Comparison of obesity-related parameters as predictors of high brachial-ankle pulse wave velocity in middle-aged and elderly people in China: A multicenter cross-sectional community-based study

Background The association between obesity-related parameters and occurrence of high brachial-ankle pulse wave velocity (baPWV) in Chinese middle-aged (40–59 years) and elderly people ( ≥ 60 years) is unknown, especially when body composition indicators are compared. A total of 3219 middle-aged and elderly subjects who were recruited from 6 community health service centers located in Hefei, Bengbu, and Chuzhou (Anhui province, Eastern China) met the inclusion criteria and had valid data. An e-health promotion system was used to collect basic health data, and baPWV and the body composition of each subject were measured. Partial correlation and binary logistic regression analyses were performed to identify associations between obesity-related parameters and high baPWV, and receiver operating characteristic (ROC) curves were analyzed to determine the optimal cutoff values and predictive capacity of high baPWV. and for (0.101 were other WHtR, waist-to-height ratio. MET, metabolic equivalent; ROC, receiver operating characteristic; RTFFM, ratio of trunk fat-free mass; SBP, systolic blood pressure; WC, waist circumference; WHtR, waist-to-height ratio.

The relationship between obesity-related parameters and risk of arteriosclerosis or hypertension varies according to sex and age. Aging reduces arterial elasticity and causes biochemical and histologic changes in arteries, resulting in increased internalization of visceral fats [6,11,19,20]. Fat is differentially distributed in men and women [21], with a higher prevalence of obesity in the latter.
Despite the increasing rates of obesity in China, few studies to date have examined the association between obesity-related parameters and incidence of high baPWV in Chinese adults, especially by comparing all indicators. The present study was carried out in order to identify the parameter (WC, BMI, WtHR, FFTI, FFI, and RTFFM) that best predicts high baPWV (> 14 m/s) in middle-aged (40-59 years) and elderly (≥ 60 years) residents of Anhui province, Eastern China, strati ed by age and sex using a cross-sectional survey study design.

Study design
We used cross-sectional data obtained from a community-based study in order to identify factors that in uence non-communicable chronic diseases and investigate the effects of health promotion through arti cial intelligence. Data on anthropometric and biochemical parameters, cardiovascular function, lifestyle, disease status, family history of disease, and mental health were collected each year using an e-health promotion system.

Participants
We invited local residents to participate in the study through 6 community health service centers located in Anhui province (Hefei, Bengbu, and Chuzhou). A total of 4529 participants aged > 18 years were surveyed between June 2018 and January 2020. Exclusion criteria were age < 40 years (n = 800); ankle-brachial index > 1.4 or < 0.89; cardiovascular diseases (n = 350); insu cient data for baPWV (n = 28) or body composition analysis (n = 132). A total of 3219 subjects (1951 women and 1264 men; mean age ± SD, 61.32 ± 9.81 years) were ultimately included in the analysis. All subjects provided written, informed consent for participating in this study and they agreed that their data would be used. The protocol of this study was approved by the Ethics Committee of Bengbu Medical College (Anhui, China; no. 2018045).

Data collection
All physical examinations were performed by trained medical staff or medical postgraduate students according to standardized procedures. Participants were questioned regarding health-related behaviors including cigarette and alcohol consumption and amount of physical activity. For cigarette consumption, total smoking during the subject's lifetime was calculated based on the quantity and weekly frequency of cigarettes that were smoked; this was extended to consumption before quitting in the case of former smokers. The amounts of alcohol in one bottle of the most popular alcoholic beverages in Anhui province are as follows: beer (500 ml, 3.2% alcohol), 17.5 g; white liquor (450 ml, 42% alcohol), 210 g; and wine (750 ml, 13.5-14% alcohol), 97.5 g. Daily alcohol consumption was calculated using these values. When data for cigarette and alcohol consumption were missing, a value of zero was assigned. Subjects were questioned about the type, duration (in minutes), and frequency (per week) of physical activities in which they engaged. According to activity codes and metabolic equivalent (MET) intensities in the Compendium of Physical Activities [22], physical activity time was determined as minutes/MET/day and missing values were assigned the median value. Data on sleep disorder, kidney disease, diabetes, dietary salt preferences, and dietary fat content were collected through self-report questionnaires, and missing values were assigned a value of zero. The details related to the questionnaires were published elsewhere [23].

Anthropometric data
Anthropometric measurements including body height, weight, and WC were obtained while subjects were standing and wearing light clothing. Height was measured with steel tape, and weight was measured with a bioelectric impedance analyzer (BX-BCA-100; Institute of Intelligent Machines, Hefei, China). WC was measured above the iliac crest and below the lowest rib margin at minimum respiration using exible leather tape as subjects were in the standing position. After obtaining the measurements, BMI and WHtR were calculated as the ratio of weight (kg) / height (m) 2 and WC (cm) / height (cm), respectively. There were no missing values in the anthropometric data.
Blood pressure measurement Blood pressure was measured using a cardiovascular function tester (BX-CFTI-100; Institute of Intelligent Machines, Hefei, China), which has a cuff that can be automatically in ated and de ated. A second measurement was automatically performed 3 min after the rst one, and the average value was recorded.
Before the measurements, participants were required to relax; sit in a chair (feet on the oor with back supported) for > 10 min; avoid caffeine, exercise, and smoking for at least 30 min prior; empty their bladder; and remove all clothing covering the location of cuff placement. During the test period participants were instructed to lie on an examination bed and there was no communication between the participant and the observer. Mean arterial pressure (MAP) was calculated as DBP + 0.4PP.

Body composition measurements
Body composition parameters were measured using a bioelectric impedance analyzer. The participants refrained from eating and drinking 3 h before measurements were performed, and were instructed to remove their socks and stand on the machine; electrodes were placed on both hands and feet, and the subjects were instructed to lift both arms upright and touch the electrodes with their hands. Fat-free mass (FFM)-including lean tissue mass and total body water-were derived from the impedance data, and fat-free tissue index (FFTI; FFM / height 2 ), fat tissue index (FTI; FM / height 2 ), FFTI/FTI, and ratio of trunk fat to free mass (RTFFM; trunk FFM / trunk weight) were calculated. baPWV (m/s) was measured immediately after blood pressure (with participants instructed to remain supine on the same examination bed without talking) using an IIM-AS-100 system (Institute of Intelligent Machines), which recorded bilateral brachial and posterior tibial-artery pressure waveforms with an oscillometric method by means of cuffs placed on participants' arms and ankles. baPWV was calculated automatically for each arterial segment as the path length divided by the corresponding time interval.
High baPWV was de ned according to the Japanese Guidelines for Noninvasive Vascular Function Test, which recommend lifestyle modi cations for a baPWV value > 14 m/s on one side, as this indicates a high risk of hypertension onset in untreated normotensive individuals [13].

Statistical analysis
Data were analyzed using SPSS v23.0 software (IBM, Armonk, NY, USA). Continuous variables are expressed as mean ± SD. The Student's t test for independent samples and Pearson's chi-squared test were used to assess the signi cance of differences in baseline characteristics between groups according to baPWV level strati ed by sex and age. Partial correlations (adjusted for age and MAP) between obesity-related parameters and baPWV were examined. Binary logistic regression models (model 1, adjusted for age; model 2, adjusted for age and MAP; and model 3, adjusted for age, MAP, cigarette consumption, physical activity, and diabetes status) were used to examine the effects of obesity-related parameters on baPWV, with the following parameters considered as independent variables: WC, BMI, WHtR, FFTI, FTI, FFTI/FTI, and RTFFM. Standardized beta coe cient was calculated as 0.5513 × partial regression coe cient × standard deviation of independent variable as in SAS statistical software (SAS Institute, Cary, NC, USA).
Receiver operating characteristic (ROC) curves were analyzed to identify the optimal cutoff points and assess the predictive capacity of obesity-related parameters for occurrence of high baPWV by age (40-59 years and ≥ 60 years) and sex, with sensitivity and speci city values reported. Optimal cutoff points for the parameters were determined according to the largest Youden's index value (sensitivity + speci city − 1). The Z test was used to evaluate the signi cances between the 2 areas under the ROC curves, and Z was calculated as (S1 − S2) / √(SE1 2 + SE2 2 ) (where S1 and S2 representing the areas under the ROC curve [AUC]) and SE1 and SE2 represent the corresponding standard errors), as in Stata software (StataCorp, College Station, TX, USA).

Participant characteristics according to baPWV strati ed by age and sex
The characteristics of the study population are presented in Table 1. The mean age of the 3219 subjects was 61.32 years (range, 40-94 years), and 61.7% (n = 1951) were women. Mean age, heart rate, SBP, and diastolic blood pressure were higher in subjects of both sexes and age categories with high baPWV value (≥ 14 m/s) as well as in those with self-reported diabetes, except in men aged ≥ 60 years (all P < 0.05). The amount of physical activity was lower in women aged ≥ 60 years with high baPWV (P < 0.05). Statistically signi cant differences were observed between high and low baPWV groups for WC, BMI, WHtR, FTI, and RTFFM in both age categories of women and men (≥ 60 years), and all of these values were higher in subjects with high baPWV strati ed by age and sex except for RTFFM, which was lower (all P < 0.05). Partial correlations between obesity-related parameters and baPWV Partial correlations (adjusted for age and MAP) between obesity-related parameters and baPWV are shown in Fig. 1

Regression analyses
Associations between all obesity-related parameters and high baPWV value were signi cant after adjusting for age in both groups of women, but in 40-59- year-old men, only the values for WC and WHtR were signi cant and in men > 60 years old, the values for all parameters were signi cant except RTFFM ( Table 2, Model 1). After adjusting for age and MAP (Model 2), the associations remained signi cant for FFTI/FTI in men aged 40-59 years; meanwhile, all parameters were signi cantly correlated with high baPWV in the same age group of women except FFTI/FTI and RTFFM, but only WC and WHtR were signi cantly correlated with high baPWV. In model 3, after adjusting for age, MAP, cigarette consumption, physical activity, and diabetes status, the associations between WC and high baPWV were signi cant in both age groups of men, while BMI in the 40-59-year age group and FFTI/FTI in the > 60 age group of men, and WC, BMI, and FTI in both age groups of women were also signi cant. In all statistically signi cant correlations, combined covariates of WHtR explained the largest proportion of the variance for dependent variables except in model 2 in the 40-59-year age group of men and model 3 in both age groups of men; R 2 ranged from 0.088 to 0.216 (beta range, 0.0001-13.7507) in Model 1, whereas the beta of RTFFM was higher than other beta values in these 3 groups of men in Models 2 and 3. Association between obesity-related parameters and high baPWV by ROC curve analysis Table 3 shows the AUCs of WC, BMI, WHtR, FFTI, and FTI for predicting high baPWV. All of these obesity-related parameters showed a reasonable predictive capacity for high baPWV in women (all with 95% con dence interval [CI] > 0.5). However, this capacity decreased for middle-aged and elderly men (95% CI < 0.5), except in the case of WC and WHtR (95% CI > 0.5 for both groups). The discriminatory power of WHtR for high baPWV was stronger in women, and was approximately 69.3% (AUC = 0.693; 95% CI: 0.647-0.739) and 66.7% (AUC = 0.667; 95% CI: 0.631-0.704) in middle-aged and elderly women, respectively.
Although the AUC for WHtR appeared higher than for other parameters in both men and women (Fig. 2), only the AUCs for WC-FFTI and WHtR-FFTI in men > 0.05). The cutoff values of the 5 obesity-related parameters with high baPWV predictive capacity by ROC curve analysis are shown in Table 3. For middle-aged and elderly men, the optimal cutoff values for WC for predicting high baPWV were 95.5 and 88.5 cm, respectively; for women, the value was 83.5 in both age groups. The optimal cutoff values for WHtR were 0.54 in middle-aged men, 0.55 in elderly men, 0.51 in middle-aged women, and 0.52 in elderly women; and the optimal cutoff values for BMI in middle-aged and elderly women were 24.08 and 23.57, respectively.

Discussion
The results of this study demonstrate that associations between obesity-related parameters and high baPWV differed between sexes and age groups. In the 2 age groups of women, WC and WHtR showed positive associations with baPWV; in middle-aged men, BMI and FTI showed positive associations, while FFTI, FFTI/FTI, and RTFFM showed negative associations. The correlation coe cients of WHtR and WC were higher than that of other parameters. WHtR and baPWV in women showed the highest correlation in the binary logistic regression analysis adjusted for covariates. However, previous studies on the association between obesity-related parameters and baPWV, arteriosclerosis, or hypertension have reported con icting ndings. BMI showed the strongest association in adults [18] or only in one sex [10,17,18]. However, others have reported results similar to ours [21,[24][25][26][27][28], including a cohort study in which subjects in the highest quartile of WHtR were 4.51 times more likely to have hypertension [29]. A systematic review also found that WHtR was the best parameter for predicting cardiometabolic risk factors, including hypertension [30]. Notably, those results showed a signi cant association in both sexes while our ndings in men were nonsigni cant, which is consistent with a previous study [5]. Few studies have examined the relationship between body composition parameters and baPWV, with only one in the last 5 years demonstrating a positive correlation between FFMI and baPWV; nonetheless, this provides evidence for the value of FFMI as a predictor of arteriosclerosis [31].
In the present work, WHtR and WC had similarly modest capacities for predicting baPWV occurrence in men, and BMI had no predictive value. WHtR, WC, and BMI had similar predictive capacities in women of both age groups, whereas WHtR had slightly stronger predictive power in elderly women. Signi cant sex differences were observed, with lower predictive capacities in men, especially those who were middle-aged. In contrast, BMI or WC was shown to have predictive value for the occurrence of hypertension [17,[32][33][34]. There were no signi cant differences in the predictive capacities of WC, BMI, and WHtR between men and women [32]; and the predictive values of BMI, WC, and WHtR were found to differ signi cantly between men and women [5], with a better performance in the latter [35]. WHtR has also been proposed as the best predictor of baPWV or hypertension [26,[36][37][38].
The results of studies can vary according to whether the analysis is strati ed by age or sex. BMI was shown to be more closely correlated with baPWV in younger subjects than in older ones [10]. Our study population included a large number of subjects aged > 40 years, with those > 60 years constituting the majority. Sex differences can also explain the discrepancies across reports. Because of metabolic adaptations during menopause, women are at greater risk than men for elevation of total and high low-density lipoprotein cholesterol after the age of 50, and are more likely to accumulate visceral fat [21]; thus, various indicators in women could show a strong association with baPWV or hypertension. Additionally, study design, statistical methods, or selection of variables for adjustment can in uence the degree of association.
The cutoff values with the best predictive capacity for high baPWV in the present work based on sensitivity and speci city differed from those reported in studies of hypertension in Asian populations; the ranges were 82.70-85.2 for men and 77.5-83.5 for women [17,32,33,36,37]. The World Health Organization Working Group on Obesity recommends WC cutoff values of 85 cm for men and 80 cm for women, which are lower than those determined here (95.5 and 88.5 cm for middle-aged and elderly men, respectively; and 83.5 and 83.5 cm for middle-aged and elderly women, respectively This study had several limitations. Firstly, it had a cross-sectional design and did not evaluate changes in the measured parameters. Secondly, the total number of participants was small, particularly the proportion of men aged 40-59. Finally, the results may not be generalizable to populations outside of Anhui.

Conclusions
In conclusion, the results of this study have implications for the health of middle-aged and elderly people in China, especially those at risk for high baPWV. We propose that WHtR and WC be used for community-based screening of women as secondary prevention of high baPWV. Moreover, using WHtR, WC in conjunction with other parameters to predict risk of high baPWV based on region, age, and sex could increase their predictive value.

Declarations
Ethics approval and consent to participate All subjects provided written, informed consent for participating in this study and they agreed that their data would be used. The protocol of this study was approved by the Ethics Committee of Bengbu Medical College (Anhui, China; no. 2018045).

Consent for publication
Not applicable.
Availability of data and material Not applicable.