The association between estimated pulse wave velocity and cardio-cerebrovascular disease risk: a cohort study

Various measures of arterial stiffness have been linked to the risk of cardiovascular disease. However, the relationship between the estimated pulse wave velocity (ePWV), a novel indicator of arterial stiffness, and cardio-cerebrovascular disease risk remains unclear. This study investigated the relationship between the ePWV and the risk of cardio-cerebrovascular diseases.

A total of 17,708 participants aged 45 years and older enrolled in the China Health and Retirement Longitudinal Study (CHARLS), conducted between 2011 and 2012, and participants with a 7-year follow-up were included. Ultimately, 8242 respondents were included in the study. The ePWV was calculated using age and mean blood pressure. Cardio-cerebrovascular diseases, including myocardial infarction, cerebral infarction, and intracerebral haemorrhage, were categorised as cardiovascular and cerebrovascular diseases. Clinical and demographic characteristics were collected. A Cox proportional hazards model was used to explore the relationship between ePWV and the risk of cardiovascular disease.

During the 7-year follow-up, 21.7% of the participants (1791/8242) developed cardio-cerebrovascular diseases. After adjusting for potential confounding factors, the ePWV was positively associated with the risk of cardio-cerebrovascular disease (adjusted hazard ratio: 1.16, 95% CI 1.11-1.22, P < 0.001). The ePWV was divided into quartiles, and regression analysis was performed. Participants in the highest ePWV quartile had a 128% higher risk of cardio-cerebrovascular disease than those in the lowest quartile. The subgroup analysis showed that the positive association between the ePWV and the risk of cardio-cerebrovascular disease remained consistent among middle-aged and older adults across different Chinese communities.

In middle-aged and older Chinese adults, the ePWV was significantly and positively associated with the risk of cardio-cerebrovascular disease, making it a reliable and innovative predictor of these conditions.