The authors have declared that no competing interests exist.
Conceived and designed the experiments: JJL. Analyzed the data: LFH XLL JJL. Wrote the paper: LFH. Collected data: LFH XLL SHL YLG JL CGZ PQ RXX NQW LXJ. Review and editing of manuscript: JJL.
Both coronary artery disease (CAD) and diabetes mellitus (DM) are associated with inflammation. However, whether and which leukocytes can predict the presence and extent of CAD in patients with DM has not been investigated. The aim of the present study was to examine the association of leukocyte and its subsets counts with the severity of CAD in patients with DM.
Three hundred and seventy-three diabetic patients who were scheduled for coronary angiography due to typical stable angina pectoris were enrolled in this study. They were classified into the three groups according to tertiles of Gensini score (GS, low group <8, n = 143; intermediate group 8∼28, n = 109; high group >28, n = 121). The relationship between the leukocyte and its subsets counts with the severity of CAD were evaluated. The data indicated that there were significant correlations between leukocyte and neutrophil counts with GS (r = 0.154 and 0.156, respectively, all P<0.003 for Pearson's correlation). Similarly, area under the receivers operating characteristic curve of leukocyte and neutrophil counts were 0.61 and 0.60 respectively (95%CI: 0.55–0.67, all P = 0.001) for predicting high GS. Multivariate logistic regression analysis demonstrated that leukocyte count was an independent predictor for high GS patients with DM (OR = 1.20, 95%CI 1.03–1.39, P = 0.023) after adjusting for conventional risk factors of CAD.
Compared with its subsets, leukocyte count appeared to be an independent predictor for the severity of CAD and the optimal cut-off value for predicting high GS (>28 points) was 5.0×109 cells/L in diabetic patients.
Since low grade of local and systemic inflammation is characteristic of all stages of atherosclerosis, multiple markers of inflammation have been intensively evaluated as potential risk factors for the development of coronary artery disease (CAD) and its complications, such as high-sensitivity C-reactive protein (hs-CRP), interleukin-6, fibrinogen, leukocyte and its subsets counts
Although leukocyte count greater than 6.7∼6.9×109 cells/L may identify individuals at high-risk of CAD, current clinical practice does not consider it a useful predictor of CAD
Furthermore, it is still unknown whether the frequency of leukocytes or of a specific leukocyte subset can be a useful predictor of CAD onset and of its severity. In the present study, we hence prospectively assessed the correlation of leukocyte and its subsets counts with the severity of CAD by Gensini Score (GS) in patients with type 2 diabetic mellitus (DM) who underwent coronary angiography.
The study complied with the Declaration of Helsinki, and was approved by the hospital ethical review board (Fu Wai Hospital & National Center for Cardiovascular Diseases, Beijing, China). Informed written consent was obtained from all patients included in this analysis.
From June 2011 through March 2012, we prospectively enrolled 373 type 2 diabetic patients (men: 70.2%) aged 31 to 79 years (average age 58.7 years) who had a typical stable exertional angina pectoris and was referred for selective coronary angiography to our center. Patients with type 1 diabetes mellitus, ACS, significant hematologic disorders (leukocytes count ≤3.5×109 cells/L or ≥20×109 cells/L), infectious or inflammatory disease, and severe liver and/or renal insufficiency were excluded from the current study. All subjects enrolled in this study underwent detailed clinical, hematologic and angiographic examination for assessment of the cardiac status. Demographic data and history of exposure to risk factors for CAD, such as smoking habits, hypertension, hyperlipidemia, obesity, DM, previous stroke, peripheral vascular disease, family history of CAD and non-cardiovascular diseases were also collected.
Hypertension was diagnosed when repeated blood pressure measurements were ≥140/90 mmHg (at least two times in different environments) or if the patient was taking anti-hypertensive drugs. DM was diagnosed in patients with fasting serum glucose level ≥6.99 mmol/L in multiple determinations or in patients under active treatment with insulin or oral hypoglycemic agents. Hyperlipidemia was considered to be present in patients with fasting total cholesterol (TC) ≥200 mg/dl or TG ≥150 mg/dl. CAD was identified in the presence of obstructive stenosis in at least 50% of the vessel lumen diameter in any of the main coronary arteries according to the diagnosis of two independent senior interventional cardiologists who performed quantitative coronary angiography (QCA) analysis. The severity of CAD was represented by GS system
Venous blood samples were obtained from each patient at baseline upon admission. The levels of leukocytes, neutrophils, lymphocytes, and monocytes were determined using an automated blood cell coulter by a Coulter LH780 Hematology Analyzer (Beckman Coulter Ireland Inc Mervue, Galway, Ireland). The levels of hemoglobin A1c (HbA1c) were measured using the Tosoh G7 Automate HPLC Analyzer (TOSOH Bioscience, Japan). The concentrations of hs-CRP were determined using immunoturbidimetry (Beckmann Assay 360, Bera, Calif., USA). Total cholesterol (TC) and triglyceride (TG) were measured by enzymatic methods and high-density lipoprotein cholesterol (HDL-C) by a direct method (Roche Diagnostics, Basel, Switzerland). Low-density lipoprotein cholesterol (LDL-C) was obtained by Friedewald's formula (if fasting triglycerides <3.39 mmol/l) or by ultracentrifugation. ApoB was measured by an immunoturbidimetric method (Tina-quant, Roche Diagnostics) calibrated against the World Health Organization/International Federation of Clinical Chemistry reference standard SP3–07. All other biomarkers were analyzed by standard hematological and biochemical tests.
Quantitative variables were expressed as mean±standard deviation (SD), and qualitative variables were expressed as numbers and percentages. Quantitative and qualitative variables were analyzed by the Kruskal–Wallis one-way analysis of variance, chi-squared statistic tests, or Student's T tests when appropriate. Correlation between variables was examined using Spearman and Pearson correlation coefficient when appropriate. Receiver operating characteristic (ROC) curves were constructed at the most discriminating cut-off point values aiming to document the predictive power of leukocyte and its subsets counts for high GS. Based on the tertiles of GS, the enrolled patients were classified into the three groups (low group<8-point, n = 143; intermediate group 8–28 points, n = 109; high group >28-point, n = 121). Predictive effect of leukocyte and its differential counts for high GS was carried by binary logistic regression models using forward stepwise selection process. A p value of less than 0.05 was considered statistically significant. Statistical studies were carried out with the SPSS program (version 19.0, SPSS, Chicago, Illinois, USA).
The cohort in the current study consisted of 373 type 2 diabetic patients admitted to the clinic for coronary angiography with an average age of 58.7±9.6 years (median = 59 years; ranged from 31 to 79 years). The mean GS was 22.9±23.2 (median = 14 points; ranged from 0 to 124 points). The baseline demographic, clinical characteristics and laboratory findings of the enrolled subjects by the tertiles of GS (low group <8, n = 143, 38.3%; intermediate group 8∼28, n = 109, 29.2%; high group >28, n = 121, 32.4%) are summarized in
Variables | Low (<8;n = 143) | Intermediate (8∼28;n = 109) | High (>28;n = 121) | P-value for trend |
P-value |
Risk factors | |||||
Age(years) | 56.7±9.9 | 60.0±9.4 | 59.8±8.9 | 0.008 | 0.121 |
Male gender | 94(65.7) | 78(71.6) | 90(74.4) | 0.291 | 0.226 |
BMI(kg/m2) | 25.7±3.3 | 24.9±2.8 | 25.7±3.0 | 0.120 | 0.447 |
Current smoking | 68(47.6) | 59(54.1) | 70(57.9) | 0.235 | 0.121 |
Hypertension | 85(59.4) | 77(70.6) | 82(67.8) | 0.145 | 0.508 |
Hyperlipidemia | 100(69.9) | 88(80.7) | 99(81.8) | 0.039 | 0.177 |
PVD | 3(2.1) | 3(2.8) | 2(1.7) | 0.847 | 0.650 |
Prior Stroke | 6(4.2) | 3(2.8) | 6(5.0) | 0.690 | 0.523 |
Family history of CAD | 7(4.9) | 13(11.9) | 17(14.0) | 0.033 | 0.064 |
Laboratory test | |||||
Leukocyte(109/L) | 6.3±1.5 | 6.2±1.6 | 6.8±1.5 | 0.003 | 0.001 |
Neutrophil (109/L) | 3.6±1.2 | 3.6±1.1 | 4.0±1.2 | 0.006 | 0.001 |
Lymphocyte(109/L) | 1.9±0.6 | 1.9±0.6 | 2.0±0.6 | 0.595 | 0.308 |
Monocyte(109/L) | 0.5±0.2 | 0.5±0.2 | 0.5±0.2 | 0.544 | 0.519 |
N/L ratio | 2.1±1.1 | 2.0±0.8 | 2.2±1.1 | 0.393 | 0.229 |
hs-CRP (mg/L) | 3.1±3.9 | 2.3±3.5 | 4.0±4.5 | 0.006 | 0.006 |
FBG (mmol/L) | 5.6±1.6 | 6.4±2.7 | 6.2±1.9 | 0.009 | 0.253 |
Hemoglobin (g/L) | 139.4±15.2 | 138.3±15.6 | 137.1±15.6 | 0.505 | 0.305 |
HbA1c (%) | 6.4±1.2 | 6.9±1.6 | 7.0±1.3 | 0.000 | 0.004 |
Platelet count(109/L) | 204.5±60.4 | 192.0±45.8 | 206.5±55.4 | 0.098 | 0.224 |
Fibrinogen(g/L) | 3.0±0.8 | 2.9±0.7 | 3.3±0.9 | 0.000 | 0.000 |
D-dimer (mg/dL) | 0.4±0.5 | 0.4±0.5 | 0.4±0.6 | 0.075 | 0.487 |
Bilirubin (umol/L) | 15.3±5.4 | 15.1±5.6 | 15.4±7.4 | 0.969 | 0.836 |
ALP (IU/L) | 64.2±17.9 | 61.6±19.1 | 62.6±17.4 | 0.517 | 0.816 |
AST(IU/L) | 19.4±13.3 | 18.5±9.2 | 17.4±10.0 | 0.342 | 0.185 |
ALT (IU/L) | 31.2±33.3 | 29.7±21.9 | 28.7±25.1 | 0.772 | 0.554 |
Creatinine (umol/L) | 73.8±15.0 | 75.6±16.4 | 78.6±14.9 | 0.041 | 0.019 |
Uric Acid (mmol/L) | 335.6±75.6 | 323.3±80.8 | 354.6±77.4 | 0.009 | 0.005 |
NT-pro-BNP(fmol/mL) | 661.1±486.8 | 667.9±485.2 | 893.5±764.8 | 0.305 | 0.000 |
LVEF (%) | 62.8±7.7 | 63.1±7.4 | 60.2±9.5 | 0.014 | 0.003 |
Lipid profile | |||||
Triglycerides(mmol/L) | 1.7±1.0 | 1.7±0.8 | 1.8±1.1 | 0.434 | 0.230 |
TC (mmol/L) | 4.0±1.0 | 4.0±0.9 | 4.1±1.1 | 0.572 | 0.360 |
LDL-C(mmol/L) | 2.3±0.9 | 2.4±0.8 | 2.5±0.9 | 0.292 | 0.121 |
HDL-C(mmol/L) | 1.1±0.3 | 1.1±0.3 | 1.0±0.2 | 0.011 | 0.009 |
Lipoprotein (a) (mg/L) | 237.7±217.5 | 190.9±211.2 | 289.7±283.6 | 0.008 | 0.007 |
apoA(g/L) | 1.4±0.3 | 1.5±0.3 | 1.4±0.3 | 0.012 | 0.057 |
apoB(g/L) | 1.0±0.3 | 1.0±0.3 | 1.1±0.3 | 0.045 | 0.015 |
Prior treatment | |||||
Aspirin | 136(95.1) | 106(97.2) | 118(97.5) | 0.501 | 0.463 |
Beta-blocker | 103(72.0) | 87(79.8) | 109(90.1) | 0.001 | 0.001 |
ACE-I/ARB | 27(18.9) | 22(20.2) | 44(36.4) | 0.002 | 0.000 |
Statin | 125(87.4) | 109(100) | 116(95.9) | 0.000 | 0.258 |
BMI = Body mass index; PVD = Peripheral vascular disease; CAD = Coronary artery disease; LVFE = Left ventricular ejection fraction; NT-pro-BNP = N-terminal pro-Brain natriuretic peptide; hs-CRP = high sensitivity C-reactive protein; N/L ratio = Neutrophil count to lymphocyte count ratio; HbA1c = Glycosylated hemoglobinA1C; FBG = Fasting blood glucose; ALP = Alkaline phosphatase; AST = Aspartate aminotransferase; ALT = Alanine aminotransferase; TC = Total cholesterol; LDL-C = Low density lipoprotein cholesterol; HDL-C = High density lipoprotein cholesterol; ACE-I = Angiotensin converting enzyme inhibitors; ARB = Angiotensin receptor blocker.
P-value obtained from analysis of variance, Kruskal-Wallis test, or chi-squared test.
P-value for high GS versus non-high (low and intermediate) GS.
To explore the relationship of leukocytes and other biomarkers in type 2 diabetic patients with CAD, a correlation evaluation was performed in the present study. Using Spearman and Pearson correlation analysis, correlation between the frequency of leukocyte and HbA1c, hs-CRP or GS was reported in
As shown in
Variables | Univariate | Multivariate | ||
O.R.(95%CI) | P-value | O.R.(95%CI) | P-value | |
Uric acid | 1.00(1.00–1.01) | 0.006 | 1.00(1.00–1.01) | 0.007 |
NT-pro-BNP | 1.00(1.00–1.00) | 0.002 | 1.00(1.00–1.00) | 0.023 |
Fibrinogen | 1.69(1.28–2.23) | 0.000 | 1.42(1.06–1.91) | 0.020 |
HbA1C | 1.24(1.07–1.44) | 0.005 | 1.23(1.05–1.45) | 0.015 |
Leukocytes | 1.28(1.10–1.47) | 0.001 | 1.20(1.03–1.39) | 0.023 |
NT-pro-BNP = N-terminal pro-Brain natriuretic peptide; HbA1c = Glycosylated hemoglobinA1c.
To our knowledge, this was the first study that focused on the association of leukocytes and its subsets counts with the severity of CAD in patients with DM. The main findings of the present study could be summarized in five aspects. First of all, DM patients with high GS (>28 points) showed the lower levels of LVEF and HDL-C but high levels of NT-pro-BNP, HbA1c, fibrinogen, serum creatinine and the inflammatory and oxidative stress biomarkers (leucocytes, neutrophils, uric acid, and hs-CRP). Secondly, in agreement with published studies on non-diabetic population, as showed in ROC curves and box graphs, the data demonstrate that elevated leukocyte and neutrophil counts might be useful discriminators of CVD severity in diabetic patients with stable CAD but not lymphocyte and monocyte counts
Numerous studies have validated the pivotal role of inflammation in the pathogenesis of atherosclerosis
There were three studies that have demonstrated the correlation of leukocyte count with CAD incidence
Therefore, the present work not only confirmed findings of previous studies but also provided novel insights concerning the role of leukocytes and its subsets in predicting the presence and the extent of CAD in diabetic patients with stable angina pectoris. Additionally, our study determined the cut-off points of leukocytes and its subsets which can be most useful for predicting increased risk of severe CAD. Furthermore, we compared the relative predictive value of differential leukocyte counts and assessed which leukocyte subset was the most valuable marker for predicting the severity of CAD in patients with DM.
Nonetheless, there are several limitations in our study. Firstly, the relatively small sample size from a single center study is a limitation. Secondly, we did not combine leukocyte and its subsets count with other nonspecific inflammatory markers such as hs-CRP, fibrinogen and HbA1c to increase the predictive ability due to the small sample size. Moreover, although leukocyte and the severity of CAD in diabetic patients in the present study are significantly associated, the power was relatively small, and we failed to evaluate the predictive power of other leukocyte subsets such as eosinophils and basophils. Finally, we did not evaluate the predictive value of leukocytes and its subsets in our population. Thus, the data should be replicated in a study with larger sample size and long term follow up.
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