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Arterial stiffness is an independent predictor for albuminuria progression among Asians with type 2 diabetes—A prospective cohort study
Journal of Diabetes and Its Complications xxx (2017) xxx–xxx
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Journal of Diabetes and Its Complications j o u r n a l h o m e p a g e : W W W. J D C J O U R N A L . C O M
Arterial stiffness is an independent predictor for albuminuria progression among Asians with type 2 diabetes—A prospective cohort study Xiao Zhang a, Serena Low a, Chee Fang Sum b, c, Subramaniam Tavintharan b, c, Yeoh Lee Ying c, Jianjun Liu a, Na Li a, Keven Anga a, Simon BM 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 c Department of Medicine, Khoo Teck Puat Hospital, Singapore 768828, Republic of Singapore d National Healthcare Group Polyclinics, Singapore 138543, Singapore b
a r t i c l e
i n f o
Article history: Received 14 September 2016 Received in revised form 14 November 2016 Accepted 1 February 2017 Available online xxxx Keywords: Type 2 diabetes Central arterial stiffness Pulse wave velocity Albuminuria progression Chronic kidney disease
a b s t r a c t Aim: Albuminuria progression has been associated with renal deterioration in type 2 diabetes (T2DM). Central arterial stiffness can aggravate systemic vasculopathy by propagating elevated systolic and pulse pressures forward, thereby accentuating global vascular injury. We aim to investigate whether central arterial stiffness is an independent predictor for albuminuria progression in a multi-ethnic T2DM Asian cohort in Singapore. Methods: In a prospective cohort, 1012 T2DM patients were assessed at baseline and after a median follow-up of 3.1 years. 880 patients with baseline normo- (urinary albumin-to-creatinine ratio (ACR) b 30 mg/g, n = 579) and microalbuminuria (ACR = 30–299 mg/g, n = 301) were divided into progression and non-progression groups according to ACR changes. Progression was defined as transition from normo- to microalbuminuria, micro- to macroalbuminuria, or normo- to macroalbuminuria. Central arterial stiffness was estimated by carotid–femoral pulse wave velocity (PWV) using applanation tonometry method. Stepwise multiple regression analysis was used to determine the predictor(s) for albuminuria progression. Results: Albuminuria progression occurred in 178 patients (20.2%). Baseline PWV was higher in progression (10.1 ± 2.9 m/s) than non-progression group (9.2 ± 2.4 m/s, p b 0.001). 1-SD increase in baseline PWV was associated with albuminuria progression (OR = 1.457, 95% CI, 1.236–1.718, p b 0.001). Stepwise regression analysis identified that baseline PWV (OR = 1.241, 95% CI, 1.033–1.490, p = 0.021), BMI (OR = 1.046, 95% CI, 1.012–1.080, p = 0.008), nature log-transformed estimated glomerular filtration rate (LneGFR) (OR = 0.320, 95% CI, 0.192–0.530, p = 0.010) and LnACR (OR = 1.344, 95% CI, 1.187–1.522, p = 0.008) are predictors for albuminuria progression. Conclusion: Increased central arterial stiffness at baseline predicted future progression of albuminuria. Our results suggest the potential benefit of ameliorating central arterial stiffness to retard albuminuria progression in T2DM. © 2017 Elsevier Inc. All rights reserved.
1. Introduction Type 2 diabetes (T2DM) is a rapidly evolving global health problem, and Asia is emerging as the epi-center of this epidemic. 1,2 The prevalence of T2DM is predicted to double from 7.3% in 1990 to 15% in 2050 in Singapore, a multi-ethnic city-state composed of three major ethnic groups (Chinese, Malays and Indians). 3 Diabetes is a major cause of chronic kidney disease (CKD). In Singapore, the burden
Disclosures: None. ⁎ Corresponding author at: Diabetes Center, Khoo Teck Puat Hospital, 90 Yishun Central, Singapore 768828, Republic of Singapore. Tel.: +65 66022353; fax: +65 66023772. E-mail address: [email protected] (S.C. Lim).
of CKD is rising in parallel with the growing prevalence of diabetes. We have recently reported a 53% prevalence of CKD among patients with T2DM in Singapore. 4 Many clinical and epidemiological studies have reported that CKD patients are at high risk of incident cardiovascular disease (CVD) and cardiovascular morbidity. 5 Although the mechanisms underlying this association are not fully elucidated, patients with CKD and CVD may have shared risk factors, such as increased arterial stiffness. 5 Arterial stiffness, a well-established biomarker of vasculopathy, has become a clinically useful predictor of complications in T2DM, including CKD. 6 Pulse wave velocity (PWV), the gold-standard index for arterial stiffness, has been widely used in clinical settings. PWV can be determined across regional arterial segments, including central (e.g., carotid–femoral) and peripheral (e.g., brachial–ankle) segments. 7 The
http://dx.doi.org/10.1016/j.jdiacomp.2017.02.004 1056-8727/© 2017 Elsevier Inc. All rights reserved.
Please cite this article as: Zhang X, et al. Arterial stiffness is an independent predictor for albuminuria progression among Asians with type 2 diabetes—A prospective cohort .... Journal of Diabetes and Its Complications (2017), http://dx.doi.org/10.1016/j.jdiacomp.2017.02.004
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X. Zhang et al. / Journal of Diabetes and Its Complications xxx (2017) xxx–xxx
effects of diabetes on arterial stiffness were suggested to be greater in central than peripheral arteries. 8 Mechanistically, central arterial stiffness can aggravate systemic vasculopathy by propagating elevated systolic and pulse pressures forward, thereby accentuating global vascular injury. 9,10 Urinary albumin-to-creatinine ratio (ACR) is an important component to predict renal dysfunction. Previous cross-sectional studies in T2DM patients have revealed a significant association of increased arterial stiffness with albuminuria.11 To date, only one longitudinal study has examined the impact of arterial stiffness on renal function in T2DM patients, a finding that increased PWV is significantly associated with incident albuminuria.12 To our knowledge, the longitudinal impact of central arterial stiffness on albuminuria progression has never been reported in Asians population with rapidly rising prevalence of T2DM and diabetic nephropathy like Singapore. In this study, we aim to evaluate whether arterial stiffness is an independent predictor for albuminuria progression in a multi-ethnic T2DM Asian cohort in Singapore. 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 completed cross-sectional study conducted between August 2011 and February 2014 including a total of 2057 adults aged 21–90 years with T2DM. 13 Inclusion and exclusion criteria of SMART2D have been previously described. 13 Longitudinal data on urinary albumin excretion were obtained from the ongoing follow-up prospective study from September 2014. In our study, a total of 1012 patients were included for analysis. 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 using a mercury sphygmomanometer on the right arm using appropriate cuff sizes. Systolic blood pressure (SBP) and diastolic blood pressure (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 ACR 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 μg/ml (Immulite; DPC, Gwynedd, UK). Estimated GFR (eGFR) was calculated based on a widely used Modified Diet in Renal Disease (MDRD) equation in patients with diabetes.14 Hemoglobin A1c (HbA1c) was measured based on monoclonal antibody agglutination reaction using a point-of-care 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. 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 Carotid–femoral PWV was measured by the foot-to-foot method using a well-validated device, SphygmoCor® (AtCor Medical, Sydney, Australia) as described previously. 15 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 9–10 pulse waves in each assessment. PWV was expressed as the distance between the two recording sites (meters) divided by transit time (seconds). 2.4. Albuminuria progression and regression The end point with regard to albuminuria progression was based on ACR measures at baseline and follow-up. Albuminuria progression was defined as transition from normo- (ACR b 30 mg/g) to microalbuminuria (ACR = 30–299 mg/g), micro- to macroalbuminuria (ACR N 300 mg/g), or normo- to macroalbuminuria. Since transitions between states were assumed to occur at any time within the observed time interval, we further divided non-progression patients into regression and remain in the same albuminuria-based state. Regression was defined as transition from macro- to micro- or albuminuria, or micro- to normoalbuminuria. The minimum follow-up period is two years, which meets the recommendation for valid determination of renal disease progression. 16 2.5. Statistical analysis Standard descriptive statistics were used to describe the characteristics of individuals with T2DM. Normally distributed continuous data were expressed as means and standard deviations (SDs). Skewed variables were expressed as median and inter-quartile range (IQR) and nature log (ln)-transformed before data analysis. Differences between groups were compared by t test or χ 2 test where appropriate. We also examined the distribution of baseline characteristics, including HbA1c, blood pressure and renal function that were possible risk factors for albuminuria progression according to PWV quartiles. Univariate analysis was performed for individual clinical and biochemical parameters to verify associated factors (Table S1). Variables that are statistically significant in univariate analysis or with putative roles in the patho-biology of renal function decline were added into multivariable logistic regression models. Stepwise binary logistic regression was used to examine the predictor(s) for albuminuria progression. We also examined the association of PWV with regression in patients with baseline micro- or macroalbuminuria using stepwise logistic regression. The following variables were incorporated as covariates in multivariate logistic regression model: age, gender, ethnicity, smoking status, HBA1c, SBP, HDL-C, LDL-C, BMI, eGFR, ACR, and usage of renin-angiotensin system (RAS) inhibitors (e.g., angiotensin-converting enzyme (ACE) inhibitors, angiotensin II receptor blockers (ARBs)). All statistical analyses were performed using STATA version 14.0 (STATA Corporation, College Station, Texas, USA). A two-tailed p value of less than 0.05 was considered as statistically significant. 3. Results Totally, 1012 T2DM patients was included in this study, including 579 (57.2%) had normoalbuminuria, 301 (29.7%) had microalbuminuria and 132 (13.0%) had macroalbuminuria at baseline. After a median follow-up of 3.1 years (range 2.1–4.2), albuminuria progression occurred in 178 patients (20.2%) out of 880 patients with baseline normo- and microalbuminuria. Table 1 shows the baseline characteristics of T2DM patients stratified by albuminuria progression. Compared with the non-progression group, the progression group had significantly higher BMI, SBP and ACR, and lower eGFR. There were no significant differences in age, gender, ethnicity, smoking status, duration of T2DM, Hb1Ac, HDL-C, LDL-C, DBP and usage of RAS inhibitors. Baseline
Please cite this article as: Zhang X, et al. Arterial stiffness is an independent predictor for albuminuria progression among Asians with type 2 diabetes—A prospective cohort .... Journal of Diabetes and Its Complications (2017), http://dx.doi.org/10.1016/j.jdiacomp.2017.02.004
X. Zhang et al. / Journal of Diabetes and Its Complications xxx (2017) xxx–xxx
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Table 1 Baseline characteristics of individuals with T2DM stratified by albuminuria progression (N = 880). Variables
Non-progression (702)
Progression (178)
Total (886)
p-Value
Entry age (y) Gender (%) Male Female Ethnicity (%) Chinese Malays Indians Smoking history (%) Current and former Never Duration of T2DM (y) Hba1c (%) HDL-C (mM) LDL-C (mM) BMI (kg/m2) eGFR (ml/min/1.73 m2)a ACR (mg/g)a SBP (mm Hg) DBP (mm Hg) PWV (m/sec) RAS medication (%)
56.5 ± 10.1
58.0 ± 10.2
56.8 ± 10.2
0.074
51.1 48.9
48.3 51.7
50.6 49.4
0.502
55.4 18.9 25.7
54.3 26.0 19.7
55.2 20.4 24.4
0.065
13.3 86.7 10.9 ± 8.7 7.6 ± 1.2 1.3 ± 0.4 2.7 ± 0.8 27.5 ± 5.0 91.5 (77.7–108.6) 12 (4–40) 135.5 ± 15.7 78.3 ± 9.2 9.2 ± 2.4 61.1
12.4 87.6 10.6 ± 8.5 7.8 ± 1.4 1.3 ± 0.3 2.7 ± 0.8 29.0 ± 5.6 80.9 (59.6–98.3) 24 (13–94) 139.7 ± 16.8 78.9 ± 9.2 10.1 ± 2.9 54.5
13.1 86.9 10.9 ± 8.7 7.7 ± 1.3 1.3 ± 0.4 2.7 ± 0.8 27.8 ± 5.2 89.9 (74.3–106.8) 15 (5–43) 136.3 ± 16.1 78.4 ± 9.2 9.4 ± 2.5 59.8
0.736 0.598 0.256 0.441 0.834 b0.001 b0.001 b0.001 0.002 0.435 b0.001 0.112
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; TC, total cholesterol; TG, total triglycerides; ACR, urine albumin-to-creatinine ratio; RAS medication, angiotensin-converting-enzyme or angiotensin receptor blockers. p b 0.05 was considered as statistically significant. a Expressed as median (inter-quartile range).
Table 2 Baseline characteristics and albuminuria progression according to quartiles of baseline PWV (N = 880).
Hba1c (%) SBP (mm Hg) DBP (mm Hg) eGFR (ml/min/1.73 m2)a ACR (mg/g)a Albuminuria progression (%)
Q1
Q2
Q3
Q4
p-Trend
7.55 ± 1.28 129.5 ± 14.2 77.8 ± 9.2 95.1 (82.8–114.6) 10 (3–30) 13.7
7.73 ± 1.34 135.3 ± 14.6 79.4 ± 9.5 89.9 (74.6–102.9) 16.5 (5–55) 18.4
7.75 ± 1.21 142.5 ± 18.5 79.0 ± 10.3 84.6 (64.2–103.2) 28 (8–91) 17.7
7.80 ± 1.31 146.3 ± 17.8 79.2 ± 8.6 75.4 (55.2–96.0) 50 (14–306) 32.0
0.460 b0.001 0.023 b0.001 b0.001 b0.001
HbA1c, hemoglobin A1c; SBP, systolic blood pressure; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; ACR, urine albumin-to-creatinine ratio. p b 0.05 was considered as statistically significant. a Expressed as median (inter-quartile range).
PWV was significantly higher in progression (10.1 ± 2.9 m/s) than the non-progression group (9.2 ± 2.4 m/s, p b 0.001). Table 2 shows baseline Hb1Ac, blood pressure and renal function, as well as percentage of albuminuria progression according to quartiles of baseline PWV level. Blood pressure increased and renal function decreased across increasing quartiles of PWV. The percentage of albuminuria progression increased significantly across increasing quartiles of PWV. One SD increase in baseline PWV was associated with increased risk of albuminuria progression (OR = 1.457, 95% CI, 1.236–1.718, p b 0.001). Stepwise multiple regression analysis identified that baseline PWV (OR = 1.241, 95% CI, 1.033–1.490, p = 0.021), BMI (OR = 1.046, 95% CI, 1.012–1.080, p = 0.008), nature log-transformed eGFR (LneGFR) (OR = 0.320, 95% CI, 0.192–0.530, p = 0.010) and LnACR (OR = 1.344, 95% CI, 1.187–1.522, p = 0.008) are predictors for albuminuria progression (Table 3). Fig. 1 shows PWV level at different albuminuria-based states during follow-up period. The majority of patients remained in the same state, with PWV level lower than progression patients while higher than regression patients. In 433 patients with baseline micro- or macroalbuminuria, regression occurred in 112 patients (25.9%). Baseline PWV was significantly higher in non-regression (10.5 ± 2.7 m/s) than regression group (9.5 ± 2.5 m/s, p = 0.001). For every 1-SD increase in
baseline PWV, there is and 32.0% decreased likelihood of albuminuria regression (OR = 0.680, 95% CI, 0.537–0.861, p = 0.001). However, the association was attenuated to marginal significance after adjustment (OR = 0.796, 95% CI, 0.618–1.023, p = 0.079) (Table 4). 4. Discussion In a multi-ethnic T2DM Asian cohort, we demonstrated that PWV could predict albuminuria progression. The other independent risk
Table 3 Odds ratio of albuminuria progression in T2DM patients (N = 880).
Univariate analysis PWV (m/s) Multivariate analysis PWV (m/s) Other covariates LnACR (mg/g) LneGFR (ml/min/1.73 m2) BMI (kg/m2)
OR (95% CI)
p-Value
1.457 (1.236–1.718)
b0.001
1.241 (1.033–1.490)
0.021
1.344 (1.187–1.522) 0.320 (0.192–0.530) 1.046 (1.012–1.080)
b0.001 0.010 0.008
PWV, pulse wave velocity; BMI, body mass index; eGFR, estimated glomerular filtration rate. p b 0.05 was considered as statistically significant.
Please cite this article as: Zhang X, et al. Arterial stiffness is an independent predictor for albuminuria progression among Asians with type 2 diabetes—A prospective cohort .... Journal of Diabetes and Its Complications (2017), http://dx.doi.org/10.1016/j.jdiacomp.2017.02.004
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X. Zhang et al. / Journal of Diabetes and Its Complications xxx (2017) xxx–xxx
Fig. 1. PWV and transition probability in every state during follow up period (N = 1012). p-Value is compared with those remained in the same state.
factors for albuminuria progression include baseline lower eGFR and higher BMI. To the best of our knowledge, it is the first study to examine the association of central arterial stiffness and albuminuria progression in a prospective study in Singapore—a multi-ethnic Asian population with rapidly rising prevalence of T2DM and diabetic kidney disease. The contribution of urinary ACR and GFR to CKD progression and CVD risk remains controversial. A recent meta-analysis revealed that ACR improved risk prediction for cardiovascular mortality and heart failure better than GFR, especially in diabetes patients. 17 Microalbuminuria represents damaged or dysfunctional vascular endothelium, whereas large quantities of albuminuria represents damaged glomerular barrier in large quantity. 18,19 Thus, albuminuria progression could reflect changes in the pathophysiology of the leakage of proteins and serves as a biomarker of progression of renal dysfunction. 18 Several longitudinal studies have suggested the prognostic importance of proteinuria upon CKD progression in diabetic CKD. For example, T2DM patients with macroalbuminuria have much more rapid decline in renal function compared with those without albuminuria in UK and Japanese populations. 20,21 In European non-diabetic population, albuminuria has been demonstrated as a continuous risk factor for progression to end-stage renal disease at all levels of eGFR. 22 Therefore, we evaluated renal outcome on the basis of ACR changes at baseline and follow-up in this study. Our finding of longitudinal association of baseline PWV with albuminuria progression agrees with the only longitudinal study of baseline PWV and renal function in T2DM patients. 12 Our study also showed good agreement in prevalence of albuminuria progression (17.6%) with that study (18.4%), although they have longer follow-up duration (median = 5.9 years). 12 However, it should be noted that two longitudinal studies reported non-association between PWV and incident albuminuria or proteinuria in non-diabetic populations. 23,24 In Framingham heart study consisted of mainly Caucasians, although carotid–femoral PWV was associated with microalbuminuria status and ACR in cross-sectional study, the longitudinal study did not find an association between baseline PWV and incident microalbuminuria. 24 The lower statistical power (34% power to detect 10% microalbuminuria incidence rate) deriving from the healthy general population may limit the longitudinal analysis.24 In a healthy and young Japanese population (mean age = 40 years), Tomiyama et al.23 mea-
Table 4 Odds ratio of albuminuria regression in T2DM patients (N = 433). OR (95% CI) Univariate analysis PWV (m/s) Multivariate analysis PWV (m/s) Other covariates LnACR (mg/g) LneGFR (ml/min/1.73 m2) Duration of T2DM (y)
p-Value
0.680 (0.537–0.861)
0.001
0.796 (0.618–1.023)
0.079
0.805 (0.658–0.985) 3.221 (1.689–6.143) 1.028 (1.000–1.057)
0.035 b0.001 0.047
PWV, pulse wave velocity; ACR, urine albumin-to-creatinine ratio; eGFR, estimated glomerular filtration rate. p b 0.05 was considered as statistically significant.
sured arterial stiffness by brachial–ankle PWV, a simple measurement. Although brachial–ankle PWV showed a good correlation with carotid– femoral PWV in some studies, it reflects more peripheral vessels than central elastic vessels.25,26 In this study, proteinuria was measured by dipstick analysis, a method with limited sensitivity and specificity for microalbuminuria detection.23 Therefore, the discrepancy may be attributable to differences in subject population, PWV measurement and proteinuria analysis. Our findings are in line with previous studies showing that high BMI and low eGFR are risk factors for albuminuria progression.27–29 In overweight T2DM patients with proteinuria, significant reduction of proteinuria has been observed after short-or long-term weight loss.30–32 Therefore, these two modifiable risk factors should be followed closely in T2DM patients. We, furthermore, examined the prevalence of albuminuria regression, and whether patients with lower PWV are likely to experience albuminuria regression than those with higher PWV as demonstrated in Japanese T2DM patients. 12 The percentage of albuminuria regression of in our study (25.9%) was higher than the reported value (18.7%). 12 However, the association between baseline PWV and regression was attenuated to marginal significance after adjustment. In our study, 104 patients with macroalbuminuria at baseline were included, but only microalbuminuria patients at baseline were included in that study. 12 We may be unable to evaluate this particular relationship with short follow-up duration and relative small sample size. We also cannot exclude chance findings due to small sample size, such as the association between longer duration of T2DM and albuminuria regression. We observed lower SBP (143.2 ± 16.1 vs.145.9 ± 19.5 mm Hg, p = 0.177) and HB1Ac (7.8 ± 1.2% vs. 8.0 ± 1.3, p = 0.174), higher HDL (1.23 ± 0.35 vs. 1.28 ± 0.35 mM, p = 0.168), and less smokers (81.1% vs. 88.4%, p = 0.174) in regression than non-regression patients, although not reaching statistical significance. Consistent with a previous report in Japanese T2DM patients, proper control of blood glucose, BP and lipid profiles, as well as unmeasured factors (i.e., lifestyle modification, medication compliance) may all contribute to albuminuria regression. 33 The exact mechanism linking increased arterial stiffness with declined renal function is not well understood yet. Previous studies have proposed several plausible mechanisms. First, because the renal glomerular afferent vessels are short, small and exposed to a high pressure, they are largely influenced by arteries stiffness.12 The increased arterial stiffness may result in glomerular hypertension through greater transmission of elevated systemic blood pressure to the glomerular capillaries, increased pulsatile stress especially in these vessels, and widened intra-renal pulse pressure, thus leading to microvascular kidney damage.34–36 Second, previous studies found associations of renal resistive index with arterial stiffness37 and rapid renal function decline,38,39 suggesting that patients with greater arterial stiffness may have a higher renal artery resistance, thereby, a more rapid decline in renal function.40,41 Finally, in the setting of diabetes, inflammation associated with hyperglycemia and arterial stiffness may have contributed to the damage of glomerular filtration barrier, resulting in the increased leakage of protein across the membrane and the development of albuminuria.42–44 The strengths of our study include the longitudinal design, a relative large sample of multi-ethnic in Singapore, and the gold
Please cite this article as: Zhang X, et al. Arterial stiffness is an independent predictor for albuminuria progression among Asians with type 2 diabetes—A prospective cohort .... Journal of Diabetes and Its Complications (2017), http://dx.doi.org/10.1016/j.jdiacomp.2017.02.004
X. Zhang et al. / Journal of Diabetes and Its Complications xxx (2017) xxx–xxx
standard measurement for assessment of arterial stiffness. We also recognized a number of limitations to our study. First, all subjects were recruited from a secondary hospital and a community-based public primary-care outpatient clinic. Whether our findings can be extended to the general population remains to be determined. Second, albuminuria status was determined by a single measurement of ACR at baseline and follow-up using from spot urine samples. However, a good correlation between spot urine protein-creatinine ratio with 24-hour urinary protein in T2DM patients was reported, suggesting that spot ACR measurement can be used as a convenient and reliable diagnostic substitute for albuminuria progression. 45 Finally, although controlling for potential confounders, we cannot rule out residual confounding from unmeasured factors that could affect arterial stiffness and albuminuria, such as social economic status and lifestyle (i.e., alcohol consumption). 46–50 In summary, our study provides the first evidence that central arterial stiffness is an independent predictor of albuminuria progression in a multi-ethnic T2DM Asian cohort in Singapore. Our results suggest the potential benefit of ameliorating central arterial stiffness (e.g., body weight management) to retard albuminuria progression in T2DM patients. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.jdiacomp.2017.02.004. Acknowledgments This work was supported by Singapore National Medical Research Council Grant PPG/AH(KTPH)/2011 and NMRC/CIRG/1398/2014. The funder has no role in study design, data collection, analysis, interpretation and manuscript writing. References 1. Ramachandran A, Snehalatha C, Shetty AS, Nanditha A. Trends in prevalence of diabetes in Asian countries. World J Diabetes. 2012;3:110-7. 2. Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract. 2010;87:4-14. 3. Phan TP, Alkema L, Tai ES, Tan KH, Yang Q, Lim WY, et al. Forecasting the burden of type 2 diabetes in Singapore using a demographic epidemiological model of Singapore. BMJ Open Diabetes Res Care. 2014;2, e000012. 4. Low SK, Sum CF, Yeoh LY, Tavintharan S, Ng XW, Lee SB, et al. Prevalence of chronic kidney disease in adults with type 2 diabetes mellitus. Ann Acad Med Singapore. 2015;44:164-71. 5. Shoji T, Emoto M, Shinohara K, Kakiya R, Tsujimoto Y, Kishimoto H, et al. Diabetes mellitus, aortic stiffness, and cardiovascular mortality in end-stage renal disease. J Am Soc Nephrol. 2001;12:2117-24. 6. Laurent S, Cockcroft J, Van Bortel L, Boutouyrie P, Giannattasio C, Hayoz D, et al. Expert consensus document on arterial stiffness: methodological issues and clinical applications. Eur Heart J. 2006;27:2588-605. 7. Kim WJ, Park CY, Park SE, Rhee EJ, Lee WY, Oh KW, et al. The association between regional arterial stiffness and diabetic retinopathy in type 2 diabetes. Atherosclerosis. 2012;225:237-41. 8. Kimoto E, Shoji T, Shinohara K, Inaba M, Okuno Y, Miki T, et al. Preferential stiffening of central over peripheral arteries in type 2 diabetes. Diabetes. 2003;52: 448-52. 9. Hatsuda S, Shoji T, Shinohara K, Kimoto E, Mori K, Fukumoto S, et al. Regional arterial stiffness associated with ischemic heart disease in type 2 diabetes mellitus. J Atheroscler Thromb. 2006;13:114-21. 10. Tsuchikura S, Shoji T, Kimoto E, Shinohara K, Hatsuda S, Koyama H, et al. Central versus peripheral arterial stiffness in association with coronary, cerebral and peripheral arterial disease. Atherosclerosis. 2010;211:480-5. 11. Prenner SB, Chirinos JA. Arterial stiffness in diabetes mellitus. Atherosclerosis. 2015;238:370-9. 12. Bouchi R, Babazono T, Mugishima M, Yoshida N, Nyumura I, Toya K, et al. Arterial stiffness is associated with incident albuminuria and decreased glomerular filtration rate in type 2 diabetic patients. Diabetes Care. 2011;34:2570-5. 13. Liu JJ, Tavintharan S, Yeoh LY, Sum CF, Ng XW, Pek SL, et al. High normal albuminuria is independently associated with aortic stiffness in patients with type 2 diabetes. Diabetic Med. 2014;31:1199-204. 14. Rognant N, Lemoine S, Laville M, Hadj-Aissa A, Dubourg L. Performance of the chronic kidney disease epidemiology collaboration equation to estimate glomerular filtration rate in diabetic patients. Diabetes Care. 2011;34:1320-2. 15. Johansen NB, Charles M, Vistisen D, Rasmussen SS, Wiinberg N, Borch-Johnsen K, et al. 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. 2012;35:2207-14.
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16. Levey AS, Gassman JJ, Hall PM, Walker WG. Assessing the progression of renal disease in clinical studies: effects of duration of follow-up and regression to the mean. Modification of Diet in Renal Disease (MDRD) study group. J Am Soc Nephrol. 1991;1:1087-94. 17. Matsushita K, Coresh J, Sang Y, Chalmers J, Fox C, Guallar E, et al. Estimated glomerular filtration rate and albuminuria for prediction of cardiovascular outcomes: a collaborative meta-analysis of individual participant data. Lancet Diabetes Endocrinol. 2015;3:514-25. 18. de Zeeuw D. Albuminuria, just a marker for cardiovascular disease, or is it more? J Am Soc Nephrol. 2005;16:1883-5. 19. Deckert T, Feldt-Rasmussen B, Borch-Johnsen K, Jensen T, Kofoed-Enevoldsen A. Albuminuria reflects widespread vascular damage. The steno hypothesis. Diabetologia. 1989;32:219-26. 20. Hoefield RA, Kalra PA, Baker PG, Sousa I, Diggle PJ, Gibson MJ, et al. The use of EGFR and ACR to predict decline in renal function in people with diabetes. Nephrol Dial Transplant. 2011;26:887-92. 21. Meguro S, Shigihara T, Kabeya Y, Tomita M, Atsumi Y. Increased risk of renal deterioration associated with low e-gfr in type 2 diabetes mellitus only in albuminuric subjects. Intern Med. 2009;48:657-63. 22. Hallan SI, Ritz E, Lydersen S, Romundstad S, Kvenild K, Orth SR. Combining gfr and albuminuria to classify CKD improves prediction of ESRD. J Am Soc Nephrol. 2009;20:1069-77. 23. Tomiyama H, Tanaka H, Hashimoto H, Matsumoto C, Odaira M, Yamada J, et al. Arterial stiffness and declines in individuals with normal renal function/early chronic kidney disease. Atherosclerosis. 2010;212:345-50. 24. Upadhyay A, Hwang SJ, Mitchell GF, Vasan RS, Vita JA, Stantchev PI, et al. Arterial stiffness in mild-to-moderate CKD. J Am Soc Nephrol. 2009;20:2044-53. 25. Sugawara J, Hayashi K, Yokoi T, Cortez-Cooper MY, DeVan AE, Anton MA, et al. brachial–ankle pulse wave velocity: an index of central arterial stiffness? J Hum Hypertens. 2005;19:401-6. 26. Tanaka H, Munakata M, Kawano Y, Ohishi M, Shoji T, Sugawara J, et al. Comparison between carotid–femoral and brachial–ankle pulse wave velocity as measures of arterial stiffness. J Hypertens. 2009;27:2022-7. 27. Cobas RA, Santos B, da Silva PC, Neves R, Gomes MB. Progression to microalbuminuria in patients with type 1 diabetes: a seven-year prospective study. Diabetol Metab Syndr. 2011;3:21. 28. Maric-Bilkan C. Obesity and diabetic kidney disease. Med Clin North Am. 2013;97: 59-74. 29. Scheven L, Halbesma N, de Jong PE, de Zeeuw D, Bakker SJ, Gansevoort RT. Predictors of progression in albuminuria in the general population: results from the PREVEND cohort. PLoS One. 2013;8:e61119. 30. Morales E, Valero MA, Leon M, Hernandez E, Praga M. Beneficial effects of weight loss in overweight patients with chronic proteinuric nephropathies. Am J Kidney Dis. 2003;41:319-27. 31. Saiki A, Nagayama D, Ohhira M, Endoh K, Ohtsuka M, Koide N, et al. Effect of weight loss using formula diet on renal function in obese patients with diabetic nephropathy. Int J Obes (Lond). 2005;29:1115-20. 32. Solerte SB, Fioravanti M, Schifino N, Ferrari E. Effects of diet-therapy on urinary protein excretion albuminuria and renal haemodynamic function in obese diabetic patients with overt nephropathy. Int J Obes (Lond). 1989;13:203-11. 33. Ono T, Shikata K, Obika M, Miyatake N, Kodera R, Hirota D, et al. Factors associated with remission and/or regression of microalbuminuria in type 2 diabetes mellitus. Acta Med Okayama. 2014;68:235-41. 34. Bidani AK, Griffin KA, Picken M, Lansky DM. Continuous telemetric blood pressure monitoring and glomerular injury in the rat remnant kidney model. Am J Physiol. 1993;265:F391-8. 35. Griffin KA, Picken MM, Churchill M, Churchill P, Bidani AK. Functional and structural correlates of glomerulosclerosis after renal mass reduction in the rat. J Am Soc Nephrol. 2000;11:497-506. 36. O'Rourke MF, Safar ME. Relationship between aortic stiffening and microvascular disease in brain and kidney: cause and logic of therapy. Hypertension. 2005;46: 200-4. 37. Ohta Y, Fujii K, Arima H, Matsumura K, Tsuchihashi T, Tokumoto M, et al. Increased renal resistive index in atherosclerosis and diabetic nephropathy assessed by Doppler sonography. J Hypertens. 2005;23:1905-11. 38. Radermacher J, Ellis S, Haller H. Renal resistance index and progression of renal disease. Hypertension. 2002;39:699-703. 39. Radermacher J, Mengel M, Ellis S, Stuht S, Hiss M, Schwarz A, et al. The renal arterial resistance index and renal allograft survival. N Engl J Med. 2003;349:115-24. 40. Chen SC, Lin TH, Hsu PC, Chang JM, Lee CS, Tsai WC, et al. Impaired left ventricular systolic function and increased brachial–ankle pulse-wave velocity are independently associated with rapid renal function progression. Hypertens Res. 2011;34: 1052-8. 41. Petersen LJ, Petersen JR, Ladefoged SD, Mehlsen J, Jensen HA. The pulsatility index and the resistive index in renal arteries in patients with hypertension and chronic renal failure. Nephrol Dial Transplant. 1995;10:2060-4. 42. Jauregui A, Mintz DH, Mundel P, Fornoni A. Role of altered insulin signaling pathways in the pathogenesis of podocyte malfunction and microalbuminuria. Curr Opin Nephrol Hypertens. 2009;18:539-45. 43. Lehoux S, Tegui A. Mechanical stress and the arterial wall. In: Safar M, O'Rourke M, eds. Arterial stiffness in hypertension. London: Elsevier; 2006. p. 75-98. 44. Silverstein DM. Inflammation in chronic kidney disease: role in the progression of renal and cardiovascular disease. Pediatr Nephrol. 2009;24:1445-52. 45. Biradar SB, Kallaganad GS, Rangappa M, Kashinakunti SV, Retnakaran R. Correlation of spot urine protein-creatinine ratio with 24-hour urinary protein in type 2 diabetes mellitus patients: a cross sectional study. J Res Med Sci. 2011;16:634-9.
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46. Fraser SD, Roderick PJ, Aitken G, Roth M, Mindell JS, Moon G, et al. Chronic kidney disease, albuminuria and socioeconomic status in the health surveys for England 2009 and 2010. J Public Health. 2014;36:577-86. 47. Plantinga Y, Ghiadoni L, Magagna A, Giannarelli C, Franzoni F, Taddei S, et al. Supplementation with vitamins c and e improves arterial stiffness and endothelial function in essential hypertensive patients. Am J Hypertens. 2007;20: 392-7.
48. Tanaka H, Safar ME. Influence of lifestyle modification on arterial stiffness and wave reflections. Am J Hypertens. 2005;18:137-44. 49. Thurston RC, Matthews KA. Racial and socioeconomic disparities in arterial stiffness and intima media thickness among adolescents. Soc Sci Med. 2009;68:807-13. 50. White SL, Polkinghorne KR, Cass A, Shaw JE, Atkins RC, Chadban SJ. Alcohol consumption and 5-year onset of chronic kidney disease: the AUSDIAB study. Nephrol Dial Transplant. 2009;24:2464-72.
Please cite this article as: Zhang X, et al. Arterial stiffness is an independent predictor for albuminuria progression among Asians with type 2 diabetes—A prospective cohort .... Journal of Diabetes and Its Complications (2017), http://dx.doi.org/10.1016/j.jdiacomp.2017.02.004
Contents lists available at ScienceDirect
Journal of Diabetes and Its Complications j o u r n a l h o m e p a g e : W W W. J D C J O U R N A L . C O M
Arterial stiffness is an independent predictor for albuminuria progression among Asians with type 2 diabetes—A prospective cohort study Xiao Zhang a, Serena Low a, Chee Fang Sum b, c, Subramaniam Tavintharan b, c, Yeoh Lee Ying c, Jianjun Liu a, Na Li a, Keven Anga a, Simon BM 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 c Department of Medicine, Khoo Teck Puat Hospital, Singapore 768828, Republic of Singapore d National Healthcare Group Polyclinics, Singapore 138543, Singapore b
a r t i c l e
i n f o
Article history: Received 14 September 2016 Received in revised form 14 November 2016 Accepted 1 February 2017 Available online xxxx Keywords: Type 2 diabetes Central arterial stiffness Pulse wave velocity Albuminuria progression Chronic kidney disease
a b s t r a c t Aim: Albuminuria progression has been associated with renal deterioration in type 2 diabetes (T2DM). Central arterial stiffness can aggravate systemic vasculopathy by propagating elevated systolic and pulse pressures forward, thereby accentuating global vascular injury. We aim to investigate whether central arterial stiffness is an independent predictor for albuminuria progression in a multi-ethnic T2DM Asian cohort in Singapore. Methods: In a prospective cohort, 1012 T2DM patients were assessed at baseline and after a median follow-up of 3.1 years. 880 patients with baseline normo- (urinary albumin-to-creatinine ratio (ACR) b 30 mg/g, n = 579) and microalbuminuria (ACR = 30–299 mg/g, n = 301) were divided into progression and non-progression groups according to ACR changes. Progression was defined as transition from normo- to microalbuminuria, micro- to macroalbuminuria, or normo- to macroalbuminuria. Central arterial stiffness was estimated by carotid–femoral pulse wave velocity (PWV) using applanation tonometry method. Stepwise multiple regression analysis was used to determine the predictor(s) for albuminuria progression. Results: Albuminuria progression occurred in 178 patients (20.2%). Baseline PWV was higher in progression (10.1 ± 2.9 m/s) than non-progression group (9.2 ± 2.4 m/s, p b 0.001). 1-SD increase in baseline PWV was associated with albuminuria progression (OR = 1.457, 95% CI, 1.236–1.718, p b 0.001). Stepwise regression analysis identified that baseline PWV (OR = 1.241, 95% CI, 1.033–1.490, p = 0.021), BMI (OR = 1.046, 95% CI, 1.012–1.080, p = 0.008), nature log-transformed estimated glomerular filtration rate (LneGFR) (OR = 0.320, 95% CI, 0.192–0.530, p = 0.010) and LnACR (OR = 1.344, 95% CI, 1.187–1.522, p = 0.008) are predictors for albuminuria progression. Conclusion: Increased central arterial stiffness at baseline predicted future progression of albuminuria. Our results suggest the potential benefit of ameliorating central arterial stiffness to retard albuminuria progression in T2DM. © 2017 Elsevier Inc. All rights reserved.
1. Introduction Type 2 diabetes (T2DM) is a rapidly evolving global health problem, and Asia is emerging as the epi-center of this epidemic. 1,2 The prevalence of T2DM is predicted to double from 7.3% in 1990 to 15% in 2050 in Singapore, a multi-ethnic city-state composed of three major ethnic groups (Chinese, Malays and Indians). 3 Diabetes is a major cause of chronic kidney disease (CKD). In Singapore, the burden
Disclosures: None. ⁎ Corresponding author at: Diabetes Center, Khoo Teck Puat Hospital, 90 Yishun Central, Singapore 768828, Republic of Singapore. Tel.: +65 66022353; fax: +65 66023772. E-mail address: [email protected] (S.C. Lim).
of CKD is rising in parallel with the growing prevalence of diabetes. We have recently reported a 53% prevalence of CKD among patients with T2DM in Singapore. 4 Many clinical and epidemiological studies have reported that CKD patients are at high risk of incident cardiovascular disease (CVD) and cardiovascular morbidity. 5 Although the mechanisms underlying this association are not fully elucidated, patients with CKD and CVD may have shared risk factors, such as increased arterial stiffness. 5 Arterial stiffness, a well-established biomarker of vasculopathy, has become a clinically useful predictor of complications in T2DM, including CKD. 6 Pulse wave velocity (PWV), the gold-standard index for arterial stiffness, has been widely used in clinical settings. PWV can be determined across regional arterial segments, including central (e.g., carotid–femoral) and peripheral (e.g., brachial–ankle) segments. 7 The
http://dx.doi.org/10.1016/j.jdiacomp.2017.02.004 1056-8727/© 2017 Elsevier Inc. All rights reserved.
Please cite this article as: Zhang X, et al. Arterial stiffness is an independent predictor for albuminuria progression among Asians with type 2 diabetes—A prospective cohort .... Journal of Diabetes and Its Complications (2017), http://dx.doi.org/10.1016/j.jdiacomp.2017.02.004
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X. Zhang et al. / Journal of Diabetes and Its Complications xxx (2017) xxx–xxx
effects of diabetes on arterial stiffness were suggested to be greater in central than peripheral arteries. 8 Mechanistically, central arterial stiffness can aggravate systemic vasculopathy by propagating elevated systolic and pulse pressures forward, thereby accentuating global vascular injury. 9,10 Urinary albumin-to-creatinine ratio (ACR) is an important component to predict renal dysfunction. Previous cross-sectional studies in T2DM patients have revealed a significant association of increased arterial stiffness with albuminuria.11 To date, only one longitudinal study has examined the impact of arterial stiffness on renal function in T2DM patients, a finding that increased PWV is significantly associated with incident albuminuria.12 To our knowledge, the longitudinal impact of central arterial stiffness on albuminuria progression has never been reported in Asians population with rapidly rising prevalence of T2DM and diabetic nephropathy like Singapore. In this study, we aim to evaluate whether arterial stiffness is an independent predictor for albuminuria progression in a multi-ethnic T2DM Asian cohort in Singapore. 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 completed cross-sectional study conducted between August 2011 and February 2014 including a total of 2057 adults aged 21–90 years with T2DM. 13 Inclusion and exclusion criteria of SMART2D have been previously described. 13 Longitudinal data on urinary albumin excretion were obtained from the ongoing follow-up prospective study from September 2014. In our study, a total of 1012 patients were included for analysis. 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 using a mercury sphygmomanometer on the right arm using appropriate cuff sizes. Systolic blood pressure (SBP) and diastolic blood pressure (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 ACR 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 μg/ml (Immulite; DPC, Gwynedd, UK). Estimated GFR (eGFR) was calculated based on a widely used Modified Diet in Renal Disease (MDRD) equation in patients with diabetes.14 Hemoglobin A1c (HbA1c) was measured based on monoclonal antibody agglutination reaction using a point-of-care 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. 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 Carotid–femoral PWV was measured by the foot-to-foot method using a well-validated device, SphygmoCor® (AtCor Medical, Sydney, Australia) as described previously. 15 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 9–10 pulse waves in each assessment. PWV was expressed as the distance between the two recording sites (meters) divided by transit time (seconds). 2.4. Albuminuria progression and regression The end point with regard to albuminuria progression was based on ACR measures at baseline and follow-up. Albuminuria progression was defined as transition from normo- (ACR b 30 mg/g) to microalbuminuria (ACR = 30–299 mg/g), micro- to macroalbuminuria (ACR N 300 mg/g), or normo- to macroalbuminuria. Since transitions between states were assumed to occur at any time within the observed time interval, we further divided non-progression patients into regression and remain in the same albuminuria-based state. Regression was defined as transition from macro- to micro- or albuminuria, or micro- to normoalbuminuria. The minimum follow-up period is two years, which meets the recommendation for valid determination of renal disease progression. 16 2.5. Statistical analysis Standard descriptive statistics were used to describe the characteristics of individuals with T2DM. Normally distributed continuous data were expressed as means and standard deviations (SDs). Skewed variables were expressed as median and inter-quartile range (IQR) and nature log (ln)-transformed before data analysis. Differences between groups were compared by t test or χ 2 test where appropriate. We also examined the distribution of baseline characteristics, including HbA1c, blood pressure and renal function that were possible risk factors for albuminuria progression according to PWV quartiles. Univariate analysis was performed for individual clinical and biochemical parameters to verify associated factors (Table S1). Variables that are statistically significant in univariate analysis or with putative roles in the patho-biology of renal function decline were added into multivariable logistic regression models. Stepwise binary logistic regression was used to examine the predictor(s) for albuminuria progression. We also examined the association of PWV with regression in patients with baseline micro- or macroalbuminuria using stepwise logistic regression. The following variables were incorporated as covariates in multivariate logistic regression model: age, gender, ethnicity, smoking status, HBA1c, SBP, HDL-C, LDL-C, BMI, eGFR, ACR, and usage of renin-angiotensin system (RAS) inhibitors (e.g., angiotensin-converting enzyme (ACE) inhibitors, angiotensin II receptor blockers (ARBs)). All statistical analyses were performed using STATA version 14.0 (STATA Corporation, College Station, Texas, USA). A two-tailed p value of less than 0.05 was considered as statistically significant. 3. Results Totally, 1012 T2DM patients was included in this study, including 579 (57.2%) had normoalbuminuria, 301 (29.7%) had microalbuminuria and 132 (13.0%) had macroalbuminuria at baseline. After a median follow-up of 3.1 years (range 2.1–4.2), albuminuria progression occurred in 178 patients (20.2%) out of 880 patients with baseline normo- and microalbuminuria. Table 1 shows the baseline characteristics of T2DM patients stratified by albuminuria progression. Compared with the non-progression group, the progression group had significantly higher BMI, SBP and ACR, and lower eGFR. There were no significant differences in age, gender, ethnicity, smoking status, duration of T2DM, Hb1Ac, HDL-C, LDL-C, DBP and usage of RAS inhibitors. Baseline
Please cite this article as: Zhang X, et al. Arterial stiffness is an independent predictor for albuminuria progression among Asians with type 2 diabetes—A prospective cohort .... Journal of Diabetes and Its Complications (2017), http://dx.doi.org/10.1016/j.jdiacomp.2017.02.004
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Table 1 Baseline characteristics of individuals with T2DM stratified by albuminuria progression (N = 880). Variables
Non-progression (702)
Progression (178)
Total (886)
p-Value
Entry age (y) Gender (%) Male Female Ethnicity (%) Chinese Malays Indians Smoking history (%) Current and former Never Duration of T2DM (y) Hba1c (%) HDL-C (mM) LDL-C (mM) BMI (kg/m2) eGFR (ml/min/1.73 m2)a ACR (mg/g)a SBP (mm Hg) DBP (mm Hg) PWV (m/sec) RAS medication (%)
56.5 ± 10.1
58.0 ± 10.2
56.8 ± 10.2
0.074
51.1 48.9
48.3 51.7
50.6 49.4
0.502
55.4 18.9 25.7
54.3 26.0 19.7
55.2 20.4 24.4
0.065
13.3 86.7 10.9 ± 8.7 7.6 ± 1.2 1.3 ± 0.4 2.7 ± 0.8 27.5 ± 5.0 91.5 (77.7–108.6) 12 (4–40) 135.5 ± 15.7 78.3 ± 9.2 9.2 ± 2.4 61.1
12.4 87.6 10.6 ± 8.5 7.8 ± 1.4 1.3 ± 0.3 2.7 ± 0.8 29.0 ± 5.6 80.9 (59.6–98.3) 24 (13–94) 139.7 ± 16.8 78.9 ± 9.2 10.1 ± 2.9 54.5
13.1 86.9 10.9 ± 8.7 7.7 ± 1.3 1.3 ± 0.4 2.7 ± 0.8 27.8 ± 5.2 89.9 (74.3–106.8) 15 (5–43) 136.3 ± 16.1 78.4 ± 9.2 9.4 ± 2.5 59.8
0.736 0.598 0.256 0.441 0.834 b0.001 b0.001 b0.001 0.002 0.435 b0.001 0.112
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; TC, total cholesterol; TG, total triglycerides; ACR, urine albumin-to-creatinine ratio; RAS medication, angiotensin-converting-enzyme or angiotensin receptor blockers. p b 0.05 was considered as statistically significant. a Expressed as median (inter-quartile range).
Table 2 Baseline characteristics and albuminuria progression according to quartiles of baseline PWV (N = 880).
Hba1c (%) SBP (mm Hg) DBP (mm Hg) eGFR (ml/min/1.73 m2)a ACR (mg/g)a Albuminuria progression (%)
Q1
Q2
Q3
Q4
p-Trend
7.55 ± 1.28 129.5 ± 14.2 77.8 ± 9.2 95.1 (82.8–114.6) 10 (3–30) 13.7
7.73 ± 1.34 135.3 ± 14.6 79.4 ± 9.5 89.9 (74.6–102.9) 16.5 (5–55) 18.4
7.75 ± 1.21 142.5 ± 18.5 79.0 ± 10.3 84.6 (64.2–103.2) 28 (8–91) 17.7
7.80 ± 1.31 146.3 ± 17.8 79.2 ± 8.6 75.4 (55.2–96.0) 50 (14–306) 32.0
0.460 b0.001 0.023 b0.001 b0.001 b0.001
HbA1c, hemoglobin A1c; SBP, systolic blood pressure; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; ACR, urine albumin-to-creatinine ratio. p b 0.05 was considered as statistically significant. a Expressed as median (inter-quartile range).
PWV was significantly higher in progression (10.1 ± 2.9 m/s) than the non-progression group (9.2 ± 2.4 m/s, p b 0.001). Table 2 shows baseline Hb1Ac, blood pressure and renal function, as well as percentage of albuminuria progression according to quartiles of baseline PWV level. Blood pressure increased and renal function decreased across increasing quartiles of PWV. The percentage of albuminuria progression increased significantly across increasing quartiles of PWV. One SD increase in baseline PWV was associated with increased risk of albuminuria progression (OR = 1.457, 95% CI, 1.236–1.718, p b 0.001). Stepwise multiple regression analysis identified that baseline PWV (OR = 1.241, 95% CI, 1.033–1.490, p = 0.021), BMI (OR = 1.046, 95% CI, 1.012–1.080, p = 0.008), nature log-transformed eGFR (LneGFR) (OR = 0.320, 95% CI, 0.192–0.530, p = 0.010) and LnACR (OR = 1.344, 95% CI, 1.187–1.522, p = 0.008) are predictors for albuminuria progression (Table 3). Fig. 1 shows PWV level at different albuminuria-based states during follow-up period. The majority of patients remained in the same state, with PWV level lower than progression patients while higher than regression patients. In 433 patients with baseline micro- or macroalbuminuria, regression occurred in 112 patients (25.9%). Baseline PWV was significantly higher in non-regression (10.5 ± 2.7 m/s) than regression group (9.5 ± 2.5 m/s, p = 0.001). For every 1-SD increase in
baseline PWV, there is and 32.0% decreased likelihood of albuminuria regression (OR = 0.680, 95% CI, 0.537–0.861, p = 0.001). However, the association was attenuated to marginal significance after adjustment (OR = 0.796, 95% CI, 0.618–1.023, p = 0.079) (Table 4). 4. Discussion In a multi-ethnic T2DM Asian cohort, we demonstrated that PWV could predict albuminuria progression. The other independent risk
Table 3 Odds ratio of albuminuria progression in T2DM patients (N = 880).
Univariate analysis PWV (m/s) Multivariate analysis PWV (m/s) Other covariates LnACR (mg/g) LneGFR (ml/min/1.73 m2) BMI (kg/m2)
OR (95% CI)
p-Value
1.457 (1.236–1.718)
b0.001
1.241 (1.033–1.490)
0.021
1.344 (1.187–1.522) 0.320 (0.192–0.530) 1.046 (1.012–1.080)
b0.001 0.010 0.008
PWV, pulse wave velocity; BMI, body mass index; eGFR, estimated glomerular filtration rate. p b 0.05 was considered as statistically significant.
Please cite this article as: Zhang X, et al. Arterial stiffness is an independent predictor for albuminuria progression among Asians with type 2 diabetes—A prospective cohort .... Journal of Diabetes and Its Complications (2017), http://dx.doi.org/10.1016/j.jdiacomp.2017.02.004
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Fig. 1. PWV and transition probability in every state during follow up period (N = 1012). p-Value is compared with those remained in the same state.
factors for albuminuria progression include baseline lower eGFR and higher BMI. To the best of our knowledge, it is the first study to examine the association of central arterial stiffness and albuminuria progression in a prospective study in Singapore—a multi-ethnic Asian population with rapidly rising prevalence of T2DM and diabetic kidney disease. The contribution of urinary ACR and GFR to CKD progression and CVD risk remains controversial. A recent meta-analysis revealed that ACR improved risk prediction for cardiovascular mortality and heart failure better than GFR, especially in diabetes patients. 17 Microalbuminuria represents damaged or dysfunctional vascular endothelium, whereas large quantities of albuminuria represents damaged glomerular barrier in large quantity. 18,19 Thus, albuminuria progression could reflect changes in the pathophysiology of the leakage of proteins and serves as a biomarker of progression of renal dysfunction. 18 Several longitudinal studies have suggested the prognostic importance of proteinuria upon CKD progression in diabetic CKD. For example, T2DM patients with macroalbuminuria have much more rapid decline in renal function compared with those without albuminuria in UK and Japanese populations. 20,21 In European non-diabetic population, albuminuria has been demonstrated as a continuous risk factor for progression to end-stage renal disease at all levels of eGFR. 22 Therefore, we evaluated renal outcome on the basis of ACR changes at baseline and follow-up in this study. Our finding of longitudinal association of baseline PWV with albuminuria progression agrees with the only longitudinal study of baseline PWV and renal function in T2DM patients. 12 Our study also showed good agreement in prevalence of albuminuria progression (17.6%) with that study (18.4%), although they have longer follow-up duration (median = 5.9 years). 12 However, it should be noted that two longitudinal studies reported non-association between PWV and incident albuminuria or proteinuria in non-diabetic populations. 23,24 In Framingham heart study consisted of mainly Caucasians, although carotid–femoral PWV was associated with microalbuminuria status and ACR in cross-sectional study, the longitudinal study did not find an association between baseline PWV and incident microalbuminuria. 24 The lower statistical power (34% power to detect 10% microalbuminuria incidence rate) deriving from the healthy general population may limit the longitudinal analysis.24 In a healthy and young Japanese population (mean age = 40 years), Tomiyama et al.23 mea-
Table 4 Odds ratio of albuminuria regression in T2DM patients (N = 433). OR (95% CI) Univariate analysis PWV (m/s) Multivariate analysis PWV (m/s) Other covariates LnACR (mg/g) LneGFR (ml/min/1.73 m2) Duration of T2DM (y)
p-Value
0.680 (0.537–0.861)
0.001
0.796 (0.618–1.023)
0.079
0.805 (0.658–0.985) 3.221 (1.689–6.143) 1.028 (1.000–1.057)
0.035 b0.001 0.047
PWV, pulse wave velocity; ACR, urine albumin-to-creatinine ratio; eGFR, estimated glomerular filtration rate. p b 0.05 was considered as statistically significant.
sured arterial stiffness by brachial–ankle PWV, a simple measurement. Although brachial–ankle PWV showed a good correlation with carotid– femoral PWV in some studies, it reflects more peripheral vessels than central elastic vessels.25,26 In this study, proteinuria was measured by dipstick analysis, a method with limited sensitivity and specificity for microalbuminuria detection.23 Therefore, the discrepancy may be attributable to differences in subject population, PWV measurement and proteinuria analysis. Our findings are in line with previous studies showing that high BMI and low eGFR are risk factors for albuminuria progression.27–29 In overweight T2DM patients with proteinuria, significant reduction of proteinuria has been observed after short-or long-term weight loss.30–32 Therefore, these two modifiable risk factors should be followed closely in T2DM patients. We, furthermore, examined the prevalence of albuminuria regression, and whether patients with lower PWV are likely to experience albuminuria regression than those with higher PWV as demonstrated in Japanese T2DM patients. 12 The percentage of albuminuria regression of in our study (25.9%) was higher than the reported value (18.7%). 12 However, the association between baseline PWV and regression was attenuated to marginal significance after adjustment. In our study, 104 patients with macroalbuminuria at baseline were included, but only microalbuminuria patients at baseline were included in that study. 12 We may be unable to evaluate this particular relationship with short follow-up duration and relative small sample size. We also cannot exclude chance findings due to small sample size, such as the association between longer duration of T2DM and albuminuria regression. We observed lower SBP (143.2 ± 16.1 vs.145.9 ± 19.5 mm Hg, p = 0.177) and HB1Ac (7.8 ± 1.2% vs. 8.0 ± 1.3, p = 0.174), higher HDL (1.23 ± 0.35 vs. 1.28 ± 0.35 mM, p = 0.168), and less smokers (81.1% vs. 88.4%, p = 0.174) in regression than non-regression patients, although not reaching statistical significance. Consistent with a previous report in Japanese T2DM patients, proper control of blood glucose, BP and lipid profiles, as well as unmeasured factors (i.e., lifestyle modification, medication compliance) may all contribute to albuminuria regression. 33 The exact mechanism linking increased arterial stiffness with declined renal function is not well understood yet. Previous studies have proposed several plausible mechanisms. First, because the renal glomerular afferent vessels are short, small and exposed to a high pressure, they are largely influenced by arteries stiffness.12 The increased arterial stiffness may result in glomerular hypertension through greater transmission of elevated systemic blood pressure to the glomerular capillaries, increased pulsatile stress especially in these vessels, and widened intra-renal pulse pressure, thus leading to microvascular kidney damage.34–36 Second, previous studies found associations of renal resistive index with arterial stiffness37 and rapid renal function decline,38,39 suggesting that patients with greater arterial stiffness may have a higher renal artery resistance, thereby, a more rapid decline in renal function.40,41 Finally, in the setting of diabetes, inflammation associated with hyperglycemia and arterial stiffness may have contributed to the damage of glomerular filtration barrier, resulting in the increased leakage of protein across the membrane and the development of albuminuria.42–44 The strengths of our study include the longitudinal design, a relative large sample of multi-ethnic in Singapore, and the gold
Please cite this article as: Zhang X, et al. Arterial stiffness is an independent predictor for albuminuria progression among Asians with type 2 diabetes—A prospective cohort .... Journal of Diabetes and Its Complications (2017), http://dx.doi.org/10.1016/j.jdiacomp.2017.02.004
X. Zhang et al. / Journal of Diabetes and Its Complications xxx (2017) xxx–xxx
standard measurement for assessment of arterial stiffness. We also recognized a number of limitations to our study. First, all subjects were recruited from a secondary hospital and a community-based public primary-care outpatient clinic. Whether our findings can be extended to the general population remains to be determined. Second, albuminuria status was determined by a single measurement of ACR at baseline and follow-up using from spot urine samples. However, a good correlation between spot urine protein-creatinine ratio with 24-hour urinary protein in T2DM patients was reported, suggesting that spot ACR measurement can be used as a convenient and reliable diagnostic substitute for albuminuria progression. 45 Finally, although controlling for potential confounders, we cannot rule out residual confounding from unmeasured factors that could affect arterial stiffness and albuminuria, such as social economic status and lifestyle (i.e., alcohol consumption). 46–50 In summary, our study provides the first evidence that central arterial stiffness is an independent predictor of albuminuria progression in a multi-ethnic T2DM Asian cohort in Singapore. Our results suggest the potential benefit of ameliorating central arterial stiffness (e.g., body weight management) to retard albuminuria progression in T2DM patients. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.jdiacomp.2017.02.004. Acknowledgments This work was supported by Singapore National Medical Research Council Grant PPG/AH(KTPH)/2011 and NMRC/CIRG/1398/2014. The funder has no role in study design, data collection, analysis, interpretation and manuscript writing. References 1. Ramachandran A, Snehalatha C, Shetty AS, Nanditha A. Trends in prevalence of diabetes in Asian countries. World J Diabetes. 2012;3:110-7. 2. Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract. 2010;87:4-14. 3. Phan TP, Alkema L, Tai ES, Tan KH, Yang Q, Lim WY, et al. Forecasting the burden of type 2 diabetes in Singapore using a demographic epidemiological model of Singapore. BMJ Open Diabetes Res Care. 2014;2, e000012. 4. Low SK, Sum CF, Yeoh LY, Tavintharan S, Ng XW, Lee SB, et al. Prevalence of chronic kidney disease in adults with type 2 diabetes mellitus. Ann Acad Med Singapore. 2015;44:164-71. 5. Shoji T, Emoto M, Shinohara K, Kakiya R, Tsujimoto Y, Kishimoto H, et al. Diabetes mellitus, aortic stiffness, and cardiovascular mortality in end-stage renal disease. J Am Soc Nephrol. 2001;12:2117-24. 6. Laurent S, Cockcroft J, Van Bortel L, Boutouyrie P, Giannattasio C, Hayoz D, et al. Expert consensus document on arterial stiffness: methodological issues and clinical applications. Eur Heart J. 2006;27:2588-605. 7. Kim WJ, Park CY, Park SE, Rhee EJ, Lee WY, Oh KW, et al. The association between regional arterial stiffness and diabetic retinopathy in type 2 diabetes. Atherosclerosis. 2012;225:237-41. 8. Kimoto E, Shoji T, Shinohara K, Inaba M, Okuno Y, Miki T, et al. Preferential stiffening of central over peripheral arteries in type 2 diabetes. Diabetes. 2003;52: 448-52. 9. Hatsuda S, Shoji T, Shinohara K, Kimoto E, Mori K, Fukumoto S, et al. Regional arterial stiffness associated with ischemic heart disease in type 2 diabetes mellitus. J Atheroscler Thromb. 2006;13:114-21. 10. Tsuchikura S, Shoji T, Kimoto E, Shinohara K, Hatsuda S, Koyama H, et al. Central versus peripheral arterial stiffness in association with coronary, cerebral and peripheral arterial disease. Atherosclerosis. 2010;211:480-5. 11. Prenner SB, Chirinos JA. Arterial stiffness in diabetes mellitus. Atherosclerosis. 2015;238:370-9. 12. Bouchi R, Babazono T, Mugishima M, Yoshida N, Nyumura I, Toya K, et al. Arterial stiffness is associated with incident albuminuria and decreased glomerular filtration rate in type 2 diabetic patients. Diabetes Care. 2011;34:2570-5. 13. Liu JJ, Tavintharan S, Yeoh LY, Sum CF, Ng XW, Pek SL, et al. High normal albuminuria is independently associated with aortic stiffness in patients with type 2 diabetes. Diabetic Med. 2014;31:1199-204. 14. Rognant N, Lemoine S, Laville M, Hadj-Aissa A, Dubourg L. Performance of the chronic kidney disease epidemiology collaboration equation to estimate glomerular filtration rate in diabetic patients. Diabetes Care. 2011;34:1320-2. 15. Johansen NB, Charles M, Vistisen D, Rasmussen SS, Wiinberg N, Borch-Johnsen K, et al. 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. 2012;35:2207-14.
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Please cite this article as: Zhang X, et al. Arterial stiffness is an independent predictor for albuminuria progression among Asians with type 2 diabetes—A prospective cohort .... Journal of Diabetes and Its Complications (2017), http://dx.doi.org/10.1016/j.jdiacomp.2017.02.004