Abstract
Introduction: Coronary artery calcium (CAC) is a well-established predictor of ASCVD events, which includes both CHD and stroke events. Given the gender and ethnic variation in CHD and stroke incidence rate, we sought to determine the predictive value of CAC for CHD and stroke risk across sex and race sub-groups of a population free of ASCVD. Hypothesis: CAC score is a better predictor of CHD risk than stroke risk and the strength of its association with ASCVD and CHD risk is stronger among men. Methods: Asymptomatic White, Black, and Hispanic participants of Multi-Ethnic Study of Atherosclerosis and Dallas Heart Study had CAC score measured at enrollment (2000-2002) and were followed for incident ASCVD events. ASCVD incidence rate and CHD-to-stroke incidence ratio across CAC score categories (0, 1-100, and >100) was assessed. Association of CAC with events was evaluated using multivariable-adjusted Cox models and multiplicative interactions between CAC and sex/race were tested. Results: Among 7,042 participants (57 years, 54% women, 36% Black, 23% Hispanic, 49% with CAC=0, and 19% with CAC >100) there were 574 ASCVD events (333 CHD, 241 strokes) over a 12-year median follow-up. The CHD-to-stroke incidence ratio increased from <1 with CAC=0 to ≥1.4 with CAC >100 in all participant groups. CAC >0 was independently associated with ASCVD and CHD risk in all groups, with a stronger association observed among men than women (p-interaction <0.05). There were no race-based interactions. CAC >0 was independently associated with stroke risk in the overall sample but not in sex/race groups, and the strength of this association was weaker than what was observed with CHD risk. Conclusions: In these multi-ethnic, population-based cohorts the CHD-to-stroke incidence ratio was higher with higher CAC score, CAC predicted CHD risk better than stroke, and CAC presence had a stronger association with ASCVD and CHD risk among men. These results may inform the utility of CAC testing for specific patient groups and event types where it is most predictive.
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