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

European Journal of Medical Research volume 30, Article number: 16 (2025) Cite this article

Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

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.

Background

Cardio-cerebrovascular diseases encompass a range of ischemic and haemorrhagic conditions affecting the heart, brain, and body [1, 2]. These diseases are primarily caused by factors such as hyperlipidaemia, increased blood viscosity, atherosclerosis, and hypertension [2], and include conditions such as heart failure, atherosclerosis, stroke, and cardiomyopathy [1], which have high incidence, disability, and mortality rates [3, 4]. Cardio-cerebrovascular diseases have become the leading cause of death from non-communicable diseases worldwide, with a mortality rate exceeding 50% [5]. According to the World Health Organization, approximately 1.79 million people died from cardio-cerebrovascular diseases in 2019, accounting for 32% of global deaths [6]. In China, these diseases account for 40% of all deaths [7, 8]. Therefore, identifying predictive indicators for the early detection of cardio-cerebrovascular diseases and implementing appropriate preventive measures can help reduce incidence and mortality rates, thereby alleviating the global economic burden on healthcare.

Arterial stiffness, also known as the loss of arterial elasticity, has been recognized as a reliable marker of changes in arterial structure and function [9, 10]. Arterial stiffness is a significant risk factor for cardio-cerebrovascular diseases and is closely associated with increased mortality from these conditions [11,12,13].

Arterial stiffness is commonly assessed using the carotid-femoral pulse wave velocity (cf-PWV). However, cf-PWV measurements require skilled personnel and specialised equipment, limiting its widespread use in clinical practice. Estimated pulse wave velocity (ePWV), a new index calculated from mean blood pressure (MBP), has been shown to reliably reflect arterial stiffness [14, 15]. Furthermore, a few studies have demonstrated the predictive value of ePWV for cardiovascular events [16, 17]; however, the research population was limited to the Kailuan community [16]. In contrast, the China Health and Retirement Longitudinal Study (CHARLS) includes respondents from both urban and rural areas across China, offering a better representation of middle-aged and older populations. The primary aim of this cohort study was to investigate the relationship between ePWV and cardio-cerebrovascular disease using data from the CHARLS database.

Methods

Data source

We analysed data from the CHARLS, a nationally representative survey designed to investigate aging issues in China and to advance related research [18]. Individuals aged 45 years and older, from 450 villages and urban communities across 150 counties and districts in 28 provinces, were included. The participants were selected using a probability sampling method proportional to the population size by employing a multistage stratified sampling approach. Our analysis incorporated data from four survey waves: 2011–2012, 2013–2014, 2015–2016, and 2017–2018. All CHARLS datasets are available at http://charls.pku.edu.cn/en. This project was approved by the Biomedical Ethics Committee of Peking University and all respondents provided informed consent.

Study design and population

We conducted a longitudinal study using data from CHARLS, which included middle-aged and older adults (aged ≥ 45 years) from Chinese communities. The 2011–2012 dataset included 17,708 participants. We excluded 9466 participants owing to missing baseline data on ePWV or cardio-cerebrovascular diseases, those under age 45 years, individuals with disabilities at baseline, or those with incomplete follow-up data from 2013–2014, 2015–2016, and 2017–2018. A total of 8242 participants were included in the final analysis (Fig. 1).

Fig. 1
figure 1

Flowchart of participant selection

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Estimated pulse wave velocity measurement

The ePWV was calculated based on the MBP and age using the following formula: ePWV = 9.587–0.402 × age + 4.560 × 10⁻3 × age2–2.621 × 10⁻5 × age2 × MBP + 3.176 × 10⁻3 × age × MBP–1.832 × 10⁻2 × MBP [14, 19]. MBP was calculated using the diastolic blood pressure (DBP) and systolic blood pressure (SBP) as follows: MBP = DBP + 0.4 × (SBP–DBP) [14, 19]. Based on previous studies [20], we defined ePWV < 10 m/s as non-arterial stiffness and ePWV ≥ 10 m/s as arterial stiffness. In addition, ePWV was divided into quartiles to form a ‘quartile categorical variable’ for analysing the risk of developing cardiovascular and cerebrovascular diseases after follow-up.

Definition of cardio-cerebrovascular diseases

Cardio-cerebrovascular diseases include cardiovascular diseases and cerebrovascular diseases. In the CHARLS cohort, the diagnosis of cardio-cerebrovascular diseases was primarily based on self-reporting by participants. These self-reports were grounded in previous medical diagnoses confirmed by physicians in hospitals using standard diagnostic procedures, such as computed tomography (CT), magnetic resonance imaging (MRI), electrocardiography (ECG), and other relevant examinations. Cardio-cerebrovascular diseases were considered present if the primary diagnosis was recorded at least once among community patients.

Covariate measurement

At baseline, trained researchers used structured questionnaires to collect information on sociodemographic characteristics and health-related factors. Sociodemographic characteristics included age, sex, education level (middle school or below, high school or above), marital status (married/partnered, other), place of residence (urban or rural), height, weight, and body mass index (BMI). Health-related factors included smoking status (no/yes) and history of alcohol consumption (no/yes). During medical examinations, skilled medical staff measured and recorded data using a professional recorder to ensure accuracy. The participants stood upright and stepped barefoot onto the base of the instrument. Vertical height was measured to an accuracy of 0.1 cm. Waist circumference was measured using a non-pull tape at the level of the navel during minimal breathing and was accurate to 0.1 cm [21, 22]. Weight (kg) was measured using a weight scale. During weighing, the participants wore inspection uniforms, stood in the middle of the digital scale with their arms close to their bodies, and stared straight ahead [23]. The BMI was defined as weight (kg)/height (m)2. Blood glucose (Glu), triglyceride (TG), and low-density lipoprotein cholesterol (LDL-C) levels were measured using fasting blood samples, and these levels were determined through the enzyme ratio method [24].

Statistical analysis

Data processing and analysis were performed using Free Statistics Software v.1.8 (based on R language) [25,26,27], available at http://www.clinicalscientists.cn/freestatistics. Categorical data were described as frequency (percentage), and group comparisons were performed using the chi-square test. Continuous data were presented as mean ± standard deviation (`x ± s), and group comparisons were analysed using ANOVA. The COX proportional hazards model was used to analyse the correlation between ePWV and cardiovascular and cerebrovascular diseases. This model included an unadjusted model and an adjusted model (adjusted for age [28], sex, education level, smoking history, history of alcohol consumption, place of residence, marital status, waist circumference, BMI, glutathione, TG, and LDL-C levels as covariates [29, 30]). Subgroup analyses were performed using the COX proportional hazards model to evaluate the impact of different factors on the risk of cardio-cerebrovascular diseases. Subgroups included age, sex, BMI, drinking status, smoking status, marital status, educational level, and place of residence. The restricted cubic spline curve was used to explore the dose–response association between ePWV and the risk of cardio-cerebrovascular disease. To address potential biases arising from missing data, we applied multiple imputation by chained equations (MICE) to supplement the missing values in our dataset. This method allows for the incorporation of uncertainty into the imputation process and is considered robust for dealing with missing at random (MAR) data. Statistical significance was set at P < 0.05.

Results

Clinical characteristics of the study population according to ePWV

Table 1 presents the clinical characteristics of the study population according to ePWV. A total of 8242 participants were included in this study, of which 3908 (47.4%) were men and 4334 (52.6%) were women. The average age of all participants was 58.2 ± 8.8 years, and the average pulse wave velocity (PWV) was 9.3 ± 1.8 m/s. The baseline characteristics of the participants according to the PWV quartiles are shown in Table 1. Compared with participants in the lowest ePWV quartile, those with higher ePWV values were generally older, more likely to be male, had lower education levels, and were more likely to smoke, drink alcohol, and have higher SBP and DBP. Compared to those in the highest ePWV quartile, participants with a lower ePWV were more likely to be married, have smaller waist circumferences, and exhibit lower TG and LDL cholesterol levels.

Table 1 Clinical characteristics of the study population according to ePWV
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Results of univariate analysis of cardio-cerebrovascular diseases

Table 2 shows the association between ePWV and the risk of cardio-cerebrovascular disease after a 7-year follow-up. In the univariate analysis, age, female sex, other marital status, rural residence, history of alcohol consumption, SBP, DBP, waist circumference, weight, BMI, blood Glu, TG, LDL-C, and ePWV were significantly associated with the risk of cardio-cerebrovascular diseases.

Table 2 Results of univariate analysis of cardio-cerebrovascular diseases
Full size table

Multivariate analysis of ePWV and the risk of cardio-cerebrovascular disease

Table 3 presents the multivariate analysis of ePWV and the risk of developing cardio-cerebrovascular disease after a 7-year follow-up. Model 1 was adjusted for age and sex. In Model 2, additional covariates were adjusted based on Model 1, including education level, alcohol consumption, smoking status, place of residence, marital status, waist circumference, BMI, and blood Glu, TG, and LDL-C levels. These quartile-based categorical variables were used as cutoff values for the regression analysis of the risk of cardio-cerebrovascular disease. After adjusting for all covariates, the hazard ratios (HRs) in the second, third, and fourth quartiles showed an increasing trend compared to those in the first quartile. Notably, participants in the fourth ePWV quartile had a 128% higher risk of cardio-cerebrovascular diseases (HR = 2.28, 95% CI 1.81–2.87) compared with those in the first quartile. The P-values for the trend tests in the models were all < 0.001, indicating a strong linear association between ePWV and cardiovascular disease.

Table 3 Multivariable-adjusted ORs and 95%CI of ePWV quartiles associated with cardio-cerebrovascular diseases
Full size table

Forest plot for the association between ePWV and cardio-cerebrovascular diseases in different subgroups

Figure 2 presents the results of the subgroup analysis conducted after the follow-up period to evaluate the impact of each 1-unit increase in ePWV on the incidence of cardio-cerebrovascular diseases across different subgroups. As shown in the forest plot, the negative association between ePWV and the risk of cardio-cerebrovascular diseases was stronger in the subgroups of male participants, those younger than 65 years, and those with a high school education or above (P-interaction = 0.049, P-interaction < 0.001, and P-interaction = 0.024, respectively). In contrast, no significant differences in the association between ePWV and the risk of cardio-cerebrovascular diseases were observed in the subgroups based on BMI, alcohol consumption, smoking history, marital status, or place of residence.

Fig. 2
figure 2

Subgroup analyses of the.ePWV and cardio-cerebrovascular diseases

Full size image

Association between ePWV and cardio-cerebrovascular diseases

We explored the relationship between ePWV and the risk of cardio-cerebrovascular disease (Fig. 3). We found a nonlinear dose–response relationship between ePWV and the risk of cardio-cerebrovascular diseases among middle-aged and older individuals (nonlinearity, P < 0.001).

Fig. 3
figure 3

Associations between ePWV with cardio-cerebrovascular diseases

Full size image

Discussion

This study examined the association between ePWV and cardio-cerebrovascular disease in a cohort of middle-aged and older aged individuals in China. The findings can be summarised as follows: First, after adjusting for relevant covariates, ePWV was independently associated with the risk of cardio-cerebrovascular disease during the follow-up period. Second, subgroup analysis showed that the positive association between ePWV and cardio-cerebrovascular disease risk remained significant across different groups of middle-aged and older individuals in Chinese communities (HR = 1.16; 95% CI 1.11–1.22; P < 0.001). Age and sex strongly influenced this association. Third, a nonlinear dose–response relationship between ePWV and the risk of cardio-cerebrovascular disease was identified (nonlinearity, P < 0.001).

Several studies have demonstrated a link between ePWV and cardiovascular outcomes as well as all-cause mortality [31,32,33]. In a cohort study based on NHANES data, Chen et al. [34] found a positive association between ePWV and all-cause mortality in patients with coronary heart disease; however, this study focused solely on patients from the United States with coronary heart disease. Further research is required to determine whether these findings apply to other populations with different conditions. Jae et al. [33], in a cohort study of middle-aged European men, found that ePWV was independently associated with the risk of stroke. However, the study was unable to explore how blood pressure variability and antihypertensive treatments influenced stroke risk due to missing data. This study included middle-aged and older-aged populations, which increases the practical applicability of our findings. Furthermore, based on a large sample of middle-aged and older individuals in China, we found that participants with high ePWV had a higher risk of developing cardio-cerebrovascular diseases. These findings provide further evidence for the link between arterial stiffness and cardio-cerebrovascular disease, potentially aiding in the early identification of patients with cardio-cerebrovascular disease and providing appropriate strategies for treatment and management.

The exact mechanisms linking arterial stiffness to cardio-cerebrovascular disease remain unclear. Current research suggests potential associations with elastin degradation [35,36,37], haemodynamic changes [38], endothelial injury [39, 40], oxidative stress [41, 42], and inflammation [41, 43]. Interleukin-6 (IL-6) [41] is a commonly studied cytokine that triggers inflammation and oxidative stress and induces the release of other molecules that can damage the heart and brain. IL-6 is closely associated with atherosclerosis, myocardial infarction, heart failure, and ischaemic stroke [41]. Moreover, increased arterial stiffness leads to hypertension and elevated pulse pressure [44]. Higher pulse pressure enhances pulse flow into the microvasculature of the organs [45]. The resulting haemodynamic stress, pulse pressure, and blood pressure variability can harm the brain and heart [46], increasing the risk of cardiovascular diseases.

Our study has several strengths. First, the study was based on a large sample size, making the findings stable and reliable. Second, the study employed a prospective design with a long-term follow-up, allowing to assess the incidence of cardio-cerebrovascular diseases. Finally, the evaluation method was simple, inexpensive, and easily applicable in clinical practice. Nonetheless, this study had some limitations that should be considered. First, due to database constraints, we could only assess cardio-cerebrovascular conditions in middle-aged and older adults. Although we adjusted for several factors, the model did not account for variables that might influence cardio-cerebrovascular risk, such as coagulation markers, which require further investigation. Second, the data were collected through self-reported surveys, which may have been subject to respondent bias, potentially affecting the accuracy of the information. Some participants may not have undergone physical examination or blood testing during the 2011 survey, may not have understood the results, or may have forgotten them. Alternatively, patients may have been unwilling to admit being diagnosed with chronic diseases such as diabetes or hypertension, resulting in important missed diagnoses, which have also been described in previous studies [47]. Finally, the study population was drawn from Chinese communities, which may limit the generalisability of the findings to other populations.

Conclusions

This study revealed that ePWV was significantly associated with the risk of cardio-cerebrovascular disease in middle-aged and older Chinese individuals. Specifically, the higher the ePWV, the greater the incidence of these diseases. Monitoring ePWV in clinical practice may help prevent the onset of cardio-cerebrovascular diseases, an approach that can help reduce disease occurrence and improve the quality of life in older adults.

Availability of data and materials

The data for this study were obtained from CHARLS (http://charls.pku.edu.cn).

Abbreviations

HR:

Hazard ratio

BMI:

Body mass index

cf-PWV:

Carotid-femoral pulse wave velocity

CHARLS:

China Health and Retirement Longitudinal Study

CI:

Confidence interval

DBP:

Diastolic blood pressure

ePWV:

Estimated pulse wave velocity

Glu:

Glucose

HR:

Hazard ratio

IL-6:

Interleukin-6

LDL-c:

Low-density lipoprotein cholesterol

MBP:

Mean blood pressure

NHANES:

National Health and Nutrition Examination Survey

PWV:

Pulse wave velocity

SBP:

Systolic blood pressure

TG:

Triglycerides

US:

United States

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Acknowledgements

We thank all the participants for their contributions to CHARLS.

Funding

Funding for this study was obtained from the ‘Guangdong Provincial Basic and Applied Basic Research Fund Enterprise Joint Fund (Public Health and Medicine and Health Field) Project (No. 2023A1515220110).

Author information

Author notes

  1. Guangyan Liu and Wenyue Sha contributed equally to this study and should be considered as co-first authors.

Authors and Affiliations

  1. Department of Geriatrics, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524000, Guangdong, China

    Guangyan Liu, Wenyue Sha, Yueying Wu, Jinhua Luo & Yu Yang

  2. Department of Geriatrics, Binhaiwan Central Hospital of Dongguan, Dongguan, 523000, Guangdong, China

    Tuming Zhang

  3. Department of Pulmonary and Critical Care Medicine, Foshan Nanhai District Fourth People’s Hospital, Foshan, 528200, Guangdong, China

    Yuying Cai

Authors
  1. Guangyan Liu
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Contributions

The research question was formulated by GYL and WYS, who also designed the study, analyzed the data, and drafted the paper., YYW, JHL and YYC contributed to the study design, data analysis, and paper revision. TMZ and YY provided assistance with research question formulation, data interpretation, and paper quality supervision. The final version of the manuscript was reviewed, feedback was provided, and confirmation was given by all authors.

Corresponding authors

Correspondence to Tuming Zhang or Yu Yang.

Ethics declarations

Ethics approval and consent to participate.

Ethical approval for the CHARLS was granted by the Institutional Review Board (IRB) of Peking University. The IRB approval number for the main household survey, including anthropometrics, was IRB00001052-11015 and the IRB approval number for biomarker collection was IRB00001052-11014. All participants provided written informed consent before enrolment in the study. The study methodology was conducted in accordance with approved guidelines.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Liu, G., Sha, W., Wu, Y. et al. The association between estimated pulse wave velocity and cardio-cerebrovascular disease risk: a cohort study. Eur J Med Res 30, 16 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40001-024-02217-4

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European Journal of Medical Research

ISSN: 2047-783X

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