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Clinical Chemistry 52: 1722-1727, 2006. First published June 1, 2006; 10.1373/clinchem.2005.059949
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Right arrow Lipids, Lipoproteins, and Cardiovascular Risk Factors
(Clinical Chemistry. 2006;52:1722-1727.)
© 2006 American Association for Clinical Chemistry, Inc.

Lipids, Lipoproteins, and Cardiovascular Risk Factors

Disparate LDL Phenotypic Classification among 4 Different Methods Assessing LDL Particle Characteristics

Wayne Ensign1, Nicole Hill2 and Christopher B. Heward2,a

1 Naval Health Research Center, San Diego, CA.
2 Kronos Science Laboratories, Inc., Phoenix, AZ.

aAddress correspondence to this author at: Kronos Science Laboratories, Inc., 2222 E. Highland Ave., Suite 220, Phoenix, AZ 85016. Fax 602-667-5623; e-mail chris.heward{at}kronoslaboratory.com.


   Abstract
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 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Background: Our study seeks to clarify the extent of differences in analytical results, from a clinical perspective, among 4 leading technologies currently used in clinical reference laboratories for the analysis of LDL subfractions: gradient gel electrophoresis (GGE), ultracentrifugation–vertical auto profile (VAP), nuclear magnetic resonance (NMR), and tube gel electrophoresis (TGE).

Methods: We collected 4 simultaneous blood samples from 40 persons (30 males and 10 females) to determine LDL subclasses in 4 different clinical reference laboratories using different methods for analysis. LDL subfractions were assessed according to LDL particle size and the results categorized according to LDL phenotype. We compared results obtained from the different technologies.

Results: We observed substantial heterogeneity of results and interpretations among the 4 methods. Complete agreement among methods with respect to LDL subclass phenotyping occurred in only 8% (n = 3) of the persons studied. NMR and GGE agreed most frequently at 70% (n = 28), whereas VAP matched least often.

Conclusions: As measurement of LDL subclasses becomes increasingly important, standardization of methods is needed. Variation among currently available methods renders them unreliable and limits their clinical usefulness.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Cardiovascular disease (CVD)1 is characterized by the progressive appearance of fibro-fatty plaque (atheroma) along the vascular endothelium and the coronary arteries(1). Early studies established that increased LDLcholesterol (LDL-C) concentrations accelerated plaque formation(2). However, increased LDL-C concentrations do not completely predict plaque formation. Chemical modification of polyunsaturated fats carried in the LDL particle can greatly enhance its atherogenicity(3)(4)(5).

Considerable heterogeneity exists in the physical properties (size and density) of LDL lipoprotein particles among groups of individuals(5). Moreover, many CVD patients do not have increased LDL-C(1)(6)(7)(8). These ambiguities were partially resolved by the demonstration of a significant association between LDL subclasses, intermediate-density lipoprotein, increased plasma triglyceride concentrations, and low plasma HDL-cholesterol(9). Adjustment for triglycerides, however, reduced the relative risk of myocardial infarction from 3 to 1.5 (95% confidence interval, 0.8–3.2), indicating a close association between certain types of LDL particle and plasma triglyceride concentration. Those investigators(9) grouped patients into type A and type B LDL patterns on the basis of the size/density distribution of the LDL particle subclasses(10)(11)(12)(13)(14)(15).

There are now suggestions that environmental and metabolic factors can influence atherogenic profiles and potentially shift a type B LDL pattern toward a type A LDL pattern(16)(17)(18). Thus, clinical use of the LDL profile as a basis for dietary counseling, lifestyle adjustments, and the effectiveness of pharmacologic agents in reducing the risk of CVD could help guide potential treatment interventions(19).

Analytical ultracentrifugation is considered to be the benchmark for determining lipoprotein subfractions(20)(21). However, other methodologies have also been used, including nondenaturing gradient gel electrophoresis (GGE)(20)(22), density gradient ultracentrifugation(23), and nuclear magnetic resonance (NMR).

Unfortunately, the estimated number of LDL subfractions is method dependent. Therefore, different methods may classify a patient’s LDL particle type differently, making comparisons only tenuous and extrapolation to patient results difficult. The increasing clinical use of these newer technologies makes accurate and consistent patient classification by different laboratories crucial. To evaluate the performance of these new technologies for determining LDL subclasses and their effects on patient classification based on LDL subclass pattern, we conducted this comparison study.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Blood samples were collected into BD Vacutainer Tubes (BD Diagnostics) from 40 apparently healthy persons (30 males) 23 to 61 years of age. Serum and EDTA-plasma were kept at ambient temperature and separated within 2 h by centrifugation at 1430g for 15 min at 4 °C, aliquoted, and then frozen at –70 °C. Samples from each person were sent to 4 different laboratories for analysis. A portion of each serum sample was shipped frozen to Atherotech (Birmingham, AL), and another portion was retained frozen at our laboratory (Kronos Science Laboratories, Phoenix, AZ) for LDL subfractionation using the LipoprintTM system (Quantimetrix). EDTA-plasma samples were shipped frozen for analysis to Liposcience (Raleigh, NC) and Berkley HeartLab (Alameda, CA). Each of the laboratories used a different method for LDL fractionation and LDL particle size determination(9).

method 1: gge
To assess LDL subfractions, Berkeley Heartlab uses LDL Segmented Gradient Gel Electrophoresis (LDL-S3GGETM). This technique separates LDL particles into 7 LDL subfractions based on particle size and shape(20)(22)(23). The LDL-S3GGE for LDL typing classifications are as follows: 26.35–28.5 nm, large LDL (pattern type A); 25.75–26.34 nm, intermediate (pattern type AB); and 22.0–25.74 nm, small LDL (pattern type B). The LDL subfractions are reported as percentages based on the area under the curve for each of the 7 subfractions. Small LDL particles correspond to the subfractions LDL IIIa and LDL IIIb. These subfractions are indicators of the "severity" of the "atherogenic lipoprotein profile".

method 2: ultracentrifugation-vertical auto profile
The Vertical Auto Profile-II, or VAP-II test (Atherotech), is derived from density gradient ultracentrifugation(24)(25). The VAP-II generates a series of absorbance curves from which proprietary software produces 6 LDL subclasses labeled as LDL1 (most buoyant) through LDL6 (most dense). LDL1 and LDL2 comprise pattern type A and LDL3 and LDL4 comprise pattern type B(26).

method 3: nmr
The laboratory report from Liposcience, Inc., contains 3 sections: a lipoprotein panel, a risk assessment panel, and a subclass panel. No references are provided for the basis of the "risk categories". The LDL size in nanometers and its classification as type A or B are also reported. The subclass panel contains the cholesterol content of the subclasses for intermediate-density lipoprotein and the triglyceride content for large, intermediate, and small VLDL(27).

method 4: tube gel electrophoresis
Quantimetrix Lipoprint LDL System(28) was used according to the manufacturer’s specifications at Kronos Science Laboratories, Inc. In this system, lipoprotein fractions are separated and summed in a weighted manner to yield an "LDLSF" score. Specific ranges of LDLSF scores correspond to different LDL phenotype patterns: normal (type A; LDLSF score <5.5), intermediate (type AB, LDLSF score 5.5–8.5), and atherogenic (type B; LDLSF score >8.5)(28).

This technology does not measure the LDL particle size directly, but estimates LDL particle size by comparing particle electrophoretic mobility with the electrophoretic mobilities of particles of known sizes. The total cholesterol of the sample must be measured independently of the Lipoprint system. The Lipoprint report contains the patient’s scan and the cholesterol concentration within each of the fractions based on retardation factor (Rf), which correspond to each of the LDL subfractions. The scan contains 7 possible LDL subfractions.

statistical analysis
For across-method comparisons, we used a repeated-measures ANOVA and pairwise contrasts. Significance was established with the Bonferroni adjustment. When the sphericity assumption was in question, the Greenhouse–Geisler adjustment for the degrees of freedom was used to determine overall statistical significance. We used the intraclass correlation coefficient to establish the agreement among the within-subject data across laboratory values. When possible, the distribution of cholesterol contained in LDL and the estimates for LDL particle sizes for each laboratory were evaluated by use of boxplots. The cholesterol, triglyceride, and major lipoprotein cholesterol values were taken from the patient reports obtained from each method. Because this report primarily focuses on LDL particle characteristics and their use for LDL phenotype classification, HDL subfractions were not included in this analysis. All LDL particle sizes were converted to nanometers for comparisons across methods. A value of 5 nm was added to the NMR estimates of LDL particle size per the laboratory’s statement included in the patient report. All statistical analyses were performed with the Statistical Package for the Social Sciences, Ver. 8.0 (SPSS, Inc.).


   Results
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 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
The distribution of LDL phenotypes among the 40 persons for each method is shown in Fig. 1 . Results varied considerably among the methods. The tube gel electrophoresis (TGE) classified 30 of 38 (79%) persons as type A pattern, whereas VAP classified only 3 of 39 (~8%) as type A. The VAP and NMR classified 21 persons (~54%) as type B, but TGE classified only 2 of the 38 persons (~5%) as having a type B pattern. TGE and GGE classified 6 and 5 persons, respectively (n = 5) as having an intermediate pattern (type AB), whereas VAP classified 2.5 times as many persons (n = 15) as type AB. Although TGE and GGE classified the same number of persons as type AB, these methods did not agree on the AB classification for the same individuals. The laboratory using the NMR methodology does not include an intermediate (type AB) classification.


Figure 1
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  		Figure 1. Histogram showing the number of persons categorized into each of the 3 LDL pattern types (A, B, or AB) by each of the 4 measurement methods (GGE, VAP, NMR, and TGE).

Patient classification data for each of the 4 methods are shown in Table 1 . Complete agreement among the laboratory methods occurred in 3 (persons 2, 5, and 11) of 39 persons (~8%). NMR matched other methods to a greatest extent (marginal totals) and VAP matched the least. NMR and GGE produced the same result for 28 of 40 (70%) individuals. When we assumed that all AB patients reported by Berkeley Heart Laboratory (GGE) could reasonably be reported as pattern A, the agreement between NMR and GGE improved to 32 of 40 (80%). Twenty persons (~50%) were similarly classified by 3 of the 4 laboratories. Eight of these 20 individuals (~40%) were classified as type A by the TGE, NMR, and GGE methods, whereas 10 of 20 (50%) were classified type B by NMR, VAP, and GGE. Two of 20 persons (10%) were classified as type B when TGE was 1 of the 3 methods.


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  		Table 1. Shows the patient classification data for each of the 4 laboratories along with results for total cholesterol, HDL-C, LDL-C, and triglycerides.

The original basis for distinguishing the LDL phenotype among individuals was LDL particle size(9). In 3 of the laboratory methods (TGE, GGE, and NMR), it was possible to determine the sizes of the main LDL particle fraction, providing the basis for LDL phenotypic classification, and in 1 case (GGE), 2 LDL particle sizes were given in the laboratory report, corresponding to LDL peaks 1 (GGE1) and 2 (GGE2). Total LDL-C could also be determined in 3 of the methods. Laboratory comparisons of LDL-C and particle size estimates are indicated in Fig. 2 .


Figure 2
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  		Figure 2. Distribution of LDL-C concentration (A) and LDL particle size (B).

(A), boxplots showing the distribution of LDL-C estimates for 3 methods in which LDL-C content was estimated. {circ} indicate outliers. (B), boxplots indicating the distribution of LDL particle sizes for the main LDL fraction used for patient classification. GGE1 and GGE2 refer to LDL peaks 1 and 2 as indicated in the patient report for the Berkeley Heart Laboratory.

We observed significant differences among methods for LDL-C concentration (Fig. 2AUp ). Although the absolute cholesterol concentrations were significantly different among methods, the within-subject LDL-C was relatively consistent (intraclass correlation = 0.904). These results indicate a degree of bias within each method, but the relative LDL-C within individuals was maintained across methodologies. The distribution of LDL particle sizes among the methods is shown in Fig. 2BUp . Differences among the methods for estimated LDL particle sizes were also significant. Unlike the LDL-C concentration, the within-subject particle size estimates were not sustained across methods (intraclass correlation = 0.246).


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
Several clinical laboratories, including the 2 largest national clinical reference laboratories, now offer LDL subfraction analysis performed with different technologies. The consistency of patient classification among laboratories is unknown because there is no standardization among the different methods. To gain insight into the consistency of LDL phenotype classification, we sent a sample from each of 40 patients to 4 different laboratories that use different methods for LDL subfraction analysis.

In this comparison study, we focused on the diagnostic classification of patient LDL pattern type. Typically, individuals with large LDL particles are classified as type A, whereas individuals with small, dense LDL particles are classified as type B and are at a greater risk for CVD(9)(15). Our results indicated only 3 of 39 patients (~8%) were classified as having the same LDL phenotype by all 4 methods. The best agreement between 2 methods was between GGE and NMR (70%); this agreement may actually be >70%, as noted in the Results section. Removal of the 5 samples classified as AB, the 3 samples classified as not available (NA), and the 1 sample classified as indeterminate (IND) from the GGE report (Table 1Up ), leaves 31 samples for comparison. The betweenlaboratory agreement for these 31 samples for the GGE and NMR methods would have been 90% (28 of 31).

The inability to consistently classify patients did not seem to be dependent on the LDL particle per se, because the inconsistency in classification was more or less evenly distributed among the methods used, suggesting that the classification is method specific. The absolute LDL-C concentration was significantly different among the compared methods, but the within-subject reproducibility (intraclass correlation = 0.904) was consistent across the methods. The LDL particle sizes were also significantly different among the methods, but unlike LDL-C, there was a poor correlation within individuals across methods. If the basis for patient classification is LDL particle size and if the particle size estimates for a given individual are different depending on the method used, then it is not surprising that patient classification across laboratories is not consistent.

As new methods for measuring LDL subclasses are introduced, standardization becomes increasingly important, particularly when these methods are based on different principles. If LDL particle size is to be meaningfully incorporated into a patient’s heart disease risk profile, then a standardization program should be considered. At the very least, a proficiency testing program for routine interlaboratory comparison of test results should be implemented. Such a program could help identify and eliminate inconsistencies among methods. In the absence of such a program, the clinical utility of current methods for determining LDL subclasses phenotype is open to question.

Although our study demonstrated disparities among various methods for differentiating LDL particle size, certain potential limitations of our study cannot be ignored. Our samples were a combination of plasma and serum (provided as requested by each laboratory), and there may be a 3% to 7% difference in the concentration of lipids between plasma and serum(29)(30). Additionally, one cannot completely ignore the physical effects of freezing and thawing on LDL particle subclasses, but several studies have demonstrated that freezing and thawing do not alter LDL phenotypes when GGE or ultracentrifugation is used for analysis(31)(32)(33). Whether this lack of an effect holds true for the other methods must be determined by further investigation.


   Footnotes
 
1 Nonstandard abbreviations: CVD, cardiovascular disease; LDL-C, LDL-cholesterol; GGE, gradient gel electrophoresis; NMR, nuclear magnetic resonance; VAP, ultracentrifugation–vertical auto profile; and TGE, tube gel electrophoresis.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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