Baseline predictors of aortic stiffness progression among multi-ethnic Asians with type 2 diabetes

Accepted Manuscript Baseline predictors of aortic stiffness progression among multi-ethnic Asians with type 2 diabetes Mei Chung Moh, Chee Fang Sum, Subramaniam Tavintharan, Keven Ang, Simon Biing Ming Lee, Wern Ee Tang, Su Chi Lim PII:

S0021-9150(17)30033-3

DOI:

10.1016/j.atherosclerosis.2017.01.031

Reference:

ATH 14944

To appear in:

Atherosclerosis

Received Date: 13 October 2016 Revised Date:

25 January 2017

Accepted Date: 25 January 2017

Please cite this article as: Moh MC, Sum CF, Tavintharan S, Ang K, Ming Lee SB, Tang WE, Lim SC, Baseline predictors of aortic stiffness progression among multi-ethnic Asians with type 2 diabetes, Atherosclerosis (2017), doi: 10.1016/j.atherosclerosis.2017.01.031. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Baseline predictors of aortic stiffness progression among multi-ethnic Asians with type 2 diabetes

RI PT

Mei Chung Moha, Chee Fang Sumb, Subramaniam Tavintharanb, Keven Anga, Simon Biing Ming

Clinical Research Unit, Khoo Teck Puat Hospital, 90 Yishun Central, Singapore 768828, Singapore

b

c

Diabetes Centre, Khoo Teck Puat Hospital, 90 Yishun Central, Singapore 768828, Singapore

M AN U

a

SC

Leec, Wern Ee Tangc, Su Chi Limb

National Healthcare Group Polyclinics, 3 Fusionopolis Link, Nexus@one-north, South Tower, # 05-

TE D

10, Singapore 138543, Singapore

Corresponding author: Diabetes Centre, Khoo Teck Puat Hospital, 90 Yishun Central, Singapore

EP

768828, Singapore. Email: [email protected] (S. C. Lim)

AC C

Keywords: type 2 diabetes, carotid-femoral pulse wave velocity, body mass index, aortic stiffness progression, multi-ethnic Asians

ACCEPTED MANUSCRIPT Abstract

Background and aims: This 3-year prospective study aimed to identify baseline parameters that predicted the progression of carotid-femoral pulse wave velocity (cf-PWV), which was used to

RI PT

evaluate aortic stiffness, among Singapore’s multi-ethnic Asians with type 2 diabetes (T2DM).

Methods: The cf-PWV was measured by the gold-standard tonometry method in 994 T2DM subjects at baseline and at follow-up. The annual rate of cf-PWV change was calculated; and individuals above the 90th percentile with rate≥1.42 m/s per year were regarded as rapid progressors (n=104). In a

M AN U

defined as follow-up cf-PWV≥10 m/s (n=188).

SC

subgroup analysis of subjects with normal cf-PWV at 1st visit (n=611), incident aortic stiffness was

Results: The total cohort (mean age:57±10 years; 53.4% Chinese, 20.4% Malay, 22.9% Indian, 3.2% ‘Others’) displayed a median annual cf-PWV progression rate of 0.2 m/s. Adjusted multivariate regression analyses showed that baseline age, cf-PWV and body mass index (BMI) constantly predicted follow-up cf-PWV, annual cf-PWV progression rate, rapid cf-PWV progression, and

TE D

incident aortic stiffness. Paradoxically, lower baseline cf-PWV was associated with elevated annual cf-PWV progression rate and rapid progressors. This inverse relationship remained significant across ethnicities after ethnic stratification. Higher BMI independently predicted cf-PWV progression in

EP

Chinese and Indians, but not in Malay and ‘Others’ ethnic groups. Increased age was a significant

AC C

predictor in Chinese and ‘Others’ ethnicities.

Conclusions: We demonstrated that baseline BMI is a modifiable independent risk factor of cf-PWV progression and incident aortic stiffness. Therefore, better obesity management may impede aortic stiffness in Singapore’s T2DM patients, especially in the Chinese and Indians.

ACCEPTED MANUSCRIPT Introduction

Cardiovascular disease (CVD) is the primary cause of morbidity and mortality in type 2 diabetes (T2DM)1. Numerous studies in different populations have shown that aortic stiffness is a powerful predictor of CVD risk2, 3. As the aorta stiffens, either due to aging or exacerbated by metabolic

RI PT

disorders such as T2DM, the velocity of the pressure waves rises and the reflected pressure waves eventually reach the heart earlier, leading to a gain in systolic blood pressure and pulse pressure along with a greater left ventricular afterload4, 5. The current gold standard for determining aortic stiffness is

SC

carotid-femoral pulse wave velocity (cf-PWV)6.

M AN U

Increased aortic stiffness may play a pivotal role in linking T2DM to CVD. Age and blood pressure are established determinants of the aortic stiffness. Other variables associated with cf-PWV consist of duration of diabetes, pulse pressure, fasting plasma glucose, body mass index (BMI), estimated glomerular filtration rate (eGFR) and use of medications including insulin and renin-angiotensin system (RAS) antagonists7, 8. Very few longitudinal studies have evaluated the predictors of cf-PWV

TE D

in T2DM. Dahlen et al. reported that sagittal abdominal diameter and systolic blood pressure (SBP) independently predicted cf-PWV measured at 4-year follow-up in a cohort of T2DM subjects aged 55-65 years old9. In a recent prospective study, factors found to be associated with aortic stiffness

EP

progression among adults with T2DM included baseline cf-PWV, SBP, heart rate, age, female gender, presence of diabetic retinopathy or nephropathy and dyslipidaemia, as well as mean HbA1c and

AC C

relative increases in SBP and heart rate during follow-up10.

Baseline predictors of aortic stiffness progression have not been characterized previously in Asians with T2DM. In this 3-year follow-up study, we aimed to identify baseline clinical factors that were associated with cf-PWV progression and incident aortic stiffness in Singapore’s multi-ethnic Asian cohort with T2DM.

Patients and methods

Patients

ACCEPTED MANUSCRIPT This is a prospective cohort study nested within the Singapore Study of MAcro-angiopathy and Micro-Vascular Reactivity in Type 2 Diabetes (SMART2D) Study. Subjects with T2DM aged between 21 and 89 years who attended the Diabetes Mellitus Centre or a primary-care polyclinic in the Northern region of Singapore were recruited into the SMART2D Study between August 2011 and

RI PT

April 2014. As previously described, the exclusion criteria consisted of subjects who were pregnant, had type 1 diabetes or cancer, or wearing a pacemaker or any device that may be affected by electric current. Participants with fasting plasma glucose levels <4.5 or >15 mmol/L, and HbA1c>108

mmol/mol (>12%) were also excluded from the study11. Ongoing follow-up of the subjects started in

SC

August 2014 with a median follow-up duration of 3 years. A questionnaire was used to collect

information on the participants’ medical history and lifestyle at baseline and at follow-up visits.

M AN U

Medications in use including insulin, metformin, statins and RAS antagonists (angiotensin converting enzyme inhibitors and angiotensin receptor blockers) were also recorded. The subjects belonged primarily to the three major Asian ethnic groups of Singapore − Chinese, Malay and Indian. The study conformed to the Declaration of Helsinki and was approved by our institution’s domain specific ethics

Clinical measurements

TE D

review board. All the participants provided written informed consent.

EP

Anthropometry including height, weight and waist circumference (WC) was measured by standard procedures. BMI was computed as weight (in kg) divided by height (in m)2. SBP and diastolic blood

AC C

pressure (DBP) were measured in a seated position using an automated blood pressure monitor (Dinamap Pro 100V2, Freiburg, Germany). Creatinine was determined by enzymatic method on the Roche/Hitachi cobas c system (Roche Diagnostic GmbH, Mannheim, Germany). The eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation. Urinary albumin from spot urine samples was assessed by a solid phase competitive chemiluminescent enzymatic immunoassay (Immulite, Siemens Healthcare Diagnostics, NY, USA), and the urinary albumincreatinine ratio (ACR) was calculated. HbA1c was measured using a point-of-care immunoassay analyser certified by the National Glycohaemoglobin Standardization Programme (DCA Vantage

ACCEPTED MANUSCRIPT Analyzer; Siemens Healthcare Diagnostics, Erlangen, Germany). Lipids were quantified by enzymatic assays using Ektachem clinical chemistry slides (Eastman Kodak, NY, USA).

cf-PWV measurement

RI PT

The cf-PWV was measured by applanation tonometry (SphygmoCor; Atcor Medical, Sydney, Australia) at baseline and at follow-up according to the manufacturer's protocol. The inter-observer and intra-observer variability of the SphygmoCor tonometry device was assessed and achieved a

SC

mean coefficient of variation of 5.7% and 5.9%, respectively. Participants were placed in supine

position and measurements were taken after a 5-min rest. Pulse waves were obtained sequentially

M AN U

from the carotid and femoral arteries. The cf-PWV was derived from the distance and transit time between the two recording sites. Direct carotid-femoral distance was corrected by a factor of 0.8 as recommended by the ESH/ESC new guideline6. Annual cf-PWV progression rate was calculated as: (cf-PWV at follow-up minus cf-PWV at baseline)/follow-up interval.

TE D

Definitions

Currently, there is no established cut-off value to define rapid progression of cf-PWV in the literature. Therefore, in this study, we defined rapid progressor of cf-PWV as annual cf-PWV progression

EP

rate≥1.42 m/s per year. This cut-off value was based on the 90th percentile of the annual cf-PWV progression rate of the whole cohort. In a subgroup analysis of individuals with normal baseline cf-

AC C

PWV (<10 m/s), incident aortic stiffness was defined as cf-PWV≥10 m/s at follow-up6. Hypertension was defined as SBP≥140 mmHg or DBP≥90 mmHg, or current treatment with RAS antagonists. Chronic kidney disease (CKD) was classified as eGFR<60 ml/min/1.73 m2. Microalbuminuria was defined as ACR of 30-300 µg/mg and macroalbuminuria as ACR>300 µg/mg.

Statistics

Statistical analyses were mainly performed using SPSS version 22 (SPSS, IL, USA). Continuous variables were presented as mean ± SD or median (interquartile range) for non-normally distributed

ACCEPTED MANUSCRIPT data. Categorical data were shown as n (%). Student’s t test or Mann Whitney U test was used for comparison of continuous variables between 2 groups. The Pearson χ2 test was used to compare categorical variables. Skewed data were log-transformed prior to regression analyses. Univariate and multivariate logistic regression, stepwise linear regression, as well as cox regression were used to

RI PT

analyze the effects of variables on cf-PWV progression and aortic stiffness development. Statistical tests of interactions of BMI/WC and ethnicity were carried out for the outcomes of annual cf-PWV progression rate, rapid cf-PWV progression and incident aortic stiffness. Variables selected a priori based on literature were considered for inclusion in the multivariate regression models to ensure that

SC

all variables with potential predictive power were not excluded. Due to multicollinearity between BMI and WC, separate models were constructed. The association between incident aortic stiffness and

indicated statistical significance.

Results

M AN U

rapid cf-PWV progression was analysed by Fisher’s exact test using a 2x2 contingency table. p<0.05

TE D

The baseline characteristics of the total cohort (n=994) are shown in Table 1. The subjects had a mean baseline age of 57 ± 10 years and consisted of 53.4% Chinese, 20.4% Malay, 22.9% Indian, and 3.2% ‘Others’. The cf-PWV increased from a baseline mean of 9.6 ± 2.6 m/s (Table 1) to 10.2 ± 3.1 m/s at

EP

3-year follow-up (Supplementary Table 1), with a median cf-PWV progression rate of 0.2 (interquartile range: 1.1) m/s per year. The determinants of baseline cf-PWV in both the BMI-based

AC C

and WC-based stepwise linear regression models were similar after covariate adjustment, composing of age, T2DM duration, heart rate, SBP, eGFR, ACR and insulin (Supplementary Table 2). Independent baseline predictors of cf-PWV recorded at the 2nd visit were BMI or WC, along with age, heart rate, SBP, cf-PWV, eGFR and use of insulin (Table 2). Among the subjects, 104 of them had annual cf-PWV progression rates above the 90th percentile (≥1.42 m/s per year) and were categorized as rapid progressors (Table 1). Compared to non/slow progressors, rapid progressors of cf-PWV displayed longer T2DM duration, higher degree of adiposity (BMI and WC), less favorable renal function (eGFR, ACR and CKD), and lower cf-PWV at

ACCEPTED MANUSCRIPT baseline. Rapid progressors also tended to have increased frequency of elevated albuminuria. No significant differences in age, gender, ethnicity, heart rate, blood pressure, hypertension status, HbA1c, lipids and medications in use (insulin, metformin, statins and RAS antagonists) were observed between the two groups.

RI PT

Among the individuals with normal baseline cf-PWV (<10 m/s), 188 developed incident aortic

stiffness as defined by cf-PWV≥10 m/s at follow-up (Table 1). People who developed aortic stiffness had older age, longer T2DM duration, and higher baseline SBP, cf-PWV and ACR than those who

SC

maintained normal cf-PWV. The high-risk group also had poorer eGFR, and increased prevalence of hypertension, CKD and albuminuria. Although not significant, there was a trend of higher BMI

M AN U

among subjects with incident aortic stiffness. In addition, the proportion of individuals using insulin, statins and RAS antagonists but not metformin was greater. The slightly lower low density lipoprotein-cholesterol level observed in this group might be attributed to the more frequent use of statins. The mean follow-up cf-PWV level was 12.1 ± 2.2 m/s in those who developed incident aortic

TE D

stiffness compared to 8.0 ± 1.3 m/s in subjects who remained unaffected.

Annual progression rate of cf-PWV was positively correlated with baseline factors including BMI and WC, and negatively correlated with cf-PWV and RAS antagonists (Table 3). In the BMI-based

EP

stepwise regression model, variables including age, BMI, SBP, cf-PWV, eGFR, and use of insulin and RAS antagonists predicted annual cf-PWV progression rate after adjustment for confounders. On the

AC C

other hand, significant determinants of annual cf-PWV progression rate in the WC-based model consisted of age, WC, SBP, cf-PWV, eGFR, and use of insulin.

As shown in Table 4, baseline age, cf-PWV, ACR and either BMI or WC were associated with rapid cf-PWV progressors after controlling for confounding variables. The risk of incident aortic stiffness was correlated with age, T2DM duration, SBP, cf-PWV, eGFR and ACR in the univariate analysis (Table 5). Noteworthily, BMI (Hazard ratio=1.035, 95%CI=1.005 − 1.065, p=0.021) but not WC became significant after adjustment for age. Incident aortic stiffness was predicted by age, Malay, BMI, SBP, and cf-PWV in the BMI-based Cox regression model (Table 5). However, when BMI was

ACCEPTED MANUSCRIPT replaced by WC, factors including age, SBP and cf-PWV, but not WC and Malay, emerged as independent predictors of incident aortic stiffness (Table 5). Strikingly, Fisher’s exact test showed strong association between incident aortic stiffness and rapid cf-PWV progression (two-sided p<0.001; Supplementary Table 3). The relative risk of developing incident aortic stiffness was 4.5

RI PT

times greater in rapid progressors than slow/non-progressors.

Test of interaction revealed an interaction between BMI and ethnicity for the outcome of annual cfPWV progression rate (p=0.014). Therefore, multivariate stepwise linear regression analysis was

SC

performed on each ethnic group separately (Supplementary Table 4). The results showed that reduced cf-PWV predicted elevated annual cf-PWV progression rate in all the ethnic groups (all p<0.001).

M AN U

None of the other metabolic variables assessed predicted cf-PWV progression in the Malays. BMI was an independent determinant in the Chinese and Indians. Both Chinese and ‘Others’ ethnic groups shared common risk factors including increased age, SBP and T2DM duration. There was no significant interaction term between WC and ethnicity for annual cf-PWV progression rate. In addition, no interaction between ethnicity and BMI or WC was observed for rapid cf-PWV

Discussion

TE D

progression and incident aortic stiffness.

EP

Aortic stiffness is a hallmark of arterial aging and can be accelerated by T2DM, leading to adverse

AC C

cardiovascular outcomes including coronary heart disease, stroke and kidney failure2, 4. In view of the pathophysiological consequences of aortic stiffness, it is critical to establish the modifiable risk factors that contribute to vascular stiffening through longitudinal studies for the development of strategies to reduce aortic stiffness. This study is the first to evaluate the baseline risk factors that contributed to cf-PWV progression and development of aortic stiffness in a 3-year prospective cohort of multi-ethnic Asian T2DM patients. The data were analysed from different perspectives to identify variables critical for predicting follow-up cf-PWV, annual rate of change in cf-PWV, rapid worsening of cf-PWV, and incident aortic stiffness. In our cohort, baseline age, cf-PWV and BMI constantly

ACCEPTED MANUSCRIPT emerged as independent risk factors in the multivariable prediction models, highlighting their importance in aorta stiffening. Cross-sectional analysis of our baseline data revealed that cf-PWV taken at the 1st visit was associated with increased age, prolonged T2DM duration, elevated heart rate and SBP, reduced renal function

RI PT

expressed as decreased eGFR or increased ACR, and insulin administration. In the prospective

analysis, baseline factors including age, heart rate, SBP, eGFR and use of insulin remained predictive of cf-PWV measured at the follow-up visit. Baseline cf-PWV strongly predicted follow-up cf-PWV.

SC

In addition, baseline BMI and WC were independent determinants of follow-up cf-PWV, suggesting the potential contribution of obesity to the development and progression of aortic stiffness. On the

M AN U

contrary, a study on 255 T2DM individuals showed that while baseline BMI and WC were correlated to cf-PWV measured at follow-up, they became statistically insignificant after controlling for covariates9. The disparity may be partly attributed to sample size difference.

Our T2DM subjects displayed a median annual cf-PWV progression rate of 0.2 m/s per year. This rate

TE D

is higher than what was reported from the Rio de Janeiro T2DM cohort study, which had a median cfPWV increase of 0.11 m/s per year10, 12. Patients with CKD seemed to display accelerated cf-PWV progression. Utescu et al. showed that the rate of change in cf-PWV was 0.84 m/s per year in

EP

haemodialysis patients13. Moreover, CKD patients without and with diabetes had an annual increase of cf-PWV rate by 0.4 m/s and 1.5 m/s per year, respectively14. However, we did not detect a

AC C

statistical difference in the annual cf-PWV progression rate between individuals with (0.28 m/s per year, interquartile: 1.69) and without (0.20 m/s per year, interquartile: 1.06) CKD in our T2DM cohort.

Several prospective studies have examined the baseline predictors of arterial stiffness progression in different populations, and have identified varying determinants for the outcome of annual progression rate. In contrast, studies characterizing the predictors of rapid cf-PWV progression and incident aortic stiffness are scarce. Among apparently healthy middle-aged White and African American men, baseline levels of adiponectin, SBP, and alcohol consumption were the main predictors of relative

ACCEPTED MANUSCRIPT annual changes in cf-PWV after 4.6 years of follow-up15. In Brazilian patients with T2DM, decreased cf-PWV, increased heart rate and SBP, older age, female, and presence of diabetic retinopathy and dyslipidaemia were associated with relative changes in cf-PWV during follow-up10. Multivariate analyses of normotensive subjects in France revealed that baseline age was the sole determinant of cf-

RI PT

PWV progression. On the other hand, older age together with elevated baseline heart rate and serum creatinine predicted annual cf-PWV progression in treated hypertensive individuals16. Among Asians, baseline factors including age, one-point carotid PWV, mean arterial pressure, and BMI were

demonstrated to influence the progression rate of arterial stiffness in Taiwan’s Han-Chinese subjects

SC

free from stroke and myocardial infarction17. Similarly, our data revealed an independent association of baseline age, BMI, and cf-PWV with annual rate of change in cf-PWV. These variables were also

M AN U

associated with rapid cf-PWV progression and incident aortic stiffness. Other predictors of annual cfPWV progression rate in our cohort were SBP, eGFR, WC, and use of insulin and RAS antagonists. Taken together, adiposity seems to be a stronger predictor of cf-PWV progression in Asians than Caucasians. Compared with the Western populations, Asian populations tend to have a higher risk of

TE D

CVD at lower BMI level18. Therefore, association between BMI and CVD risk in Asians may be more readily detectable than Caucasians.

We observed that rapid cf-PWV progessors had a heightened risk of developing aortic stiffness than

EP

slow/non progressors. Interestingly, while people with higher baseline cf-PWV were more likely to develop incident aortic stiffness, those with lower cf-PWV level were more susceptible to enhanced

AC C

cf-PWV progression. There was also a prominent negative correlation of baseline cf-PWV with annual progression rate of aortic stiffness and rapid cf-PWV progression that paradoxically implies that healthier arteries deteriorate faster. The inverse relationship was previously reported in several studies10, 17, 19, and it was postulated to be a consequence of the ceiling effects of arterial stiffness, which depict that less physiological room is available for stiffness worsening when the baseline level is already high17, 20. For this reason, baseline cf-PWV may have limited value as a predictor of cfPWV progression.

ACCEPTED MANUSCRIPT According to our data, BMI better predicted cf-PWV progression and incident aortic stiffness than WC. To date, the mechanisms underlying the association between obesity and aortic stiffness remain unclear. Research has suggested a role of adipokines such as leptin in influencing the relationship between adiposity and aortic stiffness through multiple mechanisms that cause vascular inflammation,

RI PT

proliferation, calcification, and elevated oxidative stress21, 22. Earlier work has shown that weight loss may reduce arterial stiffness. After a weight loss of 7% over 6 months, overweight/obese young adults displayed a significant median decrease of 47.5 cm/s in cf-PWV23.

SC

It is established that arterial walls stiffen due to enhanced degradation of elastin fibres, accumulation of stiffer collagen fibres and increased calcification in the media of large arteries, resulting in a greater

M AN U

PWV24. However, it is not well understood whether a gradual stiffening of the arterial wall may influence the formation and composition of carotid atherosclerotic plaques even though arterial stiffness and atherosclerosis share common risk factors such as aging and hypertension. Recent evidence suggested that increased arterial stiffness, measured by aortic PWV, was associated with carotid atherosclerotic plaques, particularly intraplaque haemorrhage, in the general population25.

TE D

Although our study was not designed to address this aspect, it would be meaningful to evaluate this association in the Singapore cohort.

EP

Stratification of our cohort by ethnicity revealed disparity is the predictive ability of the risk factors for cf-PWV progression in different ethnic groups. For instance, while baseline cf-PWV was inversely

AC C

associated with annual cf-PWV progression rate across all ethnicities, BMI was only significant in the Chinese and Indian groups, but not in the Malay and ‘Others’ groups. We have previously reported that Malays with T2DM exhibited a higher cf-PWV level than Chinese and Indians26. Similar observation was made in the participants of this study. Consistent with the concept of ceiling effects, the Malays had the lowest median annual cf-PWV progression rate (0.10 m/s per year, interquartile: 1.13), compared with Chinese (0.23 m/s per year, interquartile: 1.13), Indians (0.20 m/s per year, interquartile: 1.17) and ‘Others’ (0.15 m/s per year, interquartile: 1.00). Apart from baseline cf-PWV, we were unable to identify metabolic risk factors that predicted cf-PWV progression in the Malays.

ACCEPTED MANUSCRIPT One possibility is that the progression of aortic stiffness in Malay patients may be attributable to other medical conditions.

Although less consistent, increased SBP and heart rate, as well as reduced renal function expressed as lower eGFR or higher ACR, also exhibited predictive ability for the progression of cf-PWV in this

RI PT

study. In addition, we noted a positive relationship between the use of insulin and cf-PWV

progression, suggestive of a more serious diabetes burden. Several mechanisms have been proposed to explain the association between resting heart rate and arterial stiffness. Firstly, a higher heart rate may

SC

increase the sympathetic tone that causes endothelial dysfunction, thus leading to arterial stiffness. Secondly, resting heart rate and arterial stiffness may be linked by increased oxidative stress and

M AN U

chronic low-grade inflammation27. It has been reported that SBP accelerates arterial stiffening by increasing aortic distending pressure and reduced elasticity. In reverse, aortic stiffening elevates pressure pulsatility and therefore increases SBP28, 29. Extensive work has also revealed an association of decreased eGFR and increased albuminuria with vascular stiffness8, 11, 30, 31. Renal dysfunction may affect arterial function via mechanisms that involve impaired oxidative stress and endothelial

TE D

dysfunction32, 33. Conversely, aortic stiffness has been shown to be a significant determinant of annual decline of eGFR and incident albuminuria in T2DM patients34.

EP

To our knowledge, this is the largest prospective study that evaluated the baseline predictors of progression to aortic stiffness among T2DM patients. We used the same widely-accepted gold-

AC C

standard tonometry method to measure cf-PWV at baseline and at follow-up. A study weakness is that the intake of anti-hypertensive medications could have blunted the expected marked contribution of arterial blood pressure on aortic stiffening. Our available data on the anti-hypertensive drug classes are primarily limited to those considered as diabetic kidney disease relevant, namely angiotensin converting enzyme inhibitors and angiotensin receptor blockers. Given that T2DM patients are frequently treated for hypertension and complete information on the anti-hypertensive agents used to treat our study cohort is lacking, it is therefore not feasible for us to limit our study to individuals naïve to anti-hypertensive therapy. Although we are unable to fully account for the influence of anti-

ACCEPTED MANUSCRIPT hypertensive treatments, we sought to partially address the limitations by adjusting for the use of RAS inhibitors. These medications have been documented to be the major class of agents affecting arterial stiffness35, 36. There are other limitations to be considered. Subjects who regressed in cf-PWV were not excluded from our analysis. Both BMI and WC are crude anthropometric indices for obesity. As

RI PT

this work is limited to Singapore’s Asians with T2DM, the findings may not be generalizable beyond the study population.

In conclusion, we demonstrated in this 3-year follow-up study that baseline BMI represents a

SC

modifiable independent risk factor of cf-PWV progression and incidence of aortic stiffness. Although baseline cf-PWV persistently and robustly predicted cf-PWV progression, the relationship was

M AN U

inverse, rendering it unsuitable as a predictor for this outcome. The data suggest that effective management of obesity may impede the onset and progression of aortic stiffness in T2DM patients, especially in the Chinese and Indians. Moving forward, detailed assessment of body composition/fat distribution may reveal greater insights into the role of global and regional adiposity in aortic

TE D

stiffening.

Conflict of interest

to this manuscript.

AC C

Financial support

EP

The authors declared they do not have anything to disclose regarding conflict of interest with respect

This work was supported by the Singapore National Medical Research Council Grants PPG/AH(KTPH)/2011 and CIRG13nov045.

Author contributions

ACCEPTED MANUSCRIPT M.C.M. researched data and wrote the manuscript. K.A. researched data and reviewed the manuscript. C.F.S., S.T., S.B.M.L. and W.E.T. reviewed the manuscript. S.C.L. contributed to the discussion and reviewed/edited the manuscript.

RI PT

Acknowledgements

We thank staff from the Singapore Clinical Research Institute (SCRI) for their contribution to the study protocol and database design. Study data were collected and managed using REDCap electronic

SC

data capture tools hosted at the SCRI.

1.

M AN U

References

Fox CS. Cardiovascular disease risk factors, type 2 diabetes mellitus, and the framingham heart study. Trends Cardiovasc Med. 2010;20:90-95

2.

Vlachopoulos C, Aznaouridis K, Stefanadis C. Prediction of cardiovascular events and allcause mortality with arterial stiffness: A systematic review and meta-analysis. J Am Coll

3.

TE D

Cardiol. 2010;55:1318-1327

Mansour AS, Yannoutsos A, Majahalme N, Agnoletti D, Safar ME, Ouerdane S, Blacher J. Aortic stiffness and cardiovascular risk in type 2 diabetes. J Hypertens. 2013;31:1584-1592 Lee HY, Oh BH. Aging and arterial stiffness. Circ J. 2010;74:2257-2262

5.

Fortier C, Agharazii M. Arterial stiffness gradient. Pulse (Basel). 2016;3:159-166

6.

Van Bortel LM, Laurent S, Boutouyrie P, Chowienczyk P, Cruickshank JK, De Backer T,

AC C

EP

4.

Filipovsky J, Huybrechts S, Mattace-Raso FU, Protogerou AD, Schillaci G, Segers P, Vermeersch S, Weber T. Expert consensus document on the measurement of aortic stiffness in daily practice using carotid-femoral pulse wave velocity. J Hypertens. 2012;30:445-448

7.

Smith A, Karalliedde J, De Angelis L, Goldsmith D, Viberti G. Aortic pulse wave velocity and albuminuria in patients with type 2 diabetes. J Am Soc Nephrol. 2005;16:1069-1075

ACCEPTED MANUSCRIPT 8.

Liu JJ, Sum CF, Tavintharan S, Yeoh LY, Ng XW, Moh AM, Lee S, Tang WE, Lim SC. Obesity is a determinant of arterial stiffness independent of traditional risk factors in asians with young-onset type 2 diabetes. Atherosclerosis. 2014;236:286-291

9.

Dahlen EM, Bjarnegard N, Lanne T, Nystrom FH, Ostgren CJ. Sagittal abdominal diameter is

RI PT

a more independent measure compared with waist circumference to predict arterial stiffness in subjects with type 2 diabetes--a prospective observational cohort study. Cardiovasc Diabetol. 2013;12:55 10.

Ferreira MT, Leite NC, Cardoso CR, Salles GF. Correlates of aortic stiffness progression in

SC

patients with type 2 diabetes: Importance of glycemic control: The rio de janeiro type 2 diabetes cohort study. Diabetes Care. 2015;38:897-904

Liu JJ, Tavintharan S, Yeoh LY, Sum CF, Ng XW, Pek SL, Lee SB, Tang WE, Lim SC. High

M AN U

11.

normal albuminuria is independently associated with aortic stiffness in patients with type 2 diabetes. Diabet Med. 2014;31:1199-1204 12.

Roderjan CN, Cardoso CR, Ferreira MT, Muxfeldt ES, Salles GF. Correlates of aortic

TE D

stiffness progression in patients with resistant hypertension: Importance of clinic and ambulatory blood pressure changes. J Hypertens. 2015;33:827-834; discussion 834-825 13.

Utescu MS, Couture V, Mac-Way F, De Serres SA, Marquis K, Lariviere R, Desmeules S,

EP

Lebel M, Boutouyrie P, Agharazii M. Determinants of progression of aortic stiffness in hemodialysis patients: A prospective longitudinal study. Hypertension. 2013;62:154-160 Tholen S, Klofat K, Pan CR, Schmaderer C, Lutz J, Heemann U, Baumann M. Progression of

AC C

14.

aortic pulse wave velocity in patients with chronic kidney disease. J Clin Hypertens (Greenwich). 2013;15:833-838

15.

El Khoudary SR, Barinas-Mitchell E, White J, Sutton-Tyrrell K, Kuller LH, Curb JD, Shin C,

Ueshima H, Masaki K, Evans RW, Miura K, Edmundowicz D, Sekikawa A. Adiponectin, systolic blood pressure, and alcohol consumption are associated with more aortic stiffness progression among apparently healthy men. Atherosclerosis. 2012;225:475-480 16.

Benetos A, Adamopoulos C, Bureau JM, Temmar M, Labat C, Bean K, Thomas F, Pannier B, Asmar R, Zureik M, Safar M, Guize L. Determinants of accelerated progression of arterial

ACCEPTED MANUSCRIPT stiffness in normotensive subjects and in treated hypertensive subjects over a 6-year period. Circulation. 2002;105:1202-1207 17.

Lin LY, Liao YC, Lin HF, Lee YS, Lin RT, Hsu CY, Juo SH. Determinants of arterial stiffness progression in a han-chinese population in taiwan: A 4-year longitudinal follow-up.

18.

RI PT

BMC Cardiovasc Disord. 2015;15:100 Appropriate body-mass index for asian populations and its implications for policy and intervention strategies. Lancet. 2004;363:157-163 19.

Tomiyama H, Hashimoto H, Hirayama Y, Yambe M, Yamada J, Koji Y, Shiina K,

SC

Yamamoto Y, Yamashina A. Synergistic acceleration of arterial stiffening in the presence of raised blood pressure and raised plasma glucose. Hypertension. 2006;47:180-188 Gepner AD, Korcarz CE, Colangelo LA, Hom EK, Tattersall MC, Astor BC, Kaufman JD,

M AN U

20.

Liu K, Stein JH. Longitudinal effects of a decade of aging on carotid artery stiffness: The multiethnic study of atherosclerosis. Stroke. 2014;45:48-53 21.

Windham BG, Griswold ME, Farasat SM, Ling SM, Carlson O, Egan JM, Ferrucci L, Najjar

TE D

SS. Influence of leptin, adiponectin, and resistin on the association between abdominal adiposity and arterial stiffness. Am J Hypertens. 2010;23:501-507 22.

Katagiri H, Yamada T, Oka Y. Adiposity and cardiovascular disorders: Disturbance of the

23.

EP

regulatory system consisting of humoral and neuronal signals. Circ Res. 2007;101:27-39 Cooper JN, Buchanich JM, Youk A, Brooks MM, Barinas-Mitchell E, Conroy MB, Sutton-

AC C

Tyrrell K. Reductions in arterial stiffness with weight loss in overweight and obese young adults: Potential mechanisms. Atherosclerosis. 2012;223:485-490

24. 25.

Sun Z. Aging, arterial stiffness, and hypertension. Hypertension. 2015;65:252-256 Selwaness M, van den Bouwhuijsen Q, Mattace-Raso FU, Verwoert GC, Hofman A, Franco OH, Witteman JC, van der Lugt A, Vernooij MW, Wentzel JJ. Arterial stiffness is associated with carotid intraplaque hemorrhage in the general population: The rotterdam study. Arterioscler Thromb Vasc Biol. 2014;34:927-932

ACCEPTED MANUSCRIPT 26.

Zhang X, Liu JJ, Sum CF, Ying YL, Tavintharan S, Ng XW, Low S, Lee SB, Tang WE, Lim SC. Ethnic disparity in central arterial stiffness and its determinants among asians with type 2 diabetes. Atherosclerosis. 2015;242:22-28

27.

Park BJ, Lee HR, Shim JY, Lee JH, Jung DH, Lee YJ. Association between resting heart rate

28.

RI PT

and arterial stiffness in korean adults. Arch Cardiovasc Dis. 2010;103:246-252 Steppan J, Barodka V, Berkowitz DE, Nyhan D. Vascular stiffness and increased pulse pressure in the aging cardiovascular system. Cardiol Res Pract. 2011;2011:263585

AlGhatrif M, Lakatta EG. The conundrum of arterial stiffness, elevated blood pressure, and aging. Curr Hypertens Rep. 2015;17:12

30.

SC

29.

Rossi SH, McQuarrie EP, Miller WH, Mackenzie RM, Dymott JA, Moreno MU, Taurino C,

M AN U

Miller AM, Neisius U, Berg GA, Valuckiene Z, Hannay JA, Dominiczak AF, Delles C. Impaired renal function impacts negatively on vascular stiffness in patients with coronary artery disease. BMC Nephrol. 2013;14:173 31.

Bian SY, Guo HY, Ye P, Luo LM, Wu HM, Xiao WK, Qi LP, Yu HP, Duan LF. Association

TE D

of glomerular filtration rate with arterial stiffness in chinese women with normal to mildly impaired renal function. J Geriatr Cardiol. 2012;9:158-165 32.

Al-Nimri MA, Komers R, Oyama TT, Subramanya AR, Lindsley JN, Anderson S.

EP

Endothelial-derived vasoactive mediators in polycystic kidney disease. Kidney Int. 2003;63:1776-1784

Oberg BP, McMenamin E, Lucas FL, McMonagle E, Morrow J, Ikizler TA, Himmelfarb J.

AC C

33.

Increased prevalence of oxidant stress and inflammation in patients with moderate to severe chronic kidney disease. Kidney Int. 2004;65:1009-1016

34.

Bouchi R, Babazono T, Mugishima M, Yoshida N, Nyumura I, Toya K, Hanai K, Tanaka N,

Ishii A, Uchigata Y, Iwamoto Y. Arterial stiffness is associated with incident albuminuria and decreased glomerular filtration rate in type 2 diabetic patients. Diabetes Care. 2011;34:25702575

ACCEPTED MANUSCRIPT 35.

Shahin Y, Khan JA, Chetter I. Angiotensin converting enzyme inhibitors effect on arterial stiffness and wave reflections: A meta-analysis and meta-regression of randomised controlled trials. Atherosclerosis. 2012;221:18-33 Chen X, Huang B, Liu M, Li X. Effects of different types of antihypertensive agents on

RI PT

arterial stiffness: A systematic review and meta-analysis of randomized controlled trials. J

EP

TE D

M AN U

SC

Thorac Dis. 2015;7:2339-2347

AC C

36.

ACCEPTED MANUSCRIPT

Table 1 Baseline characteristics of subjects

Rapid progressor Total (N=994)

progressor

p value

(N=104) (N=890) 57 ± 10

57 ± 10

58 ± 9

Male, n (%)

517 (52.0)

461 (51.8)

56 (53.8)

Ethnicity, n (%) 531 (53.4)

477 (53.6)

Malay

203 (20.4)

180 (20.2)

Indian

228 (22.9)

203 (22.8)

Other

32 (3.2)

30 (3.4)

T2DM duration (years)

10 (11)

10 (11)

BMI (kg/m2)

27.6 ± 4.9

WC (cm2)

97.1 ± 13.1

Heart rate (bpm)

70.5 ± 10.7

SBP (mmHg) DBP (mmHg)

0.692

Incident AS

Non-AS (N=423)

p value (N=188)

53 ± 10

58 ± 9

<0.001

216 (51.1)

98 (52.1)

0.808

0.830

0.688

54 (51.9)

214 (50.6)

105 (55.9)

23 (22.1)

86 (20.3)

35 (18.6)

25 (24.0)

107 (25.3)

42 (22.3)

16 (3.8)

6 (3.2)

TE D

Chinese

0.164

M AN U

Age (years)

SC

Baseline variable

RI PT

Non/slow

2 (1.9) 0.016

6 (7)

10 (14)

0.011

27.5 ± 4.8

28.7 ± 5.6

0.012

27.5 ± 4.8

28.2 ± 5.2

0.099

96.7 ± 12.8

100.3 ± 14.9

0.013

96.6 ± 12.7

97.9 ± 13.1

0.273

70.5 ± 10.7

71.1 ± 11.1

0.564

69.9 ± 10.2

70.6 ± 10.4

0.431

138.8 ± 17.8

138.5 ± 17.4

141.3 ± 20.7

0.137

131.9 ± 14.5

139.5 ± 16.7

<0.001

78.9 ± 9.5

78.9 ± 9.4

79.4 ± 10.1

0.597

78.4 ± 9.6

79.6 ± 9.7

0.175

AC C

EP

10 (15)

Hypertension, n (%)

738 (74.2)

658 (74.3)

80 (77.7)

0.452

266 (63.2)

145 (78.0)

<0.001

HbA1c (%)

7.7 ± 1.3

7.7 ± 1.3

7.8 ± 1.3

0.633

7.8 ± 1.5

8.0 ± 1.6

0.239

HbA1c (mmol/mol)

60.8 ± 14.1

60.7 ± 14.1

61.4 ± 14.5

TC (mM)

4.4 ± 1.1

4.4 ± 1.1

4.3 ± 0.9

HDL-C (mM)

1.3 ± 0.4

1.3 ± 0.4

1.3 ± 0.4

RI PT

ACCEPTED MANUSCRIPT

LDL-C (mM)

2.7 ± 0.8

2.7 ± 0.8

2.7 ± 0.7

TG (mM)

1.4 (0.9)

1.4 (0.9)

cf-PWV (m/s)

9.6 ± 2.6

9.7 ± 2.7

eGFR (ml/min/1.73 m2)

87.8 ± 24.5

88.7 ± 23.9

CKD, n (%)

140 (14.1)

115 (12.9)

ACR (µg/mg)

20.0 (78.8)

19.0 (69.3)

61.2 ± 13.4

0.239

0.443

4.5 ± 1.0

4.5 ± 1.5

0.881

0.335

1.3 ± 0.3

1.3 ± 0.4

0.572

0.628

2.8 ± 0.8

2.7 ± 0.8

0.035

1.5 (0.8)

0.396

1.3 (0.9)

1.5 (0.9)

0.633

9.0 ± 2.1

0.010

7.8 ± 1.2

8.4 ± 1.1

<0.001

80.1 ± 28.2

0.001

96.6 ± 20.5

85.7 ± 22.8

<0.001

25 (24.0)

0.002

23 (5.4)

28 (14.9)

<0.001

0.002

11.0 (37.0)

22.0 (81.0)

0.002

M AN U

SC

59.7 ± 14.1

TE D

EP

Albuminuria, n (%)

0.633

35.5 (160.5)

0.003

0.063

563 (56.6)

515 (58.1)

48 (47.1)

287 (68.3)

106 (56.4)

Micro

290 (29.2)

256 (28.9)

34 (33.3)

107 (25.5)

57 (30.3)

Macro

135 (13.6)

115 (13.0)

20 (19.6)

26 (6.2)

25 (26.6)

231 (26.0)

35 (33.7)

76 (18.0)

49 (26.1)

Medications, n (%) Insulin

266 (26.8)

AC C

Normal

0.102

0.020

Metformin

825 (83.0)

744 (83.6)

81 (77.9)

0.252

362 (85.6)

155 (82.4)

0.339

Statins

819 (82.4)

728 (81.8)

91 (87.5)

0.167

331 (78.3)

163 (86.7)

0.019

RAS antagonists

578 (58.1)

518 (58.6)

60 (58.3)

RI PT

ACCEPTED MANUSCRIPT

114 (61.3)

0.005

0.946

205 (48.8)

AS, aortic stiffness; T2DM, type 2 diabetes; BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure;

SC

TC, total cholesterol; HDL-C, high density lipoprotein-cholesterol; LDL-C, low density lipoprotein-cholesterol; TG, triglyceride; cf-PWV, carotid-femoral

M AN U

pulse wave velocity; eGFR, estimated glomerular filtration rate; CKD, chronic kidney disease; ACR, urinary albumin-creatinine ratio; RAS, renin-angiotensin system.

AC C

EP

TE D

Data for qualitative variables are expressed as n (%) and quantitative variables as mean ± SD or median (interquartile range).

ACCEPTED MANUSCRIPT

Table 2 Univariate and multivariate stepwise linear regression analyses of follow-up cf-PWV. Multivariate (BMI-based)

Multivariate (WC-based)

RI PT

Age

Univariate Beta (95% CI)

p value

Beta (95% CI)

p value

Beta (95% CI)

p value

0.243 (0.057 − 0.095)

<0.001

0.136 (0.020 − 0.065)

<0.001

0.124 (0.013 − 0.063)

0.003

−

ns

Gender Reference

Male

0.007 (-0.341 − 0.433)

−

ns

M AN U

Female

SC

Variable

−

ns

−

ns

−

ns

−

ns

−

ns

−

ns

−

ns

−

ns

−

ns

−

ns

−

ns

−

ns

0.001

0.154 (0.060 − 0.135)

<0.001

na

na

<0.001

na

na

0.144 (0.018 − 0.049)

<0.001

0.003

0.063 (0.002 − 0.034)

0.030

0.064 (0.000 − 0.037)

0.047

0.816

Malay

0.020 (-0.329 − 0.631)

0.536

Indian

-0.015 (-0.570 − 0.350)

0.639

Other

-0.048 (-1.936 − 0.254)

0.132

T2DM durationa

0.256 (1.545 − 2.498)

<0.001

BMI

0.101 (0.025 − 0.104)

WC

0.124 (0.013 − 0.045)

Heart rate

0.095 (0.009 − 0.045)

EP

Reference

AC C

Chinese

TE D

Ethnicity

ACCEPTED MANUSCRIPT

0.295 (0.041 − 0.062)

<0.001

0.131 (0.012 − 0.033)

<0.001

0.113 (0.008 − 0.032)

0.001

HbA1c

0.095 (0.079 − 0.378)

0.003

−

ns

−

ns

TGa

0.043 (-0.270 − 1.520)

0.171

−

ns

−

ns

cf-PWV

0.422 (0.430 − 0.563)

<0.001

0.256 (0.223 − 0.372)

<0.001

0.241 (0.201 − 0.369)

<0.001

eGFR

-0.300 (-0.045 − -0.030)

<0.001

-0.083 (-0.019 − -0.001)

0.022

-0.090 (-0.021 − -0.002)

0.024

ACRa

0.264 (0.687 − 1.094)

<0.001

−

ns

−

ns

Insulin

0.214 (1.074 − 1.931)

<0.001

0.100 (0.244 − 1.131)

0.002

0.099 (0.193 − 1.169)

0.006

Metformin

-0.063 (-1.041 − -0.004)

0.048

−

ns

−

ns

Statins

0.102 (0.327 − 1.346)

0.001

−

ns

0.066 (0.023 − 1.024)

0.040

RAS antagonists

0.112 (0.313 − 1.099)

<0.001

−

ns

−

ns

SC

M AN U

EP

na, not applicable; ns, not significant.

AC C

Log-transformed.

TE D

Medications

a

RI PT

SBP

ACCEPTED MANUSCRIPT

Table 3 Univariate and multivariate stepwise linear regression analyses of annual cf-PWV progression rate.

Age

Univariate

Multivariate (BMI-based)

Multivariate (WC-based)

RI PT

Variable

Beta (95% CI)

p value

Beta (95% CI)

p value

Beta (95% CI)

p value

-0.046 (-0.012 − 0.002)

0.144

0.142 (0.007 − 0.023)

<0.001

0.135 (0.006 − 0.023)

<0.001

−

ns

Reference

Male

0.007 (-0.119 − 0.147)

−

ns

M AN U

Female

SC

Gender

−

ns

−

ns

−

ns

−

ns

−

ns

−

ns

−

ns

−

ns

−

ns

−

ns

−

ns

−

ns

0.002

0.148 (0.019 − 0.045)

<0.001

na

na

0.005

na

na

0.138 (0.006 − 0.016)

<0.001

0.372

−

ns

−

ns

0.592

0.136 (0.004 − 0.012)

<0.001

0.120 (0.003 − 0.012)

0.001

0.835

Malay

-0.027 (-0.235 − 0.094)

0.400

Indian

0.011 (-0.129 − 0.187)

0.718

Other

-0.034 (-0.580 − 0.170)

0.285

T2DM durationa

0.002 (-0.164 − 0.173)

0.955

BMI

0.101 (0.008 − 0.036)

WC

0.097 (0.002 − 0.013)

Heart rate

0.012 (-0.005 − 0.007)

SBP

-0.017 (-0.005 − 0.003)

EP

Reference

AC C

Chinese

TE D

Ethnicity

ACCEPTED MANUSCRIPT

0.031 (-0.026 − 0.077)

0.329

−

ns

−

ns

TGa

0.031 (-0.155 − 0.458)

0.333

−

ns

−

ns

cf-PWV

-0.421 (-0.193 − -0.147)

<0.001

-0.600 (-0.265 − -0.213)

<0.001

-0.601 (-0.276 − -0.218)

<0.001

eGFR

-0.032 (-0.004 − 0.001)

0.309

-0.091 (-0.007 − -0.001)

0.013

-0.096 (-0.008 − -0.001)

0.015

ACRa

0.001 (-0.071 − 0.073)

0.977

−

ns

−

ns

Insulin

0.042 (-0.048 − 0.252)

0.184

0.097 (0.078 − 0.383)

<0.001

0.095 (0.059 − 0.394)

0.008

Metformin

-0.025 (-0.248 − 0.108)

0.440

−

ns

−

ns

Statins

0.034 (-0.079 − 0.272)

0.280

−

ns

−

ns

RAS antagonists

-0.062 (-0.270 − 0.000)

0.050

-0.064 (-0.264 − -0.010)

0.035

−

ns

TE D

na, not applicable; ns, not significant.

EP

Log-transformed.

AC C

a

SC

M AN U

Medications

RI PT

HbA1c

ACCEPTED MANUSCRIPT

Table 4 Univariate and multivariate logistic regression model of rapid cf-PWV progressors.

Age

Univariate

Multivariate (BMI-based)

Odds ratio (95% CI)

p value

Odds ratio (95% CI)

p value

1.015 (0.094 − 1.037)

0.164

1.037 (1.007 − 1.068)

0.015

−

ns

Reference

Male

1.086 (0.722 − 1.632)

p value

1.042 (1.009 − 1.076)

0.011

−

ns

ns

−

ns

ns

−

ns

ns

−

ns

−

ns

−

ns

−

ns

−

ns

−

ns

−

ns

0.012

1.086 (1.036 − 1.137)

0.001

na

na

0.014

na

na

1.029 (1.010 − 1.049)

0.002

0.564

−

ns

−

−

0.137

−

ns

−

−

−

0.692

Ethnicity −

1.129 (0.673 − 1.893)

0.646

Indian

1.088 (0.659 − 1.797)

0.742

Others

0.589 (0.137 − 2.533)

0.477

T2DM durationa

1.909 (1.099 − 3.314)

0.022

BMI

1.051 (1.011 − 1.093)

WC

1.019 (1.004 − 1.035)

Heart rate

1.006 (0.987 − 1.025)

SBP

1.008 (0.997 − 1.020)

−

TE D

Malay

EP

Reference

AC C

Chinese

M AN U

Female

Odds ratio (95% CI)

SC

Gender

Multivariate (WC-based)

RI PT

Variable

ACCEPTED MANUSCRIPT

1.038 (0.890 − 1.212)

0.633

−

ns

−

−

TGa

1.105 (0.435 − 2.810)

0.833

−

ns

−

−

cf-PWV

0.892 (0.817 − 0.974)

0.011

0.730 (0.646 − 0.825)

<0.001

eGFR

0.987 (0.980− 0.995)

0.001

−

ns

ACRa

1.431 (1.151− 1.780)

0.001

1.413 (1.053 − 1.897)

0.021

Insulin

1.434 (0.929 − 2.212)

0.103

−

Metformin

0.677 (0.412 − 1.112)

0.123

−

Statins

1.529 (0.834 − 2.802)

0.170

−

RAS antagonists

0.986 (0.652 − 1.491)

0.946

−

TE D

na, not applicable; ns, not significant.

EP

Log-transformed.

AC C

a

<0.001

−

ns

1.456 (1.062 − 1.995)

0.020

ns

−

ns

ns

−

ns

ns

−

ns

ns

−

ns

SC

0.716 (0.627 − 0.818)

M AN U

Medications

RI PT

HbA1c

ACCEPTED MANUSCRIPT

Table 5 Univariate and multivariate Cox regression analysis of incident aortic stiffness during follow-up.

Age

Univariate

Multivariate (BMI-based)

Hazard ratio (95% CI)

p value

Hazard ratio (95% CI)

p value

1.038 (1.022 − 1.054)

<0.001

1.029 (1.008 − 1.050)

0.007

−

ns

Reference

Male

1.044 (0.784 − 1.390)

−

0.767

p value

1.025 (1.003 − 1.048)

0.024

ns

ns

−

ns

−

ns

Chinese

Reference

Malay

0.899 (0.623 − 1.298)

0.571

0.642 (0.421 − 0.980)

0.040

−

ns

Indian

0.851 (0.603 − 1.199)

0.356

−

ns

−

ns

Other

0.953 (0.422 − 2.149)

0.907

−

ns

−

ns

T2DM durationa

2.388 (1.618 − 3.525)

<0.001

−

ns

−

ns

BMI

1.015 (0.987 − 1.044)

0.304

1.045 (1.010 − 1. 081)

0.011

na

na

WC

1.002 (0.990 − 1.014)

0.723

na

na

−

ns

Heart rate

1.003 (0.989 − 1.017)

0.666

−

ns

−

ns

SBP

1.020 (1.011 − 1.029)

<0.001

1.014 (1.004 − 1.023)

0.004

1.012 (1.002 − 1.022)

0.018

EP

TE D

Reference

−

AC C

Ethnicity

M AN U

Female

Hazard ratio (95% CI)

SC

Gender

Multivariate (WC-based)

RI PT

Variable

ACCEPTED MANUSCRIPT

1.067 (0.957 − 1.190)

0.243

−

ns

−

ns

TGa

1.579 (0.872 − 2.859)

0.131

−

ns

−

ns

cf-PWV

1.386 (1.217 − 1.578)

<0.001

1.201 (1.040 − 1.387)

0.013

eGFR

0.987 (0.982− 0.992)

<0.001

−

ns

ACRa

1.349 (1.144− 1.591)

<0.001

−

ns

Insulin

1.340 (0.966 − 1.857)

0.079

−

Metformin

0.907 (0.623 − 1.321)

0.611

−

Statins

1.644 (1.079 − 2.506)

0.021

−

RAS antagonists

1.475 (1.098 − 1.981)

0.010

−

0.030

−

ns

−

ns

ns

−

ns

ns

−

ns

ns

−

ns

ns

−

ns

SC

TE D EP

na, not applicable; ns, not significant.

AC C

Log-transformed.

1.188 (1.016 − 1.388)

M AN U

Medications

a

RI PT

HbA1c

ACCEPTED MANUSCRIPT Highlights

EP

TE D

M AN U

SC

RI PT

Aortic stiffness (AS) was assessed by carotid-femoral pulse wave velocity (cf-PWV). This prospective study evaluated progression of cf-PWV in type 2 diabetes (T2DM). Baseline age, cf-PWV and BMI predicted cf-PWV progression rate and incident AS. BMI was associated with cf-PWV progression rate especially in Chinese and Indians. Obesity management may impede cf-PWV progression in Singapore’s Asians with T2DM.

AC C

• • • • •