Red Cell Distribution Width Is Independently Related to Endothelial Dysfunction in Patients With Chronic Kidney Disease

CLINICAL INVESTIGATION

Red Cell Distribution Width Is Independently Related to Endothelial Dysfunction in Patients With Chronic Kidney Disease Yalcin Solak, MD, Mahmut I. Yilmaz, MD, Mutlu Saglam, MD, Kayser Caglar, MD, Samet Verim, MD, Hilmi U. Unal, MD, Mahmut Gok, MD, Erkan Demirkaya, MD, Abduzhappar Gaipov, MD, Mehmet Kayrak, MD, Hakki Cetinkaya, MD, Tayfun Eyileten, MD, Suleyman Turk, MD and Abdulgaffar Vural, MD

Abstract: Background: Red cell distribution width (RDW) is a measure of erythrocyte size variability and has been shown as an independent predictor of mortality. The aim of this article was to evaluate the association of RDW with endothelial dysfunction in patients with chronic kidney disease (CKD). Methods: Patients with 1 to 5 stages of CKD were included in the study. Endothelial function was assessed with flowmediated dilatation (FMD). Estimated glomerular filtration rate (eGFR) and carotid intima media thickness (CIMT) were determined. Clinicodemographic characteristics, biochemical values, complete blood counts, ferritin, C-reactive protein (CRP) and cholesterol levels were recorded. Spearman’s correlation was used to determine correlates of RDW. Multivariate linear regression model was used to assess independent associates of FMD. Results: Overall, 367 patients with CKD 1 to 5 were included in the study. RDW showed a significant increase from stage 1 to stage 5 CKD. Median RDW was 13.5. Patients with RDW values higher than median had significantly lower hemoglobin, eGFR and FMD values and higher CIMT and CRP values compared with patients who had RDW values below median. RDW showed a significant positive correlation with the presence of diabetes mellitus, CIMT and CRP, whereas a significant negative correlation with eGFR, ferritin and FMD. Multivariate analysis showed independent predictors of FMD as RDW, presence of diabetes, hemoglobin, eGFR, CRP, and serum albumin. Conclusions: Multivariate regression model revealed RDW as a significant predictor of FMD independent of major confounding factors, such as diabetes, inflammation, anemia and kidney function in CKD. Key Indexing Terms: Red cell distribution width; Endothelial function; Flow-mediated dilatation; Chronic kidney disease. [Am J Med Sci 2014;347(2):118–124.]

R

ed cell distribution width (RDW) is a measure of size variability in circulating red blood cells and is routinely reported as a part of complete blood count analysis.1 Until recently, RDW has been used primarily to distinguish several types of anemias.2 However, recent studies have shown that RDW could independently predict mortality in the general population,3 acute heart failure,4 chronic heart failure,5 acute pulmonary embolism,6 myocardial infarction,7 acute kidney injury treated with continuous renal replacement therapy,8 and peripheral arterial disease.9 From the Departments of Nephrology (YS, AG, ST) and Cardiology (MK), Meram School of Medicine, Selcuk University, Konya, Turkey; and Departments of Nephrology (MIY, KC, SV, HUU, MG, ED, HC, TE) and Radiology (MS), Gülhane School of Medicine, Ankara, Turkey. Submitted August 3, 2012; accepted in revised form November 6, 2012. Supported by ERA-EDTA Long-Term Fellowship Grant (to A.G.). The authors have no financial or other conflicts of interest to disclose. Correspondence: Yalcin Solak, MD, Department of Nephrology, Meram School of Medicine, Selcuk University, Meram, Konya 42090, Turkey (E-mail: [email protected]).

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Chronic kidney disease (CKD) is characterized by an increased oxidative stress and inflammation. Coupled with other nontraditional risk factors, this accounts for the unproportionately increased cardiovascular risk burden in this patient population. Endothelial dysfunction represents the earliest changes in the process of atherosclerosis.10 However, carotid intima media thickness (CIMT) represents the more clinically advanced stage of atherosclerosis. Given very high cardiac mortality starting early during the course of CKD, we aimed to assess the association of RDW with endothelial function (measured with flow-mediated dilatation [FMD]) and CIMT.

SUBJECTS AND METHODS Study Design and Subjects This study was an observational cohort study with data collected systematically and prospectively. Between January 2008 and October 2011, 830 patients were evaluated at the Renal Unit of a tertiary care hospital for the 1st time because of suspected or manifest renal disease. All patients included in the study were diagnosed as having CKD according to the National Kidney Foundation K/DOQI Guidelines.11 To minimize confounding effects of medications that may influence endothelial dysfunction, 329 patients were excluded, including patients using angiotensinconverting enzyme inhibitors (n 5 117), angiotensin receptor blockers (n 5 104), statins (n 5 56), erythropoiesis stimulating agent (n 5 33) or supplemental vitamin pills (n 5 19). Other exclusion criteria included acute infection and unwillingness to participate in the study (n 5 32). One hundred eleven eligible patients were dropped out for the following reasons: lost to follow-up (n 5 68) and withdrew consent (n 5 44). Stages of CKD were determined using estimated glomerular filtration rate (eGFR), which was calculated via modification of diet in renal disease equation.12 In the final analyses, 357 patients were included. None of the patients in stage 5 CKD were on hemodialysis or peritoneal dialysis. The underlying etiologies for kidney disease are shown in Table 1. Hypertension was defined as systolic blood pressure $140 mm Hg or diastolic blood pressure $90 mm Hg on repeated measurements or the use of antihypertensive drugs. Thirty-two patients were on antihypertensive therapy (25 patients were treated with calcium channel antagonists, 4 with betablocker agents and 3 with loop diuretics). Local ethical committee approved the study protocol, and all patients were included in the study after signing informed consent forms. Laboratory Measurements All blood samples were obtained from patients in the morning after 12 hours of fasting, for measurement of fasting plasma glucose, serum albumin, total serum cholesterol, triglyceride, high-density lipoprotein (HDL) and low-density

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TABLE 1. Demographic and clinical characteristics of the study groups Stage 1 CKD Stage 2 CKD Stage 3 CKD (n 5 71) (n 5 71) (n 5 70) Age (yr) Sex (M/F) BMI (kg/m2) History of CVD (n) Cardiovascular episode Stroke Peripheral vascular disease Aortic aneurysm Etiology of CKD (n) Diabetes Glomerulonephritis Hypertension Polycystic kidney disease Reflux nephropathy Unknown Smoking, current (n)

Stage 4 CKD (n 5 69)

Stage 5 CKD (n 5 76)

50 (28–73) 35/36 26.5 6 2.1

52 (30–71) 36/35 26.2 6 2.9

51 (29–71) 35/35 25.6 6 2.6

56 (31–73) 35/34 25.8 6 3.0

52 (28–69) 35/41 25.5 6 2.7

6 3 5

4 2 1

7 1 —

10 — 2

18 2 2

3

—

—

—

—

15 10 7 3

19 11 17 1

17 13 15 1

18 12 7 5

17 13 13 4

2 24 31

1 22 33

1 23 31

3 24 27

5 24 36

CKD, chronic kidney disease; M, male; F, female; BMI: body mass index; CVD, cardiovascular disease.

lipoprotein cholesterol. Total plasma cholesterol, triglyceride and HDL cholesterol were measured by enzymatic colorimetric method with Olympus AU 600 auto analyzer using reagents from Olympus Diagnostics, GmbH (Hamburg, Germany). Lowdensity lipoprotein cholesterol was calculated by Friedewald’s formula.13 For the measurement of high-sensitivity C-reactive protein (hsCRP), serum samples were diluted with a ratio of 1/100 with the diluents solution. Calibrators, kit controls and serum samples were all added on each micro well with an incubation period of 30 minutes. After 3 washing intervals, 100 mL of enzyme conjugate (peroxidase-labeled anti–C-reactive protein [anti-CRP]) was added on each micro well for additional 15-minute incubation in room temperature in dark. The reaction was stopped with a stop solution, and photometric measurement was performed at the 450 nm wavelength. The amount of serum samples was calculated as milligram per liter with a graphic that was made by noting the absorbance levels of the calibrators. The serum basal insulin value was determined by the coated tube method (DPCUSA). An insulin resistance score homeostasis model assessmentinsulin resistance (HOMA-IR) was computed by the following formula14: HOMA-IR 5 fasting plasma glucose (mg/dL) 3 immunoreactive insulin (mIU/mL)/405. Proteinuria was quantified using 24-hour timed urine collection. The RDW was determined using the Pentra 120 Retic Hematology Analyzer (ABX, Montpellier, France) as part of the routine hemogram. The reference value for RDW in our laboratory is 11.7% to 14.6%. Flow-Mediated Dilatation Arterial blood pressure was determined in the morning in all patients by a physician by 3 consecutive measurements, each after a 15-minute resting period, with the mean values calculated for systolic and diastolic pressures. Endothelium-dependent vasodilatation (FMD) and endothelium-independent vasodilatation (nitroglycerine-mediated dilatation [NMD]) of the brachial artery were assessed noninvasively using high-resolution ultrasound as described by Celermajer Ó 2013 Lippincott Williams & Wilkins

et al.15 The method for the vascular assessment met the criteria that were mentioned by the International Brachial Artery Reactivity Task Force.16 Measurements were made by a single observer using an ATL 5000 ultrasound system (Advanced Technology Laboratories, Inc, Bothell, WA) with a 12-MHz probe. The subjects remained at rest in the supine position for at least 15 minutes before the examination started. Subject’s arm was comfortably immobilized in the extended position to allow consistent recording of the brachial artery 2 to 4 cm above the antecubital fossa. Three adjacent measurements of end-diastolic brachial artery diameter were made from single 2-dimensional frames. All ultrasound images were recorded on S-VHS videotape (JVC SVHS Master Video Tape NIP ST-120SV, Long Beach, CA) for subsequent blinded analysis. A pneumatic tourniquet was inflated to 200 mm Hg with obliteration of the radial pulse. After 5 minutes, the cuff was deflated. Flow measurements were made 60 seconds postdeflation. After a further 15 minutes, measurements were repeated and again 3 minutes after administration of 400 mg of sublingual glyceryl trinitrate with oral route. The maximum FMD and NMD dilatation diameters were calculated as the average of the 3 consecutive maximum diameter measurements. The FMD and NMD were then calculated as the percent change in diameter compared with baseline resting diameters. Carotid Intima Media Thickness CIMT was assessed in all study participants. The protocol used to determine CIMT was reported in detail elsewhere.17 CIMT and FMD measurements were performed ideally in the same day or within 7 days from blood extraction and biochemical assessment. Statistical Analyses All the statistical analyses were performed by using SPSS 16.0 (SPSS, Inc, Chicago, IL) statistical package. Nonnormally distributed variables were expressed as median (range) and normally distributed variables were expressed as mean 6 SD, as appropriate. A P value of ,0.05 was considered

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TABLE 2. RDW, biochemical parameters and endothelial function assessment values across CKD stages Stage 1 (eGFR [ Stage 2 (eGFR, Stage 3 (eGFR, 30– Stage 4 (eGFR, Stage 5 (eGFR \ 90) (n 5 71) 60–89) (n 5 71) 59) (n 5 70) 15–29) (n 5 69) 15) (n 5 76) BMI (kg/m²) eGFR (mL/min) SBP (mm Hg) DBP (mm Hg) Serum albumin (g/dL) Total cholesterol (mg/dL) Triglycerides (mg/dL) LDL (mg/dL) HDL (mg/dL) Calcium (mg/dL) Phosphate (mg/dL) iPTH (pg/mL) HOMA-IR index hsCRP (mg/L) 24-hour proteinuria (g/d) Hemoglobin (g/dL) RDW (%) NMD (%) Flow-mediated dilatation (%)

25.51 6 2.1 96 (90–114) 133 (113–157) 83 (71–94) 4 (3.57–4.6) 202 (160–239)

26.22 6 2.93 78 (60–89) 134 (115–163) 83 (77–92) 4.0 (3.5–4.6) 202 (172–243)

147 (103–190) 127 (85–165) 47 (31–51) 8.97 6 0.5 4.22 6 0.51 48 (23–104) 1.65 6 0.72 8 (3.2–17) 1.39 (0.38–2.45)

147 (106–170) 147 (107–179) 131 (96–167) 124.5 (98–156) 44 (26–54) 42.5 (26–50) 8.72 6 0.54 8.31 6 0.48 4.39 6 0.89 4.64 6 0.74 72 (25–155) 146 (84–254) 1.78 6 0.73 1.91 6 1.05 12 (6–16) 18 (5–23) 1.7 (0.37–3.92) 1.74 (0.57–5.15)

12.97 6 2.35 12.29 6 1.86 11.97 (10.35–14.9) 13.07 (7.8–15.4) 13 (11.8–13.8) 13 (12.4–13.8) 8.2 (5.0–9.7) 7.2 (4.5–8.3)

25.57 6 2.63 43.5 (30–58) 135 (110–180) 85 (80–95) 4.3 (3.5–4.8) 200 (171–245)

12.1 6 2.12 12.9 (10.18–17.15) 12.9 (12.0–13.8) 6.9 (5.0–8.2)

Pa

25.83 6 2.99 18 (15–29) 133 (121–170) 85 (71–95) 4.0 (3.45–4.6) 200 (159–244)

25.47 6 2.74 5 (2–14) 133 (113–170) 83 (71–93) 3.8 (3.2–4.6) 200 (159–243)

0.1 ,0.001 0.7 0.03 ,0.001 0.2

146 (124–173) 129 (98–165) 41 (28–63) 8.08 6 0.38 5.74 6 1.33 157 (118–243) 1.9 6 0.95 21 (3 47) 1.54 (0.48–4.39)

139 (103–176) 125(81–175) 43 (26–63) 8.07 6 0.41 6.44 6 1.57 269 (163–326) 1.76 6 0.66 27 (4–48) 1.65 (0.82–5.45)

*.05 0.1 0.05 ,0.001 ,0.001 ,0.001 0.3 ,0.001 ,0.001

11.47 6 2.34 15 (10.35–18) 13 (11.6–13.8) 6.2 (4.0–8.2)

10.69 6 1.94 15.28 (9.5–18.51) 12.58 (10.0–13.8) 5.5 (4.0–8.2)

,0.001 ,0.001 ,0.001 ,0.001

a Differences assessed by x2 test for categorical variables and by Kruskal-Wallis test. Statistically significant if P , 0.05. RDW, red cell distribution width; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; LDL, low-density lipoprotein; HDL, high-density lipoprotein; iPTH, parathyroid hormone; HOMA-IR, homeostasis model assessment-insulin resistance; hsCRP, high sensitivity C-reactive protein; NMD, nitroglycerine-mediated dilatation.

to be statistically significant. Chi square test was used to comparison of categorical data. The difference among groups (more than 2 groups) was assessed by Kruskal-Wallis test for the nonparametric data and by 1-way analysis of variance test for the parametric variables. After Kruskal-Wallis test, subgroup difference was tested by Mann-Whitney’s U test with Bonferroni correction. After analysis of variance test, Tukey’s test was used to determine of subgroup difference. Spearman’s rank correlation was used to determine linear association between

paired variables. Stepwise multivariate regression analysis was used to assess the predictors for FMD. Backward elimination method was used for multivariate regression model. Following variables were included in the 1st step of the model: age, gender, RDW, diabetes mellitus, body mass index (BMI), systolic blood pressure, HOMA index, albumin, Ca 3 P product, CRP, hemoglobin, eGFR, parathyroid hormone, proteinuria, smoking and total cholesterol. P . 0.10 accepted for elimination criterion from the stepwise model. Analysis was completed after 8 steps.

RESULTS

FIGURE 1. Percentages of anemic patients and patients with red cell distribution width (RDW) .14.6% in chronic kidney disease stages.

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Baseline Characteristics Overall, 367 patients with CKD (stages 1–5) were included in the study. Demographic and clinical characteristics are depicted in Table 1. None of the patients in stage 5 was receiving hemodialysis or peritoneal dialysis. There was no difference among the CKD stages in terms of mean age, BMI and gender distribution. Biochemical parameters, hemoglobin and RDW values and endothelial function measurements are shown in Table 2. The hsCRP showed a significant increase across CKD stages. Insulin resistance, expressed as HOMA-IR index, showed an increase until stage 4 and then was found to be decreased in stage 5 CKD patients. The trend was not statistically significant. Endothelial function worsened with advancing CKD stage. Both NMD and FMD were decreased while eGFR was decreasing. As expected, serum hemoglobin values showed significant reductions with advancing CKD stage. RDW showed Volume 347, Number 2, February 2014

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a significant increase from stages 1 to 5. There was only 1 patient in stage 1 CKD group whose RDW value was out of reference range (.14.6%). This number for stage 2, stage 3, stage 4 and stage 5 CKD groups were 10 (14%), 20 (28.5%), 47 (68.1%) and 46 (60.5%), respectively. RDW values of more than half of the patients in stages 4 and 5 were out of normal range. When definitions of anemia of National Kidney Foundation was considered (a hemoglobin concentration ,12 g/dL for women and ,13.5 g/dL for men), there were 30 (42.2%), 37 (52.1%), 37 (52.8%), 47 (68.1%) and 64 (84.2%) patients with anemia in groups of CKD stages 1, 2, 3, 4 and 5, respectively (Figure 1). None of the patients with anemia was receiving iron replacement or erythropoiesis stimulating agent treatment at the time of enrollment and FMD measurements. Red Cell Distribution Width We determined median value for RDW as 13.5% in the whole study cohort. The entire study cohort was then divided into 2 groups, one with RDW values below median and the other above median. Laboratory values and clinical characteristics of these groups are shown in Table 3. Patients with higher RDW values had significantly lower serum hemoglobin, lower eGFR, higher insulin resistance and lower HDL cholesterol compared with patients with lower RDW values. Both groups

were comparable in terms of BMI. FMD was found to be significantly lower in patients with higher RDW values. In contrast to FMD, despite a trend to be lower, there was no significant difference between the groups in terms of NMD. CRP was significantly higher in patients with higher RDW compared with patients with lower RDW values. Correlations of Red Cell Distribution Width Correlations of several parameters with RDW are shown in Table 4. RDW showed a significant inverse correlation with serum ferritin levels. Relation with hemoglobin level was positive but not statistically significant. RDW showed a significant positive correlation with hsCRP. Similarly, RDW levels were inversely and significantly correlated with eGFR. There was a significant inverse relationship between RDW and FMD (Figure 2). Multivariate Regression Analysis We used a multiple regression model with backward removal to determine independent associations of FMD. Variables known to influence FMD were included in the analysis. RDW appeared as an independent predictor of FMD in addition to smoking, diabetes mellitus, parathyroid hormone, albumin level and CRP (Table 5).

DISCUSSION TABLE 3. Laboratory parameters of CKD patients above and below median RDW RDW \ 13.5% RDW [ 13.5% (n 5 179) (n 5 178) P RDW (%) Hemoglobin (g/dL) iPTH (pg/mL) eGFR (mL/min) 24-hour proteinuria (mg/d) FMD (%) CIMT (mm) hsCRP (mg/L) SBP (mm Hg) DBP (mm Hg) HOMA-IR index BMI (kg/m2) Triglycerides (mg/dL) LDL (mg/dL) HDL (mg/dL) Albumin (g/dL) Calcium (mg/dL) Phosphorus (mg/dL) NMD (%)

11.99 6 0.99 12.13 6 2.19 102.77 6 71,48 65.99 6 32.52 1784.8 6 991.4 7.38 6 1.02 0.68 13.07 6 6.26 133.8 6 9.7 84.2 6 4.4 1.67 6 0.65 26.15 6 2.6 143.7 6 15.4 127.77 43.63 4.04 8.62 4.61

6 6 6 6 6

17,68 5.6 0.35 0.59 1.1

12.89 6 0.58

15.24 6 1.14 11.65 6 2.31

,0.001 0.04

184.55 6 72.6 ,0.001 31.06 6 26.96 ,0.001 1790.8 6 1035.8 0.9 6.19 6 1.07 0.82 20.3 6 9.58 135.8 6 11.4 84.2 6 5.1 1.93 6 0.97 25.68 6 2.81 145.1 6 16.6

,0.001 ,0.001 ,0.001 0.07 0.9 0.003 0.1 0.4

6 6 6 6 6

0.232 0.01 0.1 ,0.001 ,0.001

125.55 41.6 3.98 8.23 5.59

17.37 6.13 0.33 0.51 1.48

12.79 6 0.67

0.1

CKD, chronic kidney disease; RDW, red cell distribution width; iPTH, parathyroid hormone; eGFR, estimated glomerular filtration rate; FMD, flow-mediated dilatation; CIMT, carotid intima media thickness; hsCRP, high sensitivity C-reactive protein; SBP, systolic blood pressure; DBP, diastolic blood pressure; HOMA-IR, homeostasis model assessment-insulin resistance; BMI, body mass index; LDL, lowdensity lipoprotein; HDL, high-density lipoprotein; NMD, nitroglycerine-mediated dilatation.

Ó 2013 Lippincott Williams & Wilkins

The main result of this study showed that RDW is associated with endothelial dysfunction and inflammation regardless of the level of anemia. RDW increased with worsening kidney function. Despite convincing data, which show independent relation of RDW with adverse cardiovascular outcomes both in acute and

TABLE 4. Correlations of various parameters with RDWa Parameters r P FMD Hemoglobin Ferritin hsCRP NMD Diabetes Previous CVD BMI Smoking SBP HOMA index Age Gender Serum albumin 24-hour proteinuria Serum calcium Serum phosphate iPTH eGFR CIMT

20.59 20.07 20.27 0.52 20.08 0.27 0.03 20.07 0.09 0.13 0.16 0.05 0.13 20.11 20.03 20.34 0.43 0.60 20.58 0.63

,0.001 0.18 ,0.001 ,0.001 0.14 ,0.001 0.59 0.19 0.10 0.01 0.003 0.31 0.81 0.03 0.54 ,0.001 ,0.001 ,0.001 ,0.001 ,0.001

a Spearman’s rank test correlation. FMD, flow-mediated dilatation; hsCRP, high sensitivity C-reactive protein; NMD, nitroglycerine-mediated dilatation; CVD, cardiovascular disease; BMI, body mass index; SBP, systolic blood pressure; HOMA, homeostasis model assessment; iPTH, parathyroid hormone; eGFR, estimated glomerular filtration rate; CIMT, carotid intima media thickness.

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FIGURE 2. Scatter plot diagram showing an inverse correlation between flow-mediated dilatation (FMD) and red cell distribution width (RDW).

chronic disease settings, precise mechanisms of this impact have yet to be elucidated. Recent studies shed some light on potential pathophysiologic pathways by which increased RDW might operate to increase adverse outcomes. Two most commonly implicated pathways are inflammation and malnutrition. Semba et al18 evaluated serum levels of some antioxidant factors, including carotenoids, a-tocopherol, selenium, protein carbonyls, as well as interleukin-6 values and RDW in 786 moderately to

severely disabled community-dwelling elderly women. Total carotenoids and selenium was found to be significantly correlated with RDW. However, inclusion of the interleukin-6 in the model attenuated this relationship. Hence, the authors concluded that increased oxidative stress was related to an increase in RDW values, and this relation was in part mediated through inflammation. Lippi et al19 showed a strong, graded association of RDW with hsCRP and erythrocyte sedimentation rate independent of numerous confounding factors in 3,845 adult outpatients presented for health check-up. Allen et al20 also showed that RDW was a strong and independent predictor of mortality in patients with heart failure. The authors also showed that the association of elevated RDW with low hemoglobin, moderately depressed mean corpuscular volume, high erythropoietin, normal iron-binding capacity and normal ferritin are all consistent with a state of impaired iron mobilization. Association of increased RDW with inflammation also supported the latter. The results of the present study also showed a significant positive correlation between RDW and serum CRP. However, the association of RDW and FMD was independent of CRP values in multivariable regression model. However, some other studies did not show a relationship between RDW and inflammation.21 In 1,489 patients with coronary artery disease, Lappe et al22 found a significant correlation of RDW with CRP but both factors for independent predictors of allcause death during 8.4 to 15.2 years of follow-up. Förhercz et al23 evaluated 195 patients with systolic heart failure and determined correlates of RDW. In the multiple regression models, the strongest relationship for RDW was obtained with soluble transferrin

TABLE 5. Stepwise linear regression analysis to determine independent associates of flow-mediated dilation B Standard error b t a

Constant RDW DM BMI Age SBP HOMA Albumin Ca 3 P CRP Hemoglobin eGFR PTH Proteinuria Smoking Gender Total cholesterol Constantb RDW DM Albumin CRP Hemoglobin eGFR

9.911 20.114 20.338 20.004 20.002 20.001 20.005 0.273 20.007 20.018 20.166 0.015 20.001 20.005 20.142 0.173 20.002 8.601 20.117 20.374 0.320 20.018 20.164 0.017

1.157 0.030 0.110 0.016 0.004 0.004 0.056 0.128 0.005 0.007 0.020 0.003 0.001 0.000 0.084 0.084 0.002 0.706 0.029 0.100 0.122 0.006 0.019 0.002

20.184 20.120 20.009 20.021 20.010 20.003 0.077 20.064 20.130 20.311 0.429 20.049 20.032 20.059 0.072 20.029 20.190 20.133 0.090 20.136 20.307 0.499

8.567 23.820 23.071 20.248 20.612 20.273 20.090 2.127 21.520 22.642 28.348 5.029 20.614 20.882 21.694 2.072 20.832 12.177 24.088 23.736 2.619 22.841 28.455 9.328

P ,0.001 ,0.001 0.002 0.805 0.541 0.785 0.929 0.034 0.129 0.009 ,0.001 ,0.001 0.540 0.378 0.091 0.039 0.406 ,0.001 ,0.001 ,0.001 0.009 0.005 ,0.001 ,0.001

Dependent varibale (FMD); R 5 0.78, R2 5 0.59, F 5 39.7, P , 0.001 (the 1st step of the stepwise linear regression model). R 5 0.77, R2 5 0.59, F 5 86.4, P , 0.001 (the last step [8th] of the model). RDW, red cell distribution width; DM, diabetes mellitus; BMI, body mass index; SBP, systolic blood pressure; HOMA, homeostasis model assessment; Ca 3 P, Calcium-Phosphorus product; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; PTH, parathyroid hormone. a b

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receptor, soluble tumor necrosis factor receptor I, soluble tumor necrosis factor receptor II and total cholesterol. Although the authors found significant correlations between RDW and inflammatory and nutritional markers, RDW emerged as an independent predictor of mortality in this cohort. A few studies provided some possible mechanistic explanation for relationship between increased RDW and adverse cardiovascular outcomes. Afonso et al24 evaluated participants of the National Health and Nutrition Examination Survey 1999 to 2006 and found an independent association between RDW and microalbuminuria. The relationship was independent from renal function, anemia and inflammation. Kalay et al25 recently showed that in patients with slow coronary flow, serum uric acid levels and RDW were independent predictors of slow coronary flow. To our knowledge, this is the 1st study in the literature unveiling the relation between increased RDW and endothelial dysfunction. The results of our study showed an independent association between RDW and endothelial function assessed by FMD. Interestingly, this relation was independent of anemia and inflammation (CRP). Furthermore, RDW also showed a correlation with CIMT. In sum, RDW was both associated with endothelial dysfunction, as the earliest state of atherosclerotic process, and with CIMT as the more advanced stage of atherosclerotic process. Previously Lippi et al26 have shown that there is an inverse graded association between RDW and kidney function tests in a large cohort of unselected adult population. In logistic regression analysis, lower eGFR strongly predicted higher RDW levels independent of age, gender, mean corpuscular volume and hemoglobin values. Our results were in agreement with those of Lippi et al.26 RDW value increased significantly across CKD stages. To our knowledge, this is the 1st study demonstrating association of RDW with impaired endothelial function in patients with CKD. Our results provide some additional mechanistic explanation for the pathophysiologic implication of RDW in closing volume adverse events. Since increased RDW was associated with endothelial dysfunction independent of anemia and inflammation, this factor may be included among the nontraditional risk factors that pose unproportionately increased cardiovascular risk.

CONCLUSIONS In conclusion, this is the 1st study in the literature showing an inverse association between eGFR and RDW. RDW was associated with impaired endothelial function independently of anemia, inflammation and renal function. RDW was also positively correlated with CIMT. More studies should be conducted to consolidate these preliminary findings. ACKNOWLEDGMENTS The authors would like to express their sincere appreciation to FAVOR (FMF Arthritis Vasculitis and Orphan Diseases Research/www.favor.org.tr) web registries at Gulhane Military Medical Academy, Institute of Health Sciences for their support in epidemiological and statistical advisory and invaluable guidance for the preparation of the manuscript. REFERENCES

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Volume 347, Number 2, February 2014