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Maintenance of Ideal Cardiovascular Health and Coronary Artery Calcium Progression in Low-Risk Men and Women in the Framingham Heart Study

Originally published Cardiovascular Imaging. 2018;11:e006209



    Ideal cardiovascular health (CVH) is associated with a lower risk of cardiovascular disease and freedom from coronary artery calcium (CAC). Prospective data on the association between maintenance of optimal CVH and the progression of subclinical coronary atherosclerosis are limited. We assessed the influence of unfavorable versus favorable CVH on the incidence of CAC progression.

    Methods and Results—

    The study population consisted of 1119 FHS (Framingham Heart Study) participants who attended the serial FHS MDCT I and MDCT II study (Multi-Detector Computed Tomography) and had a zero Agatston CAC score at baseline. CVH status was defined using 6 CVH metrics from the American Heart Association definition. CAC progression was defined by an increase in Agatston CAC score to ≥3.4. Generalized estimating equations were applied to identify significant associations of CAC progression with both the baseline measurement of CVH and the longitudinal maintenance of CVH. After follow-up (mean, 6.1 years), we observed CAC progression in 191 participants (17.1%). Participants with unfavorable CVH at baseline had a greater risk of CAC progression (odds ratio, 2.43; 95% confidence interval, 1.40–4.23; P=0.0017). In addition, each unit decrease in ideal CVH metric was associated with an increase in CAC progression (odds ratio, 1.15; 95% confidence interval, 0.99–1.34; P=0.067), after adjustment for baseline ideal CVH metrics.


    Significant associations between an unfavorable CVH profile and CAC progression support public health measures that seek to prevent cardiovascular disease by promoting favorable CVH profiles in persons free of clinical and subclinical cardiovascular disease.


    The American Heart Association (AHA) Strategic Impact 2020 Goal provides guidelines and metrics to measure both the current and longitudinal status of cardiovascular health (CVH) with the goal of improving CVH for the US population.1 In contemporary community-based cohorts in the US population, there is a low prevalence (≤5%) of ideal CVH.2 Further, poor CVH is associated with significantly elevated morbidity and mortality rates from cardiovascular diseases (CVD).3,4 Studies demonstrating an association between maintenance of ideal CVH and the prevention of CVD from community-based population, while limited, had been reported.5 However, it remains unknown whether a change in CVH profile or maintenance of ideal CVH provides incremental benefit by preventing progression of subclinical atherosclerosis. We sought to characterize these potential associations that would facilitate the development of novel approaches for precision medicine to primary prevention of CVD in lower risk adults.

    See Editorial by Yeboah

    See Clinical Perspective

    Coronary artery calcium (CAC) is a well-recognized biomarker of the presence and burden of subclinical coronary atherosclerosis and of the progression of coronary atherosclerosis.6 Population-based studies provide compelling evidence for significant associations between an elevated CAC score and the risk of future CVD, independent of traditional CVD risk factors.7,8 Conversely, persons free of CAC carry a substantially lower risk of future CVD.9 In asymptomatic middle-aged adults with zero CAC score, however, CHD risk varies considerably across diverse populations.10,11 A significant association between CAC progression and an unfavorable CVH profile has been reported for patients with type I diabetes mellitus such that patients with an unfavorable CVH profile have a 1.5-fold higher odds of CAC progression.12 In participants of the FHS (Framingham Heart Study) at low baseline CVD risk, we tested whether a decline in CVH confers significant, additional value for the prediction of CAC progression.


    All data, methods used in the analysis, and materials will not be made directly available by the authors for purposes of reproducing the results. However, researchers are encouraged to request all data and materials through the Framingham Heart Study data request process, for which detailed information is available at

    Study Population

    The study population comprised FHS Offspring and Third-Generation participants. The FHS is a community-based prospective study designed to investigate the incidence of CVD and related risk factors.13 The study design and participant characteristics of the offspring and Third-Generation cohorts have been published.14,15 Participants who attended the Third-Generation examination 1 (from 2002 to 2005) and Offspring examination 7 (1998–2001) and who also participated in the MDCT (Multi-Detector Computed Tomography) substudy I (2002–2005) comprised the baseline study population. For the MDCT studies, we excluded participants with the following conditions: weighed ≥160 kg, pregnant, men <35 years of age, and women <40 years of age.8 For the current study, participants of the MDCT I study who were unable to attend the MDCT II study (second MDCT, 2008–2011) were also excluded. After these exclusions, the final study population consisted of 1119 participants with a zero Agatston CAC score at baseline. The study protocol was approved by the Institutional Review Board of the Boston University Medical Center, and all participants gave written informed consent.

    Determination of CVH

    To characterize ideal CVH status, we applied 6 AHA guideline metrics (total cholesterol, fasting glucose, blood pressure (BP), body mass index, cigarette smoking, and exercise) and the cut point definition for ideal, intermediate, or poor status for each CVH metric.1 Information on these 6 metrics was obtained through routine FHS examination and laboratory measurements. At each examination cycle, a physician conducted an interview and physical examination of each participant and collected information on medical history and medication use. Blood samples were collected at each examination cycle for serum cholesterol and fasting blood glucose. The ideal threshold for total cholesterol was <200 mg/dL without cholesterol-lowering medication use. Cut point levels for intermediate and poor CVH for total cholesterol were 200 to 240 and 240, respectively. Cholesterol-lowering medication user those had total cholesterol <240 mg/dL were grouped into the group of intermediate CVH. For fasting blood glucose, the ideal threshold was <100 mg/dL without diabetic medication use. Intermediate CVH group included medication user for diabetes mellitus or participants those had fasting blood glucose of 100 to 126 mg/dL. Participants with fasting blood sugar of 126 mg/dL or higher had poor CVH. Diabetes mellitus was defined by fasting blood glucose >126 mg/dL or use of diabetic medication. Systolic and diastolic BPs were generated using mean value of the physician measurements. Ideal BP was defined as systolic BP <120 and diastolic BP <80 mm Hg without antihypertension medication use. Cut points for poor BP metrics were 140 mm Hg for systolic BP or 90 mm Hg for diastolic BP. Body mass index was calculated as weight (kg) divided by the square of height (m2), and an ideal body mass index was <25 kg/m2. Cut points for intermediate and poor CVH metric were 25 and 30, respectively. Current cigarette smoking was defined as smoking ≥1 cigarettes per day in the year preceding the examination. We defined former smokers as participants who smoked at any previous examination cycle but not the current examination cycle. An ideal CVH status for cigarette smoking was defined as never having smoked. For the current study, we defined intermediate CVH status for former smoker that ideal CVH status included never smokers. Information on physical activity was collected at the examination cycle on the basis of typical hours per day for 5 activity categories, sleep, sedentary, slight activity, moderate activity, and heavy activity.16 A physical activity score was generated as the weighted sum of hours of the 5 activities with a weight factor of 1, 1.1, 1.5, 2.4, and 5, respectively, that was correlated with oxygen consumption of the activity. For the current study, we defined those with poor physical activity as those with an activity index score of ≤27.9. An intermediate status for physical activity was defined by a physical activity score between 28.0 and 36.9. The ideal CVH category for physical activity was defined by a physical activity score of ≥37.

    Information on coronary heart disease (myocardial infarction and CHD death), with follow-up up to December 31, 2013, was abstracted to identify its prevalence and examine the potential associations of atherosclerotic CVD (asCVD) with CAC progression. Using prespecified criteria, all available clinical information on each CHD diagnosis, including medical records and participant-reported information, was reviewed by the 3-physician end point panel to verify a CHD diagnosis.17 Information on cholesterol-lowering medication use was collected through on-site interview followed with prescription/label verification and standardized coding using the World Health Organization Anatomic Therapeutic Chemical classifications.

    For each CVH metric, we defined ideal, intermediate, and poor categories on the basis of the guideline recommendations for the AHA 2020 Strategic Goals. We stratified participants into 3 groups according to the number of ideal metrics: favorable (5+ ideal metrics), intermediate (3 or 4 metrics), and unfavorable (≤2 metrics). We conducted analyses of the impact of unfavorable CVH on CAC progression using either a categorical CVH profile or a semiquantitative continuous numeric CVH status based on the number of ideal CVH metrics. For the categorical CVH profile, we generated binary variables referring to unfavorable and intermediate CVH profiles. For changes in CVH profiles, 1 binary variable was defined by failure to maintain a favorable status when the starting point was a baseline favorable CVH profile. A second binary variable was defined, for participants with either an unfavorable or intermediate profile at baseline, by failure to gain ≥2 ideal CVH metrics. For the semiquantitative numeric CVH profile, we calculated the change in number of ideal CVH metrics by subtracting the number of ideal CVH metrics in follow-up compared with the number of ideal CVH metrics at baseline. For the current study, our primary analysis tested for significant association between CAC progression and numeric CVH status. Our secondary analysis tested for associations between CAC progression and categorical CVH (results are shown in the Tables in the Data Supplement).

    Measurement of Coronary Artery Calcium

    The extent of coronary artery calcium (CAC) was quantified in both the baseline and follow-up FHS MDCT studies. The comprehensive study protocol and reproducibility for FHS MDCT I have been previously reported.8 In brief, scans were conducted using an 8-slice MDCT scanner (LightSpeed Ultra; General Electric, Milwaukee, WI) with prospective ECG triggering during a single breath hold in midinspiration (≈18 s) using sequential data acquisition. A calcified lesion was defined as an area of ≥3 connected pixels with a computed tomographic (CT) attenuation >130 Hounsfield units. A modified Agatston score, calculated as the sum of lesions from each individual cross section, was generated.

    Cut points for CAC progression were defined by estimating interindividual difference with multiple scoring results for CAC. The estimates of agreement yielded a proxy for measurement noise.18 At the baseline CT scan, a second CAC scan was obtained after brief repositioning of each participant in the scanner, providing duplicate CAC Agatston scoring for 3390 participants. A total of 2803 participants had one or both CAC Agatston scores of zero. We evaluated statistical distributions for participants with at least 1 zero CAC score and observed an SD of 3.4 for those with a zero CAC score. We applied this estimate of variation to define a threshold for CAC progression for the 1119 participants who attended both the MDCT I and MDCT II examinations. In a secondary analysis, we applied a more conservative threshold Agatston score of 11.6 to define CAC progression, to account for the magnitude of change not explained by measurement alone.19

    Statistical Methods

    Demographic characteristics and CVD-related risk factors were provided as mean±SD for continuous variables and number and percent of participants for dichotomous or categorical variables. To account for within nuclear family correlation structure, we used multivariate generalized estimating equations (GEE) logistic regression to test for significant associations between CAC progression (outcome) and unfavorable CVH profile (independent variable of interest), assuming an exchangeable (compound symmetry) correlation structure. We generated 3 sequential multivariate models to characterize the significant impact of CVH profile on prediction of CAC progression. In model I, the covariates were age, sex, HDL (high-density lipoprotein) cholesterol, and triglycerides. In model II, we included the covariates in model I plus the number of ideal CVH metrics at baseline. In model III, we included covariates in model II, and we further adjusted for decreasing number of ideal CVH metrics. We also performed an additional adjustment for cholesterol-lowering medication statin use to further verify the significant association between nonideal CVH and CAC progression (model IIb and model IIIb).20 In addition to individual parameter estimation, we evaluated information of model fit to characterize potential predictors of CAC progression. Quasi-likelihood criteria for the GEE model were applied to identify the best-fitting model, and the rule of model selection was similar to that for the Akaike information criteria.21 We generated the predictive probability of CAC progression using the best-fitting GEE equation and calculated CAC progression risk by number of nonideal CVD metrics.

    We calculated participants’ asCVD risk using the Pooled Cohort Equation, and we characterized significant associations between change in asCVD risk and CAC progression using the GEE model.22 A 2-sided P value of <0.05 was considered statistically significant. All analyses were performed using SAS version 9.3.


    The mean age of the study population at baseline was 48.4 years, and the population was 58% female. At baseline, 278 (25%) had a favorable CVH profile (5+ ideal metrics), 576 (51%) had an intermediate profile, and 265 (24%) participants had an unfavorable profile (<3 ideal CVH metrics). The mean number of ideal CVH metrics was 3.5 and was similar in both men and women. Table 1 shows distributions of baseline characteristics for the 1119 FHS participants free of baseline CAC. After an average of 6.1 years of follow-up, we observed CAC progression defined by the occurrence of newly detected CAC of ≥3.4 Agatston score in 191 participants (17.1%). Among these 191 participants, the mean Agatston score change values, grouped by tertiles of Agatston score change, were 7, 21, and 63, respectively. Characteristics of ideal CVH among the 3 subgroups by increase of Agatston score were not significantly different. Table I in the Data Supplement shows the distributions of Agatston scores for the 191 participants with CAC progression.

    Table 1. Baseline Characteristics of 1119 Framingham Heart Study Participants Grouped by CAC Progression

    CAC ProgressionYesP Value*
    Age, y, mean (SD)47.72 (8.17)51.67 (10.79)<0.0001
    Female, %5949<0.0001
    BMI, kg/m2, mean (SD)26.72 (5.03)28.07 (5.64)0.009
    Systolic BP, mm Hg, mean (SD)117.22 (14.02)121.45 (13.97)0.08
    Diastolic BP, mm Hg, mean (SD)75.19 (8.86)76.02 (8.58)0.86
    Total cholesterol, mg/dL, mean (SD)193.53 (34.10)202.23 (37.78)0.08
    HDL cholesterol, mg/dL, mean (SD)56.84 (16.17)52.40 (17.21)0.007
    Triglyceride, mg/dL, mean (SD)106.43 (65.08)137.23 (89.73)<0.0001
    Fasting glucose, mg/dL, mean (SD)94.50 (13.80)100.67 (24.08)0.0002
    Physical activity score, mean (SD)36.93 (6.66)37.86 (8.46)0.09
    Cigarette smoking, %917<0.0001
    Lipid-lowering medication, %8140.04
    Antihypertensive medication, %8170.009
    Diabetes mellitus, %260.004
    CVH profile, %

    BMI indicates body mass index; BP, blood pressure; CAC, coronary artery calcium; CVH, cardiovascular health; and HDL, high-density lipoprotein.

    *Age- and sex-adjusted P value tested for significant difference by CAC progression

    In contrast to the 928 participants free of progression, participants with CAC progression possess a higher CVD risk at baseline, with a higher frequency of male sex, higher body mass index, greater level of triglycerides, lower levels of HDL cholesterol, and higher frequencies of cigarette smoking, diabetes mellitus, antihypertensive medication use, and lipid-lowering medication use. In the GEE model adjusting for age, sex, HDL cholesterol, triglycerides, and familial correlation, there were a 2.43-fold (95% confidence interval, 1.40–4.23; P=0.0017) higher odds of CAC progression in participants with an unfavorable CVH profile (Table II in the Data Supplement, model II). In regression analysis testing for significant association between number of nonideal baseline CVH metrics and CAC progression, there were a 1.24-fold (95% confidence interval, 1.08–1.42; P=0.0018) higher odds of CAC progression per increase of 1 nonideal metric (Table 2, model II). Additional adjustment for statin use did not change the significant association (Table 2, model IIb).

    Table 2. Summary of GEE Analysis for CAC Progression (≥3.4 Agatston CAC Score Increase) in 1119 Framingham Heart Study participants free of CAC at baseline

    ParameterEstimate (SE)Odds Ratio (95% CI)*P ValueQIC
    Regression model–I
     Age0.06 (0.01)1.06 (1.04–1.08)<0.0001960.15
     Female−0.68 (0.19)0.51 (0.35–0.74)0.0005
     HDL cholesterol0.001 (0.01)1.00 (0.98–1.01)0.521
     LOG (triglyceride)0.61 (0.19)1.83 (1.26–2.66)0.0014
    Regression model–II
     Age0.05 (0.01)1.05 (1.03–1.07)<0.0001952.24
     Female−0.61 (0.19)0.55 (0.37–0.8)0.0018
     HDL cholesterol0.001 (0.01)1.00 (0.98–1.01)0.5488
     LOG (triglyceride)0.42 (0.2)1.52 (1.03–2.25)0.0357
     No. of nonideal CVH metrics0.21 (0.07)1.24 (1.08–1.42)0.0018
    Regression model IIb
     Age0.05 (0.01)1.05 (1.03–1.07)<0.0001951.87
     Female−0.61 (0.19)0.54 (0.37–0.8)0.0021
     HDL cholesterol−0.004 (0.01)1.00 (0.98–1.01)0.5839
     LOG (triglyceride)0.46 (0.3)1.52 (1.02–2.22)0.0369
     Statin use0.46 (0.3)1.58 (0.87–2.85)0.1272
     No. of nonideal CVH metrics0.19 (0.07)1.22 (1.06–1.40)0.0045
    Regression model–III
     Age0.05 (0.01)1.05 (1.03–1.07)<0.0001951.28
     Female−0.62 (0.2)0.54 (0.37–0.79)0.0017
     HDL cholesterol0.001 (0.01)1.00 (0.98–1.01)0.5695
     LOG (triglyceride)0.36 (0.2)1.44 (0.97–2.14)0.0741
     No. of nonideal CVH metrics0.28 (0.08)1.33 (1.14–1.55)0.0003
     Loss of ideal CVH metrics0.14 (0.08)1.15 (0.99–1.34)0.0674
    Regression model IIIb
     Age0.05 (0.01)1.05 (1.03–1.07)<0.0001951.21
     Female−0.6 (0.19)0.54 (0.37–0.79)0.0019
     HDL cholesterol−0.004 (0.01)1.00 (0.98–1.01)0.6023
     LOG (triglyceride)0.37 (0.2)1.44 (0.97–2.15)0.0726
     Statin use0.42 (0.3)1.53 (0.85–2.76)0.1531
     No. of nonideal CVH metrics0.26 (0.08)1.33 (1.11–1.52)0.0008
     Loss of ideal CVH metrics0.13 (0.08)1.4 (0.98–1.32)0.0825

    CAC indicates coronary artery calcium; CI, confidence interval; CVH, cardiovascular health; GEE, generalized estimating equations; HDL, high-density lipoprotein; and QIC, Quasi-likelihood criteria.

    *EXP (β) on the basis of GEE parameter estimation.

    QIC, likelihood value for GEE model fit.

    Failure to gain ideal CVH metrics did not influence CAC progression after adjustment for baseline CVH profile. At follow-up, the prevalence rates for favorable, intermediate, and unfavorable CVH profiles were 15%, 45%, and 40%, respectively. In multivariable-adjusted models, there was a nonsignificant association of failure to maintain favorable CVH profile with incidence of CAC progression (Table 2, model III). We estimated the probability of developing CAC progression by number of nonideal metrics at baseline (model II). There was a marked increase in the proportion with CAC progression as the number of nonideal CVH metrics increased (Figure 1). Participants with 6 nonideal CVH metrics at baseline had a 41% risk of CAC progression; in contrast, there was a 5% risk of progression for participants with 6 ideal metrics at baseline.

    Figure 1.

    Figure 1. Generalized estimating equation (GEE) estimated probability of coronary artery calcium (CAC) progression by baseline number of nonideal cardiovascular health metrics. Predicted probability of CAC progression for 1119 FHS (Framingham Heart Study) participants free of baseline CAC by number of nonideal cardiovascular metrics. Predicted value was abstracted from GEE model for CAC progression with multiple variable adjustment. Adjusted variables included family unit (as a repeat measurement), age, female sex, generation, levels of HDL (high-density lipoprotein) cholesterol and triglyceride at the baseline. Middle circle indicates the mean value of the matrix-specific prediction, the lower/upper border lines indicate 25% and 75% of the predicted risk of CAC progression.

    In secondary analysis to examine the occurrence of clinical CVD events, there is a very low event risk in our study population. The mortality rate was zero over the follow-up period after MDCT II. Four participants developed myocardial infarction, including 2 participants who had evidence of CAC progression and 2 who were free of CAC progression.

    In sensitivity analyses, using a more conservative threshold for CAC progression (Agatston score ≥11.6; n=125; 11.1% CAC progression), we observed a significant association only for a higher number of nonideal CVH metrics. The association between baseline unfavorable CVH profile and CAC progression was not significant. For each unit increase in nonideal CVH metric, there were a 1.17-fold higher odds of CAC progression (Table 3, model II). In the overall model fit, however, an increase in nonideal CVH metrics over time did not confer additional predictive potential for CAC progression.

    Table 3. Summary of GEE Analysis for CAC Progression (≥11.6 Agatston Score Increase) in 1119 Framingham Heart Study Participants Free of CAC at Baseline

    ModelEstimate (SE)Odds Ratio (95% CI)P ValueQIC*
    Model I
     Age, y0.05 (0.01)1.05 (1.03–1.07)<0.0001754.967
     Female, yes/no−0.59 (0.23)0.55 (0.35–0.86)0.01
     HDL cholesterol, mg/dL0.001 (0.01)1.00 (0.98–1.02)0.89
     LOG (triglyceride)0.53 (0.23)1.7 (1.08–2.68)0.02
    Model II
     Age, y0.05 (0.01)1.05 (1.03–1.07)<0.0001752.743
     Female, yes/no−0.54 (0.23)0.58 (0.37–0.91)0.02
     HDL cholesterol, mg/dL0.001 (0.01)1.00 (0.98–1.02)0.91
     LOG (triglyceride)0.39 (0.25)1.48 (0.92–2.05)0.11
     No. of nonideal CVH metrics0.16 (0.08)1.17 (1.00–1.37)0.04
    Model III
     Age, y0.05 (0.01)1.05 (1.02–1.07)<0.0001753.659
     Female, yes/no−0.55 (0.23)0.58 (0.37–0.91)0.02
     HDL cholesterol, mg/dL0.001 (0.01)1.00 (0.98–1.02)0.92
     LOG (triglyceride)0.35 (0.25)1.42 (0.87–2.32)0.16
     No. of nonideal CVH metrics0.21 (0.09)1.23 (1.03–1.47)0.02
     Loss of ideal CVH metrics0.1 (0.09)1.10 (0.92–1.32)0.3

    CAC indicates coronary artery calcium; CI, confidence interval; CVH, cardiovascular health; GEE, generalized estimating equations; HDL, high-density lipoprotein; and QIC, Quasi-likelihood criteria.

    *QIC, likelihood value for GEE model fit.

    EXP (β) on the basis of GEE parameter estimation.

    Applying the American College of Cardiology/AHA recommended pooled cohort equation for status of CVH, we calculated the 10-year risk of the first asCVD and observed similar asCVD risk for the 1119 participants at the baseline and follow-up CT scans.22 For both time periods, the 191 participants with CAC progression had a significantly higher risk of asCVD (mean±SD, 10.4% ± 12.48) in contrast to that for the 928 participants free of progression (mean±SD, 4.8±6.83). Results of the GEE models adjusted for familial correlation showed significant association between 10-year asCVD risks and CAC progression (Table 4). When we grouped participants into high versus low asCVD risk groups using a cut point of 7.5% at baseline, we observed a nonsignificant association between high asCVD risk and CAC progression. Change in 10-year asCVD risk was not associated with CAC progression (Table III in the Data Supplement). Results of the separate GEE models suggested that higher asCVD risk score or number of nonideal CVH metrics predicted CAC progression (Table 4). Further, there was a stronger association between number of nonideal CVH metrics and CAC progression in contrast to the association between baseline 10-year risk of asCVD and CAC progression (Figure 2).

    Table 4. Results of GEE Testing for Significant Association Between Cardiovascular Health Profiles ([Nonideal Cardiovascular Health Metrics or 10-Year asCVD Risk] at Baseline or Follow-Up) and CAC Progression

    CAC ProgressionOdds RatioSE95% CIP Value
    Baseline asCVD risk (%)*1.02590.0131.0007–1.05160.0436
    Follow-up asCVD risk (%)1.02710.01321.0016–1.05330.0371
    Baseline nonideal metrics (count)*1.23870.08511.0827–1.41720.0018
    Follow-up nonideal metrics (count)1.23990.08081.0912–1.40880.001
    Baseline 10-y asCVD risk ≥7.5% (yes/no)*1.50480.40610.8867–2.55380.1299
    Follow-up 10-y asCVD risk ≥7.5% (yes/no)1.44860.39330.8508–2.46620.1723

    asCVD indicates atherosclerotic CVD; CAC, coronary artery calcium; CI, confidence interval; CVH, cardiovascular health; and GEE, generalized estimating equations.

    *GEE model to test for significant association between baseline CVH status and CAC progression adjusted covariates: age, female sex.

    GEE model to characterize significant association between CAC progression and CVH at follow-up included covariates of age at study baseline, female sex, and CVH status at the study baseline.

    Figure 2.

    Figure 2. Forest plots of generalized estimating equations (GEE) calculated odds ratios (95% confidence interval [CI]) of coronary artery calcium (CAC) progression in 1119 FHS (Framingham Heart Study) participants free of baseline CAC by baseline and follow-up status of Cardiovascular Health or atherosclerotic cardiovascular diseases (asCVD) risk score and the status change of cardiovascular health. GEE estimated risk of CAC progression by cardiovascular health status at baseline, follow-up, or change of cardiovascular health (CVH) profile. CVH profile was illustrated using 10-year risk of atherosclerotic CVD applied the pooled cohort equation and number of nonideal CVH metrics.


    In this community-based study of middle-aged men and women at low cardiovascular risk by virtue of having a CAC Agatston score of zero at baseline, a quarter of the population had an unfavorable CVH profile. In the follow-up period for this population, we found evidence for significant progression to CAC in 17% and a substantial increase in both intermediate and unfavorable CVH profile over 6.1 years, correlating with a concomitant decrease in favorable CVH profile. There was a significant increase in progression to CAC as a function of increases in nonideal CVH at baseline. Results of the analysis for both categorical and numeric indices of CVH did not demonstrate an incremental influence of change in CVH status over time on prediction of CAC progression. By providing quantitative estimates of the risks for CAC progression conferred by increasing numbers of nonideal CVH factors, the current study extends and adds to prior evidence that the maintenance of ideal CVH may be beneficial for primordial prevention.23

    Most persons are free from CAC in early life, but the factors predicting continued freedom from CAC have not been comprehensively characterized. Patsalas et al24 reported a high percentage of hemodialysis patients (13 of 17 [76%]) maintaining zero CAC over 30 months of follow-up. A similar rate of maintenance of zero CAC was reported for a clinical study of 422 patients referred for CAC scanning.25 A significantly lower frequency for maintaining zero CAC (33%) was reported by Alagoz et al26 in 75 kidney donors. In the current study in a community-based population, we observed that 80% of participants maintained a zero Agatston score (901/1119), not markedly different from prior findings in disease-based studies. Results of our study are consistent with other studies that describe variations in CVD and related health well-being varies within an otherwise low-risk middle-aged population free of CAC. As previously reported, 1 common predictor for both the presence of CAC at baseline and the occurrence of new CAC over time (progression) is older age.27 Further studies in larger cohorts may identify additional specific health factors at baseline and changes in health factors over time that prevent progression to subclinical coronary atherosclerosis.

    There are challenges to interpretation of CAC progression studies using repeated CAC measurements. Prior reports have noted the crucial role of interscan variability and the time-dependent changes of CAC28 and the lack of universal agreement for defining CAC progression.29 Results of our more stringent sensitivity tests confirmed our significant findings that variation in a numeric CVH profile significantly predicts CAC progression, in both the overall study population and the subset of nonstatin users. In the relatively short follow-up time after MDCT II, we noted very few CVD events, however, we observed significant variation in baseline CVH associated with CAC progression. Recent attention to precision medicine has highlighted a potential opportunity to focus on precision prevention and primordial prevention in younger persons free of clinical or subclinical disease.30,31 Our data help lay the groundwork for future studies to identify more precise CVH factors, including biomarkers, genomic markers, and imaging measures for CVD prevention.

    Strengths of the current study include our detailed characterization of CVH profile in a well-described community-based study population with repeated clinical examinations over time. We focused on participants with zero CAC Agatston score, yielding a study population with a relatively homogeneous CVD and minimal medication use. In addition, information on changes in CVD risk factors and CVD disease status was available for all participants that showed a very low CVD risk and zero CVD-related mortality for this group of participants with minimum influence by comorbidity or medication treatment.

    Nevertheless, this study had several limitations. Although we had robust data for 6 ideal CVH metrics, we lacked detailed information on dietary metrics, so we were unable to calculate the AHA ideal diet metric. However, in contrast to the other CVH metrics, the ideal diet metric status has a very low prevalence rate in the general population, so it is likely that the large majority of our participants fell in the nonideal diet category.32 The study population was primarily of European ancestry, so additional studies in multiethnic cohorts would be needed to assess the generalizability of these associations and the rate of CAC progression in other racial/ethnic populations. We also acknowledge the possibility that results may differ using other definitions of CAC progression. Multiple CT scanning and scorings for duplicate CAC were available at the baseline CT scan only. The lack of multiple scoring at the follow-up CT scan hampered the evaluation of interstudy variation. The limitation to maximize homogeneity of the study population by recruiting participants those with zero CAC includes low statistical power. Cut point for defining CAC progression was derived from a single study center that should had been verified in replication cohorts. In addition, we applied 1 method of defining progression that study findings may alter if other methods were applied. In addition, the length of follow-up (mean, 6.1 years) hamper association test for maintaining ideal CVH and incidence of CAC progression.

    In summary, the additive loss of ideal CVH factors is associated with CAC progression in low-risk middle-aged men and women with zero CAC at baseline. Significant associations between an unfavorable CVH profile and CAC progression provide supportive evidence for public health measures to prevent CVD by promoting favorable CVH profiles in persons free of both clinical and subclinical CVD.


    Guest Editor for this article was Joao A.C. Lima, MD.

    The Data Supplement is available at

    Correspondence to Christopher J. O’Donnell, MD, MPH, VA Boston Healthcare, 1400 Veterans of Foreign Wars Pkwy, Bldg 1 5B-113, Boston, MA 02132. E-mail


    • 1. Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, Greenlund K, Daniels S, Nichol G, Tomaselli GF, Arnett DK, Fonarow GC, Ho PM, Lauer MS, Masoudi FA, Robertson RM, Roger V, Schwamm LH, Sorlie P, Yancy CW, Rosamond WD; American Heart Association Strategic Planning Task Force and Statistics Committee. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond.Circulation. 2010; 121:586–613. doi: 10.1161/CIRCULATIONAHA.109.192703.LinkGoogle Scholar
    • 2. Younus A, Aneni EC, Spatz ES, Osondu CU, Shaharyar S, Roberson L, Ali SS, Ogunmoroti O, Ahmad R, Post J, Feldman T, Maziak W, Agatston AS, Veledar E, Nasir K. Prevalence of ideal cardiovascular health among adults in the United States.J Am Coll Cardiol. 2015; 66:1633–1634. doi: 10.1016/j.jacc.2015.06.1348.CrossrefMedlineGoogle Scholar
    • 3. Yang Q, Cogswell ME, Flanders WD, Hong Y, Zhang Z, Loustalot F, Gillespie C, Merritt R, Hu FB. Trends in cardiovascular health metrics and associations with all-cause and CVD mortality among US adults.JAMA. 2012; 307:1273–1283. doi: 10.1001/jama.2012.339.CrossrefMedlineGoogle Scholar
    • 4. Folsom AR, Yatsuya H, Nettleton JA, Lutsey PL, Cushman M, Rosamond WD; ARIC Study Investigators. Community prevalence of ideal cardiovascular health, by the American Heart Association definition, and relationship with cardiovascular disease incidence.J Am Coll Cardiol. 2011; 57:1690–1696. doi: 10.1016/j.jacc.2010.11.041.CrossrefMedlineGoogle Scholar
    • 5. Aatola H, Hutri-Kähönen N, Juonala M, Laitinen TT, Pahkala K, Mikkilä V, Telama R, Koivistoinen T, Lehtimäki T, Viikari JS, Raitakari OT, Kähönen M. Prospective relationship of change in ideal cardiovascular health status and arterial stiffness: the Cardiovascular Risk in Young Finns Study.J Am Heart Assoc. 2014; 3:e000532. doi: 10.1161/JAHA.113.000532.LinkGoogle Scholar
    • 6. Patel J, Blaha MJ, McEvoy JW, Qadir S, Tota-Maharaj R, Shaw LJ, Rumberger JA, Callister TQ, Berman DS, Min JK, Raggi P, Agatston AA, Blumenthal RS, Budoff MJ, Nasir K. All-cause mortality in asymptomatic persons with extensive Agatston scores above 1000.J Cardiovasc Comput Tomogr. 2014; 8:26–32. doi: 10.1016/j.jcct.2013.12.002.CrossrefMedlineGoogle Scholar
    • 7. Arad Y, Goodman KJ, Roth M, Newstein D, Guerci AD. Coronary calcification, coronary disease risk factors, C-reactive protein, and atherosclerotic cardiovascular disease events: the St. Francis Heart Study.J Am Coll Cardiol. 2005; 46:158–165. doi: 10.1016/j.jacc.2005.02.088.CrossrefMedlineGoogle Scholar
    • 8. Hoffmann U, Massaro JM, Fox CS, Manders E, O’Donnell CJ. Defining normal distributions of coronary artery calcium in women and men (from the Framingham Heart Study).Am J Cardiol. 2008; 102:1136–1141, 1141.e1. doi: 10.1016/j.amjcard.2008.06.038.CrossrefMedlineGoogle Scholar
    • 9. Sarwar A, Shaw LJ, Shapiro MD, Blankstein R, Hoffmann U, Hoffman U, Cury RC, Abbara S, Brady TJ, Budoff MJ, Blumenthal RS, Nasir K. Diagnostic and prognostic value of absence of coronary artery calcification.JACC Cardiovasc Imaging. 2009; 2:675–688. doi: 10.1016/j.jcmg.2008.12.031.CrossrefMedlineGoogle Scholar
    • 10. Nasir K, Bittencourt MS, Blaha MJ, Blankstein R, Agatson AS, Rivera JJ, Miedema MD, Miemdema MD, Sibley CT, Shaw LJ, Blumenthal RS, Budoff MJ, Krumholz HM. Implications of coronary artery calcium testing among statin candidates according to American College of Cardiology/American Heart Association Cholesterol Management Guidelines: MESA (Multi-Ethnic Study of Atherosclerosis).J Am Coll Cardiol. 2015; 66:1657–1668. doi: 10.1016/j.jacc.2015.07.066.CrossrefMedlineGoogle Scholar
    • 11. Whelton SP, Silverman MG, McEvoy JW, Budoff MJ, Blankstein R, Eng J, Blumenthal RS, Szklo M, Nasir K, Blaha MJ. Predictors of long-term healthy arterial aging: coronary artery calcium nondevelopment in the MESA Study.JACC Cardiovasc Imaging. 2015; 8:1393–1400. doi: 10.1016/j.jcmg.2015.06.019.CrossrefMedlineGoogle Scholar
    • 12. Alman AC, Maahs DM, Rewers MJ, Snell-Bergeon JK. Ideal cardiovascular health and the prevalence and progression of coronary artery calcification in adults with and without type 1 diabetes.Diabetes Care. 2014; 37:521–528. doi: 10.2337/dc13-0997.CrossrefMedlineGoogle Scholar
    • 13. Kannel WB, Dawber TR, Kagan A, Revotskie N, Stokes J. Factors of risk in the development of coronary heart disease–six year follow-up experience. The Framingham Study.Ann Intern Med. 1961; 55:33–50.CrossrefMedlineGoogle Scholar
    • 14. Feinleib M, Kannel WB, Garrison RJ, McNamara PM, Castelli WP. The Framingham Offspring Study. Design and preliminary data.Prev Med. 1975; 4:518–525.CrossrefMedlineGoogle Scholar
    • 15. Splansky GL, Corey D, Yang Q, Atwood LD, Cupples LA, Benjamin EJ, D’Agostino RB, Fox CS, Larson MG, Murabito JM, O’Donnell CJ, Vasan RS, Wolf PA, Levy D. The third generation cohort of the National Heart, Lung, and Blood Institute’s Framingham Heart Study: design, recruitment, and initial examination.Am J Epidemiol. 2007; 165:1328–1335. doi: 10.1093/aje/kwm021.CrossrefMedlineGoogle Scholar
    • 16. Kannel WB, Wilson P, Blair SN. Epidemiological assessment of the role of physical activity and fitness in development of cardiovascular disease.Am Heart J. 1985; 109:876–885.CrossrefMedlineGoogle Scholar
    • 17. Margolis JR, Gillum RF, Feinleib M, Brasch RC, Fabsitz RR. Community surveillance for coronary heart disease: the Framingham Cardiovascular Disease Survey. Methods and preliminary results.Am J Epidemiol. 1974; 100:425–436.CrossrefMedlineGoogle Scholar
    • 18. Bland JM, Altman DG. Measuring agreement in method comparison studies.Stat Methods Med Res. 1999; 8:135–160. doi: 10.1177/096228029900800204.CrossrefMedlineGoogle Scholar
    • 19. Sevrukov AB, Bland JM, Kondos GT. Serial electron beam CT measurements of coronary artery calcium: has your patient’s calcium score actually changed?AJR Am J Roentgenol. 2005; 185:1546–1553. doi: 10.2214/AJR.04.1589.CrossrefMedlineGoogle Scholar
    • 20. Waheed S, Pollack S, Roth M, Reichek N, Guerci A, Cao JJ. Collective impact of conventional cardiovascular risk factors and coronary calcium score on clinical outcomes with or without statin therapy: the St Francis Heart Study.Atherosclerosis. 2016; 255:193–199. doi: 10.1016/j.atherosclerosis.2016.09.060.CrossrefMedlineGoogle Scholar
    • 21. Pan W. Model selection in estimating equations.Biometrics. 2001; 57:529–534.CrossrefMedlineGoogle Scholar
    • 22. Goff DC, Lloyd-Jones DM, Bennett G, Coady S, D’Agostino RB, Gibbons R, Greenland P, Lackland DT, Levy D, O’Donnell CJ, Robinson JG, Schwartz JS, Shero ST, Smith SC, Sorlie P, Stone NJ, Wilson PW; American College of Cardiology/American Heart Association Task Force on Practice Guidelines. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines.J Am Coll Cardiol. 2014; 63(25pt B):2935–2959. doi: 10.1016/j.jacc.2013.11.005.CrossrefMedlineGoogle Scholar
    • 23. Gillman MW. Primordial prevention of cardiovascular disease.Circulation. 2015; 131:599–601. doi: 10.1161/CIRCULATIONAHA.115.014849.LinkGoogle Scholar
    • 24. Patsalas S, Eleftheriadis T, Spaia S, Theodoroglou H, Antoniadi G, Liakopoulos V, Passadakis P, Vayonas G, Vargemezis V. Thirty-month follow-up of coronary artery calcification in hemodialysis patients: different roles for inflammation and abnormal calcium-phosphorous metabolism?Ren Fail. 2007; 29:623–629. doi: 10.1080/08860220701395010.CrossrefMedlineGoogle Scholar
    • 25. Min JK, Lin FY, Gidseg DS, Weinsaft JW, Berman DS, Shaw LJ, Rozanski A, Callister TQ. Determinants of coronary calcium conversion among patients with a normal coronary calcium scan: what is the “warranty period” for remaining normal?J Am Coll Cardiol. 2010; 55:1110–1117. doi: 10.1016/j.jacc.2009.08.088.CrossrefMedlineGoogle Scholar
    • 26. Alagoz S, Cebi D, Akman C, Altiparmak MR, Serdengecti K, Seyahi N. Progression of coronary artery calcification in living kidney donors: a follow-up study.Nephron Clin Pract. 2014; 126:144–150. doi: 10.1159/000362169.CrossrefMedlineGoogle Scholar
    • 27. Erbel R, Lehmann N, Churzidse S, Rauwolf M, Mahabadi AA, Möhlenkamp S, Moebus S, Bauer M, Kälsch H, Budde T, Montag M, Schmermund A, Stang A, Führer-Sakel D, Weimar C, Roggenbuck U, Dragano N, Jöckel KH; Heinz Nixdorf Recall Study Investigators. Progression of coronary artery calcification seems to be inevitable, but predictable - results of the Heinz Nixdorf Recall (HNR) study.Eur Heart J. 2014; 35:2960–2971. doi: 10.1093/eurheartj/ehu288.CrossrefMedlineGoogle Scholar
    • 28. Hokanson JE, MacKenzie T, Kinney G, Snell-Bergeon JK, Dabelea D, Ehrlich J, Eckel RH, Rewers M. Evaluating changes in coronary artery calcium: an analytic method that accounts for interscan variability.AJR Am J Roentgenol. 2004; 182:1327–1332. doi: 10.2214/ajr.182.5.1821327.CrossrefMedlineGoogle Scholar
    • 29. McEvoy JW, Blaha MJ, Nasir K, Blumenthal RS, Jones SR. Potential use of coronary artery calcium progression to guide the management of patients at risk for coronary artery disease events.Curr Treat Options Cardiovasc Med. 2012; 14:69–80. doi: 10.1007/s11936-011-0154-5.CrossrefMedlineGoogle Scholar
    • 30. Gillman MW, Hammond RA. Precision treatment and precision prevention: Integrating “Below and Above the Skin”.JAMA Pediatr. 2016; 170:9–10. doi: 10.1001/jamapediatrics.2015.2786.CrossrefMedlineGoogle Scholar
    • 31. Khoury MJ, Galea S. Will precision medicine improve population health?JAMA. 2016; 316:1357–1358. doi: 10.1001/jama.2016.12260.CrossrefMedlineGoogle Scholar
    • 32. Dong C, Rundek T, Wright CB, Anwar Z, Elkind MS, Sacco RL. Ideal cardiovascular health predicts lower risks of myocardial infarction, stroke, and vascular death across whites, blacks, and Hispanics: the northern Manhattan study.Circulation. 2012; 125:2975–2984. doi: 10.1161/CIRCULATIONAHA.111.081083.LinkGoogle Scholar


    Recent efforts to decrease rates of morbidity and mortality from cardiovascular disease have focused on maintenance of ideal cardiovascular health (CVH), defined by the absence of major cardiovascular risk factors. In FHS (Framingham Heart Study) men and women free of coronary artery calcium (CAC), we examined the impact of ideal and unfavorable CVH on the progression of CAC defined by new occurrence of CAC detected by MDCT (Multi-Detector Computed Tomography) over 6 years of follow-up. We noted a 2.43-fold higher risk of CAC progression associated with an unfavorable CVH profile. Increases in CAC progression were also noted with each decrease in ideal CVH metric. Our findings support measures in individual patients and in populations to prevent the occurrence of cardiovascular disease in middle-aged adults by promoting favorable CVH.