JACC
HOME SUBSCRIPTIONS CURRENT ISSUE PAST ISSUES CARDIOSOURCE SEARCH HELP FEEDBACK
QUICK SEARCH:   [advanced]


     

J Am Coll Cardiol, 2007; 49:1841-1850, doi:10.1016/j.jacc.2007.01.076 (Published online 20 April 2007).
© 2007 by the American College of Cardiology Foundation
This Article
Right arrow Abstract Freely available
Right arrow Figures Only
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
j.jacc.2007.01.076v1
49/18/1841    most recent
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via ISI Web of Science (2)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Ruggiero, C.
Right arrow Articles by Ferrucci, L.
Right arrow Search for Related Content
PubMed
Right arrow Articles by Ruggiero, C.
Right arrow Articles by Ferrucci, L.

CLINICAL RESEARCH: WHITE CELL COUNT AND CARDIOVASCULAR MORTALITY

White Blood Cell Count and Mortality in the Baltimore Longitudinal Study of Aging

Carmelinda Ruggiero, MD*,§,*, E. Jeffrey Metter, MD*, Antonio Cherubini, MD, PhD§, Marcello Maggio, MD, PhD*, Ranjan Sen, MD, PhD{dagger}, Samer S. Najjar, MD{ddagger}, Gwen B. Windham, MD*, Alessandro Ble, MD*, Umberto Senin, MD§ and Luigi Ferrucci, MD, PhD*

* Longitudinal Studies Section, Clinical Research Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
{dagger} Laboratory of Cellular and Molecular Biology, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
{ddagger} Human Cardiovascular Studies Unit, Laboratory of Cardiovascular Science, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
§ Department of Geriatrics and Gerontology, University of Perugia, Perugia, Italy.

Manuscript received October 10, 2006; revised manuscript received January 9, 2007, accepted January 13, 2007.

* Reprint requests and correspondence: Dr. Carmelinda Ruggiero, National Institute of Aging, NIH, 3001 South Hanover Street, Baltimore, Maryland 21225. (Email: ruggieroc07{at}hotmail.it).


   Abstract
Top
 Abstract
Methods
 Results
 Discussion
 References
 
Objectives: We investigated the secular trend in white blood cell (WBC) count and the relationship between WBC count and mortality between 1958 and 2002.

Background: The WBC count is a clinical marker of inflammation and a strong predictor of mortality. Limited data exist on the WBC count secular trend and the relationship between WBC and mortality.

Methods: One thousand eighty-three women and 1,720 men were evaluated longitudinally in the Baltimore Longitudinal Study of Aging. Blood samples and medical information were collected at the study entry and every 2 years during follow-up visits. The WBC count and all-cause, cardiovascular, and cancer mortality were assessed.

Results: A downward trend in WBC count was observed from 1958 to 2002. The secular downward trend was independent of age, gender, race, smoking, body mass index, and physical activity. The WBC count was nonlinearly associated with all-cause mortality and almost linearly associated with cardiovascular mortality. Participants with baseline WBC <3,500 cells/mm3 and WBC >6,000 cells/mm3 had higher mortality than those with 3,500 to 6,000 WBC/mm3. Within each WBC group, age-adjusted mortality rates declined in successive cohorts from the 1960s to the 1990s. Participants who died had higher WBC than those who survived, and the difference was statistically significant within 5 years before death.

Conclusions: Our study provides evidence for a secular downward trend in WBC count over the period from 1958 to 2002. Higher WBC counts are associated with higher mortality in successive cohorts. We found no evidence that the decline of age-specific mortality rates that occurred from 1960 to 2000 was attributable to a secular downward trend in WBC.

Abbreviations and Acronyms
  BMI = body mass index
  CI = confidence interval
  HR = hazard ratio
  MET = metabolic unit
  WBC = white blood cell

White blood cell (WBC) count is a marker of systemic inflammation, and elevated WBC count is associated with all-cause (1) as well as cancer (2), cerebrovascular (3), and cardiovascular (4) mortality. The WBC count is an independent risk factor for cardiovascular and cerebrovascular events (3,5) and a negative prognostic factor in congenital heart disease patients (6). Accordingly, WBC count is considered a biomarker of inflammatory processes that actively contribute to vascular injury and atherosclerosis (7,8). Whether elevated WBC count directly contributes to cardiovascular disease and mortality (9) or is merely a marker of negative cardiovascular risk profile remains controversial (10,11).

Over the last century, the percentage of persons who smoke declined, whereas the percentage of obese and sedentary individuals increased substantially (12,13). Whether the longitudinal trends in these risk factors account for changes in WBC count and potentially for the association between WBC count and mortality has not been investigated. Limited data exist on the longitudinal trend of WBC and the relationship between WBC and mortality over the second half of the 20th century. With the data from the BLSA (Baltimore Longitudinal Study of Aging) trial gathered from 1958 to 2002, we studied the longitudinal trend in WBC count and the relationship between WBC and mortality.


   Methods
Top
 Abstract
 Methods
Results
 Discussion
 References
 
Study sample.   The BLSA trial is a cohort of volunteers continually recruited since 1958, primarily from the Baltimore-Washington, DC, area. Participants healthy at study entry were consecutively enrolled in the study. From 1958 to 1978 the cohort included exclusively men; women were enrolled after 1978. A general description of the BLSA trial has been previously reported (14).

In this analysis, we considered 2,803 participants (1,083 women and 1,720 men) enrolled from 1958 to 2002. Participants had a median of 4 evaluations (range 1 to 28). Men had a median of 6 visits (range 1 to 28) over 22 ± 13.6 years, and women had a median of 3 visits (range 1 to 14) over 13 ± 7.9 years: 272 men and 294 women had a single evaluation.

Participants were evaluated every 2 years at the Gerontology Research Center in Baltimore, Maryland. Follow-up visits lasted 2 to 3 days and included medical evaluations and physiological and cognitive tests performed by a physician, nurse practitioner, or physician assistant. Fasting blood samples were collected in the morning and analyzed by the clinical laboratory of the Johns Hopkins Bayview Medical Center.

WBC count.   Total WBC count (cells/mm3) was performed with standard automated clinical methodologies. Evaluating WBC trajectories over time in specific individual and in the entire sample, we found no evidence for sudden change in WBC count at the times when methods for the assessment of WBC count were changed.

Participants were grouped according to the following cut points: 3,500, 6,000, and 10,000 cells/mm3. Two of these values delimit the WBC count normal range (3,500 to 10,000 cells/mm3), whereas 6,000 is the WBC count threshold above which mortality was higher than the average population mortality. Differential blood counts were obtained starting in 1968 and are available for only part of the database. Percentages of neutrophils and lymphocytes were determined by microscopic examination of Giemsa-stained peripheral blood smear. The absolute neutrophil and lymphocyte counts were calculated applying percentage to total WBC count.

Mortality.   Participants’ vital status was determined by telephone follow-up, correspondence, and searches of the National Death Index. Cause of death was determined by a consensus of 3 physicians who reviewed death certificates, medical records, correspondences, and other available material and adjudicated as "cardiovascular," "cancer," or "other."

Covariates.   Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. Participants were categorized as smokers, former smokers, or never smokers at each visit, on the basis of self-report. Leisure time physical activity was assessed by self-report of the amount of time spent performing 97 activities on the basis of a typical day, averaged over the prior 2 years (15). Each activity was assigned a value for metabolic units (METs) (METs/min or metabolic equivalents of resting oxygen consumption/min) with the coding system described by Ainsworth (16) and Jetté (17) and categorized according to MET intensity, as low intensity (≤4 METs), moderate intensity (4.1 to 5.9 METs), and high intensity (6 METs). Leisure time physical activity was finally expressed in average total METs/day. Diabetes mellitus was defined on the basis of the 1997 American Diabetes Association criteria (18). Plasma triglycerides and total cholesterol concentrations were determined with an enzymatic method (ABA-200 ATC Biochromatic Analyzer, Abbott Laboratories, Irving, Texas). Blood pressure was measured at the brachial artery with the participants sitting and with a sphygmomanometer which was calibrated at regular intervals with a mercury standard (19).

Statistical analysis of the data.   Differences in baseline characteristics between survivors and decedents were tested with Student t or chi-square tests. In analyses limited to selected decades, WBC counts, covariate, and outcome values were derived from BLSA trial visits that occurred during that decade. Crude all-cause, cardiovascular, and cancer mortality rates were expressed as the number of events/1,000 person-years according to WBC count groups. The hypothesis that WBC count changed over time across repeated measurements as an age-independent secular trend was tested with mixed effects models on the basis of non-linear mixed effects procedure in S-PLUS 6.2 (Insightful Inc., Seattle, Washington) (20).

Proportional hazards regression models were used to determine the contribution of WBC count to mortality, with the survival functions developed by Therneau and Brambsch (21). The Martingale residuals from the proportional hazards regression models were plotted versus WBC count. The Martingale residuals represent the "excess mortality" that reflects the discrepancy between observed hazard and expected hazard of mortality (21). Exploration of the Martingale residuals further supported the WBC classification into the 4 groups (≤3,500; 3,501 to 6,000; 6,001 to 10,000; >10,000) used in this analysis.

The effect of baseline WBC count on mortality was examined by Kaplan-Meier estimates and tested by proportional hazard analysis, with the classification in 4 WBC count groups described previously. In additional analyses, we used longitudinal time-dependent survival models where the subject’s WBC count and covariates were allowed to vary over time. Time-dependent covariates in the longitudinal analyses used the Anderson-Gill formulation as a counting process (22). For each subject, time was divided into intervals between evaluations, and the covariates were based on the evaluation at the start of the interval. Thus, the independent variables can increase or decrease over time. This longitudinal time-dependent survival functions have been developed in S-PLUS 6.2 by Therneu and Brambsch (21).

Proportionality was examined with both Kaplan-Meier plots and scaled Schoenfeld residuals by time plots. Kaplan-Meier plots examined the survival function in each WBC group according to the baseline evaluations. Survival plots seemed to be proportional, with exception of group with WBC count ≤3,500. Given a covariate, Schoenfeld residuals are the covariate-value for each person who died at a specific time minus the expected value of the covariate for the risk set at that specific time. Plotting Schoenfeld residual against the log transformation of time, with the cox.zph procedure in S-PLUS 6.2, the proportionality of the hazard was confirmed in all WBC groups.

To test whether the effect of WBC count on mortality was due to a relative acute or rapidly progressing illness, survival models were also estimated in subjects who survived at least 3 or 5 years after their initial evaluation. These models had similar findings to the full models and are not reported.

All analyses were performed with S-PLUS version 6.2 (Insightful Inc.). A 2-tailed p value <0.05 and 95% confidence interval (CI) were used to indicate statistical significance.


   Results
Top
 Abstract
 Methods
 Results
Discussion
 References
 
Description of the sample.   Characteristics of the participants are reported in Table 1 according to the vital status at the end of the follow-up and in Table 2 according to the WBC groups. There were 944 deaths over 48,347 person-years of follow-up: the unadjusted mortality rate was 22.5/1,000 person-years in men and 12.7/1,000 person-years in women. Compared with survivors, those who died were more likely to be smokers, were less physically active, and had significantly worse cardiovascular risk profile. Independent of age and date, both at baseline and in subsequent follow-up visits, decedents had higher WBC count than survivors (Table 1). Women had significantly lower WBC count than men, but WBC count was not significantly correlated with age in either gender.


View this table:
[in this window]
[in a new window]

  		Table 1 Baseline Characteristics of the Study Participants According to the Vital Status at the End of the Follow-Up
 

View this table:
[in this window]
[in a new window]

  		Table 2 Baseline Characteristics of the Study Participants According to WBC Count Groupings
 
WBC count secular trend.   A secular downward trend of WBC count was observed in both genders, (linear, quadratic, and cubic effects of date, p < 0.0001) in the period from 1958 to 2002 (Figs. 1A and 1B). Independent of age, the WBC rate of change in men was –114 cells/year in 1965 and became –48 cells/year in 1995 and in women was 6 cells/year in 1985 and became –48 cells/year in 1995. The downward trend in WBC count observed in men was independent of the age of the participants at the study entry in each decade from the 1960s to the 1990s. An exception was the irregular WBC count time trends found in participants older than 80 years over all the decades (data not shown) and the higher WBC count observed in the participants enrolled at age 40 to 59.9 years in the decade 1990 to 2000. Results were similar for women, limited to the 2 decades of enrollment (data not shown). In the period from 1958 to 2002, former and current smokers had always higher WBC count than never smokers and participants with higher BMI considered as time-dependent covariates had higher WBC count. However, WBC secular downward trend was independent of smoking and/or BMI in the entire period (data not shown). On the contrary, being physically active was associated with lower WBC count. An increase in physical activities of 100 METs/min/day was associated with a decrease of 18 WBC/mm3. However, the slight changes of physical activity in the BLSA participants over time did not significantly impact the WBC count secular downward trend.


Figure 1
View larger version (18K):
[in this window]
[in a new window]
[Download PPT slide]
  		Figure 1 Longitudinal Changes in WBC Count by Years of Initial Evaluation, Separately in Men and Women

The upward trend of white blood cell (WBC) count in the oldest cohorts can be explained by participants enrolled in the first 2 enrollment periods, who developed an increase in WBC count in their very old age. Numbers in the table are for participants enrolled in different time periods and, of these, the number who were still alive at the beginning of each decade.

 
WBC count and mortality.   The relationship between WBC count and mortality was nonlinear, with a nonsignificant upward mortality trend for WBC <3,500/mm3 and a significant linear increase in mortality for WBC >3,500/mm3 (Fig. 2A). Cardiovascular mortality increased linearly with higher WBC count (Fig. 2B), whereas there was a little effect of WBC count on cancer mortality (Fig. 2C). Because of the nonlinear relationship between WBC count and all-cause mortality, 4 WBC groups were identified on the basis of the values that identify the WBC normal range in our laboratory (3,500 cells/mm3 and 10,000 cells/mm3) and the visual identification of the value of WBC associated with increased mortality (6,000 cells/mm3) on functional form of the WBC mortality curve (Fig. 2, Table 2).


Figure 2
View larger version (17K):
[in this window]
[in a new window]
[Download PPT slide]
  		Figure 2 Relationship Between Excess Mortality and WBC Count in the Entire BLSA Trial Sample

The absolute difference between the observed mortality hazard and the expected mortality hazard over time is expressed as excess mortality and plotted against white blood cell (WBC) count for the entire sample. The dashed lines represent the 95% confidence intervals.

 
The lowest crude mortality rate was found among participants with WBC 3,501 to 6,000 (13.7/1,000 person-years). Participants with WBC count below and above the range 3,501 to 6,000 experienced increased mortality. The highest mortality (30.2/1,000 person-years) was observed in participants with WBC >10,000 (Table 3, Fig. 3A). Compared with the WBC group with lowest crude mortality, participants with 6,001 to 10,000 WBC and those with WBC >10,000 had an unadjusted hazard ratio (HR) for mortality of 1.54 (95% CI 1.33 to 1.78) and 1.99 (95% CI 1.54 to 2.57), respectively (Table 3). After adjustment for confounders, the mortality HR remained significantly higher in participants with WBC 6,001 to 10,000/mm3 (HR 1.28, 95% CI 1.01 to 1.63), and in those with WBC >10,000 (HR 1.62, 95% CI 0.92 to 2.85) compared with the reference group. The small number of participants did not allow any significant conclusion in term of mortality risk associated with WBC ≤3,500, although the partial adjusted HRs tended to remain higher compared with the reference group (Table 3, Fig. 3B).


View this table:
[in this window]
[in a new window]

  		Table 3 Mortality Rate and Hazard Ratio in WBC Count Groups
 

Figure 3
View larger version (24K):
[in this window]
[in a new window]
[Download PPT slide]
  		Figure 3 Kaplan-Meier Survival Curves and Proportional Hazard Survival Plots According to WBC Count Groups

(A) Kaplan-Meier curves. (B) Proportional hazard survival plots were evaluated at mean values of the explanatory variables. In participants with white blood cell (WBC) count ≤3,500/mm3, the predicted survival was extrapolated up to 20 years of follow-up, because of the absence of events after 20 years.

 
A further analytical model that allows participants to shift between WBC count groups over time confirmed the same findings. Participants within WBC 6,000 to 10,000, who theoretically have "normal" WBC count, had 30% to 40% higher risk of mortality compared with those with WBC 3,501 to 6,000. Participants with WBC <3,500 had 3-fold higher mortality (HR 3.33, 95% CI 1.38 to 8.05), and those with WBC >10,000 had 2-fold higher mortality (HR 2.12, 95% CI 1.45 to 3.08) compared with subjects with WBC 3,501 to 6,000. These results remained statistically significant after multiple adjustments (Table 4, Models 1, 2, and 3). No significant interaction was found between gender and WBC count and between age and WBC count, suggesting that WBC is a similar risk factor for mortality in men and women and the association between WBC count and mortality is similar at different ages. In analyses restricted to participants with differential WBC count (n = 6,227 observations), the association between WBC and mortality persisted in participants with WBC ≤3,500, whereas it was no longer significant in those with WBC 6,000 to 10,000 and WBC >10,000 after adjustment for neutrophil count (Table 4, Model 4). Independent of confounders, neutrophil count remained strongly associated with mortality.


View this table:
[in this window]
[in a new window]

  		Table 4 Mortality Hazard Ratio Comparing WBC Count Groups From the Longitudinal Time-Dependent Model
 
The Kaplan-Meier survival curve summarizing the relationship between baseline WBC group membership and mortality are reported in Figure 3A. Expected survival adjusted for age, race, gender, date, BMI, smoking, physical activity, and major cardiovascular risk factors estimated by proportional hazard models are depicted in Figure 3B. The hazard associated with a given WBC group membership compared with the reference group was approximately constant over the 40 years of follow-up. Above the threshold of 3,500 WBC/mm3, the estimated mortality risk was 11% higher per each baseline additional increase of 1,000 WBC/mm3.

Interestingly, WBC count increased progressively in participants who died during the follow-up starting several years before death, whereas WBC count remained stable over time in those who survived (Fig. 4A). In the subgroup with differential count available, the rise of WBC count was mostly accounted for by neutrophils and was already significant 5 years before death (Fig. 4B). Lymphocytes remained substantially stable over the same period (Fig. 4C).


Figure 4
View larger version (11K):
[in this window]
[in a new window]
[Download PPT slide]
  		Figure 4 Longitudinal Changes in WBC, Neutrophil, and Lymphocyte Counts

Longitudinal changes of age- and date-adjusted (A) white blood cell (WBC), (B) neutrophil, and (C) lymphocyte counts observed in the BLSA participants according to time before death for participants who died during the follow-up, and time before censorship for those who were censored. Note that neutrophils and lymphocytes are limited to participants who had differential WBC count (n = 6,227). 95% confidence interval estimated by a bootstrapping method.

 
WBC and mortality across the decades.   From 1958 to 2002, the crude and the age-adjusted mortality rates decreased over time in the entire sample and in groups with WBC 3,500 to 6,000, 6,001 to 10,000, and >10.000/mm3 (Table 5). Owing to the small number of participants and deaths, the age-adjusted mortality rate could not be reliably estimated in the extreme group with WBC ≤3,500/mm3 (Table 5). However, within each cohort all-cause mortality were significantly higher in participants with WBC >6,000/mm3 than in those with WBC 3,500 to 6,000/mm3. After adjustment for age, gender, smoking, race, BMI, and systolic blood pressure, the adjusted mortality HRs for participants with WBC ≤3.500, 6,001 to 10,000, and >10,000/mm3 compared with WBC 3,500 to 6,000/mm3 tended to decline in subsequent decades from 1960 to 2000 (Table 5).


View this table:
[in this window]
[in a new window]

  		Table 5 Mortality Risk in Each WBC Count Group According to the Baseline Evaluation Across 4 Decades of Follow-Up
 

   Discussion
Top
 Abstract
 Methods
 Results
 Discussion
References
 
Over the period from 1958 to 2002, we observed a downward trend in WBC count and a parallel decline in age-specific mortality. The WBC count was nonlinearly associated with all-cause mortality and almost linearly associated with cardiovascular mortality. The independent association between WBC count and mortality was maintained in subsequent cohorts. Within WBC count strata 3,500 to 6,000, 6,001 to 10,000, and >10,000/mm3, mortality rates were progressively lower in successive cohorts from the 1960s to the 1990s, suggesting that the secular downtrend in WBC count was not a major cause of the decline in mortality.

Our findings are the first evidence of a downward trend in WBC count over the period from 1958 to 2002. The secular downward trend in WBC count might be attributed to concurrent environmental changes (i.e., less exposure to infective agents and/or improved sanitary conditions) and lifestyle modifications (i.e., smoking, physical activity, diet, and alcohol) (23,24). In the BLSA population, the percentage of current smokers declined from 40% in the early 1960s to >10% by the early 1990s, and this decline was 25% greater than in the whole U.S. population. This study confirmed the theoretical impact of smoking on WBC count and previous findings that tobacco abstinence is associated with lower WBC and neutrophil count (24,25). Additionally, we found that the WBC count secular downtrend was partially independent of changes in smoking behavior over the same period.

Obesity is associated with a proinflammatory state (26), characterized by increased C-reactive protein, interleukin-6, and WBC count (27). In the BLSA participants, an upward secular trend in BMI occurred, particularly beginning in the late 1980s (28). Although BMI and WBC count were correlated, the secular downward trend in WBC was independent of changes in BMI.

Physical activity might also affect both the WBC downward trend and the mortality via the beneficial effect of exercise on the innate and adaptive immunity. Regular exercise might positively impact the release of anti-inflammatory cytokines (29,30), T-helper1/T-helper2 imbalance and neutrophil, natural killer cell, and macrophages activity (31). In the BLSA population, we found a slight upward longitudinal trend in physical activity, particularly from 1970s to 1990s (15). Interestingly, most physically active participants were in the lowest risk WBC count group, and physical activity was inversely associated with WBC count, but we found no evidence that changes in physical activity affected secular WBC downtrend.

We showed a nonlinear relationship between WBC count and all-cause and cancer mortality and a linear relationship between WBC count and cardiovascular mortality (9). Our findings that participants with >6,000 WBC/mm3 had higher mortality risk compared with those with WBC 3,500 to 6,000/mm3 are consistent with current published data suggesting that high WBC count is a risk factor for mortality (4–9). The significant higher mortality risk, even within the normal range of WBC (6,000 to 10,000/mm3) compared with WBC 3,500 to 6,000/mm3, suggest that WBC count is a marker of subclinical disease (4,10). Although we need to use caution in interpreting the higher mortality observed in participants with WBC <3,500/mm3, because of limited sample size and small number of events, this finding might indicate that participants in this WBC group had general poor health status (32).

The secular mortality decline, observed in subsequent BLSA cohorts enrolled from the 1960s to the 1990s, is at least in part independent of WBC secular downward trend and suggests that other factors, such as the changes in the treatment of inflammatory related conditions, should be considered. In a subgroup of the BLSA participants, we found that those who died during the follow-up had a progressive elevation in WBC and neutrophil counts starting 5 years before death, whereas those who remained alive had an opposite trend in both WBC and neutrophil counts. In the same BLSA subgroup, neutrophils mainly accounted for the effect of WBC on mortality, suggesting that a specific cell type or other aspects of inflammation might cause vascular disease and be directly associated with increased mortality (8,9,33,34).

The present study has limitations. The BLSA sample includes highly educated individuals of high socioeconomic status who were considered healthy at study entry and therefore are not representative of the general population. Data from women were available only from 1978, and data from American minorities are available only from 1990. Methods of WBC determination changed over time. However, measurements were performed in the same clinical laboratory, comparability between different methods were directly verified, and sudden changes in the WBC count over time ascribable to changes in methodology were not detected. Although the major traditional cardiovascular risk factors were considered in our analysis, we had no information on inflammatory factors, high-density lipoprotein cholesterol, coagulation factors, and so forth, for which WBC might be a surrogate marker. Inclusion of these factors in the analysis might have explained to a greater extent the relationship between WBC count and mortality.

In conclusion, our study suggests a downward trend in WBC count only partially explained by behavioral changes that occurred in the BLSA participants over the period from 1958 to 2002. The age-specific mortality rate in each successive cohort of participants declined over the 44 years of follow-up, but we found little evidence that such a decline in mortality was due to the secular downward trend in WBC. However, by showing that participants with WBC >6,000/mm3 experienced higher mortality compared with those with 3,501 to 6,000 WBC/mm3, that the increased risk of mortality associated with high WBC was maintained over the entire follow-up period, and that subjects who died had progressively higher neutrophil count compared with those who survived, our study suggests that differential WBC counts should be systematically screened and factored in the cardiovascular risk profile and ultimately considered in clinical decisions concerning prescription of preventive interventions.


   Footnotes
 
This research was supported by the Intramural Research Program of the National Institute on Aging, National Institutes of Health.


   References
Top
 Abstract
 Methods
 Results
 Discussion
 References
 

  1. Weiss ST, Segal MR, Sparrow D, Wager C. Relation of FEV1 and peripheral blood leukocyte count to total mortalityThe Normative Aging Study. Am J Epidemiol 1995;142:493-498.[Abstract/Free Full Text]
  2. Erlinger TP, Muntner P, Helzlsouer KJ. WBC count and the risk of cancer mortality in a national sample of U.S. adults: results from the Second National Health and Nutrition Examination Survey mortality study Cancer Epidemiol Biomarkers Prev 2004;13:1052-1056.[Abstract/Free Full Text]
  3. Brown DW, Ford ES, Giles WH, Croft JB, Balluz LS, Mokdad AH. Associations between white blood cell count and risk for cerebrovascular disease mortality: NHANES II Mortality Study, 1976–1992 Ann Epidemiol 2004;14:425-430.[CrossRef][ISI][Medline]
  4. Margolis KL, Manson JE, Greenland P, et al. Leukocyte count as a predictor of cardiovascular events and mortality in postmenopausal women: the Women’s Health Initiative Observational Study Arch Intern Med 2005;165:500-508.[Abstract/Free Full Text]
  5. Wheeler JG, Mussolino ME, Gillum RF, Danesh J. Associations between differential leucocyte count and incident coronary heart disease: 1764 incident cases from seven prospective studies of 30,374 individuals Eur Heart J 2004;25:1287-1292.[Abstract/Free Full Text]
  6. Brennan ML, Penn MS, Van Lente F, et al. Prognostic value of myeloperoxidase in patients with chest pain N Engl J Med 2003;23:1595-1604.
  7. Mehta JL, Saldeen TG, Rand K. Interactive role of infection, inflammation and traditional risk factors in atherosclerosis and coronary artery disease J Am Coll Cardiol 1998;31:1217-1225.[Abstract/Free Full Text]
  8. Alexander RW. Inflammation and coronary artery disease N Engl J Med 1994;331:468-469.[Free Full Text]
  9. Coller BS. Leukocytosis and ischemic vascular disease morbidity and mortality: is it time to intervene? Arterioscler Thromb Vasc Biol 2005;25:658-670.[Abstract/Free Full Text]
  10. Loimaala A, Rontu R, Vuori I, et al. Blood leukocyte count is a risk factor for intima-media thickening and subclinical carotid atherosclerosis in middle-aged men Atherosclerosis 2006;188:363-369.[CrossRef][ISI][Medline]
  11. Smith MR, Kinmonth AL, Luben RN, et al. Smoking status and differential white cell count in men and women in the EPIC-Norfolk population Atherosclerosis 2003;169:331-337.[CrossRef][ISI][Medline]
  12. Smith SS, Fiore MC. The epidemiology of tobacco use, dependence, and cessation in the United States Prim Care 1999;26:433-461.[ISI][Medline]
  13. Flegal KM, Carroll, MD, Kuczmarski RJ, Johnson CL. Overweight and obesity in the United States: prevalence and trends, 1960–1994 Int J Obes Relat Metab Disord 1998;22:39-47.[CrossRef][ISI][Medline]
  14. Shock NW, Gruelich RC, Andres RA. The Baltimore Longitudinal Study of Aging. Washington, DC: U.S. Government Printing Office; 1984.
  15. Talbot LA, Metter EJ, Fleg JL. Leisure-time physical activities and their relationship to cardiorespiratory fitness in healthy men and women 18–95 years old Med Sci Sports Exerc 2000;32:417-425.
  16. Ainsworth BE, Haskell WL, Leon AS, et al. Compendium of physical activities: classification of energy costs of human physical activities Med Sci Sports Exerc 1993;25:71-80.
  17. Jette M, Sidney K, Blumchen G. Metabolic equivalents (METS) in exercise testing, exercise prescription, and evaluation of functional capacity Clin Cardiol 1990;13:555-565.[ISI][Medline]
  18. Centers for Disease Control and Prevention Ten great public health achievements—United States, 1900–1999 Morb Mortal Wkly Rep 1999;48:241-242.[Medline]
  19. Pickering TG. Principles and techniques of blood pressure measurement Cardiol Clin 2002;20:207-223.[CrossRef][Medline]
  20. Pinheiro JC, Bates DM. Mixed-Effects Models in S and S-PLUS. New York, NY: Springer; 2000.
  21. Therneau TM, Brambsch PM. Modeling Survival Data: Extending the Cox Model. New York, NY: Springer; 2000.
  22. Anderson PK, Gill, MD. Cox’s regression model for counting processes: a large sample study Ann Stat 1982;10:1100-1120.[CrossRef]
  23. Armstrong GL, Conn LA, Pinner RW. Trends in infectious disease mortality in the United States during the 20th century JAMA 1999;281:61-66.[Abstract/Free Full Text]
  24. Abel GA, Hays JT, Decker PA, Croghan GA, Kuter DJ, Rigotti NA. Effects of biochemically confirmed smoking cessation on white blood cell count Mayo Clin Proc 2005;80:1022-1028.[ISI][Medline]
  25. Frohlich M, Sund M, Lowel H, Imhof A, Hoffmeister A, Koenig W. Independent association of various smoking characteristics with markers of systemic inflammation in menResults from a representative sample of the general population (MONICA Augsburg Survey 1994/95). Eur Heart J 2003;24:1365-1372.[Abstract/Free Full Text]
  26. Dandona P, Aljada A, Bandyopadhyay A. Inflammation: the link between insulin resistance, obesity and diabetes Trends Immunol 2004;25:4-7.[CrossRef][ISI][Medline]
  27. Bastard JP, Maachi M, Lagathu C, et al. Recent advances in the relationship between obesity, inflammation, and insulin resistance Eur Cytokine Netw 2006;17:4-12.[ISI][Medline]
  28. Tucker KL, Hallfrisch J, Qiao N, Muller D, Andres R, Fleg JL. The combination of high fruit and vegetable and low saturated fat intakes is more protective against mortality in aging men than is either alone: the Baltimore Longitudinal Study of Aging J Nutr 2005;135:556-561.[Abstract/Free Full Text]
  29. Elosua R, Bartali B, Ordovas JM, Corsi AM, Lauretani F, Ferrucci L. Association between physical activity, physical performance, and inflammatory biomarkers in an elderly population: the InCHIANTI study J Gerontol A Biol Sci Med Sci 2005;60:760-767.[Abstract/Free Full Text]
  30. Petersen AM, Pedersen BK. The anti-inflammatory effect of exercise J Appl Physiol 2005;98:1154-1162.[Abstract/Free Full Text]
  31. Yan H, Kuroiwa A, Tanaka H, Shindo M, Kiyonaga A, Nagayama A. Effect of moderate exercise on immune senescence in men Eur J Appl Physiol 2001;86:105-111.[CrossRef][ISI][Medline]
  32. Gurm HS, Bhatt DL, Lincoff AM, et al. Impact of preprocedural white blood cell count on long term mortality after percutaneous coronary intervention: insights from the EPIC, EPILOG, and EPISTENT trials Heart 2003;89:1200-1204.[Abstract/Free Full Text]
  33. Kawaguchi H, Mori T, Kawano T, Kono S, Sasaki J, Arakawa K. Band neutrophil count and the presence and severity of coronary atherosclerosis Am Heart J 1996;132:9-12.[CrossRef][ISI][Medline]
  34. Chapman CML, Beiby JP, McQuillan BM, Thompson PL, Hung J. Monocyte count, but not C-reactive protein or interleukin-6, is an independent risk marker for subclinical carotid atherosclerosis Stroke 2004;35:1619-1624.[Abstract/Free Full Text]



This article has been cited by other articles:


Home page
 J. Immunol.Home page
S. von Vietinghoff and K. Ley
Homeostatic Regulation of Blood Neutrophil Counts
J. Immunol., October 15, 2008; 181(8): 5183 - 5188.
[Abstract] [Full Text] [PDF]

Home page
 J Am Coll CardiolHome page
D. S.H. Bell and J. H. O'Keefe
White Cell Count, Mortality, and Metabolic Syndrome in the Baltimore Longitudinal Study of Aging
J. Am. Coll. Cardiol., October 30, 2007; 50(18): 1810 - 1810.
[Full Text] [PDF]

Home page
 J Am Coll CardiolHome page
C. Ruggiero, A. Cherubini, S. S. Najjar, and L. Ferrucci
Reply
J. Am. Coll. Cardiol., October 30, 2007; 50(18): 1810 - 1811.
[Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Figures Only
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
j.jacc.2007.01.076v1
49/18/1841    most recent
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via ISI Web of Science (2)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Ruggiero, C.
Right arrow Articles by Ferrucci, L.
Right arrow Search for Related Content
PubMed
Right arrow Articles by Ruggiero, C.
Right arrow Articles by Ferrucci, L.

HOME SUBSCRIPTIONS CURRENT ISSUE PAST ISSUES CARDIOSOURCE SEARCH HELP FEEDBACK