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Intrapersonal Stability of Plasma Metabolomic Profiles over 10 Years among Women. Metabolites 2022; 12:metabo12050372. [PMID: 35629875 PMCID: PMC9147746 DOI: 10.3390/metabo12050372] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 04/01/2022] [Accepted: 04/11/2022] [Indexed: 11/17/2022] Open
Abstract
In epidemiological studies, samples are often collected long before disease onset or outcome assessment. Understanding the long-term stability of biomarkers measured in these samples is crucial. We estimated within-person stability over 10 years of metabolites and metabolite features (n = 5938) in the Nurses’ Health Study (NHS): the primary dataset included 1880 women with 1184 repeated samples donated 10 years apart while the secondary dataset included 1456 women with 488 repeated samples donated 10 years apart. We quantified plasma metabolomics using two liquid chromatography mass spectrometry platforms (lipids and polar metabolites) at the Broad Institute (Cambridge, MA, USA). Intra-class correlations (ICC) were used to estimate long-term (10 years) within-person stability of metabolites and were calculated as the proportion of the total variability (within-person + between-person) attributable to between-person variability. Within-person variability was estimated among participants who donated two blood samples approximately 10 years apart while between-person variability was estimated among all participants. In the primary dataset, the median ICC was 0.43 (1st quartile (Q1): 0.36; 3rd quartile (Q3): 0.50) among known metabolites and 0.41 (Q1: 0.34; Q3: 0.48) among unknown metabolite features. The three most stable metabolites were N6,N6-dimethyllysine (ICC = 0.82), dimethylguanidino valerate (ICC = 0.72), and N-acetylornithine (ICC = 0.72). The three least stable metabolites were palmitoylethanolamide (ICC = 0.05), ectoine (ICC = 0.09), and trimethylamine-N-oxide (ICC = 0.16). Results in the secondary dataset were similar (Spearman correlation = 0.87) to corresponding results in the primary dataset. Within-person stability over 10 years is reasonable for lipid, lipid-related, and polar metabolites, and varies by metabolite class. Additional studies are required to estimate within-person stability over 10 years of other metabolites groups.
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Dybos SA, Brustad ÅW, Rolfseng T, Kvam S, Olsen OE, Halgunset J, Skogseth H. RNA-Integrity and 8-Isoprostane Levels Are Stable in Prostate Tissue Samples Upon Long-Term Storage at -80°C. Biopreserv Biobank 2020; 19:2-10. [PMID: 32865438 PMCID: PMC7892308 DOI: 10.1089/bio.2019.0136] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Sampling of prostate tissue (n = 97) was performed in conjunction with planned radical prostatectomies, in collaboration with Biobank1®. The tissue used in this study was collected during the period 2003-2016, quickly frozen, and kept at -80°C until assayed in 2018. RNA extraction was performed with two different protocols (miRNeasy and mirVana™), and RNA quality was determined by measuring the RNA Integrity Number (RIN). The level of isoprostanes is widely recognized as a specific indicator of lipid peroxidation both in vitro and in vivo. The level of 8-isoprostane was measured because it is the main oxidation product of arachidonic acid, the most abundant phospholipid fatty acid. The level of 8-isoprostane was measured using enzyme immunoassay. There was no statistically significant difference in yield between the samples isolated with the mirVana protocol compared to the miRNeasy protocol. Average RIN was 2.8 units higher with the mirVana extraction protocol compared to the miRNeasy protocol (p < 0.001). For miRNeasy extractions, RINs were 7.1 for prostatectomies in 2005-2007 and 6.2 for those in 2018 (p < 0.001). For mirVana extractions, the difference in RIN score between the two groups regarding years of collection was not statistically significant. There was no significant increase in the levels of 8-isoprostane between the 2005-2007 samples and the 2018. The conclusion is that there is no oxidation of phospholipids with increasing storage time up to 15 years.
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Affiliation(s)
- Sandra Amalie Dybos
- Department of Research and Development, Biobank1, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.,Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Åge Winje Brustad
- Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway
| | - Toril Rolfseng
- Department of Research and Development, Biobank1, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Solveig Kvam
- Department of Research and Development, Biobank1, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Oddrun Elise Olsen
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Hematology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Jostein Halgunset
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Haakon Skogseth
- Department of Research and Development, Biobank1, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.,Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
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3
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Ulmer CZ, Koelmel JP, Jones CM, Garrett TJ, Aristizabal-Henao JJ, Vesper HW, Bowden JA. A Review of Efforts to Improve Lipid Stability during Sample Preparation and Standardization Efforts to Ensure Accuracy in the Reporting of Lipid Measurements. Lipids 2020; 56:3-16. [PMID: 32519378 DOI: 10.1002/lipd.12263] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Revised: 05/03/2020] [Accepted: 05/19/2020] [Indexed: 11/07/2022]
Abstract
Lipidomics is a rapidly growing field, fueled by developments in analytical instrumentation and bioinformatics. To date, most researchers and industries have employed their own lipidomics workflows without a consensus on best practices. Without a community-wide consensus on best practices for the prevention of lipid degradation and transformations through sample collection and analysis, it is difficult to assess the quality of lipidomics data and hence trust results. Clinical studies often rely on samples being stored for weeks or months until they are analyzed, but inappropriate sampling techniques, storage temperatures, and analytical protocols can result in the degradation of complex lipids and the generation of oxidized or hydrolyzed metabolite artifacts. While best practices for lipid stability are sample dependent, it is generally recommended that strategies during sample preparation capable of quenching enzymatic activity and preventing oxidation should be considered. In addition, after sample preparation, lipid extracts should be stored in organic solvents with antioxidants at -20 °C or lower in an airtight container without exposure to light or oxygen. This will reduce or eliminate sublimation, and chemically and physically induced molecular transformations such as oxidation, enzymatic transformation, and photon/heat-induced degradation. This review explores the available literature on lipid stability, with a particular focus on human health and/or clinical lipidomic applications. Specifically, this includes a description of known mechanisms of lipid degradation, strategies, and considerations for lipid storage, as well as current efforts for standardization and quality insurance of protocols.
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Affiliation(s)
- Candice Z Ulmer
- Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, 4770 Buford Hwy NE, MS F25, Atlanta, GA, 30341, USA
| | - Jeremy P Koelmel
- Department of Environmental Health Sciences, Yale School of Medicine, Yale University, 60 College Street, Room 510, New Haven, CT, 06520, USA
| | - Christina M Jones
- Chemical Sciences Division, Organic Chemical Metrology Group, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD, 20899, USA
| | - Timothy J Garrett
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, 32610, USA
| | - Juan J Aristizabal-Henao
- Center for Environmental and Human Toxicology & Department of Physiological Sciences, College of Veterinary Medicine, University of Florida, Gainesville, FL, 32611, USA
| | - Hubert W Vesper
- Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, 4770 Buford Hwy NE, MS F25, Atlanta, GA, 30341, USA
| | - John A Bowden
- Center for Environmental and Human Toxicology & Department of Physiological Sciences, College of Veterinary Medicine, University of Florida, Gainesville, FL, 32611, USA
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4
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Important Considerations for Sample Collection in Metabolomics Studies with a Special Focus on Applications to Liver Functions. Metabolites 2020; 10:metabo10030104. [PMID: 32178364 PMCID: PMC7142637 DOI: 10.3390/metabo10030104] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 03/05/2020] [Accepted: 03/07/2020] [Indexed: 12/16/2022] Open
Abstract
Metabolomics has found numerous applications in the study of liver metabolism in health and disease. Metabolomics studies can be conducted in a variety of biological matrices ranging from easily accessible biofluids such as urine, blood or feces, to organs, tissues or even cells. Sample collection and storage are critical steps for which standard operating procedures must be followed. Inappropriate sample collection or storage can indeed result in high variability, interferences with instrumentation or degradation of metabolites. In this review, we will first highlight important general factors that should be considered when planning sample collection in the study design of metabolomic studies, such as nutritional status and circadian rhythm. Then, we will discuss in more detail the specific procedures that have been described for optimal pre-analytical handling of the most commonly used matrices (urine, blood, feces, tissues and cells).
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Azad RK, Shulaev V. Metabolomics technology and bioinformatics for precision medicine. Brief Bioinform 2019; 20:1957-1971. [PMID: 29304189 PMCID: PMC6954408 DOI: 10.1093/bib/bbx170] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 11/29/2017] [Indexed: 12/14/2022] Open
Abstract
Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.
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Affiliation(s)
| | - Vladimir Shulaev
- Corresponding author: Vladimir Shulaev, Department of Biological Sciences, BioDiscovery Institute, University of North Texas, Denton, TX 76210, USA. Tel.: 940-369-5368; Fax: 940-565-3821; E-mail:
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Kim M, Jang WJ, Shakya R, Choi B, Jeong CH, Lee S. Current Understanding of Methamphetamine-Associated Metabolic Changes Revealed by the Metabolomics Approach. Metabolites 2019; 9:metabo9100195. [PMID: 31547093 PMCID: PMC6835349 DOI: 10.3390/metabo9100195] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 09/17/2019] [Accepted: 09/18/2019] [Indexed: 12/27/2022] Open
Abstract
Metabolomics is a powerful tool used in the description of metabolic system perturbations caused by diseases or abnormal conditions, and it usually involves qualitative and/or quantitative metabolome determination, accompanied by bioinformatics assessment. Methamphetamine is a psychostimulant with serious abuse potential and due to the absence of effective pharmacotherapy and a high recurrence potential, methamphetamine addiction is a grave issue. Moreover, its addiction mechanisms remain unclear, probably due to the lack of experimental models that reflect personal genetic variances and environmental factors determining drug addiction occurrence. The metabolic approach is only recently being used to study the metabolic effects induced by a variety of methamphetamine exposure statuses, in order to investigate metabolic disturbances related to the adverse effects and discover potential methamphetamine addiction biomarkers. To provide a critical overview of methamphetamine-associated metabolic changes revealed in recent years using the metabolomics approach, we discussed methamphetamine toxicity, applications of metabolomics in drug abuse and addiction studies, biological samples used in metabolomics, and previous studies on metabolic alterations in a variety of biological samples—including the brain, hair, serum, plasma, and urine—following methamphetamine exposure in animal studies. Metabolic alterations observed in animal brain and other biological samples after methamphetamine exposure were associated with neuronal and energy metabolism disruptions. This review highlights the significance of further metabolomics studies in the area of methamphetamine addiction research. These findings will contribute to a better understanding of metabolic changes induced by methamphetamine addiction progress and to the design of further studies targeting the discovery of methamphetamine addiction biomarkers and therapeutic targets.
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Affiliation(s)
- Minjeong Kim
- College of Pharmacy, Keimyung University, 1095 Dalgubeoldaero, Dalseo-gu, Daegu 42601, Korea.
| | - Won-Jun Jang
- College of Pharmacy, Keimyung University, 1095 Dalgubeoldaero, Dalseo-gu, Daegu 42601, Korea.
| | - Rupa Shakya
- College of Pharmacy, Keimyung University, 1095 Dalgubeoldaero, Dalseo-gu, Daegu 42601, Korea.
| | - Boyeon Choi
- College of Pharmacy, Keimyung University, 1095 Dalgubeoldaero, Dalseo-gu, Daegu 42601, Korea.
| | - Chul-Ho Jeong
- College of Pharmacy, Keimyung University, 1095 Dalgubeoldaero, Dalseo-gu, Daegu 42601, Korea.
| | - Sooyeun Lee
- College of Pharmacy, Keimyung University, 1095 Dalgubeoldaero, Dalseo-gu, Daegu 42601, Korea.
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Wagner-Golbs A, Neuber S, Kamlage B, Christiansen N, Bethan B, Rennefahrt U, Schatz P, Lind L. Effects of Long-Term Storage at -80 °C on the Human Plasma Metabolome. Metabolites 2019; 9:metabo9050099. [PMID: 31108909 PMCID: PMC6572224 DOI: 10.3390/metabo9050099] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 04/26/2019] [Accepted: 05/14/2019] [Indexed: 12/12/2022] Open
Abstract
High-quality biological samples are required for the favorable outcome of research studies, and valid data sets are crucial for successful biomarker identification. Prolonged storage of biospecimens may have an artificial effect on compound levels. In order to investigate the potential effects of long-term storage on the metabolome, human ethylenediaminetetraacetic acid (EDTA) plasma samples stored for up to 16 years were analyzed by gas and liquid chromatography-tandem mass spectrometry-based metabolomics. Only 2% of 231 tested plasma metabolites were altered in the first seven years of storage. However, upon longer storage periods of up to 16 years and more time differences of few years significantly affected up to 26% of the investigated metabolites when analyzed within subject age groups. Ontology classes that were most affected included complex lipids, fatty acids, energy metabolism molecules, and amino acids. In conclusion, the human plasma metabolome is adequately stable to long-term storage at −80 °C for up to seven years but significant changes occur upon longer storage. However, other biospecimens may display different sensitivities to long-term storage. Therefore, in retrospective studies on EDTA plasma samples, analysis is best performed within the first seven years of storage.
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Affiliation(s)
| | - Sebastian Neuber
- Biocrates Life Sciences AG, Eduard-Bodem-Gasse 8, 6020 Innsbruck, Austria.
| | - Beate Kamlage
- Metanomics Health GmbH, Tegeler Weg 33, 10589 Berlin, Germany.
| | | | - Bianca Bethan
- Metanomics Health GmbH, Tegeler Weg 33, 10589 Berlin, Germany.
| | | | - Philipp Schatz
- Metanomics Health GmbH, Tegeler Weg 33, 10589 Berlin, Germany.
| | - Lars Lind
- Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Dag Hammarskjöldsv 10 B, Uppsala Science Park, 75237 Uppsala, Sweden.
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Kirwan JA, Brennan L, Broadhurst D, Fiehn O, Cascante M, Dunn WB, Schmidt MA, Velagapudi V. 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). Clin Chem 2018; 64:1158-1182. [PMID: 29921725 DOI: 10.1373/clinchem.2018.287045] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 05/01/2018] [Indexed: 12/14/2022]
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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Affiliation(s)
- Jennifer A Kirwan
- Berlin Institute of Health, Berlin, Germany; .,Max Delbrück Center for Molecular Medicine, Berlin-Buch, Germany
| | - Lorraine Brennan
- UCD School of Agriculture and Food Science, Institute of Food and Health, UCD, Dublin, Ireland
| | | | - Oliver Fiehn
- NIH West Coast Metabolomics Center, UC Davis, Davis, CA
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine and IBUB, Universitat de Barcelona, Barcelona and Centro de Investigación Biomédica en Red en Enfermedades Hepáticas y Digestivas (CIBER-EHD), Madrid, Spain
| | - Warwick B Dunn
- School of Biosciences and Phenome Centre Birmingham, University of Birmingham, Birmingham, UK
| | - Michael A Schmidt
- Advanced Pattern Analysis and Countermeasures Group, Research Innovation Center, Colorado State University, Fort Collins, CO.,Sovaris Aerospace, LLC, Boulder, CO
| | - Vidya Velagapudi
- Metabolomics Unit, Institute for Molecular Medicine FIMM, HiLIFE, University of Helsinki, Helsinki, Finland.
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9
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Wood PL, Cebak JE. Lipidomics biomarker studies: Errors, limitations, and the future. Biochem Biophys Res Commun 2018; 504:569-575. [PMID: 29596837 DOI: 10.1016/j.bbrc.2018.03.188] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Accepted: 03/25/2018] [Indexed: 12/16/2022]
Abstract
Lipidomics is an ever-expanding subfield of metabolomics that surveys 3000 to 5000 individual lipids across more than 56 lipid subclasses, including lipid peroxidation products. Unfortunately, there exists a large number of publications with poor quality data obtained with unit mass resolution leading to many lipid misidentifications. This is further complicated by poor scientific oversight with regard to recognition of isobar issues, sample collection, and sample storage issues that inexplicably requires more detailed attention. Inadvertent or intentional obfuscation of relative quantification data represented as absolute quantification is a subtle but profound difference that may readers outside of the field may not realize, therefore, instigating disservice and unnecessary distrust in the scientific community. These issues need to be addressed aggressively as high quality data are essential for the translation of biomarker research to clinical practice.
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Affiliation(s)
- Paul L Wood
- Metabolomics Unit, College of Veterinary Medicine, Lincoln Memorial University, 6965 Cumberland Gap Pkwy, Harrogate, TN 37752, USA.
| | - John E Cebak
- Metabolomics Unit, College of Veterinary Medicine, Lincoln Memorial University, 6965 Cumberland Gap Pkwy, Harrogate, TN 37752, USA; Department of Medicine, DeBusk College of Osteopathic Medicine, Lincoln Memorial University, 6965 Cumberland Gap Pkwy, Harrogate, TN 37752, USA
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10
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Haid M, Muschet C, Wahl S, Römisch-Margl W, Prehn C, Möller G, Adamski J. Long-Term Stability of Human Plasma Metabolites during Storage at -80 °C. J Proteome Res 2017; 17:203-211. [PMID: 29064256 DOI: 10.1021/acs.jproteome.7b00518] [Citation(s) in RCA: 98] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Prolonged storage of biospecimen can lead to artificially altered metabolite concentrations and thus bias data analysis in metabolomics experiments. To elucidate the potential impact of long-term storage on the metabolite profile, a pooled human plasma sample was aliquoted and stored at -80 °C. During a time period of five years, 1012 of the aliquots were measured with the Biocrates AbsoluteIDQ p180 targeted-metabolomics assay at 193 time points. Modeling the concentration courses over time revealed that 55 out of 111 metabolites remained stable. The statistically significantly changed metabolites showed on average an increase or decrease of +13.7% or -14.5%, respectively. In detail, increased concentration levels were observed for amino acids (mean: + 15.4%), the sum of hexoses (+7.9%), butyrylcarnitine (+9.4%), and some phospholipids mostly with chain lengths exceeding 40 carbon atoms (mean: +18.0%). Lipids tended to exhibit decreased concentration levels with the following mean concentration changes: acylcarnitines, -12.1%; lysophosphatidylcholines, -15.1%; diacyl-phosphatidylcholines, -17.0%; acyl-alkyl-phosphatidylcholines, -13.3%; sphingomyelins, -14.8%. We conclude that storage of plasma samples at -80 °C for up to five years can lead to altered concentration levels of amino acids, acylcarnitines, glycerophospholipids, sphingomyelins, and the sum of hexoses. These alterations must be considered when analyzing metabolomics data from long-term epidemiological studies.
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Affiliation(s)
| | | | | | | | | | | | - Jerzy Adamski
- Lehrstuhl für Experimentelle Genetik, Technische Universität München , 85350 Freising-Weihenstephan, Germany.,German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
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11
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Metabolite Profiles in Sepsis: Developing Prognostic Tools Based on the Type of Infection. Crit Care Med 2017; 44:1649-62. [PMID: 27097292 DOI: 10.1097/ccm.0000000000001740] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
OBJECTIVES Currently used biomarkers insufficiently discriminate between patients with systemic inflammatory response syndrome of non-infectious origin and sepsis. The aim of this study was to identify surrogate markers that distinguish between systemic inflammatory response syndrome and sepsis as well as the underlying type of infection by targeted metabolomics. DESIGN Retrospective analysis. SETTINGS Six sites of the Hellenic Sepsis Study Group and at Jena University Hospital. PATIENTS A total of 406 patients were analyzed: 66 fulfilling criteria for diagnosis of systemic inflammatory response syndrome, 100 for community-acquired pneumonia, 112 for urinary tract infection, 83 for intra-abdominal infection and 45 for bloodstream infection. Patients were divided into test cohort (n = 268) and confirmation cohort (n = 138). INTERVENTIONS A total of 186 metabolites were determined by liquid chromatography tandem mass spectrometry. MEASUREMENTS AND MAIN RESULTS Serum concentrations of most acylcarnitines, glycerophospholipids and sphingolipids were altered in sepsis compared to systemic inflammatory response syndrome. A regression model combining the sphingolipid SM C22:3 and the glycerophospholipid lysoPCaC24:0 was discovered for sepsis diagnosis with a sensitivity of 84.1% and specificity of 85.7%. Furthermore, specific metabolites could be used for the discrimination of different types of infection. The glycerophospholipid lysoPCaC26:1 identified patients with community-acquired pneumonia in sepsis or severe sepsis/septic shock. Within severe sepsis/septic shock, patients with bloodstream infection could be discriminated by a decrease of acetylornithine. Changes of metabolites between sepsis and severe sepsis/septic shock also varied according to the underlying type of infection, showing that putrescine, lysoPCaC18:0 and SM C16:1 are associated with unfavorable outcome in community-acquired pneumonia, intra-abdominal infections and bloodstream infections, respectively. CONCLUSIONS Using a metabolomics approach, single metabolites are identified that allow a good, albeit at about 14% false positive rate of sepsis diagnosis. Additionally, metabolites might be also useful for differentiation and prognosis according to the type of underlying infection. However, confirmation of the findings in ongoing studies is mandatory before they can be applied in the development of novel diagnostic tools for the management of sepsis.
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12
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Maga-Nteve C, Vasilopoulou CG, Constantinou C, Margarity M, Klapa MI. Sex-comparative study of mouse cerebellum physiology under adult-onset hypothyroidism: The significance of GC–MS metabolomic data normalization in meta-analysis. J Chromatogr B Analyt Technol Biomed Life Sci 2017; 1041-1042:158-166. [DOI: 10.1016/j.jchromb.2016.12.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Revised: 12/06/2016] [Accepted: 12/09/2016] [Indexed: 01/21/2023]
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14
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Stringer KA, McKay RT, Karnovsky A, Quémerais B, Lacy P. Metabolomics and Its Application to Acute Lung Diseases. Front Immunol 2016; 7:44. [PMID: 26973643 PMCID: PMC4770032 DOI: 10.3389/fimmu.2016.00044] [Citation(s) in RCA: 81] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Accepted: 01/29/2016] [Indexed: 12/27/2022] Open
Abstract
Metabolomics is a rapidly expanding field of systems biology that is gaining significant attention in many areas of biomedical research. Also known as metabonomics, it comprises the analysis of all small molecules or metabolites that are present within an organism or a specific compartment of the body. Metabolite detection and quantification provide a valuable addition to genomics and proteomics and give unique insights into metabolic changes that occur in tangent to alterations in gene and protein activity that are associated with disease. As a novel approach to understanding disease, metabolomics provides a "snapshot" in time of all metabolites present in a biological sample such as whole blood, plasma, serum, urine, and many other specimens that may be obtained from either patients or experimental models. In this article, we review the burgeoning field of metabolomics in its application to acute lung diseases, specifically pneumonia and acute respiratory disease syndrome (ARDS). We also discuss the potential applications of metabolomics for monitoring exposure to aerosolized environmental toxins. Recent reports have suggested that metabolomics analysis using nuclear magnetic resonance (NMR) and mass spectrometry (MS) approaches may provide clinicians with the opportunity to identify new biomarkers that may predict progression to more severe disease, such as sepsis, which kills many patients each year. In addition, metabolomics may provide more detailed phenotyping of patient heterogeneity, which is needed to achieve the goal of precision medicine. However, although several experimental and clinical metabolomics studies have been conducted assessing the application of the science to acute lung diseases, only incremental progress has been made. Specifically, little is known about the metabolic phenotypes of these illnesses. These data are needed to substantiate metabolomics biomarker credentials so that clinicians can employ them for clinical decision-making and investigators can use them to design clinical trials.
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Affiliation(s)
- Kathleen A. Stringer
- Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, MI, USA
| | - Ryan T. McKay
- Department of Chemistry, University of Alberta, Edmonton, AB, Canada
| | - Alla Karnovsky
- Department of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, MI, USA
| | | | - Paige Lacy
- Department of Medicine, University of Alberta, Edmonton, AB, Canada
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