The Multi-Ethnic Study of Atherosclerosis (MESA) has advanced the noninvasive evaluation of subclinical atherosclerosis to predict cardiovascular disease risk beyond traditional risk factors.
This review highlights the contributions of the MESA cohort to understanding subclinical atherosclerosis and its role in cardiovascular risk prediction and public health policy.
The MESA (Multi-Ethnic Study of Atherosclerosis) is a National Heart, Lung, and Blood Institute-sponsored prospective study aimed at studying the prevalence, progression, determinants, and prognostic significance of subclinical cardiovascular disease in a sex-balanced, multiethnic, community-dwelling U.S. cohort. MESA helped usher in an era of noninvasive evaluation of subclinical atherosclerosis presence, burden, and progression for the evaluation of atherosclerotic cardiovascular disease risk, beyond what could be predicted by traditional risk factors alone. Concepts developed in MESA have informed international patient care guidelines, providing new tools to effectively guide public health policy, population screening, and clinical decision-making. MESA is grounded in an open science model that continues to be a beacon for collaborative science. In this review, we detail the original goals of MESA, and describe how the scope of MESA has evolved over time. We highlight 10 significant MESA contributions to cardiovascular medicine, and chart the path forward for MESA in the year 2021 and beyond.
“I think this study reinforces two important points. First, there is the importance of a diverse population sample in which to develop models. Second is ensuring that the relevant predictors are included. With these two things in place, the model performs well, even without the social construct of race.”
Blaha et al. (Tue,) conducted a review in Subclinical cardiovascular disease. The Multi-Ethnic Study of Atherosclerosis (MESA) has advanced the noninvasive evaluation of subclinical atherosclerosis to predict cardiovascular disease risk beyond traditional risk factors.
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