Cardiovascular disease risk: it is complicated, but race and ethnicity are key, a Bayesian network analysis.

ABSTRACT:

Background

Cardiovascular diseases are the leading cause of morbidity and mortality in the United States. Despite the complexity of cardiovascular disease etiology, we do not fully comprehend the interactions between non-modifiable factors (e.g., age, sex, and race) and modifiable risk factors (e.g., health behaviors and occupational exposures).

Objective

We examined proximal and distal drivers of cardiovascular disease and elucidated the interactions between modifiable and non-modifiable risk factors.

Methods

We used a machine learning approach on four cohorts (2005-2012) of the National Health and Nutrition Examination Survey data to examine the effects of risk factors on cardiovascular risk quantified by the Framingham Risk Score (FRS) and the Pooled Cohort Equatio

SUBMITTER: Bowles NP 

PROVIDER: S-EPMC11194318 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature