Abdominal aortic calcium (AAC) is associated with incident cardiovascular disease. However, the age- and gender-related distribution of AAC in a community-dwelling population free of standard cardiovascular disease risk factors has not been described. A total of 3,285 participants (aged 50.2 ± 9.9 years) in the Framingham Heart Study Offspring and Third Generation cohorts underwent abdominal multidetector computed tomography from 1998 to 2005. The presence and amount of AAC was quantified (Agatston score) by an experienced reader using standardized criteria. A healthy referent subsample (n = 1,656, 803 men) free of hypertension, hyperlipidemia, diabetes, obesity, and smoking was identified, and participants were stratified by gender and age (<45, 45 to 54, 55 to 64, 65 to 74, and ≥75 years). The prevalence and burden of AAC increased monotonically and supra-linearly with age in both genders but was greater in men than in women in each age group. For those <45 years old, <16% of the referent subsample participants had any quantifiable AAC. However, for those >65 years old, nearly 90% of the referent participants had >0 AAC. Across the entire study sample, AAC prevalence and burden similarly increased with greater age. Defining the 90th percentile of the referent group AAC as “high,” the prevalence of high AAC was 19% for each gender in the overall study sample. The AAC also increased across categories of 10-year coronary heart disease risk, as calculated using the Framingham Risk Score, in the entire study sample. We found AAC to be widely prevalent, with the burden of AAC associated with 10-year coronary risk, in a white, free-living adult cohort.
Necropsy studies have demonstrated that vascular calcifications are an early and significant component of many atherosclerotic plaques. Coronary artery calcium (CAC) has been studied extensively as a surrogate for atherosclerotic burden and as a predictor of future coronary heart disease (CHD). However, atherosclerosis begins to develop in the aorta before it appears in other vascular beds. Therefore, quantifying aortic calcium using widely available noninvasive imaging methods could be useful for identifying those at increased risk of developing occlusive vascular disease. In prospective epidemiologic studies, plain radiographic evidence of aortic calcific deposits in the aortic arch and the abdominal aorta and valve calcification detected by echocardiography have been associated with increased cardiovascular morbidity and mortality. We sought to describe the distribution of calcific deposits in the abdominal aorta detected using multidetector computed tomography (MDCT) in a community-based cohort of adults free of clinically apparent cardiovascular disease (CVD), to evaluate the association of abdominal aortic calcium (AAC) seen by MDCT with 10-year CHD risk defined by the Framingham Risk Score and to determine the relation between CAC and AAC.
Methods
The study sample included participants enrolled in the Framingham Offspring cohort and the Third Generation cohort. Offspring included the children and their spouses, of the original Framingham Heart Study cohort, and the Third Generation cohort were the grandchildren of the original cohort. To be included in the present study, participants were required to have attended either the Offspring Seventh Examination Cycle (1998 to 2001) or the Third Generation First Examination Cycle (2002 to 2005) and to have a complete risk factor profile (including hypertension, lipids, smoking status, body mass index, and diabetes status). The men were required to be ≥35 years old. The women were required to be ≥40 years old and not pregnant. Due to the technical factors associated with the MDCT hardware, the participants could be included only if they weighed <160 kg. Participants with clinically apparent CVD, defined by prevalent CVD, previous coronary artery bypass grafting, percutaneous stent, pacemaker/implantable cardioverter-defibrillator placement, or valve replacement, were prospectively excluded from the analysis. The institutional review boards of the Boston University Medical Center and Massachusetts General Hospital approved the study. All participants provided written informed consent.
The standard Framingham clinic examination included a physician-performed interview and physical examination and blood samples obtained in the morning after a 12-hour fast. The body mass index was determined as weight (kg) divided by the square of height (m). Obesity was defined as body mass index of ≥30 kg/m 2 . Diabetes mellitus was defined as a fasting plasma glucose of ≥126 mg/dl or treatment with insulin or a hypoglycemic agent. The participants were considered current smokers if they smoked ≥1 cigarette daily in the previous year. Hypertension was defined as systolic blood pressure of ≥140 mm Hg or diastolic blood pressure of ≥90 mm Hg on the average of 2 physician-performed measurements or by the use of antihypertensive medication. Hyperlipidemia was defined as serum total cholesterol of ≥240 mg/dl or the use of pharmacologic treatment. CVD events were adjudicated by a panel of 3 physicians, who were unaware of the MDCT data, using previously described standardized criteria. From these data, we identified a healthy nonsmoking, nonobese referent subgroup that was free of hypertension, hyperlipidemia, diabetes, and clinically apparent CVD.
The participants underwent imaging with an 8-slice MDCT scanner (LightSpeed Ultra, General Electric, Milwaukee, Wisconsin) with prospective electrocardiographic triggering during a single breath hold in mid-inspiration using sequential data acquisition, as previously described. A test breath hold was performed to ensure compliance before the scan. The scans were prospectively initiated at 50% of the RR interval, as used previously for MDCT-based measurements of CAC. The top of the S1 vertebral body was prospectively selected as the most caudal extent of the abdominal volume to be imaged. Thirty contiguous 5-mm-thick slices were obtained cranial to S1 for a total coverage of 15 cm in the Z-direction. The abdominal imaging parameters included 120 kVp, 400 mA, gantry rotation time 500 ms, and table feed 3:1. The effective radiation exposure was 2.7 mSv. The coronary imaging parameters included 120 kVp, 320 or 400 mA for a body weight of <100 or ≥100 kg, respectively, and 500-ms gantry rotation time with effective radiation exposure of 1.0 or 1.25 mSv, corresponding to 320 or 400 mA, respectively. Each participant was scanned twice consecutively.
All computed tomographic scans were analyzed by an experienced reader for the presence and amount of AAC using a commercially available workstation (Aquarius, TeraRecon, San Mateo, California). Abdominal slices cranial to the aortic bifurcation were analyzed for AAC. AAC was defined radiographically as an area of ≥3 connected pixels with an attenuation >130 HU applying 3-dimensional connectivity criteria (6 points). The Agatston score was calculated by multiplying the area of each lesion with a weighted attenuation score dependent on the maximum attenuation within the lesion as previously described by Agatston et al. The area was calculated for each calcified lesion by multiplying the number of pixels >130 HU by the pixel area (in mm 2 ) using isotropic interpolation. If an individual lesion appeared in >1 computed tomographic cross-section, the total Agatston score for the lesion was determined by summing the Agatston scores derived for each cross-section. Interobserver and intraobserver reproducibility for this method is high, as previously reported.
The distribution of AAC among the healthy referent subsample and the entire sample was categorized as percentiles of AAC (25th, 50th, 75th, and 90th), stratified by age and gender. The age- and gender-stratified healthy referent cutpoints were applied to the entire study sample to determine the number of participants with AAC scores greater than the healthy-referent 90th percentile of AAC. We prospectively selected the 90th percentile threshold. In a complementary analysis, the distribution analysis of AAC in the entire sample (at 25th, 50th, 75th, and 90th percentiles) was stratified by the 10-year CHD risk, determined by the Framingham Risk Score, for which low risk is <6%, intermediate risk is 6% to 20%, and high risk is >20%. Finally, the Spearman rank correlation coefficient (r s ) was calculated to assess the relation between AAC and CAC. (The nonparametric Spearman correlation was used because of non-normal distributions of calcium; however, as with the standard Pearson correlation, an r s >0 would suggest that AAC increases as CAC increases. The maximum possible value of r s = 1 would indicate a perfect monotonic relation between AAC and CAC; however, in contrast to Pearson correlation, Spearman correlation does not assume a linear relation between the 2 measures.) Concordance for agreement between AAC and CAC in stratifying all study participants as having high (>90th healthy referent percentile) or nonhigh (≤90th healthy referent percentile) burden of calcium, within the respective vascular beds, was assessed using the kappa statistic.
Results
A total of 3,285 Offspring and Third Generation participants meeting the study entry criteria underwent MDCT. AAC could be determined in 3,267 (99.5%, 1665 men). The baseline characteristics of these participants are listed in Table 1 . The distribution of AAC stratified by age and gender across the healthy referent subsample (n = 1656, 803 men) is listed in Table 2 . In each age and gender group, the percentage of participants who met the entry criteria for the referent subsample decreased steadily with increasing age group. In contrast, the proportion of referent participants with nonzero AAC increased with age. Among referent participants aged <45 years, fewer than 1 in 6 participants had detectable AAC. However, by age 65 years, approximately 9 of 10 referent participants had nonzero AAC. In both men and women, the AAC scores increased markedly and monotonically, in a supralinear fashion, with age. Compared to the distribution of AAC across all study participants ( Table 2 ), the referent subsample had consistently lower AAC scores within a given age and gender group, but the pattern of greater AAC burden with advancing age seen in the healthy referent sample was preserved in the entire study sample. Applying the age- and gender-specific thresholds for the 90th percentile of AAC from the referent subsample to the overall study sample ( Table 2 ), we found that 18.9% of men and 19.4% of women had AAC scores greater than the 90th percentile. The proportion of participants with greater than the 90th percentile thresholds did not differ by gender.
| Characteristic | Men | Women |
|---|---|---|
| Patients (n) | 1,665 | 1,602 |
| Age (years) | 48.8 ± 10.2 | 51.6 ± 9.6 |
| Offspring (%) | 33% | 42% |
| Hypertension (%) | 29% | 25% |
| Hyperlipidemia (%) | 23% | 20% |
| Current cigarette smoking (%) | 13% | 12% |
| Diabetes mellitus (%) | 6.1% | 4.7% |
| Body mass index (kg/m 2 ) | 28.4 ± 4.5 | 27.1 ± 7.0 |
| Men | Women | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Age (y) | ||||||||||
| <45 | 45–54 | 55–64 | 65–74 | ≥75 | <45 | 45–54 | 55–64 | 65–74 | ≥75 | |
| Referent sample (n = 803) | Referent sample (n = 853) | |||||||||
| N REF | 368 (58.5%) | 250 (46.5%) | 110 (40.7%) | 57 (35.6%) | 18 (25.4%) | 293 (71.6%) | 318 (58.1%) | 154 (43.3%) | 69 (33.3%) | 19 (22.9%) |
| N REF with AAC >0 | 57 (15.5%) | 113 (45.2%) | 90 (81.8%) | 52 (91.2%) | 18 (100.0%) | 23 (7.8%) | 72 (22.6%) | 91 (59.1%) | 60 (87.0%) | 19 (100.0%) |
| Median (IQR) | 0 (0–0) | 0 (0–26) | 109 (6–803) | 1,149 (284–3,292] | 2,340 (1,051–4,790) | 0 (0–0) | 0 (0–0) | 24 (0–295) | 270 (36–1,332) | 2,130 (1,227–4,360) |
| 90th Percentile | 7 | 231 | 1,922 | 4,914 | 8,177 | 0 | 73 | 946 | 2,263 | 5,742 |
| All Men (n = 1,665) | All Women (n = 1,602) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Participants (n) | 626 | 538 | 270 | 160 | 71 | 409 | 547 | 356 | 207 | 83 |
| Participants with AAC >0 (n) | 140 (22.4%) | 310 (57.6%) | 233 (86.3%) | 152 (95.0%) | 71 (100.0%) | 67 (16.4%) | 186 (34.0%) | 259 (72.8%) | 185 (89.4%) | 83 (100.0%) |
| Median (IQR) | 0 (0–0) | 6 (0–152) | 403 (31–1,335) | 1,998 (523–4,378) | 3,450 (1,444–7,186) | 0 (0–0) | 0 (0–34) | 127 (0–748) | 833 (138–2,493) | 2,403 (1,013–4,938) |
| 90th Percentile | 47 | 828 | 3,561 | 7,199 | 12,158 | 24 | 286 | 2,195 | 4,625 | 7,358 |
| Participants with high (>90th percentile) AAC | 102 (16.3%) | 115 (21.4%) | 51 (18.9%) | 35 (21.9%) | 12 (16.9%) | 67 (16.4) | 103 (18.8%) | 73 (20.5%) | 54 (26.1%) | 14 (19.4%) |
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