This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Cassidy, A. E. Articles by Peyser, P. A. Search for Related Content PubMed PubMed Citation Articles by Cassidy, A. E. Articles by Peyser, P. A. Related Collections Epidemiology Risk Factors Imaging

(Circulation. 2005;111:1877-1882.)
© 2005 American Heart Association, Inc.

Coronary Heart Disease

Progression of Subclinical Coronary Atherosclerosis

Does Obesity Make a Difference?

Andrea E. Cassidy, MPH; Lawrence F. Bielak, DDS, MPH; Yan Zhou, MS; Patrick F. Sheedy, II, MD; Stephen T. Turner, MD; Jerome F. Breen, MD; Philip A. Araoz, MD; Iftikhar J. Kullo, MD; Xihong Lin, PhD; Patricia A. Peyser, PhD

From the Departments of Epidemiology (A.E.C., L.F.B., P.A.P.) and Biostatistics (Y.Z., X.L.), University of Michigan, Ann Arbor, and Department of Diagnostic Radiology (P.F.S., J.F.B., P.A.A.), Division of Nephrology and Hypertension, Department of Internal Medicine (S.T.T.), and Division of Cardiovascular Diseases (I.J.K.), Mayo Clinic and Foundation, Rochester, Minn.

Correspondence to Patricia A. Peyser, PhD, Department of Epidemiology, University of Michigan, 611 Church St, Ann Arbor, MI 48104-3028. E-mail ppeyser{at}umich.edu

Received November 5, 2004; revision received January 7, 2005; accepted February 9, 2005.

	Abstract

Top Abstract Introduction Methods Results Discussion References

 
Background— Obesity is associated with coronary artery calcification (CAC), a marker of the presence and extent of subclinical coronary atherosclerosis. Obesity adds incremental information in identifying those at higher risk of coronary heart disease to traditional risk factor assessment. The present study examined associations between obesity measures and progression of CAC in those at higher (10%) and lower (<10%) 10-year coronary heart disease risk according to the Framingham risk equation.

Methods and Results— In this study, 443 asymptomatic white individuals >30 years of age (243 men) had baseline and follow-up CAC measurements an average of 8.9 years apart. Multivariable linear regression models were fit to determine associations of obesity measures at baseline with progression of CAC defined as log_e of the difference between follow-up and baseline CAC area plus 1 divided by time (in years) between examinations, adjusting for baseline CAC quantity, age, sex, baseline hypertension status, and baseline cholesterol level. Among 329 participants (74.3%) in the lower-risk group, waist circumference (P=0.024), waist-to-hip ratio (P<0.001), body mass index (P=0.036), and being overweight compared with being underweight or of normal weight (P=0.008) were each significantly positively associated with progression of CAC. Among those at higher coronary heart disease risk, no baseline obesity measures were associated with CAC progression.

Conclusions— Various measures of obesity were associated with increased progression of CAC in those at lower risk of coronary heart disease. Future studies examining the effectiveness of weight reduction strategies in reducing CAC progression among those with an otherwise favorable risk factor profile may be warranted.

Key Words: atherosclerosis • calcium • imaging • obesity • population

	Introduction

Top Abstract Introduction Methods Results Discussion References

 
More than half of US adults are overweight or obese, placing them at increased risk for morbidity and mortality from chronic diseases, including coronary heart disease (CHD).^1,2 Coronary artery calcification (CAC), a marker of subclinical coronary atherosclerosis,³ progresses over time⁴ and predicts CHD events.⁵ Although CAC is associated with measures of obesity obtained at the same time as the CAC measurement,⁶ only one study has investigated the association between obesity and progression of CAC, and that study included only those with type I diabetes.⁷ Thus, little is known about the role of obesity in the progression of CAC in asymptomatic individuals free of diabetes.

The present study examined associations between various measures of obesity and the presence, quantity, and progression of CAC over time in asymptomatic men and women >30 years of age without diabetes from a community-based study. Recently, having a body mass index (BMI) 30 kg/m² added information in predicting increased CAC quantity among those with CHD risk 20% according to traditional risk factor assessment.⁸ Therefore, we examined associations in subgroups of individuals identified as having higher or lower CHD risk on the basis of the Framingham risk equation. A better understanding of whether obesity is an independent predictor of progression in CAC quantity may identify which individuals to target for interventions to slow the progression of subclinical coronary atherosclerosis.

	Methods

Top Abstract Introduction Methods Results Discussion References

 
Study Participants
The Rochester Family Heart Study (RFHS), a community-based study of 3974 individuals 5 to 90 years of age, was conducted between 1984 and 1991.^9,10 The Epidemiology of Coronary Artery Calcification (ECAC) study, conducted between 1991 and 1998, examined 1240 participants from the RFHS and 496 individuals living in the vicinity of Rochester (Minn) who were 20 years of age at the time of recruitment for the ECAC study, who were not pregnant or lactating, and who never had coronary or noncoronary heart surgery.^11,12 Participants in the present study were recruited for a follow-up examination between December 2000 and July 2004. In general, participants were invited to return for a second examination on the basis of age (older age first) and longer time since baseline examination. Eligible family members of these individuals who also participated in the baseline study were invited to return at the same time. The Mayo Clinic and University of Michigan Institutional Review boards approved the study protocols, and participants gave written informed consent.

A total of 553 individuals >30 years of age at baseline without a previous report of myocardial infarction or stroke at baseline or follow-up had baseline and follow-up examinations available. Seven individuals with history of diabetes at baseline, 69 reporting a previous cancer diagnosis (excluding nonmelanoma skin cancer), 21 with missing data, and 13 with extreme measures of body size based on graphic inspection of the data values¹³ were excluded from analysis. The final sample size consisted of 443 white individuals (243 men).

Risk Factor Assessment
During the baseline and follow-up interviews, participants reported current medication use and history of smoking, physician-diagnosed hypertension, myocardial infarction, stroke, or diabetes. Height was measured by a wall stadiometer; weight was measured by electronic balance; and BMI was calculated (in kg/m²). Participants were categorized as overweight or obese if their BMI was 25 or 30 kg/m², respectively. Waist circumference was measured at the umbilicus; hips were measured at the level of maximal circumference; and waist-to-hip ratio was calculated. Abdominal obesity was defined from National Cholesterol Education Program/Adult Treatment Panel III criteria¹⁴ as waist circumference >102 cm in men and >88 cm in women.

Standard enzymatic methods were used to measure plasma glucose, total cholesterol, HDL cholesterol, and triglycerides after overnight fasting.¹⁰ Systolic and diastolic blood pressure levels were measured in the right arm with a random-zero sphygmomanometer (Hawksley and Sons). Three measures 2 minutes apart were taken, and the average of the second and third measurements was used. Individuals were considered hypertensive if they reported a prior diagnosis of hypertension and use of prescription antihypertensive medication or if the average systolic or diastolic blood pressure was 140 or 90 mm Hg, respectively. Participants were considered diabetic if they reported using insulin or oral hypoglycemic agents or if they reported a physician diagnosis of diabetes but were not currently taking a pharmacological agent to control their high glucose levels.

The Framingham risk equation was used to estimate the 10-year probability of CHD (10-year CHD risk) on the basis of the participant’s sex, age, systolic blood pressure, ratio of total cholesterol to HDL cholesterol, history of cigarette smoking in the past year, and diabetes status at baseline.¹⁵ National Cholesterol Education Program/Adult Treatment Panel III considers 3 categories^14,16 for 10-year CHD risk: <10%, 10% to 20%, and >20%; similar cutoffs were used in a recent article demonstrating incremental prediction benefits when obesity status is used in addition to traditional risk factor assessment.⁸ The present study participants were at relatively low to intermediate risk; only 17 individuals (3.8%) had 10-year CHD risk >20%. Thus, we defined lower or higher risk of a CHD event on the basis of 10-year risk of CHD <10% or 10%, respectively.

Electron-Beam CT Measures of CAC
CAC was measured with an Imatron C-150 electron-beam CT scanner (Imatron Inc). Protocols at baseline and follow-up were identical, as described previously.^17,18 Dual scan runs were obtained for all participants examined in 1993 and later; 138 participants examined before 1993 had one scan run at baseline.

One of 3 radiologists (P.F.S., J.F.B., P.A.A.) inspected the scan images to assess whether there was an observable problem with the scan run. If there was an observable problem, then that scan run was excluded. Most individuals (269) had 4 available measures (2 each at baseline and follow-up); 165 had 3 available measures; and 9 had 2 available measures (1 each at baseline and follow-up). When 2 scans run at a single time point were available, the average of the 2 scan runs was used to represent the quantity of CAC at that time point.

Scan runs were scored by a radiological technologist using customized computer software; the radiological technician was blinded to the results from other electron-beam CT examinations.¹⁹ CAC was defined as a hyperattenuating focus located within 5 mm of the midline of a coronary artery, 4 contiguous pixels in size, with CT numbers >130 Hounsfield units for each pixel. The area (in mm²) of each CAC focus was recorded. A CAC area 1 mm² was defined as detectable CAC. The areas 1 mm² for all CAC foci were summed for all arteries combined to provide a measure of the total quantity of CAC.

Statistical Analysis
A significance level of P=0.05 was used for all analyses. All tests were 2 sided. Higher- and lower-risk participants were considered separately. Means and SDs for continuous baseline demographic and risk factor variables and frequency and percentages for discrete baseline demographic and risk factor variables were calculated. The distributions of continuous demographic and risk factor variables were compared in higher- and lower-risk participants with a t test; nonnormally distributed baseline CAC area, follow-up CAC area, annual change in CAC area, and triglycerides were compared by use of a Wilcoxon test. The frequencies of discrete demographic and risk factor variables in the risk groups were compared with a ² test; all tests were 1 df tests except for BMI category, which was a 2 df test. For ease in interpretation, all continuous variables used as covariates in modeling procedures were centered and standardized to have mean of 0 and a SD of 1.

The distribution of CAC quantity had a mass of values at zero and a skewed set of nonzero values. Therefore, we used a 2-part model to assess the association between each obesity measure (ie, waist circumference, abdominal obesity, hip circumference, waist-to-hip ratio, BMI, and obesity categories based on BMI) and presence and quantity of CAC at baseline. Models were fit for each baseline obesity measure separately because these measures are highly correlated with each other. Age- and sex-adjusted logistic regression models were fit to examine the association between each baseline obesity measure and presence of detectable CAC at baseline. Among those with detectable baseline CAC, CAC quantity was log-transformed [ie, log_e(CAC quantity)] and age- and sex-adjusted linear regression models were fit to examine the association between baseline obesity measures and log-transformed CAC quantity.

Several CAC progression studies calculated a measure for annual change defined as follows: difference between follow-up and baseline CAC area divided by time between baseline and follow-up examinations.^20–22 Another study defined an annualized relative rate of CAC change as follows: exponentiated difference of the log-transformed CAC scores divided by time minus 1.²³ This study excluded those without detectable CAC. In the present study, progression of CAC was defined as the log annual change in CAC quantity, calculated as follows: log_e of the difference between follow-up and baseline CAC area plus 1 divided by time (in years) between baseline and follow-up examinations. Advantages of our measure are inclusion of those without detectable CAC and a log transformation of annual change to reduce the nonnormality of the distribution. If the difference between follow-up and baseline CAC quantity was <0 in our study, the difference was set to 0 (to avoid taking the log of a negative number); this included 25 participants with a negative difference.

Multivariable linear regression models were fit to determine associations between baseline obesity measurements and log annual change in CAC quantity. Models were adjusted for log_e(baseline CAC quantity+1), baseline age, male sex, baseline hypertension status, and baseline total cholesterol level. Interactions between male sex and baseline obesity measures were considered. Model fit was evaluated with residual plots, and potentially influential observations were investigated.

Parameter estimates from linear regression models with log-transformed outcome variables were exponentiated and interpreted as the estimated relative increase in the outcome variable associated with a 1-SD increase in a continuous predictor variable or a change in category for a dichotomous predictor variable.

	Results

Top Abstract Introduction Methods Results Discussion References

 
At baseline, the mean age was 51.1 years (range, 30.1 to 74.9 years), and 243 (54.9%) were male. The mean time between baseline and follow-up examinations was 8.9 years (range, 3.3 to 13.4 years). Baseline characteristics of participants by risk classification are presented in Table 1. One quarter of participants (114 of 443, 25.7%) were classified as higher risk. Overall, higher-risk participants had significantly worse risk factor distributions compared with lower-risk participants (P<0.001 for all comparisons); however, there were no significant differences between the higher- and lower-risk groups in prevalence of cigarette smoking or mean hip circumference.

View this table: [in this window] [in a new window]   TABLE 1. Baseline Participant Characteristics and Baseline and Follow-Up CAC Measurements in Those at Higher and Lower 10-Year Risk of CHD

Prevalence of detectable CAC increased over time in both higher- and lower-risk groups. At baseline and follow-up, higher-risk individuals had significantly larger CAC quantity (P<0.001 for both) compared with lower-risk individuals. The average annual change in CAC area in the higher-risk group was significantly higher than in the lower-risk group (10.2 versus 2.6 mm²/y, respectively; P<0.001; Table 1).

Association of Baseline 10-Year CHD Risk With Presence, Quantity, and Progression of CAC
Mean 10-year CHD risk in the 443 participants was 7.5±5.8%. In the entire sample of 443 participants, a 1-unit-SD increase in the 10-year CHD risk was associated with a 3.3-times-greater odds of having detectable CAC at baseline (P<0.001). Similarly, a 1-unit-SD increase in 10-year CHD risk was associated with an 80% higher CAC quantity at baseline (P<0.001) in those with CAC >0 mm² at baseline. Finally, a 1-unit-SD increase in 10-year CHD risk was associated with a 60% increase in annual change in CAC quantity, adjusted for baseline CAC quantity (P<0.001) (data not shown).

Association of Obesity Measures With Presence and Quantity of CAC Area at Baseline
As shown in Table 2, in the higher-risk group, those with abdominal obesity at baseline had 3.4-times-greater adjusted odds of having CAC compared with those without abdominal obesity (P=0.046). A 1-unit-SD increase in BMI resulted in a 2.1-times-greater adjusted odds of having CAC at baseline in the higher-risk group (P=0.041). In the higher-risk group, obese individuals had a 2.2-times-greater adjusted odds of having baseline CAC than underweight and normal-weight individuals (P=0.046).

View this table: [in this window] [in a new window]   TABLE 2. Age- and Sex-Adjusted Associations of Baseline Obesity Measures With Presence and Quantity of Detectable CAC Measured at Baseline in Those at Higher and Lower 10-Year Risk of CHD

In those at lower risk of CHD, a 1-unit increase in SD in baseline waist circumference, baseline waist-to-hip ratio, and baseline BMI was associated with a 1.7-, 2.1-, and 1.5-times-greater adjusted odds of having detectable CAC at baseline, respectively (P0.002 for all). Individuals with baseline abdominal obesity had 2.5-times-greater adjusted odds of having detectable CAC at baseline compared with those without abdominal obesity at baseline (P=0.010). Obese individuals had a 1.8-times-greater adjusted odds of having baseline CAC compared with those who were underweight or of normal weight (P=0.002).

In those at higher risk for CHD, the maximum CAC quantity was 756.7 mm²; among those with detectable CAC at baseline, a 1-unit-SD increase in baseline waist circumference was associated with a 50% increase in CAC quantity at baseline (P=0.041). In those at lower risk for CHD, the maximum CAC quantity was 534.0 mm², and a 1-unit-SD increase in baseline hip circumference was associated with a 20% decrease in CAC quantity at baseline (P=0.047).

Inferences remained the same after additionally adjusting for baseline hypertension status and baseline cholesterol level. No influential observations were detected in any model in Table 2.

Association of Obesity Measures at Baseline and Progression of CAC Quantity
After adjustment for baseline CAC quantity, baseline age, male sex, baseline hypertension status, and baseline cholesterol level, no baseline obesity measure was significantly associated with progression of CAC quantity among those at higher risk for CHD (Table 3). Only log baseline CAC quantity (P<0.001) predicted CAC progression in the higher-risk group.

View this table: [in this window] [in a new window]   TABLE 3. Association of Baseline Obesity Measures And Log Annual Change in CAC Quantity Adjusted for Log Baseline CAC Quantity, Baseline Age, Male Sex, Baseline Hypertension, and Baseline Cholesterol Level Among Those at Higher or Lower 10-Year Risk of CHD

After adjustment for log baseline CAC quantity, baseline age, male sex, baseline hypertension status, and baseline cholesterol level in those at lower risk for CHD, waist circumference (P=0.024), waist-to-hip ratio (P<0.001), BMI (P=0.036), and being overweight (P=0.008) compared with being underweight or normal weight were each positively significantly associated with progression in CAC quantity (Table 3). A 1-unit increase in the SD of baseline waist circumference, waist-to-hip ratio, and BMI was associated with an estimated 19%, 49%, and 15% increase in annual change in CAC area, respectively. Overweight individuals are expected to have a 51% increase in annual change in CAC compared with underweight and normal-weight individuals.

A small number of participants in the present study were members of the same family. In the lower-risk group, 228 individuals were from unique families, and there were 39 sibships of 2, 4 sibships of 3, 1 sibship of 5, and 1 sibship of 6; in the higher-risk group, 99 participants were from unique families, and there were 5 sibships of 2 and 1 sibship of 5. When we refit the models in Table 3 using generalized estimating equations to account for the potential correlation between observations taken from the same family, all inferences were identical to the model results ignoring genetic relationships.²⁴

Participants with diabetes were excluded a priori from the present analyses. Waist circumference and fasting glucose at baseline were significantly correlated (correlation coefficient, 0.33; P<0.001) in the total sample (n=443). When we refit the model from Table 3 that included waist circumference in those at lower risk of CHD and included fasting glucose, neither waist circumference (P=0.102) nor fasting glucose (P=0.167) was significantly associated with progression of CAC quantity. However, fasting glucose (P=0.040) was positively associated with CAC progression when waist circumference was not included in the model.

Follow-up times were similar in obese and nonobese participants. In the 57 obese individuals in the lower-risk group, the mean follow-up time was 9.4 years compared with 9.3 years in the nonobese participants (P=0.802); in the 36 obese participants in the higher-risk group, the mean follow-up time was 8.1 years compared with 8.0 years in the nonobese participants (P=0.818). Additionally, incorporation of follow-up time into the multivariable models in Table 3 did not alter the associations.

There was no evidence for sex-specific differences in the relationship between baseline obesity measures and progression of CAC. No influential observations were detected in any modeling procedures for CAC progression.

	Discussion

Top Abstract Introduction Methods Results Discussion References

 
Our findings add evidence that obesity adds valuable clinical information, independent of traditional CHD risk factors, for identifying individuals at risk for CHD who otherwise may be considered to be at low to intermediate risk.⁸ Participants in the present study who were classified as at lower risk on the basis of traditional risk factors had a 10-year CHD risk <10%. Among these participants, measures of obesity were associated with the future progression of CAC over time.

Although other studies have shown associations between obesity measures and CAC measured at the same time,⁶ this is the first study to provide evidence that obesity measures are associated with CAC progression among nondiabetic individuals. Associations between different obesity measures and CAC varied in the present study. This is not unexpected, given that waist circumference and BMI capture different aspects of adiposity. Waist circumference measures central adiposity and is associated with insulin resistance and glucose abnormalities,²⁵ whereas BMI measures overall adiposity and may be associated with different pathogenic mechanisms. Increased hip circumference, independent of increased waist circumference, may be an indicator of a benign adiposity that does not affect the abdominal organs and has been shown to be protective against cardiovascular morbidity and mortality in women.²⁶

In the present study, obesity measures were associated with baseline CAC in both risk groups. However, baseline obesity measures were associated with CAC progression only in the lower-risk group. Against this background of fewer traditional risk factors, associations with other risk factors such as obesity may be more readily identified. In addition, every other risk factor considered predicted CAC progression in the lower-risk group. In contrast, in the higher-risk group, it was interesting that baseline CAC quantity was the only predictor of CAC progression. Against a background of more traditional risk factors, as in the higher-risk group, other factors for CAC progression may be more difficult to identify.

Measures of abdominal obesity are associated with glucose abnormalities²⁵; in the present study, when we refit the multivariable model of CAC progression in the lower-risk group including waist circumference and fasting glucose, neither was statistically significantly associated with progression of CAC quantity. Individually, however, fasting glucose and baseline waist circumference each were significantly associated with increased progression. These findings suggest that the increased progression of CAC resulting from increased waist circumference or fasting glucose may occur through similar mechanisms. Thus, easily obtained abdominal size measurement during regular physical examinations or screenings may be a useful clinical tool for identifying individuals at risk for accelerated atherosclerosis progression.

Obesity is associated with an increased inflammatory response, and inflammatory processes are involved in the pathogenesis of atherosclerosis.²⁷ Weight reduction has been shown to reduce the presence of inflammatory markers²⁷ and to reduce carotid intima-media thickness in obese premenopausal women.²⁸ Lifestyle interventions, including energy restriction and vigorous exercise, were shown to reduce both glucose and insulin concentrations in response to an oral glucose tolerance test among overweight, sedentary men.²⁹ Obesity measures in this study may be surrogates for unmeasured but important factors for atherosclerosis.

Overweight/obesity is an important public health concern, and the prevention and treatment of overweight and obesity is a goal of the US Department of Health and Human Services Healthy People 2010 initiative.³⁰ Approximately 16% of US children 6 to 19 years of age are obese,¹ and there is evidence that childhood obesity predicts adult obesity.³¹ Furthermore, the effects of obesity on atherosclerosis may begin in childhood. Mahoney et al³² previously showed that childhood weight and BMI in male subjects were significantly associated with the presence of CAC detected 15 to 20 years later in young adults. In the present study, lower-risk participants who were obese were estimated to have significantly greater progression of CAC than underweight and normal-weight participants. Thus, public health interventions targeted at prevention of obesity may aid in slowing the progressive nature of atherosclerosis.

This study has several limitations. The study population is limited to white individuals, and the associations found in the present study may not be generalizable to other study populations. Obesity is related to genetic susceptibility and a variety of underlying lifestyle factors, including diet and physical inactivity. The present study was not designed to assess the relationships between these factors and CAC. Finally, we used obesity measures taken at a single time point to predict the presence, quantity, and progression of CAC; these measures may be only a proxy for the level of obesity for any given individual over his or her lifetime.

Participants (n=25) in the present study whose CAC area at follow-up was less than the CAC area at baseline were treated as having no change as part of the progression definition. The mean change in this group was –0.3 mm²/y. The median CAC area at baseline among these participants was 2.4 mm², with a maximum baseline CAC area of 18.9 mm². The negative differences between baseline and follow-up are most likely attributable to measurement errors rather than being true negative changes; thus, the treatment of these participants as having no change between baseline and follow-up is reasonable.

The present study was conducted among asymptomatic community-based research participants without diabetes who were neither selected nor self-referred on the basis of any risk factor or disease. All obesity measures were clinically obtained, minimizing potential bias associated with self-reported obesity.

In summary, obesity measures were associated with the presence and quantity of CAC. Importantly, obesity measures were associated with the progression of CAC quantity among individuals considered to be at lower risk for CHD. Interventions aimed at the primary prevention of obesity may help to retard the development and progression of CAC. Future studies examining the effectiveness of weight reduction strategies in reducing CAC progression among those with an otherwise favorable risk factor profile may be warranted.

	Acknowledgments

 
This research was supported by grant R01-HL-46292 from National Institutes of Health, by General Clinic Research Center grant MO1-RR00585 from the National Institutes of Health awarded to Mayo Clinic Rochester, and by National Human Genome Research Institute grant T32 HG00040.

	References

Top Abstract Introduction Methods Results Discussion References

 

1. Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin LR, Flegal KM. Prevalence of overweight and obesity among US children, adolescents, and adults, 1999–2002. JAMA. 2004; 291: 2847–2850.[Abstract/Free Full Text]
   
2. Klein S, Burke LE, Bray GA, Blair S, Allison DB, Pi-Sunyer X, Hong Y, Eckel RH, for the American Heart Association Council on Nutrition, Physical Activity, and Metabolism and American College of Cardiology Foundation. Clinical implications of obesity with specific focus on cardiovascular disease: a statement for professionals from the American Heart Association Council on Nutrition, Physical Activity, and Metabolism: endorsed by the American College of Cardiology Foundation. Circulation. 2004; 110: 2952–2967.[Abstract/Free Full Text]
   
3. Wexler L, Brundage B, Crouse J, Detrano R, Fuster V, Maddahi J, Rumberger J, Stanford W, White R, Taubert K. Coronary artery calcification: pathophysiology, epidemiology, imaging methods, and clinical implications. Circulation. 1996; 94: 1175–1192.[Free Full Text]
   
4. Maher JE, Bielak LF, Raz JA, Sheedy PF2nd, Schwartz RS, Peyser PA. Progression of coronary artery calcification: a pilot study. Mayo Clin Proc. 1999; 74: 347–355.[Medline] [Order article via Infotrieve]
   
5. Arad Y, Spadaro LA, Goodman K, Newstein D, Guerci AD. Prediction of coronary events with electron beam computed tomography. J Am Coll Cardiol. 2000; 36: 1253–1260.[Abstract/Free Full Text]
   
6. Snell-Bergeon JK, Hokanson JE, Kinney GL, Dabelea D, Ehrlich J, Eckel RH, Ogden L, Rewers M. 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. 2004; 28: 1594–1599.[CrossRef][Medline] [Order article via Infotrieve]
   
7. Snell-Bergeon JK, Hokanson JE, Jensen L, MacKenzie T, Kinney G, Dabelea D, Eckel RH, Ehrlich J, Garg S, Rewers M. Progression of coronary artery calcification in type 1 diabetes: the importance of glycemic control. Diabetes Care. 2003; 26: 2923–2928.[Abstract/Free Full Text]
   
8. Desai MY, Nasir K, Braunstein JB, Rumberger JA, Post WS, Budoff MJ, Blumenthal RS. Underlying risk factors incrementally add to the standard risk estimate in detecting subclinical atherosclerosis in low- and intermediate-risk middle-aged asymptomatic individuals. Am Heart J. 2004; 148: 871–877.[CrossRef][Medline] [Order article via Infotrieve]
   
9. Turner ST, Weidman WH, Michels VV, Reed TJ, Ormson CL, Fuller T, Sing CF. Distribution of sodium-lithium countertransport and blood pressure in Caucasians five to eighty-nine years of age. Hypertension. 1989; 13: 378–391.[Abstract/Free Full Text]
   
10. Kottke BA, Moll PP, Michels VV, Weidman WH. Levels of lipids, lipoproteins, and apolipoproteins in a defined population. Mayo Clin Proc. 1991; 66: 1198–1208.[Medline] [Order article via Infotrieve]
    
11. Maher JE, Raz JA, Bielak LF, Sheedy PF2nd, Schwartz RS, Peyser PA. Potential of quantity of coronary artery calcification to identify new risk factors for asymptomatic atherosclerosis. Am J Epidemiol. 1996; 144: 943–953.[Abstract/Free Full Text]
    
12. Bielak LF, Sheedy PF2nd, Peyser PA. Coronary artery calcification measured at electron-beam CT: agreement in dual scans runs and change over time. Radiology. 2001; 218: 224–229.[Abstract/Free Full Text]
    
13. Sternfeld B, Wang H, Quesenberry CP Jr, Abrams B, Everson-Rose SA, Greendale GA, Matthews KA, Torrens JI, Sowers M. Physical activity and changes in weight and waist circumference in midlife women: findings from the Study of Women’s Health Across the Nation. Am J Epidemiol. 2004; 160: 912–922.[Abstract/Free Full Text]
    
14. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA. 2001; 285: 2486–2497.[Free Full Text]
    
15. Anderson KM, Odell PM, Wilson PW, Kannel WB. Cardiovascular disease risk profiles. Am Heart J. 1990; 121: 293–298.[CrossRef]
    
16. Ford ES, Giles WH, Mokdad AH. The distribution of 10-Year risk for coronary heart disease among US adults: findings from the National Health and Nutrition Examination Survey III. J Am Coll Cardiol. 2004; 43: 1791–1796.[Abstract/Free Full Text]
    
17. Bielak LF, Rumberger JA, Sheedy PF2nd, Schwartz RS, Peyser PA. Probabilistic model for prediction of angiographically defined obstructive coronary artery disease using electron beam computed tomography calcium score strata. Circulation. 2000; 102: 380–385.[Abstract/Free Full Text]
    
18. Peyser PA, Bielak LF, Chu JS, Turner ST, Ellsworth DL, Boerwinkle E, Sheedy PF2nd. Heritability of coronary artery calcium quantity measured by electron beam computed tomography in asymptomatic adults. Circulation. 2002; 106: 304–308.[Abstract/Free Full Text]
    
19. Reed JE, Rumberger JA, Davitt PJ, Kaufmann RB, Sheedy PF. System for quantitative analysis of coronary artery calcification via electron beam computed tomography: medical imaging 1994: physiology and function from multiple dimensional images: physiology and function from multidimensional images. SPIE. 1994; 2168: 43–53.[CrossRef]
    
20. Chironi G, Simon A, Denarie N, Vedie B, Sene V, Megnien JL, Levenson J. Determinants of progression of coronary artery calcifications in asymptomatic men at high cardiovascular risk. Angiology. 2002; 53: 677–683.[Medline] [Order article via Infotrieve]
    
21. Schmermund A, Baumgart D, Mohlenkamp S, Kriener P, Pump H, Gronemeyer D, Seibel R, Erbel R. Natural history and topographic pattern of progression of coronary calcification in symptomatic patients: an electron-beam CT study. Arterioscler Thromb Vasc Biol. 2001; 21: 421–426.[Abstract/Free Full Text]
    
22. Achenbach S, Ropers D, Pohle K, Leber A, Thilo C, Knez A, Menendez T, Maeffert R, Kusus M, Regenfus M, Bickel A, Haberl R, Steinbeck G, Moshage W, Daniel WG. Influence of lipid-lowering therapy on the progression of coronary artery calcification: a prospective evaluation. Circulation. 2002; 106: 1077–1082.[Abstract/Free Full Text]
    
23. Yoon HC, Emerick AM, Hill JA, Gjertson DW, Goldin JG. Calcium begets calcium: progression of coronary artery calcification in asymptomatic subjects. Radiology. 2002; 224: 236–241.[Abstract/Free Full Text]
    
24. Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika. 1986; 73: 13–22.[Abstract/Free Full Text]
    
25. Despres JP. Abdominal obesity as important component of insulin-resistance syndrome. Nutrition. 1993; 9: 452–459.[Medline] [Order article via Infotrieve]
    
26. Heitmann BL, Frederiksen P, Lissner L. Hip circumference and cardiovascular morbidity and mortality in men and women. Obes Res. 2004; 12: 482–487.[Medline] [Order article via Infotrieve]
    
27. Sharman MJ, Volek JS. Weight loss leads to reductions in inflammatory biomarkers after a very low carbohydrate diet and a low-fat diet in overweight men. Clin Sci. 2004; 107: 365–369.[Medline] [Order article via Infotrieve]
    
28. Mavri A, Stegnar M, Sentocnik JT, Videcnik V. Impact of weight reduction on early carotid atherosclerosis in obese premenopausal women. Obes Res. 2001; 9: 511–516.[Medline] [Order article via Infotrieve]
    
29. Cox KL, Burke V, Morton AR, Beilin LJ, Puddey IB. Independent and additive effects of energy restriction and exercise on glucose and insulin concentrations in sedentary overweight men. Am J Clin Nutr. 2004; 80: 308–316.[Abstract/Free Full Text]
    
30. US Department of Health and Human Services. Healthy People 2010: Understanding and Improving Health. 2nd ed. Washington, DC: US Government Printing Office; 2000.
    
31. Whitaker RC, Wright JA, Pepe MS, Seidel KD, Dietz WH. Predicting obesity in young adulthood from childhood and parental obesity. N Engl J Med. 1997; 337: 869–873.[Abstract/Free Full Text]
    
32. Mahoney LT, Burns TL, Stanford W, Thompson BH, Witt JD, Rost CA, Lauer RM. Coronary risk factors measured in childhood and young adult life are associated with coronary artery calcification in young adults: the Muscatine Study. J Am Coll Cardiol. 1996; 27: 277–284.[Abstract]
    

This article has been cited by other articles:

				M. K. Jensen, S. E. Chiuve, E. B. Rimm, C. Dethlefsen, A. Tjonneland, A. M. Joensen, and K. Overvad Obesity, Behavioral Lifestyle Factors, and Risk of Acute Coronary Events Circulation, June 17, 2008; 117(24): 3062 - 3069. [Abstract] [Full Text] [PDF]

				E. D. Abel, S. E. Litwin, and G. Sweeney Cardiac Remodeling in Obesity Physiol Rev, April 1, 2008; 88(2): 389 - 419. [Abstract] [Full Text] [PDF]

				D. Sorajja, A. S. Gami, V. K. Somers, T. R. Behrenbeck, A. Garcia-Touchard, and F. Lopez-Jimenez Independent Association Between Obstructive Sleep Apnea and Subclinical Coronary Artery Disease Chest, April 1, 2008; 133(4): 927 - 933. [Abstract] [Full Text] [PDF]

				J. Golledge, P. Clancy, K. Jamrozik, and P. E. Norman Obesity, Adipokines, and Abdominal Aortic Aneurysm: Health in Men Study Circulation, November 13, 2007; 116(20): 2275 - 2279. [Abstract] [Full Text] [PDF]

				R. See, S. M. Abdullah, D. K. McGuire, A. Khera, M. J. Patel, J. B. Lindsey, S. M. Grundy, and J. A. de Lemos The Association of Differing Measures of Overweight and Obesity With Prevalent Atherosclerosis: The Dallas Heart Study J. Am. Coll. Cardiol., August 21, 2007; 50(8): 752 - 759. [Abstract] [Full Text] [PDF]

				E. Ingelsson, L. M. Sullivan, C. S. Fox, J. M. Murabito, E. J. Benjamin, J. F. Polak, J. B. Meigs, M. J. Keyes, C. J. O'Donnell, T. J. Wang, et al. Burden and Prognostic Importance of Subclinical Cardiovascular Disease in Overweight and Obese Individuals Circulation, July 24, 2007; 116(4): 375 - 384. [Abstract] [Full Text] [PDF]

				A. E. Cassidy-Bushrow, L. F. Bielak, P. F. Sheedy II, S. T. Turner, I. J. Kullo, X. Lin, and P. A. Peyser Coronary Artery Calcification Progression Is Heritable Circulation, July 3, 2007; 116(1): 25 - 31. [Abstract] [Full Text] [PDF]

				R. A. Kronmal, R. L. McClelland, R. Detrano, S. Shea, J. A. Lima, M. Cushman, D. E. Bild, and G. L. Burke Risk Factors for the Progression of Coronary Artery Calcification in Asymptomatic Subjects: Results From the Multi-Ethnic Study of Atherosclerosis (MESA) Circulation, May 29, 2007; 115(21): 2722 - 2730. [Abstract] [Full Text] [PDF]

				G. Fantuzzi and T. Mazzone Adipose Tissue and Atherosclerosis: Exploring the Connection Arterioscler. Thromb. Vasc. Biol., May 1, 2007; 27(5): 996 - 1003. [Abstract] [Full Text] [PDF]

				T. Mazzone, P. M. Meyer, G. T. Kondos, M. H. Davidson, S. B. Feinstein, R. B. D'Agostino Sr., A. Perez, and S. M. Haffner Relationship of Traditional and Nontraditional Cardiovascular Risk Factors to Coronary Artery Calcium in Type 2 Diabetes Diabetes, March 1, 2007; 56(3): 849 - 855. [Abstract] [Full Text] [PDF]

				T. T. Lewis, S. A. Everson-Rose, L. H. Powell, K. A. Matthews, C. Brown, K. Karavolos, K. Sutton-Tyrrell, E. Jacobs, and D. Wesley Chronic Exposure to Everyday Discrimination and Coronary Artery Calcification in African-American Women: The SWAN Heart Study Psychosom Med, May 1, 2006; 68(3): 362 - 368. [Abstract] [Full Text] [PDF]

				K. Narkiewicz Obesity and hypertension--the issue is more complex than we thought Nephrol. Dial. Transplant., February 1, 2006; 21(2): 264 - 267. [Full Text] [PDF]

This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Cassidy, A. E. Articles by Peyser, P. A. Search for Related Content PubMed PubMed Citation Articles by Cassidy, A. E. Articles by Peyser, P. A. Related Collections Epidemiology Risk Factors Imaging