli urosepsis: Emergency department database study.

Background: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection, which, if untreated, leads to multi-organ failure. One of the severe possible complications is sepsis associated encephalopathy (SAE), a neurological dysfunction occurring secondary to a severe inflammatory response. It manifests as acute cognitive dysfunction and sudden-onset dysfunctions in mental state. Uropathogenic Escherichia coli is the most common pathogen causing bacteremia, responsible for 80% of uncomplicated outpatient urinary tract infections and 40% of nosocomial infections. The study aimed to assess the difference in the severity and the course of urosepsis caused by E. coli in patients with and without septic encephalopathy. Materials and methods: This study presents a retrospective analysis of the population of urosepsis patients admitted to the Emergency Department between September 2019 and June 2022. Inflammatory parameters, urinalysis and blood cultures were performed, along with a clinical evaluation of sepsis severity and encephalopathy. The patients were then stratified into SAE and non-SAE groups based on neurological manifestations and compared according to the collected data. Results: A total of 199 septic patients were included in the study. E. coli-induced urosepsis was diagnosed in 84 patients. In this group, SAE was diagnosed in 31 (36.9%) patients (33.3% in males, 40.5% females). Patients with SAE were found to be hypotensive (p < 0,005), with a higher respiratory rate (p < 0,017) resulting in a higher mortality rate (p = 0.002) compared to non-SAE septic patients. The APACHE II score was an independent risk factor associated with a higher mortality rate. Biochemical parameters between the groups did not show any statistical importance related to the severity of urosepsis. Conclusions: The severity of urosepsis and risk of SAE development increase according to the clinical condition and underlying comorbidities. Urosepsis patients with SAE are at a higher risk of death. Patients should undergo more careful screening for the presence of SAE on admission, and more intense monitoring and treatment should be provided for patients with SAE. This study indicates the need to develop projects aiming to further investigate neuroprotective interventions in sepsis.

A multiplatform metabolomics approach for comprehensive analysis of GIST xenografts with various KIT mutations.

Metabolites in biological matrices belong to diverse chemical groups, ranging from non-polar long-chain fatty acids to small polar molecules. The goal of untargeted metabolomic analysis is to measure the highest number of metabolites in the sample. Nevertheless, from an analytical point of view, no single technique can measure such a broad spectrum of analytes. Therefore, we selected a method based on GC-MS and LC-MS with two types of stationary phases for the untargeted profiling of gastrointestinal stromal tumours. The procedure was applied to GIST xenograft samples (n = 71) representing four different mutation models, half of which were treated with imatinib. We aimed to verify the method coverage and advantages of applying each technique. RP-LC-MS measured most metabolites due to a significant fraction of lipid components of the tumour tissue. What is unique and worth noting is that all applied techniques were able to distinguish between different mutation models. However, for detecting imatinib-induced alterations in the GIST metabolome, RP-LC-MS and GC-MS proved to be more relevant than HILIC-LC-MS, resulting in a higher number of significantly changed metabolites in four treated models. Undoubtedly, the inclusion of all mentioned techniques makes the method more comprehensive. Nonetheless, for green chemistry and time and labour saving, we assume that RP-LC-MS and GC-MS analyses are sufficient to cover the global GIST metabolome.

Metabolomic and transcriptomic response to imatinib treatment of gastrointestinal stromal tumour in xenograft-bearing mice.

BACKGROUND: Although imatinib is a well-established first-line drug for treating a vast majority of gastrointestinal stromal tumours (GIST), GISTs acquire secondary resistance during therapy. Multi-omics approaches provide an integrated perspective to empower the development of personalised therapies through a better understanding of functional biology underlying the disease and molecular-driven selection of the best-targeted individualised therapy. In this study, we applied integrative metabolomic and transcriptomic analyses to elucidate tumour biochemical processes affected by imatinib treatment. MATERIALS AND METHODS: A GIST xenograft mouse model was used in the study, including 10 mice treated with imatinib and 10 non-treated controls. Metabolites in tumour extracts were analysed using gas chromatography coupled with mass spectrometry (GC-MS). RNA sequencing was also performed on the samples subset (n=6). RESULTS: Metabolomic analysis revealed 21 differentiating metabolites, whereas next-generation RNA sequencing data analysis resulted in 531 differentially expressed genes. Imatinib significantly changed the profile of metabolites associated mainly with purine and pyrimidine metabolism, butanoate metabolism, as well as alanine, aspartate, and glutamate metabolism. The related changes in transcriptomic profiles included genes involved in kinase activity and immune responses, as well as supported its impact on the purine biosynthesis pathway. CONCLUSIONS: Our multi-omics study confirmed previously known pathways involved in imatinib anticancer activity as well as correlated imatinib-relevant downregulation of expression of purine biosynthesis pathway genes with the reduction of respectful metabolites. Furthermore, considering the importance of the purine biosynthesis pathway for cancer proliferation, we identified a potentially novel mechanism for the anti-tumour activity of imatinib. Based on the results, we hypothesise metabolic modulations aiming at the reduction in purine and pyrimidine pool may ensure higher imatinib efficacy or re-sensitise imatinib-resistant tumours.

Low and high resolution gas chromatography-mass spectrometry for untargeted metabolomics: A tutorial.

GC-MS for untargeted metabolomics is a well-established technique. Small molecules and molecules made volatile by derivatization can be measured and those compounds are key players in main biological pathways. This tutorial provides ready-to-use protocols for GC-MS-based metabolomics, using either the well-known low-resolution approach (GC-Q-MS) with nominal mass or the more recent high-resolution approach (GC-QTOF-MS) with accurate mass, discussing their corresponding strengths and limitations. Analytical procedures are covered for different types of biofluids (plasma/serum, bronchoalveolar lavage, urine, amniotic fluid) tissue samples (brain/hippocampus, optic nerve, lung, kidney, liver, pancreas) and samples obtained from cell cultures (adipocytes, macrophages, Leishmania promastigotes, mitochondria, culture media). Together with the sample preparation and data acquisition, data processing strategies are described specially focused on Agilent equipments, including deconvolution software and database annotation using spectral libraries. Manual curation strategies and quality control are also deemed. Finally, considerations to obtain a semiquantitative value for the metabolites are also described. As a case study, an illustrative example from one of our experiments at CEMBIO Research Centre, is described and findings discussed.

Deprogramming metabolism in pancreatic cancer with a bi-functional GPR55 inhibitor and biased ß2 adrenergic agonist.

Metabolic reprogramming contributes to oncogenesis, tumor growth, and treatment resistance in pancreatic ductal adenocarcinoma (PDAC). Here we report the effects of (R,S')-4'-methoxy-1-naphthylfenoterol (MNF), a GPR55 antagonist and biased ß2-adrenergic receptor (ß2-AR) agonist on cellular signaling implicated in proliferation and metabolism in PDAC cells. The relative contribution of GPR55 and ß2-AR in (R,S')-MNF signaling was explored further in PANC-1 cells. Moreover, the effect of (R,S')-MNF on tumor growth was determined in a PANC-1 mouse xenograft model. PANC-1 cells treated with (R,S')-MNF showed marked attenuation in GPR55 signal transduction and function combined with increased ß2-AR/Gαs/adenylyl cyclase/PKA signaling, both of which contributing to lower MEK/ERK, PI3K/AKT and YAP/TAZ signaling. (R,S')-MNF administration significantly reduced PANC-1 tumor growth and circulating L-lactate concentrations. Global metabolic profiling of (R,S')-MNF-treated tumor tissues revealed decreased glycolytic metabolism, with a shift towards normoxic processes, attenuated glutamate metabolism, and increased levels of ophthalmic acid and its precursor, 2-aminobutyric acid, indicative of elevated oxidative stress. Transcriptomics and immunoblot analyses indicated the downregulation of gene and protein expression of HIF-1α and c-Myc, key initiators of metabolic reprogramming in PDAC. (R,S')-MNF treatment decreased HIF-1α and c-Myc expression, attenuated glycolysis, shifted fatty acid metabolism towards ß-oxidation, and suppressed de novo pyrimidine biosynthesis in PANC-1 tumors. The results indicate a potential benefit of combined GPR55 antagonism and biased ß2-AR agonism in PDAC therapy associated with the deprogramming of altered cellular metabolism.

Targeted and untargeted metabolomic approach for GDM diagnosis.

Gestational diabetes mellitus (GDM) is a disorder which manifests itself for the first time during pregnancy and is mainly connected with glucose metabolism. It is also known that fatty acid profile changes in erythrocyte membranes and plasma could be associated with obesity and insulin resistance. These factors can lead to the development of diabetes. In the reported study, we applied the untargeted analysis of plasma in GDM against standard glucose-tolerant (NGT) women to identify the differences in metabolomic profiles between those groups. We found higher levels of 2-hydroxybutyric and 3-hydroxybutyric acids. Both secondary metabolites are associated with impaired glucose metabolism. However, they are products of different metabolic pathways. Additionally, we applied lipidomic profiling using gas chromatography to examine the fatty acid composition of cholesteryl esters in the plasma of GDM patients. Among the 14 measured fatty acids characterizing the representative plasma lipidomic cluster, myristic, oleic, arachidonic, and α-linoleic acids revealed statistically significant changes. Concentrations of both myristic acid, one of the saturated fatty acids (SFAs), and oleic acid, which belong to monounsaturated fatty acids (MUFAs), tend to decrease in GDM patients. In the case of polyunsaturated fatty acids (PUFAs), some of them tend to increase (e.g., arachidonic), and some of them tend to decrease (e.g., α-linolenic). Based on our results, we postulate the importance of hydroxybutyric acid derivatives, cholesteryl ester composition, and the oleic acid diminution in the pathophysiology of GDM. There are some evidence suggests that the oleic acid can have the protective role in diabetes onset. However, metabolic alterations that lead to the onset of GDM are complex; therefore, further studies are needed to confirm our observations.

Common Strategies and Factors Affecting Off-Line Breath Sampling and Volatile Organic Compounds Analysis Using Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS).

An analysis of exhaled breath enables specialists to noninvasively monitor biochemical processes and to determine any pathological state in the human body. Breath analysis holds the greatest potential to remold and personalize diagnostics; however, it requires a multidisciplinary approach and collaboration of many specialists. Despite the fact that breath is considered to be a less complex matrix than blood, it is not commonly used as a diagnostic and prognostic tool for early detection of disordered conditions due to its problematic sampling, analysis, and storage. This review is intended to determine, standardize, and marshal experimental strategies for successful, reliable, and especially, reproducible breath analysis.

Comprehensive Metabolic Signature of Renal Dysplasia in Children. A Multiplatform Metabolomics Concept.

Renal dysplasia is a severe congenital abnormality of the kidney parenchyma, which is an important cause of end-stage renal failure in childhood and early adulthood. The diagnosis of renal dysplasia relies on prenatal or postnatal ultrasounds as children show no specific clinical symptoms before chronic kidney disease develops. Prompt diagnosis is important in terms of early introduction of nephroprotection therapy and improved long-term prognosis. Metabolomics was applied to study children with renal dysplasia to provide insight into the changes in biochemical pathways underlying its pathology and in search of early indicators for facilitated diagnosis. The studied cohort consisted of 72 children, 39 with dysplastic kidneys and 33 healthy controls. All subjects underwent comprehensive urine metabolic profiling with the use of gas chromatography and liquid chromatography coupled to mass spectrometry, with two complementary separation modes of the latter. Univariate and multivariate statistical calculations identified a total of nineteen metabolites, differentiating the compared cohorts, independent of their estimated glomerular filtration rate. Seven acylcarnitines, xanthine, and glutamine were downregulated in the urine of renal dysplasia patients. Conversely, renal dysplasia was associated with higher urinary levels of dimethylguanosine, threonic acid or glyceric acid. This is the first metabolomic study of subjects with renal dysplasia. The authors define a characteristic urine metabolic signature in children with dysplastic kidneys, irrespective of renal function, linking the condition with altered fatty acid oxidation, amino acid and purine metabolisms.

A Robust Method for Sample Preparation of Gastrointestinal Stromal Tumour for LC/MS Untargeted Metabolomics.

Gastrointestinal stromal tumour has already been well explored at the genome level; however, little is known about metabolic processes occurring in the sarcoma. Sample preparation is a crucial step in untargeted metabolomics workflow, highly affecting the metabolome coverage and the quality of the results. In this study, four liquid-liquid extraction methods for the isolation of endogenous compounds from gastrointestinal stromal tumours were compared and evaluated. The protocols covered two-step or stepwise extraction with methyl-tert-butyl ether (MTBE) or dichloromethane. The extracts were subjected to LC-MS analysis by the application of reversed-phase and hydrophilic interaction liquid chromatography to enable the separation and detection of both polar and nonpolar analytes. The extraction methods were compared in terms of efficiency (total number of detected metabolites) and reproducibility. The method was based on the stepwise extraction with MTBE, methanol, and water proved to be the most reproducible, and thus, its robustness to fluctuations in experimental conditions was assessed employing Plackett-Burman design and hierarchical modelling. While most studied factors had no effect on the metabolite abundance, the highest coefficient value was observed for the volume of MTBE added during extraction. Herein, we demonstrate the application and the feasibility of the selected protocol for the analysis of gastrointestinal stromal tumour samples. The method selected could be considered as a reference for the best characterization of underlying molecular changes associated with complex tissue extracts of GIST.

Mitochondrial Metabolism behind Region-Specific Resistance to Ischemia-Reperfusion Injury in Gerbil Hippocampus. Role of PKCßII and Phosphate-Activated Glutaminase.

Ischemic episodes are a leading cause of death worldwide with limited therapeutic interventions. The current study explored mitochondrial phosphate-activated glutaminase (GLS1) activity modulation by PKCßII through GC-MS untargeted metabolomics approach. Mitochondria were used to elucidate the endogenous resistance of hippocampal CA2-4 and dentate gyrus (DG) to transient ischemia and reperfusion in a model of ischemic episode in gerbils. In the present investigation, male gerbils were subjected to bilateral carotids occlusion for 5 min followed by reperfusion (IR). Gerbils were randomly divided into three groups as vehicle-treated sham control, vehicle-treated IR and PKCßII specific inhibitor peptide ßIIV5-3-treated IR. Vehicle or ßIIV5-3 (3 mg/kg, i.v.) were administered at the moment of reperfusion. The gerbils hippocampal tissue were isolated at various time of reperfusion and cell lysates or mitochondria were isolated from CA1 and CA2-4,DG hippocampal regions. Recombinant proteins PKCßII and GLS1 were used in in vitro phosphorylation reaction and organotypic hippocampal cultures (OHC) transiently exposed to NMDA (25 µM) to evaluate the inhibition of GLS1 on neuronal viability. PKCßII co-precipitates with GAC (GLS1 isoform) in CA2-4,DG mitochondria and phosphorylates GLS1 in vitro. Cell death was dose dependently increased when GLS1 was inhibited by BPTA while inhibition of mitochondrial pyruvate carrier (MPC) attenuated cell death in NMDA-challenged OHC. Fumarate and malate were increased after IR 1h in CA2-4,DG and this was reversed by ßIIV5-3 what correlated with GLS1 activity increases and earlier showed elevation of neuronal death (Krupska et al., 2017). The present study illustrates that CA2-4,DG resistance to ischemic episode at least partially rely on glutamine and glutamate utilization in mitochondria as a source of carbon to tricarboxylic acid cycle. This phenomenon depends on modulation of GLS1 activity by PKCßII and remodeling of MPC: all these do not occur in ischemia-vulnerable CA1.

Data-dependent normalization strategies for untargeted metabolomics-a case study.

Despite the recent advances in the standardization of untargeted metabolomics workflows, there is still a lack of attention to specific data treatment strategies that require deep knowledge of the biological problem and need to be applied after a well-thought out process to understand the effect of the practice. One of those strategies is data normalization. Data-driven assumptions are critical especially addressing unwanted variation present in the biological model as it can be the case in heterogeneous tissues, cells with different sizes or biofluids with different concentrations. Chronic kidney disease (CKD) is a widespread disorder affecting kidney structure and function. Animal models are being developed to be able to get valuable insights into the etiopathogenesis of the condition and effect of the treatments. Moreover, diagnosis and disease staging still require defining appropriate biomarkers. Untargeted metabolomics has the potential to deal with those challenges. Renal fibrosis is one of the consequences of kidney injury which greatly affects the concentration of metabolites in the same quantity of sample. To overcome this challenge, several data normalization strategies have been applied, following a multilevel normalization method with the overall aim of focussing on the relevant biological information and reducing the influence of disturbing factors. A comprehensive evaluation of the performance of the normalization strategies, both on methods assessing the intragroup variation and on the impact on differential analysis, is provided. Finally, we present evidence of the importance of biological-model-driven guided normalization methods and discuss multiple criteria that need to be taken into consideration to obtain robust and reliable data. Special concern is transmitted on the misleading conclusions that might be the consequence of inappropriate data pre-treatment solutions applied for untargeted methods. Graphical abstract.

Plasma Metabolome Alterations Associated with Extrauterine Growth Restriction.

Very preterm infants (VPI, born at or before 32 weeks of gestation) are at risk of adverse health outcomes, from which they might be partially protected with appropriate postnatal nutrition and growth. Metabolic processes or biochemical markers associated to extrauterine growth restriction (EUGR) have not been identified. We applied untargeted metabolomics to plasma samples of VPI with adequate weight for gestational age at birth and with different growth trajectories (29 well-grown, 22 EUGR) at the time of hospital discharge. A multivariate analysis showed significantly higher levels of amino-acids in well-grown patients. Other metabolites were also identified as statistically significant in the comparison between groups. Relevant differences (with corrections for multiple comparison) were found in levels of glycerophospholipids, sphingolipids and other lipids. Levels of many of the biochemical species decreased progressively as the level of growth restriction increased in severity. In conclusion, an untargeted metabolomic approach uncovered previously unknown differences in the levels of a range of plasma metabolites between well grown and EUGR infants at the time of discharge. Our findings open speculation about pathways involved in growth failure in preterm infants and the long-term relevance of this metabolic differences, as well as helping in the definition of potential biomarkers.

Short overview on metabolomics approach to study pathophysiology of oxidative stress in cancer.

Association of oxidative stress with carcinogenesis is well known, but not understood well, as is pathophysiology of oxidative stress generated during different types of anti-cancer treatments. Moreover, recent findings indicate that cancer associated lipid peroxidation might eventually help defending adjacent nonmalignant cells from cancer invasion. Therefore, untargeted metabolomics studies designed for advanced translational and clinical studies are needed to understand the existing paradoxes in oncology, including those related to controversial usage of antioxidants aiming to prevent or treat cancer. In this short review we have tried to put emphasis on the importance of pathophysiology of oxidative stress and lipid peroxidation in cancer development in relation to metabolic adaptation of particular types of cancer allowing us to conclude that adaptation to oxidative stress is one of the main driving forces of cancer pathophysiology. With the help of metabolomics many novel findings are being achieved thus encouraging further scientific breakthroughs. Combined with targeted qualitative and quantitative methods, especially immunochemistry, further research might reveal bio-signatures of individual patients and respective malignant diseases, leading to individualized treatment approach, according to the concepts of modern integrative medicine.

Quality assurance procedures for mass spectrometry untargeted metabolomics. a review.

Untargeted metabolomics, as a global approach, has already proven its great potential and capabilities for the investigation of health and disease, as well as the wide applicability for other research areas. Although great progress has been made on the feasibility of metabolomics experiments, there are still some challenges that should be faced and that includes all sources of fluctuations and bias affecting every step involved in multiplatform untargeted metabolomics studies. The identification and reduction of the main sources of unwanted variation regarding the pre-analytical, analytical and post-analytical phase of metabolomics experiments is essential to ensure high data quality. Nowadays, there is still a lack of information regarding harmonized guidelines for quality assurance as those available for targeted analysis. In this review, sources of variations to be considered and minimized along with methodologies and strategies for monitoring and improvement the quality of the results are discussed. The given information is based on evidences from different groups among our own experiences and recommendations for each stage of the metabolomics workflow. The comprehensive overview with tools presented here might serve other researchers interested in monitoring, controlling and improving the reliability of their findings by implementation of good experimental quality practices in the untargeted metabolomics study.

A review of blood sample handling and pre-processing for metabolomics studies.

Metabolomics has been found to be applicable to a wide range of clinical studies, bringing a new era for improving clinical diagnostics, early disease detection, therapy prediction and treatment efficiency monitoring. A major challenge in metabolomics, particularly untargeted studies, is the extremely diverse and complex nature of biological specimens. Despite great advances in the field there still exist fundamental needs for considering pre-analytical variability that can introduce bias to the subsequent analytical process and decrease the reliability of the results and moreover confound final research outcomes. Many researchers are mainly focused on the instrumental aspects of the biomarker discovery process, and sample related variables sometimes seem to be overlooked. To bridge the gap, critical information and standardized protocols regarding experimental design and sample handling and pre-processing are highly desired. Characterization of a range variation among sample collection methods is necessary to prevent results misinterpretation and to ensure that observed differences are not due to an experimental bias caused by inconsistencies in sample processing. Herein, a systematic discussion of pre-analytical variables affecting metabolomics studies based on blood derived samples is performed. Furthermore, we provide a set of recommendations concerning experimental design, collection, pre-processing procedures and storage conditions as a practical review that can guide and serve for the standardization of protocols and reduction of undesirable variation.

GC-MS based Gestational Diabetes Mellitus longitudinal study: Identification of 2-and 3-hydroxybutyrate as potential prognostic biomarkers.

Gestational Diabetes Mellitus (GDM) causes severe short- and long-term complications for the mother, fetus and neonate, including type 2-diabetes (T2DM) later in life. In this pilot study, GC-Q/MS analysis was applied for plasma metabolomics fingerprinting of 24 healthy and 24 women with GDM at different stages of gestation (second and third trimester) and postpartum (one and three months). Multivariate (unsupervised and supervised) statistical analysis was performed to investigate variance in the data, identify outliers and for unbiased assessment of data quality. Plasma fingerprints allowed for the discrimination of GDM pregnant women from controls both in the 2nd and 3rd trimesters of gestation. However, metabolic profiles tended to be similar after delivery. Follow up of these women revealed that 4 of them developed T2DM within 2 years postpartum. Multivariate PLS-DA models limited to women with GDM showed clear separation 3 months postpartum. In the 2nd trimester of gestation there was also a clear separation between GDM women that were normoglycemic after pregnancy and those with recognized postpartum T2DM. Metabolites that had the strongest discriminative power between these groups in the 2nd trimester of gestation were 2-hydroxybutyrate, 3-hydroxybutyrate, and stearic acid. We have described, that early GDM comprises metabotypes that are associated with the risk of future complications, including postpartum T2DM. In this pilot study, we provide evidence that 2-hydroxybutyrate and 3-hydroxybutyrate may be considered as future prognostic biomarkers to predict the onset of diabetic complications in women with gestational diabetes after delivery.

Metabolomic Fingerprinting in the Comprehensive Study of Liver Changes Associated with Onion Supplementation in Hypercholesterolemic Wistar Rats.

The consumption of functional ingredients has been suggested to be a complementary tool for the prevention and management of liver disease. In this light, processed onion can be considered as a source of multiple bioactive compounds with hepatoprotective properties. The liver fingerprint of male Wistar rats (n = 24) fed with three experimental diets (control (C), high-cholesterol (HC), and high-cholesterol enriched with onion (HCO) diets) was obtained through a non-targeted, multiplatform metabolomics approach to produce broad metabolite coverage. LC-MS, CE-MS and GC-MS results were subjected to univariate and multivariate analyses, providing a list of significant metabolites. All data were merged in order to figure out the most relevant metabolites that were modified by the onion ingredient. Several relevant metabolic changes and related metabolic pathways were found to be impacted by both HC and HCO diet. The model highlighted several metabolites (such as hydroxybutyryl carnitine and palmitoyl carnitine) modified by the HCO diet. These findings could suggest potential impairments in the energy-lipid metabolism, perturbations in the tricarboxylic acid cycle (TCA) cycle and ß-oxidation modulated by the onion supplementation in the core of hepatic dysfunction. Metabolomics shows to be a valuable tool to evaluate the effects of complementary dietetic approaches directed to hepatic damage amelioration or non-alcoholic fatty liver disease (NAFLD) prevention.

New Biochemical Insights into the Mechanisms of Pulmonary Arterial Hypertension in Humans.

Diagnosis of pulmonary arterial hypertension (PAH) is difficult due to the lack of specific clinical symptoms and biomarkers, especially at early stages. We compared plasma metabolic fingerprints of PAH patients (n = 20) with matched healthy volunteers (n = 20) using, for the first time, untargeted multiplatform metabolomics approach consisting of high-performance liquid and gas chromatography coupled with mass spectrometry. Multivariate statistical analyses were performed to select metabolites that contribute most to groups' classification (21 from liquid in both ionization modes and 9 from gas chromatography-mass spectrometry). We found metabolites related to energy imbalance, such as glycolysis-derived metabolites, as well as metabolites involved in fatty acid, lipid and amino acid metabolism. We observed statistically significant changes in threitol and aminomalonic acid in PAH patients, which could provide new biochemical insights into the pathogenesis of the disease. The results were externally validated on independent case and control cohorts, confirming up to 16 metabolites as statistically significant in the validation study. Multiplatform metabolomics, followed by multivariate chemometric data analysis has a huge potential for explaining pathogenesis of PAH and for searching potential and new more specific and less invasive markers of the disease.