Multivariate linear regression to predict association of non-invasive arterial stiffness with cardiovascular events

Background: Arterial stiffness is a crucial factor in determining an increase in systolic blood pressure and pulse pressure and can also predict the development of cardiovascular disease (CVD). The purpose of this study was to examine the relationship between arterial stiffness and future CVD.

Methods: Out of the original 9704 participants in the Mashhad stroke and heart atherosclerotic disorder (MASHAD) cohort study, we randomly selected 363 healthy participants, 226 normal subjects (who reported symptoms of CVD but were not confirmed) and 292 individuals who had experienced a major cardiovascular event. The SphygmoCor XCEL System (AtCor Medical Incorporation) was utilized to measure pulse wave velocity (PWV), central augmentation index (CAI), cardio-ankle vascular index (CAVI) and central aortic pressure (CAP). A multivariate multiple regression model was used to analyse the factors associated with non-invasive arterial stiffness parameters (PWV, CAVI, CAP and CAI) after adjusting for potential confounders. All statistical analyses were conducted using SPSS 21 with a significance level of 0.05.

Results: The mean PWV was significantly higher in patients who had experienced a confirmed CVD event (P < 0.001). The multivariate multiple regression model results, after adjusting for potential confounders, showed a significant association between PWV and the CVD group (normal vs. healthy and event vs. healthy), as well as between hypertension and obesity with PWV and diabetes with CAI (P < 0.05).

Conclusions: PWV was found to be associated with CVD and its related risk factors such as diabetes, obesity and hypertension. It may be more effective than other arterial stiffness parameters in predicting CVD in clinical settings.