Abdominal aortic aneurysm (AAA) is a permanent and localized aortic dilation, defined as aortic diameter ≥3 cm. It is an asymptomatic but potentially fatal condition because progressive enlargement of the abdominal aorta is spontaneously evolving towards rupture.Biomarkers may help to explain pathological processes of AAA expansion, and allow us to find novel therapeutic strategies or to determine the efficiency of current therapies. Metabolomics seems to be a good approach to find biomarkers of AAA. In this study, plasma samples of patients with large AAA, small AAA, and controls were fingerprinted with LC-QTOF-MS. Statistical analysis was used to compare metabolic fingerprints and select metabolites that showed a significant change. Results presented here reveal that LC-QTOF-MS based fingerprinting of plasma from AAA patients is a very good technique to distinguish small AAA, large AAA, and controls. With the use of validated PLS-DA models it was possible to classify patients according to the disease stage and predict properly the stage of additional AAA patients. Identified metabolites indicate a role for sphingolipids, lysophospholipids, cholesterol metabolites, and acylcarnitines in the development and progression of AAA. Moreover, guanidinosuccinic acid, which mimics nitric oxide in terms of its vasodilatory action, was found as a strong marker of large AAA.
Lipids are important components of biological systems, and their role can be currently investigated by the application of untargeted, holistic approaches such as metabolomics and lipidomics. Acquired data are analyzed to find significant signals responsible for the differentiation between the investigated conditions. Subsequently, identification has to be performed to bring biological meaning to the obtained results. Lipid identification seems to be relatively easy due to the known characteristic fragments; however, the large number of structural isomers and the formation of different adducts makes it challenging and at risk of misidentification. The inspection of data, acquired for plasma samples by a standard metabolic fingerprinting method, revealed multisignal formations for phosphatidylcholines, phosphatidylethanolamines, and sphingomyelins by the formation of ions such as [M + H](+), [M + Na](+), and [M + K](+) in positive ionization mode and [M - H](-), [M + HCOO](-), and [M + Cl](-) in negative mode. Moreover, sodium formate cluster formation was found for [M + H·HCOONa](+) and [H-H·HCOONa](-). The MS/MS spectrum obtained for each of the multi-ions revealed significant differences in the fragmentation, which were confirmed by the analysis of the samples in two independent research centers. After the inspection of an acquired spectra, a list of characteristic and diagnostic fragments was proposed that allowed for easy, quick, and robust lipid identification that provides information about the headgroup, formed adduct, and fatty acyl composition. This ensures successful identification, which is of great importance for the contextualization of data and results validation.
Since the start of metabolomics as a field of research, the number of studies related to cancer has grown to such an extent that cancer metabolomics now represents its own discipline. In this chapter, the applications of metabolomics in cancer studies are explored. Different approaches and analytical platforms can be employed for the analysis of samples depending on the goal of the study and the aspects of the cancer metabolome being investigated. Analyses have concerned a range of cancers including lung, colorectal, bladder, breast, gastric, oesophageal and thyroid, amongst others. Developments in these strategies and methodologies that have been applied are discussed, in addition to exemplifying the use of cancer metabolomics in the discovery of biomarkers and in the assessment of therapy (both pharmaceutical and nutraceutical). Finally, the application of cancer metabolomics in personalised medicine is presented.
Application of high-throughput technologies in metabolomics studies increases the quantity of data obtained, which in turn imposes several problems during data analysis. Correctly and clearly addressed biological question and comprehensive knowledge about data structure and properties are definitely necessary to select proper chemometric tools. However, there is a broad range of chemometric tools available for use with metabolomics data, which makes this choice challenging. Precisely performed data treatment enables valuable extraction of information and its proper interpretation. The effect of an error made at an early stage will be enhanced throughout the later stages, which in combination with other errors made at each step can accumulate and significantly affect the data interpretation. Moreover, adequate application of these tools may help not only to detect, but sometimes also to correct, biological, analytical, or methodological errors, which may affect truthfulness of obtained results. This report presents steps and tools used for LC-MS based metabolomics data extraction, reduction, and visualization. Following such steps as data reprocessing, data pretreatment, data treatment, and data revision, authors want to show how to extract valuable information and how to avoid misinterpretation of results obtained. The purpose of this work was to emphasize problematic characteristics of metabolomics data and the necessity for their attentive and precise treatment. The dataset used to illustrate metabolomics data properties and to illustrate major data treatment challenges was obtained utilizing an animal model of control and diabetic rats, both with and without rosemary treatment. Urine samples were fingerprinted employing LC-QTOF-MS.
Large-scale meta-analyses of genome-wide association studies have recently confirmed that the rs340874 single-nucleotide polymorphism in PROX1 gene is associated with fasting glycemia and type 2 diabetes mellitus; however, the mechanism of this link was not well established. The aim of our study was to evaluate the functional/phenotypic differences related to rs340874 PROX1 variants. The study group comprised 945 subjects of Polish origin (including 634 with BMI > 25) without previously known dysglycemia. We analyzed behavioral patterns (diet, physical activity), body fat distribution and glucose/fat metabolism after standardized meals and during the oral glucose tolerance test. We found that the carriers of the rs340874 PROX1 CC genotype had higher nonesterified fatty acids levels after high-fat meal (p = 0.035) and lower glucose oxidation (p = 0.014) after high-carbohydrate meal in comparison with subjects with other PROX1 genotypes. Moreover, in subjects with CC variant, we found higher accumulation of visceral fat (p < 0.02), but surprisingly lower daily food consumption (p < 0.001). We hypothesize that lipid metabolism alterations in subjects with the PROX1 CC genotype may be a primary cause of higher glucose levels after glucose load, since the fatty acids can inhibit insulin-stimulated glucose uptake by decreasing carbohydrate oxidation. Our observations suggest that the PROX1 variants have pleiotropic effect on disease pathways and it seem to be a very interesting goal of research on prevention of obesity and type 2 diabetes mellitus. The study may help to understand the mechanisms of visceral obesity and type 2 diabetes mellitus risk development.
Bariatric surgery was born in the 1950s at the University of Minnesota. From this time, it continues to evolve and, by the same token, gives new or better possibilities to treat not only obesity but also associated comorbidities. Metabolomics is also a relatively young science discipline, and similarly, it shows great potential for the comprehensive study of the dynamic alterations of the metabolome. It has been widely used in medicine, biology studies, biomarker discovery, and prognostic evaluations. Currently, several dozen metabolomics studies were performed to study the effects of bariatric surgery. LC-MS and NMR are the most frequently used techniques to study main effects of RYGB or SG. Research has yield many interesting results involving not only clinical parameters but also molecular modulations. Detected changes pertain to amino acid, lipids, carbohydrates, or gut microbiota alterations. It proves that including bariatric surgery to metabolic surgery is warranted. However, many molecular modulations after those procedures remain unexplained. Therefore, application of metabolomics to study this field seems to be a proper solution. New findings can suggest new directions of surgery technics modifications, contribute to broadening knowledge about obesity and diseases related to it, and perhaps develop nonsurgical methods of treatment in the future.
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