Статті в журналах: "3205 Medical biochemistry and metabolomics"

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Zhang, Aihua, Hui Sun, Xiuhong Wu, and Xijun Wang. "Urine metabolomics." Clinica Chimica Acta 414 (December 2012): 65–69. http://dx.doi.org/10.1016/j.cca.2012.08.016.

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Ala-Korpela, Mika. "Objective Metabolomics Research." Clinical Chemistry 64, no. 1 (January 1, 2018): 30–33. http://dx.doi.org/10.1373/clinchem.2017.274852.

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Zhang, Ai-hua, Shi Qiu, Hong-ying Xu, Hui Sun, and Xi-jun Wang. "Metabolomics in diabetes." Clinica Chimica Acta 429 (February 2014): 106–10. http://dx.doi.org/10.1016/j.cca.2013.11.037.

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Rhee, Eugene P., and Robert E. Gerszten. "Metabolomics and Cardiovascular Biomarker Discovery." Clinical Chemistry 58, no. 1 (January 1, 2012): 139–47. http://dx.doi.org/10.1373/clinchem.2011.169573.

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Abstract BACKGROUND Metabolomics, the systematic analysis of low molecular weight biochemical compounds in a biological specimen, has been increasingly applied to biomarker discovery. CONTENT Because no single analytical method can accommodate the chemical diversity of the entire metabolome, various methods such as nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) have been employed, with the latter coupled to an array of separation techniques including gas and liquid chromatography. Whereas NMR can provide structural information and absolute quantification for select metabolites without the use of exogenous standards, MS tends to have much higher analytical sensitivity, enabling broader surveys of the metabolome. Both NMR and MS can be used to characterize metabolite data either in a targeted manner or in a nontargeted, pattern-recognition manner. In addition to technical considerations, careful sample selection and study design are important to minimize potential confounding influences on the metabolome, including diet, medications, and comorbitidies. To this end, metabolite profiling has been applied to human biomarker discovery in small-scale interventions, in which individuals are extremely well phenotyped and able to serve as their own biological controls, as well as in larger epidemiological cohorts. Understanding how metabolites relate to each other and to established risk markers for diseases such as diabetes and renal failure will be important in evaluating the potential value of these metabolites as clinically useful biomarkers. SUMMARY Applied to both experimental and epidemiological study designs, metabolite profiling has begun to highlight the breadth metabolic disturbances that accompany human disease. Experimental work in model systems and integration with other functional genomic approaches will be required to establish a causal link between select biomarkers and disease pathogenesis.

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Piersigilli, Fiammetta, and Vineet Bhandari. "Metabolomics of bronchopulmonary dysplasia." Clinica Chimica Acta 500 (January 2020): 109–14. http://dx.doi.org/10.1016/j.cca.2019.09.025.

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Zhao, Ying-Yong. "Metabolomics in chronic kidney disease." Clinica Chimica Acta 422 (June 2013): 59–69. http://dx.doi.org/10.1016/j.cca.2013.03.033.

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Zhang, Ai-hua, Hui Sun, Shi Qiu, and Xi-jun Wang. "Metabolomics in noninvasive breast cancer." Clinica Chimica Acta 424 (September 2013): 3–7. http://dx.doi.org/10.1016/j.cca.2013.05.003.

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Murri, Mora, María Insenser, and Héctor F. Escobar-Morreale. "Metabolomics in polycystic ovary syndrome." Clinica Chimica Acta 429 (February 2014): 181–88. http://dx.doi.org/10.1016/j.cca.2013.12.018.

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Guasch-Ferré, Marta, Shilpa N. Bhupathiraju, and Frank B. Hu. "Use of Metabolomics in Improving Assessment of Dietary Intake." Clinical Chemistry 64, no. 1 (January 1, 2018): 82–98. http://dx.doi.org/10.1373/clinchem.2017.272344.

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Abstract BACKGROUND Nutritional metabolomics is rapidly evolving to integrate nutrition with complex metabolomics data to discover new biomarkers of nutritional exposure and status. CONTENT The purpose of this review is to provide a broad overview of the measurement techniques, study designs, and statistical approaches used in nutrition metabolomics, as well as to describe the current knowledge from epidemiologic studies identifying metabolite profiles associated with the intake of individual nutrients, foods, and dietary patterns. SUMMARY A wide range of technologies, databases, and computational tools are available to integrate nutritional metabolomics with dietary and phenotypic information. Biomarkers identified with the use of high-throughput metabolomics techniques include amino acids, acylcarnitines, carbohydrates, bile acids, purine and pyrimidine metabolites, and lipid classes. The most extensively studied food groups include fruits, vegetables, meat, fish, bread, whole grain cereals, nuts, wine, coffee, tea, cocoa, and chocolate. We identified 16 studies that evaluated metabolite signatures associated with dietary patterns. Dietary patterns examined included vegetarian and lactovegetarian diets, omnivorous diet, Western dietary patterns, prudent dietary patterns, Nordic diet, and Mediterranean diet. Although many metabolite biomarkers of individual foods and dietary patterns have been identified, those biomarkers may not be sensitive or specific to dietary intakes. Some biomarkers represent short-term intakes rather than long-term dietary habits. Nonetheless, nutritional metabolomics holds promise for the development of a robust and unbiased strategy for measuring diet. Still, this technology is intended to be complementary, rather than a replacement, to traditional well-validated dietary assessment methods such as food frequency questionnaires that can measure usual diet, the most relevant exposure in nutritional epidemiologic studies.

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Misra, Biswapriya. "Individualized metabolomics: opportunities and challenges." Clinical Chemistry and Laboratory Medicine (CCLM) 58, no. 6 (June 25, 2020): 939–47. http://dx.doi.org/10.1515/cclm-2019-0130.

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AbstractThe goal of advancing science in health care is to provide high quality treatment and therapeutic opportunities to patients in need. This is especially true in precision medicine, wherein the ultimate goal is to link disease phenotypes to targeted treatments and novel therapeutics at the scale of an individual. With the advent of -omics technologies, such as genomics, proteomics, microbiome, among others, the metabolome is of wider and immediate interest for its important role in metabolic regulation. The metabolome, of course, comes with its own questions regarding technological challenges. In this opinion article, I attempt to interrogate some of the main challenges associated with individualized metabolomics, and available opportunities in the context of its clinical application. Some questions this article addresses and attempts to find answers for are: Can a personal metabolome (n = 1) be inexpensive, affordable and informative enough (i.e. provide predictive yet validated biomarkers) to represent the entirety of a population? How can a personal metabolome complement advances in other -omics areas and the use of monitoring devices, which occupy our personal space?

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Aboud, Omran Abu, and Robert H. Weiss. "New Opportunities from the Cancer Metabolome." Clinical Chemistry 59, no. 1 (January 1, 2013): 138–46. http://dx.doi.org/10.1373/clinchem.2012.184598.

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BACKGROUND Metabolomics, the study of all metabolites produced in the body, which often includes flora and drug metabolites, is the omics approach that can be considered most closely related to a patient's phenotype. Metabolomics has a great and largely untapped potential in the field of oncology, and the analysis of the cancer metabolome to identify biofluid markers and novel druggable targets can now be undertaken in many research laboratories. CONTENT The cancer metabolome has been used to identify and begin to evaluate potential biomarkers and therapeutic targets in a variety of malignancies, including breast, prostate, and kidney cancer. We discuss the several standard techniques for metabolite separation and identification, with their potential problems and drawbacks. Validation of biomarkers and targets may entail intensive use of labor and technology and generally requires a large number of study participants as well as laboratory validation studies. The field of pharmacometabolomics, in which specific therapies are chosen on the basis of a patient's metabolomic profile, has shown some promise in the translation of metabolomics into the arena of personalized medicine. SUMMARY The relatively new approach using metabolomics has just begun to enter the mainstream of cancer diagnostics and therapeutics. As this field advances, metabolomics will take its well-deserved place next to genomics, transcriptomics, and proteomics in both clinical and basic research in oncology.

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Kirwan, Jennifer A., Rima Kaddurah-Daouk, Todd Mitchell, Tobias Pischon, Michael A. Schmidt, and Vidya Velagapudi. "Biobanking for Metabolomics and Lipidomics in Precision Medicine." Clinical Chemistry 65, no. 7 (July 1, 2019): 827–32. http://dx.doi.org/10.1373/clinchem.2018.298620.

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Wikoff, William R., Jon A. Gangoiti, Bruce A. Barshop, and Gary Siuzdak. "Metabolomics Identifies Perturbations in Human Disorders of Propionate Metabolism." Clinical Chemistry 53, no. 12 (December 1, 2007): 2169–76. http://dx.doi.org/10.1373/clinchem.2007.089011.

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Abstract Background: We applied untargeted mass spectrometry-based metabolomics to the diseases methylmalonic acidemia (MMA) and propionic acidemia (PA). Methods: We used a screening platform that used untargeted, mass-based metabolomics of methanol-extracted plasma to find significantly different molecular features in human plasma samples from MMA and PA patients and from healthy individuals. Capillary reverse phase liquid chromatography (4 μL/min) was interfaced to a TOF mass spectrometer, and data were processed using nonlinear alignment software (XCMS) and an online database (METLIN) to find and identify metabolites differentially regulated in disease. Results: Of the approximately 3500 features measured, propionyl carnitine was easily identified as the best biomarker of disease (P value 1.3 × 10−18), demonstrating the proof-of-concept use of untargeted metabolomics in clinical chemistry discovery. Five additional acylcarnitine metabolites showed significant differentiation between plasma from patients and healthy individuals, and γ-butyrobetaine was highly increased in a subset of patients. Two acylcarnitine metabolites and numerous unidentified species differentiate MMA and PA. Many metabolites that do not appear in any public database, and that remain unidentified, varied significantly between normal, MMA, and PA, underscoring the complex downstream metabolic effects resulting from the defect in a single enzyme. Conclusions: This proof-of-concept study demonstrates that metabolomics can expand the range of metabolites associated with human disease and shows that this method may be useful for disease diagnosis and patient clinical evaluation.

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Zhang, Peixu, Weiguanliu Zhang, Yue Lang, Yan Qu, Fengna Chu, Jiafeng Chen, and Li Cui. "Mass spectrometry-based metabolomics for tuberculosis meningitis." Clinica Chimica Acta 483 (August 2018): 57–63. http://dx.doi.org/10.1016/j.cca.2018.04.022.

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Tang, Yuqing, Zhou Li, Lissy Lazar, Zhiling Fang, Chunlan Tang, and Jinshun Zhao. "Metabolomics workflow for lung cancer: Discovery of biomarkers." Clinica Chimica Acta 495 (August 2019): 436–45. http://dx.doi.org/10.1016/j.cca.2019.05.012.

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Ju, Hyun Kyoung, Geum Mun Nam, Ji-Sook Min, Jae Sung Pyo, and Jae Seon Kang. "Characterization of Carbon Monoxide Fatalities by Metabolomics." Analytical Letters 49, no. 12 (February 18, 2016): 1938–47. http://dx.doi.org/10.1080/00032719.2015.1124110.

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Roback, John D., Cassandra D. Josephson, Edmund K. Waller, James L. Newman, Sulaiman Karatela, Karan Uppal, Dean P. Jones, James C. Zimring, and Larry J. Dumont. "Metabolomics of ADSOL (AS-1) Red Blood Cell Storage." Transfusion Medicine Reviews 28, no. 2 (April 2014): 41–55. http://dx.doi.org/10.1016/j.tmrv.2014.01.003.

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Trifonova, Oxana P., Dmitry L. Maslov, Elena E. Balashova, and Petr G. Lokhov. "Current State and Future Perspectives on Personalized Metabolomics." Metabolites 13, no. 1 (January 1, 2023): 67. http://dx.doi.org/10.3390/metabo13010067.

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Metabolomics is one of the most promising ‘omics’ sciences for the implementation in medicine by developing new diagnostic tests and optimizing drug therapy. Since in metabolomics, the end products of the biochemical processes in an organism are studied, which are under the influence of both genetic and environmental factors, the metabolomics analysis can detect any changes associated with both lifestyle and pathological processes. Almost every case-controlled metabolomics study shows a high diagnostic accuracy. Taking into account that metabolomics processes are already described for most nosologies, there are prerequisites that a high-speed and comprehensive metabolite analysis will replace, in near future, the narrow range of chemical analyses used today, by the medical community. However, despite the promising perspectives of personalized metabolomics, there are currently no FDA-approved metabolomics tests. The well-known problem of complexity of personalized metabolomics data analysis and their interpretation for the end-users, in addition to a traditional need for analytical methods to address the quality control, standardization, and data treatment are reported in the review. Possible ways to solve the problems and change the situation with the introduction of metabolomics tests into clinical practice, are also discussed.

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Stockard, Bradley, Huiyun Wu, Joy D. Guingab, Timothy J. Garrett, Jeffrey Rubnitz, Stanley Pounds, and Jatinder K. Lamba. "Metabolomics Profiling Reveals Markers for Chemosensitivity and Clinical Outcomes in Pediatric AML Patients." Blood 132, Supplement 1 (November 29, 2018): 1536. http://dx.doi.org/10.1182/blood-2018-99-116665.

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Abstract Acute myeloid leukemia (AML) is a clinically challenging disease with high interpatient variability in response to chemotherapy. Despite continuing advances in treatment options, current 5-year survival rates for pediatric AML are suboptimal at ~60%. The heterogeneous nature of AML contributes significantly to the variability in treatment response and survival outcomes. Several known genetic lesions and cytogenetic features contribute to disease progression. However, our understanding of how molecular mechanisms contribute to variation in treatment outcomes is still limited. Previous metabolomics studies have successfully identified significant metabolic alterations in hematological malignancies, but very few metabolomics studies have been conducted for the pediatric AML patient population. In this study, we used global and targeted metabolomics to identify differential metabolite abundance associated with chemosensitivity and treatment outcomes in pediatric AML patients. Serum metabolomics profiles were generated with serum samples obtained at diagnosis from patients treated in the multicenter AML02 study (n=94, NCT00136084). Clinical outcomes tested for association included half-maximal inhibitory concentration (IC50) of cytarabine, minimal residual disease (MRD), relapse free survival (RFS), and overall survival (OS). Global metabolomics profiling was performed using liquid chromatography/mass spectrometry (LC/MS). Targeted metabolomics profiling was generated for a select group of organic acids and acylcarnitines. The organic acid panel included eight metabolites related to the tricarboxylic acid cycle and glycolysis. The acylcarnitine panel featured 20 varieties of acylcarnitines detectable in human serum. Statistical analyses were performed using MetaboAnalyst and various R packages. A total of 3205 features were detected in the global metabolome, with 124 known metabolites and 3081 unknown features. All metabolites were used for association analysis, while annotated metabolites were used in pathway analyses. Association analysis of clinical endpoints vs. metabolome identified 10 known metabolites significantly associated with IC50 values, 17 associated with MRD, 7 associated with RFS, and 7 associated with OS (p<0.05). Targeted metabolomics generated the absolute abundance profile of 8 organic acid metabolites and 20 acylcarnitine metabolites in patient samples. Spearman correlation analysis identified five acylcarnitines significantly correlated with IC50 values. Among the significant metabolites, the most interesting is pantothenic acid, showing higher serum abundance associated with poorer IC50, MRD, and RFS outcomes. Pantothenic acid is an essential component for Coenzyme A synthesis, leading into energy production through the tricarboxylic acid cycle. A previous study has shown a reduced capacity for pantothenic acid uptake in leukemia cells resistant to daunorubicin. Our results suggest a similar relationship for pantothenic acid uptake and cytarabine resistance. Pathway enrichment analysis identified 11 metabolic pathways showing significant association with IC50 values and 12 pathways associated with MRD (FDR<0.05). Some of the most significantly associated pathways included alanine, aspartate and glutamate metabolism, arginine and proline metabolism, and pantothenic acid based CoA biosynthesis. Overall, differences in chemosensitivity and clinical outcomes appear to be most closely related to amino acid synthesis and energy production. This study identifies several metabolites and metabolic pathways significantly associated with chemosensitivity and clinical endpoints in pediatric AML patients. These results help expand on previously conducted AML pilot studies, and metabolomics studies on other cancer types, to further clarify the metabolic differences associated with interpatient variability in chemotherapy response for AML patients. Continued metabolic profiling of AML patient populations can help establish targetable pathways that can be used to improve treatment efficiency for AML. In addition, in vitro functional modeling to validate results of the metabolomics study are currently underway. Disclosures No relevant conflicts of interest to declare.

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Dwight, Trisha, Edward Kim, Talia Novos, and Roderick J. Clifton-Bligh. "Metabolomics in the Diagnosis of Pheochromocytoma and Paraganglioma." Hormone and Metabolic Research 51, no. 07 (July 2019): 443–50. http://dx.doi.org/10.1055/a-0926-3790.

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AbstractMetabolomics refers to the detection and measurement of small molecules (metabolites) within biological systems, and is therefore a powerful tool for identifying dysfunctional cellular physiologies. For pheochromocytomas and paragangliomas (PPGLs), metabolomics has the potential to become a routine addition to histology and genomics for precise diagnostic evaluation. Initial metabolomic studies of ex vivo tumors confirmed, as expected, succinate accumulation in PPGLs associated with pathogenic variants in genes encoding succinate dehydrogenase subunits or their assembly factors (SDHx). Metabolomics has now shown utility in clarifying SDHx variants of uncertain significance, as well as the accurate diagnosis of PPGLs associated with fumarate hydratase (FH), isocitrate dehydrogenase (IDH), malate dehydrogenase (MDH2) and aspartate transaminase (GOT2). The emergence of metabolomics resembles the advent of genetic testing in this field, which began with single-gene discoveries in research laboratories but is now done by standardized massively parallel sequencing (targeted panel/exome/genome testing) in pathology laboratories governed by strict credentialing and governance requirements. In this setting, metabolomics is poised for rapid translation as it can utilize existing infrastructure, namely liquid chromatography-tandem mass spectrometry (LC-MS/MS), for the measurement of catecholamine metabolites. Metabolomics has also proven tractable to in vivo diagnosis of SDH-deficient PPGLs using magnetic resonance spectroscopy (MRS). The future of metabolomics – embedded as a diagnostic tool – will require adoption by pathologists to shepherd development of standardized assays and sample preparation, reference ranges, gold standards, and credentialing.

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Zhang, Xiaoli, Ruiying Diao, Xinyue Zhu, Zesong Li, and Zhiming Cai. "Metabolic characterization of asthenozoospermia using nontargeted seminal plasma metabolomics." Clinica Chimica Acta 450 (October 2015): 254–61. http://dx.doi.org/10.1016/j.cca.2015.09.001.

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Lokhov, Petr G., Oxana P. Trifonova, Dmitry L. Maslov, Steven Lichtenberg, and Elena E. Balashova. "Personal Metabolomics: A Global Challenge." Metabolites 11, no. 11 (October 20, 2021): 715. http://dx.doi.org/10.3390/metabo11110715.

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Today, the introduction of metabolomics, like other omics sciences, into clinical practice as a personal omics test that realizes the perfect analytical capabilities of this science has become an important subject. The assembled data show that the metabolome of biosamples is a collection of highly informative and accurate signatures of virtually all diseases that are widespread in the population. However, we have not seen the emergence of personalized metabolomics in clinical practice. This article analyzes the causes of this problem. The complexity of personal metabolic data analysis and its incompatibility with widely accepted data treatment in metabolomics are shown. As a result, the impossibility of translating metabolic signatures accumulated in databases into a personal test is revealed. Problem-solving strategies that may radically change the situation and realize the analytical capabilities of metabolomics in medical laboratory practice are discussed.

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Liebisch, Gerhard, Christer S. Ejsing, and Kim Ekroos. "Identification and Annotation of Lipid Species in Metabolomics Studies Need Improvement." Clinical Chemistry 61, no. 12 (December 1, 2015): 1542–44. http://dx.doi.org/10.1373/clinchem.2015.244830.

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Kirwan, Jennifer A., Lorraine Brennan, David Broadhurst, Oliver Fiehn, Marta Cascante, Warwick B. Dunn, Michael A. Schmidt, and Vidya Velagapudi. "Preanalytical Processing and Biobanking Procedures of Biological Samples for Metabolomics Research: A White Paper, Community Perspective (for “Precision Medicine and Pharmacometabolomics Task Group”—The Metabolomics Society Initiative)." Clinical Chemistry 64, no. 8 (August 1, 2018): 1158–82. http://dx.doi.org/10.1373/clinchem.2018.287045.

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Abstract BACKGROUND The metabolome of any given biological system contains a diverse range of low molecular weight molecules (metabolites), whose abundances can be affected by the timing and method of sample collection, storage, and handling. Thus, it is necessary to consider the requirements for preanalytical processes and biobanking in metabolomics research. Poor practice can create bias and have deleterious effects on the robustness and reproducibility of acquired data. CONTENT This review presents both current practice and latest evidence on preanalytical processes and biobanking of samples intended for metabolomics measurement of common biofluids and tissues. It highlights areas requiring more validation and research and provides some evidence-based guidelines on best practices. SUMMARY Although many researchers and biobanking personnel are familiar with the necessity of standardizing sample collection procedures at the axiomatic level (e.g., fasting status, time of day, “time to freezer,” sample volume), other less obvious factors can also negatively affect the validity of a study, such as vial size, material and batch, centrifuge speeds, storage temperature, time and conditions, and even environmental changes in the collection room. Any biobank or research study should establish and follow a well-defined and validated protocol for the collection of samples for metabolomics research. This protocol should be fully documented in any resulting study and should involve all stakeholders in its design. The use of samples that have been collected using standardized and validated protocols is a prerequisite to enable robust biological interpretation unhindered by unnecessary preanalytical factors that may complicate data analysis and interpretation.

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Yin, Peiyuan, Andreas Peter, Holger Franken, Xinjie Zhao, Sabine S. Neukamm, Lars Rosenbaum, Marianna Lucio, et al. "Preanalytical Aspects and Sample Quality Assessment in Metabolomics Studies of Human Blood." Clinical Chemistry 59, no. 5 (May 1, 2013): 833–45. http://dx.doi.org/10.1373/clinchem.2012.199257.

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BACKGROUND Metabolomics is a powerful tool that is increasingly used in clinical research. Although excellent sample quality is essential, it can easily be compromised by undetected preanalytical errors. We set out to identify critical preanalytical steps and biomarkers that reflect preanalytical inaccuracies. METHODS We systematically investigated the effects of preanalytical variables (blood collection tubes, hemolysis, temperature and time before further processing, and number of freeze–thaw cycles) on metabolomics studies of clinical blood and plasma samples using a nontargeted LC-MS approach. RESULTS Serum and heparinate blood collection tubes led to chemical noise in the mass spectra. Distinct, significant changes of 64 features in the EDTA-plasma metabolome were detected when blood was exposed to room temperature for 2, 4, 8, and 24 h. The resulting pattern was characterized by increases in hypoxanthine and sphingosine 1-phosphate (800% and 380%, respectively, at 2 h). In contrast, the plasma metabolome was stable for up to 4 h when EDTA blood samples were immediately placed in iced water. Hemolysis also caused numerous changes in the metabolic profile. Unexpectedly, up to 4 freeze–thaw cycles only slightly changed the EDTA-plasma metabolome, but increased the individual variability. CONCLUSIONS Nontargeted metabolomics investigations led to the following recommendations for the preanalytical phase: test the blood collection tubes, avoid hemolysis, place whole blood immediately in ice water, use EDTA plasma, and preferably use nonrefrozen biobank samples. To exclude outliers due to preanalytical errors, inspect the biomarker signal intensities reflecting systematic as well as accidental and preanalytical inaccuracies before processing the bioinformatics data.

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Kamlage, Beate, Sandra González Maldonado, Bianca Bethan, Erik Peter, Oliver Schmitz, Volker Liebenberg, and Philipp Schatz. "Quality Markers Addressing Preanalytical Variations of Blood and Plasma Processing Identified by Broad and Targeted Metabolite Profiling." Clinical Chemistry 60, no. 2 (February 1, 2014): 399–412. http://dx.doi.org/10.1373/clinchem.2013.211979.

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Abstract BACKGROUND Metabolomics is a valuable tool with applications in almost all life science areas. There is an increasing awareness of the essential need for high-quality biospecimens in studies applying omics technologies and biomarker research. Tools to detect effects of both blood and plasma processing are a key for assuring reproducible and credible results. We report on the response of the human plasma metabolome to common preanalytical variations in a comprehensive metabolomics analysis to reveal such high-quality markers. METHODS Human EDTA blood was subjected to preanalytical variations while being processed to plasma: microclotting, prolonged processing times at different temperatures, hemolysis, and contamination with buffy layer. In a second experiment, EDTA plasma was incubated at different temperatures for up to 16 h. Samples were subjected to GC-MS and liquid chromatography–tandem mass spectrometry–based metabolite profiling (MxP™ Broad Profiling) complemented by targeted methods, i.e., sphingoids (as part of MxP™ Lipids), MxP™ Catecholamines, and MxP™ Eicosanoids. RESULTS Short-term storage of blood, hemolysis, and short-term storage of noncooled plasma resulted in statistically significant increases of 4% to 19% and decreases of 8% to 12% of the metabolites. Microclotting, contamination of plasma with buffy layer, and short-term storage of cooled plasma were of less impact on the metabolome (0% to 11% of metabolites increased, 0% to 8% decreased). CONCLUSIONS The response of the human plasma metabolome to preanalytical variation demands implementation of thorough quality assurance and QC measures to obtain reproducible and credible results from metabolomics studies. Metabolites identified as sensitive to preanalytics can be used to control for sample quality.

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Song, Zikuan, Haoyu Wang, Xiaotong Yin, Pengchi Deng, and Wei Jiang. "Application of NMR metabolomics to search for human disease biomarkers in blood." Clinical Chemistry and Laboratory Medicine (CCLM) 57, no. 4 (March 26, 2019): 417–41. http://dx.doi.org/10.1515/cclm-2018-0380.

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Abstract Recently, nuclear magnetic resonance spectroscopy (NMR)-based metabolomics analysis and multivariate statistical techniques have been incorporated into a multidisciplinary approach to profile changes in small molecules associated with the onset and progression of human diseases. The purpose of these efforts is to identify unique metabolite biomarkers in a specific human disease so as to (1) accurately predict and diagnose diseases, including separating distinct disease stages; (2) provide insights into underlying pathways in the pathogenesis and progression of the malady and (3) aid in disease treatment and evaluate the efficacy of drugs. In this review we discuss recent developments in the application of NMR-based metabolomics in searching disease biomarkers in human blood samples in the last 5 years.

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Go, Young-Mi, Dean P. Jones, and Michael Orr. "Integrated Redox Proteomics and Metabolomics to Identify Mechanisms of Cd Toxicity." Free Radical Biology and Medicine 65 (November 2013): S151. http://dx.doi.org/10.1016/j.freeradbiomed.2013.10.777.

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Pouralijan Amiri, Mohammad, Maryam Khoshkam, Reza M. Salek, Reza Madadi, Ghassem Faghanzadeh Ganji, and Ali Ramazani. "Metabolomics in early detection and prognosis of acute coronary syndrome." Clinica Chimica Acta 495 (August 2019): 43–53. http://dx.doi.org/10.1016/j.cca.2019.03.1632.

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Mussap, Michele, Roberto Antonucci, Antonio Noto, and Vassilios Fanos. "The role of metabolomics in neonatal and pediatric laboratory medicine." Clinica Chimica Acta 426 (November 2013): 127–38. http://dx.doi.org/10.1016/j.cca.2013.08.020.

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Huang, Zhisheng, Zhuoru He, Yu Kong, Zhongqiu Liu, and Lingzhi Gong. "Insight into osteoarthritis through integrative analysis of metabolomics and transcriptomics." Clinica Chimica Acta 510 (November 2020): 323–29. http://dx.doi.org/10.1016/j.cca.2020.07.010.

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Cesare Marincola, Flaminia, and Luisa Mannina. "Special Issue on “NMR-Based Metabolomics and Its Applications Volume 2”." Metabolites 10, no. 2 (January 26, 2020): 45. http://dx.doi.org/10.3390/metabo10020045.

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Over the last decade, the number of scientific publications in the metabolomics area has increased exponentially. The literature includes ~29,000 contributions (articles and reviews) during the period of 2009–2019, revealing metabolomics applications in a wide range of fields, including medical, plant, animal, and food sciences (this bibliographic data were retrieved from the SCOPUS database, searching “metabolomics” in keywords). The high applicability of this approach is due to its ability to qualitatively and quantitatively characterize the chemical profile of all the low molecular weight metabolites (metabolome) present in cells, tissues, organs, and biological fluids as end products of the cellular regulatory pathways. Thus, providing a snapshot of the phenotype of a biological system, metabolomics offers useful contributions to a comprehensive insight into the functional status of human, animal, plant, and microbe organisms. The contributions collected in this Special Issue (12 articles, one review and one technical report) report on the recent technical advances and practical applications of NMR spectroscopy to metabolomics analyses.

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Yuan, Wei, and James L. Edwards. "Capillary separations in metabolomics." Bioanalysis 2, no. 5 (May 2010): 953–63. http://dx.doi.org/10.4155/bio.10.40.

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Bujak, Renata, Emilia Daghir, Joanna Rybka, Piotr Koslinski, and Michał Jan Markuszewski. "Metabolomics in urogenital cancer." Bioanalysis 3, no. 8 (April 2011): 913–23. http://dx.doi.org/10.4155/bio.11.43.

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Sakai, Arata, Makoto Suzuki, Takashi Kobayashi, Shin Nishiumi, Kodai Yamanaka, Yuichi Hirata, Takashi Nakagawa, Takeshi Azuma, and Masaru Yoshida. "Pancreatic cancer screening using a multiplatform human serum metabolomics system." Biomarkers in Medicine 10, no. 6 (June 2016): 577–86. http://dx.doi.org/10.2217/bmm-2016-0020.

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Preez, Ilse du, Laneke Luies, and Du Toit Loots. "Metabolomics biomarkers for tuberculosis diagnostics: current status and future objectives." Biomarkers in Medicine 11, no. 2 (February 2017): 179–94. http://dx.doi.org/10.2217/bmm-2016-0287.

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Gulston, Melanie K., Christopher M. Titman, and Julian L. Griffin. "Applications of metabolomics to understanding obesity in mouse and man." Biomarkers in Medicine 1, no. 4 (December 2007): 575–82. http://dx.doi.org/10.2217/17520363.1.4.575.

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Jafari, Ameneh, Amirhesam Babajani, and Mostafa Rezaei-Tavirani. "Multiple Sclerosis Biomarker Discoveries by Proteomics and Metabolomics Approaches." Biomarker Insights 16 (January 2021): 117727192110133. http://dx.doi.org/10.1177/11772719211013352.

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Multiple sclerosis (MS) is an autoimmune inflammatory disorder of the central nervous system (CNS) resulting in demyelination and axonal loss in the brain and spinal cord. The precise pathogenesis and etiology of this complex disease are still a mystery. Despite many studies that have been aimed to identify biomarkers, no protein marker has yet been approved for MS. There is urgently needed for biomarkers, which could clarify pathology, monitor disease progression, response to treatment, and prognosis in MS. Proteomics and metabolomics analysis are powerful tools to identify putative and novel candidate biomarkers. Different human compartments analysis using proteomics, metabolomics, and bioinformatics approaches has generated new information for further clarification of MS pathology, elucidating the mechanisms of the disease, finding new targets, and monitoring treatment response. Overall, omics approaches can develop different therapeutic and diagnostic aspects of complex disorders such as multiple sclerosis, from biomarker discovery to personalized medicine.

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Ulmer, Candice Z., Anthony Maus, Jolaine Hines, and Ravinder Singh. "Challenges in Translating Clinical Metabolomics Data Sets from the Bench to the Bedside." Clinical Chemistry 67, no. 12 (November 24, 2021): 1581–83. http://dx.doi.org/10.1093/clinchem/hvab210.

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Lehmann, Rainer. "Diabetes Subphenotypes and Metabolomics: The Key to Discovering Laboratory Markers for Personalized Medicine?" Clinical Chemistry 59, no. 9 (September 1, 2013): 1294–96. http://dx.doi.org/10.1373/clinchem.2013.207993.

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Wang, Xinyang, Xinshu Zhao, Jinhui Zhao, Tongshu Yang, Fengmin Zhang, and Liyan Liu. "Serum metabolite signatures of epithelial ovarian cancer based on targeted metabolomics." Clinica Chimica Acta 518 (July 2021): 59–69. http://dx.doi.org/10.1016/j.cca.2021.03.012.

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Cuperlovic-Culf, Miroslava, Nabil Belacel, and Adrian Culf. "Integrated analysis of transcriptomics and metabolomics profiles." Expert Opinion on Medical Diagnostics 2, no. 5 (April 29, 2008): 497–509. http://dx.doi.org/10.1517/17530059.2.5.497.

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Guo, Kai, Masha G. Savelieff, Amy E. Rumora, Fadhl M. Alakwaa, Brian C. Callaghan, Junguk Hur, and Eva L. Feldman. "Plasma Metabolomics and Lipidomics Differentiate Obese Individuals by Peripheral Neuropathy Status." Journal of Clinical Endocrinology & Metabolism 107, no. 4 (November 20, 2021): 1091–109. http://dx.doi.org/10.1210/clinem/dgab844.

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Abstract Context Peripheral neuropathy (PN) is a frequent prediabetes and type 2 diabetes (T2D) complication. Multiple clinical studies reveal that obesity and dyslipidemia can also drive PN progression, independent of glycemia, suggesting a complex interplay of specific metabolite and/or lipid species may underlie PN. Objective This work aimed to identify the plasma metabolomics and lipidomics signature that underlies PN in an observational study of a sample of individuals with average class 3 obesity. Methods We performed plasma global metabolomics and targeted lipidomics on obese participants with (n = 44) and without PN (n = 44), matched for glycemic status, vs lean nonneuropathic controls (n = 43). We analyzed data by Wilcoxon, logistic regression, partial least squares–discriminant analysis, and group-lasso to identify differential metabolites and lipids by obesity and PN status. We also conducted subanalysis by prediabetes and T2D status. Results Lean vs obese comparisons, regardless of PN status, identified the most significant differences in gamma-glutamyl and branched-chain amino acid metabolism from metabolomics analysis and triacylglycerols from lipidomics. Stratification by PN status within obese individuals identified differences in polyamine, purine biosynthesis, and benzoate metabolism. Lipidomics found diacylglycerols as the most significant subpathway distinguishing obese individuals by PN status, with additional contributions from phosphatidylcholines, sphingomyelins, ceramides, and dihydroceramides. Stratifying the obese group by glycemic status did not affect discrimination by PN status. Conclusion Obesity may be as strong a PN driver as prediabetes or T2D in a sample of individuals with average class 3 obesity, at least by plasma metabolomics and lipidomics profile. Metabolic and complex lipid pathways can differentiate obese individuals with and without PN, independent of glycemic status.

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Ottosson, Filip, Einar Smith, Widet Gallo, Céline Fernandez, and Olle Melander. "Purine Metabolites and Carnitine Biosynthesis Intermediates Are Biomarkers for Incident Type 2 Diabetes." Journal of Clinical Endocrinology & Metabolism 104, no. 10 (July 24, 2019): 4921–30. http://dx.doi.org/10.1210/jc.2019-00822.

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Abstract Context Metabolomics has the potential to generate biomarkers that can facilitate understanding relevant pathways in the pathophysiology of type 2 diabetes (T2DM). Methods Nontargeted metabolomics was performed, via liquid chromatography–mass spectrometry, in a discovery case-cohort study from the Malmö Preventive Project (MPP), which consisted of 698 metabolically healthy participants, of whom 202 developed T2DM within a follow-up time of 6.3 years. Metabolites that were significantly associated with T2DM were replicated in the population-based Malmö Diet and Cancer–Cardiovascular Cohort (MDC-CC) (N = 3423), of whom 402 participants developed T2DM within a follow-up time of 18.2 years. Results Using nontargeted metabolomics, we observed alterations in nine metabolite classes to be related to incident T2DM, including 11 identified metabolites. N2,N2-dimethylguanosine (DMGU) (OR = 1.94; P = 4.9e-10; 95% CI, 1.57 to 2.39) was the metabolite most strongly associated with an increased risk, and beta-carotene (OR = 0.60; P = 1.8e-4; 95% CI, 0.45 to 0.78) was the metabolite most strongly associated with a decreased risk. Identified T2DM-associated metabolites were replicated in MDC-CC. Four metabolites were significantly associated with incident T2DM in both the MPP and the replication cohort MDC-CC, after adjustments for traditional diabetes risk factors. These included associations between three metabolites, DMGU, 7-methylguanine (7MG), and 3-hydroxytrimethyllysine (HTML), and incident T2DM. Conclusions We used nontargeted metabolomics in two Swedish prospective cohorts comprising >4000 study participants and identified independent, replicable associations between three metabolites, DMGU, 7MG, and HTML, and future risk of T2DM. These findings warrant additional studies to investigate a potential functional connection between these metabolites and the onset of T2DM.

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Salgado-Bustamante, Mariana, Ana K. Rocha-Viggiano, César Rivas-Santiago, Martín Magaña-Aquino, Jesús A. López, and Yamilé López-Hernández. "Metabolomics applied to the discovery of tuberculosis and diabetes mellitus biomarkers." Biomarkers in Medicine 12, no. 9 (September 2018): 1001–13. http://dx.doi.org/10.2217/bmm-2018-0050.

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Kalantari, Shiva, and Mohsen Nafar. "An update of urine and blood metabolomics in chronic kidney disease." Biomarkers in Medicine 13, no. 7 (May 2019): 577–97. http://dx.doi.org/10.2217/bmm-2019-0008.

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Zhu, Tao, Shanqun Li, Jiajia Wang, Chunfang Liu, Lei Gao, Yuzhen Zeng, Ruolin Mao, Bo Cui, Hong Ji, and Zhihong Chen. "Induced sputum metabolomic profiles and oxidative stress are associated with chronic obstructive pulmonary disease (COPD) severity: potential use for predictive, preventive, and personalized medicine." EPMA Journal 11, no. 4 (November 4, 2020): 645–59. http://dx.doi.org/10.1007/s13167-020-00227-w.

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AbstractChronic obstructive pulmonary disease (COPD) is a highly heterogeneous disease, and metabolomics plays a hub role in predictive, preventive, and personalized medicine (PPPM) related to COPD. This study thus aimed to reveal the role of induced sputum metabolomics in predicting COPD severity. In this pilot study, a total of 20 COPD patients were included. The induced sputum metabolites were assayed using a liquid chromatography-mass spectrometry (LC-MS/MS) system. Five oxidative stress products (myeloperoxidase (MPO), superoxide dismutase (SOD), glutathione (GSH), neutrophil elastase (NE), and 8-iso-PGF2α) in induced sputum were measured by ELISA, and the metabolomic profiles were distinguished by principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA). The Kyoto Encyclopedia of Genes and Genomes (KEGG) was used for pathway enrichment analysis, and a significant difference in induced sputum metabolomics was observed between moderate and severe COPD. The KEGG analysis revealed that the glycerophospholipid metabolism pathway was downregulated in severe COPD. Due to the critical role of glycerophospholipid metabolism in oxidative stress, significant negative correlations were discovered between glycerophospholipid metabolites and three oxidative stress products (SOD, MPO, and 8-iso-PGF2α). The diagnostic values of SOD, MPO, and 8-iso-PGF2α in induced sputum were found to exhibit high sensitivities and specificities in the prediction of COPD severity. Collectively, this study provides the first identification of the association between induced sputum metabolomic profiles and COPD severity, indicating the potential value of metabolomics in PPPM for COPD management. The study also reveals the correlation between glycerophospholipid metabolites and oxidative stress products and their value for predicting COPD severity.

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Xie, Shaobing, Hua Zhang, Zhihai Xie, Yongzhen Liu, Kelei Gao, Junyi Zhang, Shumin Xie, Fengjun Wang, Ruohao Fan, and Weihong Jiang. "Identification of Novel Biomarkers for Evaluating Disease Severity in House-Dust-Mite-Induced Allergic Rhinitis by Serum Metabolomics." Disease Markers 2021 (May 19, 2021): 1–12. http://dx.doi.org/10.1155/2021/5558458.

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The aim of this study was to identify differences in serum metabolomics profiles of house-dust-mite (HDM)-induced allergic rhinitis (AR) patients compared to controls and to explore novel biomarkers reflecting disease severity. Serum samples were collected from 29 healthy controls and HDM-induced 72 AR patients, including 30 mild patients (MAR) and 42 moderate to severe AR patients (MSAR). Metabolomics detection was performed, and orthogonal partial least square discriminate analysis was applied to assess the differences between AR patients and controls and for subgroups based on disease severity. These analysis results successfully revealed distinct metabolite signatures which distinguished MAR patients and MSAR patients from controls. MSAR patients also could be discriminated from MAR patients based on their metabolic fingerprints. Most observed metabolite changes were related to glycine, serine, and threonine metabolism, pyrimidine metabolism, sphingolipid metabolism, arginine and proline metabolism, and fatty acid metabolism. Levels of sarcosine, sphingosine-1-phosphate, cytidine, and linoleic acid significantly correlated with the total nasal symptom score and visual analogue scale in AR patients. These results suggest that metabolomics profiling may provide novel insights into the pathophysiological mechanisms of HDM-induced AR and contribute to its evaluation of disease severity.

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Mo, Liang, Bing Wei, Renji Liang, Zhi Yang, Shouzhi Xie, Shengrong Wu, and Yong You. "Exploring potential biomarkers for lung adenocarcinoma using LC-MS/MS metabolomics." Journal of International Medical Research 48, no. 4 (April 2020): 030006051989721. http://dx.doi.org/10.1177/0300060519897215.

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Background The average 5-year survival rate of lung adenocarcinoma patients is only 15% to 17%, which is primarily due to late-stage diagnosis and a lack of specific prognostic evaluations that can recommend effective therapies. Additionally, there is no clinically recognized biomarker that is effective for early-stage diagnosis. Methods Tissue samples from 10 lung adenocarcinoma patients (both tumor and non-tumor tissues) and 10 benign lung tumor samples were collected. The significantly differentially represented metabolites from the three groups were analyzed by liquid chromatography and tandem mass spectrometry. Results Pathway analysis indicated that central carbon metabolism was the top altered pathway in lung adenocarcinoma, while protein digestion and absorption, and central carbon metabolism were the top altered pathways in benign lung tumors. Receiver operating characteristic curve analysis revealed that adenosine 3′-monophosphate, creatine, glycerol, and 14 other differential metabolites were potential sensitive and specific biomarkers for the diagnosis and prognosis of lung adenocarcinoma. Conclusion Our findings suggest that the metabolomics approach may be a useful method to detect potential biomarkers in lung adenocarcinoma patients.

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Gladine, Cécile, Annika I. Ostermann, John W. Newman, and Nils Helge Schebb. "MS-based targeted metabolomics of eicosanoids and other oxylipins: Analytical and inter-individual variabilities." Free Radical Biology and Medicine 144 (November 2019): 72–89. http://dx.doi.org/10.1016/j.freeradbiomed.2019.05.012.

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