title: Sex differences in the relationship between leukocyte count and chronic kidney disease: the 2007 Korean National Health and Nutrition Examination Survey.
abstract: BACKGROUND
  Chronic kidney disease (CKD) has emerged as an independent predictor for cardiovascular disease (CVD), which is now regarded as an inflammatory disease. This study aimed to determine the association of CKD with white blood cell (WBC) count as a marker of systemic inflammation.
  
  
  METHODS
  We examined the association of WBC count with CKD in 2825 Korean adults (1155 men, 1670 women) in the 2007 Korean National Health and Nutrition Examination Survey (KNHANES). CKD was defined as either proteinuria or a glomerular filtration rate (GFR) <60 mL/min/1.73 m(2). The odds ratios (ORs) for CKD were calculated using multivariate logistic regression analysis after adjusting for confounding variables across gender-specific WBC count quartiles.
  
  
  RESULTS
  The proportion of CKD increased with increasing WBC quartiles, from 9.7% in the lowest quartile to 20.7% in the highest quartile for women. In multivariate logistic regression analysis, the corresponding odds ratios (95% confidence intervals [CIs]) for a CKD across WBC count quartiles among women were 1.00, 1.45 (0.91-2.31), 1.65 (1.03-2.63), and 2.11 (1.33-3.35), after adjusting for age, body mass index (BMI), systolic blood pressure, fasting plasma glucose, smoking status, current drinking high-density lipoprotein cholesterol (HDL-C), and triglyceride. In contrast, compared with women, men appeared to have no significant results of a relationship between WBC quartiles and CKD.
  
  
  CONCLUSIONS
  Our study shows a significant association between WBC count and the risk for CKD in women. Accordingly, potential health benefits of early detection of a higher level of WBC count may be useful for CKD risk assessment in women.
authors.affiliation: Department of Family Medicine, Yonsei University College of Medicine, Seoul, Korea.
authors.forename: Ha-Young
authors.initials: HY
authors.lastname: Na
authors.forename: Jae-Yong
authors.initials: JY
authors.lastname: Shim
authors.forename: Hye-Ree
authors.initials: HR
authors.lastname: Lee
authors.forename: Dong-Hyuk
authors.initials: DH
authors.lastname: Jung
authors.forename: Hong-Bae
authors.initials: HB
authors.lastname: Kim
authors.forename: Byoung-Jin
authors.initials: BJ
authors.lastname: Park
authors.forename: Rae-Jun
authors.initials: RJ
authors.lastname: Jung
authors.forename: Yong-Jae
authors.initials: YJ
authors.lastname: Lee
mesh_terms: D000328:Adult; D000428:Alcohol Drinking / Q000453:epidemiology / Q000523:psychology; D015415:Biomarkers; D001794:Blood Pressure / Q000502:physiology; D015992:Body Mass Index; D002784:Cholesterol / Q000097:blood; D003430:Cross-Sectional Studies; D005260:Female; D005919:Glomerular Filtration Rate / Q000502:physiology; D007722:Health Knowledge, Attitudes, Practice; D006801:Humans; D007723:Korea / Q000453:epidemiology; D007958:Leukocyte Count; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009313:National Health Programs; D009749:Nutrition Surveys; D011507:Proteinuria / Q000503:physiopathology; D051436:Renal Insufficiency, Chronic / Q000453:epidemiology*; D012907:Smoking / Q000453:epidemiology / Q000523:psychology; D011795:Surveys and Questionnaires
chemical_list: D015415:Biomarkers; D002784:Cholesterol