Review Article
Special Issue: Metabolic Disorders
The Impact of Low-Grading Inflammation on Circulating
Endothelial-Derived Progenitor Cells in Patients with
Metabolic Syndrome and Diabetes Mellitus
Alexander E. Berezin1*, Alexander A. Kremzer2, Tatyana A Berezina3 and Martovitskaya Yu.V4
1Alexander E. Berezin, Internal Medicine Department, State Medical University, 26, Mayakovsky av., Zaporozhye, 69035, Ukraine
2Alexander A. Kremzer, Clinical Pharmacology Department, State Medical University, Zaporozhye, Ukraine
3Tatyana A. Berezina, MD, Private center "Vita-Center", Zaporozhye, Ukraine
4Yulia V. Martovitskaya, Pathology Department, State Medical University, Zaporozhye, Ukraine
2Alexander A. Kremzer, Clinical Pharmacology Department, State Medical University, Zaporozhye, Ukraine
3Tatyana A. Berezina, MD, Private center "Vita-Center", Zaporozhye, Ukraine
4Yulia V. Martovitskaya, Pathology Department, State Medical University, Zaporozhye, Ukraine
*Corresponding author: Alexander E. Berezin, Internal Medicine Department, State Medical University, 26, Mayakovsky av., Zaporozhye, 69035,
Ukraine, Tel: +380612894585; E-mail:
@
Received: April 16, 2015; Accepted: June 03, 2015; Published: June 18, 2015
Citation: Berezin AE, Kremzer AA, Berezina TA, Martovitskaya YV (2015) The Impact of Low-Grading Inflammation on Circulating
Endothelial-Derived Progenitor Cells in Patients with Metabolic Syndrome and Diabetes Mellitus. J Endocrinol Diab 2(3): 8. DOI: http://dx.doi.org/10.15226/2374-6890/2/4/00126
Abstract Top
Type-2 Diabetes Mellitus (T2DM) remains a leading contributor
to cardiovascular mortality worldwide. This study was conducted
to investigate the pattern of circulating Endothelial Progenitor Cells
(EPCs) in T2DM patients in comparison with MetS subjects.
Methods: The study retrospectively evolved 101 patients (54 subjects with T2DM and 47 patients with MetS) and 35 healthy volunteers. The flow cytometry was used for predictably distinguishing cell subsets, which depend on expression of CD45, CD34, CD14, Tie-2, and VEGFR2. Biomarkers were measured at baseline of the study.
Results: Patients with MetS have demonstrated lower incidence of dyslipidemia, lower concentrations of HbA1c, fasting blood glucose, insulin, and LDL-C when compared with T2DM subjects. Higher HDL-C and HOMA-IR were found in T2DM patients than in MetS subjects. There is a significant difference between the medians of absolute numbers and frequencies of CD14+CD309+ and CD14+CD309+Tie2+ in healthy volunteers and patients with dysmetabolic disorders respectively. CD14+CD309+ and CD14+CD309+Tie2+ subsets of circulating EPCs were determined in higher concentration among MetS subjects in comparison with T2DM patients. Osteoprotegerin (OPG) and hs-C-reactive protein (CRP), improve significantly predictive model based on T2DM + number of multiple cardiovascular risk factors (MCRFs) >3 for decreased both angiopoetic phenotypes of circulating EPCs.
Conclusion: we suggest that inflammatory biomarkers (hs-CRP, OPG) may consider statistically significant predictors for decreased EPCs labeled CD14+CD309+ and CD14+CD309+Tie2+ among dysmetabolic patients without preexisting atherosclerotic lesions of coronary arteries.
Keywords: Diabetes Mellitus; Metabolic Syndrome; Circulating Endothelial-Derived Progenitor Cells; Cardiovascular Risk Factors
Methods: The study retrospectively evolved 101 patients (54 subjects with T2DM and 47 patients with MetS) and 35 healthy volunteers. The flow cytometry was used for predictably distinguishing cell subsets, which depend on expression of CD45, CD34, CD14, Tie-2, and VEGFR2. Biomarkers were measured at baseline of the study.
Results: Patients with MetS have demonstrated lower incidence of dyslipidemia, lower concentrations of HbA1c, fasting blood glucose, insulin, and LDL-C when compared with T2DM subjects. Higher HDL-C and HOMA-IR were found in T2DM patients than in MetS subjects. There is a significant difference between the medians of absolute numbers and frequencies of CD14+CD309+ and CD14+CD309+Tie2+ in healthy volunteers and patients with dysmetabolic disorders respectively. CD14+CD309+ and CD14+CD309+Tie2+ subsets of circulating EPCs were determined in higher concentration among MetS subjects in comparison with T2DM patients. Osteoprotegerin (OPG) and hs-C-reactive protein (CRP), improve significantly predictive model based on T2DM + number of multiple cardiovascular risk factors (MCRFs) >3 for decreased both angiopoetic phenotypes of circulating EPCs.
Conclusion: we suggest that inflammatory biomarkers (hs-CRP, OPG) may consider statistically significant predictors for decreased EPCs labeled CD14+CD309+ and CD14+CD309+Tie2+ among dysmetabolic patients without preexisting atherosclerotic lesions of coronary arteries.
Keywords: Diabetes Mellitus; Metabolic Syndrome; Circulating Endothelial-Derived Progenitor Cells; Cardiovascular Risk Factors
Abbreviations
CI: Confidence Interval; IQR: Inter Quartile Range; T2DM: Type 2 Diabetes Mellitus; BP: Blood Pressure; BMI: Body Mass Index; GFR: Glomerular Filtration Rate; EPCs: Endothelial Progenitor Cells; TG: Triglycerides; HDL-C: High-Density Lipoprotein Cholesterol; LDL-C: Low-Density Lipoprotein Cholesterol; hs-CRP: High Sensitive C Reactive Protein; sRANKL: Serum Receptor Activator of NF-κB ligand.
Introduction
Dysmetabolic disorders/syndrome, metabolic syndrome
(MetS), obesity, and type-2 diabetes mellitus (T2DM) remain
to be increased worldwide achieving epidemic level [1,2]. Both
metabolic states i.e. T2DM and MetS contribute Endothelial
Dysfunction (ED), atherogenesis, oxidative stress, inflammatory
responses, and they are considered causality factors of
cardiovascular diseases (CVD) [3-5]. Despite development of
dysmetabolic disorders associates with ED affecting the macroand
the microvascular systems, T2DM patients demonstrated
the most severe ED than MetS subjects [6]. Recent studies
have shown that ED associates with poor clinical outcomes in
patients with established CVD [7]. In this context, pathogenic
factors that induce endothelial dysfunction at the earlier stages
of dysmetabolic diseases might have a causality value for CVD
progression [4].
Endothelial Progenitor Cells (EPCs) is a population of mononuclear cells that expresses endothelial and progenitor markers i.e., CD34+ antigen and VEGFR-2+ vascular growth ligands (Vascular Endothelial Growth Factor Rreceptor-2), CD133+, as well as CD14+, and Tie2+ (tyrosine kinase ligand) [8]. EPCs may play a pivotal role in mechanisms of tissue repair, maturation of endothelial cells, angiogenesis, and revascularization [9]. EPCs are mobilized from bone marrow and probably from peripheral tissues upon injury in result in growth factors and inflammatory cytokines over production [10]. There are evidences regarding elevated EMPs as a marker of ED [11]. Indeed, increased production of reactive oxygen species, insulin resistance, reduced NO bioavailability have been identified as mediators of tissue injury and ED [12,13]. In contrast, T2DM has well characterized as dysmetabolic state with depletion of circulating EPCs resulting in severely reduced angiogenic capacity in vivo [14]. Although decreased number and impaired functions of CD34+CD133+ EPCs in T2DM patients might lead to increased frequency of CVD events [15,16], the role of angiogenic subsets of EPCs in T2DM and MetS is still not fully understood. The aim of the study: to investigate the pattern of circulating EMPs in T2DM patients in comparison with MetS subjects.
Endothelial Progenitor Cells (EPCs) is a population of mononuclear cells that expresses endothelial and progenitor markers i.e., CD34+ antigen and VEGFR-2+ vascular growth ligands (Vascular Endothelial Growth Factor Rreceptor-2), CD133+, as well as CD14+, and Tie2+ (tyrosine kinase ligand) [8]. EPCs may play a pivotal role in mechanisms of tissue repair, maturation of endothelial cells, angiogenesis, and revascularization [9]. EPCs are mobilized from bone marrow and probably from peripheral tissues upon injury in result in growth factors and inflammatory cytokines over production [10]. There are evidences regarding elevated EMPs as a marker of ED [11]. Indeed, increased production of reactive oxygen species, insulin resistance, reduced NO bioavailability have been identified as mediators of tissue injury and ED [12,13]. In contrast, T2DM has well characterized as dysmetabolic state with depletion of circulating EPCs resulting in severely reduced angiogenic capacity in vivo [14]. Although decreased number and impaired functions of CD34+CD133+ EPCs in T2DM patients might lead to increased frequency of CVD events [15,16], the role of angiogenic subsets of EPCs in T2DM and MetS is still not fully understood. The aim of the study: to investigate the pattern of circulating EMPs in T2DM patients in comparison with MetS subjects.
Methods
This study is a retrospective observation study and evolved
101 patients (54 subjects with T2DM and 47 patients with MetS)
and 35 healthy volunteers who were examined in three our
centers (City Hospital # 6, Regional Center of Cardiovascular
Diseases, and Regional Zaporozhye Hospital located in
Zaporozhye, Ukraine) between February 2013 and November
2013.
Study design
Study included the following: [1] already diagnosed T2DM,
[2] documented MetS,. Following were excluded from the study:
[1] patients with typical anginal symptoms, [2] subjects with
evidences of preexisting coronary artery disease, i.e. myocardial
infarction / acute coronary syndrome, [3] angiographic evidence
of asymptomatic coronary atherosclerosis obtained by contrastenhanced
multispiral tomography angiography provided prior
study entry, [4] heart failure, [5] declined glomerular filtration
rate < 60 mL/min/1.73 m2, [6] candidates for insulin therapy.
All the patients have given their informed written consent for
participation in the study.
T2DM was diagnosed with revised criteria provided by American Diabetes Association when source documents were reviewed [17]. When one or more of the following components were found (glycated hemoglobin [HbA1c] ≥ 6.5%; fasting plasma glucose ≥ 7 mmol/L; 2-h plasma glucose ≥ 11.1 mmol/L during an oral glucose tolerance test; a random plasma glucose ≥ 11.1 mmol/L; exposure of insulin or oral antidiabetic drugs; a previous diagnosis of T2DM) T2DM was determined. MetS was diagnosed based on the National Cholesterol Education Program Adult Treatment Panel III criteria [18]. Patients were enrolled in the MetS cohort when at least three of the following components were defined: waist circumference ≥ 90 cm or ≥ 80 cm in men and women respectively; high density lipoprotein (HDL) cholesterol < 1.03 mmol/l or < 1.3 mmol/l in men and women respectively; triglycerides ≥ 1.7 mmol/l; blood pressure ≥ 130/85 mmHg or current exposure of antihypertensive drugs; fasting plasma glucose ≥ 5.6 mmol/L or previously defined as T2DM or treatment with oral antidiabetic agents or insulin. Current smoking was defined as consumption of one cigarette daily for three months. Anthropometric measurements were made using standard procedures. No untreated subjects were enrolled. Patients with T2DM were treated with life-style modification, diet and orally taken antidiabetic drugs except sulfonylurea derivates and glitazones. Metformin in monotherapy or in combination with glinides and / or gliptines was given in individually optimized daily doses to be achieving full or partly full control for T2DM. Therefore, insulin was not used in enrolled patients. Subjects with MetS were treated with life-style modification and diet, therefore metformin was given in 12 patients.
T2DM was diagnosed with revised criteria provided by American Diabetes Association when source documents were reviewed [17]. When one or more of the following components were found (glycated hemoglobin [HbA1c] ≥ 6.5%; fasting plasma glucose ≥ 7 mmol/L; 2-h plasma glucose ≥ 11.1 mmol/L during an oral glucose tolerance test; a random plasma glucose ≥ 11.1 mmol/L; exposure of insulin or oral antidiabetic drugs; a previous diagnosis of T2DM) T2DM was determined. MetS was diagnosed based on the National Cholesterol Education Program Adult Treatment Panel III criteria [18]. Patients were enrolled in the MetS cohort when at least three of the following components were defined: waist circumference ≥ 90 cm or ≥ 80 cm in men and women respectively; high density lipoprotein (HDL) cholesterol < 1.03 mmol/l or < 1.3 mmol/l in men and women respectively; triglycerides ≥ 1.7 mmol/l; blood pressure ≥ 130/85 mmHg or current exposure of antihypertensive drugs; fasting plasma glucose ≥ 5.6 mmol/L or previously defined as T2DM or treatment with oral antidiabetic agents or insulin. Current smoking was defined as consumption of one cigarette daily for three months. Anthropometric measurements were made using standard procedures. No untreated subjects were enrolled. Patients with T2DM were treated with life-style modification, diet and orally taken antidiabetic drugs except sulfonylurea derivates and glitazones. Metformin in monotherapy or in combination with glinides and / or gliptines was given in individually optimized daily doses to be achieving full or partly full control for T2DM. Therefore, insulin was not used in enrolled patients. Subjects with MetS were treated with life-style modification and diet, therefore metformin was given in 12 patients.
Methods for visualization of coronary arteries
Contrast-enhanced multispiral computed tomography
angiography has been performed for all the patients with
dysmetabolic disorder prior to their inclusion in the study on
Optima СТ660 scanner (GE Healthcare, USA) using non-ionic
contrast Omnipaque (Amersham Health, Ireland) [19]. Obtained
results were interpreted by cardiologist and one of subinvestigator
independently each other before study entry.
Cardiovascular risk calculation
A 10-year cardiovascular risk for study patients was
calculated using the Framingham General Cardiovascular
Risk Score (2008) by on-line calculator and interpreted using
contemporary approaches [20].
Calculation of glomerular filtration rate
Glomerular Filtration Rate (GFR) was calculated with CKDEPI
formula [21].
Measurement of circulating biomarkers
To determine circulating biomarkers, blood samples were
collected at baseline in the morning (at 7-8 a.m.) into cooled
silicone test tubes wherein 2 mL of 5% Trilon B solution were
added. Then they were centrifuged upon permanent cooling at
6,000 rpm for 3 minutes. Plasma was collected and refrigerated
immediately to be stored at a temperature -70оС. Serum
adiponectin, RANKL and osteoprotegerin (OPG) were measured
by high-sensitive enzyme-linked immunosorbent assays using
commercial kits (R&D Systems GmbH, Wiesbaden-Nordenstadt,
Germany) according to the manufacturers' recommendations.
The inter-assay coefficients of variation were as follows:
adiponectin: 5%, RANKL: 7.0%; OPG: 8.2%.
High-sensitive C-reactive protein (hs-CRP) was measured by commercially available standard kit (R&D Systems GmbH, Wiesbaden-Nordenstadt, Germany). The intra-assay and interassay coefficients of variation were < 5%. Serum uric acid level (SUA) was determined by enzymatic methods using a Beckman Synchron LX20 chemistry analyzer. The analytical average range for SUA was 0.5–12 mg/dL.
Fasting insulin level was measured by a double-antibody sandwich immunoassay (Elecsys 1010 analyzer, F. Hoffmann- La Roche Diagnostics, Mannheim, Germany). The intra-assay and inter-assay coefficients of variation were < 5%. The lower detection limit of insulin level was 1.39 pmol/L.
High-sensitive C-reactive protein (hs-CRP) was measured by commercially available standard kit (R&D Systems GmbH, Wiesbaden-Nordenstadt, Germany). The intra-assay and interassay coefficients of variation were < 5%. Serum uric acid level (SUA) was determined by enzymatic methods using a Beckman Synchron LX20 chemistry analyzer. The analytical average range for SUA was 0.5–12 mg/dL.
Fasting insulin level was measured by a double-antibody sandwich immunoassay (Elecsys 1010 analyzer, F. Hoffmann- La Roche Diagnostics, Mannheim, Germany). The intra-assay and inter-assay coefficients of variation were < 5%. The lower detection limit of insulin level was 1.39 pmol/L.
Insulin resistance was assessed by the homeostasis
model assessment for insulin resistance (HOMA-IR)
using the following formula
HOMA-IR (mmol/L × μU/mL)=fasting glucose (mmol/L) ×
fasting insulin (μU/mL) / 22.5
Concentrations of total cholesterol (TC) (Catalog Number 3460-06, MaxDiscovery™ Cholesterol Enzymatic Assay Kit, Bioo Scientific Corporation, USA), cholesterol of high-density lipoproteins (LDL-C) (Catalog Number DZ128A-KB1, Diazyme Europe GmbH, Germany), and cholesterol of high-density lipoproteins (HDL-C) (Catalog Number DZ129A-KY1, Diazyme Europe GmbH, Germany) were measured by enzymatic colorimetric method according standardized methodology on Beckman Synchron LX20 chemistry analyzer [22].
Direct Enzymatic HbA1c Assay was used for glycated hemoglobin A1c (HbA1c) measurements (Catalog Number DZ168A-K, Diazyme Europe GmbH, Germany) on Beckman Synchron LX20 chemistry analyzer.
Concentrations of total cholesterol (TC) (Catalog Number 3460-06, MaxDiscovery™ Cholesterol Enzymatic Assay Kit, Bioo Scientific Corporation, USA), cholesterol of high-density lipoproteins (LDL-C) (Catalog Number DZ128A-KB1, Diazyme Europe GmbH, Germany), and cholesterol of high-density lipoproteins (HDL-C) (Catalog Number DZ129A-KY1, Diazyme Europe GmbH, Germany) were measured by enzymatic colorimetric method according standardized methodology on Beckman Synchron LX20 chemistry analyzer [22].
Direct Enzymatic HbA1c Assay was used for glycated hemoglobin A1c (HbA1c) measurements (Catalog Number DZ168A-K, Diazyme Europe GmbH, Germany) on Beckman Synchron LX20 chemistry analyzer.
Determination of circulating EPCs
The Flow Cytometric Technique (FCT) was used for
predictably distinguishing circulating cell subsets, which depend
on expression of CD45, CD34, CD14, Tie-2, and VEGFR2, using
High-Definition Fluorescence Activated Cell Sorter (HD-FACS)
methodology [23]. Accordingly, the cells in question were labeled
on the basis of their forward scatter characteristic (FSC) and
side scatter characteristic (SSC) profiles. The cells were directly
stained and analyzed for phenotypic expression of surface
proteins using anti-human monoclonal antibodies, including
anti-CD45 FITS (BD Biosciences, San Jose, CA, USA), anti-CD34
FITS (BD Biosciences), anti-VEGFR-2 known as anti-CD309 (BD
Biosciences), anti-Tie2 (BD Biosciences) and anti-CD14 (BD
Biosciences). The fluorescence minus one technique was used to
provide negative controls and establish positive stain boundaries.
After lysis of erythrocytes with UTILIZE wash solution, the
samples were centrifuged at 200× g for 15 min; then they were
washed twice with PBS and fixed immediately. Double- or triplepositive
events were determined using Boolean principles
("and", "not", "or", etc.). Circulating EPCs were defined as CD34/
VEGFR2 positive cells with lack of CD45 expression. From each
tube 500,000 events were analyzed. For CD14+ populations,
co-expression with Tie-2− and/or VEGFR-2− was determined
using quadrant analysis. Mononuclear cells were cultured for
functional analysis (CFUs) after FCT. Standardized cell counts
were presented as a percentage of the total of the white blood
cells count, identified as the total number of all CD45+ cells.
Statistical Analysis
Statistical analysis of the results obtained was performed in
SPSS system for Windows, Version 22 (SPSS Inc, Chicago, IL, USA).
The data were presented as mean (М) and standard deviation
(±SD) or 95% confidence interval (CI); as well as median (Ме)
and 25%-75% Interquartile Range (IQR). To compare the main
parameters of patient cohorts, two-tailed Student t-test or Mann-
Whitney U-test were used. To compare categorical variables between groups, Chi2 test (χ2) and Fisher F exact test were
used. Predictors of depleted EPCs in patients were examined in
multivariable regression analysis. A two-tailed probability value
of < 0.05 was considered as significant. between groups, Chi2 test (χ2) and Fisher F exact test were
used. Predictors of depleted EPCs in patients were examined in
multivariable regression analysis. A two-tailed probability value
of < 0.05 was considered as significant.
Results
General characteristic of patients participating in the
study was reported in Table 1. The mean age for patients with
dysmetabolic disorder and healthy volunteers was 48.34 years
and 46.12 years (P=0.68). Therefore 63.3% of dysmetabolic
disorder patients and 65.7% of healthy volunteers were
men (P=0.86). As expected, there was a significant difference
between healthy volunteers and entire cohort of enrolled
patients in BMI, waist circumference, cardiovascular risk factors
(hypertension, dyslipidemia, adherence to smoking), HOMA-IR,
lipid abnormalities, and Framingham risk score. HbA1c, fasting
blood glucose, insulin, hs-CRP, TG, sRANKL, osteoprotegerin,
and adiponectin were higher in patient cohort when compared
with healthy volunteers. Therefore, no significant increase in
circulating CD34+ subset cells (CD45+CD34+ and CD45−CD34+)
in dysmetabolic patients when compared with healthy volunteers
was found. The authors suppose that there are decreased CD14+
subsets of EPCs in dysmetabolic subjects. Indeed, there is a
significant difference between the medians of absolute numbers
and frequencies of CD14+CD309+ and CD14+CD309+Tie2+ in
healthy volunteers and patients with dysmetabolic disorders
respectively.
According to Table 2, patients with MetS have demonstrated lower incidence of dyslipidemia, lower concentrations of HbA1c, fasting blood glucose, insulin, and LDL-C when compared with T2DM subjects. Higher HDL-C and HOMA-IR were found in T2DM patients than in MetS subjects. Interestingly, similarities of circulating levels of EPCs different origin were determined in both cohorts apart from absolute numerous of CD14+CD309+ and CD14+CD309+Tie2+ EPCs. CD14+CD309+ and CD14+CD309+Tie2+ subsets of circulating EPCs were determined in higher concentration among MetS subjects in comparison with T2DM patients (P=0.18 and P=0.012 respectively).
The univariate linear correlations between both absolute numerous of EPCs with immune phenotypes labelled CD14+CD309+ and CD14+CD309+Tie2+, cardiovascular risk factors, hemodynamic performances, and other biomarkers were evaluated. The data have shown that absolute numerous of CD14+CD309+were inversely related with T2DM (r=-0.542, P=0.003), BMI (r=-0.54, P=0.001), OPG (r=-0.518, P=0.001), number of multiple cardiovascular risk factors (MCRFs) (r=- 0.486, P=0.001), hs-CRP (r=-0.478, P=0.001), sRANKL (r=-0.477, P=0.001), adiponectin (r=-0.402, P=0.001), eGFR (r=-0.398, P=0.001), Framingham risk score (r=-0.394, P=0.001), TG (r=- 0.392, P=0.001), creatinine (r=-0.387, P=0.001), SUA (r=-0.315, P < 0.001), gender (r=-0.318, P < 0.001 for male), dyslipidemia (r=-0.313, P=0.001), age (r=-0.275, P=0.001), smoking (r=- 0.212, P=0.001), and positively related with HOMA-IR (r=0.465, P=0.001).
Therefore, absolute numerous of CD14+CD309+Tie2+ EPCs
According to Table 2, patients with MetS have demonstrated lower incidence of dyslipidemia, lower concentrations of HbA1c, fasting blood glucose, insulin, and LDL-C when compared with T2DM subjects. Higher HDL-C and HOMA-IR were found in T2DM patients than in MetS subjects. Interestingly, similarities of circulating levels of EPCs different origin were determined in both cohorts apart from absolute numerous of CD14+CD309+ and CD14+CD309+Tie2+ EPCs. CD14+CD309+ and CD14+CD309+Tie2+ subsets of circulating EPCs were determined in higher concentration among MetS subjects in comparison with T2DM patients (P=0.18 and P=0.012 respectively).
The univariate linear correlations between both absolute numerous of EPCs with immune phenotypes labelled CD14+CD309+ and CD14+CD309+Tie2+, cardiovascular risk factors, hemodynamic performances, and other biomarkers were evaluated. The data have shown that absolute numerous of CD14+CD309+were inversely related with T2DM (r=-0.542, P=0.003), BMI (r=-0.54, P=0.001), OPG (r=-0.518, P=0.001), number of multiple cardiovascular risk factors (MCRFs) (r=- 0.486, P=0.001), hs-CRP (r=-0.478, P=0.001), sRANKL (r=-0.477, P=0.001), adiponectin (r=-0.402, P=0.001), eGFR (r=-0.398, P=0.001), Framingham risk score (r=-0.394, P=0.001), TG (r=- 0.392, P=0.001), creatinine (r=-0.387, P=0.001), SUA (r=-0.315, P < 0.001), gender (r=-0.318, P < 0.001 for male), dyslipidemia (r=-0.313, P=0.001), age (r=-0.275, P=0.001), smoking (r=- 0.212, P=0.001), and positively related with HOMA-IR (r=0.465, P=0.001).
Therefore, absolute numerous of CD14+CD309+Tie2+ EPCs
Table 1: General characteristic of patients participating in the study.
|
Healthy volunteers (n=35) |
Entire cohort of enrolled patients (n=101) |
P value |
Age, years |
46.12±4.22 |
48.34±7.80 |
0.68 |
males, n (%) |
23 (65.7%) |
64 (63.3%) |
0.86 |
BMI, kg/m2 |
21.5 (25-75% IQR=16.1–23.5) |
28.7 (25-75% IQR 16.5–32.4) |
0.001 |
Waist circumference, sm |
78 (25-75% IQR=63–89) |
91 (25-75% IQR=71–103) |
0.001 |
Hypertension, n (%) |
- |
68 (67.3%) |
0.001 |
Dyslipidemia, n (%) |
- |
59 (58.4%) |
0.001 |
T2DM, n (%) |
- |
54 (53.5%) |
0.001 |
MetS, n (%) |
- |
47 (46.5%) |
0.001 |
Adherence to smoking, n (%) |
6 (17.1%) |
31 (30.7%) |
0.001 |
Framingham risk score |
2.55± 1.05 |
8.12 ± 2.88 |
0.001 |
Systolic BP, mm Hg |
122±5 |
136±6 |
0.001 |
Diastolic BP, mm Hg |
72±4 |
86±6 |
0.001 |
Heart rate, beats per 1 min. |
66±6 |
72±7 |
0.01 |
GFR, mL/min/1.73 m2 |
102.1 (95% CI=91.4–113.2) |
93.1 (95% CI=79.5–109.7) |
0.12 |
HbA1c, % |
4.75 (95% CI =4.36-5.12) |
7.0 (95% CI =4.3-9.2) |
0.001 |
fasting blood glucose, mmol/L |
4.52 (95% CI =4.43-4.76) |
5.40 (95% CI =3.4-9.1) |
0.01 |
Insulin, µU/mL |
4.98 (25-75% IQR =1.5–14.1) |
15.15 (25-75% IQR =13.69-16.62) |
0.001 |
HOMA-IR, mmol/L × µU/mL |
1.01 (25-75% IQR =0.91-1.07) |
3.83 (25-75% IQR =3.47-4.20) |
0.001 |
creatinine, μmol/L |
62.1 (95% CI =55.7–82.4) |
70.5 (95% CI =59.6–88.3) |
0.24 |
Total cholesterol, mmol/L |
4.76 (95% CI =4.21-5.05) |
5.3 (95% CI =4.6-6.0) |
0.001 |
LDL-C, mmol/L |
3.10 (95% CI =2.78–3.21) |
3.60 (95% CI =3.20–4.18) |
0.001 |
HDL-C, mmol/L |
1.13 (95% CI = 1.05–1.17) |
0.94 (95% CI = 0.92–1.06) |
0.001 |
TG, mmol/L |
1,18 (95% CI = 1.07–1.30) |
1,68 (95% CI = 1.44–1.98) |
0.001 |
SUA, mmol/L |
17.1 (95% CI = 9.1–25.7) |
23.8 (95% CI = 15.8–31.3) |
0.05 |
hs-CRP, mg / L |
4.11 (25-75% IQR=0.97 – 5.03) |
7.96 (25-75% IQR=4.72 – 9.34) |
0.001 |
16.10 (25-75% IQR=2.1-30.1) |
0.002 |
||
Osteoprotegerin, pg / mL |
88.3 (25-75% IQR = 37.5-136.6) |
0.001 |
|
Adiponectin, mg / L |
6.17 (25-75% IQR = 3.44-10.15) |
13.65 (25-75% IQR = 10.12-24.93) |
0.001 |
CD45+CD34+, % |
1.90 (25-75% IQR = 1.49–2.10) |
2.10 (25-75% IQR = 1.70–2.35) |
0.66 |
CD45+CD34+, cells × 103/μL |
0.114 (25-75% IQR = 0.095–0.120) |
0.112 (25-75% IQR = 0.090–0.121) |
0.76 |
CD45−CD34+ × 10−4, % |
1.00 (25-75% IQR = 0.69–1.35) |
1.02 (25-75% IQR = 0.86–1.28) |
0.84 |
CD45−CD34+, cells × 10−1/μL |
0.06 (25-75% IQR = 0.05–0.07) |
0.057 (25-75% IQR = 0.053–0.067) |
0.86 |
CD14+CD309+ × 10−4, % |
71.00 (25-75% IQR = 61.50–96.00) |
57.00 (25-75% IQR = 43.20–81.50) |
0.02 |
CD14+CD309+, cells × 10−1/μL |
4.26 (25-75% IQR = 3.70–5.74) |
2.96 (25-75% IQR = 2.25–4.21) |
0.01 |
CD14+CD309+Tie2+ × 10−4, % |
7.70 (25-75% IQR = 4.20–12.20) |
5.50 (25-75% IQR = 3.05–8.15) |
0.04 |
CD14+CD309+Tie2+, cells × 10−1/μL |
0.465 (25-75% IQR = 0.253–0.710) |
0.270 (25-75% IQR = 0.241–0.411) |
0.01 |
Note: Data are presented as mean and ±SE or 95% CI; median and 25-75% IQR. Categorical variables are expressed as numerous (n) and percentages
(%). P-value is a comparison of mean or median variables between both cohorts (ANOVA test).
Abbreviations: CI – Confidence Interval; IQR – Inter Quartile Range; BMI - Body Mass Index, T2DM – Type 2 Diabetes Mellitus, TG – Triglycerides, BP – Blood Pressure, BMI - Body Mass Index, GFR - Glomerular Filtration Rate, EMPs – Endothelial-Derived Microparticles; HDL-C - High-Density Lipoprotein Cholesterol, LDL-C - Low-Density Lipoprotein Cholesterol, hs-CRP – High Sensitive C Reactive Protein, sRANKL – Serum Receptor Activator of NF-κB ligand.
Abbreviations: CI – Confidence Interval; IQR – Inter Quartile Range; BMI - Body Mass Index, T2DM – Type 2 Diabetes Mellitus, TG – Triglycerides, BP – Blood Pressure, BMI - Body Mass Index, GFR - Glomerular Filtration Rate, EMPs – Endothelial-Derived Microparticles; HDL-C - High-Density Lipoprotein Cholesterol, LDL-C - Low-Density Lipoprotein Cholesterol, hs-CRP – High Sensitive C Reactive Protein, sRANKL – Serum Receptor Activator of NF-κB ligand.
Table 2: Demographic, risk factors, blood pressure, circulating biomarkers, and endothelial-derived microparticles in MetS and T2DM patients.
|
MetS patients (n=47) |
T2DM patients (n=54) |
P value |
Age, years |
48.30 ± 3.94 |
48.50±6.60 |
0.88 |
males, n (%) |
30 (63.8%) |
34 (63.0%) |
0.96 |
BMI, kg/m2 |
28.2 (25-75% IQR=16.7–31.0) |
28.5 (25-75% IQR=16.8–32.1) |
0.92 |
Waist circumference, sm |
92 (25-75% IQR=69–105) |
89 (25-75% IQR=72–100) |
0.12 |
Hypertension, n (%) |
32 (68.0%) |
36 (66.7%) |
0.78 |
Dyslipidemia, n (%) |
26 (55.3%) |
33 (61.1%) |
0. 026 |
Adherence to smoking, n (%) |
16 (34.0%) |
15 (27.7%) |
0.44 |
Framingham risk score |
8.09 ± 2.12 |
8.18 ± 2.32 |
0.78 |
Systolic BP, mm Hg |
137 ± 4 |
136±5 |
0.94 |
Diastolic BP, mm Hg |
87 ± 5 |
86±4 |
0.96 |
Heart rate, beats per 1 min. |
71 ± 6 |
72±5 |
0.96 |
GFR, mL/min/1.73 m2 |
92.5 (95% CI = 83.1–107.4) |
93.8 (95% CI = 80.4–106.8) |
0.92 |
HbA1c, % |
6.82 (95% CI = 4.61-5.37) |
7.3 (95% CI = 4.3-9.1) |
0.036 |
fasting blood glucose, mmol/L |
5.46 (95% CI = 4.23-4.76) |
5.54 (95% CI = 4.49-9.0) |
0.042 |
Insulin, µU/mL |
14.2 (25-75% IQR = 12.5–15.7) |
15.6 (25-75% IQR = 12.9-16.8) |
0.048 |
HOMA-IR, mmol/L × µU/mL |
3.45 (25-75% IQR = 3.22-3.78) |
3.86 (25-75% IQR = 3.41-4.10) |
0.012 |
creatinine, μmol/L |
72.3 (95% CI = 56.1–86.9) |
71.2 (95% CI = 59.9–87.2) |
0.94 |
Total cholesterol, mmol/L |
5.3 (95% CI = 4.5-5.9) |
5.4 (95% CI = 4.8-5.8) |
0.96 |
LDL-C, mmol/L |
3.48 (95% CI = 3.30–4.07) |
3.80 (95% CI =3.20–4.20) |
0.012 |
HDL-C, mmol/L |
1.01 (95% CI = 0.90–1.13) |
0.94 (95% CI = 0.88–1.04) |
0.014 |
TG, mmol/L |
1.77 (95% CI = 1.62–1.95) |
1.45 (95% CI =1.42–1.51) |
0.044 |
SUA, mmol/L |
23.1 (95% CI = 16.2–29.7) |
23.9 (95% CI = 15.9–30.1) |
0.86 |
hs-CRP, mg / L |
7.87 (25-75% IQR = 4.92 – 9.43) |
8.10 (25-75% IQR = 4.80 – 9.54) |
0.24 |
sRANKL, pg / mL |
24.10 (25-75% IQR = 14.7-36.9) |
26.20 (25-75% IQR = 15.3-40.7) |
0.26 |
Osteoprotegerin, pg / mL |
718.5 (25-75% IQR = 572.1-846.2) |
732.1 (25-75% IQR= 587.5-866.3) |
0.38 |
Adiponectin, mg / L |
13.61 (25-75% IQR = 9.74-22.35) |
14.12 (25-75% IQR= 10.12-23.10) |
0.88 |
CD45+CD34+, % |
2.11 (25-75% IQR = 1.72–2.28) |
2.06 (25-75% IQR = 1.73–2.25) |
0.86 |
CD45+CD34+, cells × 103/μL |
0.114 (25-75% IQR = 0.094–0.122) |
0.109 (25-75% IQR = 0.091–0.117) |
0.56 |
CD45−CD34+ × 10−4, % |
1.02 (25-75% IQR = 0.89–1.25) |
1.01 (25-75% IQR = 0.88–1.22) |
0.88 |
CD45−CD34+, cells × 10−1/μL |
0.060 (25-75% IQR = 0.055–0.066) |
0.056 (25-75% IQR = 0.052–0.064) |
0.82 |
CD14+CD309+ × 10−4, % |
59.12 (25-75% IQR = 48.10–82.30) |
52.37 (25-75% IQR = 44.60–76.20) |
0.054 |
CD14+CD309+, cells × 10−1/μL |
3.35 (25-75% IQR = 2.57–4.18) |
2.71 (25-75% IQR = 2.31–3.52) |
0.018 |
CD14+CD309+Tie2+ × 10−4, % |
6.03 (25-75% IQR = 3.42–8.05) |
4.95 (25-75% IQR = 3.14–7.44) |
0.052 |
CD14+CD309+Tie2+, cells × 10−1/μL |
0.294 (25-75% IQR = 0.245–0.396) |
0.253 (25-75% IQR = 0.232–0.311) |
0.012 |
Note: Data are presented as mean and ±SE or 95% CI; median and 25-75% IQR. Categorical variables are expressed as numerous (n) and percentages
(%). P-value is a comparison of mean or median variables between both cohorts (ANOVA test).
were negatively related T2DM (r=-0.55, P=0.001), OPG (r=-0.522,
P=0.001), number of MCRFs (r=-0.492, P=0.001), hs-CRP (r=-
0.486, P=0.001), BMI (r=-0.483, P=0.001), adiponectin (r=-0.472,
P=0.001), sRANKL (r=-0.466, P=0.001), TG (r=-0.412, P=0.001),
eGFR (r=-0.392, P=0.001), Framingham risk score (r=-0.39,
P=0.001), creatinine (r=-0.365, P=0.001), dyslipidemia (r=-0.322,
P=0.001), SUA (r=-0.309, P < 0.001), gender (r=-0.303, P < 0.001
for male), age (r=-0.262, P=0.001), smoking (r=-0.211, P=0.001),
and positively related with HOMA-IR (r=0.482, P=0.001). No
significant association of absolute numerous of CD14+CD309+
and CD14+CD309+Tie2+ EPCs with fasting plasma glucose,
HbA1c, number of MetS components, means of systolic and
diastolic BP, waist circumference was found. We did not find
possible age- and gender-related correlation between metabolic
status and the presence of EPCs.
Using multivariate age and gender adjusted logistic regression analysis, independent impact of T2DM (odds ratio [OR]=1.08, P=0.003), hs-CRP per 4.50 mg/L (OR=1.12, P=0.001), OPG per 125.5 pg / mL (OR=1.14, P=0.001), number of MCRFs >3 (OR=1.15, P=0.001), HOMA-IR per 0.65 mmol/L × μU/mL (OR=1.04, P=0.001) on decreased of CD14+CD309+ EPCs was determined (Table 3). Therefore, CD14+CD309+Tie2+ EPCs were prone negatively impact by T2DM (OR=1.10, P=0.001), hs- CRP per 4.50 mg/L (OR=1.12, P=0.001), number of MCRFs >3 (OR=1.17, P=0.001), OPG per 125.5 pg / mL (OR=1.11, P=0.001), and HOMA-IR per 0.65 mmol/L × μU/mL (OR=1.06, P=0.001).
Using multivariate age and gender adjusted logistic regression analysis, independent impact of T2DM (odds ratio [OR]=1.08, P=0.003), hs-CRP per 4.50 mg/L (OR=1.12, P=0.001), OPG per 125.5 pg / mL (OR=1.14, P=0.001), number of MCRFs >3 (OR=1.15, P=0.001), HOMA-IR per 0.65 mmol/L × μU/mL (OR=1.04, P=0.001) on decreased of CD14+CD309+ EPCs was determined (Table 3). Therefore, CD14+CD309+Tie2+ EPCs were prone negatively impact by T2DM (OR=1.10, P=0.001), hs- CRP per 4.50 mg/L (OR=1.12, P=0.001), number of MCRFs >3 (OR=1.17, P=0.001), OPG per 125.5 pg / mL (OR=1.11, P=0.001), and HOMA-IR per 0.65 mmol/L × μU/mL (OR=1.06, P=0.001).
Discussion
The results of our study demonstrate that depletion of
circulating EPC number with angiopoetic capacity labeled
CD14+CD309+ and CD14+CD309+Tie2+ was defined
among dysmetabolic patients. Moreover, circulating level of
CD14+CD309+ and CD14+CD309+Tie2+ EPCs was significant
lower in T2DM persons when compared with MetS patients.
Therefore, inflammatory cytokines i.e. OPG and hs-CRP appeared
significant association with decreased CD14+CD309+ and
CD14+CD309+Tie2+ EPC subsets among dysmetabolic subjects.
We suggest that low-intensity inflammation that is common in
dysmetabolic person population may impact negatively on CVD
development through worse of endothelial reparation process
that is realized by recruitment of EPCs with angiopoetic activity.
Recent studies have shown a closely association of serum levels of OPG and hs-CRP with T2DM [24,25]. Indeed, OPG is considered a candidate for biomarker, which modulating vascular remodeling effects and CVD development and progression [26]. In fact, OPG is over expressed on endothelial and smooth muscle cells in vasculature in dysmetabolic states [27]. Moreover, epidemiological studies have examined the relations of OPG to CVD events and mortality [28-31]. Strong associations of hs-CRP with components of MetS, T2DM, other inflammatory cytokine levels, as well as CVD events were found in several investigations [32-34]. Higher level of OPG and hs-CRP in MetS and T2DM reflected chronic low grade inflammation possibly mediates the development of diabetic complications and clinical CVD outcomes through ED [35]. ED may be realized as result in altered endothelial cell activity and endothelial integrity due to dysregulation of vascular wall repair processes [36] mediatied angiogenic EPCs [37]. Indeed, there are evidences that decreased number and / or functionally impaired EPCs are one of the major risk factors for the development of micro vascular complications in dysmetabolic patients [38].
Results of the presented study have shown that increased circulating level of inflammatory cytokines in T2DM patients may lead to more much deficiency of angiogenic EPCs when compared with MetS, while frequencies and absolute numbers of other immune phenotype EPCs may be comparable. Probably, OPG and hs-CRP seems to be determinant for depleted circulating EPCs levels and relates a progress from MetS and prediabetes to T2DM. Interestingly, that glycemic control, BMI, insulin resistance were not defined as independent predictors for decreased CD14+CD309+ and CD14+CD309+Tie2+ EPCs in dysmetabolic patients, but T2DM, numbers of CVD risk factors, OPG and hs- CRP were determined. These data sort with opinion provided by investigators regarding EPCs as a potential novel marker of vascular integrity, homeostasis process, metabolic abnormalities, and cardiovascular risk [39]. As it has been postulated, the reduction and dysfunction of EPCs associates with inflammatorydependent impairment of vascular function and relates to progress from MetS to T2DM. Overall, these results suggest that vascular reparative dysfunction associated with OPG and hs- CRP over production among T2DM subjects without preexisting atherosclerotic lesions of coronary arteries is superior to MetS patients.
Recent studies have shown a closely association of serum levels of OPG and hs-CRP with T2DM [24,25]. Indeed, OPG is considered a candidate for biomarker, which modulating vascular remodeling effects and CVD development and progression [26]. In fact, OPG is over expressed on endothelial and smooth muscle cells in vasculature in dysmetabolic states [27]. Moreover, epidemiological studies have examined the relations of OPG to CVD events and mortality [28-31]. Strong associations of hs-CRP with components of MetS, T2DM, other inflammatory cytokine levels, as well as CVD events were found in several investigations [32-34]. Higher level of OPG and hs-CRP in MetS and T2DM reflected chronic low grade inflammation possibly mediates the development of diabetic complications and clinical CVD outcomes through ED [35]. ED may be realized as result in altered endothelial cell activity and endothelial integrity due to dysregulation of vascular wall repair processes [36] mediatied angiogenic EPCs [37]. Indeed, there are evidences that decreased number and / or functionally impaired EPCs are one of the major risk factors for the development of micro vascular complications in dysmetabolic patients [38].
Results of the presented study have shown that increased circulating level of inflammatory cytokines in T2DM patients may lead to more much deficiency of angiogenic EPCs when compared with MetS, while frequencies and absolute numbers of other immune phenotype EPCs may be comparable. Probably, OPG and hs-CRP seems to be determinant for depleted circulating EPCs levels and relates a progress from MetS and prediabetes to T2DM. Interestingly, that glycemic control, BMI, insulin resistance were not defined as independent predictors for decreased CD14+CD309+ and CD14+CD309+Tie2+ EPCs in dysmetabolic patients, but T2DM, numbers of CVD risk factors, OPG and hs- CRP were determined. These data sort with opinion provided by investigators regarding EPCs as a potential novel marker of vascular integrity, homeostasis process, metabolic abnormalities, and cardiovascular risk [39]. As it has been postulated, the reduction and dysfunction of EPCs associates with inflammatorydependent impairment of vascular function and relates to progress from MetS to T2DM. Overall, these results suggest that vascular reparative dysfunction associated with OPG and hs- CRP over production among T2DM subjects without preexisting atherosclerotic lesions of coronary arteries is superior to MetS patients.
Future Perspectives
Decreased numbers of circulating EPCs with angiopoetic
Table 3: The Independent Predictors of Depletion of Circulating Epcs. The Results of Age and Gender-Adjusted Multivariate Logistic Regression
Analysis.
Factors |
CD14+CD309+ EPCs |
CD14+CD309+Tie2+ EPCs |
||
OR (95% CI) |
P Value |
OR (95% CI) |
P Value |
|
Hypertension (present vs abcent ) |
0.99 (0.87–1.04) |
0.001 |
1.05 (0.97–1.11) |
0.001 |
Dyslipidemia (present vs abcent ) |
1.04 (0.92–1.22) |
0.001 |
1.02 (0.96–1.15) |
0.001 |
T2DM (present vs abcent ) |
1.08 (1.04–1.12) |
0.003 |
1.10 (1.02–1.20) |
0.001 |
BMI per 5.0 kg/ m2 |
1.05 (0.97–1.18) |
0.001 |
1.05 (0.95–1.07) |
0.002 |
hs-CRP per 4.50 mg/ L |
1.12 (1.03–1.20) |
0.001 |
1.12 (1.06–1.24) |
0.001 |
Number of MCRFs >3 |
1.15 (1.07–1.30) |
0.001 |
1.17 (1.11–1.22) |
0.001 |
TC per 0.65 mmol/ L |
1.03 (0.88–1.10) |
0.002 |
1.01 (0.98–1.10) |
0.001 |
SUA per 4.5 mmol/ L |
1.02 (0.94–1.14) |
0.001 |
1.04 (0.96–1.10) |
0.002 |
OPG per 125.5 pg / mL |
1.14 (1.07–1.26) |
0.002 |
1.11 (1.08–1.21) |
0.001 |
Adiponectin, per 9.5 mg/ L |
1.02 (0.99–1.04) |
0.002 |
1.01 (0.98–1.05) |
0.001 |
HOMA-IR per 0.65 mmol/ L × µU/ mL |
1.04 (1.00–1.06) |
0.001 |
1.06 (1.02–1.07) |
0.001 |
sRANKL per 12.5 pg / mL |
1.03 (0.98–1.07) |
0.001 |
1.02 (0.99–1.04) |
0.002 |
Abbreviations: CI – Confidence Interval; IQR – Inter Quartile Range; T2DM – Type 2 Diabetes Mellitus, BMI - Body Mass Index, EPCs - Endothelial
Progenitor Cells; TG – Triglycerides, hs-CRP – High Sensitive C Reactive Protein, sRANKL – serum receptor activator of NF-κB ligand, MCRFs-Multiple
Cardiovascular Risk Factors.
immune phenotypes labeled CD14+CD309+ and
CD14+CD309+Tie2+ among dysmetabolic patients without
preexisting atherosclerotic lesions of coronary arteries may
probably discuss as surrogate marker of endothelial integrity
and endothelial dysfunction at early stages. Future clinical
studies might focus on significance of interrelationship between
disproportion in angiopoetic immune phenotypes of EPCs and
severity of endothelial dysfunction among MetS and T2DM
patients. We suggest that decreased numbers of circulating
angiopoetic EPCs may have predicted value for cardiovascular
events and outcomes in this patient population. Therefore, it
has been postulated that serial measurements of circulating
angiopoetic EPCs in MetS and T2DM patients may be useful
for biomarker-guided therapy targeted on prevention of
cardiovascular events in dysmetabolic subjects.
Study Limitations
This study has some limitations. It is necessary to note that
a large pool of EPCs might be destroyed after blood sampling.
Therefore, preparation of isolates of EPCs in samples is the most
sophisticated step for further examination. Venous citrated blood
drawn from the fistula-free arm was performed obligatorily. We
believe that these risks are systemic, and to minimize them, we
refused to freeze the blood samples before measurement of EPCs.
Additionally, retrospective, relative small sample size may limit
the significance of the present study. The authors believe that
a greater cohort of patients with more incidences detected is
desirable to improve the credibility of the study.
In conclusion, we suggest that inflammatory biomarkers (hs-CRP, OPG) may consider statistically significant predictors for decreased both angiopoetic phenotypes of circulating EPCs labeled CD14+CD309+ and CD14+CD309+Tie2+ among dysmetabolic patients without preexisting known atherosclerotic lesions of coronary arteries.
In conclusion, we suggest that inflammatory biomarkers (hs-CRP, OPG) may consider statistically significant predictors for decreased both angiopoetic phenotypes of circulating EPCs labeled CD14+CD309+ and CD14+CD309+Tie2+ among dysmetabolic patients without preexisting known atherosclerotic lesions of coronary arteries.
Acknowledgments
We thank all patients for their participation in the
investigation, staff of the Regional Zaporozhye Hospital (Ukraine),
and the doctors, nurses, and administrative staff in Regional
Center of Cardiovascular Diseases (Zaporozhye, Ukraine) and
City Hospital # 6 (Zaporozhye, Ukraine), general practices, and
site-managed organizations that assisted with the study.
Ethical principles: All the patients have given their voluntary written informed consent for participation in the study. The study was approved by the local ethics committee of State Medical University, Zaporozhye, Ukraine. The study was performed in conformity with the Declaration of Helsinki.
Funding information: This research received no specific grant from any funding agency in the public, commercial, or notfor- profit sectors.
Ethical principles: All the patients have given their voluntary written informed consent for participation in the study. The study was approved by the local ethics committee of State Medical University, Zaporozhye, Ukraine. The study was performed in conformity with the Declaration of Helsinki.
Funding information: This research received no specific grant from any funding agency in the public, commercial, or notfor- profit sectors.
Authors' Contributions
Alexander E Berezin initiated the hypothesis and designed
the study protocol, contributed to collect, analyze and interpret
the data, including results of contrast-enhanced multispiral computed tomography angiography, performed statistical
analysis, wrote the manuscript and approved final version
of the paper. Alexander A. Kremzer contributed to enroll the
patients; interpreted data of contrast-enhanced multispiral
computed tomography angiography, collected and analyzed
the data reviewed the source documents. Tatyana A. Berezina
contributed to enroll the patients in the study and collect the data.
Martovitskaya YuV contributed in performing of cytofluometry
and interpretation of the obtained results.
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