CLN Article

Lipoprotein Subfractionation Analysis

The Continuing Quest for Improving Cardiovascular Risk Prediction

Anna Wolska, PhD, and Alan Remaley, MD, PhD

Cardiovascular disease (CVD) is the number 1 cause of death in the world. By 2030, almost 23 million people are predicted to die from CVD, mainly because of heart disease and stroke (1). Because lipoprotein particles play a key role in atherogenesis, they are useful biomarkers for assessing CVD risk as well as for monitoring lipid-lowering therapy.

Prior to the early 1950s, clinicians used total cholesterol in all the different lipoprotein fractions as the main marker for CVD risk. However, in a seminal publication in 1950, John Gofman first showed—using density gradient ultracentrifugation—that cholesterol in low density lipoproteins (LDL) was positively associated with CVD risk, whereas cholesterol in high density lipoproteins (HDL) was inversely related to CVD risk (2). During the ensuing decades, researchers have investigated the different subfractions within the major lipoprotein classes, namely chylomicrons, very low density lipoproteins (VLDL), LDL, and HDL, to determine whether analysis of lipoprotein subfractionations improves CVD risk prediction.

Only about half of patients who develop CVD appear to be at risk based on total cholesterol, making lipid subfractionation attractive for its potential to identify at risk patients who would not be detected by conventional lipid markers (3). Researchers also are exploring whether lipid subfractionation might help clinicians decide which patients at intermediate risk should be treated with lipid-lowering medications or other therapies.

There are several different methods for lipoprotein subfractionation analysis that rely upon the different physical and chemical properties of lipoproteins. This article focuses on the lipoprotein subfractionation methods shown in Table 1 that routine clinical laboratories readily perform or which reference labs offer, including gel electrophoresis, density-gradient ultracentrifugation, nuclear magnetic resonance (NMR) spectroscopy, and ion mobility analysis.

Gel Electrophoresis

Gel electrophoresis—one of the oldest methods to separate lipoproteins—does so based on a combination of charge and size (4). Although it is a relatively low-resolution technique, electrophoresis on agarose gels separates not only the major lipoprotein classes but also reveals some lipoprotein subfractions (Figure 1). Typically gel electrophoresis is a qualitative test, as the lipoproteins are stained with a dye such as Sudan black that primarily stains triglycerides and cholesteryl esters. The cholesterol content of the separated lipoproteins can, however, be quantitatively and enzymatically measured by a technique that treats the gel with cholesterol oxidase. Two Food and Drug Administration (FDA)-approved commercial kits are available.

The original Fredrickson phenotypic classification of lipid disorders, which was based on agarose gel electrophoresis (5), has largely been replaced by more accurate classifications based on DNA sequence analysis and/or other lipoprotein phenotyping tests. Lipoprotein(a) [Lp(a)], a particularly pro-atherogenic LDL-like lipoprotein, can be detected by agarose gels, but other more quantitative methods exist for measuring this subfraction.

Even with its shortcomings, gel electrophoresis still is the best method for detecting several disorders and types of lipoprotein particles. Chylomicrons, for example—difficult to measure by most other methods because of their large size—can be easily observed in agarose gels because they stay trapped near the origin of the gel. In contrast, VLDL, the only other particle that carries significant amounts of triglycerides, migrates much further into the gel. Patients with dysbetalipoproteinemia have a defect in the clearance of chylomicrons and VLDL and accumulate intermediate density lipoproteins (IDL), often referred to as remnant lipoproteins. These tend to migrate between LDL and VLDL on agarose gels. Dysbetalipoproteinemia is a relatively common disorder that affects about 1% of the population. Identifying individuals with this condition is important because they might benefit from both statins and drugs that specifically lower triglycerides.

Agarose gel electrophoresis is also one of the few methods available for detecting lipoprotein X (LpX), an abnormal lipoprotein particle that accumulates in patients with cholestasis and familial lecithin-cholesterol­ acyltransferase deficiency. Unlike other lipoproteins, LpX migrates toward the cathode in agarose gel, making it easy to distinguish from other lipoproteins.

Currently, one FDA-approved polyacrylamide tube gel electrophoresis system is available for separating LDL into seven subfractions: the LipoPrint system from Quantimetrix (6). As will be discussed further, researchers have found that small dense LDL subfractions are more closely related to CVD risk than LDL-C, most likely because of their increased ability to enter atherosclerotic plaques. Certain reference labs offer 2D-gel electrophoresis tests that separate HDL into at least five subfractions, and may also measure the amount of apolipoprotein A-I (apoA-I) in each of the five HDL subclasses (7).

Density-Gradient Ultracentrifugation

Density-gradient ultracentrifugation involves separating lipoproteins according to their density and depends on the lipid/protein ratio of lipoproteins. Chylomicrons, for example, are almost completely comprised of lipids and are very light, with a density less than water. In contrast, the smaller lipoproteins, such as HDL, are only about 50% by weight lipids with the other half made up of denser proteins, giving HDL a density range between 1.063–1.25 g/mL. Density-gradient ultracentrifugation not only forms the basis for the main nomenclature of lipoproteins but also serves as a reference method for measuring lipoproteins (8). Even so, most clinical laboratories cannot readily perform this method, and until recently it was available from reference laboratories as the Vertical Auto Profile (VAP) test II, which first separates lipoproteins by density-gradient ultracentrifugation in a vertical rotor. 

The next step is enzymatic measurement of the cholesterol distribution throughout the density gradient spectrum by continuous flow analysis. The VAP test includes HDL, LDL, and VLDL subfractionation by directly measuring the amount of cholesterol contained within each of these subfractions. The test measures IDL-C and Lp(a)-C using software that deconvolutes data embedded in the parent tracing. It also assesses total cholesterol, triglycerides, the main protein components of HDL and LDL, apoA-I, and apolipoprotein B (apoB), respectively, which can be used to estimate the particle number of these lipoproteins. VAP classifies LDL subfractions as “pattern A,” “pattern B,” or “pattern A/B.” “Pattern A” implies large, buoyant LDL particles, whereas “pattern B” implies small, dense LDL particles (Table 2), which have been strongly associated with CVD risk. The test also separates cholesterol on HDL into a larger, less dense HDL subfraction (HDL2) and small, denser HDL subfraction (HDL3) (9). Although the clinical utility for HDL subfractions is not as clear as for LDL subfractions, larger size HDL in most studies appears to be more strongly inversely related to CVD risk (10).

NMR Spectroscopy

Laboratories also use NMR spectroscopy to perform lipoprotein subfraction analysis. NMR spectroscopy quantitatively measures the spectral signals generated by the terminal methyl groups on lipids within lipoprotein particles. Unlike most other methods, NMR spectroscopy does not require physical separation of lipoproteins, and aside from separating plasma from blood cells, no pre-analytic sample processing is necessary. At least one reference laboratory offers the method, and others are developing it. The NMR LipoProfile test is currently the only NMR assay for measuring LDL-particle number (LDL-P), triglycerides, and HDL-C that has been approved by FDA. The other lipid and lipoprotein parameters that this method measures are shown in Table 3. This test also reports a lipoprotein insulin resistance score based on the lipoprotein profile that is associated with insulin resistance and diabetes risk. The position of the resonance in the NMR spectra of the terminal methyl groups on lipids is affected by the size of the lipoprotein particle, which after a deconvolution algorithm enables the laboratory to calculate the number of particles within each lipoprotein size subfraction. LDL-P is simply calculated as the sum of all the individual numbers of LDL size subfractions (11).

As illustrated in Figure 2, two individuals with similar LDL-C levels can have quite a substantial variance in LDL-P, because larger size LDL particles can carry more cholesterol (12). Researchers have found that CVD risk tracks more closely with LDL-P when there is discordance between LDL-C and LDL-P—as often occurs in patients with metabolic syndrome and diabetes (13). In the future, laboratories might obtain even more discrimination by specifically measuring the different size subfractions within VLDL, LDL, and HDL, but this is still an active area of investigation.

Ion Mobility Analysis

Both the size and concentrations of lipoprotein particle subfractions can also be measured by mass spectroscopy, using gas-phase differential electrical mobility (also known as ion mobility). This method depends on the principle that particles of a given size and charge behave differently when put in a laminar flow of air and subjected to an electric field. Quest Diagnostics has adapted this method for directly measuring the size distribution of lipoprotein particles in a range from 7 nm to about 120 nm. The analysis part of the method is automated and generates profiles of particle number and particle mass versus particle diameter. However, the plasma/serum sample requires extensive pre-analytical preparation to isolate the lipoproteins.

In this step, the laboratory first adds an albumin removal reagent to the sample, then ultracentrifuges the sample for about 2 hours to isolate the lipoprotein fraction. The laboratory then dilutes the sample in a volatile buffer and electrosprays it in a differential mobility analyser. This process yields particle number distributions within 2 minutes for HDL, LDL, IDL, and VLDL converted into particle mass distributions. Other conventional lipid and apolipoprotein tests are also included in the panel (Table 4) (14).


Numerous methods now exist for measuring lipoprotein subfractions and there is growing evidence for how such tests improve CVD risk prediction and cost-effectiveness (15). At this time, however, they are not widely used and are not recommended by national and international guidelines for use as screening tests. Like the situation with C-reactive protein as a CVD risk biomarker, they can perhaps be best used in patients with intermediate risk and/or when there appears to be discordance between a patient’s clinical presentation and conventional lipid biomarker measurements. In the case of NMR and mass spectrometry-based testing, the ability to add new markers for CVD and other diseases without much additional cost may make these approaches more attractive in the future.

*Anna Wolska, PhD, and Alan T. Remaley, MD, PhD, have received research support from LipoScience Inc., which was acquired by LabCorp Inc. in 2014.

Anna Wolska, PhD, is a postdoctoral fellow in the Lipoprotein Metabolism Section of the Pulmonary and Vascular Medicine Branch at the National Institutes of Health.
+Email: [email protected]

Alan T. Remaley, MD, PhD, is the senior investigator in the Lipoprotein Metabolism Section of the Pulmonary and Vascular Medicine Branch at the National Institutes of Health. +Email: [email protected]


1.       American Heart Association and American Stroke Association. Heart disease, stroke and research statistics at-a-glance.
(Accessed October 2016).

2.       Gofman J, Lindgren F. The role of lipids and lipoproteins in atherosclerosis. Science 1950;111:166–71.

3.       Arsenault BJ, Despres JP, Stroes ES, et al. Lipid assessment, metabolic syndrome, and coronary heart disease risk. Eur J Clin Invest 2010;40:1081–93.

4.       Noble RP. Electrophoretic separation of plasma lipoproteins in agarose gel. J Lipid Res 1968;9:693–700.

5.       Fredrickson DS, Levy RI, Lees RS. Fat transport in lipoproteins—An integrated approach to mechanisms and disorders. N Engl J Med 1967;276:148–56.

6.       Hoefner DM, Hodel SD, O’Brien JF, et al. Development of a rapid, quantitative method for LDL subfractionation with use of the Quantimetrix Lipoprint LDL system. Clin Chem 2001;47:266–74.

7.       Asztalos BF, Sloop CH, Wong L, et al. Two-dimensional electrophoresis of plasma lipoproteins: Recognition of new apo A-I-containing subpopulations. Biochim Biophys Acta 1993;1169:291–300.

8.       Chapman MJ, Goldstein S, Lagrange D, et al. A density gradient ultracentrifugal procedure for the isolation of the major lipoprotein classes from human serum. J Lipid Res 1981;22:339–58.

9.       Kulkarni KR. Cholesterol profile measurement by vertical auto profile method. Clin Lab Med 2006;26:787–802.

10.       Camont L, Chapman MJ, Kontush A. Biological activities of HDL subpopulations and their relevance to cardiovascular disease. Trends Mol Med 2011;17:594–603.

11.       Jeyarajah EJ, Cromwell WC, Otvos JD. Lipoprotein particle analysis by nuclear magnetic resonance spectroscopy. Clin Lab Med 2006;26:847–70.

12.       Mora S. Advanced lipoprotein testing and subfractionation are not (yet) ready for routine clinical use. Circulation 2009;119:2396–404.

13.       Otvos JD, Mora S, Shalaurova I, et al. Clinical implications of discordance between low-density lipoprotein cholesterol and particle number. J Clin Lipidol 2011;5:105–13.

14.       Caulfield MP, Li S, Lee G, et al. Direct determination of lipoprotein particle size and concentrations by ion mobility analysis. Clin Chem 2008;54:1307–16.

15.       Toth PP, Grabner M, Punekar RS, et al. Cardiovascular risk in patients achieving low-density lipoprotein cholesterol and particle targets. Atherosclerosis 2014;235:585–91.