Relationship between shift work and peripheral total and differential leukocyte counts in Chinese steel workers

Abstract

Objectives: Even though shift work has been suspected to be a risk factor for cardiovascular disease, little research has been done to determine the logical underlying inflammation mechanisms. This study investigated the association between shift work and circulating total and differential leukocyte counts among Chinese steel workers. Methods: The subjects were 1,654 line workers in a steel plant, who responded to a cross-sectional survey with a questionnaire on basic attributes, life style, and sleep. All workers in the plant received a periodic health checkup. Total and differential leukocytes counts were also examined in the checkup. Results: Shift workers had higher rates of alcohol use, smoking, poor sleep, poor physical exercise, and obesity than daytime workers. In further analysis, we found that the peripheral total WBC, monocyte, neutrophil, and lymphocyte counts were also greater in shift workers than in daytime workers. When subjects were divided into quartiles according to total WBC, neutrophil, monocyte, and lymphocyte counts, increased leukocyte count was associated with shift work. Using stepwise linear regression analysis, smoking, obesity, and shift work were independently associated with total WBC, monocyte, neutrophil, and lymphocyte counts. Conclusions: This study indicates that peripheral total and differential leukocyte counts are significantly higher in shift workers, which suggests that shift work may be a risk factor of cardiovascular disease. Applicable intervention strategies are needed for prevention of cardiovascular disease for shift workers.

(J Occup Health 2016; 58: 81–88)

Introduction

Shift work has been previously shown to be associated with inflammatory responses such as high-sensitive C-reactive protein (CRP) and leukocyte count¹⁾. An increase in these inflammatory indicators is known to be associated with coronary heart disease and subclinical atherosclerosis^2–4). Shift work is reported to contribute to the increased risks of sleeping difficulties and vascular diseases such as coronary heart disease (CHD) and hypertension^5–7).

The peripheral white blood cell (WBC) count has been shown to be associated with insulin resistance, type 2 diabetes^8–11), coronary artery disease (CAD)^12–15), stroke^{12, 15)}, and diabetic microvascular and macrovascular complications^{16, 17)}. The relationship between leukocytes counts and CAD has been observed in prospective and retrospective cohort studies, as well as in case-control studies, and persists after adjustment for multiple CHD risk factors, including smoking¹⁸⁾. Peripheral blood leukocytes are composed of polymorphonuclear cells, monocytes, and lymphocytes. Polymorphonuclear and mononuclear leukocytes can be activated by advanced glycation end products¹⁹⁾, oxidative stress^{20, 21)}, angiotensin II²²⁾, and cytokines²³⁾ in state of hyperglycemia. Leukocytes may be activated through the release of cytokines such as tumor necrosis factor-α (TNF-α)^{24, 25)}, transforming growth factor-β1²⁶⁾, superoxide²⁷⁾, nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB)²⁸⁾, monocyte chemoattractant protein-1, interleukin-1β, and others²⁴⁾ to participate in the pathogenesis of diabetic microvascular and macrovascular complications. Elevated differential cell counts, including eosinophil, neutrophil, and monocyte counts, also predict the future incidence of CAD^{18, 29, 30)}.

Although shift work is strongly associated with increased systemic inflammation¹⁾, little is known about the association between peripheral total and differential WBC counts and shift work. To promote primary prevention of CHD in the workplace, evaluation of the association between shift work and the total and differential WBC counts in the stage of primary prevention is considered to be important. Since total and differential WBC counts are commonly performed as clinical laboratory tests, we examined the association of shift work with total and differential WBC counts in a large population-based sample of Chinese steel workers working on a production line.

Subjects and Methods

Study design and participants

A cross-sectional survey study using questionnaires was conducted. The study hospital and factories were located in south Taiwan. A total of 3,400 employees from two steel plants were invited to participate in the study, between March and June 2010, during their routine periodic physical examinations, which were conducted by the study hospital. The study hospital was the designated periodic physical examination provider of the factories. Employees in two steel plants included daytime workers (8:00–17:00) and shift workers who worked rotating shifts comprising morning (7:00–15:00), afternoon (15:00–23:00), and night (23:00–7:00) shifts. Workplace hazardous exposures (noise, ionizing radiation, n-Hexane, manganese metal fume, and dusts) were defined according to regulations of the Taiwan Ministry of Labor. Workers on the production lines of the steel factories in this study stayed in air-conditioned control rooms and handled the automatic production process most of the time during their work hours. The duration of the production of each stock lasted about 1–1.5 hours, and the workers needed to leave the control room to check the operation of machines on-site (and possibly exposed to the hot environment), which usually took 10–15 minutes per session; their physical load was light. In the present study, only data from production line workers were included. Data were excluded from the study if workers were in charge of office work, engineering, or management or had any history or manifestation of acute or chronic inflammatory disease, allergic disease or cancer indicated in the records of their health checkups. In addition, shift workers who worked during the daytime and had a history of interrupted sleep were also excluded. Shift workers were also excluded if they provided incomplete information regarding demographics, occupational history, or sleep quality. This study was approved by the Human Research Ethics Committee of Kaohsiung E-Da Hospital, I-Shou University. Written informed consent was obtained from all participants.

Data collection and measures

Self-administered questionnaires were distributed and collected on the day of the health examination. Information on basic demographic characteristics and lifestyle, such as age, sex, job type, sleep quality, health condition, physical exercise and smoking habit, betel quid chewing, and alcohol consumption were ascertained using the questionnaire. Physical exercise was assessed by the question “How often did you exercise during the past month?” The response options were: hardly ever, once, and twice or more. For job schedules, the participants were asked whether they were daytime workers (8:00–17:00) or shift workers working rotating shifts comprising morning (7:00–15:00), afternoon (15:00–23:00), and night (23:00–7:00) shifts. Sleep quality was assessed by the question “How often did you have poor sleep during the past month?” The response options were: almost never, sometimes, and often or almost always.

The smoking status of subjects was classified as never having smoked, former smoker (quit smoking for at least 1 year), or current smoker. Drinking and betel quid chewing status were classified as never drink or chew betel quid, former drinker or betel quid chewer (quit drinking or betel quid chewing for at least 1 year), or current drinker or betel quid chewer. In this study, former and current drinkers and betel quid chewers were analyzed as a single group³¹⁾. In addition, body mass index (BMI) was calculated as weight (in kilograms) divided by height (in meters) squared. Based on the definition from the Bureau of Health Promotion, Department of Health, Taiwan, the respondents were categorized as underweight (BMI<18.5 kg/m²), normal weight (BMI 18.5–23.9 kg/m²), overweight (BMI 24.0–26.9 kg/m²), or obese (BMI ≥27.0 kg/m²).

Peripheral blood samples were taken from the antecubital vein of the workers after fasting for at least 8 hours. Complete blood cell counts and serum creatinine, serum glutamic oxaloacetic transaminase (SGOT), serum glutamic pyruvic transaminase (SGPT), glucose, HbA1c, and lipid profiles [including plasma triglycerides, total cholesterol, low- density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C)] were also examined in the health checkups and were determined in all workers by standard commercial methods with a parallel, multichannel analyzer (Hitachi 7170A) as described in our previous report³²⁾. Peripheral leukocyte analyses included total leukocyte counts and differential percentages of neutrophils, monocytes, lymphocytes, eosinophils, and basophils using an automated cell counter (XE-2100 Hematology Alpha Transportation System, Sysmex Corporation, Kobe, Japan). The absolute count of a leukocyte subtype was calculated as the product of its respective differential percentage and total leukocyte count.

Statistical analysis

The data are shown as the mean ± SD. All statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS for Windows, version 12.0, SPSS Inc, Chicago, IL, USA) software. Statistical differences in variables were compared using unpaired Student's t-tests for normally distributed variables. Categorical variables were recorded as frequencies and/or percentages, and intergroup comparisons were analyzed by the chi-square test. Simple and multiple linear stepwise regression analyses were used to examine the correlations and independence between peripheral total and differential leukocyte counts and the values of other parameters. The prevalence of shift work categorized within the quartiles of WBC counts was tested for trends. All statistical analyses were two-sided, and a p-value <0.05 was considered to be statistically significant.

Results

A total of 1,654 production line workers (1,602 men and 52 women; age, 43 ± 7 years) were included in this study. The baseline characteristics of the participants are presented in Table 1. There were 897 (54.2%) participants engaged in shift work for at least 1 year. Subjects with daytime work were older. Male gender, drinking, current smoking, hardly ever engaging in physical exercise and often or almost always having poor sleep showed significantly higher percentages among shift workers than daytime workers (p<0.01). In shift workers, obesity was reported by 25.4% of the subjects, which was significantly higher than in the daytime workers (19.3%). The systolic and diastolic blood pressure levels and percentages of betel quid chewing, former smoking, and workplace hazardous exposures were the same between the two groups.

Table 1. Clinical characteristics of the study subjects

Parameter	Shift work	Daytime work	p value
No	897	757
Male gender (n, %)	896 (99.9)	706 (93.3)	<0.0001*
Age (yrs, mean ± SD)	41.8 ± 6.5	43.3 ± 7.2	<0.0001#
Systolic blood pressure (mmHg)	125 ± 16	124 ± 16	0.161#
Diastolic blood pressure (mmHg)	79 ± 11	78 ± 10	0.051#
Alcohol use (n, %)	258 (28.8)	178 (23.5)	0.017*
Betel quid use (n, %)	16 (1.8)	6 (0.8)	0.080*
Smoking (n, %)
Never	392 (43.7)	467 (61.7)	<0.0001*
Former	95 (10.6)	78 (103)	0.849*
Current	368 (41.0)	192 (25.4)	<0.0001*
Physical exercise (n, %)
Hardly ever	193 (21.5)	102 (13.5)	<0.0001*
Once	90 (10.0)	52 (6.9)	0.021*
Twice or more	607 (67.7)	600 (79.3)	<0.0001*
Poor sleep (n, %)
Almost never	606 (67.6)	609 (80.5)	<0.0001*
Sometimes	182 (20.3)	101 (13.3)	0.0001*
Often or almost always	86 (9.6)	32 (4.2)	<0.0001*
Obesity (n, %)	228 (25.4)	146 (19.3)	0.003*
Workplace hazardous exposures (n, %)
Noise	502 (56.0)	407 (53.8)	0.156*
Ionizing radiation	3 (0.3)	2 (0.3)	0.946*
n-Hexane	6 (0.7)	5 (0.7)	0.948*
Manganese metal fume	3 (0.3)	2 (0.3)	0.946*
Dusts	20 (2.2)	14 (1.9)	0.747*

*  Chi-square test for categorical data.

^#  Independent-samples t-test for numerical data.

The peripheral total WBC, neutrophil, monocyte, lymphocyte, and eosinophil counts in shift workers were higher than those in daytime workers (Table 2). The prevalence of shift work was stratified for the quartiles of WBC counts. The frequency of shift work differed among the quartiles of the total WBC, neutrophil, monocyte, and lymphocyte counts (p<0.01), and the trend was significantly linear (p<0.01) (Fig. 1). In addition, the levels of fasting blood sugar (FBS) and SGPT in shift workers were higher than those in daytime workers, and the levels of HDL-C in shift workers were lower than those in daytime workers (Table 2).

Table 2. Biochemical characteristics of the study subjects

Parameter	Shift work	Daytime work	p value
No	897	757
Fasting blood sugar (mg/dl)	101.5 ± 20.7	99.3 ± 19.4	0.027
HbA1C (%)	5.6 ± 0.7	5.6 ± 0.6	0.562
Total cholesterol (mg/dl)	191.9 ± 33.7	193.4 ± 33.5	0.350
Triglyceride (mg/dl)	144.8 ± 111.9	136.6 ± 97.0	0.114
HDL-cholesterol (mg/dl)	46.5 ± 10.4	47.6 ± 103	0.036
LDL-cholesterol (mg/dl)	111.5 ± 29.8	111.2 ± 28.6	0.816
SGOT (U/l)	29.9 ± 13.9	29.7 ± 12.6	0.786
SGPT (U/l)	41.0 ± 29.5	37.5 ± 28.0	0.012
Creatinine (mg/dl)	1.2 ± 0.1	1.2 ± 0.8	0.218
WBC count (109/l)	6.505 ± 1.681	6.052 ± 1.448	<0.0001
Neutrophil count (109/l)	3,800 ± 1,276	3,537 ± 1,099	<0.0001
Monocyte count (109/l)	368 ±128	329 ± 109	<0.0001
Lymphocyte count (109/l)	2,126 ± 607	1,987 ± 568	<0.0001
Eosinophil count (109/l)	181 ± 127	168 ± 121	0.036
Basophil count (109/l)	29 ± 18	30 ± 20	0.286

Data are means ± SD. HDL, high-density lipoprotein; LDL, low-density lipoprotein; SGOT, serum glutamic oxaloacetic transaminase; SGPT, serum glutamic pyruvic transaminase. p values were calculated by independent-samples t-test for numerical data.

Fig. 1.

The prevalence of shift work stratified for the total WBC (A), monocyte (B), neutrophil (C), and lymphocyte counts (D). The frequency of shift work differed among the quartiles of the WBC counts (p<0.001), and the trend was significantly linear (p<0.001).

Univariate analysis revealed that the total WBC, monocyte, neutrophil, and lymphocyte counts were significantly associated with drinking, smoking, poor sleep, obesity, and shift work (Table 3). Further, multiple linear stepwise regression analysis was conducted for all these items. Significantly independent factors for the total WBC count were shown to be smoking habit (p<0.0001), poor sleep (p=0.002), obesity (p<0.0001), and shift work (p=0.001). For the monocyte count, they were shown to be age (p=0.042), smoking habit (p<0.0001), obesity (p<0.0001), and shift work (p<0.0001); for the neutrophil count, they were shown to be smoking habit (p<0.0001), poor sleep (p=0.001), obesity (p<0.0001), and shift work (p=0.020); and for the lymphocyte count, they were shown to be sex (p<0.0001), smoking habit (p<0.0001), physical exercise (p=0.024), obesity (p<0.0001), and shift work (p=0.034) (Table 4).

Table 3. Association of covariates with peripheral total and differential leukocyte counts

Factor	Total WBC count		Monocyte count		Neutrophil count		Lymphocyte count
	β	p value	β	p value	β	p value	β	p value
Sex	0.088	<0.0001	0.091	<0.0001	0.042	0.073	0.130	<0.0001
Age	−0.013	0.595	−0.059	0.012	−0.010	0.454	−0.017	0.473
Alcohol use	0.083	<0.0001	0.074	0.002	0.056	0.018	0.083	<0.0001
Smoking	0.248	<0.0001	0.271	<0.0001	0.185	<0.0001	0.208	<0.0001
Betel quid use	0.043	0.066	0.043	0.070	0.020	0.401	0.059	0.012
Physical exercise	0.064	0.008	0.039	0.107	0.045	0.068	0.070	0.004
Poor sleep	0.117	<0.0001	0.083	0.001	0.109	<0.0001	0.064	0.009
Obesity	0.160	<0.0001	0.124	<0.0001	0.121	<0.0001	0.144	<0.0001
Shift work	0.142	<0.0001	0.160	<0.0001	0.109	<0.0001	0.117	<0.0001

Table 4. Stepwise linear regression analysis of covariates associated with peripheral total and differential leukocyte counts

	Total WBC count			Monocyte count			Neutrophil count			Lymphocyte count
Factor	β	95% CI	p value	β	95% CI	p value	β	95% CI	p value	β	95% CI	p value
Sex	NA		NA	NA		NA	NA		NA	0.090	2.37–4.04	<0.0001
Age	NA		NA	−0.048	−1.67 to −0.03	0.042	NA		NA	NA		NA
Alcohol use	NA		NA	NA		NA	NA		NA	NA		NA
Smoking	0.229	0.61–0.94	<0.0001	0.263	1.29–9.38	<0.0001	0.161	6.26–12.18	<0.0001	0.198	7.71–9.32	<0.0001
Betel quid use	NA		NA	NA		NA	NA		NA	NA		NA
Physical exercise	NA		NA	NA		NA	NA		NA	0.055	5.03–7.60	0.024
Poor sleep	0.074	0.16–0.74	0.002	NA		NA	0.081	3.98–6.32	0.001	NA		NA
Obesity	0.154	0.41–0.77	<0.0001	0.106	7.35–12.37	<0.0001	0.124	4.64–6.60	<0.0001	0.120	3.84–6.24	<0.0001
Shift work	0.079	0.10–0.41	0.001	0.103	4.53–6.55	<0.0001	0.058	2.28–6.25	0.020	0.053	4.57–7.58	0.034

NA: not applicable.

Regarding the problem of collinearity amongst the independent factors, we observed that the tolerance was higher than 0.80 for all the independent predictor variables.

Discussion

In the present study, we found an independent positive association of total WBC and monocyte, neutrophil, and lymphocyte counts with shift work, cigarette smoking, and obesity. Furthermore, total WBC and neutrophil counts were also positively associated with poor sleep. Our results demonstrate that shift work and lifestyle factors may exert an important effect on peripheral total and differential leukocyte counts.

Numerous epidemiological and clinical studies have shown leukocytosis to be an independent predictor of insulin resistance, type 2 diabetes, microvascular and macrovascular complications of diabetes, future cardiovascular events, and patients with stable angina, unstable angina, or a history of myocardial infarction^8–15). Furthermore, the differential cell counts, including the eosinophil, neutrophil, and monocyte counts, also predict the future incidence of CHD^{18, 29, 30)}. However there is no research reporting about the differential leukocyte count in relation to shift work. Our study clarifies that the counts of the composite members of the WBC, especially the monocyte, neutrophil, and lymphocyte counts, are associated with shift work. To the best of our knowledge, this is the first report demonstrating that the peripheral total WBC, monocyte, neutrophil, and lymphocyte counts are associated with shift work.

Shift work disturbing daily rhythms is known to increase the risk for vascular disease such as circadian rhythm disorders and hypertension^{6, 7)}. Knutsson³³⁾ indicated that the pathological onset mechanism is deterioration of autonomic nerve regulatory function as a result of an imbalance in circadian rhythms, while others showed that hypertension and hypercholesterolemia, more often seen in shift workers, cause arteriosclerosis and easier development of CHD^{34, 35)}. It has also been indicated that shift work may affect metabolic function and glucose tolerance^{36, 37)}. All these things indicate that shift work can promote vascular changes and contribute to arteriosclerosis and CHD. In the present study, shift workers had higher FBS levels, and total and differential leukocytes counts and lower HDL-cholesterol levels than daytime workers. This finding might partly account for previous findings indicating that shift workers are more likely to develop atherosclerosis and cardiovascular and cancer morbidities^{34, 35, 38–40)}.

The present study showed that poor lifestyle factors, including obesity, cigarette smoking, and poor sleep were associated with increasing total and differential leukocytes counts. In addition, our present study also found that the rates of drinking, current smoking, hardly ever engaging in physical exercise, and poor sleep were significantly higher in shift workers than in daytime workers. It is presumed that shift work can cause poor lifestyle and thereby might contribute to higher peripheral total and differential WBC counts, leading to adverse health effects among shift workers^{41, 42)}. The effects of shift work on total and differential leukocytes counts require further study.

In this context, it is noteworthy that, in our findings, multiple regression analysis revealed that only shift work, cigarette smoking, and obesity appeared to be associated with the total WBC, monocyte, neutrophil, and lymphocyte counts and that poor sleep appeared to be associated with the total WBC and neutrophil counts. It should be noted that we excluded the possibility that the collinearity between the predictor variables influenced the effects. However, the tolerance was higher than 0.80 for all the independent predictor variables.

Shift workers are a selected population. The concept of the healthy worker effect proposes that those employees who cannot adapt to irregular working hours and those with poor health condition due to working in shifts or some other reason will more often leave shift work. This natural selection procedure may confound the differences between daytime and shift workers⁴³⁾. Our results did not provide evidence indicating that the peripheral total and differential leukocytes counts would be significantly elevated in former shift workers. Although former shift workers possibly had an elevated level of inflammation while working shifts, the levels tend to normalize after leaving irregular work shifts. Limitations of our study include its cross-sectional design, which limits our ability to infer a causal relationship between total and differential white cell counts and shift work. Another source of confounding may be due to the circadian variation in inflammatory parameters. In the present study, blood samples were drawn during the morning, the hours of the day that were not close to shift work. This decreased the possibility that a person would have had a significant phase delay in the circadian rhythm of blood leukocytes. In addition, although we controlled for other major risk factors, including potential confounders, such as drinking, betel quid chewing, and smoking, physical exercise, poor sleep, and obesity, the existence of unrecognized confounding variables is always possible. Our study did not provide serological data regarding infection or other markers of inflammation, such as C-reactive protein, tumor necrosis factor-α, or leukocyte adhesion molecules. Future studies should focus on whether white blood cells are activated to secrete specific cytokine markers associated with shift work.

In conclusion, our data indicate that the peripheral total WBC, monocyte, neutrophil, and lymphocyte counts are closely associated with and increased by shift work. These findings are in agreement with previous evidence regarding the association of inflammation and shift work^{1, 44–46)}. In contrast, previous study showed that no significant changes in monocyte count following night work can be justified by the results of a study which suggested that the elevation in blood levels of inflammatory markers is due to an increase in gene expression, not an increase in monocyte count⁴¹⁾. The results of the present study were obtained from a cross-sectional setting, and they need to be replicated in large longitudinal cohort studies. Furthermore, the mechanisms by which peripheral total and differential leukocytes counts and shift work are linked remain to be investigated.

Acknowledgments: The authors wish to thank the participating factories and their employees. The authors would also like to thank the E-Da Hospital, Kaohsiung City, Taiwan, for financially supporting this research under contract EDAHP104062.

Conflicts of interests: The authors declare that they have no conflicts of interest.

References

• 1)   Puttonen  S,  Viitasalo  K,  Härmä  M. Effect of shift work on systemic markers of inflammation. Chronobiol Int 2011; 28: 528-35.
• 2)   Hansson  GK. Immune mechanisms in atherosclerosis. Arterioscler Thromb Vasc Biol 2001; 21: 1876-90.
• 3)   Verdoia  M,  Barbieri  L,  Giovine  GD,  Marino  P,  Suryapranata  H,  De Luca  G; Novara Atherosclerosis Study Group (NAS). Neutrophil to Lymphocyte Ratio and the Extent of Coronary Artery Disease: Results From a Large Cohort Study. Angiology 2015 Mar 29. [Epub ahead of print]
• 4)   Ichihara  Y,  Ohno  J,  Suzuki  M,  Anno  T,  Sugino  M,  Nagata  K. Higher C-reactive protein concentration and white blood cell count in subjects with more coronary risk factors and/or lower physical fitness among apparently healthy Japanese. Circ J 2002; 66: 677-84.
• 5)   Puttonen  S,  Härmä  M,  Hublin  C. Shift work and cardiovascular disease - pathways from circadian stress to morbidity. Scand J Work Environ Health 2010; 36: 96-108.
• 6)   Akerstedt  T. Shift work and disturbed sleep/wakefulness. Occup Med 2003; 53: 89-94.
• 7)   Ha  M,  Park  J. Shift work and metabolic risk factors of cardiovascular disease. J Occup Health 2005; 47: 89-95.
• 8)   Schmidt  MI,  Duncan  BB,  Sharrett  AR, et al. Markers of inflammation and prediction of diabetes mellitus in adults (Atherosclerosis Risk in Communities Study): a cohort study. Lancet 1999; 353: 1649-52.
• 9)   Vozarova  B,  Weyer  C,  Lindsay  RS,  Pratley  RE,  Bogardus  C,  Tataranni  PA. High white blood cell count is associated with a worsening of insulin sensitivity and predicts the development of type 2 diabetes. Diabetes 2002; 51: 455-61.
• 10)   Ford  ES. Leukocyte count, erythrocyte sedimentation rate, and diabetes incidence in a national sample of US adults. Am J Epidemiol 2002; 155: 57-64.
• 11)   Ohshita  K,  Yamane  K,  Hanafusa  M, et al. Elevated white blood cell count in subjects with impaired glucose tolerance. Diabetes Care 2004; 27: 491-6.
• 12)   Kannel  WB,  Anderson  K,  Wilson  PW. White blood cell count and cardiovascular disease: insights from the Framingham Study. JAMA 1992; 267: 1253-6.
• 13)   Weijenberg  MP,  Feskens  EJ,  Kromhout  D. White blood cell count and the risk of coronary heart disease and all-cause mortality in elderly men. Arterioscler Thromb Vasc Biol 1996; 16: 499-503.
• 14)   Danesh  J,  Collins  R,  Appleby  P,  Peto  R. Association of fibrinogen, C-reactive protein, albumin, or leukocyte count with coronary heart disease: meta-analyses of prospective studies. JAMA 1998; 279: 1477-82.
• 15)   Lee  CD,  Folsom  AR,  Nieto  FJ,  Chambless  LE,  Shahar  E,  Wolfe  DA. 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: Atherosclerosis Risk in Communities Study. Am J Epidemiol 2001; 154: 758-64.
• 16)   Cavalot  F,  Massucco  P,  Perna  P,  Traversa  M,  Anfossi  G,  Trovati  M. White blood cell count is positively correlated with albumin excretion rate in subjects with type 2 diabetes (Letter). Diabetes Care 2002; 25: 2354-5.
• 17)   Tong  PC,  Lee  KF,  So  WY, et al. White blood cell count is associated with macro- and microvascular complications in Chinese patients with type 2 diabetes. Diabetes Care 2004; 27: 216-22.
• 18)   Madjid  M,  Awan  I,  Willerson  JT,  Casscells  SW. Leukocyte count and coronary heart disease: implications for risk assessment. J Am Coll Cardiol 2004; 44: 1945-56.
• 19)   Pertynska-Marczewska  M,  Kiriakidis  S,  Wait  R,  Beech  J,  Feldmann  M,  Paleolog  EM. Advanced glycation end products upregulate angiogenic and pro-inflammatory cytokine production in human monocyte/macrophages. Cytokine 2004; 28: 35-47.
• 20)   Shurtz-Swirski  R,  Sela  S,  Herskovits  AT, et al. Involvement of peripheral polymorphonuclear leukocytes in oxidative stress and inflammation in type 2 diabetic patients. Diabetes Care 2004; 24: 104-10.
• 21)   Hofmann  MA,  Schiekofer  S,  Isermann  B, et al. Peripheral blood mononuclear cells isolated from patients with diabetic nephropathy show increased activation of the oxidative-stress sensitive transcription factor NF-κB. Diabetologia 1999; 42: 222-32.
• 22)   Lee  FT,  Cao  Z,  Long  DM,  Panagiotopoulos  S,  Jerums  G,  Cooper  ME,  Forbes  JM. Interactions between angiotensin II and NF-kappaB-dependent pathways in modulating macrophage infiltration in experimental diabetic nephropathy. J Am Soc Nephrol 2004; 15: 2139-51.
• 23)   Scherberich  JE. Proinflammatory blood monocytes: main effector and target cells in systemic and renal disease; background and therapeutic implications. Int J Clin Pharmacol Ther 2003; 41: 459-64.
• 24)   Shanmugam  N,  Reddy  MA,  Guha  M,  Natarajan  R. High glucose-induced expression of proinflammatory cytokine and chemokine genes in monocytic cells. Diabetes 2003; 52: 1256-64.
• 25)   Guha  M,  Bai  W,  Nadler  JL,  Natarajan  R. Molecular mechanisms of tumor necrosis factor alpha gene expression in monocytic cells via hyperglycemia-induced oxidant stress-dependent and -independent pathways. J Biol Chem 2000; 275: 17728-39.
• 26)   Korpinen  E,  Groop  PH,  Fagerudd  JA,  Teppo  AM,  Akerblom  HK,  Vaarala  O. Increased secretion of TGF-beta1 by peripheral blood mononuclear cells from patients with type 1 diabetes mellitus with diabetic nephropathy. Diabet Med 2001; 18: 121-5.
• 27)   Kedziora-Kornatowska  KZ. Production of superoxide and nitric oxide by granulocytes in non-insulin-dependent diabetic patients with and without diabetic nephropathy. IUBMB Life 1999; 48: 359-62.
• 28)   Hofmann  MA,  Schiekofer  S,  Kanitz  M, et al. Insufficient glycemic control increases nuclear factor-kappa B binding activity in peripheral blood mononuclear cells isolated from patients with type 1 diabetes. Diabetes Care 1998; 21: 1310-6.
• 29)   Prentice  RL,  Szatrowski  TP,  Fujikura  T,  Kato  H,  Mason  MW,  Hamilton  HH. Leukocyte counts and coronary heart disease in a Japanese cohort. Am J Epidemiol 1982; 116: 496-509.
• 30)   Olivares  R,  Ducimetiere  P,  Claude  JR. Monocyte count: a risk factor for coronary heart disease? Am J Epidemiol 1993; 137: 49-53.
• 31)   Chung  FM,  Yang  YH,  Shieh  TY,  Shin  SJ,  Tsai  JC,  Lee  YJ. Effect of alcohol consumption on estimated glomerular filtration rate and creatinine clearance rate. Nephrol Dial Transplant 2005; 20: 1610-6.
• 32)   Lu  YC,  Wang  CP,  Hsu  CC, et al. Circulating secreted frizzled-related protein 5 (Sfrp5) and wingless-type MMTV integration site family member 5a (Wnt5a) levels in patients with type 2 diabetes mellitus. Diabetes Metab Res Rev 2013; 29: 551-6.
• 33)   Knutsson  A. Shift work and coronary heart disease. Scand J Soc Med Suppl 1989; 44: 1-36.
• 34)   Karlsson  BH,  Knutsson  AK,  Lindahl  BO,  Alfredsson  LS. Metabolic disturbances in male workers with rotating three-shift work. Results of the WOLF study. Int Arch Occup Environ Health 2003; 76; 424-30.
• 35)   Van Amelsvoort  LG,  Schouten  EG,  Kok  FJ. Impact of one year of shift work on cardiovascular disease risk factors. J Occup Environ Med 2004; 46: 699-706.
• 36)   Spiegel  K,  Leproult  R,  VanCauter  E. Impact of sleep debt on metabolic and endocrine function. Lancet 1999; 354; 1435-9.
• 37)   Ribeiro  DC,  Hampton  SM,  Morgan  L,  Deacon  S,  Arendt  J. Altered postprandial hormone and metabolic responses in a simulated shift work environment. J Endocrinol 1998; 158: 305-10.
• 38)   Bøggild  H,  Knutsson  A. Shift work, risk factors and cardiovascular disease. Scand J Work Environ Health 1999; 25: 85-99.
• 39)   Frost  P,  Kolstad  HA,  Bonde  JP. Shift work and the risk of ischemic heart disease—a systematic review of the epidemiologic evidence. Scand J Work Environ Health 2009; 35: 163-79.
• 40)   Schernhammer  ES,  Kroenke  CH,  Laden  F,  Hankinson  SE. Night work and risk of breast cancer. Epidemiology 2006; 17: 108-11.
• 41)   Khosro  S,  Alireza  S,  Omid  A,  Forough  S. Night work and inflammatory markers. Indian J Occup Environ Med 2011; 15: 38-41.
• 42)   Nakanishi  N,  Suzuki  K,  Tatara  K. Association between lifestyle and white blood cell count: a study of Japanese male office workers. Occup Med (Lond) 2003; 53: 135-7.
• 43)   Knutsson  A,  Hammar  N,  Karlsson  B. Shift workers' mortality scrutinized. Chronobiol Int 2004; 21: 1049-53.
• 44)   Sookoian  S,  Gemma  C,  Fernández Gianotti  T, et al. Effects of rotating shift work on biomarkers of metabolic syndrome and inflammation. J Intern Med 2007; 261: 285-92.
• 45)   Puttonen  S,  Härmä  M,  Hublin  C. Shift work and cardiovascular disease-pathways from circadian stress to morbidity. Scand J Work Environ Health 2010; 36: 96-108.
• 46)   Nishitani  N,  Sakakibara  H. Subjective poor sleep and white blood cell count in male Japanese workers. Ind Health 2007; 45: 296-300.