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Quantification of lipoprotein profiles by nuclear magnetic resonance spectroscopy and multivariate data analysis

Authors :
Bekzod Khakimov
Chloie Lam
Age K. Smilde
Søren Balling Engelsen
Hartmut Schäfer
Doris M. Jacobs
Gooitzen Zwanenburg
Violetta Aru
Mads Vendelbo Lind
John P. M. van Duynhoven
Huub C. J. Hoefsloot
Biosystems Data Analysis (SILS, FNWI)
Source :
TrAC : Trends in Analytical Chemistry, 94, 210-219, Aru, V, Lam, C, Khakimov, B, Hoefsloot, H C J, Zwanenburg, G, Lind, M V, Schäfer, H, van Duynhoven, J, Jacobs, D M, Smilde, A K & Engelsen, S B 2017, ' Quantification of lipoprotein profiles by nuclear magnetic resonance spectroscopy and multivariate data analysis ', Trends in Analytical Chemistry, vol. 94, pp. 210-219 . https://doi.org/10.1016/j.trac.2017.07.009, TrAC : Trends in Analytical Chemistry 94 (2017), Trends in Analytical Chemistry, 94, 210-219. Elsevier
Publication Year :
2017

Abstract

Lipoproteins and their subfraction profiles have been associated to diverse diseases including CardioVascular Disease (CVD). There is thus a great demand for measuring and quantifying the lipoproteinprofile in an efficient and accurate manner.Nuclear Magnetic Resonance (NMR) spectroscopy is uniquely able to measure the lipoprotein profile ofa blood sample non-destructively due to its sensitivity to both lipid chemistry and lipid-micellar physics.However, the NMR spectra must be scaled/regressed to a primary method of reference, such as ultracentrifugation,using multivariate regression methods.This review provides an overview of the field and explains the methods at stake.© 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license

Details

Language :
English
ISSN :
01659936
Database :
OpenAIRE
Journal :
TrAC : Trends in Analytical Chemistry, 94, 210-219, Aru, V, Lam, C, Khakimov, B, Hoefsloot, H C J, Zwanenburg, G, Lind, M V, Schäfer, H, van Duynhoven, J, Jacobs, D M, Smilde, A K & Engelsen, S B 2017, ' Quantification of lipoprotein profiles by nuclear magnetic resonance spectroscopy and multivariate data analysis ', Trends in Analytical Chemistry, vol. 94, pp. 210-219 . https://doi.org/10.1016/j.trac.2017.07.009, TrAC : Trends in Analytical Chemistry 94 (2017), Trends in Analytical Chemistry, 94, 210-219. Elsevier
Accession number :
edsair.doi.dedup.....a4d40928d5373630ecfe071b94f7b92b