BioMed Research International

BioMed Research International / 2018 / Article

Research Article | Open Access

Volume 2018 |Article ID 3104945 | 9 pages | https://doi.org/10.1155/2018/3104945

Association of Impaired Vascular Endothelial Function with Increased Cardiovascular Risk in Asymptomatic Adults

Academic Editor: Ken-ichi Aihara
Received08 May 2018
Accepted16 Sep 2018
Published02 Oct 2018

Abstract

Impaired vascular endothelial function has attracted attention as a prognostic indicator of cardiovascular prevention. The association between impaired endothelial function and cardiovascular risk in the asymptomatic population, however, has been poorly explored. We evaluated the association of brachial artery flow-mediated dilation (FMD) with Framingham-estimated 10-year cardiovascular disease (CVD) risk in subjects free of CVD, especially by cardiovascular risk profiles. In total, 680 adults aged 30-74 years were enrolled from Rongan and Rongshui of Liuzhou, Guangxi, China, through a cross-sectional study in 2015. In the full-adjusted model, the odds ratio for the estimated 10-year CVD risk comparing the low FMD (<6%) with the high FMD (≥10%) was 2.81 (95% confidence interval [CI]: 1.21, 6.53; P for trend = 0.03). In subgroup analyses, inverse associations between FMD and the estimated 10-year CVD risk were found in participants with specific characteristics. The adjusted odds ratios, comparing the 25th and the 75th percentiles of FMD, were 2.77 (95% CI: 1.54, 5.00) for aged ≥60 years, 1.77 (95% CI: 1.16, 2.70) for female, 1.59 (95% CI: 1.08, 2.35) for nonsmokers, 1.74 (95% CI: 1.02, 2.97) for hypertension, 1.59 (95% CI: 1.04, 2.44) for normal glycaemia, 2.03 (95% CI: 1.19, 3.48) for C-reactive protein ≥10 mg/L, and 1.85 (95% CI: 1.12, 3.06) for eGFR <106 mL/minute per 1.73 m2. Therefore, impaired endothelial function is associated with increased CVD risk in asymptomatic adults. This inverse association is more likely to exist in subjects with higher cardiovascular risk.

1. Introduction

The endothelium, a unique endocrine organ, plays an essential role in vascular homeostasis by secreting regulatory factors, such as nitric oxide (NO) [1, 2]. Impairment of endothelial function, mechanistically induced by the loss of NO bioavailability, is strongly regarded as a major contributor to the progression of atherosclerosis by in vitro or in vivo evidence [35]. Notably, prospective cohort studies have shown that brachial artery flow-mediated dilation (FMD), an ultrasonic technique widely used in endothelial function assessment, is a predictor of adverse cardiovascular events in populations with no apparent cardiovascular disease (CVD) [6, 7]. Although impaired endothelial function, as assessed by decreased FMD, is widely suggested as a progressive but reversible manifestation of atherogenesis, the prognostic value of FMD is still controversial in the preventive setting of atherosclerotic CVD [1, 2].

Given the current risk-based guidelines for cardiovascular prevention, a core issue in the prognostic value of FMD is to identify susceptible individuals who potentially respond to endothelial-targeted interventions among subjects free of apparent CVD. Theoretically, impaired endothelial function may be associated with adverse cardiovascular events. However, previous studies have presented discrepant findings in healthy middle-aged men [810], hypertensive or healthy postmenopausal women [1113], and healthy elderly subjects [14, 15], suggesting that the association between endothelial function and cardiovascular risk is not yet well established in the asymptomatic population, especially from the standpoint of CVD risk profiles. In fact, it is ubiquitous that risk factors, such as serum uric acid and oxidized low-density lipoprotein (LDL) cholesterol, play a different role in CVD risk under different population profiles [16].

A recent systematic review with prospective studies indicated that lower brachial FMD may increase cardiovascular risk in asymptomatic populations [17]. However, pooled risk estimates have not yet depicted subjects who are more likely to have a higher cardiovascular risk following decreased FMD. Thus, population profiles are still an open issue regarding the cardiovascular risk linked to impaired endothelial function. In this study, we aimed to evaluate the association of endothelial function with cardiovascular risk as estimated by the Framingham general CVD risk score in subjects free of apparent CVD, especially by different CVD risk profiles.

2. Methods

2.1. Study Population

We recruited 899 urban and rural adults from Rongan and Rongshui of Liuzhou, Guangxi, China, through a cross-sectional study in 2015. All participants underwent an interview, physical examinations, and laboratory examinations in sequence. Venous blood specimens were obtained after fasting for 12 hours and stored at -20°C until analysis. Among participants, we first restricted the sample to 821 participants aged 30-74 years and then excluded 15 participants with missing brachial FMD measurements, 8 participants with self-reported vasodilator use, and 5 participants with self-reported lipid-lowering treatment. We subsequently excluded 14 participants with an event of general CVD, including myocardial infarction, angina, ischaemic stroke, haemorrhagic stroke, transient ischaemic attack, or peripheral artery disease; 10 participants with other heart diseases; and 89 that were missing other covariates of interest. The final sample included 680 participants. The Medical Ethics Committee of Guangxi Medical University approved the study protocols and all participants provided written informed consent.

2.2. FMD Measurement

All the participants abstained from using vasoactive medications, smoking, consuming alcohol, caffeine, or high-fat foods the day before the brachial FMD measurement. After resting at least 15 minutes, measurement was performed using an UNEX EF38G high-resolution ultrasound system (UNEX Corporation, Nagoya, Japan) in a quiet room at a comfortable temperature. Details of the FMD measurement have been described elsewhere [18]. The brachial FMD is expressed as the percent increase in maximum diameter after reactive hyperaemia relative to the baseline brachial artery diameter.

2.3. Estimation of 10-Year CVD Risk

Information on age, sex, physician diagnosis of diabetes, use of oral hypoglycaemic medication, insulin, and antihypertension medication, and cigarette smoking was collected by a self-reported questionnaire. Serum glucose, serum total cholesterol, and serum high-density lipoprotein (HDL) cholesterol were determined using a Hitachi automatic analyser 7600-120 or 7600-020 (Hitachi, Tokyo, Japan). Blood pressure was measured using Omron HBP-9021 (Omron, Kyoto, Japan). Diabetes was defined as a self-reported physician diagnosis, a self-reported use of insulin or oral hypoglycaemic medication, or a fasting serum glucose ≥7.0 mmol/L. Smoking status was categorized as never, current, or former. Nonsmoker was defined as fewer than 100 cigarettes smoked in their entire life, current smoker was defined as at least 100 cigarettes smoked in their entire life and reported smoking cigarettes at interview, and former smoker was defined as at least 100 cigarettes smoked in their entire life but with smoking cessation [19].

The estimated 10-year CVD risk for participants aged 30-74 years was calculated using the sex-specific Framingham risk score, which included the covariates of age, total cholesterol and HDL cholesterol concentrations, treated or untreated systolic blood pressure levels, current smoking status (yes, no), and diabetes status (yes, no). The general formula of the equation is as follows:

In the formula, S10 is the 10-year baseline survival rate; is the estimated regression coefficient of the corresponding risk factor; is the value of the risk factor, which is 0 or 1 for binary variables and natural log-transformed value for continuous variables; is the corresponding mean; and n denotes the number of risk factors. Details of the algorithms for the sex-specific equations were provided elsewhere [20].

2.4. Other Variables

Information on education, ethnicity, vasodilator use, lipid-lowering treatment, and physician diagnosis of hypertension was collected by a self-reported questionnaire. Weight and height were measured during the physical examination. Body mass index (BMI) was calculated by dividing weight in kilograms by height in metres squared. Heart rate and baseline brachial artery diameter were monitored using the UNEX EF38G (UNEX Corporation, Nagoya, Japan) during the FMD measurement. Serum triglycerides, serum LDL cholesterol, serum C-reactive protein, and serum creatinine were measured using Hitachi 7600-120 or 7600-020 (Hitachi, Tokyo, Japan). Hypertension was defined as a self-reported physician diagnosis, use of antihypertensive medication, a systolic blood pressure ≥140 mmHg, or a diastolic blood pressure ≥90 mmHg. Dysglycaemia was defined as diabetes or without diabetes but a fasting serum glucose ≥6.1 mmol/L. Elevated lipid levels were defined as ≥200 mg/dL for total cholesterol, ≥150 mg/dL for triglycerides, and ≥130 mg/dL for LDL cholesterol. Low HDL cholesterol was defined as <40 mg/dL. Dyslipidaemia was defined as at least one condition of elevated lipid levels of total cholesterol, triglycerides, or LDL cholesterol or low HDL cholesterol [21, 22]. Serum creatinine was used to calculate an estimated glomerular filtration rate (eGFR) following the arithmetic in the Chronic Kidney Disease Epidemiology Collaboration equation [23].

2.5. Statistical Analysis

The brachial FMD was categorized as high (FMD ≥10%), moderate (FMD ≥6% and <10%), or low (FMD <6%), which represent good, moderate, or dysfunctional status in endothelial function, respectively [2427]. The estimated 10-year CVD risk was categorized as low (Framingham risk score ≤6%), moderate (Framingham risk score >6% and ≤20%), or high (Framingham risk score >20%) [20].

We first used binary logistic regression models to estimate the odds ratios for cardiovascular risk factors comparing the low and moderate categories of FMD to the high category of FMD. Furthermore, the association of endothelial function with estimated 10-year CVD risk was evaluated overall and from subgroups defined by age (<60 years, ≥60 years), sex (male, female), BMI (<24 kg/m2, ≥24 kg/m2), smoking (never, ever [current and former]), hypertension (yes, no), dysglycaemia (yes, no), dyslipidaemia (yes, no), C-reactive protein (<10 mg/L, ≥10 mg/L), and eGFR (<106 mL/minute per 1.73 m2, ≥106 mL/minute per 1.73 m2). Ordered logistic regression models were performed to estimate the odds ratios for estimated 10-year CVD risk comparing the low and moderate categories of FMD to the high category of FMD in overall participants and subgroups. We also estimated the odds ratios of estimated 10-year CVD risk by comparing the 25th and 75th percentiles of log-transformed FMD. P values for linear trend were obtained by including the medians for each FMD category as continuous variables in the logistic regression models. Finally, we explored the nonlinear relationship between FMD and estimated 10-year CVD risk using restricted quadratic splines with knots at the 10th, 50th, and 90th percentiles of log-transformed FMD.

For the relationship between FMD and estimated 10-year CVD risk, the logistic regression models were progressively adjusted for potential confounders. Model 1 was initially adjusted for age, sex, ethnicity, and education. Model 2 was further adjusted for BMI, smoking status, total cholesterol, HDL cholesterol, serum glucose, C-reactive protein, eGFR, and hypertension. Model 3 was further adjusted for heart rate and baseline brachial artery diameter. Statistical analyses were performed with STATA version 13.1 (StataCorp LP, College Station, TX, USA), and spline functions were conducted in R version 3.4.2 (R Foundation for Statistical Computing, Vienna, Austria). The two-sided statistical significance level was set at α = 0.05.

3. Results

Overall, the geometric medians of brachial FMD and estimated 10-year CVD risk were 8.2% and 8.3%, respectively, among the 680 study participants. On average, participants with higher estimated 10-year CVD risk were more likely to be older, men, current smokers, dyslipidaemic, hypertensive, and dysglycaemic; to have higher serum total cholesterol, LDL cholesterol, triglycerides, glucose, C-reactive protein, and baseline brachial artery diameter; and to have lower school education, serum HDL cholesterol, eGFR, and FMD (Table 1).


CharacteristicOverall (n=680)Categories of estimated 10-year CVD riskP
low risk (n=238)moderate risk (n=327)high risk (n=115)

Age (years)54.4 (0.4)45.9 (0.5)57.3 (0.4)63.8 (0.7)<0.001
Male (%)33.8 (1.8)12.2 (2.1)35.5 (2.6)73.9 (4.1)<0.001
Ethnicity, Han (%)19.3 (1.5)20.2 (2.6)16.5 (2.0)25.2 (4.1)0.11
High school education (%)11.0 (1.2)17.2 (2.4)8.2 (1.5)6.1 (2.2)0.001
BMI (kg/m2)23.0 (0.1)23.0 (0.2)23.0 (0.2)23.3 (0.3)0.55
Current smoking (%)15.3 (1.4)3.4 (1.2)12.2 (1.8)48.7 (4.7)<0.001
Total cholesterol (mg/dL)206.5 (1.9)190.5 (2.4)210.2 (2.2)229.0 (7.3)<0.001
LDL cholesterol (mg/dL)113.5 (1.4)103.7 (1.8)116.3 (1.8)126.1 (5.2)<0.001
HDL cholesterol (mg/dL)56.1 (0.5)57.5 (0.9)56.4 (0.8)52.3 (1.4)0.005
Triglycerides (mg/dL)107.8 (102.8, 113.1)86.6 (81.0, 92.6)114.6 (107.2, 122.5)142.8 (124.7, 163.6)<0.001
Serum glucose (mmol/L)5.7 (0.04)5.4 (0.03)5.8 (0.05)6.2 (0.18)<0.001
C-reactive protein (mg/L)8.8 (8.4, 9.3)7.6 (7.2, 8.2)9.3 (8.7, 10.0)10.3 (8.9, 11.9)<0.001
eGFR (mL/minute per 1.73 m2)105.7 (0.6)115.1 (0.8)102.7 (0.7)94.4 (1.6)<0.001
Dyslipidaemia (%)62.2 (1.9)47.5 (3.2)67.0 (2.6)79.1 (3.8)<0.001
Hypertension (%)42.4 (1.9)9.7 (1.9)53.8 (2.8)77.4 (3.9)<0.001
Dysglycaemia (%)21.2 (1.6)11.3 (2.1)24.2 (2.4)33.0 (4.4)<0.001
Heart rate (beat per minute)70.0 (0.4)70.4 (0.6)69.4 (0.6)70.9 (1.2)0.31
Baseline brachial artery diameter (mm)3.9 (0.02)3.7 (0.03)3.9 (0.03)4.2 (0.05)<0.001
Brachial FMD (%)8.2 (7.9, 8.6)9.2 (8.7, 9.8)8.1 (7.7, 8.6)6.7 (6.0, 7.4)<0.001
Estimated 10-year CVD risk (%)8.3 (7.8, 9.0)2.9 (2.7, 3.1)11.0 (10.6, 11.4)32.6 (30.5, 34.9)

Abbreviations: CVD, cardiovascular disease; BMI, body mass index; LDL, low-density lipoprotein; HDL, high-density lipoprotein; eGFR, estimated glomerular filtration rate; FMD, flow-mediated dilation.
One-way analysis of variance, Pearson’s chi-square, or Kruskal-Wallis test for differences across categories of estimated 10-year CVD risk.
Geometric means (95% confidence interval). Values in other results are percentages (standard errors) for categorical variables or means (standard errors) for continuous variables unless otherwise indicated.

There was no significant association between FMD and hypertension, dysglycaemia, or dyslipidaemia after adjustment for age, sex, ethnicity, education, body mass index, C-reaction protein, eGFR, smoking status, heart rate, and baseline brachial diameter (data not shown). For CVD risk, in the model adjusted for age, sex, ethnicity, and education, FMD was not significantly associated with estimated 10-year CVD risk (Table 2, Model 1; P for trend = 0.16). Further adjustment for CVD risk factors did not substantially affect this association (Table 2, Model 2; P for trend = 0.32). After further adjustment for heart rate and baseline brachial artery diameter, decreased FMD was significantly associated with higher estimated 10-year CVD risk (Table 2, Model 3; P for trend = 0.03). The fully adjusted odds ratio for estimated 10-year CVD risk comparing the low FMD (<6%) and the high FMD (≥10%) was 2.81 (95% CI: 1.21, 6.53). The corresponding odds ratio when comparing the 25th and the 75th percentiles of FMD was 1.51 (95% CI: 1.03, 2.20). Additionally, spline regression analysis showed a progressive increase in estimated 10-year CVD risk following decreased FMD from approximately 11.5% (the 75th percentile of FMD distribution) (Figure 1).


FMD (%)25th versus 75th PercentileP for trend
≥10 (n=256)6-10 (n=278)<6 (n=146)

Estimated 10-year CVD risk (%)6.58.811.4
Model 11 (reference)1.18 (0.79-1.76)1.40 (0.87-2.26)1.07 (0.86-1.33)0.16
Model 21 (reference)0.96 (0.52-1.77)1.55 (0.75-3.18)1.14 (0.83-1.57)0.32
Model 31 (reference)1.27 (0.66-2.42)2.81 (1.21-6.53)1.51 (1.03-2.20)0.03

Abbreviations: CVD, cardiovascular disease; FMD, flow-mediated dilation.
Geometric means within each category of FMD.
Model 1: adjusted for age (years), sex (male, female), ethnicity (Han, Zhuang, other), and education (<high school, ≥high school).
Model 2: further adjusted for body mass index (kg/m2), smoking status (never, former, current), total cholesterol (mg/dL), high-density lipoprotein cholesterol (mg/dL), serum glucose (mmol/L), C-reactive protein (log mg/L), estimated glomerular filtration rate (mL/minute per 1.73 m2), and hypertension (yes, no).
Model 3: further adjusted for heart rate (beat per minute) and baseline brachial artery diameter (mm).

For the specified subgroups, significant associations between decreased FMD and higher estimated 10-year CVD risk were found in participants categorized as aged ≥60 years, female, never smokers, normal glycaemia, C-reactive protein ≥10 mg/L, and eGFR <106 mL/minute per 1.73 m2 (all P for trend <0.05) (Table 3). The odds ratios for estimated 10-year CVD risk, comparing the 25th and the 75th percentiles of FMD, were 2.77 (95% CI: 1.54, 5.00), 1.77 (95% CI: 1.16, 2.70), 1.59 (95% CI: 1.08, 2.35), 1.59 (95% CI: 1.04, 2.44), 2.03 (95% CI: 1.19, 3.48), and 1.85 (95% CI: 1.12, 3.06) for the corresponding characteristics. Additionally, although there were no significant linear trends, estimated 10-year CVD risk was significantly higher when comparing the 25th and the 75th percentiles of FMD in participants with hypertension, and the corresponding odds ratio for estimated 10-year CVD risk was 1.74 (95% CI: 1.02, 2.97).


SubgroupnFMD (%)25th versus 75th PercentileP for trend
≥10 (n=256)6-10 (n=278)<6 (n=146)

Age (years)
 <604521 (reference)1.03 (0.55-1.92)1.20 (0.52-2.73)0.98 (0.66-1.44)0.71
 ≥602281 (reference)2.55 (0.87-7.53)10.94 (2.58-46.32)2.77 (1.54-5.00)0.001
Sex
 Male2301 (reference)0.96 (0.17-5.36)2.70 (0.29-25.00)1.15 (0.46-2.89)0.42
 Female4501 (reference)1.30 (0.62-2.74)4.00 (1.49-10.73)1.77 (1.16-2.70)0.02
BMI (kg/m2)
 <244421 (reference)1.18 (0.51-2.73)2.78 (0.98-7.86)1.32 (0.81-2.15)0.08
 ≥242381 (reference)1.76 (0.56-5.62)2.44 (0.46-12.94)1.84 (0.87-3.91)0.26
Smoking
 Never5251 (reference)1.31 (0.64-2.66)3.89 (1.51-10.00)1.59 (1.08-2.35)0.01
 Ever1551 (reference)0.84 (0.20-3.48)0.91 (0.18-4.57)0.70 (0.31-1.59)0.89
Hypertension
 Yes2881 (reference)1.64 (0.61-4.41)3.63 (0.97-13.62)1.74 (1.02-2.97)0.06
 No3921 (reference)1.02 (0.40-2.58)2.74 (0.82-9.17)1.27 (0.76-2.14)0.18
Dysglycaemia
 Yes1441 (reference)0.94 (0.22-3.94)3.39 (0.52-22.31)1.85 (0.87-3.95)0.31
 No5361 (reference)1.67 (0.77-3.62)3.56 (1.33-9.54)1.59 (1.04-2.44)0.02
Dyslipidaemia
 Yes4231 (reference)0.63 (0.31-1.26)1.02 (0.42-2.46)1.13 (0.71-1.79)0.70
 No2571 (reference)2.34 (0.70-7.90)4.26 (0.98-18.55)1.20 (0.68-2.13)0.05
C-reactive protein (mg/L)
 <104361 (reference)0.92 (0.38-2.18)1.37 (0.43-4.34)1.08 (0.63-1.86)0.69
 ≥102441 (reference)1.80 (0.61-5.30)7.55 (2.04-27.97)2.03 (1.19-3.48)0.005
eGFR (mL/minute per 1.73 m2)
 <1063371 (reference)0.88 (0.35-2.19)4.99 (1.54-16.15)1.85 (1.12-3.06)0.03
 ≥1063431 (reference)3.19 (1.03-9.92)1.36 (0.31-5.90)1.19 (0.60-2.37)0.40

Abbreviations: CVD, cardiovascular disease; FMD, flow-mediated dilation; BMI, body mass index; eGFR, estimated glomerular filtration rate.
Results were adjusted for age (years), sex (male, female), ethnicity (Han, Zhuang, other), education (<high school, ≥high school), body mass index (kg/m2), smoking status (never, former, current), total cholesterol (mg/dL), high-density lipoprotein cholesterol (mg/dL), serum glucose (mmol/L), C-reactive protein (log mg/L), estimated glomerular filtration rate (mL/minute per 1.73 m2), hypertension (yes, no), heart rate (beat per minute), and baseline brachial artery diameter (mm), except for the factor per se in the corresponding subgroup.

In spline analyses for subgroups, estimated 10-year CVD risk increases following decreased FMD were consistent in participants who were older, female, never smokers, hypertensive, C-reactive protein ≥10 mg/L, and eGFR <106 mL/minute per 1.73 m2. It is worth noting that the inverse association between estimated 10-year CVD risk and FMD was present for dysglycaemia and dyslipidaemia, especially within the low FMD (<6%) (Figure 2).

4. Discussion

In this study, the reduction of brachial FMD was generally associated with increased cardiovascular risk in subjects free of apparent CVD. Moreover, the inverse associations persisted in subjects with specific profiles, including aged ≥60 years, female, never smokers, hypertensive, C-reactive protein ≥10 mg/L, and eGFR <106 mL/minute per 1.73 m2. For subjects with dysglycaemia or dyslipidaemia, the inverse association was more likely to depend on a prerequisite of endothelial dysfunction.

Evidence from experimental and epidemiological studies has confirmed that endothelial dysfunction can be promoted by traditional CVD risk factors (hypertension, dysglycaemia, and dyslipidaemia) [2832]. Mechanistically, accumulating knowledge recognizes a mutual causality between impaired endothelial function and increased cardiovascular risk. However, the effect of impaired endothelial function on CVD risk factors is less known. For hypertension, a trial in normotensive humans confirmed that intravenous inhibitors of endothelium-derived NO synthase induced large increases in blood pressure [33]. Additionally, a prospective cohort study demonstrated that impaired endothelial function could predict the future development of hypertension in healthy postmenopausal women [12]. In contrast, two community-based prospective cohorts reported that impaired endothelial function did not play a key role in hypertension progression [34, 35]. Consistent with these two studies, the decreased FMD did not significantly relate to hypertension in our study. To our knowledge, no prospective study has suggested an effect of endothelial dysfunction on dysglycaemia or dyslipidaemia. There was also no evidence to support a significant relationship between deteriorated endothelial function and CVD risk factor in our study.

In terms of systematic CVD risk, prospective studies indicate that lower FMD is associated with increased future CVD events in asymptomatic populations [6, 7, 17]. Similarly, cross-sectional studies revealed an inverse relationship between FMD and the 10-year Framingham risk in 5314 Japanese adults [18] and 200 subjects free of coronary heart disease [36]. Across different characteristics, there were inconsistent findings in the association of FMD with cardiovascular risk [815]. Notably, the association between FMD and cardiovascular risk was significant in older and postmenopausal women [13, 14] but not in middle-aged men [8, 9]. In general, our study on the association between FMD and cardiovascular risk is in accordance with the previous studies. More importantly, our findings extend study characteristics in the previous studies from sociodemography to cardiovascular risk profiles and indicate a discrepancy in the relationship between endothelial function and cardiovascular risk with different characteristics.

Some underlying mechanisms have been postulated to explain the relationship between impaired endothelial function and increased cardiovascular risk, mainly involving NO bioavailability, oxidative stress, and inflammation [4, 5]. Of these molecular mechanisms, loss of NO bioavailability in the development of endothelial dysfunction is generally accompanied by elevated reactive oxygen species (ROS) and inflammation, which are thought to be the central players in atherosclerosis [3739]. Meanwhile, previous evidence has shown that endothelial dysfunction can substantially increase cardiovascular risk via pathologic alterations, such as disturbing vascular tone, activating leucocyte migration, promoting blood clotting, and disrupting arterial homeostasis [4042].

For specific characteristics, ageing or postmenopause mechanistically predisposes individuals to decreased NO bioavailability, increased oxidative stress, inflammation, and atherogenic lipid profiles, contributing to an increased risk of developing CVD [42, 43]. Increasing evidence has also shown that the adverse events of NO bioavailability, oxidative stress, and inflammation similarly exist in diabetes mellitus, hypercholesterolaemia, hypertension, and chronic kidney disease [4447]. In the present study, 88.4% of women were aged ≥60 years, postmenopausal, hypertensive, high C-reactive protein, low eGFR, dysglycaemic, or dyslipidaemic. Similarly, at least one of the above characteristics was found in 88.6% of nonsmokers or 85.8% of normal glycaemic individuals. Therefore, the subgroup of female, nonsmoking, or normal glycaemia may largely contain the adverse conditions that could alter NO bioavailability, oxidative stress, and inflammation of the vascular endothelium in this study. Regarding the nonsignificant results in ever smokers or dysglycaemia, this may be related to the small size of the study. Briefly, our findings from subgroups suggest that impaired endothelial function is more likely to increase cardiovascular risk in unfavourable cardiovascular profiles that could be promoted by molecular mechanisms, such as loss of NO bioavailability, increased oxidative stress, or inflammation.

There are some limitations in this study. The causal relationship between decreased FMD and increased CVD risk could not be determined due to the cross-sectional design, limiting a reasonable interpretation of decreased FMD as a predictor of increased CVD risk. Moreover, the 10-year CVD risk was estimated using the Framingham general CVD equations that were derived from Western populations, which may not be applicable to Chinese participants, leading to an inaccurate estimation of CVD risk. Although the Framingham general CVD equations have been shown to be relatively appropriate for Chinese populations [48], the current findings still should be interpreted with caution. Finally, FMD is generally susceptible to many factors (dietary, smoking, medication, pathological states, psychophysiological effects, etc.). Most confounding factors had been well controlled for the FMD measurements in this study. However, some potential effects on vasoconstriction derived from inherent factors, such as postmenopause, were difficult to evaluate and overcome, and this may have resulted in a misclassification of FMD.

5. Conclusions

Impaired endothelial function was generally associated with increased CVD risk in asymptomatic adults. Most importantly, the inverse association between endothelial function and CVD risk specifically presented in subjects with higher cardiovascular risk status. Growing evidence proposes a prognostic value of vascular endothelial function in both therapy and preventive care [1, 2, 41]. The current findings further suggest that endothelial function may be a favourable prognostic marker for cardiovascular prevention, with an expected benefit in the asymptomatic population at high cardiovascular risk.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This study was supported by Guangxi Natural Science Foundation (grants 2014GXNSFAA118167 and 2016GXNSFAA380050) and Natural Science Foundation of China (grants 81360422 and 81860570).

References

  1. J. A. Vita, “Endothelial function,” Circulation, vol. 124, no. 25, pp. e906–e912, 2011. View at: Publisher Site | Google Scholar
  2. A. J. Flammer, T. Anderson, D. S. Celermajer et al., “The assessment of endothelial function: From research into clinical practice,” Circulation, vol. 126, no. 6, pp. 753–767, 2012. View at: Publisher Site | Google Scholar
  3. P. J. Kuhlencordt, R. Gyurko, F. Han et al., “Accelerated atherosclerosis, aortic aneurysm formation, and ischemic heart disease in apolipoprotein E/endothelial nitric oxide synthase double-knockout mice,” Circulation, vol. 104, no. 4, pp. 448–454, 2001. View at: Publisher Site | Google Scholar
  4. M. A. Gimbrone and G. García-Cardeña, “Endothelial cell dysfunction and the pathobiology of atherosclerosis,” Circulation Research, vol. 118, no. 4, pp. 620–636, 2016. View at: Publisher Site | Google Scholar
  5. A. Pircher, L. Treps, N. Bodrug, and P. Carmeliet, “Endothelial cell metabolism: A novel player in atherosclerosis? Basic principles and therapeutic opportunities,” Atherosclerosis, vol. 253, pp. 247–257, 2016. View at: Publisher Site | Google Scholar
  6. J. Yeboah, A. R. Folsom, G. L. Burke et al., “Predictive value of brachial flow-mediated dilation for incident cardiovascular events in a population-based study: the multi-ethnic study of atherosclerosis,” Circulation, vol. 120, no. 6, pp. 502–509, 2009. View at: Publisher Site | Google Scholar
  7. M. Shechter, A. Shechter, N. Koren-Morag, M. S. Feinberg, and L. Hiersch, “Usefulness of brachial artery flow-mediated dilation to predict long-term cardiovascular events in subjects without heart disease,” American Journal of Cardiology, vol. 113, no. 1, pp. 162–167, 2014. View at: Publisher Site | Google Scholar
  8. R. T. Yan, T. J. Anderson, F. Charbonneau, L. Title, S. Verma, and E. Lonn, “Relationship between carotid artery intima-media thickness and brachial artery flow-mediated dilation in middle-aged healthy men,” Journal of the American College of Cardiology, vol. 45, no. 12, pp. 1980–1986, 2005. View at: Publisher Site | Google Scholar
  9. D. J. Green, “Exercise training as vascular medicine: direct impacts on the vasculature in humans,” Exercise and Sport Sciences Reviews, vol. 37, no. 4, pp. 196–202, 2009. View at: Google Scholar
  10. J. P. J. Halcox, A. E. Donald, E. Ellins et al., “Endothelial function predicts progression of carotid intima-media thickness,” Circulation, vol. 119, no. 7, pp. 1005–1012, 2009. View at: Publisher Site | Google Scholar
  11. R. Rossi, A. Nuzzo, A. I. Olaru, G. Origliani, and M. G. Modena, “Endothelial function affects early carotid atherosclerosis progression in hypertensive postmenopausal women,” Journal of Hypertension, vol. 29, no. 6, pp. 1136–1144, 2011. View at: Publisher Site | Google Scholar
  12. R. Rossi, E. Chiurlia, A. Nuzzo, E. Cioni, G. Origliani, and M. G. Modena, “Flow-mediated vasodilation and the risk of developing hypertension in healthy postmenopausal women,” Journal of the American College of Cardiology, vol. 44, no. 8, pp. 1636–1640, 2004. View at: Publisher Site | Google Scholar
  13. R. Rossi, A. Nuzzo, G. Origliani, and M. G. Modena, “Prognostic role of flow-mediated dilation and cardiac risk factors in post-menopausal women,” Journal of the American College of Cardiology, vol. 51, no. 10, pp. 997–1002, 2008. View at: Publisher Site | Google Scholar
  14. J. Yeboah, J. R. Crouse, F. Hsu, G. L. Burke, and D. M. Herrington, “Brachial flow-mediated dilation predicts incident cardiovascular events in older adults: the cardiovascular health study,” Circulation, vol. 115, no. 18, pp. 2390–2397, 2007. View at: Publisher Site | Google Scholar
  15. L. Lind, L. Berglund, A. Larsson, and J. Sundström, “Endothelial function in resistance and conduit arteries and 5-year risk of cardiovascular disease,” Circulation, vol. 123, no. 14, pp. 1545–1551, 2011. View at: Publisher Site | Google Scholar
  16. A. F. Cicero, M. Kuwabara, R. Johnson et al., “LDL-oxidation, serum uric acid, kidney function and pulse-wave velocity: Data from the Brisighella Heart Study cohort,” International Journal of Cardiology, vol. 261, pp. 204–208, 2018. View at: Publisher Site | Google Scholar
  17. R. T. Ras, M. T. Streppel, R. Draijer, and P. L. Zock, “Flow-mediated dilation and cardiovascular risk prediction: a systematic review with meta-analysis,” International Journal of Cardiology, vol. 168, no. 1, pp. 344–351, 2013. View at: Publisher Site | Google Scholar
  18. T. Maruhashi, J. Soga, N. Fujimura et al., “Relationship between flow-mediated vasodilation and cardiovascular risk factors in a large community-based study,” Heart, vol. 99, no. 24, pp. 1837–1842, 2013. View at: Publisher Site | Google Scholar
  19. D. J. Horne, M. Campo, J. R. Ortiz et al., “Association between Smoking and Latent Tuberculosis in the U.S. Population: An Analysis of the National Health and Nutrition Examination Survey,” PLoS ONE, vol. 7, no. 11, p. e49050, 2012. View at: Publisher Site | Google Scholar
  20. D'Agostino, “General cardiovascular risk profile for use in primary care: The framingham heart study (Circulation (2008) 117 (743-753)),” Circulation, vol. 118, no. 4, p. e86, 2008. View at: Publisher Site | Google Scholar
  21. Expert Panel on Detection Evaluation and Treatment of High Blood Cholesterol in Adults, “Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III),” Journal of the American Medical Association, vol. 285, no. 19, pp. 2486–2497, 2001. View at: Publisher Site | Google Scholar
  22. J. Y. Kim, K. H. Kweon, M. J. Kim et al., “Is nutritional labeling associated with individual health? The effects of labeling-based awareness on dyslipidemia risk in a South Korean population,” Nutrition Journal , vol. 15, no. 1, 2015. View at: Publisher Site | Google Scholar
  23. A. S. Levey, L. A. Stevens, C. H. Schmid et al., “A new equation to estimate glomerular filtration rate,” Annals of Internal Medicine, vol. 150, no. 9, pp. 604–612, 2009. View at: Publisher Site | Google Scholar
  24. T. Sawada, T. Emoto, Y. Motoji et al., “Possible association between non-invasive parameter of flow-mediated dilatation in brachial artery and whole coronary plaque vulnerability in patients with coronary artery disease,” International Journal of Cardiology, vol. 166, no. 3, pp. 613–620, 2013. View at: Publisher Site | Google Scholar
  25. H. Teragawa, M. Kato, J. Kurokawa, T. Yamagata, H. Matsuura, and K. Chayama, “Usefulness of flow-mediated dilation of the brachial artery and/or the intima-media thickness of the carotid artery in predicting coronary narrowing in patients suspected of having coronary artery disease,” American Journal of Cardiology, vol. 88, no. 10, pp. 1147–1151, 2001. View at: Publisher Site | Google Scholar
  26. S. Basyigit, S. Ozkan, M. Uzman et al., “Should Screening for Colorectal Neoplasm Be Recommended in Patients at High Risk for Coronary Heart Disease,” Medicine (United States), vol. 94, no. 20, article no. e793, 2015. View at: Publisher Site | Google Scholar
  27. T. Neunteufl, R. Katzenschlager, A. Hassan et al., “Systemic endothelial dysfunction is related to the extent and severity of coronary artery disease,” Atherosclerosis, vol. 129, no. 1, pp. 111–118, 1997. View at: Publisher Site | Google Scholar
  28. C. Ciuceis, F. Amiri, M. Iglarz, J. S. Cohn, R. M. Touyz, and E. L. Schiffrin, “Synergistic vascular protective effects of combined low doses of PPARα and PPARγ activators in angiotensin II-induced hypertension in rats,” British Journal of Pharmacology, vol. 151, no. 1, pp. 45–53, 2007. View at: Publisher Site | Google Scholar
  29. H. Ding, A. G. Howarth, M. Pannirselvam et al., “Endothelial dysfunction in Type 2 diabetes correlates with deregulated expression of the tail-anchored membrane protein SLMAP,” American Journal of Physiology-Heart and Circulatory Physiology, vol. 289, no. 1, pp. H206–H211, 2005. View at: Publisher Site | Google Scholar
  30. M. Pannirselvam, V. Simon, S. Verma, T. Anderson, and C. R. Triggle, “Chronic oral supplementation with sepiapterin prevents endothelial dysfunction and oxidative stress in small mesenteric arteries from diabetic (db/db) mice,” British Journal of Pharmacology, vol. 140, no. 4, pp. 701–706, 2003. View at: Publisher Site | Google Scholar
  31. M. Pannirselvam, S. Verma, T. J. Anderson, and C. R. Triggle, “Cellular basis of endothelial dysfunction in small mesenteric arteries from spontaneously diabetic (db/db -/-) mice: Role of decreased tetrahydrobiopterin bioavailability,” British Journal of Pharmacology, vol. 136, no. 2, pp. 255–263, 2002. View at: Publisher Site | Google Scholar
  32. L. Lind, “Flow-mediated vasodilation over five years in the general elderly population and its relation to cardiovascular risk factors,” Atherosclerosis, vol. 237, no. 2, pp. 666–670, 2014. View at: Publisher Site | Google Scholar
  33. M. Sander, B. Chavoshan, and R. G. Victor, “A large blood pressure-raising effect of nitric oxide synthase inhibition in humans,” Hypertension, vol. 33, no. 4, pp. 937–942, 1999. View at: Publisher Site | Google Scholar
  34. P. Lytsy, L. Lind, and J. Sundström, “Endothelial function and risk of hypertension and blood pressure progression: The prospective investigation of the vasculature in Uppsala seniors,” Journal of Hypertension, vol. 31, no. 5, pp. 936–939, 2013. View at: Publisher Site | Google Scholar
  35. D. Shimbo, P. Muntner, D. Mann et al., “Endothelial dysfunction and the risk of hypertension: The multi-ethnic study of atherosclerosis,” Hypertension, vol. 55, no. 5, pp. 1210–1216, 2010. View at: Publisher Site | Google Scholar
  36. T. Iguchi, Y. Takemoto, K. Shimada et al., “Simultaneous assessment of endothelial function and morphology in the brachial artery using a new semiautomatic ultrasound system,” Hypertension Research, vol. 36, no. 8, pp. 691–697, 2013. View at: Publisher Site | Google Scholar
  37. S. Karbach, P. Wenzel, A. Waisman, T. Munzel, and A. Daiber, “eNOS uncoupling in cardiovascular diseases—the role of oxidative stress and inflammation,” Current Pharmaceutical Design, vol. 20, no. 22, pp. 3579–3594, 2014. View at: Publisher Site | Google Scholar
  38. D. N. Granger, T. Vowinkel, and T. Petnehazy, “Modulation of the inflammatory response in cardiovascular disease,” Hypertension, vol. 43, no. 5, pp. 924–931, 2004. View at: Publisher Site | Google Scholar
  39. P. Libby, P. M. Ridker, and A. Maseri, “Inflammation and atherosclerosis,” Circulation, vol. 105, no. 9, pp. 1135–1143, 2002. View at: Publisher Site | Google Scholar
  40. C. Heiss, A. Rodriguez-Mateos, and M. Kelm, “Central Role of eNOS in the Maintenance of Endothelial Homeostasis,” Antioxidants & Redox Signaling, vol. 22, no. 14, pp. 1230–1242, 2015. View at: Publisher Site | Google Scholar
  41. E. Gutiérrez, A. J. Flammer, L. O. Lerman, J. Elízaga, A. Lerman, and F.-A. Francisco, “Endothelial dysfunction over the course of coronary artery disease,” European Heart Journal, vol. 34, no. 41, pp. 3175–3182, 2013. View at: Publisher Site | Google Scholar
  42. F. Paneni, C. Diaz Cañestro, P. Libby, T. F. Lüscher, and G. G. Camici, “The Aging Cardiovascular System: Understanding It at the Cellular and Clinical Levels,” Journal of the American College of Cardiology, vol. 69, no. 15, pp. 1952–1967, 2017. View at: Publisher Site | Google Scholar
  43. M. I. Fonseca, I. T. da Silva, and S. R. Ferreira, “Impact of menopause and diabetes on atherogenic lipid profile: is it worth to analyse lipoprotein subfractions to assess cardiovascular risk in women?” Diabetology & Metabolic Syndrome, vol. 9, no. 1, 2017. View at: Publisher Site | Google Scholar
  44. S. Jamwal and S. Sharma, “Vascular endothelium dysfunction: a conservative target in metabolic disorders,” Inflammation Research, pp. 1–15, 2018. View at: Google Scholar
  45. P. Rajendran, T. Rengarajan, J. Thangavel et al., “The vascular endothelium and human diseases,” International Journal of Biological Sciences, vol. 9, no. 10, pp. 1057–1069, 2013. View at: Publisher Site | Google Scholar
  46. H. Kaneto, N. Katakami, D. Kawamori et al., “Involvement of oxidative stress in the pathogenesis of diabetes,” Antioxidants & Redox Signaling, vol. 9, no. 3, pp. 355–366, 2007. View at: Publisher Site | Google Scholar
  47. M. Y. Donath and S. E. Shoelson, “Type 2 diabetes as an inflammatory disease,” Nature Reviews Immunology, vol. 11, no. 2, pp. 98–107, 2011. View at: Publisher Site | Google Scholar
  48. C. H. Lee, Y. C. Woo, J. K. Y. Lam et al., “Validation of the Pooled Cohort equations in a long-term cohort study of Hong Kong Chinese,” Journal of Clinical Lipidology, vol. 9, no. 5, pp. 640–646, 2015. View at: Publisher Site | Google Scholar

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