Coronary artery calcification and cardiovascular risk factors: impact of the analytic approach

Coronary artery calcification (CAC) may help identify novel risk factors for coronary atherosclerosis. However, analysis of CAC is challenging because of the distribution of CAC in the population. This has resulted in difficulty in interpreting and comparing results across studies. We applied several analytic approaches to CAC data in order to determine the impact of analytic methods on the association with established cardiovascular risk factors in 914 asymptomatic subjects in the Study of Inherited Risk Factors for Coronary Atherosclerosis. Multivariable analyses included: (1) linear regression of different transformations of CAC scores; (2) tobit regression of the log of (CAC+1); (3) logistic regression using CAC zero as a cut-point; and (4) ordinal logistic regression using CAC categories. Linear regression of the log CAC scores and logistic regression of CAC zero cut-point failed to detect associations with some risk factors. In contrast, linear and tobit regression of the log (CAC+1) and ordinal regression of CAC categories identified more associations and provided consistent results. Commonly applied methods of CAC analysis may fail to detect associations with cardiovascular risk factors. We present analytic approaches that are likely to provide consistent results and recommend the use of at least two distinct multivariable methods.