Abstract
Objective: Little is known about obesity measurements and coronary artery calcium (CAC) progression in older adults. We examined the sex-specific association between measures of body size and fat distribution with CAC progression.
Subjects and Methods: Participants were 156 men and 182 women (mean age 67 yr) without known heart disease who had electron-beam computed tomography for CAC at baseline and again 4.5 yr later. Obesity assessments were weight, height, body mass index, waist and hip circumference, waist to hip ratio, waist to height ratio, sc and visceral adipose tissue (SAT, VAT), and SAT to VAT ratio based on abdominal electron-beam computed tomography. CAC progression was defined as categorical (square root increased on total CAC volume score ≥2.5 mm3) and continuous variables.
Results: During the follow-up, 55% of men and 38.5% of women had CAC progression. Increased waist to hip ratio (≥0.9 for men, ≥0.85 for women) and waist to height ratio (≥0.55 for men, ≥0.54 for women) were positively and independently associated with CAC progression [median (interquartile range)] [60.8 (145) vs. 10.8 (56) mm3, P = 0.002 and 50 (153) vs. 22(84) mm3, P = 0.03, respectively]. In women but not men, an increased waist circumference (>88 cm) independently predicted CAC progression (odds ratio 3.0 95% confidence interval 1.03–8.0, P = 0.04), whereas VAT to SAT ratio predicted CAC progression in men but not women (odds ratio 2.8 95% confidence interval 1.01–7.8, P = 0.04).
Conclusion: In this study of older adults without known heart disease, abdominal obesity was an independent predictor of CAC progression. These results point to the importance of using clinical measurements of abdominal obesity to identify individuals at increased risk for atherosclerosis.
Abdominal obesity predicts coronary artery calcium progression in old adults.
Obesity is a main determinant of a cascade of metabolic problems predisposing to coronary heart disease (CHD), type 2 diabetes mellitus, dyslipidemia, and hypertension (1,2). The importance of fat distribution to CHD risk has been described extensively (1,3,4,5,6).
Coronary artery calcium (CAC) is a marker for coronary artery plaque burden and predicts future clinical events (7,8). Abdominal fat has been associated with increased CAC in cross-sectional studies of young and middle-aged adults (9,10), but visceral adiposity was not associated with CAC in a cross-sectional study of older participants with no known CHD from the Rancho Bernardo Study (11). Whether body size and fat distribution confer a greater risk for progression of atherosclerosis in older adults is not known.
We examined the sex-specific association between eight different measures of obesity and fat distribution with CAC progression among community-dwelling older adults with no history of heart disease.
Subjects and Methods
Study population
Participants were members of the Rancho Bernardo Study, a southern California community-based study established in 1972 of middle to upper-middle class, older, Caucasian adults.
This report includes surviving community-dwelling participants who attended a baseline clinic visit between 1997 and 1999 and returned for a follow-up visit between 2005 and 2006 (mean follow-up of 4.5 ± 0.5 yr). At the baseline visit, 422 participants with no known CHD (including history of angina pectoris, myocardial infarction, or coronary artery revascularization) had an electron beam computed tomography (EBCT) test for coronary artery calcification; 342 returned for the follow-up EBCT. Reasons for not returning were refusals (n = 43), deaths (n = 21), and no appointment made or kept (n = 20). In addition, four participants completed the second EBCT but refused a blood draw and were excluded from the current analyses. There were no body size differences in men who did or did not return for a follow-up visit, but women who did not return were more likely to have a higher body mass index (BMI) and central obesity.
All participants provided written informed consent at both visits. The study protocol was approved by the human research protection program at the University of California, San Diego.
Data collection
Fasting plasma glucose and low- and high-density lipoprotein (HDL) cholesterol levels were measured using enzymatic methods. Systolic and diastolic blood pressures were measured twice in seated resting subjects according to a standard protocol. Other cardiovascular risk factors including cigarette smoking (current), alcohol intake (three or more times a week), and physical activity (exercise three or more times a week) were assessed using standardized questionnaires. Medication use at the baseline visit was validated by a trained nurse who examined pills and prescriptions brought to the clinic for that purpose; medication use at the time of the first EBCT was obtained by questionnaire.
Body size assessment
Weight, height, BMI, waist circumference, hip circumference, waist to hip ratio, and waist to height ratio were assessed using a standard protocol at baseline and follow-up visits. Height and weight were measured with participants wearing light clothing and no shoes. BMI was calculated as weight (kilograms) per height (meters)2. Waist circumference was measured in a horizontal plane, midway between the inferior margin of the ribs and the superior border of the iliac crest. Hip circumference was measured as the widest circumference of the hip. Subcutaneous and visceral adipose tissue (SAT, VAT) were determined by obtaining three slices of 6 mm each at the level of L-3 or -4, using EBCT. The ratio of VAT and SAT was calculated.
CAC imaging protocol
CAC scores were assessed using an Imatron C-150 scanner (Imatron, San Francisco, CA). Heart images were obtained with 100-msec scan time. Approximately 40–45 slices were obtained using 3-mm slices starting at the level of the carina and proceeding to the level of the diaphragm. Tomography imaging was electrocardiographically triggered at 40 or 65% of the RR interval, depending on the participant’s heart rate. CAC was defined as a plaque of two or more pixels (area 0.67 mm2) with a density 130 Hounsfield units or greater. Quantitative calcium scores were calculated using the method of Agatston et al. (12) and by volumetric scores (acquired by multiplying the pixel area by the section thickness of the region of interest). The total volume scores were derived by the sum of all lesion volumes in cubic millimeters.
Statistical analyses
Descriptive analyses comparing clinical and anthropometric characteristics as well as CHD risk factors by CAC categories are expressed as mean (sd) or percentages and were compared using the Student t test, ANOVA, or χ2 tests as appropriate. Variables with skewed distributions were compared with Mann-Whitney U test and presented as median and interquartile ranges. All analyses were performed with SPSS (version 13.1; SPSS, Inc., Chicago, IL). P < 0.05 was considered significant.
CAC progression was analyzed as a continuous outcome [CAC volume scores change = CAC volume score on second visit (square root transformed) minus CAC volume score at baseline visit (square root transformed] and as categorical outcome (CAC progression yes/no). We defined categorical CAC progression as a difference between baseline and follow-up square root transformed total CAC volume score 2.5 mm3 or greater (13). Because interscan variability and error depend on CAC absolute value, this definition provides an estimate of change that is unbiased with respect to baseline CAC. In this study, the definition of CAC progression was between the mean and −1 sd of square root transformed CAC change, which is approximately equivalent to an annual increase of 30% in CAC absolute values; this increment is in accord with an early study that observed an annual 24% CAC increase in untreated individuals (14).
Baseline body size and fat distribution as continuous and categorical yes/no variables were examined as predictors of CAC progression in sex-specific analysis. Linear and logistic regression models were performed before and after adjustment for covariates known to be associated with CAC scores or associated with CAC progression in the univariate analysis including age, fasting blood glucose, and systolic blood pressure. Because most body size and fat distribution variables were highly correlated, each variable was included separately in the models. Waist circumference and VAT to SAT ratio were not correlated in men (R = 0.019, P = 0.81 and R = 0.251, P = 0.001 in men and women, respectively) but were also examined separately. The Hosmer and Lemeshow test was applied to evaluate whether the estimates of the model fit the data at an acceptable level (P > 0.05). The cut points for body fat distribution variables (BMI ≥25 kg/m2 and ≥30 kg/m2; waist circumference ≥102 cm for men and 88 cm for women; waist to hip ratio ≥0.9 for men and 0.85 for women; waist to height ratio ≥0.55 for men and 0.54 for women) were chosen based on established values in previous studies (4,15).
Results
Baseline
At baseline, the 156 men and 182 women were aged 67.6 ± 6.8 and 69.7 ± 8.1 yr, respectively; mean BMI was 27.1 ± 3.6 kg/m2 in men and 25.3 ± 4.0 kg/m2 in women; 11.7% of men and 11.9% of women were obese (BMI ≥30 kg/m2). Only 6% of men and 4.5% of women were current smokers. Men had significantly higher levels of visceral fat measurements as well as higher diastolic blood pressure, cholesterol, and fasting plasma glucose levels and lower HDL cholesterol levels than women (Table 1). As shown in Table 2, men were more likely to be overweight or obese and have increased categorical visceral obesity than women.
Table 1.
Distribution of sex-specific baseline characteristics
| Women (n = 182) | Men (n = 156) | P value | |
|---|---|---|---|
| Mean age (yr) | 67.5 ± 6.8 | 69.7 ± 8.1 | 0.007 |
| Systolic blood pressure (mm Hg) | 127.7 ± 18.5 | 130.7 ± 17.5 | 0.12 |
| Diastolic blood pressure (mm Hg) | 78.2 ± 8.0 | 75.1 ± 7.7 | 0.001 |
| Fasting blood glucose (mg/dl) | 98.6 ± 23.0 | 106.4 ± 17.5 | 0.001 |
| Total cholesterol (mg/dl) | 217.6 ± 34.3 | 197.2 ± 30.8 | <0.001 |
| LDL cholesterol (mg/dl) | 123.9 ± 31.6 | 122.5 ± 28.3 | 0.65 |
| HDL cholesterol (mg/dl) | 65 (22) | 49 (14) | <0.001 |
| Triglycerides (mg/dl) | 117 (79) | 105 (81) | 0.18 |
| Creatinine (mg/dl) | 0.8 (0.2) | 1.0 (0.2) | <0.001 |
| BMI (kg/m2) | 25.3 ± 4.0 | 27.1 ± 3.6 | <0.001 |
| Waist circumference (cm) | 79.3 ± 10.8 | 96.2 ± 10.3 | 0.001 |
| Hip girth (cm) | 96.7 ± 10.1 | 99.3 ± 9.3 | 0.01 |
| Waist to hip ratio | 0.80 (0.08) | 0.96 (0.06) | <0.001 |
| Waist to height ratio | 0.48 ± 0.06 | 0.54 ± 0.05 | <0.001 |
| Visceral fat (cm3) | 55 (40) | 88 (58) | <0.001 |
| Subcutaneous fat (cm3) | 141 (81) | 119 (63) | 0.001 |
| Visceral to sc ratio | 0.38 (0.28) | 0.70 (0.39) | <0.001 |
Mean ± sd or median (interquartile range), LDL, Low-density lipoprotein.
Table 2.
Age-adjusted distribution of sex-specific body size cut points at baseline
| Baseline percentage
|
P value | ||
|---|---|---|---|
| Men | Women | ||
| BMI (kg/m2) | |||
| 25 or greater | 74 | 47 | <0.001 |
| Less than 25 | 26 | 53 | |
| 30 or greater | 15 | 14 | 0.74 |
| Less than 30 | 85 | 86 | |
| Waist circumference (cm) | |||
| 102 or greater for men and 88 for women | 25 | 19 | 0.18 |
| Less than 102 for men and 88 for women | 75 | 81 | |
| Waist to hip ratio | |||
| 0.9 or greater for men and 0.85 for women | 92 | 26.4 | <0.001 |
| Less than 0.9 for men and 0.85 for women | 8 | 73.6 | |
| Waist to height ratio | |||
| 0.55 or greater for men and 0.54 for women | 48 | 22 | <0.001 |
| Less than 0.55 for men and 0.54 for women | 54 | 78 | |
The median CAC score was 158 mm3 (interquartile range 535; 25% was 28 and 75% was 507) in men and 10 mm3 (interquartile range 96; 25% was 0 and 75% was 96) in women. The number of participants in Agatston categories (none/minimal CAC; low; moderate; and severe) were 31, 28, 35, and 54 men and 91, 44, 28, and 14 women. There were no differences in body fat measurements between men with and without moderate/severe CAC. Women who had CAC 100 Hounsfield units or greater had higher waist (83.4 ± 12.8 vs. 79.1 ± 11.2 cm, P = 0.01), waist to hip ratio (0.84 ± 0.06 vs. 0.82 ± 0.06, P = 0.006), and waist to height ratio (0.52 ± 0.08 vs. 0.48 ± 0.07, P = 0.003) than those with none/moderate CAC.
Follow-up
During the mean 4.5-years follow-up, 55% of men and 38.5% of women had CAC progression based on categorical definition. There were no significant changes in body size or fat distribution between the baseline and follow-up visits for both men (change in mean/medians measurements: BMI +0.02 kg/m2; waist +0.8 cm; waist to hip ratio −0.01; waist to height ratio 0; VAT +5 cm3; SAT +1 cm3; VAT to SAT ratio +0.03, all P > 0.05) and women (change in mean/medians measurements: BMI −0.01 kg/m2; waist +0.7 cm; waist to hip ratio +0.01; waist to height ratio +0.01; VAT +5 cm3; SAT −1 cm3; VAT/SAT ratio +0.02, all Ps > 0.05).
Table 3 shows CAC progression by body size cut points after adjustments for age, fasting blood glucose, and systolic blood pressure; the presence of a higher waist to hip ratio (≥0.9 for men and 0.85 for women) or waist to height ratio (≥0.55 for men and 0.54 for women) at baseline was associated with greater CAC volume score progression. In the same analysis, being overweight or obese was not associated with CAC progression. Waist circumference in women, but not men, was associated with CAC increase independent of age, fasting blood glucose, systolic blood pressure, and BMI (β standardized coefficient 0.24, P = 0.04). None of the other measurements of the obesity/fat distribution variable was associated with CAC progression in sex-specific analysis.
Table 3.
CAC progression by body size cut points
| CAC total volume score change (mm3)a | P valueb | |
|---|---|---|
| BMI (kg/m2) | ||
| 25 or greater (n = 202) | 37 (117) | 0.12 |
| Less than 25 (n = 136) | 18 (94) | |
| 30 or greater (n = 49) | 17 (121) | 0.18 |
| Less than 30 (n = 289) | 33 (110) | |
| Waist circumference (cm) | ||
| 102 or greater for men and 88 for women (n = 74) | 28 (140) | 0.67 |
| Less than 102 for men and 88 for women (n = 264) | 30 (100) | |
| Waist to hip ratio | ||
| 0.9 or greater for men and 0.85 for women (n = 192) | 60.8 (145) | 0.002 |
| Less than 0.9 for men and 0.85 for women (n = 146) | 10.8 (56) | |
| Waist to height ratio | ||
| 0.55 or greater for men and 0.54 for women (n = 116) | 50 (153) | 0.03 |
| Less than 0.55 for men and 0.54 for women (n = 222) | 22 (84) | |
Median (interquartile range).
Adjusted for age, fasting glucose, and systolic blood pressure.
Three sex-specific logistic regression models were performed using CAC progression as a categorical outcome (Table 4). In women, but not men, a high waist circumference (>88 cm) predicted CAC progression before and after adjustment for age, fasting blood glucose, and systolic blood pressure. Being obese (BMI >30 kg/m2) also predicted CAC progression in women. In men, but not women, the VAT to SAT ratio predicted CAC progression before and after adjustments for all covariates.
Table 4.
Odds ratio for CAC progression by baseline anthropometric characteristics
| Women
|
P value | Men
|
P value | |||
|---|---|---|---|---|---|---|
| OR | CI | OR | CI | |||
| Age adjusted | ||||||
| Overweight (BMI >25 kg/m2) | 0.80 | 0.43–1.5 | 0.80 | 0.76 | 0.36–1.6 | 0.46 |
| Obesity (BMI >30 kg/m2) | 1.72 | 0.67–4.3 | 0.25 | 1.84 | 0.75–4.5 | 0.18 |
| Waist (>102 cm: men; >88 cm: women) | 1.72 | 0.80–3.8 | 0.19 | 1.28 | 0.61–2.6 | 0.50 |
| Waist to hip ratio (>0.9: men; >0.85: women) | 1.08 | 0.54–2.1 | 0.81 | 2.43 | 0.7–8.7 | 0.17 |
| Waist to height ratio (>0.56) | 0.80 | 0.4–1.7 | 0.55 | 1.19 | 0.63–2.2 | 0.58 |
| Visceral abdominal fat (cm3) | 1.00 | 0.99–1.0 | 0.47 | 0.99 | 0.99–1.0 | 0.27 |
| VAT to SAT ratio | 1.13 | 0.35–3.6 | 0.83 | 3.00 | 1.1–8.1 | 0.04 |
| Model 1a | ||||||
| Overweight (BMI >25 kg/m2) | 0.9 | 0.48–1.73 | 0.79 | 0.8 | 0.41–1.83 | 0.71 |
| Obesity (BMI >30 kg/m2) | 2.8 | 1.00–7.85 | 0.04 | 2.2 | 0.87–5.60 | 0.09 |
| Waist (>102 cm: men; >88 cm: women) | 2.3 | 1.00–5.70 | 0.04 | 1.5 | 0.72–3.40 | 0.24 |
| Waist to hip ratio (>0.9: men; >0.85: women) | 1.0 | 0.49–2.09 | 0.96 | 2.3 | 0.64–8.50 | 0.19 |
| Waist to height ratio (>0.56) | 1.6 | 0.73–3.66 | 0.22 | 0.9 | 0.49–1.80 | 0.90 |
| Visceral abdominal fat (cm3) | 1.0 | 0.99–1.01 | 0.96 | 0.9 | 0.99–1.00 | 0.24 |
| VAT to SAT ratio | 1.0 | 0.31–3.33 | 0.97 | 2.8 | 1.02–7.90 | 0.04 |
| Model 2a | ||||||
| Waist (>102 cm: men; >88 cm: women) | 3.03 | 1.03–8.0 | 0.04 | 1.52 | 0.58–4.0 | 0.40 |
| Waist to hip ratio (>0.9: men; >0.85: women) | 1.06 | 0.5–2.2 | 0.87 | 2.7 | 0.72–10.0 | 0.14 |
| Waist to height ratio (>0.56) | 1.90 | 0.65–5.4 | 0.24 | 0.70 | 0.30–1.70 | 0.42 |
| Visceral abdominal fat (cm3) | 1.01 | 0.99–1.02 | 0.40 | 0.99 | 0.99–1.01 | 0.30 |
| VAT to SAT ratio | 0.97 | 0.3–3.2 | 0.97 | 2.8 | 1.01–7.8 | 0.04 |
CI, Confidence interval; OR, odds ratio.
Model 1: adjusted for age, fasting plasma glucose, and systolic blood pressure; model 2: model 1 + BMI.
Further adjustment for medication use at baseline (cholesterol lowering, calcium channel blocker, β-blockers, angiotensin converting enzyme inhibitor, and aspirin) materially changed only one of these results: the association between VAT to SAT ratio and CAC progression in men was no longer statistically significant, although the trend was maintained. Further adjustment for current smoke did not change any of the results.
Discussion
In this older Caucasian cohort, excess abdominal fat accumulation at baseline predicted CAC progression in both sexes. Waist circumference was the best predictor of CAC progression in women and VAT to SAT ratio was the best predictor in men.
CAC assessed by EBCT is a marker of atherosclerotic plaque burden (16) and predicts future cardiac events independent of traditional CHD risk factors (17,18). Moreover, CAC progression is associated with worsening of plaque burden as assessed by angiography (19) as well as with increased risk of CHD events in observational studies (16,20,21,22). Several cross-sectional studies examined the association between obesity and CAC scores (9,10,13). The Coronary Artery Risk Development in Young Adults study showed that higher waist girth and waist to hip ratio were associated with CAC in 2951 African-American and white young adults (9). This was also true for middle-aged individuals in a study of 465 Chinese participants aged 40–65 yr that found an association between BMI greater than 30 kg/m2 and increased CAC scores (10).
Other cross-sectional studies compared different measurements of abdominal fat with the presence of CAC (13,23,24,25). In the Dallas Heart Study, 2744 individuals were examined and waist to hip ratio provided better discrimination of CAC than BMI or waist circumference (23). In another study of 762 middle-aged individuals, obesity assessment by tomography was not superior to BMI and waist circumference (13). However, contrary to these previous reports, in the Rancho Bernardo Study cross-sectional evaluation of obesity measurements (11), none of the fat distribution measurements discriminated those with presence of CAC. Different inclusion criteria and older age of the Rancho Bernardo Study cohort could explain these contradictory findings.
Data about fat distribution and CAC progression are limited. The main factors reported to be associated with CAC progression are advanced age, male sex, baseline CAC scores, diabetes, hypertension, and nonuse of cholesterol-lowering medication (20,21,26). In the Multi-Ethnic Study of Atherosclerosis, which evaluated risk factors for CAC progression in 5756 participants with an average of 2.4 yr between scans, besides traditional CHD risk factors, higher BMI was associated with both the risk of developing incident CAC and increases in existing calcification (27). No other body fat measurement was evaluated in that study. Cassidy et al. (28) studied 443 asymptomatic white individuals older than 30 yr (243 men) with CAC measurements 8.9 yr apart and found that waist to hip ratio, waist circumference, and BMI predicted CAC progression but only in a subset of lower-risk individuals.
We studied an older population with no known CHD; to our knowledge, this is the first report comparing fat distribution measurements in the elderly, including EBCT assessment with CAC progression. Interestingly, clinical measurements of abdominal obesity were better predictors of CAC progression than tomography assessment in women, but the opposite was true in men. The simplest possible explanation for the sexual dimorphism is the different distribution of obesity measurements and CAC by sex. As expected men were much more likely than women to have CAC at baseline. We speculate that EBCT measurements might be of added value in older men because they are more likely to be overweight and have increased visceral fat. In men the VAT to SAT ratio showed an association with CAC progression (which was not observed with waist circumference, possibly because the latter is not a good surrogate for VAT to SAT ratio). This is reinforced by the observation that the correlation between waist circumference and VAT to SAT ratio is poor in men (R = 0.019, P = 0.81) and better in women (R = 0.251, P = 0.001).
The older age of our population does not preclude the potential importance of these results. A recent study of CAC as a predictor for all-cause mortality in 3570 men and women aged 70 yr and older showed that CAC values were associated with mortality, despite their limited life expectancy (8). In another study better lifestyle and the Mediterranean diet were associated with a more than 50% lower rate of all-cause and cause-specific mortality in the elderly (29). These findings suggest that CHD risk factors are still important in old age and that perhaps it is never too late to change.
Limitations of the present study should be noted. Like all studies of the elderly, there is a possibility of survivor bias. Furthermore, by design, members of this cohort who had experienced a clinical cardiac event were ineligible for the CAC evaluation. Thus, both age and the study question excluded those with the least favorable baseline CHD risk factors, likely yielding a conservative estimate of the body size-atherosclerosis association. This idea is reinforced by the less favorable weight and waist circumference of women (but not men) who did not return for the second evaluation.
In conclusion, in this study of elderly survivors without known heart disease, abdominal obesity independently predicted CAC progression. These results point to the importance of using clinical measurements of abdominal obesity to identify individuals at increased risk for atherosclerosis and also to the importance of preventing abdominal obesity even in old age.
Footnotes
The Rancho Bernardo Study was funded by the National Institutes of Health/National Institute on Aging Grants AG07181 and AG028507 and the National Institute of Diabetes and Digestive and Kidney Diseases Grant DK31801. C.K.K. is a recipient of a grant from Coordenação de Aperfeiçoamento e Pessoal de Nivel Superior/Brasil Programa de Doutorado no Pais com Estagio no Exterior.
Disclosure Summary: The authors have nothing to disclose.
First Published Online October 21, 2009
Abbreviations: BMI, Body mass index; CAC, coronary artery calcium; CHD, coronary heart disease; EBCT, electron beam computed tomography; HDL, high-density lipoprotein; SAT, sc adipose tissue; VAT, visceral adipose tissue.
References
- Wajchenberg BL 2000 Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome. Endocr Rev 21:697–738 [DOI] [PubMed] [Google Scholar]
- Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu CY, Vasan RS, Murabito JM, Meigs JB, Cupples LA, D'Agostino Sr RB, O'Donnell CJ 2007 Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation 116:39–48 [DOI] [PubMed] [Google Scholar]
- Després JP, Lemieux I, Prud'homme D 2001 Treatment of obesity: need to focus on high risk abdominally obese patients. BMJ 322:716–720 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Schneider HJ, Glaesmer H, Klotsche J, Böhler S, Lehnert H, Zeiher AM, März W, Pittrow D, Stalla GK, Wittchen HU 2007 Accuracy of anthropometric indicators of obesity to predict cardiovascular risk. J Clin Endocrinol Metab 92:589–594 [DOI] [PubMed] [Google Scholar]
- de Koning L, Merchant AT, Pogue J, Anand SS 2007 Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: meta-regression analysis of prospective studies. Eur Heart J 28:850–856 [DOI] [PubMed] [Google Scholar]
- Gelber RP, Gaziano JM, Orav EJ, Manson JE, Buring JE, Kurth T 2008 Measures of obesity and cardiovascular risk among men and women. J Am Coll Cardiol 52:605–615 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Weintraub WS, Diamond GA 2008 Predicting cardiovascular events with coronary calcium scoring. N Engl J Med 358:1394–1396 [DOI] [PubMed] [Google Scholar]
- Raggi P, Gongora MC, Gopal A, Callister TQ, Budoff M, Shaw LJ 2008 Coronary artery calcium to predict all-cause mortality in elderly men and women. J Am Coll Cardiol 52:17–23 [DOI] [PubMed] [Google Scholar]
- Lee CD, Jacobs Jr DR, Schreiner PJ, Iribarren C, Hankinson A 2007 Abdominal obesity and coronary artery calcification in young adults: the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Am J Clin Nutr 86:48–54 [DOI] [PubMed] [Google Scholar]
- Hsu CH, Chang SG, Hwang KC, Chou P 2007 Impact of obesity on coronary artery calcification examined by electron beam computed tomographic scan. Diabetes Obes Metab 9:354–359 [DOI] [PubMed] [Google Scholar]
- Kim DJ, Bergstrom J, Barrett-Connor E, Laughlin GA 2008 Visceral adiposity and subclinical coronary artery disease in elderly adults: Rancho Bernardo Study. Obesity (Silver Spring) 16:853–858 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Agatston AS, Janowitz WR, Hildner FJ, Zusmer NR, Viamonte Jr M, Detrano R 1990 Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol 15:827–832 [DOI] [PubMed] [Google Scholar]
- Snell-Bergeon JK, Hokanson JE, Kinney GL, Dabelea D, Ehrlich J, Eckel RH, Ogden L, Rewers M 2004 Measurement of abdominal fat by CT compared to waist circumference and BMI in explaining the presence of coronary calcium. Int J Obes Relat Metab Disord 28:1594–1599 [DOI] [PubMed] [Google Scholar]
- Maher JE, Bielak LF, Raz JA, Sheedy 2nd PF, Schwartz RS, Peyser PA 1999 Progression of coronary artery calcification: a pilot study. Mayo Clin Proc 74:347–355 [DOI] [PubMed] [Google Scholar]
- 1998 Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults—The Evidence Report. National Institutes of Health. Obes Res 6 Suppl 2:51S–209S [PubMed] [Google Scholar]
- Greenland P, Bonow RO, Brundage BH, Budoff MJ, Eisenberg MJ, Grundy SM, Lauer MS, Post WS, Raggi P, Redberg RF, Rodgers GP, Shaw LJ, Taylor AJ, Weintraub WS 2007 ACCF/AHA 2007 clinical expert consensus document on coronary artery calcium scoring by computed tomography in global cardiovascular risk assessment and in evaluation of patients with chest pain: a report of the American College of Cardiology Foundation Clinical Expert Consensus Task Force (ACCF/AHA Writing Committee to Update the 2000 Expert Consensus Document on Electron Beam Computed Tomography) developed in collaboration with the Society of Atherosclerosis Imaging and Prevention and the Society of Cardiovascular Computed Tomography. J Am Coll Cardiol 49:378–402 [DOI] [PubMed] [Google Scholar]
- Kondos GT, Hoff JA, Sevrukov A, Daviglus ML, Garside DB, Devries SS, Chomka EV, Liu K 2003 Electron-beam tomography coronary artery calcium and cardiac events: a 37-month follow-up of 5635 initially asymptomatic low- to intermediate-risk adults. Circulation 107:2571–2576 [DOI] [PubMed] [Google Scholar]
- Bellasi A, Lacey C, Taylor AJ, Raggi P, Wilson PW, Budoff MJ, Vaccarino V, Shaw LJ 2007 Comparison of prognostic usefulness of coronary artery calcium in men versus women (results from a meta- and pooled analysis estimating all-cause mortality and coronary heart disease death or myocardial infarction). Am J Cardiol 100:409–414 [DOI] [PubMed] [Google Scholar]
- Tani T, Yamakami S, Matsushita T, Okamoto M, Toyama J, Fukutomi T, Itoh M 2003 Comparison of coronary artery calcium progression by electron beam computed tomography and angiographically defined progression. Am J Cardiol 91:865–867 [DOI] [PubMed] [Google Scholar]
- Raggi P, Cooil B, Ratti C, Callister TQ, Budoff M 2005 Progression of coronary artery calcium and occurrence of myocardial infarction in patients with and without diabetes mellitus. Hypertension 46:238–243 [DOI] [PubMed] [Google Scholar]
- Budoff MJ, Raggi P 2001 Coronary artery disease progression assessed by electron-beam computed tomography. Am J Cardiol 88:46E–50E [DOI] [PubMed] [Google Scholar]
- Detrano R, Guerci AD, Carr JJ, Bild DE, Burke G, Folsom AR, Liu K, Shea S, Szklo M, Bluemke DA, O'Leary DH, Tracy R, Watson K, Wong ND, Kronmal RA 2008 Coronary calcium as a predictor of coronary events in four racial or ethnic groups. N Engl J Med 358:1336–1345 [DOI] [PubMed] [Google Scholar]
- See R, Abdullah SM, McGuire DK, Khera A, Patel MJ, Lindsey JB, Grundy SM, de Lemos JA 2007 The association of differing measures of overweight and obesity with prevalent atherosclerosis: the Dallas Heart Study. J Am Coll Cardiol 50:752–759 [DOI] [PubMed] [Google Scholar]
- Nasir K, Campbell CY, Santos RD, Roguin A, Braunstein JB, Carvalho JA, Blumenthal RS 2005 The association of subclinical coronary atherosclerosis with abdominal and total obesity in asymptomatic men. Prev Cardiol 8:143–148 [DOI] [PubMed] [Google Scholar]
- Allison MA, Michael Wright C 2004 Body morphology differentially predicts coronary calcium. Int J Obes Relat Metab Disord 28:396–401 [DOI] [PubMed] [Google Scholar]
- Callister TQ, Cooil B, Raya SP, Lippolis NJ, Russo DJ, Raggi P 1998 Coronary artery disease: improved reproducibility of calcium scoring with an electron-beam CT volumetric method. Radiology 208:807–814 [DOI] [PubMed] [Google Scholar]
- Kronmal RA, McClelland RL, Detrano R, Shea S, Lima JA, Cushman M, Bild DE, Burke GL 2007 Risk factors for the progression of coronary artery calcification in asymptomatic subjects: results from the Multi-Ethnic Study of Atherosclerosis (MESA). Circulation 115:2722–2730 [DOI] [PubMed] [Google Scholar]
- Cassidy AE, Bielak LF, Zhou Y, Sheedy 2nd PF, Turner ST, Breen JF, Araoz PA, Kullo IJ, Lin X, Peyser PA 2005 Progression of subclinical coronary atherosclerosis: does obesity make a difference? Circulation 111:1877–1882 [DOI] [PubMed] [Google Scholar]
- Knoops KT, de Groot LC, Kromhout D, Perrin AE, Moreiras-Varela O, Menotti A, van Staveren WA 2004 Mediterranean diet, lifestyle factors, and 10-year mortality in elderly European men and women: the HALE project. JAMA 292:1433–1439 [DOI] [PubMed] [Google Scholar]