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• W2886084326 abstract "Human blood is a self-regenerating lipid-rich biological fluid that is routinely collected in hospital settings. The inventory of lipid molecules found in blood plasma (plasma lipidome) offers insights into individual metabolism and physiology in health and disease. Disturbances in the plasma lipidome also occur in conditions that are not directly linked to lipid metabolism; therefore, plasma lipidomics based on MS is an emerging tool in an array of clinical diagnostics and disease management. However, challenges exist in the translation of such lipidomic data to clinical applications. These relate to the reproducibility, accuracy, and precision of lipid quantitation, study design, sample handling, and data sharing. This position paper emerged from a workshop that initiated a community-led process to elaborate and define a set of generally accepted guidelines for quantitative MS-based lipidomics of blood plasma or serum, with harmonization of data acquired on different instrumentation platforms across independent laboratories as an ultimate goal. We hope that other fields may benefit from and follow such a precedent. Human blood is a self-regenerating lipid-rich biological fluid that is routinely collected in hospital settings. The inventory of lipid molecules found in blood plasma (plasma lipidome) offers insights into individual metabolism and physiology in health and disease. Disturbances in the plasma lipidome also occur in conditions that are not directly linked to lipid metabolism; therefore, plasma lipidomics based on MS is an emerging tool in an array of clinical diagnostics and disease management. However, challenges exist in the translation of such lipidomic data to clinical applications. These relate to the reproducibility, accuracy, and precision of lipid quantitation, study design, sample handling, and data sharing. This position paper emerged from a workshop that initiated a community-led process to elaborate and define a set of generally accepted guidelines for quantitative MS-based lipidomics of blood plasma or serum, with harmonization of data acquired on different instrumentation platforms across independent laboratories as an ultimate goal. We hope that other fields may benefit from and follow such a precedent. Blood plasma is a self-regenerating well-defined biological fluid that can be easily collected with minimal health risk. It is also rich in lipids and related metabolites, and its composition reflects diverse aspects of both metabolism and general human physiology in health and disease. Advances in MS, data processing algorithms and tools, databases, knowledge about lipid diversity, and the availability of a broad palette of high-quality synthetic standards have stimulated efforts toward the systematic quantification of plasma lipids in various clinical contexts. Such advances have also enabled the practical use of large biobanks assembled by generations of clinicians and clinical chemists to correlate lipid composition with the onset and progression of disease. In turn, this has triggered massive efforts toward the discovery of clinically relevant biomarkers (1.Quehenberger O. Dennis E.A. The human plasma lipidome.N. Engl. J. Med. 2011; 365: 1812-1823Crossref PubMed Scopus (234) Google Scholar, 2.Harkewicz R. Dennis E.A. Applications of mass spectrometry to lipids and membranes.Annu. Rev. Biochem. 2011; 80: 301-325Crossref PubMed Scopus (128) Google Scholar, 3.Yang K. Han X. Lipidomics: techniques, applications, and outcomes related to biomedical sciences.Trends Biochem. Sci. 2016; 41: 954-969Abstract Full Text Full Text PDF PubMed Scopus (172) Google Scholar, 4.Holčapek M. Liebisch G. Ekroos K. Lipidomic analysis.Anal. Chem. 2018; 90: 4249-4257Crossref PubMed Scopus (66) Google Scholar, 5.Hyötyläinen T. Ahonen L. Pöhö P. Orešič M. Lipidomics in biomedical research-practical considerations.Biochim. Biophys. Acta. 2017; 1862: 800-803Crossref PubMed Scopus (0) Google Scholar, 6.Pechlaner R. Kiechl S. Mayr M. Potential and caveats of lipidomics for cardiovascular disease.Circulation. 2016; 134: 1651-1654Crossref PubMed Scopus (4) Google Scholar, 7.Wigger L. Cruciani-Guglielmacci C. Nicolas A. Denom J. Fernandez N. Fumeron F. Marques-Vidal P. Ktorza A. Kramer W. Schulte A. et al.Plasma dihydroceramides are diabetes susceptibility biomarker candidates in mice and humans.Cell Reports. 2017; 18: 2269-2279Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar, 8.Hinterwirth H. Stegemann C. Mayr M. Lipidomics: quest for molecular lipid biomarkers in cardiovascular disease.Circ Cardiovasc Genet. 2014; 7: 941-954Crossref PubMed Scopus (46) Google Scholar, 9.Gorden D.L. Myers D.S. Ivanova P.T. Fahy E. Maurya M.R. Gupta S. Min J. Spann N.J. McDonald J.G. Kelly S.L. et al.Biomarkers of NAFLD progression: a lipidomics approach to an epidemic.J. Lipid Res. 2015; 56: 722-736Abstract Full Text Full Text PDF PubMed Scopus (135) Google Scholar, 10.Loomba R. Quehenberger O. Armando A. Dennis E.A. Polyunsaturated fatty acid metabolites as novel lipidomic biomarkers for noninvasive diagnosis of nonalcoholic steatohepatitis.J. Lipid Res. 2015; 56: 185-192Abstract Full Text Full Text PDF PubMed Scopus (85) Google Scholar, 11.Sokolowska M. Chen L-Y. Liu Y. Martinez-Anton A. Logun C. Alsaaty S. Cuento R.A. Cai R. Sun J. Quehenberger O. et al.Dysregulation of lipidomic profile and antiviral immunity in response to hyaluronan in patients with severe asthma.J. Allergy Clin. Immunol. 2017; 139: 1379-1383Abstract Full Text Full Text PDF PubMed Scopus (25) Google Scholar). Although these efforts have produced some promising markers and means of monitoring the severity of disease, the fundamental conclusion was that, despite the diversity of pathophysiological disturbances, the plasma lipidome remains a tightly regulated and precisely defined constellation of lipid molecules. Thus, as for common clinical plasma indexes, the time has come to establish reference concentrations for individual lipids. Studies spearheaded by the LIPID MAPS consortium (12.Quehenberger O. Armando A.M. Brown A.H. Milne S.B. Myers D.S. Merrill A.H. Bandyopadhyay S. Jones K.N. Kelly S. Shaner R.L. et al.Lipidomics reveals a remarkable diversity of lipids in human plasma.J. Lipid Res. 2010; 51: 3299-3305Abstract Full Text Full Text PDF PubMed Scopus (745) Google Scholar) and, more recently, by the National Institute of Standards and Technology (NIST) study group (13.Bowden J.A. Heckert A. Ulmer C.Z. Jones C.M. Koelmel J.P. Abdullah L. Ahonen L. Alnouti Y. Armando A.M. Asara J.M. et al.Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950-Metabolites in Frozen Human Plasma.J. Lipid Res. 2017; 58: 2275-2288Abstract Full Text Full Text PDF PubMed Scopus (150) Google Scholar) have determined consensus values of plasma lipid concentrations in the NIST Standard Reference Material (SRM) 1950 plasma (Fig. 1). Efforts are underway to establish reference values for the concentration of various lipid species for individuals of different gender and ethnicity (14.Begum H. Li B. Shui G. Cazenave-Gassiot A. Soong R. Twee-Hee Ong R. Little P. Teo Y-Y. Wenk M.R. Discovering and validating between-subject variations in plasma lipids in healthy subjects.Sci. Rep. 2016; 6: 19139Crossref PubMed Scopus (34) Google Scholar, 15.Saw W-Y. Tantoso E. Begum H. Zhou L. Zou R. He C. Ling Chan S. Wei-Lin Tan L. Wong L-P. Xu W. et al.Establishing multiple omics baselines for three Southeast Asian populations in the Singapore Integrative Omics Study.Nat. Commun. 2017; 8: 653Crossref PubMed Scopus (12) Google Scholar, 16.Begum H. Torta F. Narayanaswamy P. Mundra P.A. Ji S. Bendt A.K. Saw W-Y. Ying Teo Y. Soong R. Little P.F. et al.Lipidomic profiling of plasma in a healthy Singaporean population to identify ethnic specific differences in lipid levels and associations with disease risk factors.Clin. Mass. Spectrom. 2017; 6: 25-31Crossref Scopus (7) Google Scholar, 17.Sales S. Graessler J. Ciucci S. Al-Atrib R. Vihervaara T. Schuhmann K. Kauhanen D. Sysi-Aho M. Bornstein S.R. Bickle M. et al.Gender, contraceptives and individual metabolic predisposition shape a healthy plasma lipidome.Sci. Rep. 2016; 6: 27710Crossref PubMed Scopus (56) Google Scholar, 18.Maekawa K. Okemoto K. Ishikawa M. Tanaka R. Kumagai Y. Saito Y. Plasma lipidomics of healthy Japanese adults reveals gender- and age-related differences.J. Pharm. Sci. 2017; 106: 2914-2918Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar, 19.Ishikawa M. Maekawa K. Saito K. Senoo Y. Urata M. Murayama M. Tajima Y. Kumagai Y. Saito Y. Plasma and serum lipidomics of healthy white adults shows characteristic profiles by subjects' gender and age.PLoS One. 2014; 9: e91806Crossref PubMed Scopus (93) Google Scholar, 20.Trabado S. Al-Salameh A. Croixmarie V. Masson P. Corruble E. Fève B. Colle R. Ripoll L. Walther B. Boursier-Neyret C. et al.The human plasma-metabolome: Reference values in 800 French healthy volunteers; impact of cholesterol, gender and age.PLoS One. 2017; 12: e0173615Crossref PubMed Scopus (58) Google Scholar). We therefore speculate that the “starting phase” of plasma lipidomics is over. The lipidomics community should now make an effort to deliver concordant concentrations of individual lipids together with broad lipid class coverage, as these analyses are now routinely performed in dozens of laboratories worldwide. Despite the overall success to date, the field faces several challenges (21.Liebisch G. Ekroos K. Hermansson M. Ejsing C.S. Reporting of lipidomics data should be standardized.Biochim. Biophys. Acta. 2017; 1862: 747-751Crossref PubMed Scopus (46) Google Scholar). First, it is difficult to harmonize the published data and make them amenable to independent multidimensional data-mining by interested researchers. It appears that current efforts are filling selected pathophysiological niches, but hardly contribute to the understanding of compositional trends at a systemic level. Second, the quality of lipidomics data and the robustness of methodologies suffice for discovery research, but fall short of the common requirements for potential diagnostic applications (22.Liebisch G. Ejsing C.S. Ekroos K. Identification and annotation of lipid species in metabolomics studies need improvement.Clin. Chem. 2015; 61: 1542-1544Crossref PubMed Scopus (15) Google Scholar). Communication between research and clinical communities remains to be fully developed and there is no system in place to assess and cross-correlate plasma lipidomic profiles obtained by different laboratories in various clinical settings. This leads to an odd (and strategically unacceptable) situation where a rapid increase in the total volume of produced data does not contribute to data refinement (23.Simons K. How can omic science be improved?.Proteomics. 2018; 18: e1800039Crossref PubMed Scopus (12) Google Scholar). This position paper emerges from a workshop held in Singapore in April 2017 on this topic and whose participants committed themselves to communicating their workflows and generally agreed conclusions. The motivation to do so is founded on the belief that the community involved with MS-based lipid analysis should come together to set guidelines generally accepted in the field. To facilitate this process (possibly in an order of priority for applications), it was decided to strictly limit the discussion in this work to the lipidomic analysis of human blood (in particular, blood plasma and/or serum) and to MS as the main measurement technique, rather than other techniques, such as NMR. If successful, other applications would be expected to benefit and follow from such a precedent. Different layers of quality assurance (QA) and quality control (QC) measures are prerequisites to obtain reproducible and quantitatively concordant datasets. Batch-to-batch variations are an inherent characteristic of high-throughput analytics, irrespective of the precise nature of the analysis. This is largely recognized in clinical diagnostics, where performance verification and QC measures, including external QA programs and proficiency testing, are put in place to detect significant deviations. In fact, clinical laboratories are mostly concerned about “between-methods bias”. Data are rarely merged among different laboratory methods unless well-harmonized, and we see no conceptual reason why data concordance could not be reached for the plasma lipidome. Different QA and QC methods have been developed for MS-based metabolomics and lipidomics (5.Hyötyläinen T. Ahonen L. Pöhö P. Orešič M. Lipidomics in biomedical research-practical considerations.Biochim. Biophys. Acta. 2017; 1862: 800-803Crossref PubMed Scopus (0) Google Scholar, 24.Dunn W.B. Broadhurst D. Begley P. Zelena E. Francis-McIntyre S. Anderson N. Brown M. Knowles J.D. Halsall A. Haselden J.N. et al.Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry.Nat. Protoc. 2011; 6: 1060-1083Crossref PubMed Scopus (1162) Google Scholar, 25.Kamleh M.A. Ebbels T.M.D. Spagou K. Masson P. Want E.J. Optimizing the use of quality control samples for signal drift correction in large-scale urine metabolic profiling studies.Anal. Chem. 2012; 84: 2670-2677Crossref PubMed Scopus (94) Google Scholar, 26.Kauhanen D. Sysi-Aho M. Koistinen K.M. Laaksonen R. Sinisalo J. Ekroos K. Development and validation of a high-throughput LC-MS/MS assay for routine measurement of molecular ceramides.Anal. Bioanal. Chem. 2016; 408: 3475-3483Crossref PubMed Scopus (29) Google Scholar, 27.Broadhurst D. Goodacre R. Reinke S.N. Kuligowski J. Wilson I.D. Lewis M.R. Dunn W.B. Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies.Metabolomics. 2018; 14: 72Crossref PubMed Scopus (167) Google Scholar). However, the implementation of QA/QC strategies varies in both fields (23.Simons K. How can omic science be improved?.Proteomics. 2018; 18: e1800039Crossref PubMed Scopus (12) Google Scholar, 28.Dunn W.B. Broadhurst D.I. Edison A. Guillou C. Viant M.R. Bearden D.W. Beger R.D. Quality assurance and quality control processes: summary of a metabolomics community questionnaire.Metabolomics. 2017; 13: 50Crossref Scopus (29) Google Scholar, 29.Bowden J.A. Ulmer C.Z. Jones C.M. Koelmel J.P. Yost R.A. NIST lipidomics workflow questionnaire: an assessment of community-wide methodologies and perspectives.Metabolomics. 2018; 14: 53Crossref PubMed Scopus (9) Google Scholar). Therefore, a community-initiated approach toward generally accepted guidelines for clinical application of plasma lipidomics seems pertinent, with an ultimate goal for harmonizing data acquired on different instrumentation platforms in independent laboratories. We appreciate the challenges involved in achieving this goal. This work mostly considers analyzing the core components of the plasma lipidome, and we understand that for some physiologically important, yet low abundant or unstable, lipids for which no reliable internal standards (ISTDs) or alternative analytical methods are available, this may not be feasible, as is the case for oxidized lipids (30.Tyurina Y.Y. Domingues R.M. Tyurin V.A. Maciel E. Domingues P. Amoscato A.A. Bayir H. Kagan V.E. Characterization of cardiolipins and their oxidation products by LC-MS analysis.Chem. Phys. Lipids. 2014; 179: 3-10Crossref PubMed Scopus (28) Google Scholar, 31.Astarita G. Kendall A.C. Dennis E.A. Nicolaou A. Targeted lipidomic strategies for oxygenated metabolites of polyunsaturated fatty acids.Biochim. Biophys. Acta. 2015; 1851: 456-468Crossref PubMed Scopus (77) Google Scholar, 32.Aoyagi R. Ikeda K. Isobe Y. Arita M. Comprehensive analyses of oxidized phospholipids using a measured MS/MS spectra library.J. Lipid Res. 2017; 58: 2229-2237Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar, 33.Morgan A.H. Hammond V.J. Morgan L. Thomas C.P. Tallman K.A. Garcia-Diaz Y.R. McGuigan C. Serpi M. Porter N.A. Murphy R.C. et al.Quantitative assays for esterified oxylipins generated by immune cells.Nat. Protoc. 2010; 5: 1919-1931Crossref PubMed Scopus (33) Google Scholar). Here, we propose that such laboratory practices could be adopted by a community largely representing research and development in the area of life sciences and also in clinical testing. Therefore, recommendations for potential future adoption are organized into three main categories: pre-analytics, analytics, and post-analytics. This short write-up is not intended to be comprehensive, particularly with respect to the various subcategories addressed here (Fig. 2). Instead, as introduced above, it should serve as a working document for a growing number of subscribers. We define pre-analytics as “all procedures before the actual lipidomic analysis”. This includes study design, specification of the nature and origin of samples, collecting and communicating demographic and clinical data, and how plasma and serum are sampled and stored. Relevant guidelines for bioanalytical method validation and performance include the Food and Drug Administration (FDA) Bioanalytical Method Validation Guidelines (34.Booth B. Arnold M.E. DeSilva B. Amaravadi L. Dudal S. Fluhler E. Gorovits B. Haidar S.H. Kadavil J. Lowes S. et al.Workshop report: Crystal City V–quantitative bioanalytical method validation and implementation: the 2013 revised FDA guidance.AAPS J. 2015; 17: 277-288Crossref PubMed Scopus (79) Google Scholar, 35US Department of Health and Human Services (FDA), Center for Drug Evaluation and Research (CDER), and Center for Veterinary Medicine (CVM) Bioanalytical Method Validation: Guid­ance for Industry.https://www.fda.gov/downloads/Drugs/Guidance/ucm070107.pdf. 2018; Google Scholar), the European Medical Agency (EMA) Bioanalytical Method Validation Guideline on bioanalytical method validation (36.European Medicines Agency (EMA) Guideline on bioanalytical method validation.http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2011/08/WC500109686.pd. 2015; Google Scholar), and the Japanese Ministry of Health, Labour, and Welfare (MHLW) Guideline on Bioanalytical Method Validation in Pharmaceutical Development (37.Ministry of Health, Labour and Welfare (MHLW), Japan Guideline on Bioanalytical Method Validation in Pharmaceutical Development.http://www.nihs.go.jp/drug/BMV/250913_BMV-GL_E.pdf. 2013; Google Scholar). These guidelines were initially tailored for pharmaceutical, pharmacokinetic, and toxicokinetic applications and thus include MS as a methodology. However, many of the criteria and strategies mentioned in these guidelines are also applicable and relevant for the development and validation of lipidomic assays to be used in clinical research (38.Jiang H. Hsu F-F. Farmer M.S. Peterson L.R. Schaffer J.E. Ory D.S. Jiang X. Development and validation of LC-MS/MS method for determination of very long acyl chain (C22:0 and C24:0) ceramides in human plasma.Anal. Bioanal. Chem. 2013; 405: 7357-7365Crossref PubMed Scopus (13) Google Scholar, 39.Welford R.W.D. Garzotti M. Marques Lourenço C. Mengel E. Marquardt T. Reunert J. Amraoui Y. Kolb S.A. Morand O. Groenen P. Plasma lysosphingomyelin demonstrates great potential as a diagnostic biomarker for Niemann-Pick disease type C in a retrospective study.PLoS One. 2014; 9: e114669Crossref PubMed Scopus (55) Google Scholar). Following such guidelines will facilitate the development of clinical applications for plasma lipidomics. Laboratory methods that are developed in-house are considered “laboratory-developed tests”. They need to undergo stringent validation processes as prescribed by certain standards, e.g., the International Standards Organization 15189 (40.International Organization for Standardization (ISO) ISO 15189:2012 Medical laboratories–Requirements for quality and competence.International Organization for Standardization. 2012; Google Scholar) and Clinical Laboratory Improvement Amendments (CLIA) (41.Centers for Medicare and Medicaid Services Clinical Laboratory Improvement Amendments (CLIA).https://www.cms.gov/Regulations-and-Guidance/Legislation/CLIA. 2018; Google Scholar), and subscribe to external QA programs for monitoring of their ongoing performance. These are required as part of accreditation of a routine clinical laboratory by the relevant regulatory authority. The same validation process and subscription to external QA programs are required each time a method is applied in a different laboratory. Looking forward, guidelines and protocols used in clinical diagnostics and clinical chemistry will also be relevant for plasma lipidomic assays, including the International Standards Organization 15189 and CLIA laboratory protocols. These guidelines cover an extensive range of required topics for the accreditation of diagnostics and assays, and include training, QA/QC, administrative processes, infrastructure/facility design and management, human resources, auditing, and system design. However, all of these clinical diagnostic guidelines do not, or only superficially, cover MS-based metabolomics and lipidomics. Furthermore, only a few MS-based methods have been published that were validated according to such guidelines (42.Boulet L. Faure P. Flore P. Montérémal J. Ducros V. Simultaneous determination of tryptophan and 8 metabolites in human plasma by liquid chromatography/tandem mass spectrometry.J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2017; 1054: 36-43Crossref PubMed Scopus (20) Google Scholar, 43.Körver-Keularts I.M.L.W. Wang P. Waterval H.W.A.H. Kluijtmans L.A.J. Wevers R.A. Langhans C-D. Scott C. Habets D.D.J. Bierau J. Fast and accurate quantitative organic acid analysis with LC-QTOF/MS facilitates screening of patients for inborn errors of metabolism.J. Inherit. Metab. Dis. 2018; 41: 415-424Crossref PubMed Scopus (10) Google Scholar, 44.Roche L. Pinguet J. Herviou P. Libert F. Chenaf C. Eschalier A. Authier N. Richard D. Fully automated semi-quantitative toxicological screening in three biological matrices using turbulent flow chromatography/high resolution mass spectrometry.Clin. Chim. Acta. 2016; 455: 46-54Crossref PubMed Scopus (8) Google Scholar, 45.van den Broek I. Romijn F.P.H.T.M. Smit N.P.M. van der Laarse A. Drijfhout J.W. van der Burgt Y.E.M. Cobbaert C.M. Quantifying protein measurands by peptide measurements: Where do errors arise?.J. Proteome Res. 2015; 14: 928-942Crossref PubMed Scopus (32) Google Scholar, 46.Antonelli G. Sciacovelli L. Aita A. Padoan A. Plebani M. Validation model of a laboratory-developed method for the ISO15189 accreditation: the example of salivary cortisol determination.Clin. Chim. Acta. 2018; 485: 224-228Crossref PubMed Scopus (7) Google Scholar, 47.Chen Y. Liu Q. Yong S. Ling Teo H. Kooi Lee T. An improved reference measurement procedure for triglycerides and total glycerides in human serum by isotope dilution gas chromatography-mass spectrometry.Clin. Chim. Acta. 2014; 428: 20-25Crossref PubMed Scopus (10) Google Scholar). Recently, the Clinical Laboratory Standards Institute (CLSI) issued the CLSI C62-A guideline on MS in the clinical laboratory (48.Lynch K.L. CLSI C62-A: a new standard for clinical mass spectrometry.Clin. Chem. 2016; 62: 24-29Crossref PubMed Scopus (55) Google Scholar, 49.Clinical Laboratory Standards Institute C62-A: Liquid chromatography-Mass Spectrometry Methods: Approved Guideline.Clinical and Laboratory Standards Institute. 2014; Google Scholar). Considering such guidelines during assay development may improve the quality and adoptability of lipidomic assays for plasma analysis in clinical settings. In this position paper, we aim to highlight the critical aspects of quantitative lipidomics of human plasma. The primary focus is the application of plasma lipidomics in high-quality clinical research and the identification of biomarkers. The use of plasma lipidomics in clinical diagnostics is a logical extension to that, but is currently still a rather distant scenario. Regardless of the application, current research and development into nucleic acid, protein, and metabolite biomarkers is likely changing clinical research and diagnostic procedures over time and thus will also require new or specific guidelines. Acceptance of new procedures and the willingness to define new guidelines will depend critically on the clinical performance, usefulness, simplicity, and applicability of novel methodologies, as well as on proper communication and documentation. It is therefore up to the respective communities to define standards in line with evolving practice in contemporary and future clinical research and development. The value of a public plasma lipidomic database (e.g., for meta-analyses and the establishment of reference values) is highly dependent on the quality of the data associated with the samples. We encourage the community to put effort into collecting associated biographic and clinical data and to actively participate in the planning and implementation of novel regulations concerning data collection, anonymization, de-identification, and reporting (see also section Data sharing). Collected personal and clinical data intended for future use in publications (together with lipidomic data) should be defined at the time of application for approval by institutional review boards, so that the participants' consent forms state that the information is being collected ethically, thereby allowing full use of the collected data. We suggest that the minimum set of personal and clinical data collected along with plasma/serum samples should be subject age, gender, BMI, ethnicity, fasting status, and prescription medications, including drugs directly affecting lipid metabolism (e.g., nonsteroidal anti-inflammatory drugs, anticoagulants, and statins) and also drugs with insufficiently characterized metabolic impact (i.e., hormones, including contraceptives, steroids, and diuretics) (17.Sales S. Graessler J. Ciucci S. Al-Atrib R. Vihervaara T. Schuhmann K. Kauhanen D. Sysi-Aho M. Bornstein S.R. Bickle M. et al.Gender, contraceptives and individual metabolic predisposition shape a healthy plasma lipidome.Sci. Rep. 2016; 6: 27710Crossref PubMed Scopus (56) Google Scholar, 50.Nakamura H. Kim D.K. Philbin D.M. Peterson M.B. Debros F. Koski G. Bonventre J.V. Heparin-enhanced plasma phospholipase A2 activity and prostacyclin synthesis in patients undergoing cardiac surgery.J. Clin. Invest. 1995; 95: 1062-1070Crossref PubMed Google Scholar, 51.Meikle P.J. Wong G. Tan R. Giral P. Robillard P. Orsoni A. Hounslow N. Magliano D.J. Shaw J.E. Curran J.E. et al.Statin action favors normalization of the plasma lipidome in the atherogenic mixed dyslipidemia of MetS: potential relevance to statin-associated dysglycemia.J. Lipid Res. 2015; 56: 2381-2392Abstract Full Text Full Text PDF PubMed Scopus (26) Google Scholar, 52.Mazaleuskaya L.L. Lawson J.A. Li X. Grant G. Mesaros C. Grosser T. Blair I.A. Ricciotti E. FitzGerald G.A. A broad-spectrum lipidomics screen of antiinflammatory drug combinations in human blood.JCI Insight. 2016; 1: e87031Crossref PubMed Google Scholar). It should also include significant medical conditions (e.g., affected with chronic disease). Recent research suggests that the spectrum of drugs affecting the lipidome composition is broad, and metabolic side-effects, being harmless per se, might bias the outcome of epidemiological studies. We therefore suggest providing detailed data on all given, prescribed, self-medicated drugs and health supplements together with lipidomics datasets and, if applicable, to analyze datasets for potential confounding effects of these medications. Specific populations, such as pediatric cohorts, may require different/additional sets of relevant variables. The submission of additional parameters is strongly encouraged and for adults should include: diabetic/insulin status, HDL/LDL/triacylglycerol (TG) values, blood pressure, full blood count, C-peptide, C-reactive protein, smoking status, alcohol consumption, diet, intake of dietary supplements, type and frequency of exercise, and other information on lifestyle. Recoding of socio-economic indicators can also be of value, as these may offer information about dietary and environmental exposure. This data collection must be within the practice guidelines of local institutional review boards and legislation related to human biomedical research and personal data protection, but also with an outlook toward depositing the data in internationally accessible repositories. The latter generally mandates strict separation of identification keys from the individuals involved in the research. Plasma and serum are two distinct matrices, and the lipid profiles of plasma and serum obtained from the same blood draw differ (53.Yu Z. Kastenmuller G. He Y. Belcredi P. Moller G. Prehn C. Mendes J. Wahl S. Roemisch-Margl W. Ceglarek U. et al.Differences between human plasma and serum metabolite profiles.PLoS One. 2011; 6: e21230Crossref PubMed Scopus (232) Google Scholar, 54.Liu X. Hoene M. Wang X. Yin P. Häring H-U. Xu G. Lehmann R. Serum or plasma, what is the difference? Investigations to facilitate the sample material selection decision making process for metabolomics studies and beyond.Anal. Chim. Acta. 2018; Google Scholar). Serum is obtained from coagulated blood, whereby various compounds, including lipids and lipid-modifying enzymes, are released in extracellular vesicles or in soluble forms from platelets, leukocytes, and erythrocytes during the clotting process. The coagulation process therefore leads to generation or degradation of species in a lipid class-dependent manner. This can strongly affect the abundances of lysophospholipids (lyso-PLs), sphingosine 1-phosphates (S1Ps), prostaglandins, leukotrienes, resolvi" @default.
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• W2886084326 title "MS-based lipidomics of human blood plasma: a community-initiated position paper to develop accepted guidelines" @default.
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