The inherent accuracy of 1H NMR spectroscopy to quantify plasma lipoproteins is subclass dependent

Ala‐Korpela, Mika; Lankinen, Niko; Salminen, Aino; Suna, Teemu; Soininen, Pasi; Laatikainen, Reino; Ingman, Petri; Jauhiainen, Matti; Taskinen, Marja‐Riitta; Héberger, Károly; Kaski, Kimmo

doi:10.1016/j.atherosclerosis.2006.04.020

Cited by 53 publications

(45 citation statements)

References 23 publications

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“…2, the spectral regions which correlate with the biochemical variables for the high-density lipoprotein (HDL) particles (see Table 1) are those in the low-frequency sides of the CH 3 and (-CH 2 -) n resonances at around 0.85 and 1.25 ppm, respectively, as well as the phospholipid N(CH 3 ) 3 groups at around 3.18 ppm. These are exactly the spectral regions known to be the best representatives for these lipoprotein lipids [12, 13,22]. The pronounced role of the N(CH 3 ) 3 resonances in the association for HDL particles is also seen from the LMWM window in Fig.…”

Section: Resultssupporting

confidence: 71%

“…Furthermore, using various regression analysis methods, we illustrate the quantitative nature of the metabolite information present in a 1 H NMR metabonomic data set of serum, not only for the wellestablished case of lipoprotein lipids [12,13], but also for apolipoprotein components [14] and for low-molecularweight metabolites [15]. These data make it biochemically comprehensible that our quantitative 1 H NMR metabonomics set up, together with appropriate statistical modelling, gives rise to competitive diagnostic performance with respect to an advanced set of biochemical serum variables in the case of diabetic nephropathy in T1DM patients.…”

Section: Introductionmentioning

confidence: 99%

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Diagnosing diabetic nephropathy by 1H NMR metabonomics of serum

Mäkinen

Soininen

Forsblom

et al. 2006

Magn Reson Mater Phy

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Section: Resultssupporting

confidence: 71%

Section: Introductionmentioning

confidence: 99%

Diagnosing diabetic nephropathy by 1H NMR metabonomics of serum

Mäkinen

Soininen

Forsblom

et al. 2006

Magn Reson Mater Phy

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“…Some of these assays may be amenable to routine diagnostic testing and relate to HDL function, and hence CVD risk. The best example of this is the use of nuclear magnetic resonance (NMR) spectroscopy for measuring HDL particle number, which has been shown in several large studies to be superior to HDL-C in predicting future cardiovascular events (4,183,184 ). NMR can also be used to quantify specific HDL subfractions, such as apoB-containing lipoproteins, as well as LDL and HDL particle size and distribution, which may also be predictive of increased mortality in a wide range of disease states (185)(186)(187)(188)(189)(190).…”

Section: Hdl Assays Based On Physical Propertiesmentioning

confidence: 99%

The Changing Face of HDL and the Best Way to Measure It

Karathanasis¹,

Freeman

Gordon

et al. 2017

Clinical Chemistry

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BACKGROUND: HDL cholesterol (HDL-C) is a commonly used lipid biomarker for assessing cardiovascular health. While a central focus has been placed on the role of HDL in the reverse cholesterol transport (RCT) process, our appreciation for the other cardioprotective properties of HDL continues to expand with further investigation into the structure and function of HDL and its specific subfractions. The development of novel assays is empowering the research community to assess different aspects of HDL function, which at some point may evolve into new diagnostic tests. CONTENT:This review discusses our current understanding of the formation and maturation of HDL particles via RCT, as well as the newly recognized roles of HDL outside RCT. The antioxidative, antiinflammatory, antiapoptotic, antithrombotic, antiinfective, and vasoprotective effects of HDL are all discussed, as are the related methodologies for assessing these different aspects of HDL function. We elaborate on the importance of protein and lipid composition of HDL in health and disease and highlight potential new diagnostic assays based on these parameters.

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“…However, the UCFbased lipoprotein work is most often restricted regarding the analyzed subfractions. This is because the fi ner the density ranges used for the isolation, the more tedious and expensive the analyses become ( 44 ). Consequently, it would generally be benefi cial to computationally enhance the UCF-based lipoprotein data as illustrated in this work.…”

Section: Rationale For the In Silico Lipoprotein Phenotypingmentioning

confidence: 99%

Characterization of metabolic interrelationships and in silico phenotyping of lipoprotein particles using self-organizing maps

Kumpula

Mäkelä

Mäkinen

et al. 2010

Journal of Lipid Research

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protein particles using self-organizing maps. J. Lipid Res . 2010. 51: 431-439. Supplementary key words metabolism • lipids • ultracentrifugation • subfractions • unsupervised data analysisLipoprotein metabolism plays a key role in health and disease. Related measures, such as HDL and LDL cholesterol, are in common use to describe individuals' overall metabolic status and the potential risk for atherosclerosis and vascular complications. However, lipoprotein metabolism appears signifi cantly more complex than just an interplay between HDL and LDL. Thus, information on lipoprotein subpopulations is presently needed to appreciate the various counteracting metabolic phenomena and also to more accurately assess the risk for various vascular outcomes ( 1-3 ).It is well established that the liver plays a central role in the apolipoprotein B (apoB) particle cascade, i.e . , the endogenous transport system of lipids to various tissues, by producing and secreting VLDL particles into the circulation ( 4-6 ). This new SOM approach was applied here to analyze and interpret the individual multivariate lipoprotein lipid data. LogicA characteristic feature of the SOMs is their ability to map nonlinear relations in multidimensional data sets into visually more approachable, typically two-dimensional planes of nodes. The overall concept of SOM analysis is illustrated in Fig. 1 . The input data to the SOM from each case i , i.e., from each plasma sample in this particular application, contain a number of variables used to form a vector. The SOM algorithm ( 21, 26 ) then transforms the input data vectors into a two-dimensional map in which each node j,k ( j goes over the rows and k over the columns, total of J rows and K columns) will be represented by a single feature vectorrepresenting the original N dimensional space, i.e . , the input data. After the self-organizing process, the point density of the feature vectors follows roughly the probability density of the data, thereby making SOM a valuable tool for detecting similarities and groupings in a data set. The training algorithm is rather simple (and also robust to missing values), and it is easy to visualize the resulting maps. The feature vectors of the neighboring nodes in the two-dimensional map are similar to each other and thereby, importantly, the individuals ending up in nodes close by are similar also in the original N dimensional space ( 21,24,27 ).The visualization phase of the SOM analysis is two-fold: fi rst, to look at potential constellations of nodes (feature vectors) formed that would describe similar individuals (groups) in the original variable space; second, to depict input (or other related) variables over the two-dimensional map in order to obtain a quick overview of their distribution and values in different nodes, i.e., in the case of each feature vector. In other words, each node describes a model individual, which, in turn, bares a link to the individuals specifi ed in the original N dimensional space. The SOM algorithm, thus, offers the possibility t...

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The inherent accuracy of 1H NMR spectroscopy to quantify plasma lipoproteins is subclass dependent

Cited by 53 publications

References 23 publications

Diagnosing diabetic nephropathy by 1H NMR metabonomics of serum

Diagnosing diabetic nephropathy by 1H NMR metabonomics of serum

The Changing Face of HDL and the Best Way to Measure It

Characterization of metabolic interrelationships and in silico phenotyping of lipoprotein particles using self-organizing maps

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