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The Extended Lipid Panel Assay is an in vitro diagnostic test for quantitative determination of Total Cholesterol, High Density Lipoprotein Cholesterol, and Triglycerides in human serum and Apolipoprotein B in human serum. Values for Total Cholesterol, High Density Lipoprotein Cholesterol, Triglycerides and Apolipoprotein B are calculated by the Vantera® Clinical Analyzer. · Total Cholesterol measurements are used in the diagnosis and treatment of disorders involving excess cholesterol in the blood, lipid and lipoprotein metabolism disorders. · High Density Lipoprotein Cholesterol measurements are used in the diagnosis and treatment of lipid disorders (such as diabetes mellitus), atherosclerosis, and various liver and renal diseases. · Triglyceride measurements are used in the diagnosis and treatment of patients with diabetes mellitus, nephrosis, liver obstruction, other diseases involving lipid metabolism, or various endocrine disorders. · Apolipoprotein B measurements are used in the diagnosis and treatment of lipid disorders and atherosclerosis.

The Extended Lipid Panel Assay involves the acquisition of a 400 MHz proton NMR spectrum of serum or plasma, passing the spectral information through a Partial Least Squares (PLS) regression model, and deriving analyte concentrations from the spectrum based on the trained PLS model. The proton NMR spectrum of serum and plasma is replete with information from the lipids packaged in lipoproteins. The spectrum consists of multiple proton signals emanating from the TG, cholesteryl esters and free cholesterol present in chylomicrons, VLDL, LDL and HDL, out of which the methylene and methyl proton signals are the most abundant. NMR spectra were recorded for several hundred to several thousand representative serum specimens for which the TG, TC, HDL-C and ApoB were chemically measured. Using a PLS regression routine, the spectral information in the combined methylene and methyl region (0.56 - 1.40 ppm) was trained against the chemical measurements where the information is connected through latent variables. Cross-validation was performed with PRESS statistics to optimize the regression model with an appropriate number of latent variables. Once trained with sufficient number of specimens, for any test specimen spectrum, the spectral information is then converted into lipid or ApoB concentrations through the optimum number of 24 to 27 latent variables for which the regression coefficients were known from the predictor matrix.