Abstract
Interarm blood pressure difference (IAD) is a risk factor for peripheral artery disease and cardio‐cerebral vascular disease (CCVD). The current study examines the association of IAD with stroke and coronary heart disease in a Chinese community. A cross‐sectional study was conducted in Pudong New Area in Shanghai, China. A total of 10 657 residents aged 15 years and older were randomly selected through three‐stage sampling. Volunteers had systolic and diastolic blood pressure (BP) measured in both arms at recruitment, and IAD was defined in both arms as the absolute difference in BP. Medical records of study participants were reviewed by investigators to confirm measurements. Logistic regression models were used to assess the association between systolic interarm blood pressure difference (sIAD) and diastolic interarm blood pressure difference (dIAD) with stroke and coronary heart disease. Compared with dIAD <5 mm Hg, the multivariate adjusted odds ratio (OR) of stroke prevalence was 1.357 (95% CI 0.725‐2.542, P = 0.034) for dIAD ≥20 mm Hg and 1.702 (95% CI1.025‐2.828, P = 0.040) for dIAD between 15 and 19 mm Hg, and the multivariate adjusted OR of coronary heart disease prevalence was 1.726 (95% CI 1.093‐2.726, P = 0.019) for dIAD ≥20 mm Hg and 1.498 (95% CI 0.993‐2.261, P = 0.044) for dIAD between 15 and 19 mm Hg. The relationship between cardio‐cerebral vascular disease and dIAD was significant in a Chinese community population. Further cohort studies are needed to investigate the association of different levels of IAD with the incidence of cardiovascular and cerebrovascular diseases and subsequent mortality.
Keywords: cardio‐cerebral vascular disease, coronary heart disease, cross‐sectional study, Interarm blood pressure difference, stroke
1. INTRODUCTION
Identifying patients who are at a high risk for CCVD is necessary to target more aggressive medical therapies for primary prevention.1 It is recommended to measure blood pressure (BP) in both arms at patients’ initial evaluation because there are differences in the BP values measured in both arms.2, 3 A review of series reported prevalences of the IAD ranging from 12.0% to 18.4% for a sIAD ≥20 mm Hg and from 13.0% to 33.7% for a dIAD ≥10 mm Hg in different populations3; substantial evidence has indicated that IAD, especially sIAD, is an independent risk factor for ischemic stroke,4 left ventricular hypertrophy,5 and other fatal and nonfatal cardiovascular events.6, 7, 8, 9, 10 It is still unclear whether coronary heart disease (CHD) or cerebrovascular disease is more associated with sIAD/dIAD of 10 or 15 mm Hg or more. Meanwhile, sIAD and dIAD may associate with CHD and cerebrovascular disease diversely11; a study performed in general population found that exaggerated absolute dIAD (≥5 mm Hg) but not sIAD was associated with left ventricular mass index. Johansson et al12 explained that DBP might play more important role in the early phase of cardiovascular disease. Hu et al13 found that dIAD (but not sIAD) was associated with the flow‐mediated dilatation of arm, which was an early index of arterial endothelium lesion, existing research on this topic has yielded inconsistent and equivocal results.
Most previous studies of the association of IAD with CCVD were performed in Western populations with small sample sizes. The MESA study (Multi‐Ethnic Study of Atherosclerosis)14 reported that the prevalence of IAD was lower in Asian populations which have different genetic characteristics, lifestyles, environmental factors, and other CCVD risk factors than that in people of other ethnicities. Besides, CCVD has become a major and increasing health burden worldwide, there is a need for simple screening tools that are easier to apply and cost‐effective to assist in the early identification of cardio‐cerebral vascular events. However, simple screening tools are not widely used in communities of China.
Therefore, we designed this study to estimate the prevalence of the IAD in participants in a Chinese community and to examine the association between IAD with cardio‐cerebral vascular disease risk.
2. MATERIALS AND METHODS
2.1. Ethics statement
The study was approved by the local ethics committee. All volunteers were given a detailed description of the study, and their written consent was obtained. The study was performed in accordance with the Declaration of Helsinki and the Ethical Committee of Center for Disease Prevention and Control of Pudong New Area, Shanghai, China.
2.2. Background and study design
Chronic Disease and Its Risk Factor Surveillance is a cross‐sectional study conducted by the Shanghai Pudong New Area Center for Disease Control and Prevention in 2013. Participants were randomly sampled from permanent residents of the Pudong New Area of Shanghai, China. A multistage, stratified, cluster, random sampling design was employed based on regional and socioeconomic disparities. In the first stage, all 38 streets in the area were classified into three groups according to residents’ average social economic status, and four streets from each group were randomly selected (12 streets altogether, including 352 communities). In the second stage, a total of 34 communities were randomly selected from the 12 selected streets, resulting in a total of 93 606 eligible residents. In the third stage, 11.0% of the eligible population in each community were calculated as the expected number of participants. A house number was randomly selected to be the first family interviewed using a random digit table. All eligible subjects in the selected families were recruited until the expected number of participants was achieved. A total of 12 382 eligible residents aged 15 years or older were randomly selected, among whom 10 657 were surveyed by structured questionnaire and had biological samples collected. The response rate was 86.1%. Exclusion criteria were being younger than 15 years old, having a serious physical or mental disorder, and being pregnant. Signed informed consent was obtained from all subjects.
2.3. Demographic and clinical measurements
Blood pressure was simultaneously measured in both arms using separate cuff hoses in each electronic sphygmomanometer (HEM‐7012; Omron Inc) which was calibrated according to manufacturer's recommendations while participants were in a sitting position. Trained technicians measured BP in both arms after the subject had rested for about 5 minutes. The BP was measured two times at 2‐minute intervals. A third measurement was taken if the difference between the first two exceeded 3 mm Hg, and the values were averaged. Subjects with systolic blood pressure (SBP) ≥140 mm Hg or diastolic blood pressure (DBP) ≥90 mm Hg were measured two more times within the following month for confirmation of hypertension. The interarm BP difference was defined as right‐arm BP minus left‐arm BP. Then, the absolute value of the interarm BP difference was calculated.
Weight and height were recorded with participants wearing light indoor clothing and no shoes. Both height and body weight were measured twice, with tolerances of <1 cm for height and <1 kg for weight. The mean of the two measurements was used in analyses. Body mass index (BMI) was calculated as weight/height squared (kg/m2). Clinical information was collected by interview, including smoking and drinking habits, current drug intake, and personal history of diabetes, among other factors. Current smokers were defined as those who had smoked cigarettes on one or more days for at least the previous 6 months. Alcohol consumption and tea consumption were classified as regular drinker (at least three times per week for at least the previous 6 months) or irregular drinker. Physical activity was defined as participating in sports activities at least once per week in the previous 5 years.
After 10 hours of overnight fasting, all subjects were asked to provide 10 mL of fasting blood for biochemical analysis at the Pudong New Area People's Hospital. Oral glucose tolerance tests (OGTTs) were conducted in subjects who had not been diagnosed with diabetes mellitus. Blood samples were detected within 2 hours of collection for further analysis. Tests included the following items: blood routine, fasting plasma glucose, 2‐hour postload plasma glucose, triglycerides (TG), high‐density lipoprotein cholesterol (HDL‐C), low‐density lipoprotein cholesterol (LDL‐C), total cholesterol (TC), and C‐reactive protein.
2.4. Definitions
Subjects were asked whether they have had physician‐diagnosed symptomatic stroke and/or CHD. Study investigators with no knowledge of the participants’ self‐reported risk factor status reviewed the medical records for further confirmation. Stroke included ischemic stroke (embolic or thrombotic) and hemorrhagic stroke (subarachnoid hemorrhage or intracerebral hemorrhage) or unknown cause, CHD included angina, myocardial infarction, silent myocardial ischemia, and ischemic heart failure. Hypertension was defined as having an SBP ≥140 mm Hg or DBP ≥90 mm Hg, using antihypertensive drugs, or having a self‐reported history of hypertension. Diabetes referred to self‐reported history of type 2 diabetes, elevated fast plasma glucose (FPG ≥ 7.0 mmol/L), or elevated 2‐hour plasma glucose (2 h PG ≥ 11.1 mmol/L).
2.5. Statistical analysis
For database management and statistical analysis, we used SPSS software (version 22.0; SPSS Inc). Subjects were classified into different groups on the basis of sIAD and dIAD. Chi‐square and one‐way ANOVA tests were used to compare the categorical and numeric variables among groups. In the continuous data analyses, logistic regression models were performed to test the multivariate adjusted association of sIAD/dIAD with stroke/CHD, controlling for additional covariates. The associations of CCVD with sIAD and dIAD were estimated first in model 1 adjusted for age, sex, SBP, and DBP. Model 2 was adjusted for variables in model 1 plus marital status, smoking, regular drinker, working night shift, lack of exercise, history of diabetes, history of hypertension, sedentary time, body mass index, waist‐to‐hip ratio, waist height ratio, heart rate, fasting glucose, total cholesterol, triglycerides, HDL cholesterol, LDL cholesterol, and C‐reactive protein. All P values were two‐tailed, and a P value <0.05 was considered statistically significant.
3. RESULTS
3.1. Characteristics of the study population
Included subjects had a mean age of 57.2 ± 13.5 years, and 4016 participants (37.7%) were male. The mean value of sIAD/dIAD was 8.2/5.1 mm Hg. There were 351 stroke patients and 659 coronary heart disease patients among the study population.
The demographic, clinical characteristics and laboratory findings of participants according to the different ranges of sIAD/dIAD are presented in Table 1. Approximately 37.5% of participants had a sIAD of <5 mm Hg, 25.8% between 5 and 10 mm Hg, 15.0% between 10 and 15 mm Hg, 8.3% between 15 and 20 mm Hg, and 7.7% had a sIAD of more than 20 mm Hg. Meanwhile, approximately 53.9% of participants had a dIAD of <5 mm Hg, 26.7% between 5 and 10 mm Hg, 9.5% between 10 and 15 mm Hg, 2.5% between 15 and 20 mm Hg, and 1.8% had a dIAD of more than 20 mm Hg.
Table 1.
Characteristics of the sample by level of sIAD and dIAD
| Variable | sIAD, mm Hg | dIAD, mm Hg | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
<5 n = 4000 |
5‐9 n = 2754 |
10‐14 n = 1599 |
15‐19 n = 880 |
≥20 n = 824 |
P value |
<5 n = 5745 |
5‐9 n = 2842 |
10‐14 n = 1016 |
15‐19 n = 263 |
≥20 n = 190 |
P value | |
| Male | 1464 (36.6%) | 1004 (36.5%) | 578 (36.1%) | 346 (39.3%) | 352 (42.7%) | 0.006 | 2087 (36.3%) | 1079 (38.0%) | 405 (39.9%) | 98 (37.3%) | 74 (38.9%) | 0.206 |
| Married | 3494 (87.4%) | 2390 (86.8%) | 1403 (87.7%) | 777 (88.3%) | 715 (86.8%) | <0.001 | 5030 (87.6%) | 2484 (87.4%) | 886 (87.2%) | 216 (82.1%) | 162 (85.3%) | 0.001 |
| Somking | 844 (21.1%) | 624 (22.7%) | 369 (23.1%) | 224 (25.5%) | 226 (27.4%) | <0.001 | 1267 (22.1%) | 660 (23.2%) | 249 (24.5%) | 62 (23.6%) | 49 (25.8%) | 0.305 |
| Regular drinker | 524 (13.1%) | 370 (13.4%) | 225 (14.1%) | 128 (14.5%) | 128 (15.5%) | 0.349 | 772 (13.4%) | 398 (14.0%) | 140 (13.8%) | 47 (17.9%) | 18 (9.5%) | 0.120 |
| Night shift | 772 (19.3%) | 574 (20.8%) | 352 (22.0%) | 177 (20.1%) | 188 (22.8%) | 0.202 | 1124 (19.6%) | 614 (21.6%) | 209 (20.6%) | 71 (27.0%) | 45 (23.7%) | 0.042 |
| Lack of exercise | 3000 (75.0%) | 2029 (73.7%) | 1190 (74.4%) | 643 (73.1%) | 613 (74.4%) | 0.680 | 4289 (74.7%) | 2093 (73.6%) | 750 (73.8%) | 193 (73.4%) | 149 (78.4%) | 0.567 |
| Diabetes | 701 (18.9%) | 494 (18.5%) | 291 (18.4%) | 184 (20.9%) | 221 (27.0%) | <0.001 | 1043 (19.2%) | 506 (18.3%) | 226 (22.6%) | 58 (22.2%) | 58 (30.7%) | <0.001 |
| Hypertension | 1441 (36.0%) | 1072 (38.9%) | 674 (42.2%) | 433 (49.2%) | 437 (53.0%) | <0.001 | 2201 (38.3%) | 1162 (40.9%) | 470 (46.3%) | 135 (51.3%) | 89 (46.8%) | <0.001 |
| Age, y | 56.43 ± 13.69 | 56.70 ± 13.32 | 58.14 ± 12.63 | 59.71 ± 12.37 | 61.69 ± 11.60 | <0.001 | 57.11 ± 13.30 | 57.10 ± 13.18 | 58.82 ± 13.11 | 61.95 ± 11.29 | 61.79 ± 13.34 | <0.001 |
| Sedentary time | 9.03 ± 3.41 | 8.72 ± 3.32 | 8.59 ± 3.30 | 8.73 ± 3.52 | 8.82 ± 3.33 | <0.001 | 8.81 ± 3.39 | 8.88 ± 3.43 | 8.81 ± 3.15 | 8.63 ± 3.44 | 9.14 ± 3.45 | 0.500 |
| BMI, kg/m2 | 24.74 ± 3.73 | 24.97 ± 4.36 | 25.06 ± 3.55 | 25.42 ± 3.47 | 25.60 ± 3.56 | <0.001 | 24.83 ± 3.73 | 25.06 ± 4.23 | 25.35 ± 3.54 | 25.62 ± 3.50 | 25.69 ± 3.63 | <0.001 |
| WHR | 0.86 ± 0.07 | 0.87 ± 0.07 | 0.87 ± 0.06 | 0.87 ± 0.06 | 0.87 ± 0.06 | <0.001 | 0.86 ± 0.07 | 0.87 ± 0.06 | 0.87 ± 0.07 | 0.88 ± 0.07 | 0.88 ± 0.07 | <0.001 |
| WHtR | 0.51 ± 0.06 | 0.51 ± 0.06 | 0.52 ± 0.06 | 0.52 ± 0.06 | 0.53 ± 0.06 | <0.001 | 0.51 ± 0.06 | 0.51 ± 0.06 | 0.52 ± 0.06 | 0.53 ± 0.06 | 0.53 ± 0.06 | <0.001 |
| SBP of right arm, mm Hg | 136.16 ± 20.76 | 138.34 ± 21.68 | 142.92 ± 22.01 | 147.71 ± 22.75 | 154.38 ± 25.38 | <0.001 | 138.48 ± 21.75 | 140.69 ± 22.52 | 145.95 ± 23.47 | 149.20 ± 23.47 | 148.86 ± 26.21 | <0.001 |
| SBP of left arm, mm Hg | 136.02 ± 20.72 | 137.24 ± 21.59 | 139.38 ± 21.83 | 141.53 ± 21.84 | 142.05 ± 23.86 | <0.001 | 136.58 ± 20.94 | 138.24 ± 21.80 | 141.66 ± 22.31 | 143.57 ± 21.83 | 143.19 ± 28.44 | <0.001 |
| DBP of right arm, mm Hg | 84.47 ± 11.18 | 85.50 ± 11.63 | 86.87 ± 11.52 | 89.07 ± 12.02 | 91.86 ± 14.03 | <0.001 | 84.92 ± 10.58 | 86.62 ± 11.82 | 88.95 ± 13.09 | 91.98 ± 14.87 | 92.93 ± 24.57 | <0.001 |
| DBP of left arm, mm Hg | 84.44 ± 10.88 | 85.51 ± 11.52 | 86.36 ± 11.73 | 87.84 ± 12.08 | 88.49 ± 12.37 | <0.001 | 84.81 ± 10.55 | 86.32 ± 11.56 | 87.43 ± 12.82 | 88.54 ± 14.22 | 88.64 ± 20.43 | <0.001 |
| Heart rate, Beats/min | 79.12 ± 11.15 | 79.71 ± 11.39 | 80.18 ± 11.78 | 80.29 ± 12.13 | 81.23 ± 13.05 | <0.001 | 79.33 ± 11.22 | 80.11 ± 11.90 | 80.35 ± 12.13 | 80.66 ± 12.40 | 81.35 ± 12.76 | 0.001 |
| FBG, mmol/L | 5.82 ± 1.47 | 5.94 ± 1.82 | 5.92 ± 1.64 | 6.02 ± 1.69 | 6.24 ± 1.95 | <0.001 | 5.88 ± 1.58 | 5.89 ± 1.65 | 6.11 ± 1.96 | 6.07 ± 1.97 | 6.37 ± 2.07 | <0.001 |
| TG, mmol/L | 1.69 ± 1.56 | 1.69 ± 1.33 | 1.68 ± 1.25 | 1.69 ± 1.14 | 1.75 ± 1.49 | 0.787 | 1.68 ± 1.37 | 1.71 ± 1.46 | 1.73 ± 1.52 | 1.64 ± 1.22 | 1.76 ± 1.35 | 0.565 |
| TC, mmol/L | 5.52 ± 1.14 | 5.50 ± 1.09 | 5.53 ± 1.07 | 5.64 ± 1.15 | 5.63 ± 1.16 | 0.003 | 5.53 ± 1.12 | 5.52 ± 1.10 | 5.63 ± 1.22 | 5.57 ± 1.01 | 5.58 ± 1.12 | 0.069 |
| LDL, mmol/L | 3.09 ± 1.00 | 3.10 ± 1.00 | 3.12 ± 0.99 | 3.21 ± 1.05 | 3.23 ± 1.08 | <0.001 | 3.11 ± 1.01 | 3.11 ± 0.98 | 3.20 ± 1.09 | 3.17 ± 0.97 | 3.17 ± 1.03 | 0.064 |
| HDL, mmol/L | 1.39 ± 0.35 | 1.36 ± 0.33 | 1.35 ± 0.32 | 1.36 ± 0.33 | 1.34 ± 0.32 | <0.001 | 1.38 ± 0.34 | 1.36 ± 0.34 | 1.36 ± 0.33 | 1.36 ± 0.34 | 1.35 ± 0.33 | 0.043 |
| CRP, mg/L | 1.29 ± 4.11 | 1.34 ± 5.74 | 1.34 ± 3.77 | 1.51 ± 5.54 | 1.37 ± 3.94 | 0.803 | 1.27 ± 4.39 | 1.41 ± 5.24 | 1.50 ± 5.38 | 1.36 ± 2.78 | 1.31 ± 2.29 | 0.568 |
Data given as number (%) or mean (95% confidence interval).
Abbreviations: BMI, body mass index; CHD, coronary heart disease; CRP, C‐reactive protein; DBP, diastolic blood pressure; dIAD, diastolic interarm blood pressure difference; FBG, fasting blood glucose; HDL, high‐density lipoprotein; LDL, low‐density lipoprotein; SBP, systolic blood pressure; sIAD, systolic interarm blood pressure difference; TC, total cholesterol; TG, triglycerides; WHR, waist‐to‐hip ratio; WHtR, waist‐to‐height ratio.
Being male, married, a smoker, and having a history of hypertension and/or diabetes were associated with higher sIAD levels. The mean values of age, sedentary time, body mass index, waist‐to‐hip ratio, waist height ratio, SBP/DBP of both arms, heart rate, serum fasting glucose, total cholesterol, and LDL cholesterol increased with increasing sIAD (P < 0.01). The mean value of HDL cholesterol decreased with increasing sIAD (P < 0.01).
Being married, working night shifts and a history of hypertension and/or diabetes were associated with higher dIAD levels. The mean values of age, body mass index, waist‐to‐hip ratio, waist height ratio, SBP/DBP of both arms, heart rate, and serum fasting glucose increase with increasing dIAD (P < 0.01). The mean value of HDL cholesterol decreased with increasing dIAD (P < 0.05).
Increased dIAD was associated with stroke and coronary heart disease. Figure 1 described the percentage distributions of stroke and coronary heart disease across different IAD groups (<5, 5‐9, 10‐14, 15‐19, and ≥20 mm Hg). Analysis using chi‐square test showed a statistically significant positive association between dIAD and stroke (P = 0.003) and coronary heart disease (P < 0.001) depicted in Figure 1.
Figure 1.
The percentage of stroke (A) and coronary heart disease (B) of participants by sIAD and dIAD categories
3.2. Analysis of the correlations between IAD and the prevalence of CCVD
Results from regression analyses for categorical sIAD/dIAD are shown in Table 2. Compared with dIAD <5 mm Hg, OR of stroke prevalence was 1.856 (95% CI 1.113‐3.096, P = 0.018) for dIAD between 15 and 19 mm Hg in model 1 adjusted for age, sex, SBP, and DBP and OR of coronary heart disease prevalence was 1.727 (95% CI 1.089‐2.737, P = 0.020) for dIAD ≥20 mm Hg and 1.607 (95% CI 1.071‐2.412, P = 0.022) for dIAD between 15 and 19 mm Hg. Relationships persisted in model 2 even after adjustment for more covariates including marital status, smoking, regular drinker, working night shifts, lack of exercise, history of diabetes, history of hypertension, sedentary time, body mass index, waist‐to‐hip ratio, waist height ratio, heart rate, fasting glucose, total cholesterol, triglycerides, HDL cholesterol, LDL cholesterol, and C‐reactive protein; compared with dIAD <5 mm Hg, the multivariate adjusted odds ratio (OR) of stroke prevalence was 1.357 (95% CI 0.725‐2.542, P = 0.034) for dIAD ≥20 mm Hg and 1.702 (95% CI1.025‐2.828, P = 0.040) for dIAD between 15 and 19 mm Hg, and the multivariate adjusted OR of coronary heart disease prevalence was 1.726 (95% CI 1.093‐2.726, P = 0.019) for dIAD ≥20 mm Hg and 1.498 (95% CI 0.993‐2.261, P = 0.044) for dIAD between 15 and 19 mm Hg. Moreover, coronary heart disease showed significant associations with dIAD levels in model 1 (P trend = 0.038) even adjusted for additional covariates in model 2 (P trend = 0.045), whereas coronary heart disease was not significantly related to sIAD levels in both models (P trend = 0.156; P trend = 0.336). The relationship between cardio‐cerebral vascular disease and dIAD was significant other than sIAD in a Chinese community population. Visual depictions of observed associations in model 1 and model 2 are provided in Figure 2.
Table 2.
Regression coefficients for the association of sIAD/dIAD with stroke and coronary heart disease
| IAD, mm Hg | sIAD | dIAD | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Model 1 | Model 2 | Model 1 | Model 2 | |||||||||
| OR | 95% CI | P value | OR | 95% CI | P value | OR | 95% CI | P value | OR | 95% CI | P value | |
| Stroke | ||||||||||||
| <5 | Reference | Reference | Reference | Reference | ||||||||
| 5‐9 | 0.816 | 0.616‐1.080 | 0.155 | 0.756 | 0.569‐1.005 | 0.054 | 0.983 | 0.759‐1.275 | 0.899 | 0.916 | 0.706‐1.189 | 0.509 |
| 10‐14 | 0.845 | 0.610‐1.169 | 0.308 | 0.785 | 0.565‐1.089 | 0.147 | 0.912 | 0.623‐1.333 | 0.633 | 0.834 | 0.572‐1.216 | 0.345 |
| 15‐19 | 0.825 | 0.556‐1.225 | 0.341 | 0.786 | 0.530‐1.165 | 0.786 | 1.856 | 1.113‐3.096 | 0.018 | 1.702 | 1.025‐2.828 | 0.040 |
| ≥20 | 0.745 | 0.497‐1.117 | 0.154 | 0.722 | 0.486‐1.073 | 0.108 | 1.465 | 0.777‐2.763 | 0.238 | 1.357 | 0.725‐2.542 | 0. 034 |
| P for trend | 0.120 | 0.075 | 0.156 | 0.336 | ||||||||
| Coronary heart disease | ||||||||||||
| <5 | Reference | Reference | Reference | Reference | ||||||||
| 5‐9 | 0.897 | 0.727‐1.108 | 0.313 | 0.841 | 0.676‐1.045 | 0.119 | 0.926 | 0.762‐1.126 | 0.443 | 0.892 | 0.731‐1.089 | 0.262 |
| 10‐14 | 1.068 | 0.843‐1.353 | 0.583 | 0.996 | 0.781‐1.269 | 0.972 | 1.001 | 0.760‐1.319 | 0.994 | 0.943 | 0.713‐1.246 | 0.678 |
| 15‐19 | 0.930 | 0.689‐1.255 | 0.635 | 0.903 | 0.668‐1.219 | 0.505 | 1.607 | 1.071‐2.412 | 0.022 | 1.498 | 0.993‐2.261 | 0.044 |
| ≥20 | 0.947 | 0.702‐1.278 | 0.723 | 0.938 | 0.698‐1.259 | 0.669 | 1.727 | 1.089‐2.737 | 0.020 | 1.726 | 1.093‐2.726 | 0.019 |
| P for trend | 0.856 | 0.882 | 0.038 | 0.045 | ||||||||
Model 1: Adjusted for age, sex, systolic blood pressure, and diastolic blood pressure.
Model 2: Adjusted for variables in model 1 plus marital status, smoking, regular drinker, working night shift, lack of exercise, history of diabetes, history of hypertension, sedentary time, body mass index, waist‐to‐hip ratio, waist height ratio, heart rate, fasting glucose, total cholesterol, triglycerides, HDL cholesterol, LDL cholesterol, and C‐reactive protein.
Abbreviations: dIAD, diastolic interarm blood pressure difference; OR, odds ratio; sIAD, systolic interarm blood pressure difference.
Figure 2.
Odds ratios for the association between sIAD/dIAD and stroke and coronary heart disease. The results for model 1 are provided for stroke (A) and coronary heart disease (C). Results for model 2 are displayed for stroke (B) and coronary heart disease (D). IAD, interarm blood pressure difference; sIAD, systolic interarm blood pressure difference; dIAD, diastolic interarm blood pressure difference. Model 1 was adjusted for age, sex, systolic blood pressure, and diastolic blood pressure. Model 2 was adjusted for variables in model 1 plus marital status, smoking, regular drinker, working night shift, lack of exercise, history of diabetes, history of hypertension, sedentary time, body mass index, waist‐to‐hip ratio, waist height ratio, heart rate, fasting glucose, total cholesterol, triglycerides, high‐density lipoprotein, low‐density lipoprotein, and C‐reactive protein
4. DISCUSSION
In the clinical setting, cases with an interarm difference in BP are occasionally found,15, 16 but the underlying mechanism explaining these differences are unclear.17 The prevalence of sIAD ≥10 mm Hg (31.0%) in our study is higher than that in a previous reports by Lane et al,18 accordance with some previous reports by Clark19 and Agarwal.20 The prevalence of dIAD ≥10 mm Hg (13.8%) in our study is similar to that the previous report by Lane18 and Harrison,21 higher than that in reports by Verberk.22 As the significant correlation between IAD and age, the high average age of the subjects (57.2 ± 13.5 years) may be a potential explanation for the high prevalence of IAD in our study. This finding suggests that the IAD is common in community residents of China and emphasize the importance of measuring IAD in clinical practice among Chinese populations.
The findings from this study indicated that individuals with higher dIAD were associated with higher prevalence of stroke and coronary heart disease (Figure 1). Increased dIAD (15‐19 mm Hg and ≥20 mm Hg) was significantly associated with stroke and CHD, even after adjusting for known cardio‐cerebral vascular disease risk factors in model 2 (Figure 2). The analysis of the relationship between sIAD and cardio‐cerebral vascular disease prevalence did not reach statistical significance, but the associations were in expected directions (Figure 1). In contrast to several previous studies, it was possible to adjust for a variety of traditional confounders, as well as for levels of CRP, lack of exercise, working night shifts, sedentary time, heart rate, and history of hypertension and diabetes, which further reduced potential bias. IAD might provide additional information related to stroke and CHD compared with models based on known traditional information in a Chinese community population.
Several studies have investigated the relationship between IAD and multiple health outcomes at the population level. Chang et al23 found that IAD ≥10 mm Hg was associated with the presence and increased burden of cerebral small‐vessel diseases in noncardioembolic stroke patients. Another study suggested that an IAD of ≥10 mm Hg could be a useful indicator for the risks of early neurological deterioration, poor functional outcome, and mortality.24 Recently, increasing attention has been paid to better understanding the clinical outcome of sIAD, the most reported include subclinical atherosclerosis,25 left ventricular hypertrophy,5 aortic aneurysms,15 aortic dissection, and cardiovascular disease.10 Inconsistent with above studies, three studies4, 5, 26 compared cerebrovascular (stroke, cerebral atherosclerosis, and transient ischemic attack) outcomes among groups defined by sIAD of 10 mm Hg or more, and one study26 compared outcomes at cutoff of 15 mm Hg or more, cumulative estimates were not statistically significant at either cutoff.
Most studies emphasize the relationship between sIAD and cardiovascular and cerebrovascular diseases, but there are still a few studies analyzing the relationship between dIAD and cardio‐cerebrovascular disorders. A study performed in general population found that exaggerated absolute interarm DBP (≥5 mm Hg) but not interarm SBP was associated with left ventricular mass index.12 Johansson et al explained that DBP might play more important role in the early phase of cardiovascular disease, which was consistent with our study. In addition, Hu et al13 found that interarm DBP difference (but not SBP difference) was associated with the flow‐mediated dilatation of arm, which was an early index of arterial endothelium lesion. A Chinese study program found that subjects with the high dIAD (≥4 mm Hg) showed significantly higher risk of intracranial arterial stenosis and the participants with the high sIAD (≥6 mm Hg) showed significantly higher risk of extracranial arterial stenosis in the categorical study diversely.27 Dezhi Hong and colleagues28 suggest that one‐arm exercise can lead to a significant dIAD.
As the IAD is an easily applied clinical tool, an appreciation of the measurement of the IAD which was noninvasive is recommended to accurately diagnosis and manage of hypertension, as the current guidelines of American College of Cardiology Foundation and American Heart Association recommend assessing at the initial visit.29 However, in daily practice these guidelines may be ignored with BP often being measured only in the arm that is most accessible in examination. sIAD is often associated with an anatomical correlation between carotid or atherosclerotic disease in the subclavian artery. Patients with carotid artery disease usually have multiple grades of atherosclerotic pathology. In bilateral compromised out flow in the subclavian arteries, this may still underestimate the central aortic pressure even in the presence of IAD. Given that the guidelines recommend treating hypertension to specific targets based on BP readings, underestimating BP might result in inadequate treatment of hypertension, which may indirectly lead to an increased incidence of cardiovascular and cerebrovascular diseases.
This study has limitations. First, because of its retrospective character, we could assess the association between IAD and a history of stroke and CHD but not report on a predictor for cardiovascular and cerebrovascular diseases. A long‐term cohort study is needed to elucidate the concrete effect of IAD on the morbidity and mortality of cardio‐cerebral vascular disease, particularly for dIAD, and to establish whether these findings can be generalized. Second, besides measuring the bilateral brachial blood pressures difference between the two arms, some new examination methods should be enrolled such as electrocardiogram and carotid ultrasound.
5. CONCLUSION
In conclusion, we found diverse associations between interarm SBP/DBP differences and cardio‐cerebral vascular disease. The characterization of dIAD was associated with cardio‐cerebral vascular disease may be helpful and effective in clinical settings in China.
CONFLICT OF INTEREST
The authors declared no conflict of interest.
AUTHOR CONTRIBUTIONS
Yu Siyu, Zhou Yi, Wu Kang, Zhou Xianfeng, and Ruan Xiaonan conceived and designed the experiments; Yu Siyu, Zhou Yi, Wu Kang, Zhou Xianfeng, Yang Yi, Qiu Hua, Liu Xiaolin, and Ke Juzhong performed the experiments; Yu Siyu and Zhou Xianfeng analyzed the data; Yu Siyu, Zhou Yi, Wu Kang, Zhou Xianfeng, Yang Yi, Qiu Hua, Liu Xiaolin, Ke Juzhong, Wang Xiaonan, Li Zhitao, Chen Xiaodan, and Ruan Xiaonan contributed materials/analysis tools.
Supporting information
ACKNOWLEDGMENTS
We gratefully acknowledge the efforts of the investigators, research coordinators, and committee members of each study. The organization of the study group, the lists of members of the study team, and their contribution statement are provided in the online‐only Data Supplement.
Yu S, Zhou Y, Wu K, et al. Association of interarm blood pressure difference with cardio‐cerebral vascular disease: A community‐based, cross‐sectional study. J Clin Hypertens. 2019;21:1115–1123. 10.1111/jch.13604
Y. Siyu and Z. Yi are equal contributors.
Funding information
This study was supported by a grant from the General Project of Scientific Research of Shanghai Municipal Health Commission (No. 201740297).
REFERENCES
- 1. Greenland P, Smith SJ, Grundy SM. Improving coronary heart disease risk assessment in asymptomatic people: role of traditional risk factors and noninvasive cardiovascular tests. Circulation. 2001;104:1863‐1867. [DOI] [PubMed] [Google Scholar]
- 2. Clark CE, Campbell JL, Evans PH, Millward A. Prevalence and clinical implications of the inter‐arm blood pressure difference: a systematic review. J Hum Hypertens. 2006;20:923‐931. [DOI] [PubMed] [Google Scholar]
- 3. Clark CE. Difference in blood pressure between arms might reflect peripheral vascular disease. BMJ. 2001;323:399‐400. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Kim J, Song TJ, Song D, et al. Interarm blood pressure difference and mortality in patients with acute ischemic stroke. Neurology. 2013;80:1457‐1464. [DOI] [PubMed] [Google Scholar]
- 5. Su HM, Lin TH, Hsu PC, et al. Association of interarm systolic blood pressure difference with atherosclerosis and left ventricular hypertrophy. PLoS ONE. 2012;7:e41173. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Clark CE, Campbell JL, Powell RJ. The interarm blood pressure difference as predictor of cardiovascular events in patients with hypertension in primary care: cohort study. J Hum Hypertens. 2007;21:633‐638. [DOI] [PubMed] [Google Scholar]
- 7. Singh S, Sethi A, Singh M, Khosla K, Grewal N, Khosla S. Simultaneously measured inter‐arm and inter‐leg systolic blood pressure differences and cardiovascular risk stratification: a systemic review and meta‐analysis. J Am Soc Hypertens. 2015;9:640‐650. [DOI] [PubMed] [Google Scholar]
- 8. Clark CE, Taylor RS, Shore AC, Ukoumunne OC, Campbell JL. Association of a difference in systolic blood pressure between arms with vascular disease and mortality: a systematic review and meta‐analysis. Lancet. 2012;379:905‐914. [DOI] [PubMed] [Google Scholar]
- 9. Zhou M, Gao Z, Chen F, Xu H, Dong X, Ma L. The inter‐arm systolic blood pressure difference and risk of cardiovascular mortality: a meta‐analysis of cohort studies. Clin Exp Hypertens. 2016;38:317‐324. [DOI] [PubMed] [Google Scholar]
- 10. Weinberg I, Gona P, O'Donnell CJ, Jaff MR, Murabito JM. The systolic blood pressure difference between arms and cardiovascular disease in the Framingham Heart Study. Am J Med. 2014;127:209‐215. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Chang Y, Choi GS, Lim SM, Kim YJ, Song TJ. Interarm systolic and diastolic blood pressure difference is diversely associated with cerebral atherosclerosis in noncardioembolic stroke patients. Am J Hypertens. 2017;31:35‐42. [DOI] [PubMed] [Google Scholar]
- 12. Johansson JK, Puukka PJ, Jula AM. Interarm blood pressure difference and target organ damage in the general population. J Hypertens. 2014;32:260‐266. [DOI] [PubMed] [Google Scholar]
- 13. Hu W, Li J, Su H, et al. The inter‐arm diastolic blood pressure difference induced by one arm ischemia: a new approach to assess vascular endothelia function. PLoS ONE. 2014;9:e84765. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Aboyans V, Kamineni A, Allison MA, et al. The epidemiology of subclavian stenosis and its association with markers of subclinical atherosclerosis: the Multi‐Ethnic Study of Atherosclerosis (MESA). Atherosclerosis. 2010;211:266‐270. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Tanaka Y, Fukui M, Tanaka M, et al. The inter‐arm difference in systolic blood pressure is a novel risk marker for subclinical atherosclerosis in patients with type 2 diabetes. Hypertens Res. 2014;37:548‐552. [DOI] [PubMed] [Google Scholar]
- 16. Clark CE, Taylor RS, Shore AC, Campbell JL. Prevalence of systolic inter‐arm differences in blood pressure for different primary care populations: systematic review and meta‐analysis. Br J Gen Pract. 2016;66:e838‐e847. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Clark CE, Aboyans V. Interarm blood pressure difference: more than an epiphenomenon. Nephrol Dial Transplant. 2015;30:695‐697. [DOI] [PubMed] [Google Scholar]
- 18. Lane D, Beevers M, Barnes N, et al. Inter‐arm differences in blood pressure: when are they clinically significant? J Hypertens. 2002;20:1089‐1095. [DOI] [PubMed] [Google Scholar]
- 19. Clark CE, Powell RJ. The differential blood pressure sign in general practice: prevalence and prognostic value. Fam Pract. 2002;19:439‐441. [DOI] [PubMed] [Google Scholar]
- 20. Agarwal R, Bunaye Z, Bekele DM. Prognostic significance of between‐arm blood pressure differences. Hypertension. 2008;51:657‐662. [DOI] [PubMed] [Google Scholar]
- 21. Harrison EJ, Roth GM, Hines EJ. Bilateral indirect and direct arterial pressures. Circulation. 1960;22:419‐436. [DOI] [PubMed] [Google Scholar]
- 22. Verberk WJ, Kessels AG, Thien T. Blood pressure measurement method and inter‐arm differences: a meta‐analysis. Am J Hypertens. 2011;24:1201‐1208. [DOI] [PubMed] [Google Scholar]
- 23. Chang Y, Lee SA, Lee SH, Lee EH, Kim YJ, Song TJ. Interarm blood pressure difference has various associations with the presence and burden of cerebral small‐vessel diseases in noncardioembolic stroke patients. J Clin Neurol. 2019;15:159‐167. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Chang Y, Kim J, Kim MH, Kim YJ, Song TJ. Interarm blood pressure difference is associated with early neurological deterioration, poor short‐term functional outcome, and mortality in noncardioembolic stroke patients. J Clin Neurol. 2018;14:555‐565. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Cao K, Xu J, Shangguan Q, et al. Association of an inter‐arm systolic blood pressure difference with all‐cause and cardiovascular mortality: An updated meta‐analysis of cohort studies. Int J Cardiol. 2015;189:211‐219. [DOI] [PubMed] [Google Scholar]
- 26. Clark CE, Campbell JL, Powell RJ, Thompson JF. The inter‐arm blood pressure difference and peripheral vascular disease: cross‐sectional study. Fam Pract. 2007;24:420‐426. [DOI] [PubMed] [Google Scholar]
- 27. Wang Y, Zhang J, Qian Y, et al. Association of inter‐arm blood pressure difference with asymptomatic intracranial and extracranial arterial stenosis in hypertension patients. Sci Rep. 2016;6:29894. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Hong D, Wang J, Su H, et al. One arm exercise induces significant interarm diastolic blood pressure difference. Blood Press Monit. 2011;16:134‐137. [DOI] [PubMed] [Google Scholar]
- 29. Aronow WS, Fleg JL, Pepine CJ, et al. ACCF/AHA 2011 expert consensus document on hypertension in the elderly: a report of the American College of Cardiology Foundation Task Force on Clinical Expert Consensus Documents. Circulation. 2011;123:2434‐2506. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials