Association of WBC count and glucose metabolism among Chinese population aged 40 years and over

diabetes research and clinical practice 82 (2008) 132–138

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Association of WBC count and glucose metabolism among Chinese population aged 40 years and over Jing-Yan Tian a,1, Yan Yang a,b,1, Qi Cheng a,b,*, Hong-Er Huang c, Rui Li d, Guo-Xin Jiang b,e, Shi-You Liu c, Xiao-Ying Li a, Guang Ning a,** a

Ruijin Hospital affiliated to Shanghai JiaoTong University School of Medicine, Shanghai Institute of Endocrine and Metabolic Diseases, Shanghai Clinical Center for Endocrine and Metabolic Diseases, 197 Ruijin Er Lu, Shanghai 200025, China b School of Public Health, Shanghai JiaoTong University, 227 Chong Qing Nan Road, Shanghai 200025, China c Baoshan District Center for Disease Control and Prevention, Shanghai 201900, China d Shanghai Municipal Center of Disease Control and Prevention, Shanghai 200336, China e Department of Public Health Sciences, Karolinska Institute, Stockholm 17177, Sweden

article info

abstract

Article history:

Chronic subclinical inflammation may be involved in the pathogenesis of Type 2 diabetes. We

Received 19 September 2007

examined whether elevated WBC count, a marker of inflammation, was associated with

Received in revised form

worsening of glucose tolerance among Chinese population aged 40 years and over. Based on

19 May 2008

the 75 g OGTT, 1016 subjects aged from 40 to 88 years were classified into four groups: NFG/NGT

Accepted 30 May 2008

(n = 299), isolated IFG (n = 213), IGT (n = 213) and Type 2 diabetes (n = 291). We compared the

Published on line 8 August 2008

WBC count among the four groups and investigated relevant variables associated significantly with the WBC count. The IGT and Type 2 diabetes groups had a significantly higher WBC count

Keywords:

than the NFG/NGT and isolated IFG groups. By stepwise regression analyses, we found that

White blood cell

waist circumference, DBP, total cholesterol, HDL cholesterol and 2-h PG showed an indepen-

Type 2 diabetes

dent association with the WBC count. In the analysis stratified by sex and smoking status, WBC

IFG

count was independently associated with age and triglycerides in males, whereas it was

IGT

associated with BMI, SBP, triglycerides and 2-h PG in females. BMI, SBP, triglycerides and 2-h PG showed an independent association with WBC count in subjects who never smoked. We concluded that an increase in WBC count was associated with the deterioration of glucose tolerance. WBC count was associated with lipid metabolism in males and with various components of the metabolic syndrome in females and subjects who never smoked. # 2008 Elsevier Ireland Ltd. All rights reserved.

1.

Introduction

Chronic subclinical inflammation has been recognized to play a central role in both initiation and progression of Type 2

diabetes and cardiovascular diseases [1,2]. Activation of the inflammation may be detected by an increase in a number of markers, including WBC count, C-reactive protein, interleukin-6, plasminogen activator inhibitor-1, etc. [3–6].

* Corresponding author at: Tel.: +86 21 53063743; fax: +86 21 53063743. ** Corresponding author at: Tel.: +86 21 64370045x665344; fax: +86 21 64373514. E-mail addresses: [email protected] (Q. Cheng), [email protected] (G. Ning). 1

Joint first authors. Abbreviations: WBC, white blood cell; NFG/NGT, normal fasting glucose/normal glucose tolerance; iIFG, isolated impaired fasting glucose; IGT, impaired glucose tolerance; HDL, high density lipoprotein; LDL, low density lipoprotein; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; 2-h PG, 2-h postchallenge plasma glucose. 0168-8227/$ – see front matter # 2008 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.diabres.2008.05.014

diabetes research and clinical practice 82 (2008) 132–138

An abundance of evidence in Pima Indians and in other populations supported the hypothesis that altered markers of inflammation are associated with the later development of Type 2 diabetes [7–9]. Moreover, prospective studies had shown that WBC count was independently associated with increased coronary heart disease (CHD) incidence and related mortality in the general population [10,11]. Furthermore, WBC count has been reported to be associated with the incident CHD among diabetic patients [12]. The association of a chronic subclinical inflammation, as assessed by elevated WBC count, with various abnormal glucose metabolism, such as IFG, IGT and Type 2 diabetes, has been well documented in various populations and in certain ages [13–17]. However, few investigations have focused on the Chinese population aged 40 years and over. In the present study, we examined the association of WBC count, a marker of inflammation, and glucose metabolism in the Chinese population between 40 and 88 years of age. Our aims were to examine (1) whether an elevated WBC count was associated with a worsening of glucose tolerance and (2) relevant variables that may have a significant association with the WBC count.

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As a result, 1016 people (94% of the random sample) with valid and complete information were subjected to the present study. This study was approved by the local ethics committee and informed consent was obtained from all the participants.

2.2.

Measurements

2.

Subjects and methods

Anthropometric measurements included height, weight, waist circumference, body mass index (kg/m2) and systolic/ diastolic blood pressure. Body weight, height and waist circumference were measured to the nearest 0.1 kg and 0.1 cm, respectively. Blood pressure was measured three times on the right arm after the subjects had rested for at least 5 min in the sitting position and the average of the last two measurements was used for analysis. All interviews were conducted by 20 well-trained medical workers. After 3 days during which time the subjects were instructed to eat at least 150 g of carbohydrates per day, a 75 g oral glucose tolerance test was performed. Plasma glucose concentration was determined immediately after blood centrifugation by a hexose-kinase method whereas serum lipid profiles were measured by the automatic analyzer method. WBC count was determined using an automated cell counter. All tests were made in the same laboratory.

2.1.

Subjects

2.3.

The subjects were from an epidemiological study on Type 2 diabetes conducted in 2004 in Youyi Community, Baoshan District, Shanghai, China. We investigated the participants for health screening. All participants were asked to bring their medical and pharmacy records with them to the first interview and all of their information was reviewed. Furthermore, all subjects with hypertension and/or dyslipidemia were treated with appropriate medications. In our study, we included 5071 people aged 40 years and over, accounting for 50.5% of all eligible inhabitants with permanent residence in the 10 sectors of Youyi Community. No significant difference was found with respect to age and sex when comparing the 5071 people to the census information. A random sample of 1081 people was selected from the 5071 people for extensive examinations; this random sample provided the information for the present study. Characteristics between the sample (1081 people) and the remaining (3990 people) were compared and no significant difference was found between the two groups with regard to age, sex, SBP, DBP and fasting capillary glucose. To minimize the confounding effect of infection, only subjects having a WBC count within the normal range ((4.0– 10.0)  109 cell/l) were included in the analysis. Subjects with acute infection were excluded according to the information provided by our interview, medical records, physical examination and routine laboratory tests. Subjects who had liver dysfunction (aspartate transaminase 80 units/l and/or alanine transaminase 80 units/l) and renal dysfunction (creatinine 106 mmol/l) were excluded. Furthermore, subjects with the treatment that might affect WBC count, such as for hyperthyroidism, immune and hematological diseases, were also excluded.

Definitions and categorical cut points

Based on the 75 g oral glucose tolerance test, the subjects were classified into four groups according to 2003 American Diabetes Association (ADA) recommendations [18]: normal fasting glucose (NFG)/normal glucose tolerance (NGT) (FPG <5.6 mmol/l and 2-h PG <7.8 mmol/l), n = 299; isolated impaired fasting glucose (FPG between 5.6 and 6.9 mmol/l and 2-h PG <7.8 mmol/l), n = 213; impaired glucose tolerance (FPG <7.0 mmol/l and 2-h PG between 7.8 and 11.0 mmol/l), n = 213; and Type 2 diabetes (FPG 7.0 mmol/l or a 2-h PG level 11.1 mmol/l), n = 291. Cigarette smoking status was divided into: (1) current smoking, i.e. one cigarette or more per day; (2) former smoking, i.e. given up smoking for at least 3 months and (3) never smoking.

2.4.

Statistical analysis

Descriptive data were shown as mean  S.D. (unless indicated otherwise). Linear regression analyses with adjustment for age and sex and smoking status were applied to compare variables among the different groups and Fisher’s least significant difference t-tests were conducted in multiple comparisons. The values for WBC count and triglycerides were logarithmically transformed before statistical analysis like regression analyses to approximate normal distributions. Pearson’s correlation coefficients were calculated to determine whether a significant relationship existed between the WBC count and other relevant clinical factors. Furthermore, stepwise multiple regression analyses were performed to assess the independent relationship between the WBC count and other variables that showed significant associations. All the statistical analyses were performed using SPSS 11.0. All statistical tests were two-tailed and

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diabetes research and clinical practice 82 (2008) 132–138

Table 1 – Mean WBC count and clinical characteristics NFG/NGT

Isolated IFG

IGT

DM

N (male/female) Age (years)

299 (105/194) 60.5  10.3

213 (101/111) 60.2  9.1

213 (80/133) 63.5  9.2*,y

291 (126/165) 61.3  9.7z

Smoking Never smokers (no.) Former smokers (no.) Current smokers (no.) WBC (106 cell/l) (95% CI) BMI (kg/m2) Waist circumference (cm) SBP (mmHg) DBP (mmHg) Triglycerides (mmol/l) Total cholesterol (mmol/l) HDL (mmol/l) LDL (mmol/l) FPG (mmol/l) 2-h PG (mmol/l)

235 17 47 5553 (3900–8400) 24.0  3.0 81.1  8.0 132  22 77  10 1.12 (1.06–1.20) 4.68  0.80 1.52  0.36 2.62  0.64 5.03  0.48 5.94  1.02

154 16 43 5632 (3800–8200) 25.0  3.1* 84.5  9.0* 136  21* 80  11* 1.27 (1.18–1.36) 4.86  0.89* 1.47  0.37 2.78  0.71* 5.94  0.31* 6.32  1.08

178 11 24 5843*,y (4000–8430) 25.4  3.2* 85.2  8.4* 142  22* 81  10* 1.51*,y (1.41–1.62) 4.88  0.90* 1.38  0.33*,y 2.76  0.76* 5.81  0.55* 9.07  0.94*,y

212 18 61 6134*,y (4100–9000) 26.5  3.9*,y,z 88.1  9.5*,y,z 145  22*,y,z 82  11*,y 1.78*,y,z (1.66–1.91) 5.04  1.22* 1.33  0.38*,y 2.83  0.80* 7.78  2.03*,y,z 13.67  3.99*,y,z

Data are presented as mean  S.D. WBC counts and triglycerides were shown as geometric means with 95% CI. A generalized linear model with adjustment for age and sex and smoking status was applied to compare variables among four groups. *P  0.05 vs. NFG/NGT group; yP  0.05 vs. isolated IFG group; zP  0.05 vs. IGT group.

P-value 0.05 was considered statistically significantly different.

3.

Results

There were 412 males and 604 females in this study. The mean age of the sample was 61.3  9.7 years, 62.2  9.8 years for males and 60.6  9.7 years for females. The geometric mean WBC counts (95% CI) were 5956 (4100–8600)  106 cell/l in males and 5683 (3900–8400)  106 cell/l in females, and there were no significant differences after adjustment for age and smoking status. The geometric mean WBC counts (95% CI) were 5700 (3900–8400)  106 cell/l in those who had never smoked (n = 779), 5847 (3640–8650)  106 cell/l in the former smokers (n = 62) and 6199 (4180–8920)  106 cell/l in the current smokers (n = 105). No significant difference was found in the WBC count in those who had never smoked and former smokers, while the number of cases was relatively smaller. The WBC count of the current smokers was

significantly higher than that in the other two groups (P < 0.05). The subjects with isolated IFG (iIFG) had significantly higher BMI, waist circumference, SBP and DBP, total cholesterol, LDL, FPG and 2-h PG levels, compared with those of the NFG/NGT group (Table 1). The subjects with IGT had significantly higher age, triglycerides, 2-h PG and lower HDL levels than those in the iIFG group. Compared with the subjects with IGT, the subjects with Type 2 diabetes had significantly higher levels of BMI, waist circumference, SBP, triglycerides, FPG and 2-h PG. Moreover, the IGT and Type 2 diabetes groups had higher WBC count than the NFG/NGT and iIFG groups. The correlation coefficients between logarithmically transformed WBC count and the other anthropometric and metabolic variables are shown in Tables 2 and 3. The WBC count was positively correlated with BMI, waist circumference, SBP and DBP, triglycerides, total cholesterol, LDL, FPG and 2-h PG, and negatively correlated with HDL in the overall subjects of the study. In stratified analysis, the WBC count

Table 2 – Relationship between ln WBC and clinical factors by sex Overall (n = 1016)

Age BMI Waist circumference SBP DBP Triglycerides Total cholesterol HDL cholesterol LDL cholesterol FPG 2-h PG

Males (n = 412)

Females (n = 604)

r

P

r

P

r

0.01 0.19 0.21 0.14 0.15 0.20 0.12 0.18 0.07 0.09 0.16

0.675 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.030 0.007 <0.001

0.13 0.18 0.19 0.02 0.07 0.21 0.21 0.19 0.17 0.05 0.09

<0.001 <0.001 <0.001 0.682 0.188 <0.001 <0.001 <0.001 0.001 0.325 0.070

0.05 0.19 0.20 0.22 0.20 0.21 0.12 0.16 0.03 0.10 0.22

P 0.211 <0.001 <0.001 <0.001 <0.001 <0.001 0.004 <0.001 0.518 0.013 <0.001

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diabetes research and clinical practice 82 (2008) 132–138

Table 3 – Relationship between ln WBC and clinical factors by smoking status Never smokers (n = 779)

Age BMI Waist circumference SBP DBP Triglycerides Total cholesterol HDL cholesterol LDL cholesterol FPG 2-h PG

Former smokers (n = 62)

Current smokers (n = 175)

r

P

r

P

r

0.02 0.19 0.19 0.20 0.19 0.21 0.13 0.18 0.06 0.08 0.19

0.631 <0.001 <0.001 <0.001 <0.001 <0.001 0.001 <0.001 0.078 0.019 <0.001

0.08 0.29 0.25 0.06 0.09 0.23 0.23 0.13 0.16 0.13 0.23

0.565 0.024 0.048 0.653 0.479 0.073 0.069 0.310 0.216 0.325 0.075

0.07 0.13 0.17 0.01 0.01 0.16 0.16 0.15 0.09 0.03 0.06

P 0.354 0.096 0.023 0.904 0.931 0.035 0.038 0.047 0.249 0.739 0.424

Table 4 – Stepwise multiple regression analyses with ln WBC as the dependent variable by sex Overall (n = 1016) S.E.

b Age BMI Waist circumference SBP DBP Triglycerides Total cholesterol HDL cholesterol LDL cholesterol FPG 2-h PG

– NS 0.120

Males (n = 412)

NS NS – – NS

NS 0.001** NS 0.148 0.161

0.007** 0.022**

0.280 0.257

NS NS 0.058

– 0.088

0.003* NS

0.139

0.000** NS

0.098 0.013** 0.031**

NS – –

0.015*

S.E.

b

0.001**

0.151 0.001**

0.098

S.E.

b

Females (n = 604)

0.017* NS NS – NS

0.116

0.028**

–: the variable was not considered because no correlation was found; NS: the variable was not accepted as significant for stepwise analysis. *P  0.05; **P  0.01.

Table 5 – Stepwise multiple regression analyses with ln WBC as the dependent variable by smoking status Never smokers (n = 779) b Age BMI Waist circumference SBP DBP Triglycerides Total cholesterol HDL cholesterol LDL cholesterol FPG 2-h PG

Former smokers (n = 62)

S.E.

b

0.002**

0.287

– 0.101

0.000** NS

0.120

0.015** NS NS – NS

0.093

0.023*

– –

0.010* NS – – – – – – – –

S.E.

b

–

NS 0.121

S.E.

Current smokers (n = 175)

0.173

0.002* – – NS NS NS – – –

–: the variable was not considered because no correlation was found; NS: the variable was not accepted as significant for stepwise analysis. *P  0.05; **P  0.01.

showed significant relationship with age, BMI, waist circumference, triglycerides, total cholesterol, LDL and HDL in males, whereas with BMI, waist circumference, SBP and DBP, triglycerides, total cholesterol, HDL, FPG and 2-h PG in females (Table 2). The WBC count showed a significant relationship with BMI, waist circumference, SBP and DBP, triglycerides, total choles-

terol, HDL, FPG and 2-h PG in those who had never smoked (Table 3). The WBC count was also significantly correlated with BMI and waist circumference in the former smokers. As to the current smokers, the WBC count was positively correlated with waist circumference, total cholesterol and triglycerides, and negatively correlated with HDL cholesterol.

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diabetes research and clinical practice 82 (2008) 132–138

By stepwise multiple regression analysis (Tables 4 and 5), waist circumference, DBP, total cholesterol, HDL and 2-h PG showed an independent association with the WBC count in the overall subjects of the study (adjusted R2 = 0.088). In stratified analysis by sex, age, total cholesterol and HDL were independently associated with WBC count in males (adjusted R2 = 0.127), whereas BMI, SBP, triglycerides and 2-h PG were independently associated with WBC count (adjusted R2 = 0.091) in females. In the subjects who had never smoked, BMI, SBP, triglycerides and 2-h PG were independently associated with WBC count (adjusted R2 = 0.085). With former smokers, BMI showed a significant association with the WBC count (adjusted R2 = 0.083), whereas waist circumference was significantly associated with the WBC count in the current smokers (adjusted R2 = 0.030).

4.

Discussion

The association between the WBC count and the glucose tolerance among Chinese population aged 40 years and over was confirmed by our findings. The WBC count was positively correlated with 2-h PG in the general population, females and those who had never smoked. An increase in WBC count was associated with the deterioration of glucose tolerance. The WBC counts in the IGT and Type 2 diabetes groups were significantly higher than that in the NFG/NGT and iIFG groups. With regard to the fact that the WBC count was higher in the IGT and Type 2 diabetes groups, our results were consistent with the previous findings from our institute. Compared with the normal group, Yuan et al. [19] found that serum high sensitive CRP, an additional sensitive marker of inflammation for future cardiovascular events, was significantly increased in the IGT and Type 2 diabetes groups, and there was no significant difference between the last two groups. We found that an increased WBC count was associated with the deterioration of glucose tolerance in our study. However, the mechanism that could explain the relationships has not yet been clarified. The study in Pima Indians [13] found that a high WBC was associated with a worsening of insulin sensitivity, which might predict the development of Type 2 diabetes. The mechanism concerning the positive relationship with WBC count and 2-h PG was not fully understood in our study. Other studies [3] have revealed glucose inducing the production of mitochondrial reactive oxygen species (ROS) in endothelial cells, thereby initiating a proinflammatory cascade. Van Oostrom et al. confirmed that an acute glucose load increased plasma IL-8 levels and concomitantly resulted in a WBC count increment. To clarify the difference in the cardiovascular risk individuals with iIFG and IGT, the previous studies have compared various clinical factors in Caucasians [20,21] and Japanese population [22], but little difference had been found. The study [24] reported that the iIFG group and isolated IGT group had similar cardiovascular risk factors. However, Ohshita et al. found that subjects with IGT had a significantly higher WBC count, as well as higher BMI, waist circumference, SBP and DBP, triglycerides and lower HDL cholesterol levels, unlike subjects with IFG in 4720 nondiabetic Japanese men aged 24–84 years [23].

In the present study, we assessed the differences of inflammatory markers and other relevant factors between iIFG and IGT among Chinese population aged 40 years and over. The results of our study demonstrated that the WBC count was significantly increased in IGT subjects, but not in iIFG subjects. Moreover, subjects with IGT had significantly higher age, triglycerides, 2-h PG and lower HDL levels than the iIFG group. Our findings are in agreement with the results from previous large prospective cohort studies [24,25] which demonstrated a higher risk of cardiovascular disease and death in the subjects with IGT than in those with IFG. As to Type 2 diabetes, subjects had significantly higher levels of BMI, waist circumference, SBP, triglycerides, FPG and 2-h PG, by comparison with the subjects with IGT. Our results indicated that increased subclinical inflammation might be one of the mechanisms of the elevated cardiovascular risk in the subjects with IGT and Type 2 diabetes. Since IGT and Type 2 diabetes have the same WBC count, it is important to conduct intervention procedures for the subjects with IGT in order to decrease the incidence of Type 2 diabetes and cardiovascular diseases. Furthermore, we found that the WBC count was correlated with various components of the metabolic syndrome, including BMI, waist circumference, SBP, DBP, triglycerides, total cholesterol, HDL, LDL, FPG and 2-h PG. By stepwise multiple regression analyses, the WBC count was independently related to waist circumference, DBP, HDL and 2-h PG (adjusted R2 = 0.088). In the analysis stratified by sex and smoking status, WBC count was independently associated with age and lipid metabolism in males (adjusted R2 = 0.127), whereas it was associated with various components of the metabolic syndrome in females (adjusted R2 = 0.081) and those who had never smoked (adjusted R2 = 0.085). Our results are consistent with several previous studies [26,27] which concluded that WBC count was correlated with various components of the metabolic syndrome. Regarding current smokers, stepwise regression analyses yielded adjusted R2-value of 0.030 (P < 0.05). Although these values signify that there is a significant association, the low R2-value indicates that other factors must also have an influence on WBC count. In the present study, the WBC count among current smokers was significantly higher than that with those who had never smoked or with former smokers. As to those who never smoked and former smokers, no significant difference was found. Therefore, to give up smoking early and completely might be beneficial for the prevention of the activation of the chronic subclinical inflammation. The present study had several limitations. First, we assessed WBC count because it was one of the most common laboratory tests. Further studies of other inflammatory markers, such as CRP, are required to confirm the association with glucose metabolism among Chinese population. Second, our results confirmed that chronic subclinical inflammation might be one of the mechanisms contributing to the elevated cardiovascular risk for the subjects with abnormal glucose metabolism. However, a cross-sectional study cannot provide information on a causal relationship. Therefore, more evidence about increased cardiovascular risk in the subjects with IGT and Type 2 diabetes requires further investigation.

diabetes research and clinical practice 82 (2008) 132–138

In conclusion, an elevated WBC count, although still within the normal range, is associated with the deterioration of glucose tolerance among Chinese population aged 40 years and over. WBC count was associated with the lipid metabolism in males and with various components of the metabolic syndrome in females as well as with people who had never smoked. Chronic subclinical inflammation may contribute to the elevated cardiovascular risk in the subjects with IGT and Type 2 diabetes. Therefore, the complete and early giving up of smoking and conducting intervention procedures for subjects with IGT tends to ameliorate the chronic subclinical inflammation, thereby decreasing the elevated cardiovascular risk.

Acknowledgements This study was supported by funds from the Major State Basic Research Development Program of China (973 Program), Shanghai Science and Technology Commission (04DZ19502), and Endocrine and Metabolic Division, E-institutes of Shanghai Universities, Shanghai, China. We thank David M. Proctor III for carefully proofreading this paper.

Conflict of interest There are no conflicts of interest.

references

[1] M.I. Schmidt, B.B. Duncan, A.R. Sharrett, G. Lindberg, P.J. Savage, S. Offenbacher, et al., Markers of inflammation and prediction of diabetes mellitus in adults (Atherosclerosis Risk in Communities study): a cohort study, Lancet 353 (1999) 1649–1652. [2] R. Ross, Atherosclerosis—an inflammatory disease, N. Engl. J. Med. 340 (1999) 115–126. [3] J.S. Yudkin, I. Juhan-Vague, E. Hawe, S.E. Humphries, G. di Minno, M. Margaglione, et al., Low-grade inflammation may play a role in the etiology of the metabolic syndrome in patients with coronary heart disease: the HIFMECH study, Metabolism 53 (2004) 852–857. [4] R. Retnakaran, A.J.G. Hanley, P.W. Connelly, S.B. Harris, B. Zinman, Elevated C-reactive protein in Native Canadian children: an ominous early complication of childhood obesity, Diabetes Obes. Metab. 8 (2006) 483–491. [5] A. Festa, R.B. D’Agostino, R.P. Tracy, S.M. Haffner, Elevated levels of acute phase proteins and plasminogen activator inhibitor-1 (PAI-1) predict the development of type 2 diabetes mellitus: the Insulin Resistance Atherosclerosis Study (IRAS), Diabetes 51 (2002) 1131–1137. [6] P.M. Ridker, C.H. Hennekens, J.E. Buring, N. Rifai, C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women, N. Engl. J. Med. 342 (2000) 836–843. [7] D.J. Freeman, J. Norrie, N. Sattar, R.D. Neely, S.M. Cobbe, I. Ford, et al., Pravastatin and the development of diabetes mellitus: evidence for a protective treatment effect in the West of Scotland Coronary Prevention Study, Circulation 103 (2001) 357–362.

137

[8] R.S. Lindsay, J. Krakoff, R.L. Hanson, P.H. Bennett, W.C. Knowler, Gamma globulin levels predict type 2 diabetes in the Pima Indian population, Diabetes 50 (2001) 1598–1603. [9] W.C. Knowler, D.J. Pettitt, M.F. Saad, P.H. Bennett, Diabetes mellitus in the Pima Indians: incidence, risk factors and pathogenesis, Diabetes Metab. Rev. 6 (1990) 1–27. [10] C.D. Lee, A.R. Folsom, F.J. Nieto, L.E. Chambless, E. Shahar, D.A. Wolfe, White blood cell count and incidence of coronary heart disease and ischemic stroke and mortality from cardiovascular disease in African–American and white men and women, Am. J. Epidemiol. 154 (2001) 758–764. [11] D.W. Brown, W.H. Giles, J.B. Croft, White blood cell count: an independent predictor of coronary heart disease mortality among a national cohort, J. Clin. Epidemiol. 54 (2001) 316–322. [12] I. Saito, A.R. Folsom, F.L. Brancati, B.B. Duncan, L.E. Chambless, P.G. McGovern, Nontraditional risk factors for coronary heart disease incidence among persons with diabetes: the Atherosclerosis Risk in Communities (ARIC) study, Ann. Intern. Med. 133 (2000) 81–91. [13] B. Vozarova, C. Weyer, R.S. Lindsay, R.E. Pratley, C. Bogardus, P.A. Tataranni, High white blood cell count is associated with a worsening of insulin sensitivity and predicts the development of type 2 diabetes, Diabetes 51 (2002) 455–461. [14] N. Nakanishi, H. Yoshida, Y. Matsuo, K. Suzuki, K. Tatara, White blood-cell count and the risk of impaired fasting glucose or type II diabetes in middle-aged Japanese men, Diabetologia 45 (2002) 42–48. [15] P.C. Tong, K.F. Lee, W.Y. So, M.H. Ng, W.B. Chan, M.K. Lo, et al., White blood cell count is associated with macro- and microvascular complications in Chinese patients with type 2 diabetes, Diabetes Care 27 (2004) 216–222. [16] Y.Y. Wang, S.Y. Lin, P.H. Liu, B.M. Cheung, W.A. Lai, Association between hematological parameters and metabolic syndrome components in a Chinese population, J. Diabetes Complicat. 18 (2004) 322–327. [17] L.K. Chen, M.H. Lin, Z.J. Chen, S.J. Hwang, S.T. Chiou, Association of insulin resistance and hematologic parameters: study of a middle-aged and elderly Chinese population in Taiwan, J. Chin. Med. Assoc. 69 (2006) 248–253. [18] Expert Committee on the Diagnosis and Classification of Diabetes Mellitus, Report of the expert committee on the diagnosis and classification of diabetes mellitus, Diabetes Care 27 (Suppl. 1) (2004) S5–S14. [19] G. Yuan, L. Zhou, J. Tang, Y. Yang, W. Gu, F. Li, et al., Serum CRP levels are equally elevated in newly diagnosed type 2 diabetes and impaired glucose tolerance and related to adiponectin levels and insulin sensitivity, Diabetes Res. Clin. Pract. 72 (2006) 244–250. [20] H. Larsson, G. Berglund, F. Lindgarde, B. Ahre´n, Comparison of ADA and WHO criteria for diagnosis of diabetes and glucose intolerance (Letter), Diabetologia 41 (1998) 1124–1125. [21] P.A. Metcalf, R.K. Scragg, Comparison of WHO and ADA criteria for diagnosis of glucose status in adults, Diabetes Res. Clin. Pract. 49 (2000) 169–180. [22] DECODA Study Group, International Diabetes Epidemiology Group, Cardiovascular risk profile assessment in glucoseintolerant Asian individuals: an evaluation of the World Health Organization two-step strategy: the DECODA Study (Diabetes Epidemiology: Collaborative Analysis of Diagnostic Criteria in Asia), Diabet. Med. 19 (2002) 549–557. [23] K. Ohshita, K. Yamane, M. Hanafusa, H. Mori, K. Mito, M. Okubo, et al., Elevated white blood cell count in subjects with impaired glucose tolerance, Diabetes Care 27 (2004) 491–496.

138

diabetes research and clinical practice 82 (2008) 132–138

[24] M. Tominaga, H. Eguchi, H. Manaka, K. Igarashi, T. Kato, A. Sekikawa, Impaired glucose tolerance is a risk factor for cardiovascular disease, but not impaired fasting glucose: the Funagata Diabetes Study, Diabetes Care 22 (1999) 920–924. [25] DECODE Study Group, European Diabetes Epidemiology Group, Glucose tolerance and cardiovascular mortality: comparison of fasting and 2-hour diagnostic criteria, Arch. Intern. Med. 161 (2001) 397–404.

[26] M.Y. Desai, D. Dalal, R.D. Santos, J.A. Carvalho, K. Nasir, R.S. Blumenthal, Association of body mass index, metabolic syndrome, and leukocyte count, Am. J. Cardiol. 97 (2006) 835–838. [27] V. Lohsoonthorn, B. Dhanamun, M.A. Williams, Prevalence of metabolic syndrome and its relationship to white blood cell count in a population of Thai men and women receiving routine health examinations, Am. J. Hypertens. 19 (2006) 339–345.