title: Differential white blood cell count and all-cause mortality in the Korean elderly.
abstract: The circulating white blood cell (WBC) count has been considered a good biomarker of systemic inflammation, but the predictive value of this inexpensive and universally obtained test result has not been fully explored in the elderly. The objective of this study was to assess the independent association of WBC count and its individual components with mortality in an elderly population. We studied a total of 9996 participants (age ≥65 years) who underwent routine health examinations at the 2 healthcare centers affiliated with Seoul National University. Mortality data were obtained from the National Statistics Office of Korea. The mean age of the study population was 69.7 (SD 4.3) years, and 5491 of the subjects (54.9%) were male. The median length of follow-up was 44.9 months (range, 1.2-78.7 months). There were 118 deaths (1.2%) during the follow-up period. The leading cause of death was cancer. Compared with the survivors, the deceased subjects were older, predominantly male, had increased levels of inflammatory markers, and had poor nutritional status. A significant difference in mortality was identified among patients in different WBC and WBC subtype quartile groups. Cox proportional hazards analysis indicated that monocyte count (HR: 5.18, 95% CI: 2.44-11.02) was a strongest predictor of all-cause mortality than total WBC count (HR: 1.57, 95% CI: 0.88-2.80), granulocyte count (HR: 2.11, 95% CI: 1.15-3.88), and lymphocyte count (HR: 1.11, 95% CI: 0.66-1.86), even after adjusting for possible confounding variables. Monocyte counts were associated with an increased risk of cardiovascular and cancer-related mortality in the elderly population. In conclusion, the total WBC count is an independent predictor of mortality in older adults, but the monocyte subtype provides greater predictive ability.
authors.affiliation: Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
authors.forename: Kwang-Il
authors.initials: KI
authors.lastname: Kim
authors.forename: Jaebong
authors.initials: J
authors.lastname: Lee
authors.forename: Nam Ju
authors.initials: NJ
authors.lastname: Heo
authors.forename: Sejoong
authors.initials: S
authors.lastname: Kim
authors.forename: Ho Jun
authors.initials: HJ
authors.lastname: Chin
authors.forename: Ki Young
authors.initials: KY
authors.lastname: Na
authors.forename: Dong-Wan
authors.initials: DW
authors.lastname: Chae
authors.forename: Cheol-Ho
authors.initials: CH
authors.lastname: Kim
authors.forename: Suhnggwon
authors.initials: S
authors.lastname: Kim
mesh_terms: D000367:Age Factors; D000368:Aged; D044466:Asian People; D002318:Cardiovascular Diseases / Q000097:blood / Q000208:ethnology / Q000401:mortality; D002423:Cause of Death; D016009:Chi-Square Distribution; D005260:Female; D006801:Humans; D007249:Inflammation / Q000097:blood* / Q000208:ethnology / Q000401:mortality*; D018836:Inflammation Mediators / Q000097:blood; D053208:Kaplan-Meier Estimate; D007958:Leukocyte Count; D008297:Male; D015999:Multivariate Analysis; D009369:Neoplasms / Q000097:blood / Q000208:ethnology / Q000401:mortality; D009752:Nutritional Status; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D056910:Republic of Korea / Q000453:epidemiology; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors
chemical_list: D018836:Inflammation Mediators