Methods

MESA was initiated by the National Heart, Lung, and Blood Institute (Bethesda, Maryland) to characterize subclinical cardiovascular disease (CVD) and its progression. A full description of the study design and recruitment process has been previously reported. A total of 6,814 free-living people without clinically apparent CVD, aged 45 to 84 years, were recruited from 6 United States communities (Baltimore City and County, Maryland; Chicago, Illinois; Forsyth County, North Carolina; Los Angeles County, California; New York, New York; and St. Paul, Minnesota) from July 2000 to August 2002. Recruitment targeted 4 ethnic groups (white, black, Hispanic, or Chinese). The participants were excluded if they self-reported CVD, including angina or previous cardiovascular procedures such as percutaneous coronary interventions, coronary bypass or valve surgery, or pacemaker/defibrillator implantation. The institutional review boards at each participating institution approved the MESA protocol, and each participant provided informed written consent for study participation and data analysis before enrollment. (The Clinical Trial Registration ClinicalTrials.gov registry unique identifier is NCT00005487 , available at www.clinicaltrials.gov/ct2/show/NCT00005487 .)

All MESA participants underwent a baseline examination that included CT examination of the chest. Follow-up testing, including repeat chest CT examination, was performed in 2 stages. One half of the cohort returned from September 2002 to January 2004, and the remainder returned from March 2004 to July 2005, with an average between-scan interval of 1.6 to 3.2 years. Of the 6,814 participants initially enrolled in MESA, 5 were excluded subsequently because of postenrollment identification of antecedent cardiovascular events. Of this group, the 5,880 MESA participants for whom a follow-up CT examination was performed were included in the present analyses.

Information regarding the participants’ demographic data and medical history, including age, smoking history, and medication use, was self-reported. Although participants reporting CVD or valve disease were excluded at baseline, no specific screening was done to detect subclinical aortic stenosis. Subjects who self-reported their race/ethnicity as white, black, Hispanic, or Chinese were eligible for inclusion; other races/ethnicities were excluded from MESA enrollment.

Additional covariate data were obtained from the baseline examination. Continuous variables (body mass index, blood pressure, lipid parameters, fibrinogen, and creatinine) were normalized by their standard deviations. Hyperglycemia and impaired fasting glucose were categorized according to the 2003 American Diabetic Association fasting criteria algorithm. Plasma lipoprotein parameters were obtained after a 12-hour fast and sent to a central lipid laboratory (Fairview University Medical Center, Minneapolis, Minnesota) for analysis, as reported separately. Because of skewed data, the values for triglycerides and C-reactive protein were log-transformed.

Serial CT scans were obtained at each of the 4 centers using electron beam tomography or multidetector CT scanners. To adjust for intersubject and intrasubject differences in scanner types, the images were calibrated individually using calcium phantoms; the analyses also included adjustments for scanner pair. Full details concerning the equipment, scanning methods, and quality control in MESA, including image calibration and interscanner reproducibility, have been previously reported. Additionally, the equivalency of AVC quantification across scanner type was established. The spatial resolution was 1.38 mm ³ for electron beam tomography (0.68 × 0.68 × 3.00 mm) and 1.15 mm ³ for multidetector computed tomography (0.68 × 0.68 × 2.50 mm).

All studies were processed at a central reading center (Harbor-UCLA Research and Education Institute, Los Angeles, California), where the AVC scores were measured retrospectively by a single, reader (J.T.) who was unaware of the subject’s details. AVC was defined as any calcified lesion residing within the aortic valve leaflets. Calcification of the aortic annulus, sinuses, ascending aorta, or coronary arteries was excluded. AVC was quantified using the Agatston method, which accounts for both lesion area and calcium density (using Hounsfield brightness). Single lesion measurements were summed to give an overall Agatston score. If AVC was not detected, the Agatston score was recorded as 0.

The MESA participants were categorized by the presence (Agatston score >0) or absence (Agatston score 0) of AVC on baseline imaging. Those without AVC at baseline were deemed at risk of incident AVC and included in the analyses of risk associations for incident AVC. All subjects with baseline AVC were included in the AVC progression analyses, regardless of the magnitude of, or direction of change in, the Agatston scores. For the main progression analysis, AVC progression was determined by the absolute between-scan change in the Agatston score. Because of a right skew, the Agatston score distributions are reported as the median and interquartile range (IQR). A comparison of the rates of AVC incidence and progression between the older and younger subjects was performed using t tests, with graphic depiction of age-group differences using Lowess smooth curves.

The prevalence of incident calcification in the present cohort was <10%; thus, the odds ratio was used to estimate the relative risk. Univariate analyses were adjusted for follow-up interval, age, and gender. Multivariate logistic (incidence analysis) and linear (progression analysis) regression models were constructed using the backward selection method, in which the variables were initially entered into the model, with removal of the least significant variable and iterative reanalysis. Robust linear regression analysis was used to downweight the effects of potential outliers. The follow-up interval and scanner pair were forced into all models, and if one component of a categorical variable reached significance, all components of that variable were included. The threshold for removal and addition was p ≥0.10 and p <0.05, respectively.

Because the best method for modeling AVC progression is unknown, additional exploratory analyses were performed. Previous studies have identified baseline AVC severity as a predictor of progression; thus, analyses were performed that included the baseline Agatston scores in the model. Additional stratified analyses were performed to assess the influence of the baseline Agatston score among those with low and high baseline scores. In addition to considering the absolute change in Agatston score, AVC progression was alternatively modeled multiplicatively as a log transformation of the difference in Agatston score and as a relative change in Agatston score [defined as the difference in log(Agatston score + 25)]. This transformation provided a roughly normal distribution of relative progression and was used previously when considering coronary artery calcification in the MESA.

Statistical analyses were performed using Stata, version 10.1, for Windows (StataCorp LP, College Station, Texas). Statistical significance was defined as p <0.05, and the relative risk is reported with the 95% confidence intervals.

Results

Of the 5,880 subjects with follow-up CT examinations, the mean between-scan interval was 2.4 ± 0.9 years. A total of 5,142 subjects (87%) did not have AVC on the baseline CT examination, and 738 (13%) had prevalent AVC, with a median Agatston score of 56 (IQR 19 to 137). As shown in Figure 1 , the former group included the population at risk of incident AVC, and the latter group defined the population at risk of AVC progression. The baseline demographic characteristics of this population are listed in Table 1 . This population was relatively young (age 61.8 ± 10.1 years), ethnically diverse (60% nonwhite), and rather healthy (74% normoglycemic and 64% normotensive). A total of 16% of the cohort was taking lipid-lowering therapy, with 15% taking statin medications, and the baseline lipid levels were relatively normal (low-density lipoprotein 117 ± 31 mg/dl; high-density lipoprotein 51 ± 15 mg/dl; triglycerides 112 mg/dl, IQR 77 to 161). Renal function was well preserved, with only 1.5% of the population having creatinine levels >1.5 mg/dl.

Flow diagram for MESA participants, categorized by aortic valve calcification status.

Of the 5,142 participants without AVC at baseline, 210 (4.1%) developed AVC during the follow-up period, with an annualized incidence rate of 1.7%/year. For those with incident AVC, the median Agatston score on the follow-up scan was 13 (IQR 6 to 39). The rates of incident AVC were increased in men and those with diabetes and hypertension compared to women and those without diabetes or hypertension ( Table 2 ). Additionally, a marked increase was seen in the AVC incidence rate in the older age groups ( Figure 2 ). Subjects aged 70 to 79 years had a sixfold greater rate of incident AVC than did subjects aged 50 to 54 years (3.5%/year vs 0.6%/year, p <0.001). Men had slightly greater incidence rates across all age groups, but no significant age-gender interaction was found in the incident rate of AVC ( Figure 2 ).

Average incidence rate of AVC (% per year) by yearly age increments among those free of AVC at baseline (n = 5,142), for both men (squares) and women (circles). The size of the scatter points is weighted for the number at risk at each age category; nonlinear Lowess smooth curves are displayed for the full cohort (solid) , women (dotted) , and men (dashed). A marked increase in AVC incidence rate was seen with advancing age.

On univariate analyses (controlling for between-scan interval, age, and gender), the risk factors associated with incident AVC were age, male gender, body mass index, antihypertensive therapy, diabetes, current smoking, total cholesterol/high-density lipoprotein cholesterol ratio, and the use of lipid-lowering therapies ( Table 3 ). Participants with incident AVC had greater triglyceride levels and total/high-density lipoprotein cholesterol ratios, but similar levels of low-density lipoprotein and high-density lipoprotein cholesterol ( Table 1 ). No significant associations were found between incident AVC and C-reactive protein, fibrinogen, or creatinine.

In the fully adjusted model ( Table 3 ), positive associations with incident AVC were found for age, male gender, body mass index, current smoking, and the use of both antihypertensive and lipid-lowering medications. In contrast, creatinine >1.1 mg/dl demonstrated a negative association with incident AVC. Repeat sensitivity analyses using a cutoff definition for incident AVC of an Agatston score of >10 showed similar findings.

Of the 738 subjects with prevalent AVC at baseline, the median rate of change in the Agatston score was 2 U/year (IQR −21 to 37). In contrast to incident AVC, a correlation between age and AVC progression did not appear ( Figure 3 ). On univariate analyses (controlling for between-scan interval, scanner type, age, and gender), the risk factors associated with greater AVC progression included only male gender and baseline Agatston score. In the primary backward regression analysis ( Table 4 ), which did not include the baseline Agatston score in the model, only male gender was associated with an increased rate of AVC progression, although age was borderline significant. In contrast, greater diastolic blood pressures were associated with a lower rate of AVC progression.

(A) Mean annualized rate of AVC progression (Agatston units/year) by yearly age increments among those with detectable AVC at baseline (n = 738). The size of the scatter points is weighted for the number at risk at each age category, with nonlinear Lowness smooth curve (solid) displayed. (B) Mean annualized rate of AVC progression (Agatston units/year) grouped by baseline Agatston scores among those with detectable AVC at baseline (n = 738). Baseline Agatston scores were log-transformed to reduce skew.

Table 4

Risk of aortic valve calcium (AVC) progression among those with aortic valve calcium (AVC) Agatston score >0 at baseline (n = 738): univariate and multivariate stepwise backward linear regression models

Characteristic	Univariate Model ⁎		Fully Adjusted Model †
	β ‡ (95% CI)	p Value	β ‡ (95% CI)	p Value
Baseline Agatston score	0.31 (0.19, 0.42)	<0.001	Not included
Age	14.4 (−1.9, 30.8)	0.08	18.3 (−0.1, 36.8)	0.05
Men	32.9 (0.7, 65.1)	0.04	37.5 (6.6, 68.5)	0.02
Race/ethnicity
White	Referent
Chinese	−52.2 (−86.6, −17.7)	0.003
Black	−33.5 (−69.8, 2.7)	0.07
Hispanic	−21.9 (−63.6, 19.9)	0.30
Body mass index	12.7 (−5.7, 31.1)	0.18	15.0 (−1.5, 31.4)	0.08
Blood pressure
Systolic	−5.0 (−14.2, 4.3)	0.29
Diastolic	−16.1 (−33.7, 1.5)	0.07	−15.2 (−28.9, −1.5)	0.03
Antihypertensive therapy	−12.8 (−46.8, 21.2)	0.46
Diabetes mellitus	−27.2 (−60.5, −6.2)	0.11
Smoking status
Never	Referent
Former	33.7 (−1.5, 69.0)	0.06
Current	5.3 (−35.6, 46.1)	0.80
Pack-years smoking §	7.1 (−4.0, 18.2)	0.20
Total cholesterol/high-density lipoprotein ratio	−6.1 (−21.7, 9.5)	0.44
Lipid-lowering therapy	−1.6 (−37.8, 34.6)	0.93
C-reactive protein	9.0 (−10.9, 29.0)	0.38
Fibrinogen	17.5 (−14.8, 49.8)	0.29
Creatinine (mg/dl)
≤0.9	−7.3 (−49.8, 35.1)	0.73
1.0	Referent
≥1.1	−12.5 (−60.2, 35.2)	0.61

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