CAC Volume and Density: Interactions and Predictive Value

Study Questions:

What is the interaction between coronary artery calcium (CAC) volume or CAC density with each other, and with age, sex, ethnicity, the atherosclerotic cardiovascular disease (ASCVD) risk score, diabetes status, and renal function, and, using differing CAC scores, what is the improvement over the ASCVD risk score in risk prediction and reclassification?


A total of 3,398 MESA participants free of clinical CVD but with CAC >0 at baseline were followed for incident CVD events. Average CAC density score was calculated by dividing the Agatston score by their respective area scores. The adjusted models for both coronary heart disease (CHD) and CVD were then stratified by the following variables: quartiles of CAC volume; age <65 versus >65 years; sex; four ethnic groups; ASCVD 10-year risk score <10%, 10% to 19%, and >20%; diabetes; and estimated glomerular filtration rate <60 versus >60. Each model was adjusted for the ASCVD risk score.


Approximately 58% were male, baseline mean age was 66 years, and mean ASCVD 10-year risk score was 0.19 ± 0.14. Mean Agatston score was 293 (range 1-6,316). During a median 11.0 years of follow-up, there were 390 CVD events (11.5%), 264 of which were CHD (7.8%). With each standard deviation (SD) increase of In CAC volume (1.62), risk of CHD increased 73% (p < 0.001) and risk of CVD increased 61% (p < 0.001). Conversely, each SD increase of CAC density (0.69) was associated with 28% lower risk of CHD (p < 0.001) and 25% lower risk of CVD (p < 0.001). CAC density was inversely associated with risk at all levels of CAC volume (i.e., no interaction was present). In multivariable Cox models, significant interactions were present for CAC volume with age and ASCVD risk score for both CHD and CVD, and CAC density with ASCVD risk score for CVD. Hazard ratios were generally stronger in the lower risk groups. Receiver-operating characteristic area under the curve and net reclassification index analyses showed better prediction by CAC volume than by Agatston, and the addition of CAC density to CAC volume further significantly improved prediction.


The inverse association between CAC density and incident CHD and CVD events is robust across strata of other CVD risk factors. Added to the ASCVD risk score, CAC volume and density provided the strongest prediction for CHD and CVD events, and the highest correct reclassification.


The value of the CAC score for discriminating risk is underestimated in this study since those with zero calcium were excluded so as to determine the relative value of plaque calcium density. The evidence that calcium density is inversely related to CHD and CVD events is consistent with studies suggesting that it is associated with plaque healing in contrast to long noncalcified and mildly calcified plaque. As suggested by the authors, future research should focus on integrating density into CAC scoring and identifying clinical situations where such advanced risk stratification might be most useful.

Clinical Topics: Noninvasive Imaging, Prevention

Keywords: Atherosclerosis, Cardiac Imaging Techniques, Cardiovascular Diseases, Diabetes Mellitus, Ethnic Groups, Glomerular Filtration Rate, Plaque, Atherosclerotic, Primary Prevention, Risk Factors, Renal Insufficiency

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