Abstract
BACKGROUND AND OBJECTIVE: We aimed to compare the predictive value of long-term variability in systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse pressure (PP) over a 10-year period for cardiovascular diseases (CVDs). METHODS: A prospective cohort of 44,938 participants were categorized into four groups based on the quartiles of variability of SBP, DBP, and PP. Cox proportional hazards regression models were applied to assess the impact of SBP, DBP, and PP variability on CVDs. The predictive value of SBP, DBP, and PP variability for CVDs and specific cardiovascular outcomes was evaluated based on the China-PAR model. RESULTS: During a mean follow-up period of 5.04 years, compared to the Q1, the HR and 95 %CI of CVDs was 1.28 (1.11,1.48) in the Q4 of SBP, 1.33 (1.17,1.51) in the Q4 of DBP, and 1.25 (1.06,1.47) in the Q4 of PP (all P < 0.01). When incorporated into the China-PAR model, the C-index for SBP, DBP, and PP variability in predicting CVDs were 0.6866, 0.6787, and 0.6822, with SBP variability offering the strongest predictive improvement to the China-PAR model. Subgroup analysis revealed a significant interaction between age and SBP, DBP, and PP variability (P < 0.01). CONCLUSION: Increases in SBP, DBP, and PP variability independently elevate the risk of CVDs and its subtypes, regardless of absolute blood pressure levels. Furthermore, SBP, DBP, and PP variability can improve the predictive value of the China-PAR model for CVDs risk, with SBP variability demonstrating the strongest predictive capacity.