Ethnic disparity in central arterial stiffness and its determinants among Asians with type 2 diabetes

Atherosclerosis 242 (2015) 22e28

Contents lists available at ScienceDirect

Atherosclerosis journal homepage: www.elsevier.com/locate/atherosclerosis

Ethnic disparity in central arterial stiffness and its determinants among Asians with type 2 diabetes Xiao Zhang a, Jian Jun Liu a, Chee Fang Sum b, c, Yeoh Lee Ying c, Subramaniam Tavintharan b, c, Xiao Wei Ng a, Serena Low a, Simon B.M. Lee d, Wern Ee Tang d, Su Chi Lim b, c, * a

Clinical Research Unit, Khoo Teck Puat Hospital, Singapore 768828, Republic of Singapore Diabetes Centre, Khoo Teck Puat Hospital, Singapore 768828, Republic of Singapore Department of Medicine, Khoo Teck Puat Hospital, Singapore 768828, Republic of Singapore d National Healthcare Group Polyclinics, Singapore 138543, Republic of Singapore b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 1 April 2015 Received in revised form 29 May 2015 Accepted 16 June 2015 Available online 18 June 2015

Objective: We previously reported ethnic disparity in adverse outcomes among Asians with type 2 diabetes (T2DM) in Singapore. Central arterial stiffness can aggravate systemic vasculopathy by propagating elevated systolic and pulse pressures forward, thereby accentuating global vascular injury. We aim to study ethnic disparity in central arterial stiffness and its determinants in a multi-ethnic T2DM Asian cohort. Methods: Arterial stiffness was estimated by carotid-femoral pulse wave velocity (PWV) and augmentation index (AI) using applanation tonometry method in Chinese (N ¼ 1045), Malays (N ¼ 458) and Indians (N ¼ 468). Linear regression model was used to evaluate predictors of PWV and AI. Results: PWV was higher in Malays (10.1 ± 3.0 m/s) than Chinese (9.7 ± 2.8 m/s) and Indians (9.6 ± 3.1 m/ s) (P ¼ 0.018). AI was higher in Indians (28.1 ± 10.8%) than Malays (25.9 ± 10.1%) and Chinese (26.1 ± 10.7%) (P < 0.001). Malays remain associated with higher PWV (b ¼ 0.299, P ¼ 0.048) postadjustment for age, gender, duration of diabetes, hemoglobin A1c, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), soluble receptor for advanced glycation endproducts, urinary albumin-to-creatinine ratio, and insulin usage, which were all independent predictors of PWV. Indians remain associated with higher AI (b ¼ 2.776, P < 0.001) post-adjustment for age, gender, BMI, SBP, DBP, and height, which were independent predictors of AI. These variables explained 27.7% and 33.4% of the variance in PWV and AI respectively. Conclusions: Malays and Indians with T2DM have higher central arterial stiffness, which may explain their higher risk for adverse outcomes. Modifying traditional major vascular risk factors may partially alleviate their excess cardiovascular risk through modulating arterial stiffness. © 2015 Elsevier Ireland Ltd. All rights reserved.

Keywords: Type 2 diabetes Arterial stiffness Pulse wave velocity Augmentation index

1. Introduction Type 2 diabetes (T2DM) is a rapidly evolving global health issue [1] and Asia is the epi-center of this worldwide epidemic [2]. Singapore, a multi-ethnic city-state composed of three major ethnic groups (Chinese, Malays and Indians), progresses from third-world to first-world in a short span of 50 years and witnesses dramatic

* Corresponding author. Diabetes Center, Khoo Teck Puat Hospital, 90 Yishun Central, Singapore 768828, Republic of Singapore. E-mail address: [email protected] (S.C. Lim). http://dx.doi.org/10.1016/j.atherosclerosis.2015.06.019 0021-9150/© 2015 Elsevier Ireland Ltd. All rights reserved.

changes in lifestyle and living environment. It has been predicted that its T2DM prevalence will double from 7.3% in 1990 to 15% in 2050 [3]. Diabetes mellitus is a major risk factor for ischemic heart disease (IHD) [4]. It has been reported that white people are less likely to develop IHD than their black counterparts in USA [5,6]. In the last decade, such ethnic disparity has also been reported among Asians with T2DM in Singapore, showing that Indians and Malays had higher IHD and cardiovascular mortality compared to Chinese [7e10]. Our recent prospective studies among Asians with T2DM in Singapore revealed that Malays and Indians had higher risk for diabetes outcomes than Chinese, such as end-stage renal failure

X. Zhang et al. / Atherosclerosis 242 (2015) 22e28

and acute myocardial infarction, respectively [11]. However, the mechanisms underlying this ethnic disparity remain largely unclear. Arterial stiffness, a well-established biomarker of vasculopathy, may be an important link between diabetes and higher cardiovascular disease risk. It has evolved to become a clinically useful independent predictor of both macro- and micro-vascular complications in T2DM [12]. Pulse wave velocity (PWV) is widely used as the “gold-standard” index for arterial stiffness. PWV measured at different sites may reflect the atherosclerotic alterations at central or peripheral arteries. Augmentation index (AI) is a surrogate measurement of wave reflection and arterial stiffness derived from central arterial pressure waveform [13]. Previous studies revealed that the impacts of potential risk factors on arterial stiffness varied among different vascular beds, suggesting that arterial stiffening is a systemic process with regional variation [14]. Arterial stiffness is increased in T2DM, and the effects of diabetes were greater in central arteries than in peripheral arteries [15]. Hatsuda et al. found that IHD was more closely associated with central arterial stiffness than peripheral arterial stiffness in T2DM patients, suggesting the key role of central arterial stiffness in macro-vascular injury [16]. Mechanistically, stiffening of central arteries not only increases systolic blood pressure (SBP), cardiac afterload, left ventricular mass, and oxygen demand, but also decreases diastolic blood pressure (DBP) and coronary blood flow during the diastole, which may ultimately result in increased pulse pressures, thereby accentuating global vascular injury [14,16]. Evidence on ethnic disparity in arterial stiffness has begun to accumulate. For example, in healthy populations, blacks and South Asians had significant higher PWV than white counterparts [17,18]. In a Brazilian T2DM admixed population, African descent had higher PWV than Amerindians [19]. In patients with metabolic syndrome, compared with Chinese, Malays had higher PWV and AI [20]. Given that we have observed ethnic disparity in adverse diabetic outcomes and the putative pivotal role of central arterial stiffness in mediating these vascular injuries, we hypothesized that there is ethnic-dependent differential arterial stiffness, which may explain the above disparity. Therefore, we aim to investigate any ethnic differences in carotid-femoral PWV and AI and their determinants in a multi-ethnic T2DM Asian cohort. 2. Materials and methods 2.1. Study population and design The Singapore Study of Macro-angiopathy and Micro-vascular Reactivity in Type 2 Diabetes (SMART2D) is a cross-sectional study conducted between August 2011 and February 2014 including a total of 2057 adults aged 21e90 years with T2DM [21]. The participants were recruited consecutively from a secondary hospital and a neighboring primary-care public outpatient clinic in equal proportion in Singapore. Diagnosis of T2DM was based on American Diabetes Association criteria [22]. The exclusion criteria included: Type 1 diabetes, pregnant subjects, subjects with active inflammation (e.g. systemic lupus erythromatosis) and cancer, subjects taking non-steroid anti-inflammation drugs on the same day of clinical/vascular/biomedical assessment, subjects on oral steroids equivalent to >5 mg/day prednisolone, those who could not fulfill the informed consent process and those who wore a pacemaker or any device that may be affected by electric current. After phlebotomy, those with fasting glucose <4.5 mmol/l or >15.0 mmol/l, and subjects with HbA1c > 108 mmol/mol (>12%) were also excluded from the study. The aim of SMART2D is to investigate traditional and novel risk factors associated with diabetes complications and their associations with endothelial

23

dysfunction and vascular stiffness. In our study, 1990 individuals were selected, including 1053 Chinese, 474 Indians and 463 Malays. This study has been approved by our institution's domain-specific ethics review committee. Individual written informed consent was obtained prior to enrollment in the study. 2.2. Clinical and biochemical measurement Body mass index (BMI) was calculated as body weight (kg)/ height (m)2. Blood pressure was measured after 5 min of seated rest using a mercury sphygmomanometer on the right arm using appropriate cuff sizes. SBP and DBP were calculated from the average of three most consistent readings. At the end of the 5 min rest, heart rate was assessed by an OMRON®digital blood pressure monitor. Urinary albumin-to-creatinine ratio (UACR) was determined by urinary creatinine measured by enzymatic method on Roche/Hitachi cobas c system (Roche Diagnostic GmbH, Mannheim, Germany) and albumin measured by a solid-phase competitive chemiluminescent enzymatic immunoassay with a lower detection limit of 2.5 mg/ml (Immulite; DPC, Gwynedd, UK). Estimated glomerular filtration rate (eGFR) was calculated based on a widely used Modified Diet in Renal Disease (MDRD) equation in patients with diabetes [23]. Hemoglobin A1c (HbA1c) was measured based on monoclonal antibody agglutination reaction using a point-ofcare immunoassay analyzer (DCA Vantage Analyzer; Siemens, Erlangen, Germany) certified by National Glycohemoglobin Standardization Program. High-density lipoprotein-cholesterol (HDL-C) and low-density lipoprotein-cholesterol (LDL-C) were quantified by enzymatic method using Kodak Ektachem chemistry slides. Total triglycerides was quantified by enzymatic colorimetric method on Roches/Hitachi cobas c system. Total soluble receptor for advanced glycation end-products (sRAGE) was quantified by ELISA (R&D Systems, Minneapolis, MN) according to manufacturer's protocol. The intra- and inter-assay coefficients of variation were 5.7% and 7.7%, respectively. The sensitivity reported by the manufacturer was 4.12 pg/ml. 2.3. Assessment of arterial stiffness Arterial stiffness was assessed using SphygmoCor® (AtCor Medical, Sydney, Australia), a well-validated device that has been shown to have a high level of intra- and inter-observer reproducibility in different populations, including diabetic patients [24,25]. Carotid-femoral PWV was measured by the foot-to-foot method as described previously [26]. Briefly, the pulse waveform was captured using a high-fidelity applanation tonometer at the carotid and subsequently at the femoral artery. Consecutive waves were gated by electrocardiogram and the transit time of the waves between carotid and femoral sites was calculated. The average of transit time was calculated based on 9e10 pulse waves in each assessment. Surface distances were measured by means of a tape ruler over the body surface. PWV was expressed as the distance between the two recording sites (meters) divided by transit time (seconds). In order to ensure a reliable measurement of PWV, the standard deviation of measurements should be equal to or less than 10%. The first and second systolic peaks of the radial and the derived aortic pressure waveforms were identified automatically by the SphygmoCor® software. AI was calculated from aortic pressure waveform as [(second systolic peak-first systolic peak)/pulse pressure  100] [12]. Only results passing the quality control algorithm by the manufacturer were used for analysis. 2.4. Statistical analysis Standard descriptive statistics were used to describe the

24

X. Zhang et al. / Atherosclerosis 242 (2015) 22e28

characteristics of individuals with T2DM. Normally distributed continuous data were expressed as means and standard deviations (SDs). Two skewed variables (UACR and total triglycerides) were expressed as median and inter-quartile range and nature log (ln)transformed before data analysis. Differences among ethnicity were compared by one-way ANOVA or c2 tests where appropriate. Univariate analysis was performed for individual clinical and biochemical parameters to verify associated factors (Table S1). Variables that are statistically significant in univariate analysis (p < 0.1) were added into the multivariable linear regression models based on their putative roles in the patho-biology of arterial stiffness. PWV or AI was entered as dependent variable. Age, gender and ethnicity were entered as the main confounders (model 1). Because of the influence of height on AI [27], height has been suggested to be controlled for in AI analysis [28]. In our study, Malays were shorter (1.59 ± 0.09 m) than Indians (1.61 ± 0.09 m, p ¼ 0.025) and Chinese (1.62 ± 0.09 m, p < 0.001), so height was added for AI analysis in model 1. In model 2, duration of diabetes and HbA1c were added to adjust for disease burden and glycemic control. The model was further adjusted for vascular risk factors in model 3 (e.g. BMI, SBP, DBP, HDL-C, LDL-C, total triglycerides and sRAGE), renal function markers in model 4 (e.g. eGFR and UACR), and commonly used medications in diabetes in model 5 (e.g. angiotensin-converting-enzyme (ACE), angiotensin receptor blockers (ARBs), statins, fibrates, insulin, metformin, and sulphonylureas). All statistical analysis was performed using IBM SPSS (Version 22). A two-tailed p value of less than 0.05 was considered as statistically significant. 3. Results Table 1 shows the characteristics of T2DM patients stratified by ethnicity. There are less male in Malays and Indians than Chinese. Compared with Chinese, Malays and Indians were younger, had

shorter duration of diabetes and higher BMI. Malays were shorter, have higher level of Hb1Ac, LDL-C, sRAGE, and heart rate and lower HDL-C than Chinese and Indians. We observed lower eGFR and higher UACR in Malays than Chinese and Indians. There were no significant differences in current smoker percentage, DBP and usage of three commonly used medications in diabetes (e.g. statins, fibrates, and sulphonylureas) in Malays compared with Indians and Chinese. PWV level was significantly higher in Malays (10.1 ± 3.0 m/s) than Chinese (9.7 ± 2.8 m/s, p ¼ 0.025) and marginally significant higher than Indians (9.6 ± 3.1 m/s, p ¼ 0.052). AI was significantly higher in Indians (28.1 ± 10.8%) than Malays (25.9 ± 10.1%, p ¼ 0.005) and Chinese (26.1 ± 10.7%, p ¼ 0.002). Post-hoc pairwise difference in PWV level between Chinese and Malays, and AI between Chinese and Indians remained statistically significant after Bonferroni correction. Table 2 shows the associations of ethnicity with the clinical and biochemical determinants of arterial stiffness by multivariable linear regression models. PWV adjusted for age and gender in Malays was 0.704 m/s (95% CI 0.402e1.005, p < 0.001) faster than that in Chinese whereas the difference in PWV level between Indians and Chinese was not statistically significant (p ¼ 0.201, model 1). Further adjustment for duration of diabetes and HbA1c did not attenuate differences in PWV among the three ethnicities, showing that PWV in Malays was 0.702 m/s faster than that in Chinese (95% CI 0.406e0.998, p < 0.001) and PWV level between Indians and Chinese remains non-significant (p ¼ 0.186, model 2). In model 3, vascular risk factors including BMI, SBP, DBP, heart rate, HDL-C, LDL-C, total triglycerides, and sRAGE were further added to examine the effect of obesity, hypertension and dyslipidemia on ethnic disparity. We observed an attenuated but statistically significant difference, showing that PWV in Malays was 0.414 m/s faster than that in Chinese (95% CI 0.123e0.706, p ¼ 0.005), and PWV level between Indians and Chinese remains non-significant

Table 1 Clinical and biochemical characteristics of individuals with T2DM stratified by ethnicity (n ¼ 1990). Variables

Total (1990)

Malay (463)

Indian (474)

Chinese (1053)

P-value

Age Male gender (%) Height (m) Duration of diabetes (yrs) HbA1C (%) Current smokers (%) BMI (kg/m2) SBP (mmHg) DBP (mmHg) Heart rate (bpm) HDL-C (mM) LDL-C (mM) TG (mM)a eGFR (ml/min/1.73 m2) UACR (mg/g)a PWV (m/s) AI (%) sRAGE (pg/ml) Usage of medication (%) ACE inhibitors ARBs Statins Fibrates Insulin Metformin Sulphonylurea

57.5 ± 10.8 52.9 1.61 ± 0.09 11.4 ± 9.1 7.8 ± 1.3 8.8 27.7 ± 5.2 141.2 ± 19.4 79.1 ± 9.6 71.0 ± 11.0 1.3 ± 0.4 2.8 ± 0.8 1.4 (1.04e1.96) 84.7 ± 33.2 24 (7e112) 9.8 ± 2.9 26.5 ± 10.6 905.2 ± 557.2

56.0 ± 9.8 45.1 1.59 ± 0.09 9.8 ± 7.8 8.0 ± 1.5 10.8 29.8 ± 5.7 142.4 ± 19.8 79.8 ± 9.6 72.7 ± 11.4 1.3 ± 0.3 2.9 ± 0.9 1.6 (1.1e2.1) 80.3 ± 37.3 41 (12e202) 10.1 ± 3.0 25.9 ± 10.1 977.6 ± 708.0

56.6 ± 9.6 48.3 1.61 ± 0.09 11.1 ± 8.8 7.8 ± 1.3 7.6 28.3 ± 5.3 138.4 ± 18.5 79.4 ± 9.6 71.0 ± 10.6 1.2 ± 0.4 2.7 ± 0.8 1.3 (1.0e1.7) 88.7 ± 29.1 16 (5e54) 9.6 ± 3.1 28.1 ± 10.8 868.2 ± 494.2

58.5 ± 11.5 54.7 1.62 ± 0.09 12.2 ± 9.7 7.7 ± 1.2 8.4 26.5 ± 4.7 142.1 ± 19.5 78.7 ± 9.6 70.2 ± 10.9 1.3 ± 0.4 2.7 ± 0.8 1.4 (1.0e2.0) 84.7 ± 33.1 24 (6e118) 9.7 ± 2.8 26.1 ± 10.7 890.1 ± 504.0

<0.001 0.001 <0.001 <0.001 <0.001 0.265 <0.001 0.001 0.070 <0.001 <0.001 <0.001 0.001 0.001 0.001 0.018 <0.001 0.005

36.4 27.2 81.4 10.2 29.0 81.2 48.9

45.2 23.9 83.8 11.3 36.2 78.3 49.2

37.7 19.7 80.9 7.9 28.7 85.6 52.1

31.9 32.0 80.6 10.8 26.0 80.6 47.3

<0.001 <0.001 0.329 0.150 <0.001 0.012 0.215

HbA1c, hemoglobin A1c; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; PWV, pulse wave velocity; AI, augmentation index; TG, total triglycerides; UACR, urine albumin-tocreatinine ratio; ACE, angiotensin-converting-enzyme; RAGE, soluble receptor for advanced glycation end products; ARBs, angiotensin receptor blockers. p < 0.05 was considered as statistically significant and these values are listed in bold type. a Expressed as median (inter-quartile range) and log-transformed before data analysis.

X. Zhang et al. / Atherosclerosis 242 (2015) 22e28

25

Table 2 Variables associated with PWV in multivariable linear regression models in individuals with T2DM (n ¼ 1990). Variables

Entry age (yrs) Gender Male Female Ethnicity Chinese Malay Indian T2DM duration (yrs) HbA1c (%) BMI (kg/m2) SBP (mmHg) DBP (mmHg) Heart rate (bpm) HDL-C (mM) LDL-C (mM) LnTG (mM) LnsRAGE (pg/ml) LneGFR (ml/min) LnUACR (mg/mg) ACE inhibitors ARBs Statins Fibrates Insulin Metformin Sulphonylurea

Model 1

Model 2

<0.001

0.088

<0.001

0.077

<0.001

0.072

<0.001

0.075

<0.001

0.007

0.300

0.013

0.446

<0.001

0.351

0.005

0.372

0.003

<0.001 0.201 e e e e e e e e e e e e e e e e e e e

0.702 0.196 0.051 0.293

<0.001 0.186 <0.001 <0.001 e e e e e e e e e e e e e e e e e

0.414 0.184 0.041 0.235 0.078 0.043 0.016 0.026 0.128 0.114 0.086 0.114 e e e e e e e e e

0.005 0.201 <0.001 <0.001 <0.001 <0.001 0.034 <0.001 0.500 0.122 0.521 0.292 e e e e e e e e e

0.294 0.225 0.033 0.226 0.068 0.039 0.017 0.024 0.115 0.115 0.197 0.241 0.261 0.114 e e e e e e e

0.048 0.117 <0.001 <0.001 <0.001 <0.001 0.027 <0.001 0.541 0.114 0.145 0.032 0.057 <0.001 e e e e e e e

0.299 0.238 0.022 0.151 0.065 0.039 0.016 0.023 0.128 0.099 0.191 ¡0.243 0.280 0.096 0.008 0.219 0.096 0.320 0.613 0.221 0.157

0.048 0.105 0.003 0.002 <0.001 <0.001 0.041 <0.001 0.502 0.185 0.161 0.033 0.056 0.004 0.952 0.138 0.532 0.097 <0.001 0.070 0.345

e e e e e e e e e e e e e e e e e

P

P

b

Model 5

0.096

Ref 0.704 0.195 e e e e e e e e e e e e e e e e e e e

b

Model 4

P

Ref 0.333

b

Model 3

b

b

P

P

HbA1c, hemoglobin A1c; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; PWV, pulse wave velocity; AI, augmentation index; TG, total triglycerides; UACR, urine albumin-tocreatinine ratio; sRAGE, soluble receptor for advanced glycation end products; ACE, angiotensin-converting-enzyme, ARBs, angiotensin receptor blockers. p < 0.05 was considered as statistically significant and these values are listed in bold type.

(p ¼ 0.201). In model 4, additionally adjustment for renal function by eGFR and UACR further attenuated the difference, showing that PWV in Malays was 0.294 m/s faster than that in Chinese (95% CI 0.002e0.586, p ¼ 0.048), and PWV level between Indians and Chinese remains non-significant (p ¼ 0.117). Further adjustment for usage of commonly used medications in diabetes did not attenuate the difference, showing that PWV in Malays was 0.299 m/s faster than that in Chinese (95% CI 0.003e0.595, p ¼ 0.048), and PWV level between Indians and Chinese remains non-significant (p ¼ 0.105) (model 5). AI adjusted for age, gender and height in Indians was 2.174% (95% CI 1.161e3.188, p < 0.001) higher than that in Chinese, and remained significantly higher in model 2 (2.216%, 95% CI 1.200e3.232, p < 0.001), model 3 (2.846%, 95% CI 1.869e3.822, p < 0.001), model 4 (2.917%, 95% CI 1.935e3.898, p < 0.001) and model 5 (2.776%, 95% CI 1.778e3.775, p < 0.001). Notably, the association of AI with Indians was not attenuated after adjustment for all variables. The difference in AI between Malays and Chinese remained non-significant in all four models (Table 3). Backward multiple linear regression model revealed that these variables collectively explained 27.7% and 33.4% variance in PWV and AI, respectively (Table 4). 4. Discussion In a large multi-ethnic T2DM Asian cohort, we demonstrated that Malay and Indian had higher central arterial stiffness measured by PWV or AI respectively compared to Chinese. Most of the major vascular risk factors (e.g. age, male gender, duration of diabetes, BMI, HBA1c, SBP, DBP, heart rate, LDL-C, and sRAGE) were independent predictors of PWV or AI and were less favorable in Malays and Indians (Tables 1e3). However, after adjustment for these risk factors, Malays and Indians remain significantly

associated with higher PWV (Table 2) and AI (Table 3), respectively. Taken together, the higher adverse outcomes in Malays and Indians, which we previously reported, may be attributable to higher central arterial stiffness. All these major vascular risk factors collectively explained 27.7% and 33.4% variance in PWV level and AI. To the best of our knowledge, this is the first study on the association of ethnicity with central arterial stiffness in a large multi-ethnic Asians with T2DM. Our finding supports the published data regarding ethnic disparity in arterial stiffness. In young adults, compared with whites, blacks had higher PWV or larger annual PWV increases in healthy and T2DM subjects [17,18,29]. In healthy adults, difference in PWV or AI was observed between African descent and Amerindian, Malays and Chinese, and Africans and British whites [19,20,30]. The mechanistic impact of ethnicity on vascular health is complex and may be composite outcome of non-biological factors (e.g. social economic status) and biological factors, such as insulin resistance, vessel wall composition (e.g. collagen concentration) and central hemodynamics. [17e20], [29,30]. In our study, duration of diabetes is shorter in Malays and Indians than Chinese. We hypothesize that diagnosis of diabetes may have been delayed in the ethnic-minorities, due to the asymptomatic nature of glucose-intolerance at its early phase. This notion is supported by the greater burden of diabetes-related co-morbidities (e.g. albuminuria) and less favorable metabolic profile (e.g. higher HbA1c) consistent with chronicity of diabetes among Malays and Indians [31]. We found lower PWV but higher AI in women than men. Since the average age is less than 60, the protective effect of estrogen on vasculature may contribute to the lower PWV in women [32]. Literature has suggested that AI varies inversely with height [27] and women have higher AI across the age span in general [33]. In our study, women (58.3 ± 9.9) are older than men (56.7 ± 11.5, p ¼ 0.059) and significantly shorter (1.54 ± 0.06 m)

26

X. Zhang et al. / Atherosclerosis 242 (2015) 22e28

Table 3 Variables associated with AI in multivariable linear regression models in individuals with T2DM (n ¼ 1990). Variables

Entry age (yrs) Gender Male Female Ethnicity Chinese Malay Indian Height (m) T2DM duration (yrs) HbA1c (%) BMI (kg/m2) SBP (mmHg) DBP (mmHg) HDL-C (mM) LDL-C (mM) LnTG (mM) LnsRAGE (pg/ml) LneGFR (ml/min) LnUACR (mg/mg) ACE inhibitors ARBs Statins Fibrates Insulin Metformin Sulphonylurea

Model 1

Model 2

Model 3

0.249

<0.001

0.254

<0.001

0.189

<0.001

0.189

<0.001

0.194

<0.001

Ref. 2.647

<0.001

2.632

<0.001

3.355

<0.001

3.458

<0.001

3.408

<0.001

Ref. 0.506 2.174 29.180 e e e e e e e e e e e e e e e e e e

0.337 <0.001 <0.001 e e e e e e e e e e e e e e e e e e

0.462 2.216 29.251 0.012 0.040 e e e e e e e e e e e e e e e e

0.386 <0.001 <0.001 0.626 0.809 e e e e e e e e e e e e e e e e

0.259 2.846 29.695 0.024 0.051 0.322 0.095 0.179 0.438 0.412 0.376 0.484 e e e e e e e e e

0.614 <0.001 <0.001 0.310 0.748 <0.001 <0.001 <0.001 0.504 0.105 0.416 0.197 e e e e e e e e e

0.188 2.917 29.397 0.035 0.003 0.331 0.083 0.185 0.575 0.438 0.608 0.366 0.237 0.210 e e e e e e e

0.718 <0.001 <0.001 0.158 0.987 <0.001 <0.001 <0.001 0.382 0.087 0.196 0.350 0.620 0.060 e e e e e e e

0.024 2.776 29.223 0.053 0.108 0.321 0.079 0.192 0.525 0.335 0.489 0.468 0.331 0.187 0.132 0.203 0.746 1.167 0.998 0.405 0.347

0.965 <0.001 <0.001 0.052 0.533 <0.001 <0.001 <0.001 0.431 0.206 0.307 0.244 0.522 0.104 0.776 0.696 0.165 0.085 0.062 0.342 0.554

P

P

b

Model 5

P

В

b

Model 4

b

P

b

P

HbA1c, hemoglobin A1c; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; PWV, pulse wave velocity; AI, augmentation index; TG, total triglycerides; UACR, urine albumin-tocreatinine ratio; sRAGE, soluble receptor for advanced glycation end products; ACE, angiotensin-converting-enzyme; ARBs, Angiotensin receptor blockers. p < 0.05 was considered as statistically significant and these values are listed in bold type.

than men (1.67 ± 0.07 m, p < 0.001). Therefore, it is reasonable that we observed lower AI in women. The observed associations of PWV with heart rate, BMI, sRAGE and ACR are consistent with previous findings from our [34e36] and other studies [37,38]. The possible mechanisms underlying the effect of heart rate on arterial stiffening include fatigue and fracture of elastic fibers in the arterial wall and abnormalities of blood atherogenic lipoprotein fractions [37]. sRAGE is a multi-ligand receptor on vascular cells that plays a key role in inflammatory processes and oxidative stress generation [39]. Inflammatory and oxidative stress related to excessive adiposity, sRAGE and renal impairment are key processes in the pathogenesis of T2DM, which might be the rational linkage of arterial stiffness with ethnicity [40]. Although we (r2 ¼ 0.151) and some studies found good correlation of AI with PWV [41,42], they reflect different properties of the arterial tree and may have shared and unshared pathogenic drivers (e.g. inflammation). Previous studies found that in response to inflammation, PWV level increased while AI remained at normal level or decreased [43,44]. In hypertension patients, Gedikli et al. reported that low total antioxidative capacity levels were associated with AI but not PWV, which may be explained by the more impact of oxidative stress on peripheral arteries than aorta, and the nonparallel changes between aortic stiffness and wave reflection indices [45]. Therefore, the differential pathogenic-substrates (albeit overlap) of PWV and AI may, at least partially, explain different findings when arterial stiffness is estimated using PWV and AI in our study. We found attenuated association of PWV in Malays when major vascular risk factors and renal function were added, indicating the need of effective management of weight, blood pressure and heart rate in high risk populations like Malays. We also speculated that the attenuation might be due to loss of statistical power because of increased missing data in models 3 and 4. However, adjustment for these risk factors did not attenuate association of AI in Indians,

suggesting the existence of other unobserved mechanisms accounting for the higher AI in Indians. Non-traditional risk factors associated with both ethnicity and AI in T2DM, such as homocysteine level [46,47], should be considered in future analysis for AI. The strengths of our study include a large sample of multiethnic and high risk population living in Singapore, a small citystate with rapidly rising prevalence of diabetes. It offers a unique opportunity to study ethnic disparity among individuals sharing relatively homogeneous environmental experience and access to health care. We used PWV, the gold standard measurement for assessment of arterial stiffness. We also recognize that our study is subject to a number of limitations. First, all subjects were recruited from a secondary hospital and a community-based public primarycare outpatient clinic in equal proportion. Whether our findings can be extended to the general population remains to be determined. Second, the cross-sectional design of our study precludes any causal-inference between vascular risk factors and arterial stiffness. Finally, although we used matching and multivariable models to control potential confounders, we cannot exclude unmeasured potential factors that could determine the difference in arterial stiffness across three ethnic groups, such as genetic and environmental factors. The angiotensin II type 1 receptor gene 1166C allele frequency was lower in Chinese (6.6%) than Malays (7.7%), which might have an influence on PWV [48,49]. Compared with Indians and Malays, local studies reported higher consumption of coffee and vitamin C [50,51], C-reactive protein level [52] and socioeconomic status [53] in Chinese, which have all been associated arterial stiffness [54e56]. These unmeasured factors may interact with traditional risk factors, resulting in the ethnic disparity in arterial stiffness. Further studies examining these markers are needed to clarify the roles of lifestyle, inflammation and genetic factors in arterial stiffness. In conclusion, higher central arterial stiffness in Malays and

X. Zhang et al. / Atherosclerosis 242 (2015) 22e28 Table 4 Variables associated with PWV or AI in parsimonious backward linear regression models in individuals with T2DM (n ¼ 1990). Variables PWV (r2 ¼ 0.277) Entry age (yrs) Gender Male Female Ethnicity Chinese Malay Indian T2DM duration (yrs) HbA1c (%) BMI (kg/m2) SBP (mmHg) DBP (mmHg) Heart rate (bpm) LnsRAGE (pg/ml) LnUACR (mg/mg) Insulin AI (r2 ¼ 0.334) Entry age (yrs) Gender Male Female Ethnicity Chinese Malay Indian Height (m) BMI (kg/m2) SBP (mmHg) DBP (mmHg)

Unstandardized b

Standardized b

P-value

0.079

0.292

<0.001

Ref. 0.425

N/A

<0.001

Ref. 0.275 0.262 0.027 0.140 0.068 0.039 0.016 0.024 0.234 0.118 0.580

N/A N/A 0.085 0.064 0.120 0.258 0.054 0.090 0.043 0.090 0.091

0.041 0.065 <0.001 0.007 <0.001 <0.001 0.030 <0.001 0.031 <0.001 <0.001

0.191

0.194

<0.001

Ref. 3.444

N/A

<0.001

Ref. 0.177 2.822 29.632 0.333 0.092 0.181

N/A N/A 0.259 0.165 0.169 0.164

0.726 <0.001 <0.001 <0.001 <0.001 <0.001

HbA1c, hemoglobin A1c; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; PWV, pulse wave velocity; AI, augmentation index; UACR, urine albumin-to-creatinine ratio; sRAGE, soluble receptor for advanced glycation end products.

Indians with T2DM may explain their excess adverse diabetesrelated outcomes compared to other ethnicities. While several modifiable major vascular risk factors (e.g. obesity, hypertension, dyslipidemia) are important predictors of PWV and AI, ethnicity remains an independent determinant of arterial stiffness. Sources of funding This work was supported by Singapore National Medical Research Council Grant PPG/AH(KTPH)/2011. The funder has no role in study design, data collection, analysis, interpretation and manuscript writing. Disclosures None. Acknowledgments We thank Dr Darren E. J. Seah for his assistance in subject recruitment. We also thank staff from the Singapore Clinical Research Institute (SCRI) for their contribution to study protocol and database design. Study data were collected and managed using REDCap electronic data capture tools hosted at SCRI. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.atherosclerosis.2015.06.019.

27

References [1] J.E. Shaw, R.A. Sicree, P.Z. Zimmet, Global estimates of the prevalence of diabetes for 2010 and 2030, Diabetes Res. Clin. Pract. 87 (2010) 4e14. [2] A. Ramachandran, C. Snehalatha, A.S. Shetty, A. Nanditha, Trends in prevalence of diabetes in Asian countries, World J. diabetes 3 (2012) 110e117. [3] T.P. Phan, L. Alkema, E.S. Tai, K.H. Tan, Q. Yang, W.Y. Lim, Y.Y. Teo, C.Y. Cheng, X. Wang, T.Y. Wong, K.S. Chia, A.R. Cook, Forecasting the burden of type 2 diabetes in Singapore using a demographic epidemiological model of Singapore, BMJ Open Diabetes Res. care 2 (2014) e000012. [4] Diabetes mellitus: a major risk factor for cardiovascular disease. A joint editorial statement by the american diabetes association; the national heart, lung, and blood institute; the juvenile diabetes foundation international; the national institute of diabetes and digestive and kidney diseases; and the American heart association, Circulation 100 (1999) 1132e1133. [5] A.K. Jha, P.D. Varosy, A.M. Kanaya, D.B. Hunninghake, M.A. Hlatky, D.D. Waters, C.D. Furberg, M.G. Shlipak, Differences in medical care and disease outcomes among black and white women with heart disease, Circulation 108 (2003) 1089e1094. [6] J.E. Keil, S.E. Sutherland, R.G. Knapp, D.T. Lackland, P.C. Gazes, H.A. Tyroler, Mortality rates and risk factors for coronary disease in black as compared with white men and women, N. Engl. J. Med. 329 (1993) 73e78. [7] K.K. Yeo, B.C. Tai, D. Heng, J.M. Lee, S. Ma, K. Hughes, S.K. Chew, K.S. Chia, E.S. Tai, Ethnicity modifies the association between diabetes mellitus and ischaemic heart disease in Chinese, Malays and Asian Indians living in Singapore, Diabetologia 49 (2006) 2866e2873. [8] C.Y. Hong, K.S. Chia, K. Hughes, S.L. Ling, Ethnic differences among Chinese, Malay and Indian patients with type 2 diabetes mellitus in Singapore, Singapore Med. J. 45 (2004) 154e160. [9] R. Dalan, M. Jong, R. Choo, D.E. Chew, M.K. Leow, Predictors of cardiovascular complication in patients with diabetes mellitus: a 5-year follow-up study in a multiethnic population of Singapore: Credence ii study, Int. J. Cardiol. 169 (2013) e67e69. [10] S. Ma, J. Cutter, C.E. Tan, S.K. Chew, E.S. Tai, Associations of diabetes mellitus and ethnicity with mortality in a multiethnic Asian population: data from the 1992 Singapore national health survey, Am. J. Epidemiol. 158 (2003) 543e552. [11] J. Liu, S. Lim, L. Yeoh, C. Su, K. Low, B. Aswin, A. Chia, S. Fun, S. Tavintharan, C. Sum, Long term follow-up of cardiovascular outcome in multiethnic asians with type 2 diabetes mellitus (abstract), Singapore Med. J. 55 (2014) s29. [12] S. Laurent, J. Cockcroft, L. Van Bortel, P. Boutouyrie, C. Giannattasio, D. Hayoz, B. Pannier, C. Vlachopoulos, I. Wilkinson, H. Struijker-Boudier, European network for non-invasive investigation of large A. expert consensus document on arterial stiffness: methodological issues and clinical applications, Eur. heart J. 27 (2006) 2588e2605. [13] G.F. Mitchell, Y. Lacourciere, J.M. Arnold, M.E. Dunlap, P.R. Conlin, J.L. Izzo Jr., Changes in aortic stiffness and augmentation index after acute converting enzyme or vasopeptidase inhibition, Hypertension 46 (2005) 1111e1117. [14] S. Tsuchikura, T. Shoji, E. Kimoto, K. Shinohara, S. Hatsuda, H. Koyama, M. Emoto, Y. Nishizawa, Central versus peripheral arterial stiffness in association with coronary, cerebral and peripheral arterial disease, Atherosclerosis 211 (2010) 480e485. [15] E. Kimoto, T. Shoji, K. Shinohara, M. Inaba, Y. Okuno, T. Miki, H. Koyama, M. Emoto, Y. Nishizawa, Preferential stiffening of central over peripheral arteries in type 2 diabetes, Diabetes 52 (2003) 448e452. [16] S. Hatsuda, T. Shoji, K. Shinohara, E. Kimoto, K. Mori, S. Fukumoto, H. Koyama, M. Emoto, Y. Nishizawa, Regional arterial stiffness associated with ischemic heart disease in type 2 diabetes mellitus, J. Atheroscler. Thrombosis 13 (2006) 114e121. [17] R.P. Wildman, G.N. Farhat, A.S. Patel, R.H. Mackey, S. Brockwell, T. Thompson, K. Sutton-Tyrrell, Weight change is associated with change in arterial stiffness among healthy young adults, Hypertension 45 (2005) 187e192. [18] S. Li, W. Chen, S.R. Srinivasan, G.S. Berenson, Influence of metabolic syndrome on arterial stiffness and its age-related change in young adults: the bogalusa heart study, Atherosclerosis 180 (2005) 349e354. [19] P.C. Santos, O. Alvim Rde, N.E. Ferreira, R. de Sa Cunha, J.E. Krieger, J.G. Mill, A.C. Pereira, Ethnicity and arterial stiffness in brazil, Am. J. Hypertens. 24 (2011) 278e284. [20] A. Aminuddin, Z. Zakaria, A.F. Fuad, J. Kamsiah, F. Othman, S. Das, Y. Kamisah, H.S. Qodriyah, Z. Jubri, N.A. Nordin, W.Z. Ngah, High c reactive protein associated with increased pulse wave velocity among urban men with metabolic syndrome in Malaysia, Saudi Med. J. 34 (2013) 266e275. [21] J.J. Liu, S. Tavintharan, L.Y. Yeoh, C.F. Sum, X.W. Ng, S.L. Pek, S.B. Lee, W.E. Tang, S.C. Lim, Study SD, High normal albuminuria is independently associated with aortic stiffness in patients with type 2 diabetes, Diabet. Med. A J. Br. Diabet. Assoc. 31 (2014) 1199e1204. [22] American Diabetes A, Diagnosis and classification of diabetes mellitus, Diabetes Care 29 (Suppl. 1) (2006) S43eS48. [23] N. Rognant, S. Lemoine, M. Laville, A. Hadj-Aissa, L. Dubourg, Performance of the chronic kidney disease epidemiology collaboration equation to estimate glomerular filtration rate in diabetic patients, Diabetes Care 34 (2011) 1320e1322. [24] Technical note: 9-validation and reproducibility of sphygmocor px. [25] I.B. Wilkinson, S.A. Fuchs, I.M. Jansen, J.C. Spratt, G.D. Murray, J.R. Cockcroft,

28

[26]

[27]

[28]

[29]

[30]

[31] [32] [33]

[34]

[35]

[36]

[37]

[38]

[39]

[40] [41]

X. Zhang et al. / Atherosclerosis 242 (2015) 22e28 D.J. Webb, Reproducibility of pulse wave velocity and augmentation index measured by pulse wave analysis, J. Hypertens. 16 (1998) 2079e2084. N.B. Johansen, M. Charles, D. Vistisen, S.S. Rasmussen, N. Wiinberg, K. BorchJohnsen, T. Lauritzen, A. Sandbaek, D.R. Witte, Effect of intensive multifactorial treatment compared with routine care on aortic stiffness and central blood pressure among individuals with screen-detected type 2 diabetes: the addition-denmark study, Diabetes Care 35 (2012) 2207e2214. Yasmin, M.J. Brown, Similarities and differences between augmentation index and pulse wave velocity in the assessment of arterial stiffness, QJM Mon. J. Assoc. Physicians 92 (1999) 595e600. J.I. Davies, A.D. Struthers, Pulse wave analysis and pulse wave velocity: a critical review of their strengths and weaknesses, J. Hypertens. 21 (2003) 463e472. A.S. Shah, L.M. Dolan, Z. Gao, T.R. Kimball, E.M. Urbina, Racial differences in arterial stiffness among adolescents and young adults with type 2 diabetes, Pediatr. Diabetes 13 (2012) 170e175. J.A. Chirinos, J.G. Kips, M.J. Roman, J. Medina-Lezama, Y. Li, A.J. Woodiwiss, G.R. Norton, Yasmin, L. Van Bortel, J.G. Wang, J.R. Cockcroft, R.B. Devereux, I.B. Wilkinson, P. Segers, C.M. McEniery, Ethnic differences in arterial wave reflections and normative equations for augmentation index, Hypertension 57 (2011) 1108e1116. E.A. Gale, Glucose control in the ukpds: what did we learn? Diabet. Med. A J. Br. Diabet. Assoc. 25 (Suppl. 2) (2008) 9e12. M.E. Mendelsohn, R.H. Karas, The protective effects of estrogen on the cardiovascular system, N. Engl. J. Med. 340 (1999) 1801e1811. J.H. Janner, N.S. Godtfredsen, S. Ladelund, J. Vestbo, E. Prescott, Aortic augmentation index: reference values in a large unselected population by means of the sphygmocor device, Am. J. Hypertens. 23 (2010) 180e185. J.J. Liu, C.F. Sum, S. Tavintharan, L.Y. Yeoh, X.W. Ng, A.M. Moh, S. Lee, W.E. Tang, S.C. Lim, for SDS, Obesity is a determinant of arterial stiffness independent of traditional risk factors in Asians with young-onset type 2 diabetes, Atherosclerosis 236 (2014) 286e291. M.C. Moh, C.F. Sum, B.C. Lam, X.W. Ng, C. Su, S. Tavintharan, L.Y. Yeoh, M.D. Wong, S.B. Lee, W.E. Tang, S.C. Lim, Evaluation of body adiposity index as a predictor of aortic stiffness in multi-ethnic asian population with type 2 diabetes, Diabetes Vasc. Dis. Res. Off J. Int. Soc. Diabetes Vasc. Dis. 12 (2014) 111e118. Y. Ester Chai Kheng, L. Jian-Jun, Y. Lee Ying, T. Subramaniam, S. Chee Fang, N. Xiao Wei, T. Wan Ching, P. Sharon, L. Su Chi, Soluble receptor for advanced glycation end products (srage) is associated with arterial stiffness only in normalbuminuric type 2 diabetes, The Endocrine Society's 95th Annual Meeting and Expo, In Cardiometabolic Risk & Vasc. Biol. (2013). p. SUN-766SUN-766. P. Lantelme, C. Mestre, M. Lievre, A. Gressard, H. Milon, Heart rate: an important confounder of pulse wave velocity assessment, Hypertension 39 (2002) 1083e1087. R. Sa Cunha, B. Pannier, A. Benetos, J.P. Siche, G.M. London, J.M. Mallion, M.E. Safar, Association between high heart rate and high arterial rigidity in normotensive and hypertensive subjects, J. Hypertens. 15 (1997) 1423e1430. A.M. Schmidt, S.D. Yan, S.F. Yan, D.M. Stern, The multiligand receptor rage as a progression factor amplifying immune and inflammatory responses, J. Clin. Investigation 108 (2001) 949e955. J.C. Pickup, Inflammation and activated innate immunity in the pathogenesis of type 2 diabetes, Diabetes Care 27 (2004) 813e823. C.M. McEniery, Yasmin, I.R. Hall, A. Qasem, I.B. Wilkinson, J.R. Cockcroft, A. Investigators, Normal vascular aging: differential effects on wave reflection and aortic pulse wave velocity: the anglo-cardiff collaborative trial (acct),

J. Am. Coll. Cardiol. 46 (2005) 1753e1760. [42] A. Adji, K. Hirata, M.F. O'Rourke, Clinical use of indices determined noninvasively from the radial and carotid pressure waveforms, Blood Press. Monit. 11 (2006) 215e221. [43] C. Vlachopoulos, I. Dima, K. Aznaouridis, C. Vasiliadou, N. Ioakeimidis, C. Aggeli, M. Toutouza, C. Stefanadis, Acute systemic inflammation increases arterial stiffness and decreases wave reflections in healthy individuals, Circulation 112 (2005) 2193e2200. [44] K.M. Maki-Petaja, F.C. Hall, A.D. Booth, S.M. Wallace, Yasmin, P.W. Bearcroft, S. Harish, A. Furlong, C.M. McEniery, J. Brown, I.B. Wilkinson, Rheumatoid arthritis is associated with increased aortic pulse-wave velocity, which is reduced by anti-tumor necrosis factor-alpha therapy, Circulation 114 (2006) 1185e1192. [45] O. Gedikli, S. Ozturk, H. Yilmaz, M. Baykan, A. Kiris, I. Durmus, K. Karaman, C. Karahan, S. Celik, Low total antioxidative capacity levels are associated with augmentation index but not pulse-wave velocity, Heart Vessels 24 (2009) 366e370. [46] M. Shargorodsky, M. Boaz, S. Pasternak, R. Hanah, Z. Matas, A. Fux, Y. Beigel, M. Mashavi, Serum homocysteine, folate, vitamin b12 levels and arterial stiffness in diabetic patients: which of them is really important in atherogenesis? Diabetes/Metabolism Res. Rev. 25 (2009) 70e75. [47] F.P. Cappuccio, R. Bell, I.J. Perry, J. Gilg, P.M. Ueland, H. Refsum, G.A. Sagnella, S. Jeffery, D.G. Cook, Homocysteine levels in men and women of different ethnic and cultural background living in England, Atherosclerosis 164 (2002) 95e102. [48] G.N. Thomas, R.P. Young, B. Tomlinson, K.S. Woo, J.E. Sanderson, J.A. Critchley, Renin-angiotensin-aldosterone system gene polymorphisms and hypertension in Hong kong Chinese, Clin. Exp. Hypertens. 22 (2000) 87e97. [49] A. Rehman, A.H. Rasool, L. Naing, T.M. Roshan, A.R. Rahman, Influence of the angiotensin ii type i receptor gene 1166a > c polymorphism on bp and aortic pulse wave velocity among malays, Ann. Hum. Genet. 71 (2007) 86e95. [50] H. Uemura, S. Katsuura-Kamano, M. Yamaguchi, M. Nakamoto, M. Hiyoshi, K. Arisawa, Consumption of coffee, not green tea, is inversely associated with arterial stiffness in Japanese men, Eur. J. Clin. Nutr. 67 (2013) 1109e1114. [51] K. Hughes, C.N. Ong, Vitamins, selenium, iron, and coronary heart disease risk in Indians, Malays, and Chinese in Singapore, J. Epidemiol. Community Health 52 (1998) 181e185. [52] R. Dalan, M. Jong, S.P. Chan, R. Hawkins, R. Choo, B. Lim, M.L. Tan, M.K. Leow, High-sensitivity c-reactive protein concentrations among patients with and without diabetes in a multiethnic population of Singapore: Credence study, Diabetes, Metab. Syndrome Obes. Targets Ther. 3 (2010) 187e195. [53] E.E. Khaing Nang, E.Y. Khoo, A. Salim, E.S. Tai, J. Lee, R.M. Van Dam, Patterns of physical activity in different domains and implications for intervention in a multi-ethnic asian population: a cross-sectional study, BMC Public Health 10 (2010) 644. [54] Y. Plantinga, L. Ghiadoni, A. Magagna, C. Giannarelli, F. Franzoni, S. Taddei, A. Salvetti, Supplementation with vitamins c and e improves arterial stiffness and endothelial function in essential hypertensive patients, Am. J. Hypertens. 20 (2007) 392e397. [55] F. Anan, T. Masaki, Y. Umeno, T. Iwao, H. Yonemochi, N. Eshima, T. Saikawa, H. Yoshimatsu, Correlations of high-sensitivity c-reactive protein and atherosclerosis in Japanese type 2 diabetic patients, Eur. J. Endocrinology/ European Fed. Endocr. Soc. 157 (2007) 311e317. [56] R.C. Thurston, K.A. Matthews, Racial and socioeconomic disparities in arterial stiffness and intima media thickness among adolescents, Soc. Sci. Med. 68 (2009) 807e813.