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• W2000681997 abstract "HomeCirculationVol. 120, No. 17Letter by Ala-Korpela et al Regarding Article, “Lipoprotein Particle Profiles by Nuclear Magnetic Resonance Compared With Standard Lipids and Apolipoproteins in Predicting Incident Cardiovascular Disease in Women” Free AccessLetterPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessLetterPDF/EPUBLetter by Ala-Korpela et al Regarding Article, “Lipoprotein Particle Profiles by Nuclear Magnetic Resonance Compared With Standard Lipids and Apolipoproteins in Predicting Incident Cardiovascular Disease in Women” Mika Ala-Korpela Pasi Soininen Markku J. Savolainen Mika Ala-KorpelaMika Ala-Korpela Computational Medicine Research Group, Institute of Clinical Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland Search for more papers by this author Pasi SoininenPasi Soininen NMR Metabonomics Laboratory, Laboratory of Chemistry, Department of Biosciences, University of Kuopio, Kuopio, Finland Search for more papers by this author Markku J. SavolainenMarkku J. Savolainen Department of Internal Medicine, Clinical Research Center, University of Oulu and Biocenter Oulu, Oulu, Finland Search for more papers by this author Originally published27 Oct 2009https://doi.org/10.1161/CIRCULATIONAHA.109.864124Circulation. 2009;120:e149To the Editor:There has been increasing interest in the applications of proton (1H) nuclear magnetic resonance (NMR) spectroscopy of serum in clinical and epidemiological research.1 The article by Mora et al2 made an important contribution by demonstrating that the standard lipid or apolipoprotein measurements and the 1H NMR measures on the number and size of lipoprotein particles are comparable relative to cardiovascular disease risk prediction. However, the authors did not discuss that 1H NMR can also quantify serum and lipoprotein lipids, as independently demonstrated by Otvos et al3 and by us.4The 1H NMR from lipoprotein particles originate from all the lipid constituents of the particles, the signal position being dependent on the size of the entire particle.1 Thus, the true measure recorded by 1H NMR is the total NMR-visible lipid amount that, via various mathematical and spectral analysis methods, can be converted into information on lipoprotein subclasses. Thus, the spectral analysis is the primary issue in defining the capability of 1H NMR-based lipoprotein analytics.1 Mora et al2 adopted a curve-fitting approach3 to estimate the total NMR-visible lipid amounts and then transformed these data to lipoprotein particle concentrations by approximating the lipoprotein particle diameter and core lipid volume and mass.2,3 We have previously used a similar curve-fitting method4 or various regression modeling approaches1 to isolate the different lipoprotein categories. However, our convention has been to transform the mathematically isolated total NMR-visible lipid amounts to lipoprotein fraction–specific triglyceride or cholesterol concentrations by assuming an averaged lipid composition of the lipoprotein particles at a certain NMR chemical shift range. Nevertheless, the key is to recognize that both lipoprotein lipid and particle concentrations are only different approximations based on the actual 1H NMR measure (ie, the total NMR-visible lipid amount).1H NMR-based lipoprotein lipid quantification has been shown to be analytically good for the main lipoprotein fractions and their major lipid constituents, namely, very-low–density lipoprotein triglycerides, low-density and high-density lipoprotein cholesterol, and serum triglyceride and cholesterol concentrations.1,3,4 These earlier analytical demonstrations that 1H NMR spectroscopy can also be used to quantify standard lipids put the conclusion by Mora et al2 into a new perspective: If the use of standard lipids were recommended, those can indeed be also obtained by a single 1H NMR spectroscopic measurement. Furthermore, we have recently shown that serum triglyceride, cholesterol, and high-density lipoprotein cholesterol enable estimation of apolipoprotein B and A-I concentrations.5 Thus, 1H NMR spectroscopic measurements of standard lipids together with the computational estimation of apolipoprotein B and A-I can thus be an appealing cost-effective alternative for the tedious separate measurements of several lipids and apolipoproteins. In addition, the use of 1H NMR in a metabonomics fashion1 is able to provide quantitative molecular data not only on lipoproteins but on many additional and clinically important metabolites.1 Therefore, it is tempting to envision that 1H NMR of serum might eventually replace the standard lipid measurements with holistic multimetabolic risk phenotyping.DisclosuresNone. References 1 Ala-Korpela M. Critical evaluation of 1H NMR metabonomics of serum as a methodology for disease risk assessment and diagnostics. Clin Chem Lab Med. 2008; 46: 27–42.CrossrefMedlineGoogle Scholar2 Mora S, Otvos JD, Rifai N, Rosenson RS, Buring JE, Ridker PM. Lipoprotein particle profiles by nuclear magnetic resonance compared with standard lipids and apolipoproteins in predicting incident cardiovascular disease in women. Circulation. 2009; 119: 931–939.LinkGoogle Scholar3 Jeyarajah EJ, Cromwell WC, Otvos JD. Lipoprotein particle analysis by nuclear magnetic resonance spectroscopy. Clin Lab Med. 2006; 26: 847–870.CrossrefMedlineGoogle Scholar4 Ala-Korpela M, Korhonen A, Keisala J, Hörkkö S, Korpi P, Ingman LP, Jokisaari J, Savolainen MJ, Kesäniemi YA. 1H NMR-based absolute quantitation of human lipoproteins and their lipid contents directly from plasma. J Lipid Res. 1994; 35: 2292–2304.CrossrefMedlineGoogle Scholar5 Niemi J, Mäkinen VP, Heikkonen J, Tenkanen L, Hiltunen Y, Hannuksela ML, Jauhiainen M, Forsblom C, Taskinen MR, Kesäniemi YA, Savolainen MJ, Kaski K, Groop PH, Kovanen PT, Ala-Korpela M. Estimation of VLDL, IDL, LDL, HDL2, apoA-I and apoB from the Friedewald inputs– apoB and IDL, but not LDL, are associated with mortality in type 1 diabetes. Ann Med. 2009; 41: 451–461.CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited By Li C, Liang C, Chen Z, Di Y, Zheng S, Wei S and Sun Z (2021) Surface modification of calcium carbonate: A review of theories, methods and applications碳酸钙的表面改性: 理论、 方法和应用综述, Journal of Central South University, 10.1007/s11771-021-4795-6, 28:9, (2589-2611), Online publication date: 1-Sep-2021. Keun H (2018) NMR Spectroscopy of Serum and Plasma NMR-based Metabolomics, 10.1039/9781782627937-00085, (85-132) Cao P, Pan H, Xiao T, Zhou T, Guo J and Su Z (2015) Advances in the Study of the Antiatherogenic Function and Novel Therapies for HDL, International Journal of Molecular Sciences, 10.3390/ijms160817245, 16:8, (17245-17272) Soininen P, Kangas A, Würtz P, Suna T and Ala-Korpela M (2015) Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Cardiovascular Epidemiology and Genetics, Circulation: Cardiovascular Genetics, 8:1, (192-206), Online publication date: 1-Feb-2015. Männistö V, Simonen M, Soininen P, Tiainen M, Kangas A, Kaminska D, Venesmaa S, Käkelä P, Kärjä V, Gylling H, Ala-Korpela M and Pihlajamäki J (2014) Lipoprotein subclass metabolism in nonalcoholic steatohepatitis, Journal of Lipid Research, 10.1194/jlr.P054387, 55:12, (2676-2684), Online publication date: 1-Dec-2014. Jelenkovic A, Bogl L, Rose R, Kangas A, Soininen P, Ala-Korpela M, Kaprio J and Silventoinen K (2014) Association between serum fatty acids and lipoprotein subclass profile in healthy young adults: Exploring common genetic and environmental factors, Atherosclerosis, 10.1016/j.atherosclerosis.2013.12.053, 233:2, (394-402), Online publication date: 1-Apr-2014. Erkkilä A, Schwab U, Lehto S, de Mello V, Kangas A, Soininen P, Ala-Korpela M and Uusitupa M (2014) Effect of fatty and lean fish intake on lipoprotein subclasses in subjects with coronary heart disease: A controlled trial, Journal of Clinical Lipidology, 10.1016/j.jacl.2013.09.007, 8:1, (126-133), Online publication date: 1-Jan-2014. Jelenkovic A, Bogl L, Rose R, Kangas A, Soininen P, Ala-Korpela M, Kaprio J and Silventoinen K (2013) Association of height and pubertal timing with lipoprotein subclass profile: Exploring the role of genetic and environmental effects, American Journal of Human Biology, 10.1002/ajhb.22381, 25:4, (465-472), Online publication date: 1-Jul-2013. Wang J, Stančáková A, Soininen P, Kangas A, Paananen J, Kuusisto J, Ala-Korpela M and Laakso M (2012) Lipoprotein subclass profiles in individuals with varying degrees of glucose tolerance: a population-based study of 9399 Finnish men, Journal of Internal Medicine, 10.1111/j.1365-2796.2012.02562.x, 272:6, (562-572), Online publication date: 1-Dec-2012. Tukiainen T, Kettunen J, Kangas A, Lyytikainen L, Soininen P, Sarin A, Tikkanen E, O'Reilly P, Savolainen M, Kaski K, Pouta A, Jula A, Lehtimaki T, Kahonen M, Viikari J, Taskinen M, Jauhiainen M, Eriksson J, Raitakari O, Salomaa V, Jarvelin M, Perola M, Palotie A, Ala-Korpela M and Ripatti S (2011) Detailed metabolic and genetic characterization reveals new associations for 30 known lipid loci, Human Molecular Genetics, 10.1093/hmg/ddr581, 21:6, (1444-1455), Online publication date: 15-Mar-2012. Stančáková A, Paananen J, Soininen P, Kangas A, Bonnycastle L, Morken M, Collins F, Jackson A, Boehnke M, Kuusisto J, Ala-Korpela M and Laakso M (2011) Effects of 34 Risk Loci for Type 2 Diabetes or Hyperglycemia on Lipoprotein Subclasses and Their Composition in 6,580 Nondiabetic Finnish Men, Diabetes, 10.2337/db10-1655, 60:5, (1608-1616), Online publication date: 1-May-2011. Würtz P, Soininen P, Kangas A, Mäkinen V, Groop P, Savolainen M, Juonala M, Viikari J, Kähönen M, Lehtimäki T, Raitakari O and Ala-Korpela M (2011) Characterization of systemic metabolic phenotypes associated with subclinical atherosclerosis, Mol. BioSyst., 10.1039/C0MB00066C, 7:2, (385-393) Inouye M, Kettunen J, Soininen P, Silander K, Ripatti S, Kumpula L, Hämäläinen E, Jousilahti P, Kangas A, Männistö S, Savolainen M, Jula A, Leiviskä J, Palotie A, Salomaa V, Perola M, Ala‐Korpela M and Peltonen L (2010) Metabonomic, transcriptomic, and genomic variation of a population cohort, Molecular Systems Biology, 10.1038/msb.2010.93, 6:1, (441), Online publication date: 1-Jan-2010. October 27, 2009Vol 120, Issue 17 Advertisement Article InformationMetrics https://doi.org/10.1161/CIRCULATIONAHA.109.864124PMID: 19858422 Originally publishedOctober 27, 2009 PDF download Advertisement SubjectsEpidemiologyMetabolism" @default.
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