Repeatability and reproducibility of lipoprotein particle profile measurements in plasma samples by ultracentrifugation

Background: Characterization of lipoprotein particle profiles (LPPs) (including main classes and subclasses) by means of ultracentrifugation (UC) is highly requested given its clinical potential. However, rapid methods are required to replace the very labor-intensive UC method and one solution is to calibrate rapid nuclear magnetic resonance (NMR)-based prediction models, but the reliability of the UC-response method required for the NMR calibration has been largely overlooked. Methods:  This study provides a comprehensive repeatability and reproducibility study of various UC-based lipid measurements (cholesterol, triglycerides [TGs], free cholesterol, phospholipids, apolipoprotein [apo]A1 and apoB) in different main classes and subclasses of 25 duplicated fresh plasma samples and of 42 quality control (QC) frozen pooled plasma samples of healthy individuals. Results: Cholesterol, apoA1 and apoB measurements were very repeatable in all classes (intraclass correlation coefficient [ICC]: 92.93%-99.54%). Free cholesterol and phospholipid concentrations in main classes and subclasses and TG concentrations in high-density lipoproteins (HDL), HDL subclasses and low-density lipoproteins (LDL) subclasses, showed worse repeatability (ICC: 19.21%-99.08%) attributable to low concentrations, variability introduced during UC and assay limitations. On frozen QC samples, the reproducibility of cholesterol, apoA1 and apoB concentrations was found to be better than for the free cholesterol, phospholipids and TGs concentrations. Conclusions: This study shows that for LPPs measurements near or below the limit of detection (LOD) in some of the subclasses, as well as the use of frozen samples, results in worsened repeatability and reproducibility. Furthermore, we show that the analytical assay coupled to UC for free cholesterol and phospholipids have different repeatability and reproducibility. All of this needs to be taken into account when calibrating future NMR-based models

Insight in modulation of inflammation in response to diclofenac intervention: a human intervention study

Background. Chronic systemic low-grade inflammation in obese subjects is associated with health complications including cardiovascular diseases, insulin resistance and diabetes. Reducing inflammatory responses may reduce these risks. However, available markers of inflammatory status inadequately describe the complexity of metabolic responses to mild anti-inflammatory therapy. Methods. To address this limitation, we used an integrative omics approach to characterize modulation of inflammation in overweight men during an intervention with the non-steroidal anti-inflammatory drug diclofenac. Measured parameters included 80 plasma proteins, >300 plasma metabolites (lipids, free fatty acids, oxylipids and polar compounds) and an array of peripheral blood mononuclear cells (PBMC) gene expression products. These measures were submitted to multivariate and correlation analysis and were used for construction of biological response networks. Results. A panel of genes, proteins and metabolites, including PGE2 and TNF-alpha, were identified that describe a diclofenac-response network (68 genes in PBMC, 1 plasma protein and 4 plasma metabolites). Novel candidate markers of inflammatory modulation included PBMC expression of annexin A1 and caspase 8, and the arachidonic acid metabolite 5,6-DHET. Conclusion. In this study the integrated analysis of a wide range of parameters allowed the development of a network of markers responding to inflammatory modulation, thereby providing insight into the complex process of inflammation and ways to assess changes in inflammatory status associated with obesity. Trial registration. The study is registered as NCT00221052 in clinicaltrials.gov database. © 2010 van Erk et al; licensee BioMed Central Ltd

Corrigendum to “Quantification of lipoprotein profiles by nuclear magnetic resonance spectroscopy and multivariate data analysis”

The authors regret that there was an error in the published Table 2. Wrong literature reference numbers were given in Table 2 making the interpretation of the table very confusing to the reader. Table 2 should be replaced with the following corrected table.</p

Human Blood Lipoprotein Predictions from 1H NMR Spectra: Protocol, Model Performances, and Cage of Covariance

Lipoprotein subfractions are biomarkers for the early diagnosis of cardiovascular diseases. The reference method, ultracentrifugation, for measuring lipoproteins is time-consuming, and there is a need to develop a rapid method for cohort screenings. This study presents partial least-squares regression models developed using 1H nuclear magnetic resonance (NMR) spectra and concentrations of lipoproteins as measured by ultracentrifugation on 316 healthy Danes. This study explores, for the first time, different regions of the 1H NMR spectrum representing signals of molecules in lipoprotein particles and different lipid species to develop parsimonious, reliable, and optimal prediction models. A total of 65 lipoprotein main and subfractions were predictable with high accuracy, Q2 of >0.6, using an optimal spectral region (1.4-0.6 ppm) containing methylene and methyl signals from lipids. The models were subsequently tested on an independent cohort of 290 healthy Swedes with predicted and reference values matching by up to 85-95%. In addition, an open software tool was developed to predict lipoproteins concentrations in human blood from standardized 1H NMR spectral recordings