One of the primary challenges regarding chronic kidney disease (CKD) diagnosis is the absence of reliable methods to detect early-stage kidney damage. A metabolomic approach is expected to broaden the current diagnostic modalities by enabling timely detection and making the prognosis more accurate. Analysis performed on urine has several advantages, such as the ease of collection using noninvasive methods and its lower protein and lipid content compared with other bodily fluids. This review highlights current trends in applied analytical methods, major discoveries concerning pathways, and investigated populations in the context of urine metabolomic research for CKD over the past five years. Also, we are presenting approaches, instrument upgrades, and sample preparation modifications that have improved the analytical parameters of methods. The onset of CKD leads to alterations in metabolism that are apparent in the molecular composition of urine. Recent works highlight the prevalence of alterations in the metabolic pathways related to the tricarboxylic acid cycle and amino acids. Including diverse patient cohorts, using numerous analytical techniques with modifications and the appropriate annotation and explanation of the discovered biomarkers will help develop effective diagnostic models for different subtypes of renal injury with clinical applications.

Mass-spectrometry-based assays nowadays play an essential role in biomedical research and clinical applications. There are different types of commercial mass spectrometers on the market today, and triple quadrupole (QqQ) is one of the time-honored systems. Here, we overview the main areas of QqQ applications in biomedicine and assess the current level, evolution, and trends in the use of QqQ in these areas. Relevant data were extracted from the Scopus database using the specified terms and Boolean operators defined for each field of the QqQ application. We also discuss the recent advances in QqQ and QqQ-based analytical platforms, which promote the clinical application of these systems, and explain the indicated substantial increase in triple quadrupole use in biomedicine. The number of biomedical studies utilizing QqQ increased 2–3 times this decade. Triple quadrupole is most intensively used in the field of endocrine research and testing. On the contrary, the relative rate of immunoassay utilization—a major competitor of chromatography–mass spectrometry—decreased in this area as well as its use within Therapeutic drug monitoring (TDM) and forensic toxicology. Nowadays, the applications of high-resolution accurate mass (HRAM) mass spectrometers in the investigated areas represent only a small fraction of the total amount of research using mass spectrometry; however, their application substantially increased during the last decade in the untargeted search for new biomarkers.

Inflammatory bowel diseases are extremely common throughout the world. However, in most cases, it is asymptomatic at the initial stage. Therefore, it is important to develop non-invasive diagnostic methods that allow identification of the IBD risks in a timely manner. It is well known that gastrointestinal microbiota secrete volatile compounds (VOCs) and their composition may change in IBD. We propose a non-invasive method to identify the dynamics of IBD development in the acute and remission stage at the level of VOCs in model of dextran sulfate sodium (DSS) with chemically induced colitis measured by headspace GC/MS (HS GC/MS). Methods: VOCs profile was identified using a headspace GC/MS (HS GC/MS). GC/MS data were processed using MetaboAnalyst 5.0 and GraphPad Prism 8.0.1 software. The disease activity index (DAI) and histological method were used to assess intestinal inflammation. The peak of intestinal inflammation activity was reached on day 7, according to the disease activity index. Histological examination data showed changes in the intestine due to different stages of inflammation. As the acute inflammation stage was reached, the metabolomic profile also underwent changes, especially at the short-fatty acids level. A higher relative amounts of acetic acid (p value < 0.025) and lower relative amounts of propanoic acid (p value < 0.0005), butanoic acid (p value < 0.005) and phenol 4-methyl- (p value = 0.053) were observed in DSS7 group on day 7 compared to the control group. In remission stage, disease activity indexes decreased, and the histological picture also improved. But metabolome changes continued despite the withdrawal of the DSS examination. A lower relative amounts of propanoic acid (p value < 0.025), butanoic acid (p value < 0.0005), pentanoic acid (p value < 0.0005), and a significant de-crease of hexanoic acid (p value < 0.0005) relative amounts were observed in the DSS14 group compared to the control group on day 14. A model of DSS-induced colitis in rats was successfully implemented for metabolomic assessment of different stages of inflammation. We demonstrated that the ratios of volatile compounds change in response to DSS before the appearance of standard signs of inflammation, determined by DAI and histological examination. Changes in the volatile metabolome persisted even after visual intestine repair and it confirms the high sensitivity of the microbiota to the damaging effects of DSS. The use of HS GC/MS may be an important addition to existing methods for assessing inflammation at early stages.

According to the World Health Organization, ischemic stroke is the second leading cause of death in the world. Frequently, it is caused by brachiocephalic artery (BCA) atherosclerosis. Timely detection of atherosclerosis and its unstable course can allow for a timely response to potentially dangerous changes and reduce the risk of vascular complications. Omics technologies allow us to identify new biomarkers that we can use in diagnosing diseases. This research included 90 blood plasma samples. The study group comprised 52 patients with severe atherosclerotic lesions BCA, and the control group comprised 38 patients with no BCA atherosclerosis. Targeted and panoramic lipidomic profiling of their blood plasma was carried out. There was a statistically significant difference (p < 0.05) in the values of the indices saturated fatty acids (FAs), unsaturated FAs, monounsaturated FAs, omega-3, and omega-6. Based on the results on the blood plasma lipidome, we formed models that have a fairly good ability to determine atherosclerotic lesions of the brachiocephalic arteries, as well as a model for identifying unstable atherosclerotic plaques. According only to the panoramic lipidome data, divided into groups according to stable and unstable atherosclerotic plaques, a significant difference was taken into account: p value < 0.05 and abs (fold change) > 2. Unfortunately, we did not observe significant differences according to the established plasma panoramic lipidome criteria between patients with stable and unstable plaques. Omics technologies allow us to obtain data about any changes in the body. According to our data, statistically significant differences in lipidomic profiling were obtained when comparing groups with or without BCA atherosclerosis.

Schizophrenia is one of the most severe chronic mental disorders that is currently diagnosed and categorized through subjective clinical assessment of complex symptoms. At present, there is a recognized need for an objective, unbiased clinical test for schizophrenia diagnosis at an early stage and categorization of the disease. This can be achieved by assaying low-molecular-weight biomarkers of the disease. Here we give an overview of previously conducted research on the discovery of biomarkers of schizophrenia and focus on the studies implemented with the use of GC-MS and the least invasiveness of biological samples acquisition. The presented data demonstrate that GC-MS is a powerful instrumental platform for investigating dysregulated biochemical pathways implicated in schizophrenia pathogenesis. With this platform, different research groups suggested a number of low molecular weight biomarkers of schizophrenia. However, we recognize an inconsistency between the biomarkers or biomarkers patterns revealed by different groups even in the same matrix. Moreover, despite the importance of the problem, the number of relevant studies is limited. The intensification of the research, as well as the harmonization of the analytical procedures to overcome the observed inconsistencies, can be indicated as future directions in the schizophrenia bio-markers quest.

Background: Atherosclerosis and its associated chronic inflammation of the arterial wall disrupt fatty acid metabolism, leading to changes in plasma fatty acid composition. These alterations can be used to improve disease diagnosis and risk stratification by the development and application of specific lipidomic indices. Objectives: The objectives of this study are to evaluate the performance of conventional fatty acid indices and enhance diagnostic efficiency in atherosclerosis by introducing novel index based on plasma PUFA n-6 and n-3 content (Omega-6/3 Balance Index, O6/3-BI), as well as the perspective SFA/MUFA ratio (stearic/oleic acid ratio, C18:0/C18:1n-9) and a logit function combining PUFA and SFA/MUFA biomarkers. Methods: Plasma fatty acids were quantified by LC-MS/MS in healthy controls (n = 50) and patients with carotid atherosclerosis (n = 52), stratified by atorvastatin, rosuvastatin, or no statin therapy. The conventional indices (the Omega-3 Status (EPA + DHA), AA/EPA, and the omega-6/omega-3 ratio), and pathway ratios (C18:0/C18:1n-9; and C20:4n-6/C22:4n-6), as well as the newly introduced PUFA index and combined PUFA-SFA/MUFA logit function, were calculated. Their diagnostic performance for distinguishing atherosclerosis was assessed by a receiver operating characteristic (ROC) analysis with the cross-validation and calculation of Cliff’s Δ effect size. Results: The conventional parameters demonstrated a poor to low discrimination ability of the atherosclerosis patients’ groups from healthy controls (area under the ROC curve, AUC 0.548–0.711). In statin-treated patients, these conventional markers lost significance. The newly introduced PUFA index and SFA/MUFA ratio demonstrated improved patients’ discrimination with AUC 0.734–0.780 for the former and strong predictive power with AUC 0.831–0.858 for the latter marker and maintained their diagnostic value under statin therapy. The most significant positive effect size was observed for the SFA/MUFA ratio with Cliff’s Δ = 0.67–0.71. The combined PUFA-SFA/MUFA logit function also demonstrated a strong predictive power with AUC = 0.880 (Cliff’s Δ = −0.76), outperforming any single index. Conclusions: The newly introduced lipidomic index based on the PUFA content, SFA/MUFA ratio, and a logit function combining PUFA-SFA/MUFA biomarkers demonstrated a substantially better discrimination of atherosclerosis-related fatty acid metabolic disturbances than conventional fatty acid biomarkers.