Background: Coronary artery calcium (CAC) and left ventricular diastolic dysfunction (LVDD) are strong predictors of cardiovascular events and share common risk factors. However, their independent association remains unclear.

Methods: In the Project Baseline Health Study (PBHS), 2082 participants underwent cardiac-gated, non-contrast chest computed tomography (CT) and echocardiography. The association between left ventricular (LV) diastolic function and CAC was assessed using multidimensional network and multivariable-adjusted regression analyses. Multivariable analysis was conducted on continuous LV diastolic parameters and categorical classification of LVDD and adjusted for traditional cardiometabolic risk factors. LVDD was defined using reference limits from a low-risk reference group without established cardiovascular disease, cardiovascular risk factors or evidence of CAC, (n ​= ​560). We also classified LVDD using the American Society of Echocardiography recommendations.

Results: The mean age of the participants was 51 ​± ​17 years with 56.6% female and 62.6% non-Hispanic White. Overall, 38.1% had hypertension; 13.7% had diabetes; and 39.9% had CAC >0. An intertwined network was observed between diastolic parameters, CAC score, age, LV mass index, and pulse pressure. In the multivariable-adjusted analysis, e', E/e', and LV mass index were independently associated with CAC after adjustment for traditional risk factors. For both e' and E/e', the effect size and statistical significance were higher across increasing CAC tertiles. Other independent correlates of e' and E/e' included age, female sex, Black race, height, weight, pulse pressure, hemoglobin A1C, and HDL cholesterol. The independent association with CAC was confirmed using categorical analysis of LVDD, which occurred in 554 participants (26.6%) using population-derived thresholds.

Conclusion: In the PBHS study, the subclinical coronary atherosclerotic disease burden detected using CAC scoring was independently associated with diastolic function.

Gov Identifier: NCT03154346.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10870833PMC
http://dx.doi.org/10.1016/j.jcct.2022.06.003DOI Listing

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