More precise estimation of the atherogenic lipid parameters could improve identification of residual risk beyond what is possible using traditional lipid risk factors. The aim of the present study was to explore the association between advanced analysis of lipoprotein subfractions and the prevalence of coronary artery calcium. Consecutive participants at intermediate cardiovascular risk who were undergoing computed tomographic assessment of coronary calcium (calcium score) were included. Using a validated ultracentrifugation method (the vertical autoprofile II test), cholesterol in eluting lipoprotein subfractions [i.e., low- (LDL), very-low-, intermediate-, and high-density lipoprotein subclasses, lipoprotein (a), and predominant LDL distribution] was directly quantified. A total of 410 patients were included (29% women, mean age 57 years), of whom 297 (72.4%) had coronary artery calcium. LDL pattern B (predominance of small dense particles) emerged as an independent predictor of coronary calcium after adjustment for traditional risk factors (odds ratio 4.46, 95% confidence interval 1.19 to 16.7). However, after additional stratification for dyslipidemia, as defined by conventional lipid profiling, a statistically significant prediction was only retained for high-density lipoprotein subfraction 2 (odds ratio 3.45, 95% confidence interval 2.03 to 50.1) and “real” LDL (odds ratio 6.10, 95% confidence interval 1.26 to 23.41) in the normolipidemia group and for lipoprotein (a) (odds ratio 7.81, 95% confidence interval 1.41 to 43.5) in the dyslipidemic group. In conclusion, advanced assessment of the lipoprotein subfractions [i.e., LDL pattern B, high-density lipoprotein subfraction 2, “real” LDL, and lipoprotein (a)] using the vertical autoprofile II test provided additional information to that of conventional risk factors on the prevalence of coronary artery calcium in patients at intermediate cardiovascular risk.
Evidence from large epidemiologic studies and clinical trials has established the importance of traditional lipid risk factors and their management. However, most cardiovascular events have not been prevented. More precise estimation of atherogenic lipid parameters could improve the identification of residual risk beyond that of traditional lipid risk factors. A variety of assays subfractionate lipoprotein particles according to size, density, or charge. Density gradient ultracentrifugation, such as in the vertical autoprofile II test (Atherotech, Birmingham, Alabama), measures the relative distribution of cholesterol within various lipoprotein subfractions, quantifying the cholesterol content of low-density (LDL), very-low-density, intermediate-density (IDL), and high-density (HDL) lipoprotein subclasses, and lipoprotein (a) [Lp(a)], real LDL [(LDL-R) calculated by subtracting Lp(a) and IDL from LDL]. Density gradient ultracentrifugation also determines the predominant LDL size distribution (e.g., A, large buoyant LDL particles; A/B, intermediate pattern; and B, small particles). The aim of the present study was to explore the association between these advanced lipid parameters and the prevalence of coronary artery calcium (CAC)—a widely accepted surrogate marker of prevalent coronary atherosclerosis and a very strong risk marker for incident cardiovascular events, including myocardial infarction, stroke, and cardiovascular death.
Methods
Consecutive patient undergoing cardiovascular risk assessment at a preventive cardiology outpatient program of the Harbor-UCLA Medical Center (Los Angeles, California) were considered for inclusion. Participants without a history, or evidence, of cardiovascular disease at intermediate cardiovascular risk (10% to 20% 10-year risk of a major cardiovascular disease event) according to the Framingham risk score who were undergoing computed tomographic assessment of CAC score were included.
Age and ethnicity were self-reported. Cardiovascular risk factors were measured or collected. These included hypertension (systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg, or a history of physician-diagnosed hypertension and taking antihypertensive medication), diabetes (fasting glucose ≥126 mg/dl or taking antidiabetic medication), a family history of premature heart disease, tobacco consumption, and the lipid profile. The lipid profile was defined on the basis of high LDL cholesterol, low HDL cholesterol, and/or high triglyceride (TG) levels. Normolipidemia was defined as HDL >40 mg/dl in women and >50 mg/dl in men, LDL cholesterol <160 mg/dl, and TG <150 mg/dl. Dyslipidemia included the following categories: hypercholesterolemia—LDL cholesterol ≥160 mg/dl and TG <150 mg/dl; hypertriglyceridemia—HDL >40 mg/dl in women and >50 mg/dl in men, LDL cholesterol <160 mg/dl, and TG ≥150 mg/dl; isolated low HDL—HDL ≤40 mg/dl in women and ≤50 mg/dl in men, LDL cholesterol <160 mg/dl, and TG <150 mg/dl; metabolic dyslipidemia—HDL ≤40 mg/dl in women and ≤50 mg/dl in men, LDL cholesterol <160 mg/dl, and TG ≥150 mg/dl; and combined dyslipidemia—LDL cholesterol ≥160 mg/dl and TG ≥150 mg/dl. For analysis purposes, the study population was stratified into those with and without dyslipidemia.
CAC was assessed using electron beam tomographic imaging (GE, Milwaukee, Wisconsin). The typical acquisition parameters were used in a 40-slice study (3-mm slice thickness, 3-mm table increment, 100-ms acquisition times), as previously described. The CAC score of each lesion was calculated by multiplying the lesion area by a density factor derived from the maximum Hounsfield units within this area, as described by Agatston et al. The density factor was assigned as follows: 1 for lesions whose maximum density was 130 to 199 HU, 2 for lesions 200 to 299 HU, 3 for lesions 300 to 399 HU, and 4 for lesions >400 HU. A total CAC score was determined by summing the individual lesion scores at each anatomic site. The presence of CAC was defined by a score of >0 Agatston units.
The lipid and lipoprotein measurements were performed using fasting (12-hour) ethylenediaminetetraacetic acid plasma. The cholesterol associated with individual lipoprotein subfractions was quantified using the vertical autoprofile II method, a validated ultracentrifugation method that involves direct cholesterol measurements in eluting fractions, including very-low-density lipoprotein, IDL, 4 LDL subfractions (from the most buoyant LDL 1 to the most dense LDL 4 ), 3 LDL density/size subtype patterns (B, predominant, small, dense LDL particles; A, predominant, large, buoyant LDL particles, and A/B, an intermediate pattern), Lp(a), and HDL subfractions HDL 2 and HDL 3 . A value for LDL, designated “real LDL” (LDL-R), was calculated from all fractions containing true LDL particles (LDL 1–4 ) and excluded the contributions of IDL and Lp(a) included in the standard LDL measurements. All vertical autoprofile II lipid measurements were performed by Atherotech (Birmingham, Alabama).
Normally distributed continuous variables are described as the mean ± SD and were compared using the t test. Non-normally distributed continuous variables are described as the median and interquartile range and were compared using the Mann-Whitney U test. Categorical variables are described as numbers and/or percentages and were compared using the chi-square test. Multiple regression analysis was performed to determine the independent predictors for the presence of CAC (score >0 Agatston units) using the significant univariate predictors (i.e., age, gender, diabetes, hypertension, family history, smoking status, body mass index, and lipid parameters) as covariates. For the multivariate analysis, advanced lipid parameters were dichotomized according to cutoffs proposed by risk-identifying cohort studies and the current guidelines: LDL-R >100 mg/dl, HDL 2 >10 mg/dl in men and >15 mg/dl in women, HDL 3 >20 mg/dl in men and >25 mg/dl in women, Lp(a) >50 mg/dl, very-low-density lipoprotein >30 mg/dl, and IDL >20 mg/dl. A p value of ≤0.05 was considered statistically significant. Statistical analyses were performed using PASW, version 18.0.0, for Windows.
Results
Of 508 eligible patients who had undergone cardiovascular screening, 410 had complete results from the computed tomographic calcium scan and demographic characteristics available and were included in the final analysis. The participants were 57 years old on average, almost 1/3 were women (28.8%), almost 60% were white ( Table 1 ). Of the 410 participants, 198 (48%) had dyslipidemia, of whom, 162 (81.8%) had CAC compared to 135 participants (63.7%) with CAC in the normolipidemia group.
| Variable | All (n = 410) | CAC | ||
|---|---|---|---|---|
| Absent (n = 113) | Present (n = 297) | p Value | ||
| Women | 118 (28.8%) | 46 (40.7%) | 60 (20.2%) | <0.001 |
| Age (yrs) | 57.3 ± 11 | 50.3 ± 10 | 59.0 ± 10 | |
| White | 239 (58.3%) | 50 (44.3%) | 194 (65.3%) | <0.001 |
| Hypertension | 135 (32.9%) | 25 (22.1%) | 122 (41.1%) | 0.002 |
| Diabetes mellitus | 45 (11.0%) | 6 (5.3%) | 45 (15.2%) | 0.001 |
| Current smoking | 61 (14.9%) | 11 (9.7) | 54 (18.2%) | 0.103 |
| Body mass index (kg/m 2 ) | 28 ± 4 | 27 ± 8 | 29 ± 6 | 0.092 |
| Total cholesterol (mg/dl) | 197 ± 45 | 214 ± 57 | 191 ± 40 | 0.216 |
| Low-density lipoprotein (mg/dl) | 128 ± 39 | 138 ± 39 | 124 ± 36 | 0.061 |
| High-density lipoprotein (mg/dl) | 0.016 | |||
| Median | 46 | 48 | 44 | |
| Interquartile range | 39–58 | 40–64 | 37–56 | |
| Triglycerides (mg/dl) | 0.102 | |||
| Median | 125 | 126 | 100 | |
| Interquartile range | 79–188 | 50–168 | 55–153 | |
| Low-density lipoprotein–real (mg/dl) | 108 ± 34 | 102 ± 32 | 112 ± 32 | 0.161 |
| High-density lipoprotein subtype 2 (mg/dl) | 12 ± 7 | 13 ± 7 | 11 ± 7 | 0.006 |
| High-density lipoprotein subtype 3 (mg/dl) | 26 ± 6 | 27 ± 6 | 25 ± 6 | 0.069 |
| Very-low-density lipoprotein (mg/dl) | 0.090 | |||
| Median | 16 | 19 | 15 | |
| Interquartile range | 10–25 | 7–24 | 8–22 | |
| Intermediate density lipoprotein (mg/dl) | 14 ± 8 | 17 ± 8 | 13 ± 7 | 0.508 |
| Lipoprotein (a) (mg/dl) | 7.5 ± 4.2 | 8.4 ± 3.6 | 7.1 ± 3.8 | 0.009 |
| Low-density lipoprotein pattern B | 205 (50%) | 27 (24%) | 178 (60%) | 0.011 |
Overall, 297 participants (72.4%) had CAC. On average, the participants with CAC were older, more likely to be men and white, and had a greater prevalence of traditional risk factors ( Table 1 ). In terms of lipid parameters, HDL (specifically, HDL 2 ), Lp(a), and LDL pattern B were significantly different between patients with and without CAC. Across decreasing quartiles of HDL 2 concentration, the prevalence and extent of CAC increased, with a reverse association observed across LDL density and size patterns from A (predominantly large, buoyant LDL) to B (predominantly small, dense LDL particles; Figure 1 ). We analyzed the cohort for both CAC >0 and CAC >100. The results were similar using either cutpoint (data not shown).
In a multivariate model adjusting for demographic characteristics (e.g., age, gender, ethnicity) and traditional risk factors (e.g., hypertension, diabetes, family history, body mass index, and smoking), none of the explored lipid parameters predicted the presence of CAC with reliable statistical significance, except for LDL pattern B. However, after stratification for dyslipidemia, LDL-R and HDL 2 emerged as independent predictors of CAC in patients with normal lipid levels, and Lp(a) emerged as an independent predictor of CAC in patients with dyslipidemia ( Figure 2 ).
