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Increased arterial pressure volume index and cardiovascular risk score in China

  • Open Access
  • 01-12-2023
  • Research
Published in:

Abstract

Background and objective

The new non-invasive arterial stiffness indices, arterial pressure volume index (API) is explored as a novel marker of residual stress in the wall of the peripheral muscular arteries at zero-stress state in clinical settings. The present study aimed to study the association of API with cardiovascular disease (CVD) risk in China (China-PAR).

Methods

According to China-PAR score, participants were divided into three groups: low risk (< 5%), medium risk (5–9.9%), and high risk (≥ 10.0%). API ≥ 31 was defined as high API, and the incidences of high API were compared. Logistic regression models were used to analyze the risk factors of high API and high risk China-PAR categories. The association between China-PAR and API was analyzed by restrictive cubic spline.

Results

The study included 4311 participants. After adjustments for confounding factors, high API was independent factor associated with high risk China-PAR categories, and the probability of high API was 1.366 times higher than that in normal API subjects. While, the independent factors associated with high API were BMI, blood pressure and heart rate. Furthermore, API had a significant U-shaped association with China-PAR. CVD risk was lowest with API of 19 units, the fastest increase at 26 units and the flattest starting point at 59 units.

Conclusion

API, an indicator of arterial stiffness and residual stress, had a U-shaped association with China-PAR score and might play an important role in predicting CVD risk in Chinese natural populations.
Title
Increased arterial pressure volume index and cardiovascular risk score in China
Authors
Lin Jin
Mengjiao Zhang
Lei Sha
Mengmeng Cao
Lanyue Tong
Qingqing Chen
Cuiqin Shen
Lianfang Du
Liping Liu
Zhaojun Li
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Cardiovascular Disorders / Issue 1/2023
Electronic ISSN: 1471-2261
DOI
https://doi.org/10.1186/s12872-022-03035-4
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Case-Based Insights teaser image/© Eva Künzel