Abstract
Interarm systolic blood pressure difference (IASBPD) can predict cardiovascular disease. To investigate the relationship between IASBPD and cardiovascular disease risk factors, a total of 1426 individuals were studied. Blood pressure was assessed simultaneously and IASBPD was expressed as the absolute difference value (|R−L|). Cardiovascular disease risk factors were compared between the high IASBPD group (IASBPD ≥10 mm Hg) and the normal IASBPD group (IASBPD <10 mm Hg). An increased prevalence of hypertension, body mass index, and systolic and diastolic blood pressure were observed in the high IASBPD group (P<.05), associated with the enhanced mean values of intima–media thickness and maximum intima–media thickness (P<.05). Brachial‐ankle pulse wave velocity was increased, while ankle‐brachial index was lower in the high IASBPD group (P<.05). Multivariate logistic regression analysis revealed that IASBPD ≥10 mm Hg was positively associated with body mass index (odds ratio, 1.077; P=.002) and systolic blood pressure (odds ratio, 1.032; P<.001), and negatively associated with ankle‐brachial index (odds ratio, 0.038; P<.001).
Keywords: ABI, blood pressure difference, BMI, hypertension
What is known about the topic
Arm and leg blood pressure difference—ankle‐brachial index—is associated with the incidence of cardiovascular disease.
Interarm blood pressure difference is associated with cardiovascular morbidity and mortality.
What this study adds
Increased interarm blood pressure difference is positively correlated to systolic blood pressure, body mass index, and ankle‐brachial index independently.
1. Introduction
The lower ankle‐brachial index (ABI) is associated with the incidence of cardiovascular disease (CVD) and peripheral artery disease (PAD). The question is raised “what does the systolic blood pressure (SBP) difference between the two arms indicate?” It has been reported that interarm SBP difference (IASBPD) is associated with peripheral vascular disease, which has been considered a predictor of CVD.1 A previous study demonstrated that CVD can be predicted when IASBPD is ≥10 mm Hg.2 However, the association between IASBPD and cardiovascular morbidity and mortality remains elusive.
Current hypertension guidelines suggest that blood pressure (BP) assessment in both arms should be used as an initial evaluation parameter of hypertension. An interarm BP difference of <10 mm Hg can be considered a normal condition, whereas the difference of ≥20/10 mm Hg demands further attention and investigation.3 The BP difference between arms was first reported more than 100 years ago, but its clinical significance was not recognized until recently. A meta‐analysis showed that the prevalence of IASBPD ≥10 mm Hg was 19.6%,4 indicating that this is not a small probability event and PAD is also not the unique reason for IASBPD. Knowledge of the interarm BP difference remains limited, which could be important for accurate BP measurement and treatment decisions, and may be a potential risk marker for CVD. Although a questionnaire survey revealed that a consensus has been reached among 77% of physicians that BP should be measured in both arms, only 30% of respondents agreed with this recommendation, while over half disagreed, and a mere 13% adhered to it.5 These studies show that most of the clinicians ignored the importance of measurement of BP in both arms because they did not realize the clinical significance.
Carotid intima–media thickness (IMT) and plaque formation are associated with CVD,6 as well as brachial‐ankle pulse wave velocity (ba‐PWV).7 The relationship among interarm BP difference and carotid atherosclerosis and ba‐PWV remains unknown, which may partially elucidate the mechanism in which interarm BP difference can predict CVD. Our study evaluates the relationship between interarm SBP and CVD risk factors.
2. Methods
2.1. Population
A survey within the Pingguoyuan community in the Shijingshan District of Beijing was conducted from September to December 2007. Among the 42 500 residents in the community, volunteers 18 years or older were invited to participate in the study. One community was selected by cluster sampling, which was then subjected to proportion sampling protocol. At the end of the investigation, 1497 people were recruited, and 1426 people who had ba‐PWV measurement were included in the analysis. The study was approved by the institutional review board of Peking University First Hospital. Informed consent was obtained from all participants.
2.2. Measurement of ba‐PWV and ABI
ABI and ba‐PWV were measured with the VP1000 vascular profiler (Omron Colin, Tokyo, Japan) after at least 5 minutes of rest. Details of the measurement have been previously described.8 Left and right arm BP can be obtained simultaneously by the oscillometric method with this device. Left‐ and right‐side ba‐PWV and ABI were measured simultaneously, and a higher value of ba‐PWV was employed in this study, whereas the lower value of the ABI was used for calculation.
2.3. Measurement of cardiovascular risk factors
Body mass index (BMI) was calculated according to height and weight parameters. ABI is associated with the incidence of CVD. A fasting blood sample was collected for analysis of total cholesterol, triglycerides, and low‐ and high‐density lipoprotein cholesterol using standard protocols from the Beijing Hypertension League Institute. Consumers of tobacco during the research period and participants who had a smoking history were identified as smokers. Hypertension was defined by the history and SBP ≥140 mm Hg and/or diastolic BP (DBP) ≥90 mm Hg measured by VP1000 during this survey. Diabetes was diagnosed according to medical history and fasting glucose ≥7.0 mmol/L and 2‐hour glucose ≥11.1 mmol/L after oral glucose tolerance test. Stroke, including cerebral infarction, intracerebral hemorrhage, and transient ischemic attack, and myocardial infarction were determined according to medical history.
2.4. Carotid artery ultrasound
Carotid ultrasonography was conducted by the General Electric vivid i apparatus (GE Healthcare, Chicago, IL, USA) equipped with a high‐resolution 10‐MHz linear array transducer. Optimal longitudinal and transverse B‐scan images were obtained and stored on a compact disc. Data were obtained by an experienced sonographer in the central laboratory of echocardiography of Peking University First Hospital. The examination and measurement method were performed as previously described.9 Three measurements were obtained for each site at 5‐mm intervals at the end of cardiac diastole. For each individual, carotid IMT was determined as the average of IMT values in 36 sites, including three points at the anterior and posterior wall of the common carotid artery, carotid bifurcation, and internal carotid artery of both sides. Plaques were avoided when measuring.10 Carotid plaque was defined as a focal part protruding into the lumen with maximum IMT ≥1.5 mm or a focal raised lesion >0.5 mm with or without flow disturbance. Reproducibility of carotid IMT measurement of these two groups was conducted, which showed that the reproducibility of IMT measurements according to this protocol was acceptable. Better reproducibility was found when measuring the mean IMT than maximum IMT.11
2.5. Statistical analysis
IASBPD was defined as the absolute value of left arm SBP minus the right arm SBP measured by the VP1000. The IASBPD increasing group was defined as increasing IASBPD ≥10 mm Hg.1 Normal distribution data are shown as mean±standard deviation. Comparisons were performed of different variables between the IASBPD <10 mm Hg group and the IASBPD ≥10 mm Hg group, measurement data using Student t test, and count data using chi‐square test. A univariate logistic regression model was used to analyze the association of IASBPD increasing and age, sex, BMI, hypertension, diabetes, smoking, SBP, total cholesterol, triglycerides, low‐ and high‐density lipoprotein cholesterol, ABI, PWV, IMT, and plaque. A multivariate logistic regression model was then used to analyze the association of IASBPD increasing and other CVD risk factors. A P value of <.05 was considered statistically significant according to two‐tailed analysis. All analyses were performed by SPSS software version 14.0 (SPSS Inc, Chicago, IL, USA).
3. Results
The Gaussian distribution was not observed in the value of IASBPD, and the cutoff values of 10 and 15 mm Hg were in the 90th and 97.5th percentile, respectively, (1). Demographic data of the patients are shown in Table 1. The mean age of the patients was 51±14 years, and the prevalence of hypertension was higher in the high IASBPD group (40.5% vs 22.6%, P<.05). Weight, BMI, SBP, and DBP were also higher in the high IASBPD group (P<.05). The mean values of IMT and maximum IMT were increased to a greater level in the high IASBPD group, when compared with the normal IASBPD group (P<.05). Accordingly, ba‐PWV (1546±406 vs 1524±393, P<.05) was higher whereas ABI (1.04±0.16 vs 1.09±0.16, P<.05) was lower in the high IASBPD group (Table 1). Left arm SBP was higher than right arm SBP (133±21 vs 131±20 mm Hg, P<.05), while right arm DBP was higher than that in the left arm (Table 2).
Figure 1.
Distribution of interarm systolic blood pressure difference (IASBPD)
Table 1.
Comparison of the General Characteristics of the Two Groups
| All (N=1426) | IABPD <10 mm Hg (N=1276) | IABPD ≥10 mm Hg (N=150) | P Value | |
|---|---|---|---|---|
| Male sex, % | 41.9 | 41.3 | 46.7 | .221 |
| Age, y | 51±14 | 51±14 | 53±14 | .041 |
| Smoking, % | 30.2 | 29.9 | 32.0 | .638 |
| MI, % | 0.8 | 0.9 | 0 | .627 |
| Stroke, % | 3.4 | 3.1 | 6.0 | .087 |
| Diabetes, % | 15.0 | 14.7 | 18.0 | .278 |
| Hypertension, % | 34.2 | 32.0 | 53.3 | .00 |
| Height, cm | 163.18±8.53 | 163.12±8.55 | 163.68±8.38 | .449 |
| Weight, kg | 68.22±12.43 | 67.70±12.13 | 72.69±13.94 | .00 |
| BMI, kg/m2 | 25.53±3.65 | 25.36±3.56 | 27.00±4.03 | .00 |
| SBP, mm Hg | 134±21 | 132±20 | 149±21 | .000 |
| DBP, mm Hg | 81±11 | 81±11 | 86±12 | .000 |
| Fasting glucose, mmol/L | 5.47±1.64 | 5.46±1.64 | 5.57±1.71 | .412 |
| TC, mmol/L | 4.95±0.88 | 4.94±0.88 | 5.01±0.92 | .313 |
| TGs, mmol/L | 1.78±1.48 | 1.78±1.53 | 1.74±1.02 | .766 |
| LDL‐C, mmol/L | 2.90±0.73 | 2.89±0.73 | 3.01±0.77 | .065 |
| HDL‐C, mmol/L | 1.40±0.35 | 1.41±0.35 | 1.35±0.30 | .091 |
| Mean IMT, mm | 0.50±0.14 | 0.50±0.14 | 0.53±0.12 | .002 |
| Maximum IMT, mm | 0.90±0.22 | 0.90±0.22 | 0.96±0.22 | .002 |
| Carotid plaque, % | 35.1 | 35.0 | 36.7 | .718 |
| ba‐PWV, cm/s | 1546±406 | 1525±391 | 1722±483 | .00 |
| ABI | 1.07±0.10 | 1.07±0.10 | 1.03±0.13 | .00 |
Abbreviations: ABI, ankle‐brachial index; ba‐PWV, brachial‐ankle pulse wave velocity; BMI, body mass index; DBP, diastolic blood pressure; HDL‐C, high‐density lipoprotein cholesterol; IABPD, interarm blood pressure difference; IMT, intima–media thickness; LDL, low‐density lipoprotein cholesterol; MI, myocardial infarction; SBP, systolic blood pressure; TC, total cholesterol; TGs, triglycerides.
Table 2.
Blood Pressure Difference Between the Two Arms
| Right Arm SBP, mm Hg | Left Arm SBP, mm Hg | Right Arm DBP, mm Hg | Left Arm DBP, mm Hg |
|---|---|---|---|
| 131±20 | 133±21a | 80±11 | 79±12a |
Abbreviations: DBP, diastolic blood pressure; SBP, systolic blood pressure.
P<.001.
Univariate logistic regression analysis showed that some CVD risk factors including age, BMI, hypertension, SBP, ba‐PWV, mean IMT, and ABI were associated with IASBPD increasing (P<.05; Table 3).
Table 3.
Relationship Between IASBPD Increasing (≥10 mm Hg) and Other Cardiovascular Disease Risk Factors by Univariate Logistic Regression Model
| B | Wald Test | OR (95% CI) | P Value | |
|---|---|---|---|---|
| Sex (male as reference) | −0.218 | 1.583 | 0.804 (0.573–1.129) | .208 |
| Age, per y | 0.013 | 4.915 | 1.014 (1.002–1.026) | .027 |
| BMI, per kg/m2 | 0.118 | 26.578 | 1.125 (1.076–1.177) | <.001 |
| Hypertension | 0.888 | 25.975 | 2.431 (1.728–3.422) | <.001 |
| Diabetes | 0.246 | 1.172 | 1.278 (0.820–1.994) | .279 |
| Smoking | 0.097 | 0.271 | 1.101 (0.766–1.584) | .603 |
| SBP, per mm Hg | 0.036 | 78.471 | 1.036 (1.028–1.044) | <.001 |
| LDL‐C | 0.203 | 3.092 | 1.225 (0.977–1.536) | .079 |
| HDL‐C | −0.496 | 3.513 | 0.609 (0.363–1.023) | .061 |
| TC | 0.086 | 0.794 | 1.090 (0.902–1.317) | .373 |
| TGs | −0.016 | 0.070 | 0.984 (0.874–1.108) | .791 |
| ba‐PWV | 0.001 | 30.411 | 1.001 (1.001–1.001) | <.001 |
| Carotid plaque | 0.075 | 0.173 | 1.077 (0.758–1.531) | .678 |
| Mean IMT | 1.697 | 8.139 | 5.458 (1.701–17.514) | .004 |
| ABI | −3.450 | 22.189 | 0.032 (0.008–0.133) | <.001 |
Abbreviations: ABI, ankle‐brachial index; ba‐PWV, brachial‐ankle pulse wave velocity; BMI, body mass index; CI, confidence interval; HDL‐C, high‐density lipoprotein cholesterol; IASBPD, interarm systolic blood pressure difference; IMT, intima–media thickness; LDL, low‐density lipoprotein cholesterol; OR, odds ratio; SBP, systolic blood pressure; TC, total cholesterol; TGs, triglycerides.
Multiple logistic regression analysis (forward conditional) showed that IASBPD ≥10 mm Hg was positively associated with BMI (odds ratio, 1.077; 95% confidence interval, 1.027–1.130; P=.002) and SBP (OR, 1.032; 95% CI, 1.024–1.041 [P<.001]), and negatively associated with ABI (OR, 0.051; 95% CI, 0.009–0.273 [P=.001]; Table 4).
Table 4.
Relationship Between IASBPD Increasing (≥10 mm Hg) and Other Cardiovascular Disease Risk Factors by Multivariate Logistic Regression Model (Forward Conditional)
| B | Wald Test | OR (95% CI) | P Value | |
|---|---|---|---|---|
| SBP | 0.032 | 56.897 | 1.032 (1.024–1.041) | <.001 |
| BMI | 0.074 | 7.366 | 1.077 (1.027–1.130) | .002 |
| ABI | −3.375 | 18.137 | 0.038 (0.008–0.171) | <.001 |
Variables in the equation include age, sex, body mass index (BMI), hypertension, diabetes, smoking, systolic blood pressure (SBP), low‐ and high‐density lipoprotein cholesterol, and ankle‐brachial index (ABI). Abbreviations: CI, confidence interval; IASBPD, interarm systolic blood pressure difference; OR, odds ratio.
4. Discussion
The interarm BP difference is a common event in the general population, but previous studies have focused on the measurement of double‐arm BP at the same time to reduce the misdiagnosis of hypertension.12 Failure to recognize the interarm BP difference may lead to inadequate treatment of hypertensive patients and result in a delay in the diagnosis of hypertension. Thus, it is very important to measure BP in both arms. One study showed that BP measured in only one arm would lead to about 30% of hypertensive patients being misdiagnosed as normotensive.13 An earlier study showed that the interarm BP difference was consistent only when obstructive arterial disease was present. Clinically meaningful interarm differences were not reproducible in the absence of obstructive arterial disease and were subjected to random variation.14 A recent clinical study has shown that the IASBPD is a potential marker of peripheral vascular disease and a predictor of CVD.2
Another previous study showed that in 79% of patients, the interarm BP difference was not reproduced after 1 year.15 The method to evaluate the interarm BP difference is important. One clinical study suggested that IASBPD was smaller when simultaneously compared with sequential BP measurement.16 Therefore, IASBPD evaluated with automatic measurements simultaneously should estimate a patient's true IASBPD.17 In our study, to evaluate the true IASBPD, we measured BP in both upper limbs simultaneously. A previous meta‐analysis has suggested that a simultaneous, automated repeated measurement method with one or two machines is an ideal choice for epidemiological study.16
Pooled prevalence of IASBPD ≥10 mm Hg from four independent studies was 19.6%.4 One study demonstrated that interarm BP difference was common in young healthy patients, with an interarm BP difference >10 mm Hg recorded in 111 (12.6%) and 77 (8.8%) patients for SBP and DBP,18 which could not be contributed to obstruction of the arm artery only. The exact reason of interarm BP difference without obstruction of the arm artery was unknown, which should be studied further. In our study, 10 mm Hg as the cut point value of IASBPD was in the 90th percentile, which implied that the prevalence of IASBPD ≥10 mm Hg in this group of patients was similar to previous studies.
Our research was consistent with the study by Grossman and colleagues,19 which showed that high IASBPD appears more frequently in seniors than in younger patients. Univariate logistic regression analysis showed that CVD risk factors such as age, BMI, hypertension, SBP, ba‐PWV, mean values of IMT, and ABI were associated with IASBPD increasing. Multivariate logistic regression showed that IASBPD >10 mm Hg was positively related to SBP and BMI and negatively related to ABI. A study from Japan showed that patients with hypertension, hypercholesterolemia, obesity, elevated glycated hemoglobin, and low ABI had a significant increase in the risk of an absolute SBP difference >10 mm Hg,20 which is partly in agreement with our current study. Another study showed that interarm BP difference was unrelated to age, BMI, and heart rate, but related to SBP in young and healthy patients.19
Whether BP is higher in the right arm or the left arm in the general population is uncertain. One study showed that BP in the right arm is higher,21 while another showed the opposite result.21 In our study, SBP in the left arm was higher than that in the right arm, while DBP in the left arm was lower than that in the right arm.
Why increased IASBPD predicts CVD is controversial, especially in populations without a history of obvious occlusive arm artery disease. One study showed that interarm SBP could be a diagnostic marker for subclinical atherosclerosis in patients with type 2 diabetes. Results from multiple linear regression analysis demonstrated that IASBPD was an independent determinant of maximum IMT, mean IMT, and ABI.22 Another study also showed that IASBPD ≥10 mm Hg was related to left ventricular hypertrophy.23 Our study demonstrated the same findings that IMT and ba‐PWV were higher in the increased IASBPD group. Increased IASBPD is related to subclinical atherosclerosis, which may partially explain why IASBPD can predict CVD.
Our sample was selected by cluster sampling and the proportion sampling method was then employed, with the nature distribution of IASBPD shown in the community population. Further research needs to identify whether IASBPD ≥10 mm Hg is caused by upper arm artery occlusion. However, a meta‐analysis has shown that IASBPD of ≥10 mm Hg or ≥15 mm Hg is associated with PAD in cross‐sectional studies.2 Therefore, we can speculate that some patients may have occlusive artery disease. Further studies are expected to clarify the relationship between the two clinical conditions.
5. Conclusions
Routine measurement of interarm BP may provide a simple and effective screening method for the presence of PAD. This study adds valuable information to detect interarm BP difference, not only to improve measurement and management of hypertension but also to consider the CVD risk evaluation among patients. Detection of an interarm BP difference should prompt consideration of further vascular assessment and aggressive management of risk factors.
Conflict of Interest
The authors have no conflicts of interest to disclose.
Ma W, Zhang B, Yang Y, et al. Correlating the relationship between interarm systolic blood pressure and cardiovascular disease risk factors. J Clin Hypertens. 2017;19:466–471. 10.1111/jch.12987
References
- 1. 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]
- 2. Clark CE, Taylor RS, Shore AC, Campbell JL. The difference in blood pressure readings between arms and survival: primary care cohort study. BMJ. 2012;44:e1327. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. National Institute for Health and Care Excellence . Hypertension: the clinical management of primary hypertension in adults [CG127]. NICE; 2011. [Google Scholar]
- 4. 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]
- 5. Heneghan C, Perera R, Mant D, Glasziou P. Hypertension guideline recommendations in general practice: awareness, agreement, adoption, and adherence. Br J Gen Pract. 2007;57:948–952. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Simon A, Megnien JL, Chironi G. The value of carotid intima‐media thickness for predicting cardiovascular risk. Arterioscler Thromb Vasc Biol. 2010;30:182–185. [DOI] [PubMed] [Google Scholar]
- 7. Itahara T, Ono K, Tsuchida A, et al. Impact of brachial‐ankle pulse wave velocity and ankle‐brachial blood pressure index on mortality in hemodialysis patients. Am J Kidney Dis. 2005;46:688–696. [DOI] [PubMed] [Google Scholar]
- 8. Yamashina A, Tomiyama H, Takeda K, et al. Validity, reproducibility, and clinical significance of noninvasive brachial‐ankle pulse‐wave velocity measurement. Hypertens Res. 2002;25:359–364. [DOI] [PubMed] [Google Scholar]
- 9. Liu L, Zhao F, Yang Y, et al. The clinical significance of carotid intima media thickness in cardiovascular diseases: a survey in Beijing. J Hum Hypertens. 2008;22:259–265. [DOI] [PubMed] [Google Scholar]
- 10. Howard G, Sharrett AR, Heiss G, et al. Carotid artery intima‐medial thickness distribution in general populations as evaluated by B‐mode ultrasound. ARIC Investigators. Stroke. 1993;24:1297–1304. [DOI] [PubMed] [Google Scholar]
- 11. Yang Y, Zhao F, Qi LT, et al. Quality control of ultrasonic measurement of carotid artery intima‐media thickness. J Geriatr Cardiol. 2009;6:222–226. [Google Scholar]
- 12. 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]
- 13. Poon LC, Kametas N, Strobl I, Pachoumi C, Nicolaides KH. Inter‐arm blood pressure differences in pregnant women. BJOG. 2008;115:1122–1130. [DOI] [PubMed] [Google Scholar]
- 14. Eguchi K, Yacoub M, Jhalani J, Gerin W, Schwartz JE, Pickering TG. Consistency of blood pressure differences between the left and right arms. Arch Intern Med. 2007;167:388–393. [DOI] [PubMed] [Google Scholar]
- 15. Kleefstra N, Houweling ST, Meyboom‐de Jong B, Bilo HJ. Measuring the blood pressure in both arms is of little use; longitudinal study into blood pressure differences between both arms and its reproducibility in patients with diabetes mellitus type 2. Ned Tijdschr Geneeskd. 2007;27:1509–1514. [PubMed] [Google Scholar]
- 16. 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]
- 17. Lohmann FW, Eckert S, Verberk WJ. Interarm differences in blood pressure should be determined by measuring both arms simultaneously with an automatic oscillometric device. Blood Press Monit. 2011;16:37–42. [DOI] [PubMed] [Google Scholar]
- 18. Grossman A, Prokupetz A, Gordon B, Morag‐Koren N, Grossman E. Inter‐arm blood pressure differences in young, healthy patients. J Clin Hypertens (Greenwich). 2013;15:575–578. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Grossman A, Weiss A, Beloosesky Y, Morag‐Koren N, Green H, Grossman E. Inter‐arm blood pressure difference in hospitalized elderly patients‐is it consistent? J Clin Hypertens (Greenwich). 2014;16:518–523. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Kimura A, Hashimoto J, Watabe D, et al. Patient characteristics and factors associated with inter‐arm difference of blood pressure measurements in a general population in Ohasama, Japan. J Hypertens. 2004;22:2277–2283. [DOI] [PubMed] [Google Scholar]
- 21. 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]
- 22. 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]
- 23. Su HM, Lin TH, Hsu PC, et al. Association of bilateral brachial‐ankle pulse wave velocity difference with peripheral vascular disease and left ventricular mass index. PLoS One. 2014;2:e88331. [DOI] [PMC free article] [PubMed] [Google Scholar]