Potential and Caveats of Lipidomics for Cardiovascular Disease

HomeCirculationVol. 134, No. 21Potential and Caveats of Lipidomics for Cardiovascular Disease Free AccessEditorialPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessEditorialPDF/EPUBPotential and Caveats of Lipidomics for Cardiovascular Disease Raimund Pechlaner, MD, PhD, Stefan Kiechl, MD and Manuel Mayr, MD, PhD Raimund PechlanerRaimund Pechlaner From Department of Neurology, Medical University of Innsbruck, Austria (R.P., S.K.); and King's British Heart Foundation Centre, King's College London, United Kingdom (M.M.). , Stefan KiechlStefan Kiechl From Department of Neurology, Medical University of Innsbruck, Austria (R.P., S.K.); and King's British Heart Foundation Centre, King's College London, United Kingdom (M.M.). and Manuel MayrManuel Mayr From Department of Neurology, Medical University of Innsbruck, Austria (R.P., S.K.); and King's British Heart Foundation Centre, King's College London, United Kingdom (M.M.). Originally published18 Oct 2016https://doi.org/10.1161/CIRCULATIONAHA.116.025092Circulation. 2016;134:1651–1654Other version(s) of this articleYou are viewing the most recent version of this article. Previous versions: November 22, 2016: Previous Version 1 Articles, see p 1629 and p 1637Since the seminal publications from the Framingham study in the mid-60s,1 the measurement of lipid levels, mainly of total cholesterol, total triglycerides, and low-density lipoprotein and high-density lipoprotein cholesterol, is routine clinical practice for cardiovascular disease (CVD) and lipid-lowering therapy. A more detailed assessment of the lipid composition, that is, the molecular species that constitute the lipid classes, is not widely used, mainly because of the caveats of assessing the lipidome. The human lipidome is estimated to include thousands of molecular lipid species with functional diversity. The molecular lipid species within a lipid class share a modular composition with fatty acids being attached to a common backbone. Although a characteristic head group within the backbone defines the lipid class, the diversity of molecular lipid species derives from the conjugated fatty acids.2 The conjugated fatty acids can differ in their carbon chain length, the number, position, and configuration (cis or trans) of their double bonds, and the position and type (acyl, alkyl, or vinyl) of linkage to the backbone.Lipidomics refers to the comprehensive profiling of lipids, facilitated by recent advances in mass spectrometry (MS).2 The identified molecular lipid species are designated by abbreviations that combine the description of the lipid class with the characteristics of the conjugated fatty acids.3 For example, a diglyceride (DG) with 2 fatty acids of 12 and 22 carbon atoms and 0 and 5 double bonds, respectively, both linked by an acyl linkage, can be described either as DG(12:0/22:5), or as DG(34:5). Most MS studies report the numbers of carbon atoms and double bonds either for the individual fatty acids or in total, but do not assign the precise positions of the fatty acids at the backbone or of the double bonds within the individual fatty acids.Sample extraction is key for lipidomic analysis. Traditionally, a 2-phase method has been used. The most popular one is the Folch extraction method with chloroform/methanol.4 Methanol precipitates the proteins, whereas chloroform ensures effective extraction of a broad range of lipid classes from the precipitated lipoproteins. The 2-phase extraction, however, is laborious and not very amenable to automation. Single-phase methods are less time demanding, but concerns have been raised that the lipid extraction might be less uniform and less efficient.5Liquid chromatography is applicable to a broad range of lipid classes and, thus, the principal separation method used in lipidomics. Chromatographic separation of the lipid extract enhances sensitivity and specificity by reducing sample complexity, but can also add technical variability.6 Before MS analysis, the lipids have to be ionized by electrospray ionization. Then, the mass-to-charge ratio of the lipid species can be determined by MS. Next, fragmentation is commonly induced by tandem MS to enable the structural elucidation of the lipid species. For quantitation, stable isotope-labeled standards are spiked into the samples before lipid extraction to account for technical variation. The addition of authentic standards (targeted MS analysis) enhances accuracy of analyte identification and quantitation in comparison with untargeted MS analysis without authentic standards.7 Because of economic considerations and lack of availability, a limited set of standards is commonly used, and relative quantitation is achieved by linear extrapolation through response factors.6This issue of Circulation features 2 studies that break new ground in relating lipid species to cardiometabolic outcomes.Alshehry and coworkers8 investigated 310 lipid species within 22 lipid classes in almost 4000 subjects with diabetes mellitus and found a signature of 42 lipid species associated with incident CVD, cardiovascular death, or both. The lipid signature determined by using targeted liquid chromatography-electrospray ionization-tandem MS was dominated by glycerophospholipids and sphingolipids. Addition of select lipid species to conventional risk factors entailed moderate improvements in CVD risk prediction, and replication in independent patient samples was in part successful. Lipids included in the cardiovascular events model were: phosphatidylcholine (PC)(O-36:1), cholesteryl ester (CE)(18:0), phosphatidylethanolamine(O-36:4), PC(28:0), lyso-PC(20:0), PC(35:4), lyso-PC(18:2). Lipids included in the cardiovascular death model were: PC(O-36:1), DG(16:0/22:5), sphingomyelin(34:1), PC(O-36:5). Alshehry and colleagues integrated their findings for PCs and phosphatidylethanolamines with underlying enzymatic pathways.8 Only by considering lipid species in the context of their metabolism can new mechanistic knowledge be generated.Syme and coworkers,9 using targeted liquid chromatograph-electrospray ionization-MS, report the first lipidomic study conducted in adolescents. In almost 1000 participants, they measured 69 PCs, ie, members of the glycerophospholipid class, of which 21 were associated with at least 1 CVD risk factor out of visceral adiposity, blood pressure, insulin resistance, and atherogenic dyslipidemia. PC(16:0/2:0), a platelet-activating factor possessing a 2-carbon acetyl at its sn-2 position, and PC(14:1/0:0), a lysophosphatidylcholine possessing only 1 fatty acid of 14 carbon atoms, showed the strongest inverse and direct associations with CVD risk factors, respectively. The findings by Syme and colleagues point toward the detrimental effects of CVD risk factors on biologically active molecular lipid species, such as PC(16:0/2:0),9 that may impact platelet activation.At first glance, the difference in the final selection of molecular lipid species is apparent, also with regard to previous lipidomics studies on cardiometabolic disease.10–15 This is not unexpected given the sources of variability in lipidomics measurements (Figure). However, there are also consistencies.Download figureDownload PowerPointFigure. Lipidomics for CVD. Summary of the potential for clinical translation and the current caveats of lipid profiling. Sources of variability include the clinical characteristics, such as medication (heparin administration and lipid-lowering therapy), preanalytical variation (blood sampling and storage), and the different methods for lipid extraction, MS measurements, and statistical analysis of the multidimensional, highly correlated lipid data, as well. Standardization will be required for any future clinical application of lipidomics profiling for CVD. CVD indicates cardiovascular disease; MS, mass spectrometry; and T2DM, type 2 diabetes mellitus.A number of studies targeting CVD and diabetes mellitus unraveled associations with cholesteryl esters (CEs),13,15 predominantly of species with shorter-chain and more saturated fatty acids, such as CE(16:0) and CE(16:1). The relevance of CE is expected to decline with statin therapy. About 40% of the diabetic patients evaluated by Alshehry et al8 received lipid-lowering medication. Yet, CE(16:0) still emerged as significantly associated with incident CVD, and CE(18:0) was in their final selection.Triacylglycerols (TGs) were part of adverse lipid signatures identified previously.12–15 Alshehry et al8 detected a signal of protection for TG(56:6), but no TG species were in their final selection. Potential reasons for this discrepancy are the limited variability of TG levels in diabetic dyslipidemia, the use of lipid-lowering medication including fibrates, lifestyle-modifying strategies including diet, and the single-phase extraction procedure applied in this and another study, which failed to obtain an association for TG.8,12 Single-phase extraction methods may result in less efficient extraction of TG.5Lyso-PCs relate to the risk of CVD, diabetes mellitus, and other metabolic abnormalities in most studies available so far, including those published in the current issue of Circulation,7,8 but the selected molecular species and their directionality differ. It remains to be defined whether this reflects biological variation as suggested8,9 or arises from methodological differences in the measurements.The perhaps most consistent finding in the published lipidomic studies so far is a general shift in fatty acid chain length across multiple lipid classes in men and women. Species with shorter or more saturated fatty acid substituents confer risk, whereas species possessing longer or polyunsaturated fatty acids offer protection.8,13 This is corroborated by a large metabolic profiling study using nuclear magnetic resonance spectroscopy, an entirely different technological platform from MS: plasma monounsaturated fatty acids (which are generated from saturated fatty acids by the action of stearoyl coenzyme A desaturase-1), were again directly associated with incident CVD, whereas polyunsaturated fatty acids showed an inverse association.16 This is in line with previous research on dietary fatty acids and CVD.17 It also implicates a role for hepatic de novo lipogenesis, the endogenous synthesis of fatty acids from glucose and fructose, which is influenced by lifestyle and diet. Hepatic de novo lipogenesis produces mostly saturated fatty acids with 16 to 18 carbon atoms and, at most, a single double bond. Therapeutic inhibitors of de novo lipogenesis have been developed and studied in obesity.A necessary next step toward clinical application of lipidomics is the standardization of the measurement methods. Lipidomics has potential in the context of cardiometabolic disease, but requires methodological standardization and better interlaboratory reproducibility before its power can be harnessed toward clinical application. Another important consideration is that most plasma lipids are associated with apolipoproteins, and thus associations of lipid species with CVD may partly reflect associations of apolipoproteins with CVD. Apart from apolipoprotein A1 and apolipoprotein B, other apolipoproteins have not been extensively explored in the context of lipidomics measurements to date. It will be interesting to see which of the 2 postgenomic technologies, lipidomics or proteomics, will deliver new biomarkers with clinical utility for CVD.18,19Sources of FundingProf Mayr is a Senior Research Fellow of the British Heart Foundation (FS/13/2/29892). This work was supported by the National Institute for Health Research Biomedical Research Center based at Guy's and St Thomas' National Health Service Foundation Trust and King's College London in partnership with King's College Hospital and an excellence initiative (Competence Centers for Excellent Technologies, COMET) of the Austrian Research Promotion Agency FFG: "Research Center of Excellence in Vascular Ageing, Tyrol, VASCage" (K-Project number 843536).DisclosuresThe authors have filed patents on biomarkers for cardiovascular disease, including molecular lipids.FootnotesThe opinions in this article are not necessarily those of the editors or of the American Heart Association.Circulation is available at http://circ.ahajournals.org.Correspondence to: Manuel Mayr, MD, PhD, King's British Heart Foundation Centre, King's College London, 125 Coldharbour Lane, London SE5 NU, United Kingdom. E-mail [email protected]References1. Kannel WB, Dawber TR, Friedman GD, Glennon WE, McNamara PM. Risk factors in coronary heart disease. An evaluation of several serum lipids as predictors of coronary heart disease; The Framingham Study.Ann Intern Med. 1964; 61:888–899.CrossrefMedlineGoogle Scholar2. Hinterwirth H, Stegemann C, Mayr M. Lipidomics: quest for molecular lipid biomarkers in cardiovascular disease.Circ Cardiovasc Genet. 2014; 7:941–954. doi: 10.1161/CIRCGENETICS.114.000550.LinkGoogle Scholar3. Fahy E, Cotter D, Sud M, Subramaniam S. Lipid classification, structures and tools.Biochim Biophys Acta. 2011; 1811:637–647. doi: 10.1016/j.bbalip.2011.06.009.CrossrefMedlineGoogle Scholar4. Folch J, Lees M, Sloane Stanley GH. A simple method for the isolation and purification of total lipides from animal tissues.J Biol Chem. 1957; 226:497–509.CrossrefMedlineGoogle Scholar5. Liu Z, Rochfort S, Cocks BG. Optimization of a single phase method for lipid extraction from milk.J Chromatogr A. 2016; 1458:145–149. doi: 10.1016/j.chroma.2016.06.055.CrossrefMedlineGoogle Scholar6. Xu H, Valenzuela N, Fai S, Figeys D, Bennett SA. Targeted lipidomics - advances in profiling lysophosphocholine and platelet-activating factor second messengers.FEBS J. 2013; 280:5652–5667. doi: 10.1111/febs.12423.CrossrefMedlineGoogle Scholar7. Baig F, Pechlaner R, Mayr M. Caveats of untargeted metabolomics for biomarker discovery.J Am Coll Cardiol. 2016; 68:1294–1296. doi: 10.1016/j.jacc.2016.05.098.CrossrefMedlineGoogle Scholar8. Alshehry ZH, Mundra PA, Barlow CK, Mellett NA, Wong G, McConville MJ, Simes J, Tonkin AM, Sullivan DR, Barnes EH, Nestel PJ, Kingwell BA, Marre M, Neal B, Poulter NR, Rodgers A, Williams B, Zoungas S, Hillis GS, Chalmers J, Woodward M, Meikle PJ. Plasma lipidomic profiles improve on traditional risk factors for the prediction of cardiovascular events in type 2 diabetes mellitus.Circulation. 2016; 134:1637–1650. doi: 10.1161/CIRCULATIONAHA.116.023233.LinkGoogle Scholar9. Syme C, Czajkowski S, Shin J, Abrahamowicz M, Leonard G, Perron M, Richer L, Veillette S, Gaudet D, Strug L, Wang Y, Xu H, Taylor G, Paus T, Bennett S, Pausova . Glycerophosphocholine metabolites and cardiovascular disease risk factors in adolescents: a cohort study.Circulation. 2016; 134:1629–1636. doi: 10.1161/CIRCULATIONAHA.116.022993.LinkGoogle Scholar10. Meikle PJ, Wong G, Tsorotes D, Barlow CK, Weir JM, Christopher MJ, MacIntosh GL, Goudey B, Stern L, Kowalczyk A, Haviv I, White AJ, Dart AM, Duffy SJ, Jennings GL, Kingwell BA. Plasma lipidomic analysis of stable and unstable coronary artery disease.Arterioscler Thromb Vasc Biol. 2011; 31:2723–2732. doi: 10.1161/ATVBAHA.111.234096.LinkGoogle Scholar11. Ganna A, Salihovic S, Sundström J, Broeckling CD, Hedman AK, Magnusson PK, Pedersen NL, Larsson A, Siegbahn A, Zilmer M, Prenni J, Arnlöv J, Lind L, Fall T, Ingelsson E. Large-scale metabolomic profiling identifies novel biomarkers for incident coronary heart disease.PLoS Genet. 2014; 10:e1004801. doi: 10.1371/journal.pgen.1004801.CrossrefMedlineGoogle Scholar12. Fernandez C, Sandin M, Sampaio JL, Almgren P, Narkiewicz K, Hoffmann M, Hedner T, Wahlstrand B, Simons K, Shevchenko A, James P, Melander O. Plasma lipid composition and risk of developing cardiovascular disease.PLoS One. 2013; 8:e71846. doi: 10.1371/journal.pone.0071846.CrossrefMedlineGoogle Scholar13. Stegemann C, Pechlaner R, Willeit P, Langley SR, Mangino M, Mayr U, Menni C, Moayyeri A, Santer P, Rungger G, Spector TD, Willeit J, Kiechl S, Mayr M. Lipidomics profiling and risk of cardiovascular disease in the prospective population-based Bruneck study.Circulation. 2014; 129:1821–1831. doi: 10.1161/CIRCULATIONAHA.113.002500.LinkGoogle Scholar14. Rhee EP, Cheng S, Larson MG, Walford GA, Lewis GD, McCabe E, Yang E, Farrell L, Fox CS, O'Donnell CJ, Carr SA, Vasan RS, Florez JC, Clish CB, Wang TJ, Gerszten RE. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans.J Clin Invest. 2011; 121:1402–1411. doi: 10.1172/JCI44442.CrossrefMedlineGoogle Scholar15. Meikle PJ, Wong G, Barlow CK, Weir JM, Greeve MA, MacIntosh GL, Almasy L, Comuzzie AG, Mahaney MC, Kowalczyk A, Haviv I, Grantham N, Magliano DJ, Jowett JB, Zimmet P, Curran JE, Blangero J, Shaw J. Plasma lipid profiling shows similar associations with prediabetes and type 2 diabetes.PLoS One. 2013; 8:e74341. doi: 10.1371/journal.pone.0074341.CrossrefMedlineGoogle Scholar16. Würtz P, Havulinna AS, Soininen P, Tynkkynen T, Prieto-Merino D, Tillin T, Ghorbani A, Artati A, Wang Q, Tiainen M, Kangas AJ, Kettunen J, Kaikkonen J, Mikkilä V, Jula A, Kähönen M, Lehtimäki T, Lawlor DA, Gaunt TR, Hughes AD, Sattar N, Illig T, Adamski J, Wang TJ, Perola M, Ripatti S, Vasan RS, Raitakari OT, Gerszten RE, Casas JP, Chaturvedi N, Ala-Korpela M, Salomaa V. Metabolite profiling and cardiovascular event risk: a prospective study of 3 population-based cohorts.Circulation. 2015; 131:774–785. doi: 10.1161/CIRCULATIONAHA.114.013116.LinkGoogle Scholar17. Siri-Tarino PW, Chiu S, Bergeron N, Krauss RM. Saturated fats versus polyunsaturated fats versus carbohydrates for cardiovascular disease prevention and treatment.Annu Rev Nutr. 2015; 35:517–543. doi: 10.1146/annurev-nutr-071714-034449.CrossrefMedlineGoogle Scholar18. Mayr M, Zampetaki A, Willeit P, Willeit J, Kiechl S. MicroRNAs within the continuum of postgenomics biomarker discovery.Arterioscler Thromb Vasc Biol. 2013; 33:206–214. doi: 10.1161/ATVBAHA.112.300141.LinkGoogle Scholar19. Lindsey ML, Mayr M, Gomes AV, Delles C, Arrell DK, Murphy AM, Lange RA, Costello CE, Jin YF, Laskowitz DT, Sam F, Terzic A, Van Eyk J, Srinivas PR; American Heart Association Council on Functional Genomics and Translational Biology, Council on Cardiovascular Disease in the Young, Council on Clinical Cardiology, Council on Cardiovascular and Stroke Nursing, Council on Hypertension, and Stroke Council. Transformative impact of proteomics on cardiovascular health and disease: a scientific statement from the American Heart Association.Circulation. 2015; 132:852–872. doi: 10.1161/CIR.0000000000000226.LinkGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited By Wang T, Sun L, Xu L, Zhao T, Feng J, Yu L, Wu J and Li H (2021) Prevalence of dyslipidemia and gene polymorphisms of ABCB1 and SLCO1B1 in Han, Uygur, Kazak, Hui, Tatar, Kirgiz, and Sibe populations with coronary heart disease in Xinjiang, China, Lipids in Health and Disease, 10.1186/s12944-021-01544-3, 20:1, Online publication date: 1-Dec-2021. Harshfield E, Koulman A, Ziemek D, Marney L, Fauman E, Paul D, Stacey D, Rasheed A, Lee J, Shah N, Jabeen S, Imran A, Abbas S, Hina Z, Qamar N, Mallick N, Yaqoob Z, Saghir T, Rizvi S, Memon A, Rasheed S, Memon F, Qureshi I, Ishaq M, Frossard P, Danesh J, Saleheen D, Butterworth A, Wood A and Griffin J (2019) An Unbiased Lipid Phenotyping Approach To Study the Genetic Determinants of Lipids and Their Association with Coronary Heart Disease Risk Factors, Journal of Proteome Research, 10.1021/acs.jproteome.8b00786, 18:6, (2397-2410), Online publication date: 7-Jun-2019. Burla B, Arita M, Arita M, Bendt A, Cazenave-Gassiot A, Dennis E, Ekroos K, Han X, Ikeda K, Liebisch G, Lin M, Loh T, Meikle P, Orešič M, Quehenberger O, Shevchenko A, Torta F, Wakelam M, Wheelock C and Wenk M (2018) MS-based lipidomics of human blood plasma: a community-initiated position paper to develop accepted guidelines, Journal of Lipid Research, 10.1194/jlr.S087163, 59:10, (2001-2017), Online publication date: 1-Oct-2018. Hu T and Zhang J (2017) Mass-spectrometry-based lipidomics, Journal of Separation Science, 10.1002/jssc.201700709, 41:1, (351-372), Online publication date: 1-Jan-2018. Kaabia Z, Poirier J, Moughaizel M, Aguesse A, Billon-Crossouard S, Fall F, Durand M, Dagher E, Krempf M and Croyal M (2018) Plasma lipidomic analysis reveals strong similarities between lipid fingerprints in human, hamster and mouse compared to other animal species, Scientific Reports, 10.1038/s41598-018-34329-3, 8:1, Online publication date: 1-Dec-2018. Alesi S, Ghelani D, Rassie K and Mousa A (2021) Metabolomic Biomarkers in Gestational Diabetes Mellitus: A Review of the Evidence, International Journal of Molecular Sciences, 10.3390/ijms22115512, 22:11, (5512) November 22, 2016Vol 134, Issue 21 Advertisement Article InformationMetrics © 2016 American Heart Association, Inc.https://doi.org/10.1161/CIRCULATIONAHA.116.025092PMID: 27756782 Originally publishedOctober 18, 2016 Keywordslipidsatherosclerosiscoronary artery diseasebiomarkersmass spectrometryPDF download Advertisement