Small artery elasticity is decreased in patients with systemic lupus erythematosus without increased intima media thickness

Thirty consecutive patients (26 females, 4 males) fulfilling the American College of Rheumatology criteria for SLE [13], who attended the out-patient clinic of the University Medical Center Groningen, were included (Table 1). Aiming at markers for early detection of atherosclerosis, patients with a history of CVD, including ischemic heart disease (ICD-9 classification 410 to 414), cerebrovascular accidents or peripheral vascular disease, were excluded. Other exclusion criteria were pregnancy, diabetes mellitus, renal insufficiency (creatinine > 140 μmol/l) and active disease, defined as SLE Disease Activity Index (SLEDAI) > 4 [14]. Thirty age- and sex-matched healthy subjects were recruited as negative controls and 20 patients with still untreated essential hypertension (EH) were included as positive controls (Table 2). Hypertension was defined as a systolic blood pressure = 140 mmHg and/or a diastolic blood pressure = 90 mmHg, based on at least three measurements, and/or the use of antihypertensive drugs. Possible secondary causes of hypertension had to be excluded. The same exclusion criteria as for SLE patients applied to EH patients. All patients and controls were Caucasians, except for two SLE patients of Asian origin. The local research ethics committee gave approval for the study and written informed consent was obtained from each participant.

All traditional cardiovascular risk factors were assessed. Body mass index, smoking status, and family history of CVD (considered positive if first-degree relatives suffered from CVD before 60 years of age) were recorded. Dyslipideamia was defined as plasma cholesterol above 5.0 mmol/L and/or plasma low density lipoprotein (LDL) cholesterol above 3.0 mmol/L and/or triglycerides above 1.7 mmol/l and/or high density lipoprotein (HDL) cholesterol below 1.2 mmol/L in women and below 1.0 mmol/L in men and/or use of lipid lowering drugs [15]. Creatinine clearance was estimated using the Cockcroft-Gault formula. Use of prednisolone, azathioprine, and hydroxychloroquine was categorized as current and none, and daily dose was recorded. All measurements and blood sampling were performed after an overnight fast to exclude postprandial effects.

Arterial elasticity was assessed by pulse-wave analysis using the CR-2000 (Hypertension Diagnostics, Eagan, MN, USA). The CR-2000 records and analyses the blood pressure waveform data from the Arterial Pulse Pressure Sensor. SAE was estimated from a computerized pulse contour analysis using a Windkessel model [16]. Measurements were performed after 10 minutes of acclimatization in a temperature controlled room (22°C) with the subject in supine position. The arterial pulse pressure sensor was placed over the right and subsequently over the left radial artery. A blood pressure cuff was placed on the opposite arm to record blood pressure (BP). The average of three readings of BP and SAE of both the left and right arm were used for analysis. To assess reproducibility of this technique, 10 subjects of each group were studied by a single investigator on two separate occasions, with three weeks interspace. The intra-individual coefficient of variation was 11.2% for SAE.

Tissue AGE accumulation was assessed as skin autofluorescence (AF) on the ventral site of the lower arm with the AGE-Reader (DiagnOptics BV, Groningen, The Netherlands) [17]. In short, the AGE-Reader consists of a tabletop box containing a black light excitation light source (peak wavelength approximately 365 nm). Light emitted from the skin is measured with an integrated spectrometer. Measurement is fully automated and takes approximately 30 seconds to perform, giving an average value over 50 individual scans. The measurement was performed at non-lesional skin. Skin AF is calculated by dividing the mean value of the emitted light intensity per nm between 420 and 600 nm by the mean value of the excitation light intensity per nm between 300 and 420 nm, expressed as arbitrary units (AU). Repeated measurements on one day in controls and diabetic patients showed an overall Altman error percentage of five percent [17].

Details of the method, as used in this study, have been described by de Groot et al. [18]. IMT was determined at the common carotid artery (CCA), the bulb and the internal carotid artery (ICA) at left and right sites using an Acuson 128XP ultrasound system with 7 MHz linear array transducers (Acuson Corp., Silicon Valley, California, USA). A B-mode image was obtained after which a probe was positioned perpendicular to the far wall, showing an intima-media complex over approximately one centimeter. Mean IMT (the mean of the segment studied) and the maximum IMT (the highest IMT value found among the segment studied) were determined. As endpoint we used the mean of the mean (mean IMT) and the mean of the maximum (mean to max IMT) of the far wall IMT of the six imaged carotid segments. From studies on repeatability, the error of variation in measurement is calculated as 0.03 mm for the carotid far wall IMT.

Fasting cholesterol, HDL, LDL, triglycerides, creatinine, glycated haemoglobin (HbA1c) and C-reactive protein (CRP) levels were measured by routine techniques. In patients, levels of complement C3 and C4 were measured by nephelometry, and levels of antibodies to double stranded DNA (dsDNA) by 125I Farr assay. Additionally, serum and plasma samples were stored at -20°C for determination of levels of markers of EC activation. Serum levels of vascular cell adhesion molecule-1 (VCAM-1) (R&D Systems, Abingdon, UK) and thrombomodulin (TM) (Diaclone, Besancon, France) were measured according to the manufacturer's instructions. Levels of von Willebrand factor (vWf) were determined using an in-house ELISA as described [19].

Power analysis revealed that 30 subjects in each group had to be included to detect a difference in SAE of 2.0 ml/mmHg × 100 with a standard deviation of 2.8 at a significance level of 0.05 with a power of 80%.

Characteristics of patients and age- and sex-matched controls are presented in Tables 1 and 2. Concerning traditional cardiovascular risk factors, more SLE patients were smoking compared to healthy controls (30% vs 10%, respectively) and SLE patients more often used antihypertensive drugs compared to healthy controls (30% vs 0%, respectively). Patients with essential hypertension had increased diastolic and systolic blood pressure and more often used statins compared to healthy controls.

SAE was decreased in SLE patients (P = 0.01) and EH patients (P < 0.01) compared to healthy controls (Table 2). Since the increased prevalence of hypertension and smoking among SLE patients could have influenced results, we reanalysed data after exclusion of subject with hypertension and smokers. After exclusion of both subjects with hypertension and smokers, SAE remained significantly decreased in SLE patients (n = 14) compared to controls (n = 27, P = 0.021).

Mean to max IMT and mean IMT did not differ between SLE patients and healthy controls, whereas both the mean to max IMT and mean IMT were increased in EH patients compared to healthy controls (P < 0.05 and P = 0.01, respectively) (Table 2).

AGE accumulation was increased in SLE patients (P = 0.02) and in EH patients (P < 0.001) compared to healthy controls (Table 2).

Markers of EC activation are presented in Table 2. Levels of vWf and sVCAM-1 were elevated in SLE patients compared to healthy controls (P < 0.001 and P < 0.001, respectively) but not in EH patients. Levels of TM did not differ between SLE patients and healthy controls, whereas levels of TM were increased in EH patients compared to healthy controls (P = 0.04). Levels of CRP were increased in SLE patients (P < 0.05) but not in EH patients.

Univariate and multivariate analysis was performed to assess whether the difference in SAE between SLE patients and healthy controls could be explained by traditional cardiovascular risk factors. Factors that related to SAE in univariate analysis and factors that significantly differed between SLE patients and healthy controls (Table 2) were included in multivariate analysis. This analysis revealed that age, gender, smoking, and SLE are independently associated with SAE (Table 3). In addition, we assessed whether SAE and other vascular parameters differed between SLE patients with and SLE patients without hypertension, no significant differences were found (Table 4).

Univariate analysis performed on data of SLE patients and healthy controls together revealed inverse correlations between SAE and AGE accumulation (r = -0.336, P = 0.009), CRP (r = -0.307, P = 0.022) and creatinine clearance (r = 0.376, P = 0.004). In SLE patients, SAE inversely correlated to AGE accumulation (r = -0.370, P = 0.044). Concerning disease related factors, SAE was positively correlated to creatinine clearance (r = 0.440, P = 0.017) and inversely correlated to CRP (r = -0.429, P = 0.025). No significant correlations were found between SAE and IMT.