INTRODUCTION

Brachial pulse pressure (PP), calculated as the difference between SBP and DBP, reflects the stiffness of the aorta and large arteries [1]. An increase in SBP or a decrease in DBP can lead to an elevated PP. High SBP increases left ventricular and arterial load, whereas low DBP reduces coronary perfusion in the heart, both contributing to the biological plausibility of PP as a risk factor for cardiovascular disease (CVD) [1,2]. Population-based cohort studies showed close associations between wide PP and the incidence of CVD [3,4].

Previous studies have suggested sex differences in the PP distributions by age, yet with inconsistent patterns in different populations, based on studies with limited sample sizes [5]. In China, several regional surveys with limited sample sizes and different age coverages reported different prevalence rates of wide PP. For instance, a study among Han Chinese aged 18–74 years in Hebei, Zhejiang, and Guangxi Provinces reported a prevalence of 6.7% [6], whereas the Yiwu Elderly Cohort Study among 5030 adults older than 65 years living in Jiangsu Province reported 54.2% participants as wide PP [7]. However, there is insufficient information from large-scale nationwide studies on the age-specific and sex-specific distributions of PP among the Chinese population.

Body fat accumulation may worsen arterial stiffness through multiple mechanisms, such as insulin resistance [8], oxidative stress [9], or activation of the sympathetic nervous system [10]. Several observational studies performed in Western populations have found that BMI and waist circumference were associated with elevated PP, with varying strengths and directions [11,12]. For the same BMI, Asians have a higher percentage of body fat than Europeans, linked with increased cardiovascular risk at a relatively lower BMI [13,14], whereas little is known regarding the associations between different body composition and wide PP in Asian populations.

In the present study, we aimed to examine the distribution of PP by age and sex; and assess the association of different body composition with PP using baseline data from a large nationwide cohort study in China.

METHODS Study design and participants

We utilized the baseline data of the China Kadoorie Biobank (CKB). Detailed design has been described previously [15,16]. From 25 June 2004 to 15 July 2008, the baseline survey was conducted in 10 geographic regions in China (five urban and five rural) based on local disease patterns and related risk factors. Potentially eligible residents aged 35–74 years were invited to participate in the baseline survey, and about one-third of the residents responded. Individuals slightly outside the target age range were also included to encourage participation, leading to the final participants’ age range of 30–79 years. The current study used version 15.0 of the data release, including 512 715 participants at baseline. Participants with extreme blood pressure (SBP <80 mmHg or SBP ≥250 mmHg; DBP <40 mmHg or DBP ≥150 mmHg, n = 170) or body composition (BMI<15 kg/m2 or BMI ≥40 kg/m2, n = 512), having missing values on body fat percentage (BF%, n = 240), or with clinically diagnosed hypertension or taking antihypertensive medications (n = 77 589) were excluded. Therefore, 434 200 participants were finally included in the current study. Ethical approval for the CKB study was obtained from the Ethical Review Committee of the Chinese Center for Disease Control and Prevention (Approval number: 005/2004, 9.7.2004; Beijing, China), the Oxford Tropical Research Ethics Committee, (Approval number: 025–04, 3.2.2005; University of Oxford, the United Kingdom). All participants provided written informed consent.

Data collection

At the baseline survey, trained health workers collected information on socioeconomic characteristics, lifestyle, and medical history using an electronic questionnaire. With participants wearing light clothes and no shoes, trained staff measured participants for standing height, weight, waist circumference, hip circumference, and BF%. Standing height was measured to the nearest 0.1 cm using a stadiometer. Weight and BF% were obtained simultaneously using a body composition analyzer (TANITA-TBF-300GS; Tanita Corporation, Tokyo, Japan). BMI was calculated as weight in kilograms divided by the square of height in meters (kg/m2). Waist circumference and hip circumference were measured to the nearest 0.1 cm using nonstretchable tapes. Waist circumference was measured horizontally at the midpoint between the lowest rib and the superior margin of the iliac crest. Hip circumference was measured at the most prominent part of the hip. Waist-hip ratio (WHR) was calculated as waist circumference divided by hip circumference. Fat mass was obtained as BF% multiplied by the weight. Fat-free mass was calculated as weight minus fat mass. The fat mass index (FMI) or fat-free mass index (FFMI) was calculated as the fat mass or fat-free mass in kilograms divided by the square height in meters (kg/m2). After participants rested in a seated position for at least 5 min, blood pressure was measured twice by a professional staff using a digital sphygmomanometer (model UA-779; A&D Medical, Tokyo, Japan). If the difference between the first two SBP measurements was greater than 10 mmHg, an additional measurement was obtained, and the results of the last two measurements were recorded. PP was calculated as the difference between SBP and DBP. The average of the two measured PP was used for the analysis. As in most previous studies, wide PP was defined as PP above 65 mmHg [7,17].

Statistical analysis

The mean ± standard deviation (SD) described the continuous data, and the percentage presented the categorical data for the overall population and by PP categories (i.e. normal and wide PP). The t test (for continuous variables) or chi-square test (for categorical variables) was used to compare the differences between normal and wide PP groups. For each body composition measure, adjusted mean values of PP were calculated for each sex-specific quintile group using multiple linear regression, with adjustment for baseline age (continuous variable), study area (10 categories), education level (no formal education, primary school, middle/high school, or college/university categories), smoking (never, occasional, ex-regular, or regular), alcohol drinking (never, occasional, ex-regular, or regular), physical activity level [metabolic equivalent of task hours (MET-h)/day], and monthly mean outdoor temperature (MMOT) in model 1. Wherever appropriate, additional adjustment was made for waist circumference in model 2 or BMI in model 3. Multiple linear regression was used to estimate the mean difference in PP associated with 1 SD higher level of body composition, with adjustment for covariates as above. Odds ratios (ORs) and 95% confidence intervals (CIs) of wide PP by body composition quintiles were examined using multivariable logistic regression models, adjusting for the same covariates as in the linear regression models. ORs between tertiles of PP and quintiles of body composition measures were further analyzed using multinomial logistic regression, adjusting for covariates as mentioned above. Chi-square tests for trend and heterogeneity were applied to the log OR and their SEs. All statistical tests were conducted with R version 3.3.2 (R Foundation for Statistical Computing, Vienna, Austria). A two-sided P value of less than 0.05 was considered statistically significant.

RESULTS Population characteristics

Of all participants, 14.3% were categorized as wide PP. Body composition, including BMI, BF%, waist circumference, WHR, FMI and FFMI were significantly higher in those with wide PP. Participants with wide PP were also older and physically inactive than their counterparties (Table 1).

TABLE 1 - Characteristics of the participants Characteristic All Normal PPa Wide PPb P value N 434200 372287 61913 Age (years) 50.8 ± 10.4 49.4 ± 9.8 59.1 ± 10.0 <0.001 Female (%) 41.3 41.3 41.0 0.204 Urban residence (%) 57 55.9 63.7 <0.001 Education level (%) <0.001  No formal education 17.4 15.1 31.1  Primary school 31.8 30.6 38.5  Middle or high school 44.9 47.8 27.6  College or university 5.9 6.4 2.8 Household income (%) <0.001  <10 000 yuan/year 28.7 27.5 36  10 000–19 999 yuan/year 29.1 29.3 28.2  20 000–34 999 yuan/year 24.5 25 21.8  ≥35 000 yuan/year 17.6 18.2 14.1 Smoking status (%) <0.001  Never 61.5 61.5 61.4  Occasional 5.8 5.9 5.2  Ex-regular 5.3 5 7.2  Regular 27.4 27.6 26.3 Alcohol drinking (%) <0.001  Never 44.4 43.3 50.7  Occasional 33.1 34 27.7  Ex-regular 1.4 1.3 1.9  Regular 21.1 21.3 19.8 Physical activity (MET-h/day) 21.9 ± 13.9 22.4 ± 13.9 18.7 ± 13.5 <0.001 BMI (kg/m2) 23.4 ± 3.2 23.3 ± 3.2 23.8 ± 3.5 <0.001 BF% 27.5 ± 8.2 27.4 ± 8.1 28.1 ± 8.9 <0.001 WC (cm) 79.4 ± 9.4 79.1 ± 9.3 81.2 ± 10.0 <0.001 WHR 0.88 ± 0.07 0.87 ± 0.07 0.89 ± 0.07 <0.001 FMI (kg/m2) 6.6 ± 2.7 6.6 ± 2.7 6.9 ± 3.0 <0.001 FFMI (kg/m2) 16.8 ± 1.7 16.8 ± 1.6 16.9 ± 1.7 <0.001

Values are mean ± SD or percentages as indicated. Overall group difference was determined by t test or chi-square test for continuous or categorical variables, respectively. BF%: body fat percentage; MET, metabolic equivalent task; PP, pulse pressure; WC, waist circumference; WHR, waist–hip ratio.

aNormal PP less than 65 mmHg.

bWide PP at least 65 mmHg.

Distribution of pulse pressure by age

Overall, women had a lower mean PP than men until around the mean menopausal age of 52 years, after which PP in women was greater and increased faster with age (Fig. 1). A similar pattern was observed in urban and rural areas, although the age point when women and men had the same mean PP was much lower in rural areas than in urban areas (about 48 vs. 60 years).

FIGURE 1:

Distribution of pulse pressure by residence and sex. (a) Whole participants. (b) Urban participants. (c) Rural participants. PP, pulse pressure. Squares represent the mean of PP and vertical lines represent the corresponding 95% confidence intervals (CIs).

Associations between body composition and wide pulse pressure

After adjustment for covariates (model 1), dose-dependent relationships were observed between different body composition, such as BMI, BF%, and waist circumference and wide PP (P for trend <0.001) (Fig. 2). The ORs of BMI, BF%, and waist circumference in both men and women exceeded 2.00 by comparing the extreme quintiles. In men, after further adjustment for waist circumference (model 2), the association between BMI and wide PP was slightly attenuated, with the OR (95% CI) shifting from 2.30 (2.19–2.41) to 1.65 (1.53–1.79) for the fifth quintile; for BF%, the OR (95% CI) shifted from 2.02 (1.93–2.12) to 1.28 (1.20–1.37) for the fifth quintile. However, for waist circumference, this association was substantially weakened after additional adjustment for BMI, whereby the OR (95% CI) for the fifth quintile shifted from 2.12 (2.03–2.23) to 1.11 (1.03–1.20). A similar change in the ORs was observed after further adjustment among women.

FIGURE 2:

Adjusted odds ratios of wide pulse pressure by quintile of body composition in men and women. Model 1, adjusted for age, study area, education level, smoking, alcohol drinking, physical activity (MET-h/day) and local outdoor temperature; model 2, additionally adjusted for waist circumference based on model 1; model 3, additionally adjusted for BMI based on model 1. BF%, body fat percentage; OR, odd ratio; PP, pulse pressure; WC, waist circumference.

Differences in pulse pressure per standard deviation increase in body composition

Figure 3 shows the difference in PP per one sex-specific SD increase of different body composition. Overall, in model 1, BMI had the strongest association with PP (men: 1.83 mmHg/SD; women: 1.79 mmHg/SD), followed by waist circumference (men: 1.67 mmHg/SD; women: 1.63 mmHg/SD), FMI (men: 1.60 mmHg/SD; women: 1.66 mmHg/SD), FFMI (men: 1.57 mmHg/SD; women: 1.37 mmHg/SD), BF% (men: 1.42 mmHg/SD; women: 1.52 mmHg/SD), WHR (men: 1.42 mmHg/SD; women: 1.33 mmHg/SD), respectively. After additional adjustment for waist circumference (model 2), this association was modestly attenuated for BMI, whereas associations were significantly attenuated for other body composition, such as FMI, FFMI, and BF%. Interestingly, the association of FMI and FFMI with PP greatly differed between men and women. In men, PP was more strongly associated with FFMI than with FMI (0.91 vs. 0.67 mmHg, P < 0.05), whereas in women, the opposite was observed (0.72 vs. 1.01 mmHg, P < 0.05). For measures of central adiposity, such as waist circumference and WHR, their associations with PP were substantially attenuated after additional adjustment for BMI (model 3).

FIGURE 3:

Higher pulse pressure per standard deviation of each body composition by sex. Model 1, adjusted for age, study area, education level, smoking, alcohol drinking, physical activity (MET-h/day) and local outdoor temperature; model 2, BMI and waist circumference (WC) were mutually adjusted for each other. BF% was adjusted for WC. Squares represent the mean of PP and horizontal lines represent the corresponding 95% CIs. BF%, body fat percentage; PP, pulse pressure; WC, waist circumference.

Adjusted means of pulse pressure by body composition and sex

Figure 4 shows the adjusted mean PP by different body composition. In model 1, the PP difference between the first and fifth quintiles of BMI, BF%, and waist circumference exceeded 4.0 mmHg. After further adjustment for waist circumference, the PP difference of BMI slightly attenuated to 3.1 and 2.9 mmHg for men and women, respectively. However, for BF% and waist circumference, the PP difference was reduced mainly after further adjustment for BMI. For BF%, it decreased from 4.0 and 4.2 mmHg to 0.8 and 1.4 mmHg for men and women, respectively. Similarly, for waist circumference, it changed from 4.6 and 4.5 mmHg to 0.5 and 0.6 mmHg for men and women, respectively. In model 1, each 10 kg/m2 higher BMI was associated with 5.8 mmHg higher PP in men and 5.4 mmHg higher PP in women. After additional adjustment for waist circumference (model 2), the strengths of these associations were attenuated by 16% in men and 19% in women (to 4.9 and 4.4 mmHg). For BF%, each extra 10% in BF% was associated with 2.3 mmHg higher PP in men and 2.2 mmHg higher PP in women in model 1; after additional adjustment for waist circumference (model 2), the association was 78% (male) or 59% (male) attenuated to 0.5 and 0.9 mmHg. Moreover, each 10 cm higher waist circumference was associated with 1.8 mmHg higher PP in both men and women in model 1. However, this association nearly disappeared, with a 78% attenuation to 0.4 mmHg in both men and women after further adjustment with BMI. When FMI and FFMI were considered separately, FFMI showed somewhat stronger associations with PP in different models. Moreover, the association with FMI was attenuated largely by additional adjustment for waist circumference (Figure S1, https://links.lww.com/HJH/C274).

FIGURE 4:

Pulse pressure vs. BMI, BF% and waist circumference in men (a–c) and women (d–f). Model 1, adjusted for age, study area, education level, smoking, alcohol drinking, physical activity (MET-h/day) and local outdoor temperature; model 2, additionally adjusted for waist circumference based on model 1; model 3, additionally adjusted for BMI based on model 1. Squares represent the mean of PP and vertical lines represent the corresponding 95% confidence intervals (CIs). BF%, body fat percentage; PP, pulse pressure; WC, waist circumference.

Stratified results

Figure 5 shows the association between 10-unit body composition and wide PP by strata of the basic characteristics. Although there was some statistically significant heterogeneity across subgroups of participants, consistent positive associations were observed between body composition and wide PP in all subgroups.

FIGURE 5:

Adjusted odds ratios of wide pulse pressure by body composition and others. Squares represent the mean of PP and horizontal lines represent the corresponding 95% CIs. BF%, body fat percentage; MET, metabolic equivalent task; OR, odd ratio; PP, pulse pressure; WC, waist circumference.

DISCUSSION

In this cross-sectional study of over 400 000 Chinese adults without diagnosed hypertension, PP was positively associated with age, with 3.8 and 5.9 mmHg increments every 10 years in men and women, respectively. After mutual adjustment for each other, the association between BMI and wide PP was stronger than other body composition. Interestingly, PP was more strongly associated with FFMI (than with FMI) in men, whereas in women, the opposite was true, that is, more closely related to FMI.

Ageing is often accompanied by disruption and destruction of the elastic laminae of the arteries and changes in the collagen-to-elastin ratio, which may induce enhanced stiffness and, consequently, higher PP [18,19]. In women, we did observe an increase in PP with age. Interestingly, the positive trend of PP with age was not that clear in younger men (before 50 years old). A meta-analysis included 22 articles with data from 27 populations that showed similar sex differences in PP trends with aging [5]. The age-dependent decline in stroke volume in men contributed to a corresponding narrowing of PP until age 50 years. Subsequent decreases in arterial compliance counteracted the effect of reduced stroke volume on PP and dominated the elevation of PP [20]. Compared with men, persistent elevation of PP may be attributable to the lower cardiac output in women, making its reduction insufficient to offset the effects of arterial stiffness [21,22]. Meanwhile, the lower cardiac output of women may also account for their lower PP levels than men by the age of around 50 years, as we observed. Since then, PP in women has exceeded that in men. It may be attributed to the absence of estrogenic protection of the arterial vessels as they progress through menopause. In the postmenopausal cohort, blood pressure-independent improvement in pulse wave velocity with estrogen replacement therapy [23] and improvement in carotid artery stiffness with estrogen administration [24] also supported this phenomenon. In the CKB cohort, rural women have menopause earlier than urban women [25], which was somewhat consistent with our observation that rural women overtook the equivalent male PP levels earlier than urban women.

In agreement with some previous studies [11,26], we found that BMI exhibited the strongest association with PP. However, some other studies suggested that measures of central adiposity, such as waist circumference and WHR, provided additional information beyond BMI to predict wider PP [12,27]. Most of these studies were conducted among white people with limited sample sizes. The inconsistent results may be related to Chinese individuals having higher trunk fat than white people for the same BMI [28], and higher trunk fat is associated with greater arterial stiffness [29]. Higher FMI was strongly associated with arterial stiffness in children [30], adolescents [31], and adults [32]. Similarly, it has been shown that elevated FFMI was associated with arterial stiffness in middle-aged adults [33]. Therefore, the sex differences in the association of FMI and FFMI with PP may be attributed to their different body composition characteristics. Previous studies have shown that men have a higher mean FFMI but a lower mean FMI than women, irrespective of age and ethnicity [34,35]. This phenomenon may endorse that FMI is more relevant in the process of arterial stiffness in women, whereas FFMI is more critical in men's arterial stiffness process.

The underlying mechanisms associated with fat mass and elevated PP have been widely recognized, such as dysfunction of adipose tissue and activation of the sympathetic nervous system [10,36]. However, limited evidence remained for the association between fat-free mass and greater PP. One possible reason is that higher lean mass leads to higher circulating blood volume, increasing left ventricular stroke volume and cardiac output [37]. The elevation in BMI may be a consequence of the increase in both FMI and FFMI. Therefore, the mechanisms mentioned above may play a role in the elevation of PP because of higher BMI.

Several limitations of the present study warrant clarification. First, the current study is a cross-sectional design with no temporal association evidence. Second, the age range of the population in the current study was 30–80 years, with uncertain generalizability to younger populations. Third, potential biochemical confounders were not available in the present study, thus may limit the association robustness. Moreover, without a uniform definition of wide PP, the ORs in our study could be different if we set different cut-off points for wide PP. However, when we set the outcome as the highest tertile of PP, a predictor of coronary mortality [38], the associations were not substantially changed (Figure S2, https://links.lww.com/HJH/C274). A large nationwide sample enabled us to have sufficient statistical power to compare different body composition; by excluding individuals with clinically diagnosed hypertension, causal inversions arising from lifestyle alterations after diagnosis of hypertension can be minimized; MMOT data for each region were adjusted, and hence the potential confounding effect of temperature on blood pressure could be minimized.

In conclusion, using data from more than 400 000 Chinese adults without diagnosed hypertension, this study found that the age trend of PP showed different patterns by sex. BMI is more closely associated with PP than other body composition. Furthermore, the association of FMI and FFMI, which constitute BMI, with PP differed by sex. In the future, large-scale prospective studies and mechanistic studies remain warranted to corroborate our results.

ACKNOWLEDGEMENTS

We thank the China Kadoorie Biobank study that provided the data (DAR-2019-00031), the Chinese Center for Disease Control and Prevention, the Chinese Ministry of Health, the National Health and Family Planning Commission of China, and 10 provincial/regional Health Administrative Departments. The most important acknowledgement is to the participants in the study and the members of the survey teams in each of the 10 regional centers, as well as to the project development and management teams based at Beijing, Oxford, and the 10 regional centers.

Funding support: the study was supported by the National Natural Science Foundation of China (82173504, 82011530197, 82192901, 2192904, 82192900), the National Key Research and Development Program of China (2016YFC0900500, 2016YFC0900501, 2016YFC0900504), the Kadoorie Charitable Foundation in Hong Kong, Wellcome Trust in the UK (088158/Z/09/Z, 104085/Z/14/Z, 104085/Z/14/Z) and the International Exchanges 2019 Cost Share (191251).

Conflicts of interest

There are no conflicts of interest.

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