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There is a paucity of data regarding relations of apolipoproteins (apolipoprotein B [ApoB] and apolipoprotein A-1 [Apo A-1]), lipoprotein particle measures (low-density lipoprotein particle concentration [LDLp] and high-density lipoprotein particle concentration [HDLp]), and lipoprotein cholesterol measures (low-density lipoprotein cholesterol [LDL-C], non–high-density lipoprotein cholesterol [non– HDL-C], and high-density lipoprotein cholesterol [HDL-C]) with atherosclerotic plaque burden, plaque eccentricity, and lipid-rich core presence as a marker of high-risk plaques.
Carotid artery magnetic resonance imaging was performed in 1,670 Atherosclerosis Risk in Communities study participants. Vessel wall and lipid cores were measured; normalized wall index (NWI), standard deviation (SD) of wall thickness (measure of plaque eccentricity) were calculated; and lipid cores were detected in vessels with ≥1.5 mm thickness. Fasting concentrations of cholesterol, ApoB and Apo A-1, and LDLp and HDLp were measured.
Measures of plaque burden (carotid wall volume, wall thickness, and NWI) were positively associated with atherogenic cholesterol and lipoproteins (p<0.05 for total cholesterol, LDL-C, non–HDL-C, ApoB, and LDLp), but not with HDL-C, Apo A-1, or HDLp. SD of wall thickness was associated with total cholesterol (p 0.01) and non-HDL-C (p 0.02). Although measures of atherogenic or anti-atherogenic cholesterol or lipoprotein were not individually associated with detection of a lipid-rich core, their ratios (total cholesterol/HDL-C, non–HDL-C/ HDL-C, and LDLp/HDLp) were associated with lipid-rich core presence (p≤0.05).
Extent of carotid atherosclerosis is associated with atherogenic cholesterol and lipoproteins. Atherogenic/anti-atherogenic cholesterol or particle ratios were associated with presence of a detectable lipid-rich core.
It has been suggested that measures of low-density lipoprotein (LDL) particles (Apolipoprotein B [ApoB] or LDL particle number [LDLp] measured by nuclear magnetic resonance [NMR] spectroscopy) perform better in cardiovascular risk prediction than the traditional cholesterol measures, LDL cholesterol (LDL-C) and non– high-density lipoprotein cholesterol (non–HDL-C).1, 2. Though this remains debatable in terms of clinical outcomes, there is a paucity of data regarding how these measures of particle number or cholesterol content of the particles relate to measures of either atherosclerotic plaque burden or presence of lipid-rich necrotic core, one of the features of high risk plaques.3
Atherosclerosis is a dynamic process with a balance between the cholesterol brought into the intima of the large arteries via atherogenic particles and reverse cholesterol transport mediated by high-density lipoprotein (HDL) particles.4, 5 Based upon this lipoprotein model of atherogenesis,4, 5 the use of ratios of atherogenic/anti-atherogenic cholesterol or lipoprotein measures should provide more information regarding assessment of plaque burden or lipid-rich core presence compared with the use of these measures individually.
Although carotid intima-media thickness (CIMT) can reliably assess presence or absence of plaque and provides useful prognostic information,6 magnetic resonance imaging (MRI) of the carotid arteries extends CIMT measurements to provide useful information regarding plaque burden, vascular remodeling, and the presence of a detectable lipid-rich necrotic core.7-10 Similarly, normalized wall index [NWI] (carotid wall area divided by total vessel area including lumen area) has been used as a measure of plaque burden, and takes into account inherent differences in wall areas among vessels of different diameters.11-13 In addition, standard deviation of the wall thickness measurement has been shown to be a marker of plaque eccentricity.11 Both NWI and standard deviation of the wall thickness have also been shown to be associated with a prior history of coronary heart disease in a recent publication.11
Thus the aim of our analysis was to identify which atherogenic or anti-atherogenic cholesterol or particle parameters can predict the burden of atherosclerosis in the carotid arteries, plaque eccentricity, and the presence of a detectable lipid-rich core. We also aimed to identify whether the use of atherogenic/anti-atherogenic cholesterol (or lipoprotein) ratios can provide information regarding plaque burden and lipid-rich core presence beyond that provided by the use of atherogenic and antiatherogenic cholesterol (or lipoprotein) parameters alone.
Methods for the ARIC MRI study have been described previously.10, 14 Relevant details are described here. The study sample consisted of members of the Atherosclerosis Risk in Communities (ARIC) study cohort who participated in the ARIC Carotid MRI substudy in 2004–2005 (Year 18). The ARIC study is a population-based cohort study of cardiovascular disease incidence among African American and Caucasian adults (n=15,792)15.
The ARIC MRI sampling strategy included approximately 1,200 participants with carotid artery wall thickness (maximum over 6 sites: left and right of the common, bifurcation, internal) that was at least >68th percentile as measured by carotid B-mode ultrasound on ARIC study visits 3 (1993–1995) and 4 (1996–1998). Intima-media thickness (IMT) cut-offs were 1.35, 1.00, 1.28, and 1.22 mm in Forsyth County, Jackson, Minneapolis, and Washington County, respectively, representing the 73rd, 69th, 73rd, and 68th percentiles of maximal IMT. A cohort random sample of approximately 800 participants whose carotid intima-media thickness was <68th percentile was also included. Ineligibility criteria for the carotid MRI substudy included standard contra-indications to the MRI exam or to the contrast agent, carotid revascularization on either side for the low CIMT group or on the selected side for imaging for the high CIMT group, and difficulties in understanding questions or in completing the informed consent.
Protocols for fasting glucose, blood pressure, height and weight measurements were identical at the baseline ARIC cohort examination and 18 years later at the Carotid MRI substudy examination. The study was approved by the institutional review committees of all participating centers, and all participants provided written informed consent.
Twelve-hour fasting plasma lipid assays were performed on participants of the ARIC MRI substudy. Plasma samples were collected on ice using EDTA as the anticoagulant at the time of the MRI visit. Total cholesterol, triglycerides, and HDL-C were measured enzymatically and expressed in mg/dL units. LDL-C levels were calculated using the Friedewald equation.16 Non-HDL-C levels were calculated as total cholesterol minus HDL-C. Blind duplicate coefficient of variation for total cholesterol, HDL-C, and triglycerides were 2.0%, 3.0%, and 2.7%, respectively.
Apolipoproteins (ApoB and Apo A-1) were measured in frozen plasma using a commercially available immunoturbidimetric assay (OLYMPUS®, Olympus America Inc). The inter-assay coefficient of variation for Apo A-I and ApoB were 3.9% and 7.2%, respectively. Intra-assay coefficient of variation for Apo A-1 and ApoB were 1.5% and 1.7%, respectively. Reliability coefficients based on 120 blinded split QC samples were 0.93 and 0.95 for ApoA-I and ApoB, respectively.
Frozen EDTA plasma samples were thawed, and a 200 μL aliquot withdrawn, refrozen, and shipped on dry ice to Liposcience, Inc. (Raleigh, NC) for NMR lipoprotein particle analysis. Using this technique, particle concentrations of lipoproteins of different sizes were calculated from the measured amplitudes of their spectroscopically distinct lipid-methyl group.17 The NMR variables examined in this manuscript were total LDL particle concentration (LDLp) expressed in nmol/L units and total HDL particle concentration (HDLp) expressed in μmol/L units.
The methods for acquisition and interpretation of the MRI images collected in the Carotid MRI substudy have been described previously.10, 14 Briefly, a contrast-enhanced MRI exam of the thickest 1.6-cm segment of the thicker carotid artery was performed according to a standard protocol on a 1.5T whole-body scanner as follows: A 3-dimensional time-of-flight magnetic resonance angiogram (MRA) was acquired through both carotid bifurcations. Detailed black blood MRI (BBMRI) images were then acquired through the extracranial carotid bifurcation, known to have a thicker maximum wall by the most recent ultrasound study, unless the contralateral carotid bifurcation wall appeared to the technologist to be thicker on the MRA. These BBMRI images consisted of 16 axial T1-weighted, fat-suppressed slices (thickness=2 mm; acquired in-plane resolution=0.51 × 0.58 mm2; total longitudinal coverage=3.2 cm) oriented perpendicular to the vessel and centered at the thickest part of the internal or common carotid artery wall. These 16 slices were acquired 5 minutes after the intravenous injection of gadodiamide (Omniscan, GE Amersham), 0.1 mmol/kg body weight, with a power injector.
Seven readers were trained to interpret the MRI images and contour the wall components using specialized software (VesselMass, Leiden University Medical Center). Readers were blinded to the characteristics of the study population. Each reader drew contours to delineate the lumen, outer wall, lipid core, and calcification. Eight slices centered at the slice with the thickest wall were analyzed. All exams were assigned quality scores by the reader based on image quality and protocol adherence; exams that failed were not analyzed.
Using semi-automated software, vessel walls were divided into 12 radial segments, and mean thickness values were generated for each segment (Figure 1). Standard deviation of the wall thickness measurement was also computed and provides a measure of the plaque eccentricity. Area measurements were calculated for the lipid core and calcification contours. Volumetric data were computed by integrating area measurements over all 8 slices examined.
Maximum segmental wall thickness (in mm) was defined as the maximum wall thickness of 12 segments at the slice with the largest lipid contour area. Vessel wall area and lumen area (in cm2) were computed at the slice with the largest mean segmental wall thickness. Volume measurements for total wall volume (in mm3) was computed by integrating area measurements over 8 slices. Normalized wall index (NWI) was then calculated. NWI was derived as carotid wall area divided by the total vessel area. The total vessel area in these measurements includes the lumen area. Lipid-rich core was represented as a binary variable (present or absent).
All analyses were weighted and appropriately accounted for the stratified random sampling design of the ARIC Carotid MRI substudy. Analyses were conducted using SAS version 9.1 for descriptive statistics or SUDAAN for domain analysis. Wall thickness and wall volume were analyzed in the full data set. Due to the resolution constraints of the MRI scan, we restricted consideration of lipid core to those 1,131 participants whose maximum wall thickness was ≥1.5 mm. Only 4 lipid cores were excluded using this cut point. Among the 1,131 participants who were included in the analyses, a total of 542 participants had an identifiable lipid-rich core.
For continuous MRI variables, standardized regression coefficients (β) are presented for linear regression models, standardizing by one standard deviation (SD) of exposure and outcome with adjustment for covariates. These β coefficients can be interpreted as number of SD difference in the dependent variable (e.g., total wall volume) associated with a single SD difference in plasma cholesterol or lipoprotein level or their ratio. For the dichotomous lipid-rich core variable (presence or absence), standardized odds ratio (OR) are presented for logistic regression models adjusting for covariates. These ORs can be interpreted as the odds for the presence of lipid-rich core associated with a single SD difference in plasma cholesterol or lipoprotein level or their ratio. Covariates included for adjustment were age, race, gender, smoking status, body mass index, blood glucose, hypertensive medication use, cholesterol-lowering medication use, aspirin use, diabetic medication use, hs-CRP, and plasma triglyceride concentration. Similarly, the coefficients of determination (R2) were described for each model including the above-mentioned covariates as well as lipid or lipoprotein particle measure of interest. R2 can be interpreted as proportion of the variance in the dependent variable (e.g., total wall volume) as explained by the set of independent variables in the model including the cholesterol or lipoprotein particle concentration parameter of interest.
Because the presence of lipid-rich core on carotid MRI has been shown to highly correlate with carotid wall thickness,10 we conducted additional analyses to examine the presence of lipid-rich core, while controlling for carotid wall thickness in addition to the covariates mentioned above.
Final analyses, after excluding those with insufficient quality or missing MRI variables (n=336) and those missing lipid measurement (n=60) data, included 1,670 participants. Baseline characteristics and baseline lipid and lipoprotein levels for the current study participants are shown in Table 1. The mean age of this cohort was 71 years with 49% men and a predominance of Caucasians (78%). The proportion of participants with history of diabetes, hypertension, current smoking, or statin medication use was 24%, 68%, 9%, and 39%, respectively. More than half of the patients fulfilled the National Heart, Lung and Blood Institute/American Heart Association criteria for metabolic syndrome.18 Mean levels of total cholesterol, LDL-C, non-HDL-C, ApoB and LDLp were 193 mg/dL, 113 mg/dL, 144 mg/dL, 98 mg/dL and 1148 nmol/L, respectively. Mean levels of HDL-C, ApoA-1 and HDLp were 49 mg/dL, 131 mg/dL, and 35 μmol/L, respectively.
As expected, LDL-C was correlated with ApoB (r=0.74), and LDLp (r=0.66), and non-HDL-C was even more strongly correlated (r=0.85 for ApoB; r=0.77 for LDLp). HDL-C levels had a high correlation with Apo A-1 levels (r=0.80), and a more modest correlation with HDLp (r = 0.63).
Among the atherogenic lipid measures, total cholesterol, LDL-C, non-HDL-C, ApoB, and LDLp were all associated with maximum wall thickness as well as carotid wall volumes in fully adjusted models. It can also be noted from Table 2 that the standardized beta coefficients (β) were numerically higher for total cholesterol (β=0.13), and non-HDL-C (β=0.13) compared with LDL-C (β=0.09) for total wall volume. The β for total cholesterol and non–HDL-C were also higher than those obtained for ApoB 0=0.06) and LDLp (β=0.08).
Anti-atherogenic cholesterol (HDL-C) or lipoprotein parameters (Apo A-1, HDLp) were not associated with carotid wall volume or maximum wall thickness. Although total cholesterol/ HDL-C ratio and non-HDL-C/ HDL-C ratio were associated with carotid wall volume, they were not associated with an improvement in model prediction (R2 values) compared with the use of atherogenic cholesterol or lipoprotein parameters alone.
Normalized wall index was associated with atherogenic cholesterol (or lipoproteins) and the ratio of atherogenic/anti-atherogenic cholesterol or lipoproteins, but not when anti-atherogenic cholesterol or lipoproteins parameters were used alone. Similarly, plaque eccentricity was positively associated with total cholesterol and non-HDL-C in the fully adjusted models.
Among persons with arteries ≥1.5 mm in thickness, the associations between the presence of detectable lipid-rich core and various atherogenic or anti-atherogenic lipoprotein cholesterol/particle measures as well as their ratios on carotid MRI are shown in Table 3. Although neither atherogenic nor anti-atherogenic lipoprotein measures could individually predict the presence of lipid-rich core in the plaques, ratios of total cholesterol/ HDL-C, non-HDL-C/ HDL-C, and LDLp/HDLp were associated with the presence of lipid-rich core. Apo B/Apo A-1 ratio was not significantly associated with presence of lipid-rich core in fully adjusted models.
Because detection of lipid-rich core is highly dependent on wall thickness,10 we subsequently added maximum wall thickness to the adjustment model (Table 4). The overall model prediction (R2) was substantially improved by adding maximum wall thickness to the model, but the associations between the presence of lipid-rich core and atherogenic/anti-atherogenic cholesterol as well as lipoprotein ratios were attenuated. On the other hand, the inverse associations between the presence of lipid-rich core and Apo A-1 (OR=0.84, p=0.08) or HDLp (OR=0.84, p=0.06) became borderline significant.
Our findings indicate that measures of carotid artery plaque burden and plaque eccentricity were positively associated with atherogenic particle number or atherogenic cholesterol content (especially total cholesterol and non–HDL-C). Although the various measures of anti-atherogenic lipids were not associated with measures of plaque burden when examined individually, the ratio of atherogenic/anti-atherogenic lipids was associated with lipid-rich core presence in arteries of >1.5 mm thickness. This association between the presence of lipid-rich core and atherogenic/anti-atherogenic ratios was attenuated once maximum wall thickness was included in the adjustment model, but the association between anti-atherogenic lipoprotein measures and the presence of lipid-rich core approached significance after including maximum wall thickness in the adjustment model. The strengths of association for carotid plaque burden assessed using total cholesterol and non–HDL-C were numerically higher compared with LDL-C, ApoB, and LDLp.
Several cholesterol and particle measures of atherogenic lipoproteins have been associated with measures of plaque burden. In a prior manuscript,19 both small and large LDLp subclasses were associated with carotid intima media thickness (a marker of carotid plaque burden). Similarly, LDL-C has been shown to be associated with carotid IMT.20, 21 Our findings confirm these earlier observations when carotid MRI is used to measure atherosclerosis burden. We show that all the measures of atherogenic cholesterol or particle number are indeed associated with measures of plaque burden using carotid MRI.
In addition, two other observations could be made. First, the β coefficients were higher for total cholesterol and non–HDL-C compared with LDL-C. This is not surprising because non–HDL-C measures cholesterol content within all the atherogenic particles (very-low-density lipoproteins, intermediate-density lipoproteins, and lipoprotein(a) in addition to LDL).22-24 Therefore, non–HDL-C use may provide a more comprehensive assessment of the plaque burden. Second, we show that the strength of association (β coefficient) as well as the overall model prediction (R2) is at least as strong or stronger with measures of cholesterol content (total cholesterol and non–HDL-C) compared with measures of atherogenic particle number (ApoB or LDLp).
Presence of lipid-rich core identified by carotid MRI has been shown to have a very strong association with wall thickness,10 and our models for core which included wall thickness had substantially larger R2 compared with those without wall thickness. This could be due to the fact that carotid MRI is not sensitive for detecting very small lipid-rich cores. Individuals with a higher burden of atherosclerosis (as evidenced by having thicker carotid walls) are expected to have larger lipid-rich cores. We also showed that after adjusting for maximum wall thickness which is a measure of the size of the atherosclerotic lesion, the atherogenic parameters or the ratios of atherogenic/anti-atherogenic parameters were not associated with lipid-rich core presence. Interestingly, the negative association between HDLp (or Apo A-1) and presence of lipid-rich core increased after adjustment was made for maximum wall thickness, and the association became borderline significant. These results indicate that although atherogenic lipoproteins are the primary determinants of plaque burden, the development of detectable lipid rich cores in large plaques may be influenced by low levels of HDL.
Our findings have potentially important research implications in terms of which MRI parameters to follow when therapies that lower atherogenic lipids or raise anti-atherogenic lipids are used. Our results indicate that measures of plaque burden could be monitored for disease progression when evaluating the impact of therapies that lower atherogenic lipids, whereas lipid-rich necrotic core presence might be an important imaging parameter to follow with therapies that markedly improve the ratio of atherogenic/anti-atherogenic lipids by both reducing the atherogenic cholesterol (or lipoproteins) and raising anti-atherogenic cholesterol (or lipoproteins). Our results showed that although atherogenic lipids can explain the variance in measures of plaque burden in the artery wall to a greater extent, the development of plaques with large lipid-rich necrotic cores is more likely to occur when anti-atherogenic lipid levels are low.
The cross-sectional nature of this study does not allow us to imply causal relationships; however, the findings are valuable for generating hypotheses. We only evaluated the associations between cholesterol and lipoprotein parameters, and measures of plaque burden and presence of lipid-rich core. Though there are studies suggesting that wall thickness, wall area, NWI, and measures of plaque eccentricity can predict cardiovascular outcomes,11 we did not evaluate cardiovascular outcomes in this cohort given limited statistical power. Our associations for the presence of lipid-rich core were attenuated once wall thickness was entered as a covariate in the adjusted models. This observation is not surprising as a prior study from the ARIC MRI cohort has shown that wall thickness itself is very highly correlated with the presence of a lipid-rich core.10 Similarly, the resolution constraints of the MRI limit the ability to fully characterize the presence of lipid-rich core in small plaques.
The strengths of this study include relatively large number of patients in the ARIC MRI cohort and extensive quality control measures for the carotid MRI as well as lipid and lipoprotein measurements in the ARIC study. Moreover, representation from both African American and Caucasian participants makes our results generalizable to a broader population. To our knowledge, this is the largest study evaluating the associations between measures of cholesterol content and particle number and carotid plaque characteristics using carotid MRI as the imaging modality.
We conclude the measures of plaque burden in the carotid arteries are positively associated with both cholesterol and particle measures of lipoproteins. Measures of plaque eccentricity are also positively associated with total cholesterol and non-HDL-C. Although anti-atherogenic cholesterol or lipoproteins are not directly associated with carotid plaque burden, the development of detectable lipid rich cores in large plaques could be influenced by low levels of anti-atherogenic lipoproteins.
The authors thank the staff and participants of the ARIC study for their important contributions. The authors would also like to thank Ms. Joanna A. Brooks, BA, for her editorial assistance.
Funding, Conflicts of Interest, and Disclosures of Financial Support: The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C) with the ARIC carotid MRI examination funded by U01HL075572-01. Dr. Virani is supported by a Department of Veterans Affairs Health Services Research and Development Service (HSR&D) Career Development Award (CDA-09-028).
Dr. Otvos is executive vice president and chief scientific officer of LipoScience.
Dr. Nambi has research collaboration with GE and has served on a virtual advisory board for Roche.
Dr. Virani has received research support (to the institution and not to the individual) from Merck and Co, Inc., and the National Football League Charities.
Dr. Ballantyne has received research support (to the institution and not to the individual) from Abbott, AstraZeneca, Bristol-Myers Squibb, diaDexus, GlaxoSmithKline, Kowa, Merck, Novartis, Roche, Sanofi-Synthelabo, Takeda, NIH, American Diabetes Association, and the American Heart Association; is a consultant for Abbott, Adnexus, Amylin, AstraZeneca, Bristol-Myers Squibb, Esperion, Genentech, GlaxoSmithKline, Idera Pharma, Kowa, Merck, Novartis, Omthera, Resverlogix, Roche, Sanofi-Synthelabo, and Takeda; is on the speakers bureau for Abbott, AstraZeneca, GlaxoSmithKline, and Merck; and, has received honorarium from Abbott, AstraZeneca, GlaxoSmithKline, Merck, Sanofi-Synthelabo, and Takeda.
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