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Original Contribution |

Education, 15-Year Risk Factor Progression, and Coronary Artery Calcium in Young Adulthood and Early Middle Age:  The Coronary Artery Risk Development in Young Adults Study FREE

Lijing L. Yan, PhD, MPH; Kiang Liu, PhD; Martha L. Daviglus, MD, PhD; Laura A. Colangelo, MS; Catarina I. Kiefe, MD, PhD; Stephen Sidney, MD, MPH; Karen A. Matthews, PhD; Philip Greenland, MD
[+] Author Affiliations

Author Affiliations: Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Ill (Drs Yan, Liu, Daviglus, and Greenland, and Ms Colangelo); Department of Health Economics and Management, Guanghua School of Management, Peking University, Beijing, China (Dr Yan); Division of Preventive Medicine, University of Alabama at Birmingham and Birmingham Veterans Affairs Medical Center (Dr Kiefe); Kaiser Permanente Research Division, Oakland, Calif (Dr Sidney); and Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pa (Dr Matthews).

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JAMA. 2006;295(15):1793-1800. doi:10.1001/jama.295.15.1793.
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Context The inverse association between education and cardiovascular disease is well established, but little is known about the relationship between education and subclinical disease, which is free from medical access and treatment-related influences, or about possible mediating pathways for these relationships.

Objective To examine the association of education with coronary artery calcium (CAC), an indicator of subclinical atherosclerosis, and cardiovascular risk factors, and their changes as potential mediators.

Design, Setting, and Participants A population-based, prospective, observational study (Coronary Artery Risk Development in Young Adults [CARDIA]) of 2913 eligible participants (44.9% black; 53.9% women) recruited from 4 metropolitan areas (Birmingham, Ala; Chicago, Ill; Minneapolis, Minn; and Oakland, Calif) in both the baseline (1985-1986, ages 18-30 years) and year 15 examinations (2000-2001, ages 33-45 years). Education (year 15) was classified into less than high school (n = 128), high school graduate (n = 498), some college (n = 902), college graduate (n = 764), and more than college (n = 621).

Main Outcome Measure Presence of CAC, measured twice by computed tomography (mean total Agatston score >0) at year 15.

Results Overall CAC prevalence in this sample was 9.3%. After adjusting for age, race, and sex, the odds ratios (ORs) for having CAC were 4.14 (95% confidence interval [CI], 2.33-7.35) for less than high school education, 1.89 (95% CI, 1.23-2.91) for high school graduate, 1.47 (95% CI, 0.99-2.19) for some college, and 1.24 (95% CI, 0.84-1.85) for college graduate compared with those participants with more than a college education (P for trend<.001). This was also consistent within each of the 4 race-sex groups. Adjustment for baseline systolic blood pressure, smoking, waist circumference, physical activity, and total cholesterol reduced the ORs to 2.61 (95% CI, 1.40-4.85) for less than high school, 1.38 (95% CI, 0.88-2.17) for high school graduate, 1.17 (95% CI, 0.78-1.77) for some college, and 1.13 (95% CI, 0.76-1.69) for college graduate compared with more than a college education (P for trend = .01), and only slightly attenuated by further adjustment for 15-year changes in risk factors.

Conclusion Education was inversely associated with the prevalence of CAC, an association partially explained by baseline risk factors and minimally by 15-year changes in risk factors.

Figures in this Article

Many studies have documented that education is inversely associated with a wide array of clinical disease outcomes and mortality.13 Its effect on cardiovascular disease and coronary heart disease in particular is among the most consistent and pronounced.2 Despite abundant evidence linking education and health, causal pathways by which education influences health are poorly understood. Education may reduce the risks of clinical diseases through factors influencing overt disease, such as symptom recognition, access to treatment, disease care–seeking behaviors, health literacy, and adherence to medical treatment advice,35 and physicians may have potentially different attitudes, reactions, and treatment patterns for overt diseases toward patients of different socioeconomic backgrounds.6 Subclinical diseases, on the other hand, are not directly affected by the potential mediators of the education-overt disease association listed above. Therefore, they may be useful in elucidating pathways linking education and cardiovascular outcomes. Investigation of subclinical cardiovascular disease among young and early middle-aged adults may be particularly informative because most young adults do not have clinical manifestations of cardiovascular disease, limiting the potential to study frank diseases and mortality as primary health outcomes. Furthermore, young adulthood is a relatively early critical period during the life course for risk factor development and disease trajectories.

In recent years, coronary artery calcium (CAC) has attracted much research attention as an indicator of subclinical coronary artery disease. It is a noninvasive measure of the calcified component of atherosclerotic plaque of the coronary arteries and has been shown to be strongly related to the extent of atherosclerosis and to be an independent predictor of cardiac events.713 Although there are a few published articles addressing the relationship between education and subclinical carotid disease among older individuals with the measure of carotid intimal media thickness (IMT),1423 only 1 study conducted among postmenopausal women used the measure of CAC.21 Carotid IMT and CAC represent atherosclerotic burdens in 2 different arterial beds. Coronary artery calcium is a direct measure of coronary atherosclerosis, whereas carotid IMT is a more general indicator of atherosclerosis. Although carotid IMT and CAC are correlated,24,25 CAC has recently been shown to outperform carotid IMT as a predictor of coronary artery disease.26 No studies have examined the relationship between education and CAC (or carotid IMT) in young and early middle-aged individuals.

Besides treatment-related influences, cardiovascular risk factors and their changes over time represent another potential pathway linking education with clinical and subclinical outcomes. Education is inversely related to established cardiovascular risk factors, such as cigarette smoking, blood pressure (BP), lipids, obesity, and physical activity, particularly among white individuals.2730 These risk factors also influence clinical3134 and subclinical diseases, including CAC.3537 However, few studies have specifically examined whether education is associated with progression of risk profile from young adulthood to middle age3840 and whether such an association, if it exists, could explain the likely relationship of education with subclinical diseases. None of the previous studies on education and subclinical diseases cited herein included any measure of risk factor changes over time. Thus, we used data from a large biracial cohort of young adults followed up for 15 years to examine the relationship of education with CAC and the potential roles of cardiovascular risk factor progression in explaining any associations observed.

The Coronary Artery Risk Development in Young Adults (CARDIA) study is a multicenter, longitudinal study of the evolution of coronary artery disease risk factors in young adults. Details of the study design are published elsewhere.41 Briefly, the cohort included 5115 black and white adults aged 18 to 30 years at baseline (year 0: 1985-1986) recruited from 4 metropolitan areas (Birmingham, Ala; Chicago, Ill; Minneapolis, Minn; and Oakland, Calif) and reexamined at year 15 (2000-2001, ages 33-45 years). Within each center, the sample was designed to comprise approximately equal numbers of participants by sex, race (self-defined: black or white), age (18-24 years or 25-30 years), and education (≤high school or >high school). All examinations were approved by the institutional review boards at each institution, and written informed consent was obtained from each study participant. Because some participants in this young adult cohort had not completed their education at baseline (year 0), we used self-reported educational level at year 15 as our main education measure. Education was classified into 5 groups, according to years of schooling completed: less than 12 (<high school), 12 (high school graduate), 13 to 15 (some college), 16 (college graduate), and more than 16 (>college degree).

Coronary Artery Calcium

Coronary artery calcium was measured at the year-15 examination when participants were aged 33 to 45 years by computed tomography (CT) of the chest. Electron beam CT (Imatron C-150; GE Imatron, San Francisco, Calif) or multidetector CT scanners (GE Lightspeed; General Electric Co, Fairfield, Conn; and Siemens VZ, Siemens AG, Munich, Germany) obtained contiguous 2.5- to 3-mm-thick transverse images from the root of the aorta to the apex of the heart in 2 sequential electrocardiogram-gated scans. Scan data were transmitted electronically to the CARDIA CT Reading Center, where a trained technician examined each image and identified potential foci of CAC using specially developed image-processing software. An expert investigator reviewed and adjudicated all discordant scan pairs. A total calcium score was computed by summing the Agatston score of calcified lesions within each artery (left main, left circumflex, left anterior descending, and right) and across all arteries.42 The mean scores of the 2 scans were used in the analyses. All readers were blinded to participant characteristics and to image data from the other paired scan. Details of the scanning protocol are published, demonstrating high between-reader and within-reader reproducibility.43

Risk Factor Progression

A standardized protocol41 was followed in each examination to measure BP, cholesterol, height, weight, waist circumference, smoking, and physical activity. All technicians were centrally trained and certified. Resting BP was measured at three 1-minute intervals with a Hawksley random-zero sphygmomanometer (WA Baum Co, Copaigue, NY), and the second and third measurements were averaged. Lipids and lipoproteins were measured in fasting plasma samples and determined using enzymatic procedures44 or estimated by the Friedewald equation.45 Trained interviewers obtained information on medication use, including antihypertensive and lipid-lowering medications. Body height, weight, and waist circumference (at the minimum waist girth) were measured in light clothing without shoes. Smoking status and number of cigarettes smoked per day were obtained from standardized questionnaires. Physical activity was assessed by a standard instrument, and energy expenditure for all moderate and vigorous activities was calculated in exercise units.46 Risk factor progression was characterized by changes over 15 years from baseline to year 15 (year 15 − year 0).

Study Sample

Of the 3672 participants (74% of the surviving cohort) examined at the year-15 follow-up, 3043 had CT scans for quantification of CAC score. We excluded pregnant women, persons with coronary artery disease, or persons with missing values (n = 130). The final sample consisted of 560 black men, 748 black women, 783 white men, and 822 white women. According to baseline data, persons who had missing data or who were lost to follow-up were more likely to be black, younger, less educated, and smokers than participants in the study sample.

Statistical Analyses

Baseline (year 0) characteristics and 15-year changes in major cardiovascular risk factors were computed for the 5 educational groups and compared with the reference group (>college degree) using analysis of variance or logistic regression. The presence of CAC was defined as having a positive score (mean total Agatston score >0) with alternative cut points (10, 20, 100, and 400) also examined. Continuous CAC scores were not investigated due to the low prevalence of CAC in this young adult cohort and the skewness of the data. The age-adjusted prevalence (percentage) of CAC across the 5 educational strata was computed for the total cohort and stratified by sex and race. Logistic regression examined the odds ratios (ORs) and 95% confidence intervals (CIs) of having CAC at year 15 with education entered as 4 dummy variables in 3 models: model 1 (adjusted for age, sex, and race), model 2 (all variables in model 1 and additionally adjusted for baseline systolic BP, total cholesterol levels, waist circumference, cigarettes smoked per day, and physical activity), and model 3 (all variables in model 2 and additionally adjusted for 15-year changes in these 5 risk factors, as continuous variables in the model based on the definition above [year 15 − year 0]). Other risk factors (eg, antihypertensive medication use, lipid-lowering medication use, diastolic BP, low-density lipoprotein cholesterol, and body mass index [BMI, calculated as weight in kilograms divided by height in meters squared]) were examined in additional analyses. Linear trend across the 5 educational strata was tested with education as an ordinal variable, coded 1 to 5, in logistic regression. Analyses were conducted with SAS statistical software version 9.1 (SAS Institute Inc, Cary, NC). P<.05 was considered statistically significant.

At baseline, the mean (SD) age of the study cohort was 25.2 (3.6) years. Persons with higher education at year 15 were older and disproportionately female and white (Table 1). Education was significantly and inversely related to systolic BP, smoking, waist circumference, and BMI measured at baseline. A strong positive relationship was observed between education and physical activity. No or weak relationships were observed between education and diastolic BP and lipid profiles.

Table Graphic Jump LocationTable 1. Baseline Characteristics of Study Participants by Educational Level at Year 15 (1985-2001)*

Baseline and year-15 risk factors were highly correlated, with Pearson correlation coefficient ranging from 0.46 (for systolic BP) to 0.77 (for BMI) (all P<.001). A strong and graded inverse association was found for education and 15-year changes in BP; the higher the education, the smaller the increase in levels of systolic and diastolic BP and the lower the prevalence of antihypertensive medication at year 15 (Table 2). The smoking prevalence at year 15 was also inversely associated with education. Although the 2 better educated groups had larger decreases in physical activity over 15 years, the dose-response positive relationship between education and physical activity remained at year 15. No clear patterns emerged for changes in lipids. Average increases in waist circumference and BMI over 15 years were higher for those participants with less education, but highest for those with some college.

Table Graphic Jump LocationTable 2. 15-Year Changes in Cardiovascular Risk Factors and Selected Year 15 Characteristics by Educational Level at Year 15 (1985-2001)*

In this sample, the overall mean prevalence of CAC score of more than 0 at year 15 (ages 33-45 years) was 9.3% (10.5% in black men, 17.2% in white men, 4.4% in black women, and 5.4% in white women). After adjusting for age, sex, and race, the prevalence of CAC (defined at various cut points of 0, 10, 20, 100, and 400) was inversely associated with education, with highest prevalence for those individuals with less than a high school education (Table 3). The relationship between education and age-adjusted prevalence of CAC score of more than 0 was consistent for both sexes and races and each of the 4 race-sex groups (Figure).

Table Graphic Jump LocationTable 3. Prevalence Adjusted by Age, Sex, and Race of Having CAC by Educational Level for the Total Sample (2000-2001)
Figure. Age-Adjusted Prevalence of Having Coronary Artery Calcium by Educational Level, 2000-2001
Graphic Jump Location

Adjusted for age and race for results on men and women (first 2 data sets in left panel) and adjusted for age and sex for results on black and white adults (second 2 data sets in left panel). P values for linear trend across the 5 educational strata were obtained by entering education as an ordinal variable (coded as 1-5) in logistic regression.

In age-, race-, and sex-adjusted analyses, the OR of CAC score of more than 0 for individuals with less than a high school education was 4.14 (95% CI, 2.33-7.35) vs more than a college education (Table 4, model 1). High school graduates also had significantly higher odds of having CAC. Adjustment for baseline risk factors reduced the effect sizes for most educational groups by approximately half (model 2), while further adjustment for 15-year changes in these risk factors only slightly attenuated the results (model 3). All baseline risk factors, except physical activity, were significant predictors of the presence of CAC; the only change variable achieving statistical significance was systolic BP (P = .004). Similar relationships were observed with stratification by race or sex, use of alternative risk factors (diastolic instead of systolic BP, low-density lipoprotein instead of total cholesterol, BMI instead of waist circumference), addition of antihypertensive or lipid-lowering medication use in the models, alternative CAC cut points, and change in risk factors defined as subtracting the average of years 0 and 2 values from the average of years 10 and 15 values instead of the difference between year 0 and year 15 only (data not shown).

Table Graphic Jump LocationTable 4. Adjusted Odds Ratios of Having Coronary Artery Calcium at Year 15 (1985-2001)*

For the biracial young adult and early middle-aged cohort (ages 33-45 years) with a wide spectrum of educational levels, we found that educational level was inversely associated with prevalence of CAC in a graded dose-response fashion, with particularly higher prevalence for individuals with less than a high school degree; cardiovascular risk factors assessed 15 years before the measurement of CAC partially, but not completely, explained this association; and baseline (ages 18-30 years) and year-15 (ages 33-45 years) risk factors were highly correlated. Further adjustment for 15-year changes in risk factors only slightly attenuated the existing relationship.

Education has been shown to be strongly related to clinically diagnosed diseases and mortality, especially cardiovascular, by many epidemiological studies.13,47 Population-based research on education and subclinical cardiovascular diseases has been reported only in recent years, made possible by advancement in noninvasive measurement of atherosclerosis (eg, CAC by CT scan48 and carotid IMT by ultrasound imaging). Most previous studies on education and subclinical cardiovascular disease used carotid IMT as the main outcome and were conducted among middle-aged or older adults. In 1995, the cross-sectional Atherosclerosis Risk in Communities study by Diez-Roux et al14 showed that carotid IMT (average of common and internal wall thicknesses) was inversely associated with education, income, and occupation in 12 476 middle-aged men and women. Furthermore, the association between these socioeconomic status indicators and carotid IMT progression over 9 years was inverse in white adults but positive among black adults.22 Black adults have a higher risk for cardiovascular diseases than white adults do49; however, several studies,50 including the CARDIA data,35,51 have shown that black adults have lower prevalence of CAC. This apparent paradox is not well understood. Nonetheless, we observed an inverse association between education and CAC for white as well as black adults.

The inverse relationships of education with established cardiovascular risk factors are hypothesized to play an important role in the effects of education on both clinical and subclinical disease. Consistent with most previous research,15,16,20,21 we found that the strength of the association between education and CAC was attenuated by adjustment of these risk factors, with statistically significant association remaining only for individuals with less than a high school education. We further demonstrated that lower education was strongly related to worsening of BP and obesity, as well as higher prevalence of smoking and physical inactivity over 15 years. However, the influence of 15-year changes in most risk factors on CAC, independent of their initial levels, was not significant.

Partially through other indicators of socioeconomic status, such as occupational status and income level, education has also been shown to affect access to health care, ability to navigate the health care system, and adherence to medical therapy and pharmacological regimens.35 However, the association between education and CAC observed in our study was not affected by these factors related to access or treatments because CAC is not symptomatic and the study was conducted among a relatively healthy cohort of young and early middle-aged adults without concurrent overt diseases. Although the overall prevalence of CAC was low in our study, some significant differences in CAC across educational group were detected among these early middle-aged men and women.

To our knowledge, our study is the first to demonstrate a relationship between education and CAC, a powerful marker of subclinical coronary artery disease, among young and early middle-aged black and white adults. Its other unique contributions and features include extensive examination of risk factors and their progression as one potential mechanism linking education and health, made possible by the standardized repeated risk factor measurements with 15 years of follow-up; advancement of our understanding of factors related to subclinical coronary artery disease among young and early middle-aged adults, free from confounding by symptomatic disease, health care access, or treatment-related influences; and the relatively large sample size approximately balanced on age, race, sex, and education, and the standardized data collection and rigorous quality control of the CARDIA study.

The results of our study should be interpreted in light of its limitations. Education is assessed by years of completed schooling; no measures of the quality of education are available, a potentially important aspect in determining the impact of education. Although the age-adjusted results showed consistent patterns of higher prevalence of CAC among the least educated across the 4 race-sex groups, the numbers of participants in the lower educational groups were too small to permit race and sex−specific multivariable analyses. Additionally, our findings based on a cohort from 4 urban areas may not be generalizable to other populations. The fact that participants lost to follow-up had on average lower education may produce selection bias, which most likely led to conservative estimates of the differences in CAC between low and high education.

In conclusion, we found that education is inversely related to the prevalence of CAC in early middle age with particularly high risk for individuals with less than a high school degree. These findings are partially explained by risk factors assessed 15 years before the measurement of CAC and by increase in these risk factors over time. Although residual confounding is an issue that cannot be ruled out in an observational study, our findings suggest that the well-known education–health outcomes association includes explanatory pathways beyond risk factor profile differences and beyond differential access to care or disease management. Examples of such pathways include lifestyle factors (eg, diet and sleep patterns) and psychosocial factors (eg, depression and job strain). Pathways and mechanisms linking education and subclinical disease remain to be further explored.3 Fundamental changes in preventive measures very early in life are required to address social and economic disparities in health. In addition, integrated prevention and intervention strategies effective for less educated persons are also needed.3

Corresponding Author: Lijing L. Yan, PhD, MPH, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 680 N Lake Shore Dr, Suite 1102, Chicago, IL 60611 (lijing@northwestern.edu).

Author Contributions: Dr Yan and Ms Colangelo had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Yan, Liu, Daviglus, Greenland.

Acquisition of data: Liu, Sidney.

Analysis and interpretation of data: Yan, Liu, Daviglus, Colangelo, Kiefe, Matthews, Greenland.

Drafting of the manuscript: Yan.

Critical revision of the manuscript for important intellectual content: Yan, Liu, Daviglus, Colangelo, Kiefe, Sidney, Matthews, Greenland.

Statistical analysis: Yan, Liu, Colangelo.

Obtained funding: Liu, Sidney.

Administrative, technical, or material support: Yan, Daviglus, Matthews, Greenland.

Study supervision: Yan, Daviglus.

Financial Disclosures: None reported.

Funding/Support: This study was funded by grants N01-HC-48047 through N01-HC-48050 and N01-HC-95095 from the National Heart, Lung, and Blood Institute, National Institutes of Health.

Role of the Sponsor: The National Heart, Lung, and Blood Institute participated in the design and conduct of the CARDIA study, in the collection of the data, and in the review and approval of the manuscript. The Institute did not participate in the analysis or interpretation of the data, or in the preparation of the manuscript.

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Bild DE, Folsom AR, Lowe LP.  et al.  Prevalence and correlates of coronary calcification in black and white young adults: the Coronary Artery Risk Development in Young Adults (CARDIA) study.  Arterioscler Thromb Vasc Biol. 2001;21:852-857
PubMed   |  Link to Article
Chambless LE, Folsom AR, Davis V.  et al.  Risk factors for progression of common carotid atherosclerosis: the Atherosclerosis Risk in Communities Study, 1987-1998.  Am J Epidemiol. 2002;155:38-47
PubMed   |  Link to Article
Schmermund A, Baumgart D, Gorge G.  et al.  Measuring the effect of risk factors on coronary atherosclerosis: coronary calcium score versus angiographic disease severity.  J Am Coll Cardiol. 1998;31:1267-1273
PubMed   |  Link to Article
Winkleby MA. Accelerating cardiovascular risk factor change in ethnic minority and low socioeconomic groups.  Ann Epidemiol. 1997;7:S96-S103
Link to Article
Burke GL, Bild DE, Hilner JE, Folsom AR, Wagenknecht LE, Sidney S. Differences in weight gain in relation to race, gender, age and education in young adults: the CARDIA study.  Ethn Health. 1996;1:327-335
PubMed   |  Link to Article
Tyroler HA. The influence of socioeconomic factors on cardiovascular disease risk factor development.  Prev Med. 1999;29:S36-S40
PubMed   |  Link to Article
Friedman GD, Cutter GR, Donahue RP.  et al.  CARDIA: study design, recruitment, and some characteristics of the examined subjects.  J Clin Epidemiol. 1988;41:1105-1116
PubMed   |  Link to Article
Agatston AS, Janowitz WR, Hildner FJ, Zusmer NR, Viamonte M Jr, Detrano R. Quantification of coronary artery calcium using ultrafast computed tomography.  J Am Coll Cardiol. 1990;15:827-832
PubMed   |  Link to Article
Carr JJ, Nelson JC, Wong ND.  et al.  Calcified coronary artery plaque measurement with cardiac CT in population-based studies: standardized protocol of Multi-Ethnic Study of Atherosclerosis (MESA) and Coronary Artery Risk Development in Young Adults (CARDIA) study.  Radiology. 2005;234:35-43
PubMed   |  Link to Article
Warnick GR. Enzymatic methods for quantification of lipoprotein lipids.  Methods Enzymol. 1986;129:101-123
PubMed
Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge.  Clin Chem. 1972;18:499-502
PubMed
Bild DE, Jacobs DR Jr, Sidney S, Haskell WL, Anderssen N, Oberman A. Physical activity in young black and white women: the CARDIA study.  Ann Epidemiol. 1993;3:636-644
PubMed   |  Link to Article
Kitagawa E, Hauser P. Differential Mortality in the United States: A Study in Socioeconomic Epidemiology. Cambridge, Mass: Harvard University Press; 1973
Budoff MJ, Cohen MC, Garcia MJ.  et al.  ACCF/AHA clinical competence statement on cardiac imaging with computed tomography and magnetic resonance: a report of the American College of Cardiology Foundation/American Heart Association/American College of Physicians Task Force on Clinical Competence and Training.  J Am Coll Cardiol. 2005;46:383-402
PubMed   |  Link to Article
Thom T, Haase N, Rosamond W.  et al.  Heart disease and stroke statistics—2006 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee.  Circulation. 2006;113:e85-e151
PubMed   |  Link to Article
Bild DE, Detrano R, Peterson D.  et al.  Ethnic differences in coronary calcification: the Multi-Ethnic Study of Atherosclerosis (MESA).  Circulation. 2005;111:1313-1320
PubMed   |  Link to Article
Loria C, Detrano R, Liu K.  et al.  Sex and race differences in prevalence and predictors of early coronary calcification: the CARDIA study [abstract].  Circulation. 2002;105:e86-e119

Figures

Figure. Age-Adjusted Prevalence of Having Coronary Artery Calcium by Educational Level, 2000-2001
Graphic Jump Location

Adjusted for age and race for results on men and women (first 2 data sets in left panel) and adjusted for age and sex for results on black and white adults (second 2 data sets in left panel). P values for linear trend across the 5 educational strata were obtained by entering education as an ordinal variable (coded as 1-5) in logistic regression.

Tables

Table Graphic Jump LocationTable 1. Baseline Characteristics of Study Participants by Educational Level at Year 15 (1985-2001)*
Table Graphic Jump LocationTable 2. 15-Year Changes in Cardiovascular Risk Factors and Selected Year 15 Characteristics by Educational Level at Year 15 (1985-2001)*
Table Graphic Jump LocationTable 3. Prevalence Adjusted by Age, Sex, and Race of Having CAC by Educational Level for the Total Sample (2000-2001)
Table Graphic Jump LocationTable 4. Adjusted Odds Ratios of Having Coronary Artery Calcium at Year 15 (1985-2001)*

References

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Cirera L, Tormo MJ, Chirlaque MD, Navarro C. Cardiovascular risk factors and educational attainment in Southern Spain: a study of a random sample of 3091 adults.  Eur J Epidemiol. 1998;14:755-763
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Iribarren C, Luepker RV, McGovern PG, Arnett DK, Blackburn H. Twelve-year trends in cardiovascular disease risk factors in the Minnesota heart survey: are socioeconomic differences widening?  Arch Intern Med. 1997;157:873-881
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Chang CL, Shipley MJ, Marmot MG, Poulter NR. Can cardiovascular risk factors explain the association between education and cardiovascular disease in young women?  J Clin Epidemiol. 2002;55:749-755
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Lynch JW, Kaplan GA, Cohen RD, Tuomilehto J, Salonen JT. Do cardiovascular risk factors explain the relation between socioeconomic status, risk of all-cause mortality, cardiovascular mortality, and acute myocardial infarction?  Am J Epidemiol. 1996;144:934-942
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Kilander L, Berglund L, Boberg M, Vessby B, Lithell H. Education, lifestyle factors and mortality from cardiovascular disease and cancer: a 25-year follow-up of Swedish 50-year-old men.  Int J Epidemiol. 2001;30:1119-1126
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Liu K, Cedres LB, Stamler J.  et al.  Relationship of education to major risk factors and death from coronary heart disease, cardiovascular diseases and all causes: findings of three Chicago epidemiologic studies.  Circulation. 1982;66:1308-1314
PubMed   |  Link to Article
Bild DE, Folsom AR, Lowe LP.  et al.  Prevalence and correlates of coronary calcification in black and white young adults: the Coronary Artery Risk Development in Young Adults (CARDIA) study.  Arterioscler Thromb Vasc Biol. 2001;21:852-857
PubMed   |  Link to Article
Chambless LE, Folsom AR, Davis V.  et al.  Risk factors for progression of common carotid atherosclerosis: the Atherosclerosis Risk in Communities Study, 1987-1998.  Am J Epidemiol. 2002;155:38-47
PubMed   |  Link to Article
Schmermund A, Baumgart D, Gorge G.  et al.  Measuring the effect of risk factors on coronary atherosclerosis: coronary calcium score versus angiographic disease severity.  J Am Coll Cardiol. 1998;31:1267-1273
PubMed   |  Link to Article
Winkleby MA. Accelerating cardiovascular risk factor change in ethnic minority and low socioeconomic groups.  Ann Epidemiol. 1997;7:S96-S103
Link to Article
Burke GL, Bild DE, Hilner JE, Folsom AR, Wagenknecht LE, Sidney S. Differences in weight gain in relation to race, gender, age and education in young adults: the CARDIA study.  Ethn Health. 1996;1:327-335
PubMed   |  Link to Article
Tyroler HA. The influence of socioeconomic factors on cardiovascular disease risk factor development.  Prev Med. 1999;29:S36-S40
PubMed   |  Link to Article
Friedman GD, Cutter GR, Donahue RP.  et al.  CARDIA: study design, recruitment, and some characteristics of the examined subjects.  J Clin Epidemiol. 1988;41:1105-1116
PubMed   |  Link to Article
Agatston AS, Janowitz WR, Hildner FJ, Zusmer NR, Viamonte M Jr, Detrano R. Quantification of coronary artery calcium using ultrafast computed tomography.  J Am Coll Cardiol. 1990;15:827-832
PubMed   |  Link to Article
Carr JJ, Nelson JC, Wong ND.  et al.  Calcified coronary artery plaque measurement with cardiac CT in population-based studies: standardized protocol of Multi-Ethnic Study of Atherosclerosis (MESA) and Coronary Artery Risk Development in Young Adults (CARDIA) study.  Radiology. 2005;234:35-43
PubMed   |  Link to Article
Warnick GR. Enzymatic methods for quantification of lipoprotein lipids.  Methods Enzymol. 1986;129:101-123
PubMed
Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge.  Clin Chem. 1972;18:499-502
PubMed
Bild DE, Jacobs DR Jr, Sidney S, Haskell WL, Anderssen N, Oberman A. Physical activity in young black and white women: the CARDIA study.  Ann Epidemiol. 1993;3:636-644
PubMed   |  Link to Article
Kitagawa E, Hauser P. Differential Mortality in the United States: A Study in Socioeconomic Epidemiology. Cambridge, Mass: Harvard University Press; 1973
Budoff MJ, Cohen MC, Garcia MJ.  et al.  ACCF/AHA clinical competence statement on cardiac imaging with computed tomography and magnetic resonance: a report of the American College of Cardiology Foundation/American Heart Association/American College of Physicians Task Force on Clinical Competence and Training.  J Am Coll Cardiol. 2005;46:383-402
PubMed   |  Link to Article
Thom T, Haase N, Rosamond W.  et al.  Heart disease and stroke statistics—2006 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee.  Circulation. 2006;113:e85-e151
PubMed   |  Link to Article
Bild DE, Detrano R, Peterson D.  et al.  Ethnic differences in coronary calcification: the Multi-Ethnic Study of Atherosclerosis (MESA).  Circulation. 2005;111:1313-1320
PubMed   |  Link to Article
Loria C, Detrano R, Liu K.  et al.  Sex and race differences in prevalence and predictors of early coronary calcification: the CARDIA study [abstract].  Circulation. 2002;105:e86-e119

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