Risk Factor Differences for Aortic Versus Coronary Calcified Atherosclerosis
The Multiethnic Study of Atherosclerosis
Objective—The goal of this study was to compare and contrast coronary artery calcium (CAC) with abdominal aortic calcium (AAC) in terms of their associations with traditional and novel cardiovascular disease (CVD) risk factors.
Methods and Results—We measured both AAC and CAC using computed tomography scans in 1974 men and women aged 45 to 84 years from a multiethnic cohort. Traditional and novel CVD risk factors were examined separately in relation to AAC and CAC, using logistic regression for qualitative categorical comparisons and multiple linear regression for quantitative continuous comparisons. AAC was significantly associated with cigarette smoking and dyslipidemia and showed no gender difference. In contrast, CAC showed much weaker associations with smoking and dyslipidemia and a strong male predominance. Age and hypertension were associated similarly and significantly with AAC and CAC. Novel risk factors generally showed no independent association with either calcium measure, although in subset analyses, phosphorus, but not calcium, was related to CAC. The receiver operating characteristic curves for the qualitative results and the r2 values for the quantitative analyses were both much higher for AAC than for CAC.
Conclusion—AAC showed stronger correlations with most CVD risk factors than did CAC. The predictive value of AAC compared with CAC for incident CVD events remains to be evaluated.
In the Multi-Ethnic Study of Atherosclerosis (MESA), cardiovascular disease (CVD) risk factors are modestly associated with coronary artery calcium (CAC) assessed by computed tomography (CT) in both cross-sectional1 and longitudinal progression analyses.2 These results are concordant with other studies.3,4 Less attention has been paid to risk factors for abdominal aortic calcium (AAC), another important measure of subclinical CVD. The degree to which CAC and AAC are similar pathophysiologic processes is not clear. A study of 650 patients with full body scans suggested somewhat stronger correlations for hypercholesterolemia, cigarette smoking, and diabetes with AAC than CAC.5 Two smaller studies, limited to women, showed only minor differences in the associations between standard CVD risk factors and CAC versus AAC.6,7 A recent study in MESA compared AAC and thoracic aortic calcium, but CAC was not included.8 However, no data have been published comparing CT quantitative assessment of AAC and CAC with both standard and newer risk factors. In a study ancillary to the MESA, we quantified both AAC and CAC by CT scans, and we compared and contrasted standard and novel CVD risk factor associations for AAC and CAC in a cross-sectional analysis.
MESA is a prospective cohort study investigating subclinical atherosclerosis in 6814 individuals aged 45 to 84 years without known clinical CVD at baseline. Women made up 53% of the cohort, and 4 ethnic groups were represented: 38% white, 28% black, 22% Hispanic, and 12% Chinese. The cohort was recruited and initially examined from 2000 to 2002 at 6 field centers: Baltimore, Md; Chicago, Ill; Los Angeles, Calif; New York, NY; St Paul, Minn; and Winston-Salem, NC. Individuals were excluded if they had clinical CVD, including physician-diagnosed myocardial infarction, angina, stroke, transient ischemic attack, or heart failure; use of nitroglycerin; or current atrial fibrillation or had undergone a procedure related to CVD. A detailed description of the study design, recruitment methods, examination components, and data collection has been published.9
After the MESA study began an ancillary study measured AAC and CAC at the same examination in a random sample of MESA participants, recruited during follow-up visits between August 2002 and September 2005 from 5 of the 6 MESA field centers: those above with the exception of Baltimore. Of 2202 MESA participants recruited, 2172 agreed to participate, and 1990 satisfied eligibility criteria; 1974 participants had complete CT scanning of their abdominal aorta. Additional details about the MESA study design have been published elsewhere and are available online at http://www.mesa-nhlbi.org.
Risk Factor Assessment
Standardized questionnaires at the baseline examination were used to obtain information about participant demographics, medical history, and medication usage, including current blood pressure (BP)– and cholesterol-lowering medications. Height, weight, and BP were measured at the baseline examination. Body mass index was calculated as weight (kg)/ height (m2). Resting BP was measured 3 times in the seated position using a Dinamap automated sphygmomanometer, and the average of the 2nd and 3rd readings was used for this analysis. Blood samples were obtained after a 12-hour fast to measure glucose, total cholesterol, high-density lipoprotein cholesterol (HDL-C), triglycerides, and creatinine. Estimated glomerular filtration rate was calculated using the 4-variable Modification of Diet in Renal Disease equation. Low-density lipoprotein cholesterol (LDL-C) was calculated using the Friedewald equation.
Diabetes was classified as having a fasting blood glucose >125 mg/dL or the self-reported use of hypoglycemic medications. Impaired fasting glucose status was defined as fasting glucose 101 to 125 mg/dL. Participants were classified by never, former, or current use of cigarettes, and pack-years of cigarette smoking in ever smokers was calculated.
The following novel risk factors were also measured: C-reactive protein (CRP), interleukin-6 (IL-6), fibrinogen, homocysteine, d-dimer, factor VIII, plasmin antiplasmin complex, insulin, and Chlamydia pneumoniae titer. CRP and fibrinogen were measured by immunonephelometry using the BNII instrument (N High Sensitivity CRP, N Antiserum to Human Fibrinogen, Dade Behring Inc., Deerfield, Ill). IL-6 was measured by ultrasensitive ELISA (Quantikine HS Human IL-6 Immunoassay; R&D Systems, Minneapolis, Minn). Homocysteine was measured by fluorescence polarization immunoassay (IMx Homocysteine Assay, Axis Biochemicals ASA, Oslo, Norway) using the IMx Analyzer (Abbott Diagnostics, Abbott Park, Ill). Factor VIII coagulant activity and d-dimer were determined using the STA-R automated analyzer (STA-Deficient VIII, Liatest D-DI, Diagnostica Stago, Parsippany, NJ). Plasmin antiplasmin complex was measured using a 2-site ELISA that detects only plasmin in complex with α2-antiplasmin, not free plasmin or α2-antiplasmin, so it is a marker of plasmin generation.10 Insulin was determined using the Linco Human Insulin Specific RIA Kit (Linco Research, Inc., St. Charles, Mo). IgG antibodies to C. pneumoniae were detected using a microimmunofluorescent antibody assay using a 2-stage sandwich procedure for the qualitative and semiquantitation detection (Focus Technologies, Cypress, Calif).
Subclinical Vascular Disease Assessment
To measure AAC, electron-beam CT scanners were used at Northwestern University and University of California, Los Angeles (Imatron C-150). These were set as follows: scan collimation of 3 mm, slice thickness of 6 mm, and reconstruction using 25 6-mm slices with 35-cm field of view and normal kernel. Multidetector CT mode scanners were used at the remaining 3 field centers (Columbia University, Wake Forest University, and University of Minnesota field centers; Sensation 64, GE Lightspeed, Siemens S4+ Volume Zoom and Siemens Sensation 16). Images were reconstructed in a 35-cm field of view with 5-mm slice thickness. All scan scores were brightness adjusted with a standard phantom.
Noncontrast CT images were analyzed centrally using a standard protocol by the MESA CT Reading Center. Calcium in the wall of the distal abdominal aorta in the 8-cm segment proximal to the aortic bifurcation was measured. Calcification was identified as a plaque of ≥1 mm2 with a density of >130 Hounsfield units and quantified using the previously described Agatston scoring method.11
At the same scanning examination, CAC was measured using either electron-beam tomography (3 sites) or multidetector CT (2 sites). Participants were scanned twice consecutively, and each scan was read by a single trained physician-reader independently at a centralized reading center (Harbor-University of California, Los Angeles, Medical Center/Los Angeles Biomedical Research Institute, Torrance, Calif). The methodology for acquisition and interpretation of the scans, as well as reproducibility of the readings, has been reported previously.12 The results from the 2 scans were averaged to provide a more accurate point estimate of the amount of calcium present. The Agatston score was calculated as previously described,11 and scores were adjusted using a standard calcium phantom that was scanned along with the participant.13 The phantom contained 4 bars of known calcium density, and it calibrated the X-ray attenuation level between measurements conducted on different machines. This was important, as scanners were changed between baseline and follow-up at 3 of the 6 sites. Any detectable calcium was defined as a CAC score greater than 0; a minimum focus of calcification was based on at least 4 contiguous voxels, which resulted in identification of calcium of 1.15 mm3 for the multidetector CT scanners and 1.38 mm3 for the electron-beam tomography scanners.12 The nominal section thickness was 3.0 mm for electron-beam tomography scanners and 2.5 mm for multidetector CT. The distribution of CAC in MESA at baseline by age, gender, and ethnicity has been published previously.14
For potential risk factors, we tabulated the mean value and distribution of continuous variables across first those with zero versus nonzero AAC, and then zero versus nonzero CAC. Two sets of multivariate models were used. The first set of models was logistic regression and separately compared the zero versus nonzero AAC and zero versus nonzero CAC groups. In logistic regression models, each potential risk factor was first adjusted for age, gender, and ethnicity. Risk factors that were significant (P<0.05) for either AAC or CAC were included in a second multivariate model that included adjustment for each of the other risk factors. Receiver operating characteristic (ROC) curves were calculated alternately for prediction of the presence of AAC and for the presence of CAC, first using only age, gender, and ethnicity and then using all the significant risk factors. Additional analyses included gender-specific models.
In the second set of models, multiple linear regression was used with either lnAAC+1 or lnCAC+1 as the dependent variable, and r2 values were calculated. Because the natural logarithm of the calcium scores was used, 1 was added to each score to include the zero scores. Using the risk factors selected in the final logistic model above, the linear models predicted in lnAAC+1 and lnCAC+1 respectively. Analyses were performed using Stata 10.0 (College Station, Texas). All probability values were 2-tailed (α=0.05).
Figure 1 shows the distribution of AAC and CAC in the study population, with percentages in parentheses. 21.1% had neither AAC nor CAC, 21.1% had AAC only, 7.3% had CAC only, and 50.5% had both AAC and CAC. Thus, 1413 (997+416; 71.6%) had nonzero AAC and 1141 (997+144; 57.8%) had nonzero CAC. Among those with both AAC and CAC, the Spearman correlation for the amount of AAC and CAC was r=0.38, P<0.0001.
Table 1⇑ shows the 10th, 50th, and 90th percentile Agatston scores and distribution of potential risk factors stratified by absence or presence of AAC and CAC. The Agatston scores for AAC were much higher than those for CAC, such that the 50th percentile for AAC and 90th percentile for CAC were similar. In these unadjusted analyses, with the exception of C. pneumoniae titer (and calcium and phosphorous in the subset), analysis of variance showed that each of these variables differed significantly for either the AAC or the CAC comparison.
After adjustment of each of these variables for age, gender, and ethnicity, the variables that showed no significant association in these logistic models in either the AAC or CAC analysis were education, estimated glomerular filtration rate, homocysteine, d-dimer, plasmin antiplasmin complex, and C. pneumoniae titer, so these variables were not further considered. Among the 5 BP variables, systolic BP and hypertension treatment were arbitrarily selected for a second model to avoid colinearity. This second model added each of the remaining variables from Table 1⇑, each adjusted for each other. Variables lacking significant relationships in the second model were income, body mass index, triglycerides, CRP, IL-6, fibrinogen, insulin, and factor VIII. Gender-specific analyses showed similar results, so both genders were combined to maximize power in these analyses.
Variables included in the final logistic models are shown in Table 2. Differences in numbers between tables reflect missing data for 1 or more variables. Note that the odds ratios (ORs) presented are not good approximations of the relative risk because the prevalence of both AAC and CAC was relatively high. Of interest is that none of the novel risk factors met the selection criteria for Table 2, and neither calcium nor phosphorous was significant in the subset. For AAC, there was a positive risk factor association for age and inverse associations for black and Hispanic ethnicity. There were strong associations for the smoking variables; OR, 1.72 for past smoking and 3.32 for current smoking, and an independent additional OR of 1.88 per 20 pack years. HDL-C showed a strong inverse association (OR 0.77 per SD), LDL-C a strong positive association (OR 1.52 per SD), and lipid medication use was additionally and independently significant (OR=2.43). Systolic BP and hypertensive medication use showed independent positive associations.
Associations for CAC were similar to AAC for age, ethnicity, and systolic BP, and the hypertensive medication association, although significant, was less strong. CAC showed a strong male predominance (OR=2.83), in sharp contrast to AAC, which was not associated with gender. Current smoking was much weaker for CAC, and the pack-years association was null. HDL-C was not significantly associated with CAC, and the associations for LDL-C and lipid medications were weaker than for AAC.
Figure 2a shows ROC curves for the prediction of AAC, and Figure 2b shows ROC curves for the prediction of CAC, with the black lines using only age, gender, and ethnicity and the red lines using all of the risk factors in Table 2. The C-statistics were higher for AAC, and the difference between the minimally and fully adjusted curves were more than twice as large for AAC (+0.065) than for CAC (+0.024), indicating that a greater proportion of the variance in AAC versus CAC was explained by risk factors beyond age, gender, and ethnicity.
Table 3 shows the results of the multiple linear regression analyses separately for lnAAC+1 and lnCAC+1. In these models, risk variables are predicting the extent of calcified plaque in participants, rather than the presence or absence as in Table 2. Table 3 shows risk estimates per SD change for continuous variables and percentage change for categorical variables. The results of these models were quite consistent with the results in Table 2. For the full cohort, age, gender, and ethnic associations were similar to the results in Table 2. The smoking variables were much stronger for AAC. The lipid variables were again much stronger predictors of lnAAC+1 than lnCAC+1. The r2 for the lnAAC+1 model was larger (0.439) than for the lnCAC+1 model (0.314). In the subset with calcium and phosphorous measurements, there was no association between calcium and either lnAAC+1 or lnCAC+1. However, there was a strong positive association between phosphorous and lnCAC+1 (coefficient=0.21, P<0.01) but not lnAAC+1 (coefficient=0.04, P=0.59).
In sensitivity analyses, we compared the logistic models for AAC with and without adjustment for CAC and the CAC models with and without adjustment for AAC. Results were similar, although, as expected, risk factor differences were sharpened by adjustment for the other (correlated) calcium score. In addition, mutinomial logistic regression was used to evaluate risk factor differences across the 4 mutually exclusive groups in Figure 1: neither AAC nor CAC, AAC only, CAC only, and both AAC and CAC. Results again were similar to the results in Table 2 for the CAC only and AAC only phenotypes, and the results for the AAC and CAC phenotype were similar to those for AAC only. However, the smaller numbers per group made the results less stable.
Two previous small studies have suggested little difference in the association of standard CVD risk factors for atherosclerotic calcification of the coronary arteries compared with the distal aorta.6,7 One study did suggest possibly stronger risk factor correlations for AAC than CAC.5 Estrogen alone, but not combination hormone replacement therapy, has been correlated with less CAC progression in women.15 One study reported no independent association in men or women of AAC with any of several sex hormones measured.16 Earlier studies did not evaluate novel risk factors such as inflammatory and thrombotic factors.
AAC and CAC were both common in this cohort free of clinical CVD at baseline. The major finding was a stronger association of most standard CVD risk factors with AAC compared with CAC. Both measures were positively correlated to a similar degree with age and hypertension. In contrast, the results for gender and ethnicity were markedly different. CAC was much more common in men (OR=2.83), whereas AAC was not (OR=0.91). The inverse association between AAC and black ethnicity was stronger than for CAC.
Two major cardiovascular risk factors, cigarette smoking and dyslipidemia, also showed clear differences. AAC was strongly and significantly associated with all smoking measures, whereas CAC showed much weaker associations. For example, for AAC, the OR for current smoking was 3.32, whereas for CAC the OR was 1.51. For the dyslipidemia measures, the associations were again stronger for AAC. HDL-C was strongly protective for AAC (OR per SD=0.77) but null for CAC (OR=0.91). LDL-C showed a stronger positive association for AAC (OR=1.52) than for CAC (OR=1.22). Lipid-lowering medication was more strongly associated with AAC (OR=2.43) than with CAC (OR=1.62).
Models exploring risk factor relationships with the continuous distribution of AAC and CAC (Table 3) were quite consistent with the categorical models above and actually accentuated the AAC versus CAC differences somewhat. The subset models in Table 3 adjusting also for calcium and phosphorous were consistent with recent studies showing that higher serum phosphorus levels are associated with CAC in community living populations.17,18 The lack of association of phosphorous with AAC requires further study.
Recent in vitro and autopsy studies have provided new insights to mechanisms contributing to deposition of calcium within atherosclerotic lesions and within the arterial media.19,20 These studies demonstrate that higher extracellular phosphorus may activate the calcific process through a sodium phosphate transporter on the cell surface, Pit-1, which appears both necessary and sufficient for calcification to ensue.19 It is possible that the process begins in the necrotic core of atherosclerotic lesions and later transforms vascular smooth muscle cells to an osteoblast-like phenotype, involving deeper layers of the arterial wall including the arterial media.20 The positive association of phosphorous with CAC in our data are consistent with these concepts. Others have argued that intimal (atherosclerotic) and medial calcification are distinct vasculopathies.21,22 To our knowledge, the relative distribution of calcium (intimal versus medial) in the coronary arteries versus aorta in community-living humans is unknown. Whether these factors may help explain the relatively dramatic differences in association with traditional CVD risk factors between these 2 anatomic sites observed in this study is speculative at present and is an important area for future study.
Sensitivity analyses adjusting the AAC models for CAC and vice versa showed even greater differences in risk factor associations. However, the interpretation of such models is unclear, because one outcome (eg, AAC) is being adjusted for another correlated outcome (eg, CAC) rather than for a risk factor.
Multiple novel risk markers were studied, and in univariate analysis all but 1 differed significantly for either AAC or CAC (Table 2). Such markers have shown strong correlations with CVD events in some studies.23 However, in these data, none remained significantly associated with AAC or CAC after adjusting for standard cardiovascular risk factors.
It is unclear why AAC would show much stronger associations with cigarette smoking and dyslipidemia than would CAC. Much evidence suggests that cigarette smoking is a somewhat stronger risk factor for peripheral versus central atherosclerosis,24 and in a separate analysis in this cohort, smoking was the strongest risk factor for aortic diameter and aortic diameter was also correlated with AAC.25 A recent review highlights the potential link between cigarette smoking, matrix metalloproteinases, and vascular disease.26 However, the weak smoking association for CAC was a surprise. Similarly, some evidence suggests lower HDL-C and higher triglycerides are somewhat better correlated with peripheral than central atherosclerosis, but the opposite appears to be true for LDL-C,24 and in contrast we found a weaker association of LDL-C with CAC than with AAC. In these data overall, AAC showed stronger correlations with CVD risk factors than did CAC, with the notable exception of gender.
Comparison of the results here with differences between AAC and thoracic aortic calcium in MESA indicate that risk factors in general are somewhat stronger for AAC than thoracic aortic calcium but that risk factors for thoracic aortic calcium are stronger than for CAC. Interestingly, compared with women, men are significantly more likely to have CAC but significantly less likely to have thoracic aortic calcium, with no gender difference for AAC.8
Our study has potential limitations. Parathormone was not measured, and calcium and phosphorous were available only on a subset. Although participants in the aortic calcium study were a random sample of those participating in the baseline examination, the baseline cohort was not a random sample of the multiethnic US population but rather was selected with varying field site-specific criteria.9 Thus, although these results are representative of the 5 participating MESA sites, they cannot necessarily be extrapolated to the general adult population. In addition, the risk factor measures were collected at the baseline examination between 2000 and 2002, whereas the AAC and CAC scans for this report were collected between 2002 and 2005. Thus, there was a lag between risk factor and scan data. This could have influenced the strength of associations, with some nondifferential attenuation likely, but would not reasonably have affected the markedly differential associations of risk factors with AAC versus CAC presented here.
Strengths of our study include structured, validated protocols with phantom calibration for AAC and CAC measures, as well as a standardized assessment of risk variables, with central reading centers for all variables from the 5 participating field centers.
In conclusion, AAC was strongly associated with age, hypertension, smoking, and dysplidemia, and it varied sharply with ethnicity. In contrast, CAC showed a strong male predominance, and although associated with age and hypertension, showed weaker associations with smoking and dyslipidemia, unexpected findings for an atherosclerotic measure. CAC, especially higher levels, has proved strongly predictive of future CVD events,27,28 as has the presence of AAC measured by standard lumbar radiographs.29,30 CAC has also been shown in the MESA to significantly improve risk prediction beyond standard risk factors.31 Future analyses in this cohort will explore the quantitative association by CT of AAC with future CVD events, taking into account CAC, to determine whether AAC predicts events independent of the CAC burden.
Sources of Funding
This research was supported by NIH Grants HL72403 and HL091217 and by contracts N01-HC-95159 through N01-HC-95169 with the National Heart, Lung, and Blood Institute.
Received on: April 19, 2010; final version accepted on: August 11, 2010.
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