Quantification of Lipoprotein Subclasses by Proton Nuclear Magnetic Resonance–Based Partial Least-Squares Regression Models

Petersen, Morten Krogh; Dyrby, Marianne; Toubro, Søren; Engelsen, Søren Balling; Nørgaard, Lars; Pedersen, Henrik Toft; Dyerberg, Jørn

doi:10.1373/clinchem.2004.046748

Cited by 62 publications

(61 citation statements)

References 19 publications

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“…However, the cause of this phenomenon is not clear but might be due to problems with an accurate measurement of this fraction by the ultracentrifugation method. Overall, the results from this study are in agreement with previously published studies where chemometric methods for the quantification of human lipoproteins from NMR spectra were used Petersen et al 2005). However, in this study models using the full NMR spectrum could not be obtained wherefore selection of minor parts (intervals) of the spectrum was required in order to obtain reasonable prediction models of the cholesterol distribution in rat plasma.…”

Section: Discussionsupporting

confidence: 90%

“…This could probably be avoided by using a larger test set than the one available for the present study. The relative prediction error (RMSECV) of the VLDL measurement is moderately higher than for the other accepted models (IDL not taken into account here) and this trend was also observed in a previous study on human blood by Petersen et al (2005). However, the cause of this phenomenon is not clear but might be due to problems with an accurate measurement of this fraction by the ultracentrifugation method.…”

Section: Discussionsupporting

confidence: 76%

“…Therefore, an alternative method to measure the lipoprotein profile in rodents is needed. Previous studies in humans Petersen et al 2005;Bathen et al 2000) have taken advantage of the proton nuclear magnetic resonance (NMR) spectra of plasma since these contains latent information about the concentration of lipoproteins and the use of partial least-square (PLS) regression models may allow specific extraction of this information. In the present study we employ this approach on rat plasma samples, which is to our best knowledge still unexplored.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2009

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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.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionsupporting

confidence: 76%

Section: Introductionmentioning

confidence: 99%

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

et al. 2009

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“…In fact, diffusion-edited 1 H NMR has recently been applied, together with partial least squares regression or multi-way chemometrics, to quantify lipoproteins from plasma, including subclasses (51-53). In two of these studies (51,52), the NMR experimentation was performed at 303 K, a temperature at which the core cholesterol esters of LDL particles are likely to be partly in a liquid crystalline state, thereby affecting their NMR visibility. However, in a recent study by Dyrby and co-workers (53), the NMR experimentation was performed at 318 K, i.e.…”

Section: Resultsmentioning

confidence: 99%

¹H NMR metabonomics of plasma lipoprotein subclasses: elucidation of metabolic clustering by self‐organising maps

Suna¹,

Salminen²,

Soininen³

et al. 2007

NMR in Biomedicine

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(1)H NMR spectra of plasma are known to provide specific information on lipoprotein subclasses in the form of complex overlapping resonances. A combination of (1)H NMR and self-organising map (SOM) analysis was applied to investigate if automated characterisation of subclass-related metabolic interactions can be achieved. To reliably assess the intrinsic capability of (1)H NMR for resolving lipoprotein subclass profiles, sum spectra representing the pure lipoprotein subclass part of actual plasma were simulated with the aid of experimentally derived model signals for 11 distinct lipoprotein subclasses. Two biochemically characteristic categories of spectra, representing normolipidaemic and metabolic syndrome status, were generated with corresponding lipoprotein subclass profiles. A set of spectra representing a metabolic pathway between the two categories was also generated. The SOM analysis, based solely on the aliphatic resonances of these simulated spectra, clearly revealed the lipoprotein subclass profiles and their changes. Comparable SOM analysis in a group of 69 experimental (1)H NMR spectra of serum samples, which according to biochemical analyses represented a wide range of lipoprotein lipid concentrations, corroborated the findings based on the simulated data. Interestingly, the choline-N(CH(3))(3) region seems to provide more resolved clustering of lipoprotein subclasses in the SOM analyses than the methyl-CH(3) region commonly used for subclass quantification. The results illustrate the inherent suitability of (1)H NMR metabonomics for automated studies of lipoprotein subclass-related metabolism and demonstrate the power of SOM analysis in an extensive and representative case of (1)H NMR metabonomics.

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“…Also the NMR technique for measuring lipoprotein subclasses cannot a priori be extrapolated to size measurements obtained by gradient gel electrophoresis (Witte et al, 2004). However, available studies do suggest a good correlation between the ultracentrifugation-based method and NMR in predicting a profile of a increased risk of cardiovascular disease (Petersen et al, 2005).…”

Section: Discussionmentioning

confidence: 99%

Effects of marine n-3 fatty acid supplementation on lipoprotein subclasses measured by nuclear magnetic resonance in subjects with type II diabetes

et al. 2007

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Objective: To measure effects of fish oil supplements on lipoprotein subclasses by nuclear magnetic resonance (NMR) in subjects with type II diabetes and relate them to insulin sensitivity. Design: Two-armed, parallel, placebo-controlled, randomized. Subjects: Normotriglyceridemic subjects with type II diabetes without insulin treatment were given either fish oil (n ¼ 12, median intake 5.9 g/day total n-3 fatty acids (FA) (1.8 g 20:5n-3, 3.0 g 22:6n-3)) or corn oil (n ¼ 14, 8.5 g/day 18:2n-6 FA). Methods: Size and concentration of lipoproteins subclasses were measured by NMR, insulin sensitivity by hyperinsulinemic, isoglycemic clamps. Results: After 9 weeks, there were differences between those treated with fish and corn oil with respect to very low-density lipoprotein (VLDL) size (median À15 vs þ 0.6%, P ¼ 0.001), particle concentrations of large VLDL (À99 vs À4.1%, P ¼ 0.041) and small high-density lipoprotein (HDL) (À12 vs þ 10%, P ¼ 0.051). Compared with corn oil fish oil tended to increase HDL size and small low-density lipoprotein ( LDL) concentration (P ¼ 0.063 and 0.068, respectively, for differences between groups). There was no effect on oxidized LDL. Insulin sensitivity (glucose utilization) decreased in the fish oil group compared with the corn oil group (P ¼ 0.049). The decrease in insulin sensitivity did not correlate with the effects on lipoprotein subclasses. Conclusions: A high intake of n-3 FA exerts effects on several lipoprotein subclasses without obvious influence from changes in insulin sensitivity.

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Quantification of Lipoprotein Subclasses by Proton Nuclear Magnetic Resonance–Based Partial Least-Squares Regression Models

Cited by 62 publications

References 19 publications

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

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

¹H NMR metabonomics of plasma lipoprotein subclasses: elucidation of metabolic clustering by self‐organising maps

Effects of marine n-3 fatty acid supplementation on lipoprotein subclasses measured by nuclear magnetic resonance in subjects with type II diabetes

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Quantification of Lipoprotein Subclasses by Proton Nuclear Magnetic Resonance–Based Partial Least-Squares Regression Models

Cited by 62 publications

References 19 publications

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

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

1H NMR metabonomics of plasma lipoprotein subclasses: elucidation of metabolic clustering by self‐organising maps

Effects of marine n-3 fatty acid supplementation on lipoprotein subclasses measured by nuclear magnetic resonance in subjects with type II diabetes

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¹H NMR metabonomics of plasma lipoprotein subclasses: elucidation of metabolic clustering by self‐organising maps