Augmentation index,but not pulse wave velocity,is associated with disease activity in rheumatoid arthritis

Abstract

Rheumatoid arthritis (RA) is associated with premature atherosclerosis and increased risk of cardiovascular disease (CVD). The purpose of this study was to assess the relationship between disease activity and arterial stiffness indexes for CVD in RA patients. Two hundred twenty-two female RA patients were retrospectively recruited. Non-invasive arterial stiffness was assessed by brachial-ankle PWV (baPWV) and ankle-brachial index (ABI) using pulse wave analyses and the augmentation index (AIx). The AIx was classified into two groups of normal (grade 0) and abnormal (grade 1, 2, and 3). The baPWV was associated with only age and rheumatoid factor titer, but not disease activity index, DAS28-ESR. Univariate analysis indicated that AIx was related to body mass index, disease duration, and DAS28-ESR. In addition, the baPWV was associated with DAS28-ESR and disease duration after adjusting for confounding factors (β = 0.147, p = 0.032 and β = 0.183, p = 0.034, respectively). Multivariate logistic regression analysis showed that the tendency for abnormal AIx increased as DAS28-ESR increased, and the result was significant (p for trend = 0.026). The results of this study indicated that the relationship between AIx and DAS28-ESR suggests that disease activity could affect arterial stiffness in RA.

Keywords

rheumatoid arthritis arterial stiffness pulse wave velocity augmentation index

Introduction

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease characterized by synovial inflammation with infiltration of inflammatory and immune cells, neovascularization, and bone erosion by osteoclast activation that results in disability and poor quality of life.¹ Research has established that patients with RA have increased risk of cardiovascular disease (CVD) caused by premature atherosclerosis compared to the general population.^2,3 This increase in CVD is considered the primary cause for the decrease in overall life expectancy and higher mortality rate in RA patients.⁴

Previous prospective cohort studies have confirmed that traditional CV risk factors such as hypertension, diabetes, hyperlipidemia, family history of CVD, and body mass index are important in development of CVD in RA patients.^5,6 However, the causal relationship for development of CVD in RA patients is not sufficiently explained by traditional risk factors alone.⁷ Systemic inflammation, therapeutic agents for RA, and an RA-related specific genetic background also have been considered as other CV risk factors in RA.^8,9 This suggests that additional mechanisms are related to the increased risk of CVD in RA. Some clinical studies have reported that the risk of CVD could be regulated by management of disease activity in RA.^10,11 Recently, the European League Against Rheumatism (EULAR), which is focused on the causal relationship between RA and CVD, presented guidelines for preventing and treating CVD in patients with RA by lowering disease activity through appropriate treatment for RA and controlling traditional CV risk factors.¹²

Atherosclerosis is an important pathological mechanism in CVD, and early detection is important to minimize damage to vascular endothelial cells in the preclinical stage. Arterial stiffness assessment is an important index for predicting CVD events.¹³ Non-invasive tests are used commonly in clinical practice to evaluate arterial stiffness, which include pulse wave velocity (PWV), augmentation index (AIx), and ankle-brachial index (ABI).^14,15 Several studies have demonstrated that patients with RA showed higher PWV and AIx compared to healthy controls.^16,17

In Korea, RA patients also have a higher prevalence of CVD than the general population.¹⁸ In a recent study for the awareness and recognition of CVD in RA patients, only 39.5% of patients accurately recognized the risk of CVD.¹⁹ The relationship between systemic inflammation and risk of CVD in RA remains under debate. The purpose of this study was to evaluate the correlation between disease activity and non-invasive arterial stiffness markers for CVD in Korean RA patients.

Subjects and Methods

Study population

Patients with RA who were admitted to the Department of Rheumatology at Daegu Catholic University Medical Center from November 2020 to March 2021 and who met the 2010 American College of Rheumatology (ACR) classification criteria were enrolled in this study.²⁰ Patients with underlying different autoimmune or inflammatory rheumatic diseases such as systemic lupus erythematosus, Sjogren syndrome, ankylosing spondylitis, mixed connective tissue disease, and systemic sclerosis were excluded.

A total of 222 female patients who underwent an arterial stiffness test retrospectively enrolled in this study. The arterial stiffness test was performed to evaluate the risk of CVD regardless of preexisting CV symptoms or signs. This study was approved by the Institutional Review Board (IRB No. CR 21–032) of Daegu Catholic University Medical Center and the written informed consent was waived by IRB.

Assessment of clinical information

Sex, age (years), and disease duration (years) were determined through medical records. Information about the use of therapeutic drugs was investigated based on the drugs prescribed at the time of study enrollment. Treatment drugs were nonsteroidal anti-inflammatory drugs (NSAIDs), glucocorticoids, conventional synthetic disease-modifying antirheumatic drugs (csDMARDs), targeted synthetic DMARDs (tsDMARDs), and biological DMARDs (bDMARDs). Conventional synthetic antirheumatic drugs included methotrexate, hydroxychloroquine, sulfasalazine, azathioprine, leflunomide, and tacrolimus, and the tsDMARDs included tofacitinib and baricitinib. The bDMARDs were classified into tumor necrosis factor inhibitors (TNF inhibitors) and non-TNF inhibitors. TNF inhibitors included infliximab, adalimumab, etanercept, and golimumab, and non-TNF inhibitors included tocilizumab and abatacept.

In addition, evaluation of acute phase reactants was performed using the erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) measured at the time of the arterial stiffness test. Rheumatoid factor (RF) and anti-cyclic citrullinated peptide (anti-CCP) antibody also were identified.

Height (cm) and weight (kg) were measured using a body composition analyzer. Body mass index (BMI) was measured automatically by the formula BMI = weight (kg)/height (m²).

Pulse wave velocity measurement

PWV was measured using a VP-1000 (Colin Co. Ltd, Komaki, Japan) machine after allowing sufficient stability for about 10 min in a lying position, since the value is affected by heart rate and blood pressure.²¹ The machine was a device that records blood pressure, electrocardiogram (ECG), and heart sounds simultaneously. The cuffs were wrapped around both upper forearms and both ankles to measure the blood pressure of the limbs, ECG electrodes were attached to both wrists to record the heart rhythm, and a microphone to record heart sounds was placed on the left edge of the sternum.

In addition, the cuff was connected to a plethymographic sensor to determine the volume pulse form, and blood pressure was measured with an oscillometric pressure sensor. The PWV (cm/s) was defined as the value obtained by dividing the distance (cm) between two points by the pulse wave transmission time (seconds). This study used the brachial-ankle PWV (baPWV) measured from the brachial artery and ankle.

The ankle-brachial index (ABI) was defined as the ratio of systolic blood pressure measured at the ankle and systolic blood pressure measured at the brachial artery.²² The test results were analyzed by classifying the subjects into two groups of normal (grade 0) and abnormal (grade 1, 2, 3) using the automatically calculated result values for analysis.

Augmentation index measurement

The AIx was determined by measuring radial arterial pressure by placing a probe in the position where the pulse was best palpated in the radial artery of the left hand in a seated position after 10 min of rest using an HEM-9000AI (Omron, Japan).²³ At the same time, blood pressure was measured in the right upper arm. This method is used to calculate late systolic blood pressure of the radial artery pulse wave by substituting brachial artery blood pressure. The method measures aortic blood pressure by substituting the blood pressure of the brachial artery into the formula for the generalized transfer function from the pulse wave detected in the radial artery.

AIx is expressed as a percentage by dividing the difference between the height of the pulse changed by the reflected wave of the aortic waveform and the height of the pulse caused by central arterial pressure cardiac contraction. AIx@75 is an index normalized for AIx based on a heart rate of 75 bpm. The results were classified into two groups of normal (grade 0) and abnormal AIx (grade 1, 2, and 3).

Assessment of disease activity in RA

Disease activity in RA was evaluated using a Disease Activity Score of 28 joints (DAS28). DAS28-ESR used the tender joint count (TJC) and swollen joint count (SJC) for the 28 joints, the visual analogue scale (VAS) that was evaluated by the patient, and the ESR. The result is calculated based on the following formula: DAS28-ESR = 0.56x√TJC + 0.28x√SJC + 0.70xln (ESR) + 0.014xVAS.

The disease activity status of RA patients at the time of this study was classified into remission (<2.6), low (≥2.6 and ≤3.2), moderate (>3.2 and ≤5.1), and high (>5.1) based on DAS28-ESR.²⁴

Statistical analysis

Variable values were expressed as mean value ± standard deviation (SD) or frequency (percentage, %). The normality of the data was verified using the Kolmogorov–Smirnov test, showing a normal distribution. Student’s t-test was used to identify difference between the two groups, and chi-square test was performed for frequency distribution analysis. The correlation analysis between variables was verified using the Pearson correlation coefficient (r). Multivariate regression analysis was used to identify variables related to the AIx@75. Odd ratio (OR) was evaluated using multivariable logistic regression analysis to identify variables related to abnormal AIx. In this study, the csDMARDs and ts/bDMARDs groups were compared according to therapeutic agent characteristics. The statistical significance level was set to p < 0.05. The software used for statistical analysis of this study was SPSS/WIN version 25.0 (IBM Corp, Armink, NY, USA).

Results

Baseline characteristics of the study population

A total of 222 female RA patients was enrolled in this study, and the mean age was 61.0 years (Table 1). The mean BMI of the study subjects was 23.3 kg/m², and the mean systolic and diastolic blood pressure were 133.1 mmHg and 74.4 mmHg, respectively. Analysis of comorbid diseases indicated that 43 (19.4%), 25 (11.3%), and 7 (3.2%) patients had hypertension, diabetes mellitus, and preexisting CVD, respectively. The mean disease duration was 11.4 years. The mean titers of RF and anti-CCP antibody were 93.2 (IU/ml) and 313.7 (U/ml), respectively. Table 1 showed disease activity indexes including ESR, CRP, number of tender joints, number of swollen joints, and patient pain score (VAS); and DAS28-ESR was 3.5 ± 1.2.

Table 1.

Baseline characteristics of enrolled rheumatoid arthritis patients.

Variables	Results
Demographics
Age (year)	61.0±8.4
Height (cm)	156.0±5.7
Weight (kg)	56.5±8.4
Body mass index (kg/m²)	23.3±3.2
Systolic blood pressure (mmHg)	133.1±17.5
Diastolic blood pressure (mmHg)	74.4±9.7
Comorbidities
Hypertension	43 (19.4)
Diabetes mellitus	25 (11.3)
Cardiovascular diseases	7 (3.2)
Biochemistry markers
Glucose (mg/dL)	105.5±27.1
Total cholesterol (mg/dL)	186.5±31.8
Disease duration (year)	11.4±8.4
Rheumatoid factor (IU/mL)	93.2±131.6
Anti-cyclic citrullinated peptide (U/mL)	313.7±198.9
Disease activity indices
Erythrocyte sedimentation rate (mm/hr)	28.2±21.0
C-reactive protein (mg/L)	5.2±12.2
Tender joint count	3.2±3.3
Swollen joint count	0.8±2.1
Patient VAS (mm)	31.2±25.4
DAS28-ESR	3.5±1.2
Medications
CsDMARDs	222 (100.0)
Glucocorticoid	190 (85.6)
bDMARDs	79 (35.6)
TNF inhibitors	48 (60.8)
non-TNF inhibitors	31 (39.2)
TsDMARDs	20 (9.0)

Data were described as mean ± standard deviation or number (%).

Abbreviation: DAS: disease activity score; ESR: erythrocyte sedimentation rate; CRP: C-reactive protein; VAS: visual analogue scale; csDMARDs: conventional synthetic DMARDs; bDMARDs: biological DMARDs; tsDMARDs: targeted synthetic DMARDs; TNF: tumor necrosis factor.

All patients used at least one csDMARD, and 85.6% (n = 190) of patients received glucocorticoids. bDMARDs and tsDMARDs were used by 79 patients and 20 patients, respectively. Of the patients receiving bDMARDs, 60.8% (n = 48) were taking TNF inhibitors.

Analysis of arterial stiffness indexes

The baPWV, AIx, and ABI as surrogates for arterial stiffness are described in Table 2. The mean values of baPWV and ABI measured from both sides were 1606.6 ± 361.0 cm/s and 1.16 ± 0.1, respectively. The mean AIx was 87.1% and 84.2% of patients had a normal AIx.

Table 2.

Values of arterial stiffness in rheumatoid arthritis patients.

Variables	Results
Arterial stiffness
Right baPWV	1598.6±347.9
Left baPWV	1614.7±381.2
Mean baPWV	1606.6±361.0
Right ABI	1.16±0.1
Left ABI	1.16±0.1
Mean ABI	1.16±0.1
AIx	87.1±11.8
Normal	187 (84.2)
Grade I	26 (11.7)
Grade II	7 (3.2)
Grade III	2 (0.9)

Abbreviation: baPWV: brachial-ankle pulse wave velocity; ABI: ankle-brachial index; AIx: augmentation index.

Correlations between arterial stiffness indexes and clinical variables

Analysis of the correlations between arterial stiffness indexes and clinical variables indicated that baPWV was positively correlated with age and RF titer (p < 0.001, p = 0.018, respectively) (Table 3). AIx had a statistically significant correlation with BMI, disease duration, and DAS28-ESR (p = 0.011, p = 0.001, p = 0.006, respectively). Similarly, Alx@75 was also correlated with BMI, disease duration, and DAS28-ESR (p = 0.004, p = 0.001, and p < 0.001, respectively). However, there was no statistically significant relationship between ABI and clinical variables.

Table 3.

Correlation of arterial stiffness markers and other variables in rheumatoid arthritis patients (n = 222).

	Mean baPWV		Mean ABI		AIx		AIx@75
Variables	r	p	r	p	r	P	r	p
Age	0.545	<0.001	0.106	0.115	0.109	0.107	0.078	0.249
BMI	−0.081	0.228	−0.029	0.666	−0.171	0.011	−0.194	0.004
Disease duration	0.118	0.080	0.027	0.693	0.224	0.001	0.216	0.001
DAS28-ESR	−0.089	0.185	−0.042	0.536	0.184	0.006	0.242	<0.001
RF	0.159	0.018	−0.023	0.733	0.079	0.238	0.112	0.096
Anti-CCP	−0.072	0.288	0.052	0.439	0.058	0.390	0.031	0.647

Data were described as correlation coefficients (r).

Abbreviation: BMI: body mass index; DAS: disease activity score; ESR: erythrocyte sedimentation rate; RF: Rheumatoid factor; Anti-CCP: Anti-Cyclic citrullinated peptide baPWV: brachial-ankle pulse wave velocity; ABI: ankle-brachial index; AIx: augmentation index.

Comparison for differences in clinical variables according to AIx

The difference in clinical variables was measured between the normal (n = 187) and abnormal (n = 31) AIx groups (Figure 1). In this analysis, patients with abnormal AIx values showed statistically significantly lower BMIs than those with normal AIx values (23.5 ± 3.2 vs 22.1 ± 2.9, p = 0.025). There was also a statistical difference for DAS28-ESR and ESR between the two groups, and the values of these variables were higher in patients with abnormal AIx compared to those with normal AIx (3.4 ± 1.2 vs 3.9 ± 1.4 for DAS28-ESR and 26.9 ± 19.6 vs 35.1 ± 26.2 for ESR). Age, RF titer, anti-CCP antibody titer, patient VAS score, CRP, baPWV, and ABI were not statistically different between the groups.

Figure 1.

Comparison of variables between normal and abnormal augmentation index in rheumatoid arthritis (n = 222).

Analysis for clinical variables related with AIx

BMI, disease duration, and DAS28-ESR related toAIx@75, shown in Table 3, were identified. In the age-adjusted Model 1, DAS28-ESR and disease duration were positively related (β = 0.249, p =< 0.001 and β = 0.209, p = 0.002) (Table 4). But, BMI was negatively related with AIx@75 (β = −0.138, p = 0.041). Consistently, AIx@75 was related with BMI, disease duration, and DAS28-ESR. Model 3 analysis showed that patients with higher DAS28-ESR and longer disease duration had higher AIx@75 values, whereas BMI lost statistical significance.

Table 4.

Determination of correlation factors for AIx@75 in rheumatoid arthritis patients (n = 222).

	Model 1		Model 2		Model 3
Variables	B	p	B	p	B	p
BMI	−0.138	0.041	−0.141	0.038	−0.128	0.062
DAS28-ESR	0.249	<0.001	0.251	<0.001	0.238	0.001
Disease duration	0.209	0.002	0.206	0.003	0.204	0.003

Linear regression models were used: Model 1, adjusted for age; Model 2, adjusted for age, hypertension, diabetes mellitus, and cardiovascular disease; Model 3, additionally adjusted for steroid and ts/bDMARDs.

Abbreviation: AIx: augmentation index; BMI: body mass index; DAS: disease activity score; ESR, erythrocyte sedimentation rate.

Analysis of clinical variables related with abnormal AIx

The correlation analysis of abnormal AIx and RA-related variables included RF, anti-CCP antibody, DAS28-ESR, disease duration, comorbid diseases, glucocorticoid, and use of ts/bDMARD. The RF, anti-CCP antibodies, DAS28-ESR, disease duration, glucocorticoid use, and comorbid diseases were not significantly different based on AIx.

In Model 1, as DAS28-ESR increased, there was a tendency for abnormal AIx values (p for trend = 0.079) (Table 5). After adjusting for age, body mass index, and blood pressure as confounding variables, patients with higher DAS28-ESR and that used ts/bDMARDs showed higher AIx. Similarly, in Model 3, abnormal AIx was found in cases with higher DAS28-ESR and in patients that used ts/bDMARDs (p for trend = 0.026). Specifically, patients with high disease activity showed increased risk of abnormal AIx compared to those in a remission state (OR 4.277, 95% CI 1.163–15.362).

Table 5.

Determination of clinical variables for abnormal AIx in rheumatoid arthritis patients (n = 222).

	Model 1	Model 2	Model 3
RF
Absence	1.00 (reference)	1.00 (reference)	1.00 (reference)
Presence	1.639 (0.930–5.800)	1.628 (0.446–5.933)	1.661 (0.449–6.139)
Anti-CCP
Absence	1.00 (reference)	1.00 (reference)	1.00 (reference)
Presence	1.561 (0.931–1.015)	1.637 (0.445–6.022)	1.548 (0.397–6.033)
DAS28-ESR
Remission	1.00 (reference)	1.00 (reference)	1.00 (reference)
Low	0.443 (0.124–1.584)	0.364 (0.096–0.1385)	0.449 (0.115–1.745)
Moderate	1.084 (0.424–2.771)	0.970 (0.364–2.585)	1.238 (0.438–3.494)
High	2.677 (0.825–8.690)	3.287 (0.950–11.373)	4.227 (1.163–15.362)
p For trend^a	0.079	0.033	0.026
Disease duration
<11 years	1.00 (reference)	1.00 (reference)	1.00 (reference)
≥11 years	1.020 (0.488–2.131)	0.893 (0.414–1.930)	0.820 (0.374–1.801)
Comorbidities	—	—	—
Absence	1.00 (reference)	1.00 (reference)	1.00 (reference)
Presence	0.850 (0.365–1.980)	0.705 (0.289–1.724)	0.706 (0.276–1.803)
Glucocorticoid	—	—	—
Non-user	1.00 (reference)	1.00 (reference)	1.00 (reference)
User	1.416 (0.462–4.344)	1.059 (0.328–3.418)	1.112 (0.333–3.710)
ts/bDMARDs
Non-user	1.00 (reference)	1.00 (reference)	1.00 (reference)
User	2.074 (0.990–4.342)	2.322 (1.065–5.060)	2.549 (1.145–5.677)
p	0.053	0.034	0.022

Logistic regression models were used to calculate ORs and 95% CIs: Model 1, adjusted for age; Model 2, adjusted for age, BMI, SBP, and DBP; Model 3, additionally adjusted for glucose and total cholesterol.

^aLikelihood ratio test for trend.

Abbreviation: AIx: augmentation index; DAS: disease activity score; ESR: erythrocyte sedimentation rate; RF: Rheumatoid factor; Anti-CCP: Anti-Cyclic citrullinated peptide; ts/bDMARDs: targeted synthetic/biologic disease-modifying antirheumatic drugs.

Discussion

It is well known that RA has a higher prevalence and mortality rate in cases with CVD compared with the general population. Previous studies have identified traditional risk factors for CV events such as hyperlipidemia, diabetes mellitus, and BMI for patients with RA,^5,6 similar to the general population. However, the increased incidence of myocardial infarction in patients without traditional CV risk factors suggests that the increase in CVD in RA patients contributes to other mechanisms associated with inflammatory responses.^7,25 Disease-specific factors have been investigated to determine the impact on development of the development of CVD in RA, including the duration of disease, RA-related autoantibodies, degree of inflammatory response, and therapeutic drugs such as glucocorticoid.²⁶ The objective of this study was to analyze the relationships between disease activity and arterial stiffness indexes in Korean RA patients, and the main findings revealed that AIx is correlated with DAS28-ESR and disease duration, and abnormal AIx is related to DAS28-ESR and use of ts/bDMARDs.

The inflammatory response is a critical mechanism for progression of atherosclerosis. Chronic inflammatory reactions lead to endothelial cell activation and dysfunction, resulting in atherosclerotic changes in the endothelium of the artery.²⁷ RA and atherosclerosis share inflammatory and immunological pathogenic mechanisms.²⁸ Several studies have suggested that RA patients with higher inflammation had increased risk of more severe and frequent CV events. One study followed 24,989 patients for about 2.7 years to assess the association between disease activity and risk of CVD and found that lower CDAI disease activity resulted in a lower incidence of CVD.¹⁰ A study that analyzed Nijmegen in an early RA inception cohort demonstrated that low DAS28 (≤3.2) was associated with a lower risk of CVD compared to DAS28 > 3.2 (HR 0.65, 95% CI 0.43 to 0.99).¹¹ The incidence of atherosclerotic plaques increased in patients with high swollen joint number and CRP in RA patients who underwent carotid ultrasound follow-up visits for about 3 years.²⁹ Additionally, results of an analysis with the Framingham Risk Score confirmed that the risk of CVD was increased. These findings provide evidence to suggest a close relationship between the inflammatory response of RA and CVD caused by progressive atherosclerosis.

Multiple studies have evaluated the degree of atherosclerosis in the preclinical stage using non-invasive arterial stiffness indexes such as PWV and AIx to assess the risk of CVD of RA.^16,17,29,30 The PWV and AIx were statistically higher in RA patients than in the healthy control group, which suggests that these markers could be used as predictors for CVD.^16,17,30 However, data on clinical variables related to these arterial stiffness indexes in RA patients are lacking. Taverner et al. reported that PWV and AIx were not related to the inflammatory variables and disease activity of RA.³¹ However, the present study showed that DAS28-ESR was positively correlated with AIx in linear regression analysis. However, it is necessary to consider the moderate disease activity of the patients participating in this study, with an average DAS28-ESR of 3.5. Based on the DAS28-ESR, it cannot be excluded that the bias of the study population’s disease activity affects AIx. And DAS28-ESR also was confirmed as a determinant of abnormal AIx in logistic regression analysis. Furthermore, our results indicated that baPWV was not related to disease activity index in RA, although it was significantly associated with age and RF titer even in linear regression analysis using confounding factors (supplementary Table 1). These results suggest that disease activity indicators such as DAS28-ESR can be an alternative marker for predicting the degree of arterial stiffness.

Several studies have shown that risk of CVD in patients with RA increases with increasing disease duration.^32,33 In an analysis of 185 female RA patients with disease duration of at least 2 years, Kao et al. reported an increased risk of coronary artery calcification in patients with disease duration of 8 to 13 years (OR 3.03, 95% CI 1.06–8.66).³² In addition, the incidence of CVD increased as the disease duration increased in early RA patients.³⁴ Therefore, research indicates that disease duration is a major risk factor for CVD. In comparison, a recent study of RA patients in Spain demonstrated that baPWV and Aix, which are indicators of arterial stiffness, were not related to disease duration.³¹ In contrast, our study found a positive correlation between disease duration and AIx, but not baPWV.

Among the current therapeutic agents for RA, NSAIDs and glucocorticoids can increase the risk of CVD development.²⁶ Recently, TNF inhibitors, which are one of the bDMARDs that have a strong anti-inflammatory effect for RA, showed favorable effects not only on inflammation, but also lipid metabolism, which suggested the possibility of reducing the risk of CVD.³⁵

Additionally, analysis of 983 patients treated with TNF inhibitors indicated that the incidence of CVD was significantly lower than for patients that did not receive TNF inhibitor treatment (95% CI 0.25–0.85, p = 0.013).³⁶ PWV was statistically significantly reduced before and after 12 weeks of TNF inhibitor administration in RA patients, and PWV measured at 12 weeks was improved to a level similar to that of the healthy controls.¹⁷ These results suggest that bDMARDs, including TNF inhibitors, have a beneficial effect on arterial stiffness as biomarkers for CV events. However, in this study, administration of ts/bDMARDs was a determinant of abnormal AIx. This is a paradox since such treatments are expected to beneficially affect parameters of vascular function following the reduction of the inflammatory burden. Nevertheless, patients on DMARDs are more likely those with the more active disease and probably more extensive atherosclerotic disease. Therefore, it could be related to higher disease activity in patients that receive biological therapeutic agents, compared with patients that did not use ts/bDMARDs.

Subclinical atherosclerosis can be detected using ABI as a non-invasive test to evaluate arterial stiffness, in addition to PWV and AIx. The clinical utility of ABI for evaluating arterial stiffness or subclinical atherosclerosis is still debated. ABI was considered to be beneficial for detecting atherosclerosis or decreased peripheral arterial blood flow in RA patients.^37,38 In contrast, the usefulness of the ABI test for evaluating CVD development or progression has been questioned.³⁹ Similar to the results of the latter study, our study demonstrated that ABI was not correlated with traditional risk factors or RA-specific factors in patients with RA. Therefore, further research is needed to determine the relationships between ABI and RA-related indicators.

BMI-based on obesity has been well established to be closely associated with increased CVD risk in general population and more firmly in patients with RA.^5,6 In this study, patients with abnormal AIx showed lower BMI than patients with normal AIx, but statistical significance was lost in the analysis using various confounding variables. The findings of low BMI in patients with abnormal ABI may be due to the following several reasons. First, mechanical techniques such as ultrasound, computed tomography, and dual-energy X-ray absorptiometry may be more accurate than BMI in order to accurately measure the body composition of RA patients.⁴⁰ Second, because the number of patients with abnormal ABI is too small, the results may be lack of consistency.

There are several limitations for interpreting the results of this study. First, this study was conducted as a cross-sectional study in which subjects were retrospectively recruited within a specific period. The correlation between disease activity and arterial stiffness or atherosclerosis in RA could be determined, but there was a limitation for clearly clarifying the causal relationship. A prospective cohort study in a large study population is needed to confirm the trend of changes in arterial stiffness markers based on changes in disease activity. Second, there were no healthy control subjects to be compared with RA patients. Therefore, it was not possible to determine whether the arterial stiffness of RA patients was increased compared to healthy controls. Additional case–control studies that include a control group should be conducted to address this limitation. Third, there might be a selection bias for study subject enrollment. It is hypothesized that there is a higher likelihood of undergoing the arterial stiffness tests required for this study for patients with more stable disease activity. Fourth, due to the lack of clinical information on the traditional CV risk factors, such as smoking or a family history of CVD, these variables were not considered sufficiently in the analysis for disease activity of RA. Fifth, a total of 222 patients with rheumatoid arthritis participated in this study, and it was not evaluated whether this was a sufficient sample size. In order to prove this hypothesis, a study with a more appropriate number of patients is needed in the future.

Conclusion

Increased risk of CVD development in RA was not clearly explained by traditional CV risk factors. This study demonstrated that higher disease activity DAS28-ESR values had abnormal arterial stiffness, as demonstrated by AIx, even considering some traditional risk factors. This finding suggests that RA-related inflammatory response contributes to increased arterial stiffness or premature atherosclerosis.

Supplemental Material

sj-pdf-1-eji-10.1177_1721727X221090158 – Supplemental Material for Augmentation index, but not pulse wave velocity, is associated with disease activity in rheumatoid arthritisds

Supplemental Material, sj-pdf-1-eji-10.1177_1721727X221090158 for Augmentation index, but not pulse wave velocity, is associated with disease activity in rheumatoid arthritis by Jung-Yoon Choe, Seo-Hyeon Park and Seong-Kyu Kim in European Journal of Inflammation

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Seong-Kyu Kim

Supplemental material

Supplemental material for this article is available online.

References

Scott

Wolfe

Huizinga

. Rheumatoid arthritis. The Lancet 2010; 376: 1094–1108.

Radner

Lesperance

Accortt

, et al. Incidence and prevalence of cardiovascular risk factors among patients with rheumatoid arthritis, psoriasis, or psoriatic arthritis. Arthritis Care Res 2017; 69: 1510–1518.

Maradit-Kremers

Crowson

Nicola

, et al. Increased unrecognized coronary heart disease and sudden deaths in rheumatoid arthritis: a population-based cohort study. Arthritis Rheum 2005; 52: 402–411.

Jacobsson

LTH

Knowler

Pillemer

, et al. Rheumatoid arthritis and mortality. A longitudinal study in Pima Indians. Arthritis Rheum 1993; 36: 1045–1053.

Gonzalez

Kremers

Crowson

, et al.

Do cardiovascular risk factors confer the same risk for cardiovascular outcomes in rheumatoid arthritis patients as in non-rheumatoid arthritis patients?

Ann Rheum Dis 2008; 67(1): 64–69.

Solomon

Curhan

Rimm

, et al. Cardiovascular risk factors in women with and without rheumatoid arthritis. Arthritis Rheum 2004; 50: 3444–3449.

Del Rincón

Williams

Stern

, et al. High incidence of cardiovascular events in a rheumatoid arthritis cohort not explained by traditional cardiac risk factors. Arthritis Rheum 2001; 44(12): 2737–2745.

Zhang

Chen

Delzell

, et al. The association between inflammatory markers, serum lipids and the risk of cardiovascular events in patients with rheumatoid arthritis. Ann Rheum Dis 2014; 73(7): 1301–1308.

Gonzalez-Gay

Gonzalez-Juanatey

Lopez-Diaz

, et al. HLA-DRB1 and persistent chronic inflammation contribute to cardiovascular events and cardiovascular mortality in patients with rheumatoid arthritis. Arthritis Rheum 2007; 57(1): 125–132.

10.

Solomon

Reed

Kremer

, et al. Disease activity in rheumatoid arthritis and the risk of cardiovascular events. Arthritis Rheumatol 2015; 67(6): 1449–1455.

11.

Arts

Fransen

Den Broeder

, et al. Low disease activity (DAS28≤3.2) reduces the risk of first cardiovascular event in rheumatoid arthritis: a time-dependent cox regression analysis in a large cohort study. Ann Rheum Dis 2017; 76(10): 1693–1699.

12.

Agca

Heslinga

Rollefstad

, et al. EULAR recommendations for cardiovascular disease risk management in patients with rheumatoid arthritis and other forms of inflammatory joint disorders: 2015/2016 update. Ann Rheum Dis 2017; 76(1): 17–28.

13.

Willum Hansen

Staessen

Torp-Pedersen

, et al. Prognostic value of aortic pulse wave velocity as index of arterial stiffness in the general population. Circulation 2006; 113(5): 664–670.

14.

Laurent

Cockcroft

Van Bortel

, et al. Expert consensus document on arterial stiffness: methodological issues and clinical applications. Eur Heart J 2006; 27(21): 2588–2605.

15.

Mancini

Dahlöf

Díez

. Surrogate markers for cardiovascular disease: structural markers. Circulation 2004; 109(25 Suppl 1): IV22–30.

16.

Pieringer

Schumacher

Stuby

, et al .Augmentation index and large-artery remodeling in patients with longstanding rheumatoid arthritis compared with healthy controls. Semin Arthritis Rheum 2009; 39(3): 163–169.

17.

Mäki-Petäjä

Hall

Booth

, et al. Rheumatoid arthritis is associated with increased aortic pulse-wave velocity, which is reduced by anti-tumor necrosis factor-alpha therapy. Circulation 2006; 114(11): 1185–1192.

18.

Lee

Song

Choi

, et al. Relationship of rheumatoid arthritis and coronary artery disease in the Korean population: a nationwide cross-sectional study. Adv Rheumatol 2019; 59(1): 40.

19.

Boo

Froelicher

, et al. Knowledge and perception of cardiovascular disease risk among patients with rheumatoid arthritis. PLoS One 2017; 12(4): e0176291.

20.

Aletaha

Neogi

Silman

, et al. 2010 Rheumatoid arthritis classification criteria: An American college of rheumatology/European league against rheumatism collaborative initiative. Arthritis Rheum 2010; 62(9): 2569–2581.

21.

Kim

Lee

, et al. Soluble tumor necrosis factor receptors and arterial stiffness in patients with coronary atherosclerosis. Am Journal Hypertens 2017; 30(3): 313–318.

22.

Lee

Chu

, et al. Effects of heart rate on brachial-ankle pulse wave velocity and ankle-brachial pressure index in patients without significant organic heart disease. Angiology 2007; 58(1): 67–74.

23.

Takazawa

Kobayashi

Shindo

, et al. Relationship between radial and central arterial pulse wave and evaluation of central aortic pressure using the radial arterial pulse wave. Hypertens Res 2007; 30(3): 219–228.

24.

van Riel

. The development of the disease activity score (DAS) and the disease activity score using 28 joint counts (DAS28). Clin Exp Rheumatol 2014; 32(5 Suppl 85): S–74.

25.

Solomon

Karlson

Rimm

, et al. Cardiovascular morbidity and mortality in women diagnosed with rheumatoid arthritis. Circulation 2003; 107(9): 1303–1307.

26.

Sen

González-Mayda

Brasington

Jr . Cardiovascular disease in rheumatoid arthritis. Rheum Dis Clin North America 2014; 40(1): 27–49.

27.

Schillinger

Exner

Mlekusch

, et al. Inflammation and carotid artery-risk for atherosclerosis study (ICARAS). Circulation 2005; 111(17): 2203–2209.

28.

Pasceri

Yeh

ETH

. A tale of two diseases. Circulation 1999; 100(21): 2124–2126.

29.

Giles

Post

Blumenthal

, et al. Longitudinal predictors of progression of carotid atherosclerosis in rheumatoid arthritis. Arthritis Rheum 2011; 63(11): 3216–3225.

30.

Avalos

Chung

Oeser

, et al. Increased augmentation index in rheumatoid arthritis and its relationship to coronary artery atherosclerosis. The J Rheumatology 2007; 34(12): 2388–2394.

31.

Taverner

Paredes

Ferré

, et al. Assessment of arterial stiffness variables in patients with rheumatoid arthritis: a mediation analysis. Scientific Rep 2019; 9(1): 4543.

32.

Kao

Krishnaswami

Cunningham

, et al. Subclinical coronary artery calcification and relationship to disease duration in women with rheumatoid arthritis. J Rheumatology 2008; 35(1): 61–69.

33.

Chung

Oeser

Raggi

, et al. Increased coronary-artery atherosclerosis in rheumatoid arthritis: relationship to disease duration and cardiovascular risk factors. Arthritis Rheum 2005; 52(10): 3045–3053.

34.

Kremers

Crowson

Therneau

, et al. High ten-year risk of cardiovascular disease in newly diagnosed rheumatoid arthritis patients: a population-based cohort study. Arthritis Rheum 2008; 58(8): 2268–2274.

35.

Popa

Netea

Radstake

, et al. Influence of anti-tumour necrosis factor therapy on cardiovascular risk factors in patients with active rheumatoid arthritis. Ann Rheum Dis 2005; 64(2): 303–305.

36.

Jacobsson

Turesson

Gülfe

, et al. Treatment with tumor necrosis factor blockers is associated with a lower incidence of first cardiovascular events in patients with rheumatoid arthritis. J Rheumatology 2005; 32(7): 1213–1218.

37.

Kurt

Temiz

Gokmen

, et al.

Can the ankle brachial pressure index (ABPI) and carotis intima media thickness (CIMT) be new early stage markers of subclinical atherosclerosis in patients with rheumatoid arthritis?

Wien Klin Wochenschr 2015; 127(13–14): 529–534.

38.

Guellec

Bressollette

Gueguen

, et al.

Is routine ankle-brachial pressure index evaluation useful in rheumatoid arthritis?

Joint Bone Spine 2013; 80(1): 111–113.

39.

Marcos de Frutos

Abad Pérez

Suárez Fernández

. Usefulness of the ankle-brachial index as a survey method for subclinical vascular disease in patients with rheumatoid arthritis. Reumatol Clín 2014; 10(4): 268–269.

40.

Stavropoulos-Kalinoglou

Metsios

Koutedakis

, et al. Obesity in rheumatoid arthritis. Rheumatology 2011; 50(3): 450–462.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.05 MB