Association of variability in hemoglobin A1c with cardiovascular diseases and mortality in Chinese patients with type 2 diabetes mellitus — A retrospective population-based cohort study

Journal of Diabetes and Its Complications xxx (2016) 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

Association of variability in hemoglobin A1c with cardiovascular diseases and mortality in Chinese patients with type 2 diabetes mellitus — A retrospective population-based cohort study Eric Yuk Fai Wan a, b,⁎, Colman Siu Cheung Fung a, Daniel Yee Tak Fong b, Cindy Lo Kuen Lam a a b

Department of Family Medicine and Primary Care, the University of Hong Kong, Hong Kong School of Nursing, the University of Hong Kong, Hong Kong

a r t i c l e

i n f o

Article history: Received 17 March 2016 Received in revised form 12 April 2016 Accepted 26 May 2016 Available online xxxx Keywords: Diabetes mellitus HbA1c Variability Cardiovascular diseases Mortality

a b s t r a c t Aims: This study aimed to investigate the association between variability in HbA1c and incidence of cardiovascular disease (CVD) event and mortality among Chinese primary care patients with Type 2 diabetes mellitus (T2DM). Methods: A retrospective cohort study was conducted on 91,866 T2DM patients aged ≥18 years without any history of CVD. Variability in HbA1c, was measured by standard deviation (SD), associated with the risks of CVD and all-cause mortality were evaluated using Cox proportional hazards regression analysis by age groups ( 65 and ≥ 65 years old)." to "Variability in HbA1c was measured by standard deviation (SD) The association between Variability in HbA1c and the incidence of CVD and all-cause mortality were evaluated using Cox proportional hazards regression analysis by age groups ( 65 and ≥ 65 years old). Results: Over a median follow-up of 58.5 months, our study identified a positive linear relationship between variability in HbA1c and incidence of CVD and all-cause mortality in the younger and older groups. For every 1-SD increase in HbA1c, the risk of CVD events in the older group only increased by 15.2% (95% CI: 1.026–1.293), and the risks of all-cause mortality in both age groups increased by 49.5% (95% CI: 1.154–1.936) and 77.8% (95% CI: 1.563–2.024), respectively. Conclusions: The HbA1c variability independently of the mean HbA1c level may provide additional valuable information as a potential predictor for the development of CVD and all-cause mortality in diabetic patients, particularly for the elderly patients. © 2016 Elsevier Inc. All rights reserved.

1. Introduction Diabetes mellitus (DM) is an important public health issue, affecting 387 million people worldwide and contributing to 10% of the global mortality of which approximately 70% are attributed to cardiovascular diseases (CVD) (Gilmer et al., 2005; International Diabetes Federation, 2015). Due to population aging and the increasing prevalence of obesity all over the world, the number of diabetic patients has been forecast to rise globally to 552 million by 2030 (Whiting, Guariguata, Weil, & Shaw, 2011). In most international guidelines for diabetes management, reducing the level of blood sugar, measured by Hemoglobin A1c (HbA1c), to optimal level is a well-recognized goal to minimize the risk of CVD and premature death (American Diabetes Association, 2015; Conflict of Interest: Competing interests none declared. ⁎ Corresponding author at: Department of Family Medicine and Primary Care, the University of Hong Kong, 3/F Ap Lei Chau Clinic, 161 Main Street, Ap Lei Chau, Hong Kong. Tel.: +852 2552 4690; fax: +852 2814 7475. E-mail addresses: [email protected] (E.Y.F. Wan), [email protected] (C.S.C. Fung), [email protected] (D.Y.T. Fong), [email protected] (C.L.K. Lam).

International Diabetes Federation Guideline Development Group, 2014). Apart from the optimal level, there is an emerging concern about the deleterious effect of variability in blood glucose among diabetic populations (Monnier, Colette, & Owens, 2008; Nalysnyk, Hernandez‐Medina, & Krishnarajah, 2010). Many studies also suggested that the glycemic variability is a potential predictor for diabetic complications and mortality and may play a vital role in clinical risk assessment (Cheng et al., 2014; Gorst et al., 2015). Literature has demonstrated that the short term effect on higher level of fluctuation in blood glucose from hour to hour or between days increased the risk of morbidity and mortality (Nalysnyk et al., 2010). Nevertheless, a few studies have investigated the long term effect of variability in HbA1c (Gorst et al., 2015). Understanding the long term impact of glycemic variability to prevent CVD and mortality is needed to inform clinical practice and policy planning. Currently there are no population-based study investigating the adverse effects of variability in HbA1c on CVD and mortality among patients with Type 2 DM. Two meta-analyses concluded that variability in HbA1c was associated with the worsening of renal function (Cheng et al., 2014; Gorst et al., 2015). Nonetheless, most of the previous literature

http://dx.doi.org/10.1016/j.jdiacomp.2016.05.024 1056-8727/© 2016 Elsevier Inc. All rights reserved.

Please cite this article as: Wan, E.Y.F., et al., Association of variability in hemoglobin A1c with cardiovascular diseases and mortality in Chinese patients with type 2 diabetes m..., Journal of Diabetes and Its Complications (2016), http://dx.doi.org/10.1016/j.jdiacomp.2016.05.024

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E.Y.F. Wan et al. / Journal of Diabetes and Its Complications xxx (2016) xxx–xxx

focused only on either patients with type 1 DM or the development of microvascular complications but not on other outcomes among patients with Type 2 DM. Only two observational studies in Hong Kong and Japan revealed that variability in HbA1c was associated with incidence of CVD (Bouchi et al., 2012; Luk et al., 2013). Meanwhile, four prior studies in the United Stated, Denmark, Japan and Taiwan showed the association between variability in HbA1c and all-cause mortality (Hirakawa et al., 2014; Ma et al., 2012; Skriver, Sandbæk, Kristensen, & Støvring, 2015; Takao, Matsuyama, Yanagisawa, Kikuchi, & Kawazu, 2014). A recent systematic review and mate-analysis pointed out that most of these studies lack adjustment for potential confounders such as comorbidities and drug medications (Gorst et al., 2015). Moreover, there is no literature conducted to evaluate the impact of variability in HbA1c on the adverse events by age groups. As the guideline from American Diabetes Association and International Diabetes Federation suggested a potential difference in the impact of HbA1c levels between younger and older patients (American Diabetes Association, 2015; International Diabetes Federation Guideline Development Group, 2014), stratification according to age group (e.g. b 65 and ≥65 years) could be a vital way to investigate the association between variability in HbA1c and all-cause mortality. Moreover, due to genetic and environmental factors, substantial differences in disease risk were found across racial and ethnic groups; therefore, the data from one population cannot be generalized to other populations (Byrne & Wild, 2011; Forouhi & Sattar, 2006). More population-based studies examining the relationship between variability in HbA1c and the incidence of CVD event and mortality are necessary to confirm the influence of variability in HbA1c on clinical outcomes. Regarding the importance of glycemia control on the management of DM, the knowledge of variability in HbA1c can provide further information in addition to the mean HbA1c to assist clinicians in setting the management plan for diabetic patients. This study aimed to investigate the association between variability in HbA1c and incidence of CVD event and mortality among Chinese primary care patients with Type 2 DM. 2. Materials and methods 2.1. Study design This was a population-based retrospective cohort study on Chinese primary care patients aged 18 years or above, who were clinically diagnosed with Type 2 DM and who had no prior history of CVD. Data were obtained from patients who had received primary care services from any one of the total 74 General Out-Patient Clinics (GOPC) of the Hong Kong Hospital Authority (HA) between 1 August 2008 and 31 December 2009. The data obtained were part of a territory-wide study evaluating the quality of care and outcomes of a government-funded diabetic risk assessment and management program (Fung et al., 2012). The HA is the governing body of all public-sector hospitals and primary care clinics, managing one-third of DM patients in Hong Kong. Patients who had been given a clinical diagnosis of Type 2 DM were identified via a search of the computerized database of HA using the International Classification of Primary Care-2 (ICPC-2) code of ‘T90’. The date of first record of HbA1c was defined as baseline. Each patient was followed up until the date of incidence of an outcome event, the date of all-cause mortality or last follow-up as censoring until 31 December 2013, whichever occurred first. Ethics approval of this study was granted by all local Institutional Review Board across the territory. 2.2. Cardiovascular diseases and mortality identification The study outcomes of interest included three events: 1) CVD event included coronary heart disease (Ischemic heart disease, myocardial infarction, coronary death and sudden death were coded as ICPC-2 of

K74 to K76 or International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) of 410.x, 411.x to 414.x, 798.x), stroke (fatal and non-fatal were coded as ICPC-2 of K89 to K91 or ICD-9-CM of 430.x to 438.x), or heart failure (coded as ICPC-2 of K77 or ICD-9-CM of 428.x), 2) all-cause mortality and 3) composite of CVD and all-cause mortality. 2.3. HbA1c variability assessment and baseline measurements Baseline HbA1c and subsequent HbA1c records before an outcome event occurred were retrieved and used for data analysis. The HbA1c readings in patient records at baseline and every 6 months after baseline before an outcome event occurred were used and retrieved for data analysis. The mean HbA1c value of a particular patient was defined as the average of all the available HbA1c readings. For this analysis, patients with less than 5 HbA1c measurements were excluded. Various measurements of HbA1c visit-to-visit variability were used, including: 1) standard derivation (SD) of HbA1c levels as a primary measurement; 2) coefficient of variation (CV), defined as the percentage of the ratio of the standard deviation to the mean of the cohort; 3) variability independent of mean (VIM), defined as p V I M ¼ SD  ðMx Þ where x is the mean HbA1c value, M is the average value of mean HbA1c in the cohort and p is the regression coefficient, on the basis of regressing natural logarithm of SD on natural logarithm of the multiplication of x and M. 4) Residual standard deviation (RSD), defined as the square root of the residual mean square from fitting a linear mixed effects model. 5) Average real variability (ARV), defined as the average absolute difference between consecutive measurements; 6) Successive variation, defined as the square root of the average squared difference between successive HbA1c measurements. These indices of variability were widely adopted to investigate the association between variability in clinical parameter and the incidence of morbidity and mortality (Bangalore, Breazna, DeMicco, Wun, & Messerli, 2015; Hirakawa et al., 2014; Webb, Fischer, & Rothwell, 2011). Baseline covariates consisted of patient’s socio-demographics, clinical parameters, disease characteristics and treatment modalities. Socio-demographics included: age, gender, smoking status and drinking habit. Clinical parameters included: body mass index, waist-to-hip ratio, systolic blood pressure and diastolic blood pressure, lipid profile (Low-density lipoprotein-cholesterol and total cholesterol to high-density lipoprotein cholesterol ratio), triglyceride and urine albumin to creatinine ratio. Disease characteristics included: self-reported duration of diabetes mellitus and family history of diabetes mellitus. Hypertension was defined as the clinical diagnosis with ICPC-2 code of “K86” or “K87”. The chronic kidney disease was defined as patient having an estimated glomerular filtration rate b60 ml/min/1.73 m 2. Treatment modalities included: the baseline use of anti-hypertensive drugs, oral anti-diabetic drugs, insulin and lipid-lowering agents. All laboratory assays were performed in laboratories accredited by the College of American Pathologists, the Hong Kong Accreditation Service or the National Association of Testing Authorities, Australia. 2.4. Data analysis Missing data (other than HbA1c) were handled by multiple imputation method (Royston, 2004). This method was designed to increase the power of the analysis and produce models that are more statistically reliable and applicable within clinical practice (Moons, Donders, Stijnen, & Harrell, 2006; Schafer & Graham, 2002; Steyerberg & van Veen, 2007). It allows the inclusion of patients with incomplete data in analyses to minimize unnecessary biases (Clark & Altman, 2003; Royston, 2004). In this study, each missing value was imputed by the chained equation method 5 times, attaining a relative efficiency of 95% (Rubin, 2004; Van Buuren, Boshuizen, & Knook, 1999). For each

Please cite this article as: Wan, E.Y.F., et al., Association of variability in hemoglobin A1c with cardiovascular diseases and mortality in Chinese patients with type 2 diabetes m..., Journal of Diabetes and Its Complications (2016), http://dx.doi.org/10.1016/j.jdiacomp.2016.05.024

E.Y.F. Wan et al. / Journal of Diabetes and Its Complications xxx (2016) xxx–xxx

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Table 1 Characteristics among the subjects after multiple imputation. Total (N = 91,866)

Age b65 at baseline (N = 52,876)

Age ≥65 at baseline (N = 38,990)

55.8% 44.2% 62.58±10.75

52.7% 47.3% 55.01±6.49

60.1% 39.9% 72.86±5.63

79.7% 20.3%

80.3% 19.7%

79.0% 21.0%

97.1% 2.9%

96.6% 3.4%

97.8% 2.2%

Clinical parameters at baseline BMI, kg/m2 Waist hip ratio HbA1c, % SBP, mmHg DBP, mmHg LDL-C, mmol/L TC/HDL-C ratio Triglyceride, mmol/L Urine ACR

25.47±3.85 0.93±0.20 7.43±1.36 135.88±17.09 75.59±10.03 3.10±0.83 4.35±1.28 1.67±1.12 7.18±34.79

25.85±4.00 0.93±0.20 7.55±1.45 133.78±16.71 77.96±9.68 3.11±0.83 4.40±1.30 1.73±1.22 5.24±24.48

24.95±3.57 0.94±0.19 7.26±1.20 138.72±17.19 72.38±9.58 3.08±0.84 4.27±1.25 1.60±0.97 9.80±44.63

b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎

Disease characteristics at baseline Duration of DM, years Family history of DM Diagnosed hypertension Chronic kidney disease

6.82±6.39 44.7% 67.7% 11.4%

5.71±5.56 55.5% 57.9% 3.8%

8.34±6.95 30.1% 81.0% 21.6%

b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎

Treatment modalities at baseline Anti-hypertensive drugs used Oral anti-diabetic drug used Insulin used Lipid-lowering agents used

65.8% 79.7% 0.7% 5.9%

56.9% 79.0% 0.7% 6.0%

77.8% 80.6% 0.8% 5.7%

b0.001⁎ b0.001⁎ 0.035⁎ 0.035⁎

HbA1c during follow-up Number of HbA1c measurements Mean HbA1c SD CV VIM RSD ARV SV

6.47±1.30 7.22±0.89 0.62±0.46 0.59±0.36 8.29±5.52 0.55±0.41 1.17±0.73 0.72±0.55

6.60±1.33 7.33±0.93 0.65±0.47 0.59±0.37 8.62±5.65 0.58±0.41 1.21±0.78 0.76±0.56

6.30±1.23 7.08±0.81 0.57±0.43 0.58±0.36 7.86±5.30 0.51±0.39 1.11±0.65 0.67±0.52

b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎

Socio-demographics at baseline Gender Female Male Age, years Smoking status Never smoked Ever smoked Drinking habit Non drinker Current drinker

P-value b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎

Note: All parameters were expressed as mean ± SD, percentages as appropriate. CVD = Cardiovascular Disease; DM = Diabetes Mellitus; BMI = Body Mass Index; HbA1c = Hemoglobin A1c; SBP = Systolic Blood Pressure; DBP = Diastolic Blood Pressure; LDL-C = Low-Density Lipoprotein-Cholesterol; TC = Total Cholesterol; HDL-C = High-Density Lipoprotein-Cholesterol; ACR = Albumin/Creatinine Ratio; eGFR=estimated Glomerular Filtration Rate; SD = Standard Deviation; CV = Coefficient of Variation; VIM = Variation Independent of Mean; RSD = Residual Standard Deviation; ARV = Average Real Variability; SV = Successive Variation. ⁎ Significant difference (P b 0.05) by univariate linear or logistic regression.

of the 5 imputed datasets, the same analysis was performed with the five sets of results combined using Rubin’s combination rules (Rubin, 2004). Descriptive statistics were used to describe the characteristics of the cohort. The incidence rate was estimated by an exact 95% confidence interval (CI) based on a Poisson distribution (Ulm, 1990). The indicators of variability in HbA1c associated with the incidence of CVD and mortality were examined using multivariable Cox proportional hazards regression. Three different models for each indicator of variability in HbA1c were used to calculate the hazard ratio (HR) for the outcomes per 1-SD increase of variability in HbA1c. The first model treated the indicator of variability in HbA1c as a continuous variable with adjustments for all baseline covariates and the number of HbA1c measurements during follow-up. To investigate whether variability in HbA1c provides additional information aside from mean HbA1c level which was curvilinear associated with CVD and all-cause mortality shown in our previous study and other literature (Arnold & Wang, 2013; Kontopantelis et al., 2015; Wan, Fung, Wong, Chin, & Lam, 2016), the second model was modified based on the first one

with the adjustment to the mean HbA1c and square of mean HbA1c values. The third model was developed by modifying based on the second model with the adjustment to the difference in HbA1c between baseline and last record in order to adjust the trend of HbA1c over times. The proportional hazards assumption was assessed by examining plots of the scaled Schoenfeld residuals against time for the covariates. Presence of multicollinearity was also checked by examining the variance inflation factor. The nonlinear relationship between the indicators of variability in HbA1c and the outcomes was evaluated by the restricted cubic splines with three knots in Cox models (Durrleman & Simon, 1989). As the guideline from American Diabetes Association suggested a potential difference in the impact of HbA1c levels between younger and older patients (American Diabetes Association, 2015), subgroup analysis was stratified by baseline age (b65 and ≥ 65 years). Sensitivity analyses were performed to avoid the potential bias due to severe disease at baseline. Firstly, patients with follow-up duration less than 3 years were excluded. Secondly, patients with less than three HbA1c measurements in the first two years after baseline were excluded.

Please cite this article as: Wan, E.Y.F., et al., Association of variability in hemoglobin A1c with cardiovascular diseases and mortality in Chinese patients with type 2 diabetes m..., Journal of Diabetes and Its Complications (2016), http://dx.doi.org/10.1016/j.jdiacomp.2016.05.024

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E.Y.F. Wan et al. / Journal of Diabetes and Its Complications xxx (2016) xxx–xxx

All significance tests were two-tailed and those with a p-value less than 0.05 were considered statistically significant. The statistical analysis was performed in STATA Version 13.0. 3. Results A total of 126,974 Chinese patients with Type 2 DM and aged 18 years old or above were identified. These patients had at least one previous HbA1c measurement, and received HA GOPC services between 1 August 2008 and 31 December 2009. After excluding 9433 patients with a prior CVD history, 152 patients without follow-up and 25,523 patients with less than five HbA1c measurements after baseline, the remaining 91,866 diabetic patients were included in the data analysis. Data completion rate for each baseline factor was shown in the Supplementary Table 1. Table 1 summarizes the baseline characteristics using multiple imputations for the entire cohort by age groups. For the whole group, the mean age was 62.6 years (SD: 10.8), women made up 55.8% of the group and mean duration of diabetes was 6.8 years (SD: 6.4). The number of HbA1c measurements during follow-up period was 6.47 (SD: 1.3), the mean HbA1c was 7.22% (SD: 0.89) and SD HbA1c was 0.62% (SD: 0.46). There were 57.6% (52,876) and 42.4% (38,990) patients in the younger and the older age groups, respectively. There were significant differences in baseline characteristics including socio-demographics, clinical parameters, disease characteristics and treatment modalities between groups. Compared with the older group, the younger group had higher levels in all indicators of variability in HbA1c. Table 2 demonstrates the number and incidence rates for CVD events and all-cause mortality by age groups. After 58.5 months of median follow-up, unadjusted incidence rates for CVD event, all-cause mortality and composite of both events were 6.2, 3.2 and 8.7 per 1000 person-years, respectively. As expected, the incidence rates of CVD and all-cause mortality in the older group were higher than those of the younger group. Multivariable Cox proportional hazard regressions were performed on the dependent variables of CVD events and all-cause mortality and results were shown in Table 3. The variance inflation factor ranged from 1.01 to 2.63 and the scatter plots of the scaled Schoenfeld residual against survival time showed zero slopes, indicating absence of multicollinearity and no substantial deviation from the proportional hazard assumption. In the first model, all indicators of variability in HbA1c were associated with significant increase in the risk of CVD, all-cause mortality and composite of both events after adjustment with

all baseline covariates. The results of the subgroup analyses by age stratification also echoed with the results in overall cohort. After controlling the mean achieved HbA1c levels in the second model and the difference in HbA1c between baseline and last follow-up record in the third model, the effect of variability in HbA1c among the younger group on CVD was statistically insignificant but other results in both the younger and older groups demonstrated similar relationships between all measurements of HbA1c variability and the risk of CVD and all-cause mortality when compared with the first model shown in Table 3. Fig. 1 shows no non-linear relationship between variability in HbA1c and the outcome events by restricted cubic spline in Cox models. These results indicated that there were positive linear association between HbA1c variability and CVD and all-cause mortality, and variability in HbA1c was an independent predictor of CVD and all-cause mortality independently of the HbA1c levels except for CVD in the younger group. Moreover, the influence of variability in HbA1c on the risk of CVD and all-cause mortality among the older group was relatively higher. For every 1-SD increase in HbA1c, the risk of CVD, all-cause mortality and composite of both events among the younger group increased by 1.7% (Adjusted HR: 1.017; 95% CI: 0.852–1.215), 49.5% (Adjusted HR: 1.495; 95% CI: 1.154–1.936) and 15.7% (Adjusted HR: 1.157; 95% CI: 0.998–1.342), respectively. Meanwhile, every 1-SD increase in HbA1c was associated with 15.2% (Adjusted HR: 1.152; 95% CI: 1.026–1.293) increase in CVD, 77.8% (Adjusted HR: 1.778; 95% CI: 1.563–2.024) increase in all-cause mortality and 34.1% (Adjusted HR: 1.341; 95% CI: 1.224–1.469) increase in composite of both events among the older group. Supplementary Table 2 illustrates the sensitivity analysis which elucidates the effect of variability in HbA1c on the risk of CVD and mortality. After excluding the patients with follow-up period less than 3 years or with less than three HbA1c measurements in the first two years after baseline, similar relationships were obtained compared to the main analysis. 4. Discussion This study is the first population-based cohort study conducted to date investigating the association between variability in HbA1c and incidence of CVD event and all-cause mortality among Chinese patients with Type 2 DM managed in primary care stratified by age group. Our study identified a positive linear relationship between all measurements of variability in HbA1c and incidence of CVD event and all-cause mortality in both age groups, indicating that the high levels of HbA1c variability elevated the risks for adverse events. Compared to the younger group, the detrimental effects of variability in HbA1c on the risks of CVD and all-cause mortality in the older group were

Table 2 Incidence rate of cardiovascular diseases (CVD) and all-cause mortality among subjects.

CVD Cumulative cases with event Cumulative incidence rate Person-years Medium follow-up (Months) Incidence rate (95% CI)a All-cause mortality Cumulative cases with event Cumulative incidence rate Person-years Medium follow-up (Months) Incidence rate (95% CI)a CVD or all-cause mortality Cumulative cases with event Cumulative incidence rate Person-years Medium follow-up (Months) Incidence rate (95% CI)a a

Total (N = 91,866)

Age b65 at baseline (N = 52,876)

Age ≥65 at baseline (N = 38,990)

2755 3.0% 5,326,201 58.5 6.21 (5.98–6.44)

826 1.6% 3,073,040 58.5 3.23 (3.01–3.45)

1929 4.9% 2,253,161 58.5 10.27 (9.83–10.74)

1,415 1.5% 5,351,276 58.5 3.17 (3.01–3.34)

289 0.5% 3,081,225 58.5 1.13 (1.00–1.26)

1,126 2.9% 2,270,051 58.5 5.95 (5.61–6.31)

3,847 4.2% 5,326,201 58.5 8.67 (8.40–8.95)

1,078 2.0% 3,073,040 58.5 4.21 (3.97–4.47)

2,769 7.1% 2,253,161 58.5 14.75 (14.21–15.31)

Incidence rate (Cases/1000 Person-years) with 95% CI based on Poisson distribution.

Please cite this article as: Wan, E.Y.F., et al., Association of variability in hemoglobin A1c with cardiovascular diseases and mortality in Chinese patients with type 2 diabetes m..., Journal of Diabetes and Its Complications (2016), http://dx.doi.org/10.1016/j.jdiacomp.2016.05.024

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Table 3 Adjusted hazard ratios for incidence of cardiovascular diseases (CVD), all-cause mortality and composite of CVD and mortality by multivariable Cox proportional hazards regression. CVD Adjusted HR (95% CI) Model 1: Variability in HbA1c + adjustments Overall SD 1.327 (1.229–1.431) CV 1.020 (1.013–1.026) VIM 1.135 (1.028–1.253) RSD 1.192 (1.135–1.252) ARV 1.253 (1.152–1.364) SV 1.212 (1.138–1.291) Age b 65 SD 1.273 (1.111–1.460) CV 1.015 (1.003–1.027) VIM 0.966 (0.796–1.171) RSD 1.168 (1.077–1.265) ARV 1.218 (1.045–1.421) SV 1.166 (1.040–1.308) Age ≥ 65 SD 1.359 (1.240–1.490) CV 1.023 (1.015–1.030) VIM 1.223 (1.089–1.372) RSD 1.210 (1.137–1.287) ARV 1.275 (1.152–1.411) SV 1.238 (1.149–1.335)

All-cause mortality

CVD or all-cause mortality

P-value

Adjusted HR (95% CI)

P-value

Adjusted HR (95% CI)

P-value

b0.001⁎ b0.001⁎ 0.012⁎ b0.001⁎ b0.001⁎ b0.001⁎

1.723 (1.574–1.886) 1.046 (1.038–1.054) 1.806 (1.627–2.004) 1.422 (1.341–1.509) 1.771 (1.611–1.946) 1.527 (1.421–1.642)

b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎

1.461 1.029 1.347 1.272 1.431 1.320

(1.375–1.553) (1.024–1.034) (1.248–1.455) (1.223–1.323) (1.339–1.529) (1.256–1.388)

b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎

0.001⁎ 0.014⁎ 0.724 b0.001⁎ 0.012⁎ 0.009⁎

1.826 (1.509–2.209) 1.049 (1.032–1.066) 1.774 (1.411–2.232) 1.486 (1.331–1.658) 1.871 (1.527–2.292) 1.574 (1.346–1.840)

b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎

1.424 1.025 1.182 1.254 1.391 1.280

(1.272–1.595) (1.015–1.035) (1.013–1.380) (1.173–1.339) (1.226–1.577) (1.164–1.407)

b0.001⁎ b0.001⁎ 0.033⁎ b0.001⁎ b0.001⁎ b0.001⁎

b0.001⁎ b0.001⁎ 0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎

1.687 (1.521–1.871) 1.045 (1.036–1.054) 1.816 (1.613–2.044) 1.387 (1.292–1.488) 1.729 (1.554–1.925) 1.508 (1.390–1.637)

b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎

1.477 1.031 1.419 1.278 1.444 1.337

(1.373–1.588) (1.025–1.037) (1.299–1.551) (1.217–1.343) (1.335–1.561) (1.260–1.418)

b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎

b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎

1.270 1.020 1.371 1.301 1.202 1.169

(1.179–1.368) (1.014–1.026) (1.269–1.481) (1.216–1.392) (1.108–1.304) (1.101–1.242)

b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎

b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ 0.001⁎ 0.001⁎

1.144 1.012 1.232 1.170 1.044 1.050

(0.997–1.314) (1.001–1.023) (1.054–1.440) (1.032–1.326) (0.892–1.222) (0.935–1.179)

0.056 0.037⁎ 0.009⁎ 0.014⁎ 0.592 0.413

b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎

1.335 1.024 1.429 1.366 1.272 1.224

(1.221–1.459) (1.017–1.031) (1.307–1.563) (1.257–1.484) (1.156–1.400) (1.140–1.314)

b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎

b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎

1.278 1.021 1.390 1.303 1.200 1.169

(1.183–1.380) (1.015–1.027) (1.283–1.507) (1.216–1.395) (1.105–1.302) (1.099–1.243)

b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎

0.002⁎ b0.001⁎ b0.001⁎ 0.001⁎ 0.003⁎ 0.010⁎

1.157 1.013 1.258 1.176 1.041 1.049

(0.998–1.342) (1.001–1.025) (1.064–1.486) (1.033–1.338) (0.887–1.222) (0.930–1.183)

0.053 0.033⁎ 0.007⁎ 0.014⁎ 0.620 0.439

b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎

1.341 1.024 1.447 1.367 1.270 1.224

(1.224–1.469) (1.017–1.031) (1.319–1.587) (1.257–1.486) (1.154–1.398) (1.139–1.315)

b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎ b0.001⁎

Model 2: Variability in HbA1c + Mean HbA1c + (Mean HbA1c)2 + adjustments Overall SD 1.094 (0.997–1.201) 0.058 1.689 (1.514–1.885) CV 1.008 (1.001–1.015) 0.035⁎ 1.043 (1.035–1.052) VIM 1.150 (1.041–1.271) 0.006⁎ 1.840 (1.656–2.044) RSD 1.141 (1.050–1.239) 0.002⁎ 1.685 (1.519–1.868) ARV 0.978 (0.882–1.086) 0.680 1.729 (1.542–1.939) SV 1.026 (0.950–1.108) 0.508 1.491 (1.368–1.625) Age b 65 SD 0.990 (0.837–1.171) 0.909 1.576 (1.250–1.987) CV 0.999 (0.986–1.012) 0.878 1.040 (1.022–1.059) VIM 0.990 (0.814–1.204) 0.918 1.909 (1.515–2.405) RSD 1.046 (0.901–1.214) 0.559 1.553 (1.251–1.929) ARV 0.875 (0.722–1.060) 0.173 1.547 (1.202–1.992) SV 0.930 (0.808–1.072) 0.318 1.370 (1.132–1.657) Age ≥ 65 ⁎ SD 1.154 (1.030–1.292) 0.013 1.747 (1.541–1.982) CV 1.012 (1.004–1.021) 0.006⁎ 1.045 (1.035–1.055) VIM 1.229 (1.094–1.380) 0.001⁎ 1.830 (1.624–2.062) RSD 1.184 (1.069–1.312) 0.001⁎ 1.780 (1.579–2.006) ARV 1.033 (0.912–1.169) 0.613 1.786 (1.569–2.033) SV 1.077 (0.983–1.180) 0.112 1.533 (1.391–1.690) Model 3: Variability in HbA1c + Mean HbA1c + (Mean HbA1c)2 + change HbA1c + adjustments Overall SD 1.099 (0.998–1.210) 0.055 1.702 (1.519–1.907) CV 1.008 (1.001–1.016) 0.032⁎ 1.044 (1.036–1.053) VIM 1.160 (1.046–1.286) 0.005⁎ 1.876 (1.681–2.093) RSD 1.143 (1.051–1.243) 0.002⁎ 1.682 (1.514–1.870) ARV 0.977 (0.880–1.085) 0.664 1.720 (1.532–1.931) SV 1.026 (0.949–1.109) 0.519 1.488 (1.363–1.625) Age b 65 SD 1.017 (0.852–1.215) 0.850 1.495 (1.154–1.936) CV 1.001 (0.987–1.015) 0.877 1.037 (1.016–1.058) VIM 1.021 (0.831–1.255) 0.844 1.859 (1.435–2.410) RSD 1.064 (0.913–1.240) 0.430 1.481 (1.180–1.860) ARV 0.885 (0.728–1.075) 0.218 1.478 (1.137–1.921) SV 0.942 (0.814–1.090) 0.421 1.305 (1.065–1.600) Age ≥ 65 SD 1.152 (1.026–1.293) 0.017⁎ 1.778 (1.563–2.024) CV 1.012 (1.003–1.021) 0.007⁎ 1.047 (1.037–1.057) VIM 1.232 (1.092–1.389) 0.001⁎ 1.881 (1.664–2.126) 1.182 (1.066–1.311) 0.002⁎ 1.793 (1.587–2.025) RSD ARV 1.029 (0.908–1.166) 0.658 1.787 (1.569–2.036) SV 1.074 (0.979–1.178) 0.132 1.541 (1.396–1.700)

CVD = Cardiovascular Diseases; HR = Hazards Ratio; SD = Standard Deviation; CV = Coefficient of Variation; VIM = Variation Independent of Mean; RSD = Residual Standard Deviation; ARV = Average Real Variability; SV = Successive Variation. Notes: Change HbA1c was defined as the difference in HbA1c between baseline and last follow-up record. Hazard ratios were adjusted for age, gender, smoking status, drinking habit, body mass index, waist-to-hip ratio, low-density lipoprotein-cholesterol, total cholesterol to high-density lipoprotein cholesterol ratio, triglyceride, urine albumin to creatinine ratio, self-reported duration of diabetes mellitus, family history of diabetes mellitus, hypertension, chronic kidney disease, the use of anti-hypertensive drugs, insulin and lipid-lowering agents at baseline and number of HbA1c measurements during follow-up. ⁎ Significant difference (P b 0.05) by multivariable Cox proportional hazards regression.

Please cite this article as: Wan, E.Y.F., et al., Association of variability in hemoglobin A1c with cardiovascular diseases and mortality in Chinese patients with type 2 diabetes m..., Journal of Diabetes and Its Complications (2016), http://dx.doi.org/10.1016/j.jdiacomp.2016.05.024

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higher. The impact of variability in HbA1c on the risks of CVD events in the older group and all-cause mortality in both groups remained significant after the adjustments of mean HbA1c and difference in HbA1c between baseline and last follow-up record, suggesting that HbA1c variability may provide additional valuable information as a potential predictor for the incidence in diabetic population. Our findings on variability in HbA1c among Chinese diabetic population were consistent with the results of earlier studies in other populations with Type 2 DM in which a positive linear association between HbA1c variability and the incidence of CVD and all-cause mortality was demonstrated. A post-hoc analysis of the ADVANCE (Action in Diabetes and Vascular Disease: Preterax and Diamicron MR controlled Evaluation) trial identified that every 1-SD increase in HbA1c was associated with a 34% (Adjusted HR: 1.34; 95% CI: 1.18–1.53) increase in the risk of all-cause mortality (Hirakawa et al., 2014). Another study with 15.9 years follow-up period in Japan also found that the risk of all-cause mortality was elevated 3-fold in every 1-SD increase in HbA1c (Adjusted HR: 3.17; 95% CI: 1.43–7.03) (Takao et al., 2014). The two studies conducted in Denmark and Taiwan concluded that the higher SD in HbA1c as a categorical variable was associated with higher risk of all-cause mortality (Ma et al., 2012; Skriver et al., 2015). Regarding the risk of CVD events, the small-sized local literature showed that the risk was increased by 27% (Adjusted

HR: 1.27; 95% CI: 1.15–1.40) for every 1-SD increase in HbA1c (Luk et al., 2013). The previous research among Japanese population also illustrated that the risk of CVD in high level SD of HbA1c group was greater compared to the low level in SD of HbA1c group (Bouchi et al., 2012). A recent meta-analysis also summarized that the higher HbA1c variability was associated with CVD (risk ratio 1.27, 95% CI: 1.15–1.40) and mortality (risk ratio 1.34, 95% CI: 1.18–1.53) (Gorst et al., 2015). The current study confirmed the harmful effect of variability in HbA1c on the risk of CVD and all-cause mortality among diabetic population. While anti-diabetic drug treatment decreases the risk of CVD and mortality, the diabetic population still has a remarkably higher risk compared to the normal population. Therefore, novel efficacious treatment or drug medication should be further investigation to attenuate the effect of HbA1c variability for further prevention against the incidence of CVD and all-cause mortality. Many studies also supported the HbA1c variability as a potential predictor for the incidence of the CVD event and all-cause mortality (Bouchi et al., 2012; Hirakawa et al., 2014; Luk et al., 2013; Ma et al., 2012; Skriver et al., 2015; Takao et al., 2014). The results in the present analyses found the same agreement that variability in HbA1c was independent of mean HbA1c among overall cohort. Nevertheless, our subgroup analyses by age stratification showed that the variability in HbA1c among the younger group may not be a potential predictor

(A1)

(B1)

(C1)

(A2)

(B2)

(C2)

(A3)

(B3)

(C3)

Fig. 1. Adjusted hazard ratios for incidence of (A) cardiovasular diseases (CVD), (B) all-cause mortality and (C) composite of CVD and mortality by (1) overall cohort (2) younger group (age b65 years) (3) older group (age ≥65 years) by multivariable Cox proportional hazards regression. Hazard ratios were adjusted for age, gender, smoking status, drinking habit, body mass index, waist-to-hip ratio, systolic and diastolic blood pressure, low-density lipoprotein-cholesterol, total cholesterol to high-density lipoprotein cholesterol ratio, triglyceride, urine albumin to creatinine ratio, self-reported duration of diabetes mellitus, family history of diabetes mellitus, hypertension, the stage of chronic kidney disease, the use of anti-hypertensive drugs, oral anti-diabetic drugs, insulin and lipid-lowering agents at baseline, and number of HbA1c measurements during follow-up, mean of glycated hemoglobin A1c, square of mean of glycated hemoglobin A1c, the difference in HbA1c between baseline and last record during follow-up period. Dashed lines indicated 95% confidence intervals.

Please cite this article as: Wan, E.Y.F., et al., Association of variability in hemoglobin A1c with cardiovascular diseases and mortality in Chinese patients with type 2 diabetes m..., Journal of Diabetes and Its Complications (2016), http://dx.doi.org/10.1016/j.jdiacomp.2016.05.024

E.Y.F. Wan et al. / Journal of Diabetes and Its Complications xxx (2016) xxx–xxx

for CVD event. Compared to the older group, all indicators of variability in HbA1c among the younger group were higher, which was also the pattern observed in the other studies (Luk et al., 2013; Takao et al., 2014). This observation implied that the predictor power of variability in HbA1c on the risk of CVD among the younger group may be relatively lower. In other words, the variability in HbA1c among the older group was potentially more sensitive to the risk of CVD. Although direct comparisons of study findings were infeasible due to methodological differences such as the definition of HbA1c variability, temporal changes and modifications in unmeasured risk factors or interventions, the HbA1c variability should be plausible and conceivable as an indicator of care process. Further researches are required to standardize the definition of HbA1c variability and evaluate whether HbA1c variability serves as a useful predictor and a valuable therapeutic target in clinical practice. There were several possible mechanisms behind the associations between variability in HbA1c and the risks of developing CVD and all-cause mortality. In the pathophysiological rationale, intermittent hyperglycemia rather than chronic hyperglycemia exaggerates the production of reactive oxygen, impairs endothelial function and induces long-lasting epigenetic changes and the release of cytokines (Keating & El-Osta, 2013; Monnier et al., 2006). Development of accelerated atherosclerosis related to inflammation, oxidative stress and endothelial dysfunction will lead to increased risk of CVD and mortality (Brownlee, 2001; Ceriello & Ihnat, 2010). Compared to chronic hyperglycemia, intermittent hyperglycemia had higher levels of apoptosis and lower levels of the insulin secretory capacity in pancreatic beta cells (Del Guerra et al., 2007; Kim et al., 2010). The less pancreatic beta cell function may deteriorate the glycemic control which is involved in the progression of CVD and mortality. On the other hand, hypoglycemia is another potential connection between glucose fluctuation and the incidence of CVD and all-cause mortality. Some studies revealed that glucose variability was associated with the risk of hypoglycemia (Murata, Hoffman, Shah, Wendel, & Duckworth, 2004; Skyler et al., 2009). Risk of hypoglycemia may contribute to the increase in the progression of CVD and mortality though induction of inflammation, blood coagulation abnormality, sympathoadrenal response and endothelial dysfunction (Desouza, Bolli, & Fonseca, 2010; Saisho, 2014). Although the relationship between long term HbA1c variability and the adverse events was not well established, more researches should be conducted to investigate the mechanisms between long term glucose variability and the incidence of CVD and all-cause mortality. 4.1. Strengths and limitations of this study Several strengths of this study included the large population of diabetic patients involved which is highly representative of the Chinese diabetic population in Hong Kong. The clinical characteristics were captured by the HA’s computerized administrative database which allowed access to relevant information such as anthropometric and laboratory data. In order to obtain less biased results, multiple imputations were used to replace the missing data. There were also several limitations. Firstly, our design was retrospective cohort study which is less highly ranked in terms of the hierarchy of evidence. To confirm the association between long-term HbA1c variability and incidence of CVD event and all-cause mortality among Chinese diabetic patients, a more convincing study design like RCT or prospective cohort study would be required. Nonetheless, it may not be feasible to conduct such high-evidence trial under the primary care setting in reality. The common limitations of RCTs included high attrition rates, low number of incident events, short follow-up times and strict subject’s inclusion criteria that reduce the applicability to diabetic patients in clinical practices. In addition, several studies showed that cohort studies and RCTs obtained comparable findings (Benson & Hartz, 2000; Concato,

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Shah, & Horwitz, 2000). Therefore, using retrospective cohort studies, particularly from primary care based cohorts, may reflect the general clinical practice, and enabled data over 5 years to be collected in a time and cost-efficient manner. Secondly, SD may be overestimated for the patients who kept on having improvement or deterioration in HbA1c during follow-up period. Nevertheless, the results of model 3 and other indicators of variability in HbA1c such as RSD, which is a measurement of variability completely independent of changes over times, showed consistent results. Thereupon, our findings may not be affected by this limitation. Thirdly, drug adherence, quality of life, infections and lifestyle interventions such as regular exercise and diet modification which contribute to the risks of CVD and mortality (Gorst et al., 2015) were not measured in the present analysis. However, the disease characteristics such as duration of diabetes, and key clinical parameters like body mass index, waist-to-hip ratio, blood pressure, and lipid, which, to a certain extent, reflect the intensity of disease severity and lifestyle modification, have been considered. Lastly, the large numbers of observations may make p-value irrelevant and the long-term effects of variability in HbA1c on the risks of CVD event and all-cause mortality are uncertain among Chinese diabetic patients. Further longitudinal studies are warranted to reappraise the association between variability in HbA1c and incidence of CVD events and mortality, especially in younger patient. A follow-up period of 10 years or longer should be conducted. 5. Conclusions This large population-based cohort study showed a positive linear association between variability in HbA1c and incidence of CVD and all-cause mortality among Chinese primary care patients with Type 2 DM. Compared to the younger group, the detrimental effects of HbA1c variability on the risks of CVD and all-cause mortality in the older group were higher. The HbA1c variability may provide additional valuable information as a potential predictor for the development of CVD and all-cause mortality in diabetic population. Apart from the change towards the optimal target, clinician should be cautious about the fluctuation along with the direction of the change for further prevention against the incidence of CVD and all-cause mortality. Ethics approval The study was approved by the Institutional Review Board (IRB) and Ethics Committees (EC) of the HKU/HA HKW Cluster (UW 10-369) on 30 September, 2010, the HA HKE Cluster (HKEC-2010-093) on 21 December, 2010, the CUHK-NTE Cluster (CRE-2010.543) on 4 January, 2011, the HA KE/KC Cluster (KC/KE-10-0210/ER-3) on 17 December, 2010, the HA KW Cluster (KW/EX/10-137(34-04)) on 28 February, 2011, the HA NTW Cluster (NTWC/CREC/1091/12) on 3 October, 2012. Relationship with industry No investigators in this study have any relationship with industry. Funding This study has been funded by the Hong Kong Hospital Authority (Ref. no. 8011014157) and the Health and Health Services Research Fund, Food and Health Bureau, HKSAR Commissioned Research on Enhanced Primary Care Study (Ref. no. EPC-HKU-2). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acknowledgments The authors wish to acknowledge the contributions of the multidisciplinary risk-stratification based diabetes mellitus management program

Please cite this article as: Wan, E.Y.F., et al., Association of variability in hemoglobin A1c with cardiovascular diseases and mortality in Chinese patients with type 2 diabetes m..., Journal of Diabetes and Its Complications (2016), http://dx.doi.org/10.1016/j.jdiacomp.2016.05.024

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teams and Statistics and Workforce Planning Department at the Hong Kong Hospital Authority. Finally, we would like to thank Mr Anca Chan and Ms Karina Chan for providing administrative supports. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.jdiacomp.2016.05.024. References American Diabetes Association (2015). Standards of medical care in diabetes—2015. Diabetes Care, 38, S70–S76. Arnold, L. W., & Wang, Z. (2013). The HbA1c and all-cause mortality relationship in patients with type 2 diabetes is J-shaped: A meta-analysis of observational studies. The review of diabetic studies: RDS, 11, 138–152. Bangalore, S., Breazna, A., DeMicco, D. A., Wun, C. -C., & Messerli, F. H. (2015). Visit-tovisit low-density lipoprotein cholesterol variability and risk of cardiovascular outcomes: Insights from the TNT Trial. Journal of the American College of Cardiology, 65, 1539–1548. Benson, K., & Hartz, A. J. (2000). A comparison of observational studies and randomized, controlled trials. New England Journal of Medicine, 342, 1878–1886. Bouchi, R., Babazono, T., Mugishima, M., Yoshida, N., Nyumura, I., Toya, K., ... Ishii, A. (2012). Fluctuations in HbA1c are associated with a higher incidence of cardiovascular disease in Japanese patients with type 2 diabetes. Journal of Diabetes Investigation, 3, 148–155. Brownlee, M. (2001). Biochemistry and molecular cell biology of diabetic complications. Nature, 414, 813–820. Byrne, C. D., & Wild, S. H. (2011). The metabolic syndrome. John Wiley & Sons. Ceriello, A., & Ihnat, M. (2010). ‘Glycaemic variability’: A new therapeutic challenge in diabetes and the critical care setting. Diabetic Medicine, 27, 862–867. Cheng, D., Fei, Y., Liu, Y., Li, J., Xue, Q., Wang, X., & Wang, N. (2014). HbA1C variability and the risk of renal status progression in diabetes mellitus: A meta-analysis. PloS One, 9, e115509. http://dx.doi.org/10.1371/journal.pone.0115509. Clark, T. G., & Altman, D. G. (2003). Developing a prognostic model in the presence of missing data: An ovarian cancer case study. Journal of Clinical Epidemiology, 56, 28–37. Concato, J., Shah, N., & Horwitz, R. I. (2000). Randomized, controlled trials, observational studies, and the hierarchy of research designs. New England Journal of Medicine, 342, 1887–1892. Del Guerra, S., Grupillo, M., Masini, M., Lupi, R., Bugliani, M., Torri, S., ... Mosca, F. (2007). Gliclazide protects human islet beta‐cells from apoptosis induced by intermittent high glucose. Diabetes/Metabolism Research and Reviews, 23, 234–238. Desouza, C. V., Bolli, G. B., & Fonseca, V. (2010). Hypoglycemia, diabetes, and cardiovascular events. Diabetes Care, 33, 1389–1394. Durrleman, S., & Simon, R. (1989). Flexible regression models with cubic splines. Statistics in Medicine, 8, 551–561. Forouhi, N. G., & Sattar, N. (2006). CVD risk factors and ethnicity—a homogeneous relationship? Atherosclerosis Supplements, 7, 11–19. Fung, C. S., Chin, W. Y., Dai, D. S., Kwok, R. L., Tsui, E. L., Wan, Y. F., ... Lam, C. L. (2012). Evaluation of the quality of care of a multi-disciplinary risk factor assessment and management programme (RAMP) for diabetic patients. BMC Family Practice, 13, 116. Gilmer, T. P., O’Connor, P. J., Rush, W. A., Crain, A. L., Whitebird, R. R., Hanson, A. M., & Solberg, L. I. (2005). Predictors of health care costs in adults with diabetes. Diabetes Care, 28, 59–64. Gorst, C., Kwok, C., Aslam, I., Buchan, I., Kontopantelis, E., Myint, P., ... Mamas, M. (2015). Long-term glycaemic variability and risk of adverse outcomes: A systematic review and meta-analysis. Diabetes Care, 38, 2354–2369. http://dx.doi.org/10.2337/dc15-1188. Hirakawa, Y., Arima, H., Zoungas, S., Ninomiya, T., Cooper, M., Hamet, P., ... Woodward, M. (2014). Impact of visit-to-visit glycemic variability on the risks of macrovascular and microvascular events and all-cause mortality in type 2 diabetes: The ADVANCE trial. Diabetes Care, 37, 2359–2365. International Diabetes Federation (2015). IDF diabetes atlas (7 ed.) (Brussels). International Diabetes Federation Guideline Development Group (2014). Global guideline for type 2 diabetes. Diabetes Research and Clinical Practice, 104, 1.

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Please cite this article as: Wan, E.Y.F., et al., Association of variability in hemoglobin A1c with cardiovascular diseases and mortality in Chinese patients with type 2 diabetes m..., Journal of Diabetes and Its Complications (2016), http://dx.doi.org/10.1016/j.jdiacomp.2016.05.024