NMR and interval PLS as reliable methods for determination of cholesterol in rodent lipoprotein fractions

Publication: Research - peer-reviewJournal article – Annual report year: 2010

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Risk of cardiovascular disease is related to cholesterol distribution in different lipoprotein fractions. Lipoproteins in rodent model studies can only reliably be measured by time- and plasma-consuming fractionation. An alternative method to measure cholesterol distribution in the lipoprotein fractions in rat plasma is presented in this paper. Plasma from two rat studies (n = 68) was used in determining the lipoprotein profile by an established ultracentrifugation method and proton nuclear magnetic resonance (NMR) spectra of replicate samples was obtained. From the ultracentrifugation reference data and the NMR spectra, an interval partial least-square (iPLS) regression model to predict the amount of cholesterol in the different lipoprotein fractions was developed. The relative errors of the prediction models were between 12 and 33% and had correlation coefficients (r) between 0.96 and 0.84. The models were tested with an independent test set giving prediction errors between 19 and 46% and r between 0.96 and 0.76. Prediction of High, Low and Very Low Density Lipoprotein (HDL, LDL and VLDL) and total cholesterol was conducted in a study where rats had been supplemented with two doses of air-dried apple-powder. No significant difference in LDL, VLDL and total cholesterol was observed between the groups. The high apple-powder (20%) group had significantly lower HDL cholesterol (11%, P = 0.0452) than the control group. It is concluded that the iPLS approach yielded excellent regression models and thus univocal established chemometric analysis of NMR spectra of rat plasma as a strong and efficient way to quantify lipoprotein fractions in rat studies.
Original languageEnglish
JournalMetabolomics
Publication date2010
Volume6
Issue1
Pages129-136
ISSN1573-3882
DOIs
StatePublished
CitationsWeb of Science® Times Cited: 9

Keywords

  • Targeted metabolomics, Lipoproteins, Apple, iPLS, NMR
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