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Original Investigation |

Leukocyte Count as a Predictor of Cardiovascular Events and Mortality in Postmenopausal Women:  The Women’s Health Initiative Observational Study FREE

Karen L. Margolis, MD, MPH; JoAnn E. Manson, MD, DrPH; Philip Greenland, MD; Rebecca J. Rodabough, MS; Paul F. Bray, MD; Monika Safford, MD; Richard H. Grimm Jr, MD, PhD; Barbara V. Howard, PhD; Annlouise R. Assaf, PhD; Ross Prentice, PhD; Women’s Health Initiative Research Group
[+] Author Affiliations

A complete listing of the Women’s Health Initiative Research Group is given in a box at the end of this article.Author Affiliations: Hennepin County Medical Center, Minneapolis, Minn (Drs Margolis and Grimm); Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass (Dr Manson); Northwestern University, Chicago, Ill (Dr Greenland); Fred Hutchinson Cancer Research Center, Seattle, Wash (Ms Rodabough and Dr Prentice); Baylor College of Medicine, Houston, Tex (Dr Bray); University of Alabama, Birmingham (Dr Safford); Medstar Research Institute/Howard University, Washington, DC (Dr Howard); Brown University, Pawtucket, RI (Dr Assaf).


Arch Intern Med. 2005;165(5):500-508. doi:10.1001/archinte.165.5.500.
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Published online

Background  Increasing evidence supports a role for inflammation in the atherosclerotic process. The role of the leukocyte count as an independent predictor of risk of a first cardiovascular disease (CVD) event remains uncertain. Our objective was to describe the relation between the baseline white blood cell (WBC) count and future CVD events and mortality in postmenopausal women.

Methods  In this prospective cohort study set in 40 US clinical centers, the study population comprised 72 242 postmenopausal women aged 50 to 79 years, free of CVD and cancer at baseline, enrolled in the Women’s Health Initiative Observational Study. Main outcome measures included incident fatal coronary heart disease (CHD), nonfatal myocardial infarction, stroke, and total mortality.

Results  At baseline, the mean ± SD age of the women was 63 ± 7.3 years, 84% were white, 4% had diabetes, 35% had hypertension, and 6% were current smokers. The mean WBC count was 5.8 ± 1.6×109 cells/L. During a mean of 6.1 years of follow-up, there were 187 CHD deaths, 701 nonfatal myocardial infarctions, 738 strokes, and 1919 deaths from all causes. Compared with women with WBC counts inthe first quartile (2.5-4.7×109 cells/L), women in the fourth quartile (6.7-15.0×109 cells/L) had over a 2-fold elevated risk for CHD death (hazard ratio, 2.36; 95% confidence interval, 1.51-3.68), after multivariable adjustment for age, race, diabetes, hypertension, smoking, hypercholesterolemia, body mass index, alcohol intake, diet, physical activity, aspirin use, and hormone use. Women in the upper quartile of the WBC count also had a 40% higher risk for nonfatal myocardial infarction, a 46% higher risk for stroke, and a 50% higher risk for total mortality. In multivariable models adjusting for C-reactive protein, the WBC count was an independent predictor of CHD risk, comparable in magnitude to C-reactive protein.

Conclusions  The WBC count, a stable, well-standardized, widely available and inexpensive measure of systemic inflammation, is an independent predictor of CVD events and all-cause mortality in postmenopausal women. A WBC count greater than 6.7×109 cells/L may identify high-risk individuals who are not currently identified by traditional CVD risk factors.

Figures in this Article

Increasing evidence supports a role for inflammation in the atherosclerotic process.1,2 Initiation, growth, and complications of atherosclerotic plaques are each judged to be an inflammatory response to vascular injury,3,4 and inflammatory markers and cytokines originating in the heart, vessel walls, macrophages, adipose tissue, and liver have been associated with the risk of coronary events.5 In light of the multitude of pathobiological factors involved in inflammation, a large number of targets for measurement have been proposed to identify and monitor the inflammatory process in patients with, or at risk for, coronary heart disease (CHD). These include proinflammatory factors such as oxidized low-density lipoproteins, proinflammatory cytokines (eg, interleukin 1 and tumor necrosis factor-α), adhesion molecules (eg, intercellular adhesion molecule 1 and selectins), inflammatory stimuli with hepatic effects (eg, interleukin 6), or the products of the hepatic stimulation, such as serum amyloid A, C-reactive protein (CRP), and other acute-phase reactants.6 In addition, indicators of cellular responses to inflammation, such as elevated white blood cell (WBC) count, have also been considered.6

As early as 1954, Cole et al7 made the observation that patients with myocardial infarction (MI) with elevated WBC counts had a 4-fold higher risk of death compared with patients with WBC counts in the normal range. Since then, prospective studies have suggested a relation between higher total leukocyte count and cardiovascular disease (CVD) events and mortality.830 Furthermore, in the Multiple Risk Factor Intervention Trial (MRFIT), a decline in the WBC count over time was associated with reduced CHD mortality.14 Although the WBC count is associated with other established CVD risk factors, most notably cigarette smoking, many studies have found an independent association of WBC counts and CVD risk.1316,18,2030 A number of the studies cited above have included women, although only a few presented data stratified by sex.13,17,20,21,24,28 Only 2 studies found a positive relationship between the leukocyte count and future cardiovascular events in women after adjusting for other CVD risk factors.20,28

The Women’s Health Initiative (WHI) Observational Study (WHI-OS) is a multicenter longitudinal cohort study of 93 676 postmenopausal women, composed of diverse racial/ethnic and socioeconomic groups. At baseline, participants in the WHI had leukocyte counts measured, in addition to giving an extensive history and undergoing a physical examination. Because of its large size and broad representation of women from across the United States, this cohort provides an opportunity to determine whether the association of WBC count with future cardiovascular events is present in postmenopausal women and to examine the independence of this association from other known CVD risk factors and biomarkers. In this article, we describe the relation between the baseline leukocyte count and future cardiovascular events in women enrolled in the WHI Observational Study who were initially free of clinical CVD and cancer.

STUDY POPULATION

As described elsewhere, the WHI has clinical trial and observational study components.31,32 The latter component is an ongoing prospective cohort study of postmenopausal women, and is designed to examine the association between clinical, socioeconomic, behavioral, and dietary risk factors and the subsequent incidence of health outcomes. Between September 1, 1994, and December 31, 1998, the WHI-OS enrolled 93 676 women aged 50 to 79 years at 40 clinical centers throughout the United States.

Participants were recruited from areas surrounding the 40 clinical centers in 24 states and the District of Columbia.33 Women were eligible to participate in the WHI-OS if they were postmenopausal; unlikely to change residence or die within 3 years; did not have complicating conditions such as alcoholism, drug dependency, or dementia; and were not enrolled in the WHI, or any other clinical trial. The baseline characteristics of the WHI-OS cohort have been described in detail.34 All participants provided informed consent using materials approved by institutional review boards at each center.

Participants entered the WHI-OS by expressing initial interest in either the diet modification or hormone therapy arms of the WHI Clinical Trial but proved ineligible or unwilling to participate or responded to a direct invitation to be screened for the WHI-OS. More than 80% of WHI-OS participants preferred to participate in an observational rather than interventional component of WHI or did not meet the requirements for the diet modification part of the clinical trial (fat intake >32% of calories and ≤10 meals per week away from home). Other common reasons for participation in the WHI-OS were closure of the appropriate age clinical trial stratum (about 10%) or a history of breast cancer (about 5%). The following participants were excluded from the original cohort of 93 676 for these analyses: 1635 with a missing WBC count, 141 with a WBC count less than 2.5×109 cells/L, 213 with a WBC count greater than 15.0×109 cells/L, 12 075 with any cancer diagnosis at baseline except nonmelanoma skin cancer, 7992 women with a history of CVD at baseline, and 1423 women with missing data on CVD at baseline. Some women had more than 1 exclusion criterion, yielding a final sample of 72 242.

DATA COLLECTION

Participants underwent initial screening visits, during which personal information, medical history, health-related habits, and medication and vitamin use were assessed. Anthropometric measurements, blood pressure, and fasting blood specimens were obtained. The blood collection took place in the morning after a 12-hour tobacco-free fast. The hemogram sample was collected in a tube containing the anticoagulant edetic acid. These samples were analyzed at local laboratories at each of the 40 WHI Clinical Centers. Certified staff performed physical measurements and obtained blood samples at the baseline clinic visit. Women were asked to specify their race/ethnicity from 6 categories: American Indian or Alaskan Native, Asian or Pacific Islander, black or African American (not of Hispanic origin), Hispanic/Latino, non-Hispanic white, and other. Women were considered to have previous cancer or CVD if they self-reported a history of any type of cancer except nonmelanoma skin cancer, myocardial infarction (MI), stroke, angina, congestive heart failure, coronary revascularization, or peripheral arterial disease. Participants were asked whether they had ever been told by a physician that they had hypertension or high blood pressure, diabetes, or high blood glucose when they were not pregnant, or high cholesterol that required taking pills. Family history of MI at a young age in first-degree relatives (men <55 years and women <65 years), past or current smoking status, aspirin use, and frequency of alcohol consumption were queried. Fiber intake, fruit and vegetable intake, and polyunsaturated-saturated fatty acid ratio were obtained using a validated food frequency questionnaire based on instruments previously used in large-scale dietary intervention trials.35,36 A participant was considered a current or former hormone therapy user if she used an estrogen or progesterone containing pill or patch for at least 3 months following menopause. Recreational physical activity was assessed by questions on the frequency and duration of several types of recreational activity, and metabolic equivalent task scores were computed as the product of days per week, minutes per day, and the metabolic equivalent task value for each activity.37

FOLLOW-UP AND ASCERTAINMENT OF CASES

The WHI-OS follow-up was conducted by annual mailed self-administered medical update questionnaires (except for year 3, when participants attended a clinical follow-up visit). Participants mailed their completed questionnaires to their local clinical center for data entry and outcomes processing. As of August 31, 2003, the response rates for medical history updates from years 1 through 6 were 96%, 94%, 96%, 94%, 94%, and 93%, respectively; 1.8% of the WHI-OS participants had been lost to follow-up, an additional 1.8% had stopped follow-up, and 4.2% had died.

At each annual contact, initial reports of treatment or hospitalization for “problems with the heart or circulation, stroke, or transient ischemic attack” were obtained using a self-administered questionnaire. Medical records and death certificates were obtained and reviewed by a trained local physician adjudicator to verify all events. Coronary heart disease death was defined as death consistent with CHD as the underlying cause plus 1 or more of the following: hospitalization for MI within 28 days of death, previous angina or MI and no potentially lethal noncoronary cause of death, death related to a procedure for coronary artery disease, or death certificate consistent with CHD as the underlying cause. The diagnosis of acute MI was established according to an algorithm adapted from standard criteria38 that included clinical symptoms, cardiac enzymes and troponin levels, and electrocardiogram readings. Stroke diagnosis was based on the rapid onset of a persistent neurological deficit attributable to an obstruction or rupture of the arterial system supported by imaging studies when available. The neurological deficit must have lasted more than 24 hours, unless death supervened or there was a demonstrable radiographic lesion compatible with acute stroke. A sample of the locally verified events was reviewed by central cardiovascular adjudicators. For the WHI-OS, the agreement of central review with local adjudication was 79% for CHD death and 82% for MI. Although strokes were not centrally adjudicated for the WHI-OS, the agreement of central review with local adjudication in the WHI clinical trials was 91% for stroke.

A previously published ancillary study from the WHI-OS, using a prospective, nested case-control design, measured total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and CRP on stored baseline serum samples with a high-sensitivity assay.39 Among 75 343 women with no history of CVD or cancer, 304 women with incident first MI or death from CHD during 2.9 years of follow-up were defined as cases and matched by age, smoking status, ethnicity, and follow-up time with 304 study participants who remained event free. In this study, a CRP level in the upper quartile was independently associated with about a 2-fold increase in the risk of developing CHD, after matching for the above variables, and after adjusting for TC/HDL-C ratio, body mass index, hypertension, diabetes, family history of premature coronary artery disease, exercise frequency, alcohol consumption, and use of hormone therapy. We performed additional logistic regression analyses using this data set with WBC count as the main predictor variable.

STATISTICAL ANALYSIS

To describe participant characteristics across levels of WBC count, WBC was categorized using quartile divisions and cross-tabulations were examined. Hazard ratios (HRs) and nominal 95% confidence intervals (CIs) from Cox proportional hazards regression analyses are reported for the outcomes CHD death, nonfatal MI, stroke, total CVD events (CHD death, MI, or stroke) and total mortality. An additional 430 participants with no follow-up and 5551 participants without complete case data for all covariates included in the multivariate modeling were excluded from all Cox regression models (n = 66 261). The initial model adjusted only for age, race, and ethnicity. The fully adjusted model also included baseline hypertension, diabetes, hypercholesterolemia, smoking, body mass index, alcohol intake, polyunsaturated-saturated fatty acid ratio, dietary fiber, fruit and vegetable intake, physical activity, and current use of aspirin or hormone therapy. Follow-up time for each woman was accrued from enrollment to the date of CVD event, loss to follow-up, or administrative censoring date (August 31, 2003). Mean length of follow-up for the cohort was 6.1 years (range, 0.002-8.9 years).

The assumption of proportionality was tested by including indicators for the upper 3 WBC count quartiles, product terms between these indicators and follow-up time, and using a likelihood ratio procedure to test for zero coefficients for the 3 product terms. The assumption was met for all of the outcomes included in the analysis. Trends across WBC count quartiles were assessed by including a variable that equaled the median of the WBC values within the pertinent quartile. Corresponding P values are reported. Stratified Cox models were also examined separately by age, race/ethnicity, smoking, history of diabetes, hypertension, body mass index, and history of hypercholesterolemia, adjusting for all other variables previously listed for the fully adjusted models. Interactions of age, race/ethnicity, smoking, and diabetes with WBC count quartile were tested separately for each covariate, using likelihood ratio tests, comparing fully adjusted Cox models with and without the interaction terms.

Logistic regression modeling was performed using the WHI-OS case-control data to examine the effect of WBC count, as well as the joint effect of WBC count × CRP, on CHD. A progression of 3 models (crude, adjusted for TC/HDL-C ratio and CRP, and fully adjusted) was used to examine the main effect of WBC count by quartile on CHD. The joint effect of WBC count × CRP level quartiles was examined after adjusting for TC/HDL-C ratio. Models were based on case-control pairs for whom data were available on all variables of interest. All statistical analyses were performed using the SAS System for Windows (version 9.00; SAS Inc, Cary, NC).

Table 1 shows the baseline characteristics of the WHI-OS participants in this analysis by quartile of WBC count. Age increased across the WBC count quartiles, with women in the fourth quartile about 1.5 years older, on average, than women in the first quartile. Blacks and Asian/Pacific Islanders were more likely to be in the first WBC count quartile compared with members of other racial/ethnic groups. Women in lower income categories were more likely to be in the upper WBC count quartiles. There was a striking increase in the prevalence of cardiovascular risk factors (diabetes, hypertension, hypercholesterolemia, family history of MI, and current smoking) across the WBC quartiles. Alcohol intake of at least 1 drink per week, fiber intake, fruit and vegetable intake, polyunsaturated-saturated fatty acid ratio and physical activity were inversely related to WBC count quartile; whereas body size and both systolic and diastolic blood pressure were positively related to WBC count. Use of aspirin and both unopposed estrogen and combined estrogen and progestin increased across the WBC count quartiles. All of these differences were significant at P<.001.

Table Graphic Jump LocationTable 1. Baseline Characteristics of WHI-OS Participants Free of CVD at Baseline, by WBC Count Quartile*

The association between WBC count quartile and incident cardiovascular events is shown in Table 2. Included in this analysis were 187 CHD deaths, 701 nonfatal MIs, 738 strokes, 1510 total CVD events, and 1919 deaths from all causes. Each of these events had a strong and graded association with WBC count quartile in the age and race/ethnicity adjusted models. The strength of the association was attenuated by further adjustment for other CVD risk factors but still remained statistically significant for all outcomes. After multivariable adjustment, compared with women with WBC counts in the first quartile, women in the fourth quartile had a more than 2-fold elevated risk for CHD death and 40% to 50% higher risks for nonfatal MI, stroke, total CVD, and total mortality. A secondary analysis was performed for the outcome of total CVD events using deciles of the WBC count. Starting in the ninth decile (WBC, 6.8-7.6×109 cells/L) and 10th decile (WBC, 7.7-15.0×109 cells/L) the HRs were significantly elevated compared with the first decile: 1.30 (95% CI, 1.02-1.65; P = .03) and 1.56 (95% CI, 1.23-1.98; P<.001), respectively. When WBC count was modeled as a continuous variable, the HR for total CVD events per 1.0×109-cells/L increase in WBC count was 1.11 (95% CI, 1.08-1.15; P<.001).

Table Graphic Jump LocationTable 2. Association Between WBC Quartile and Incident Cardiovascular Events in WHI-OS Participants Free of Cancer and CVD at Baseline*

In the 608 women included in the WHI-OS case-control study of CHD events, the median CRP level in cases was 0.33 mg/dL and in controls was 0.25 mg/dL (P<.001). The TC/HDL-C ratio was 4.2 in cases and 3.7 in controls (P<.001).39 A WBC count in the upper quartile was associated with a more than 2-fold increase in events even after adjusting for multiple other risk factors including CRP level and TC/HDL-C ratio (Table 3 and Table 4). In the fully adjusted model, the odds ratio for CHD events for the fourth vs first WBC count quartile was 2.36 (95% CI, 1.33-4.19; P for trend, .01). In contrast, the odds ratio for CHD events for the fourth vs first CRP level quartile was 1.95 (95% CI, 0.95-4.01; P for trend, .02). Table 5 presents logistic regression analyses of the risk of CHD based on the joint relationship between CRP and WBC count, adjusted for TC/HDL-C ratio. The referent group is the first quartile of both the WBC count and CRP. The risk of CHD was generally close to unity in the categories defined by the lower 3 quartiles of WBC count × CRP, and was more than doubled in the upper quartile of most joint categories, with an additive nearly 7-fold elevation of risk for women with WBC count and CRP in the upper quartile of both biomarkers (odds ratio, 6.8; 95% CI, 2.7-16.9; P<.001).

Table Graphic Jump LocationTable 3. Crude and Adjusted Odds Ratios* for Coronary Heart Disease According to Baseline WBC Count Quartiles
Table Graphic Jump LocationTable 4. Crude and Adjusted Odds Ratios* for Coronary Heart Disease According to Baseline CRP Level Quartiles
Table Graphic Jump LocationTable 5. Odds Ratios (95% CIs) From a Bivariate Logistic Regression Model* Predicting Coronary Heart Disease Based on the Joint WBC Count×CRP Level Relationship

We examined risk of total CVD events for the highest compared with the lowest WBC count quartiles in subgroups defined by age, race, and other CVD risk factors (Figure). For age, race, and CVD risk factor subgroups, the HRs and 95% CIs were consistent with the 50% excess risk seen in the whole cohort, and tests for interaction did not reveal any evidence for effect modification. For women without current smoking, diabetes, hypertension, obesity, or history of hypercholesterolemia, the adjusted HR for the fourth vs first quartile was 1.70 (95% CI, 1.28-2.27; P for trend, <.001).

Place holder to copy figure label and caption
Figure.

Hazard ratios (95% confidence intervals) for cardiovascular event in the highest compared with the lowest white blood cell count quartiles in subgroups of age, race, and cardiovascular risk factors. All models were adjusted for age, race/ethnicity, diabetes, hypertension, high cholesterol, smoking status, body mass index, alcohol intake, physical activity, aspirin use, dietary fiber, fruit and vegetable intake, polyunsaturated/saturated fatty acid ratio, and prior use of hormone therapy. BMI indicates body mass index (calculated as weight in kilograms divided by the square of height in meters); CVD, cardiovascular disease.

Graphic Jump Location

These data indicate that the WBC count is an independent predictor of CHD events, stroke, and all-cause mortality in postmenopausal women. The upper quartile appears to be a reasonable threshold for elevated risk and is approximated by a WBC count of greater than 6.7× 109 cells/L. Furthermore, a WBC count in the upper quartile is associated with an increased risk of cardiovascular events in subgroups of postmenopausal women without other risk factors, including older age, smoking, diabetes, hypertension, and obesity. The WBC count remains a significant predictor of CHD events even after further adjustment for lipids and CRP.

Few previous studies have assessed the WBC count as a predictor of CVD events in generally healthy women, and none has also controlled for CRP. A meta-analysis including nearly 6000 subjects from 14 population-based studies concluded that the risk ratio for CHD events in the upper vs lower tertile of WBC count was 1.5 (95% CI, 1.4-1.6).40 Of the 3 studies that included women and presented results stratified by sex,10,20,24 only the first National Health and Nutrition Examination Survey (NHANES I) epidemiologic and follow-up study found a positive association between the WBC count and CHD events in women after adjustment for other risk factors. Several subsequent studies have found a significant relationship between the WBC count and cardiovascular events in women. In the NHANES II mortality study, women in the upper tertile of WBC count had an adjusted CHD mortality HR of 1.7.28 In the Atherosclerosis Risk in Communities (ARIC) prospective study, the risk of incident stroke was doubled in women with WBC counts in the upper quartile.29 In contrast, in NHANES I no significant associations of the WBC count with stroke were observed in men or women after adjustment for smoking, despite a similar number of events21 (625 strokes in NHANES I vs 708 in ARIC).

In a recent American Heart Association/Centers for Disease Control and Prevention scientific statement,6 many measures of the inflammatory process were considered for their potential utility in the clinical setting for CHD risk assessment. Factors regarded as important for selection of new markers included the following: ability to standardize the assay and to control measurement variability; independence of the new measure from established risk factors; association with cardiovascular end points in observational studies and clinical trials; presence of population norms to guide interpretation of results; ability to improve the overall prediction beyond that of traditional risk factors; generalization of results to various population groups; and acceptable cost of the assays. Based on these parameters, CRP was judged potentially capable of having utility in the clinical assessment of inflammation and CHD risk evaluation.6 Considering the factors outlined for assessment of the utility of inflammatory markers for CHD risk assessment, the WBC count appears comparable to CRP.

Prospective studies and nested case-control studies have shown a graded dose-response relationship between levels of CRP and higher long-term risk for future cardiovascular events among apparently healthy individuals, and CRP has been suggested as an adjunct to traditional risk factor measurement in those with intermediate levels of cardiovascular risk.6 To our knowledge, there are no previously published studies in which WBC count and CRP have been compared head-to-head in individuals without known CVD or dyslipidemia. However, in patients with acute coronary syndromes, WBC count and CRP were found to be independent, additive predictors of 6-month mortality.41 In a prospective study of patients with angiographically proven coronary artery disease and no recent MI, adjusting for CRP eliminated the association of the WBC count with long-term mortality.42 In a nested case-control study of dyslipidemic men enrolled in the Helsinki Heart Study, the joint effect of a high CRP level and high WBC count on incident CHD events was additive.43

It is not known whether leukocytes are involved directly in the pathogenesis of cardiovascular events or are only a risk marker for other factors causing the disease. The independence of the risk associated with higher WBC counts from other risk factors suggests that the relationship may in fact be causal. Plausible biological mechanisms also exist to support a causal link. Monocytes contribute to atherogenesis by giving rise to foamy macrophages and reactive oxygen species and have been implicated as one of the leukocyte types associated with CHD events.10,19 Both macrophages and lymphocytes secrete proinflammatory cytokines, and mast cells secrete serine proteases that activate matrix metaloproteases.44 Monocytes also participate in vascular thrombosis via interactions with platelets45 and are a rich source of highly thrombogenic tissue factor.

A number of limitations of this analysis must be considered. Only 1 measurement of WBC was performed, and the analyses were done in 40 local laboratories on automated counters. Multiple measurements in a central laboratory would have reduced measurement error and increased the precision of our results; thus, our current results are likely to be underestimates of the true associations because of nondifferential misclassification. The participants in WHI were generally healthy, well-educated volunteers; therefore, our results may not apply to the general population, despite the broad geographic representation of the 40 clinical centers. Another important issue is that other laboratory measurements were performed only in the nested case-control study and in a 1% subsample of the WHI-OS cohort; therefore, we are unable to adjust for blood markers of cardiovascular risk, such as lipoproteins, clotting factors, and other inflammatory markers in the entire cohort. However, we included the self-report of elevated cholesterol level requiring medication in our multivariable models. The similar relative risk estimate that we obtained for the upper quartile of the WBC count in the WHI-OS case-control sample suggests that the results would have changed little with the addition of the above blood measurements.

In summary, we have demonstrated that a WBC count in the upper quartile is independently associated with cardiovascular events and death in older women after adjustment for traditional risk factors. This offers a stable, well-standardized, widely available and inexpensive measure of systemic inflammation. These data add to available evidence in men suggesting a similar link and suggest that the predictive role of the WBC count is independent ofCRP. Cardiovascular risk categorization by inflammatory markers, including the WBC count, may identify high-risk individuals who are not currently identified by traditional risk factors; further studies are needed to assess the effectiveness of risk reduction in these patients.

Box Section Ref ID

The Women’s Health Initiative Research Group

Program Office: (National Heart, Lung, and Blood Institute, Bethesda, Md) Barbara Alving, Jacques Rossouw, Linda Pottern.

Clinical Coordinating Center: (Fred Hutchinson Cancer Research Center, Seattle, Wash) Ross Prentice, Garnet Anderson, Andrea LaCroix, Ruth E. Patterson, Anne McTiernan; (Wake Forest University School of Medicine, Winston-Salem, NC) Sally Shumaker, Pentti Rautaharju; (Medical Research Labs, Highland Heights, Ky) Evan Stein; (University of California at San Francisco) Steven Cummings; (University of Minnesota, Minneapolis) John Himes; (University of Washington, Seattle) Bruce Psaty.

Clinical Centers: (Albert Einstein College of Medicine, Bronx, NY) Sylvia Wassertheil-Smoller; (Baylor College of Medicine, Houston, Tex) Jennifer Hays; (Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass) JoAnn Manson; (Brown University, Providence, RI) Annlouise R. Assaf; (Emory University, Atlanta, Ga) Lawrence Phillips; (Fred Hutchinson Cancer Research Center, Seattle) Shirley Beresford; (George Washington University Medical Center, Washington, DC) Judith Hsia; (Harbor-UCLA Research and Education Institute, Torrance, Calif) Rowan Chlebowski; (Kaiser Permanente Center for Health Research, Portland, Ore) Cheryl Ritenbaugh; (Kaiser Permanente Division of Research, Oakland, Calif) Bette Caan; (Medical College of Wisconsin, Milwaukee) Jane Morley Kotchen; (MedStar Research Institute/Howard University, Washington, DC) Barbara V. Howard; (Northwestern University, Chicago/Evanston, Ill) Linda Van Horn; (Rush-Presbyterian St Luke’s Medical Center, Chicago) Henry Black; (Stanford Center for Research in Disease Prevention, Stanford University, Stanford, Calif) Marcia L. Stefanick; (State University of New York at Stony Brook, Stony Brook, NY) Dorothy Lane; (The Ohio State University, Columbus) Rebecca Jackson; (University of Alabama at Birmingham) Cora Beth Lewis; (University of Arizona, Tucson/Phoenix, Ariz) Tamsen Bassford; (University at Buffalo, Buffalo, NY) Maurizio Trevisan; (University of California at Davis, Sacramento) John Robbins; (University of California at Irvine, Orange) Allan Hubbell; (University of California at Los Angeles) Howard Judd; (University of California at San Diego, La Jolla/Chula Vista, Calif) Robert D. Langer; (University of Cincinnati, Cincinnati, OH) Margery Gass; (University of Florida, Gainesville/Jacksonville) Marian Limacher; (University of Hawaii, Honolulu) David Curb; (University of Iowa, Iowa City/Davenport) Robert Wallace; (University of Massachusetts/Fallon Clinic, Worcester) Judith Ockene; (University of Medicine and Dentistry of New Jersey, Newark) Norman Lasser; (University of Miami, Miami, Fla) Mary Jo O’Sullivan; (University of Minnesota, Minneapolis) Karen Margolis; (University of Nevada, Reno) Robert Brunner; (University of North Carolina, Chapel Hill) Gerardo Heiss; (University of Pittsburgh, Pittsburgh, Pa) Lewis Kuller; (University of Tennessee, Memphis) Karen C. Johnson; (University of Texas Health Science Center, San Antonio) Robert Brzyski; (University of Wisconsin, Madison) Catherine Allen; (Wake Forest University School of Medicine, Winston-Salem, NC) Gregory Burke; (Wayne State University School of Medicine/Hutzel Hospital, Detroit, Mich) Susan Hendrix.

Correspondence: Karen L. Margolis, MD, MPH, Berman Center for Outcomes and Clinical Research, 825 S Eighth St, Suite 440, Minneapolis, MN 55404 (margo006@umn.edu)

Accepted for Publication: December 3, 2004.

Financial Disclosure: Dr Assaf is an employee of Pfizer Inc.

Funding/Support: The WHI program is funded by the National Heart, Lung, and Blood Institute, US Department of Health and Human Services, Bethesda. The funding source participated in the design and conduct of the study and approved the final manuscript but was not involved in the analysis or interpretation of the data. Dr Margolis received support from an award from the National Heart, Lung, and Blood Institute (K23 HL03996).

Additional Information: Dr Margolis had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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Schlant  RForman  SStamler  JCanner  P The natural history of coronary heart disease: prognostic factors after recovery from myocardial infarction. Circulation 1982;66401- 414
PubMed Link to Article
Lowe  GMachado  SKrol  WBarton  BForbes  C White blood cell count and haematocrit as predictors of coronary recurrence and myocardial infarction. Thromb Haemost 1985;54700- 703
PubMed
Grimm  RNeaton  JLudwig  W Prognostic importance of the white blood cell count for coronary, cancer, and all-cause mortality. JAMA 1985;2541932- 1937
PubMed Link to Article
Yarnell  JBaker  ISweetnam  P  et al.  Fibrinogen, viscosity, and white blood cell count are major risk factors for ischemic heart disease: the Caerphilly and Speedwell Collaborative heart disease studies. Circulation 1991;83836- 844
PubMed Link to Article
Phillips  ANeaton  JCook  DGrimm  RShaper  A Leukocyte count and risk of major coronary heart disease events. Am J Epidemiol 1992;13659- 70
PubMed
Kannel  WAnderson  KWilson  P White blood cell count and cardiovascular disease. JAMA 1992;2671253- 1256
PubMed Link to Article
Manttari  MManninen  VKoskinen  P  et al.  Leukocytes as a coronary risk factor in a dyslipidemic male population. Am Heart J 1992;123873- 877
PubMed Link to Article
Olivares  RDucimetiere  PClaude  J Monocyte count: a risk factor for coronary heart disease? Am J Epidemiol 1993;13749- 53
PubMed
Gillum  RIngram  DMakuc  D. White blood cell count, coronary heart disease, and death: the NHANES 1 Epidemiological Follow-up Study. Am Heart J 1993;128855- 863
PubMed Link to Article
Gillum  RIngram  DMakuc  D White blood cell count and stroke incidence and death. Am J Epidemiol 1994;139894- 902
PubMed
Weijenberg  MFeskens  EKromhout  D White blood cell count and the risk of coronary heart disease and all-cause mortality in elderly men. Arterioscler Thromb Vasc Biol 1996;16499- 503
PubMed Link to Article
Sweetnam  PThomas  HYarnell  JBaker  IElwood  P Total and differential leukocyte counts as predictors of ischemic heart disease: the Caerphilly and Speedwell Studies. Am J Epidemiol 1997;145416- 421
PubMed Link to Article
Folsom  AWu  KRosamond  WSharrett  AChambless  L Prospective study of hemostatic factors and incidence of coronary heart disease: the Atherosclerosis Risk in Communities (ARIC) Study. Circulation 1997;961102- 1108
PubMed Link to Article
Folsom  ARosamond  WShahar  E  et al. The Atherosclerosis Risk in Communities (ARIC) Study Investigators, Prospective study of markers of hemostatic function with risk of ischemic stroke. Circulation 1999;100736- 742
PubMed Link to Article
Packard  CO'Reilly  DCaslake  M  et al.  Lipoprotein-associated phospholipase A2 as an independent predictor of coronary heart disease. N Engl J Med 2000;3431148- 1155
PubMed Link to Article
Saito  IFolsom  ABrancati  FDuncan  BChambless  LMcGovern  P Nontraditional risk factors for coronary heart disease incidence among persons with diabetes: the Atherosclerosis Risk in Communities (ARIC) Study. Ann Intern Med 2000;13381- 91
PubMed Link to Article
Brown  DGiles  WCroft  J White blood cell count: an independent predictor of coronary heart disease mortality among a national cohort. J Clin Epidemiol 2001;54316- 322
PubMed Link to Article
Lee  CFolsom  AChambless  LShahar  EWolfe  D 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 2001;154758- 764
PubMed Link to Article
Hajj-Ali  RZareba  WEzzeddine  RMoss  A Relation of the leukocyte count to recurrent cardiac events in stable patients after acute myocardial infarction. Am J Cardiol 2001;881221- 1224
PubMed Link to Article
Women's Health Initiative Study Group, Design of the Women's Health Initiative Clinical Trial and Observational Study. Control Clin Trials 1998;1961- 109
PubMed Link to Article
Anderson  GManson  JWallace  R  et al.  Implementation of the Women’s Health Initiative Study Design. Ann Epidemiol 2003;13S5- S17
PubMed Link to Article
Hays  JHunt  JHubbell  F  et al.  The Women’s Health Initiative recruitment methods and results. Ann Epidemiol 2003;13S18- S77
PubMed Link to Article
Langer  RWhite  ELewis  CKotchen  JHendrix  STrevisan  M The Women’s Health Initiative Observational Study: baseline characteristics of participants and reliability of baseline measures. Ann Epidemiol 2003;13S107- S121
PubMed Link to Article
Block  GWoods  MPotosky  AClifford  C Validation of a self-administered diet history questionnaire using multiple diet records. J Clin Epidemiol 1990;431327- 1335
PubMed Link to Article
Patterson  RKristal  ATinker  LCarter  RBolton  MAgurs-Collins  T Measurement characteristics of the Women's Health Initiative food frequency questionnaire. Ann Epidemiol 1999;9178- 187
PubMed Link to Article
Ainsworth  BHaskell  WLeon  A  et al.  Compendium of physical activities: classification of energy costs of human physical activities. Med Sci Sports Exerc 1993;2571- 80
PubMed Link to Article
Ives  DFitzpatrick  ABild  D Surveillance and ascertainment of cardiovascular events: the Cardiovascular Health Study. Ann Epidemiol 1995;5278- 285
PubMed Link to Article
Pradhan  AManson  JRossouw  J  et al.  Inflammatory biomarkers, hormone replacement therapy, and incident coronary heart disease: prospective analysis from the Women's Health Initiative Observational Study. JAMA 2002;288980- 987
PubMed Link to Article
Danesh  JCollins  RAppleby  PPeto  R Association of fibrinogen, c-reactive protein, albumin, or leukocyte count with coronary heart disease. JAMA 1998;2791477- 1482
PubMed Link to Article
Sabatine  MMorrow  DCannon  C  et al.  Relationship between baseline white blood cell count and degree of coronary artery disease and mortality in patients with acute coronary syndromes: a TACTICS-TIMI 18 substudy. J Am Coll Cardiol 2002;401761- 1768
PubMed Link to Article
Bickel  CRupprecht  HBlankenberg  S  et al.  Relation of markers of inflammation (C-reactive protein, fibrinogen, von Willenbrand factor, and leukocyte count) and statin therapy to long-term mortality in patients with angiographically proven coronary artery disease. Am J Cardiol 2002;89901- 908
PubMed Link to Article
Kervinen  HPalosuo  TManninen  VTenkanen  LVaarala  OMänttäri  M Joint effects of C-reactive protein and other risk factors on acute coronary events. Am Heart J 2001;141580- 585
PubMed Link to Article
Libby  P Inflammation in atherosclerosis. Nature 2002;420868- 874
PubMed Link to Article
Dinerman  JMehta  J Endothelial, platelet and leukocyte interactions in ischemic heart disease: insights into potential mechanisms and their clinical relevance. J Am Coll Cardiol 1990;16207- 222
PubMed Link to Article

Figures

Place holder to copy figure label and caption
Figure.

Hazard ratios (95% confidence intervals) for cardiovascular event in the highest compared with the lowest white blood cell count quartiles in subgroups of age, race, and cardiovascular risk factors. All models were adjusted for age, race/ethnicity, diabetes, hypertension, high cholesterol, smoking status, body mass index, alcohol intake, physical activity, aspirin use, dietary fiber, fruit and vegetable intake, polyunsaturated/saturated fatty acid ratio, and prior use of hormone therapy. BMI indicates body mass index (calculated as weight in kilograms divided by the square of height in meters); CVD, cardiovascular disease.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Baseline Characteristics of WHI-OS Participants Free of CVD at Baseline, by WBC Count Quartile*
Table Graphic Jump LocationTable 2. Association Between WBC Quartile and Incident Cardiovascular Events in WHI-OS Participants Free of Cancer and CVD at Baseline*
Table Graphic Jump LocationTable 3. Crude and Adjusted Odds Ratios* for Coronary Heart Disease According to Baseline WBC Count Quartiles
Table Graphic Jump LocationTable 4. Crude and Adjusted Odds Ratios* for Coronary Heart Disease According to Baseline CRP Level Quartiles
Table Graphic Jump LocationTable 5. Odds Ratios (95% CIs) From a Bivariate Logistic Regression Model* Predicting Coronary Heart Disease Based on the Joint WBC Count×CRP Level Relationship

References

Tracy  R Inflammation in cardiovascular disease. Circulation 1998;972000- 2002
PubMed Link to Article
Ross  R Atherosclerosis: an inflammatory disease. N Engl J Med 1999;340115- 126
PubMed Link to Article
Libby  PRidker  P Novel inflammatory markers for coronary risk. Circulation 1999;1001148- 1150
PubMed Link to Article
Plutzky  J Inflammatory pathways in atherosclerosis and acute coronary syndromes. Am J Cardiol 2001;8810K- 15K
PubMed Link to Article
Rader  D Inflammatory markers of coronary risk. N Engl J Med 2000;3431179- 1182
PubMed Link to Article
Pearson  TMensah  GAlexander  R  et al.  Markers of inflammation and cardiovascular disease: application to clinical and public health practice: a statement for healthcare professionals from the Centers for Disease Control and Prevention and the American Heart Association. Circulation 2003;107499- 511
PubMed Link to Article
Cole  DSingian  EKate  L The long-term prognosis following myocardial infarction, and some factors which affect it. Circulation 1954;9321- 334
PubMed Link to Article
Friedman  GKlatsky  ASiegelaub  A The leukocyte count as a predictor of myocardial infarction. N Engl J Med 1974;2901275- 1278
PubMed Link to Article
Zalokar  JRichard  JClaude  J Leukocyte count, smoking, and myocardial infarction. N Engl J Med 1981;304465- 468
PubMed Link to Article
Prentice  RSzatrowski  TFujikura  TKato  HMason  MHamilton  H Leukocyte counts and coronary heart disease in a Japanese cohort. Am J Epidemiol 1982;116496- 509
PubMed
Prentice  RSzatrowski  TKato  HMason  M Leukocyte counts and cerebrovascular disease. J Chronic Dis 1982;35703- 714
PubMed Link to Article
Schlant  RForman  SStamler  JCanner  P The natural history of coronary heart disease: prognostic factors after recovery from myocardial infarction. Circulation 1982;66401- 414
PubMed Link to Article
Lowe  GMachado  SKrol  WBarton  BForbes  C White blood cell count and haematocrit as predictors of coronary recurrence and myocardial infarction. Thromb Haemost 1985;54700- 703
PubMed
Grimm  RNeaton  JLudwig  W Prognostic importance of the white blood cell count for coronary, cancer, and all-cause mortality. JAMA 1985;2541932- 1937
PubMed Link to Article
Yarnell  JBaker  ISweetnam  P  et al.  Fibrinogen, viscosity, and white blood cell count are major risk factors for ischemic heart disease: the Caerphilly and Speedwell Collaborative heart disease studies. Circulation 1991;83836- 844
PubMed Link to Article
Phillips  ANeaton  JCook  DGrimm  RShaper  A Leukocyte count and risk of major coronary heart disease events. Am J Epidemiol 1992;13659- 70
PubMed
Kannel  WAnderson  KWilson  P White blood cell count and cardiovascular disease. JAMA 1992;2671253- 1256
PubMed Link to Article
Manttari  MManninen  VKoskinen  P  et al.  Leukocytes as a coronary risk factor in a dyslipidemic male population. Am Heart J 1992;123873- 877
PubMed Link to Article
Olivares  RDucimetiere  PClaude  J Monocyte count: a risk factor for coronary heart disease? Am J Epidemiol 1993;13749- 53
PubMed
Gillum  RIngram  DMakuc  D. White blood cell count, coronary heart disease, and death: the NHANES 1 Epidemiological Follow-up Study. Am Heart J 1993;128855- 863
PubMed Link to Article
Gillum  RIngram  DMakuc  D White blood cell count and stroke incidence and death. Am J Epidemiol 1994;139894- 902
PubMed
Weijenberg  MFeskens  EKromhout  D White blood cell count and the risk of coronary heart disease and all-cause mortality in elderly men. Arterioscler Thromb Vasc Biol 1996;16499- 503
PubMed Link to Article
Sweetnam  PThomas  HYarnell  JBaker  IElwood  P Total and differential leukocyte counts as predictors of ischemic heart disease: the Caerphilly and Speedwell Studies. Am J Epidemiol 1997;145416- 421
PubMed Link to Article
Folsom  AWu  KRosamond  WSharrett  AChambless  L Prospective study of hemostatic factors and incidence of coronary heart disease: the Atherosclerosis Risk in Communities (ARIC) Study. Circulation 1997;961102- 1108
PubMed Link to Article
Folsom  ARosamond  WShahar  E  et al. The Atherosclerosis Risk in Communities (ARIC) Study Investigators, Prospective study of markers of hemostatic function with risk of ischemic stroke. Circulation 1999;100736- 742
PubMed Link to Article
Packard  CO'Reilly  DCaslake  M  et al.  Lipoprotein-associated phospholipase A2 as an independent predictor of coronary heart disease. N Engl J Med 2000;3431148- 1155
PubMed Link to Article
Saito  IFolsom  ABrancati  FDuncan  BChambless  LMcGovern  P Nontraditional risk factors for coronary heart disease incidence among persons with diabetes: the Atherosclerosis Risk in Communities (ARIC) Study. Ann Intern Med 2000;13381- 91
PubMed Link to Article
Brown  DGiles  WCroft  J White blood cell count: an independent predictor of coronary heart disease mortality among a national cohort. J Clin Epidemiol 2001;54316- 322
PubMed Link to Article
Lee  CFolsom  AChambless  LShahar  EWolfe  D 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 2001;154758- 764
PubMed Link to Article
Hajj-Ali  RZareba  WEzzeddine  RMoss  A Relation of the leukocyte count to recurrent cardiac events in stable patients after acute myocardial infarction. Am J Cardiol 2001;881221- 1224
PubMed Link to Article
Women's Health Initiative Study Group, Design of the Women's Health Initiative Clinical Trial and Observational Study. Control Clin Trials 1998;1961- 109
PubMed Link to Article
Anderson  GManson  JWallace  R  et al.  Implementation of the Women’s Health Initiative Study Design. Ann Epidemiol 2003;13S5- S17
PubMed Link to Article
Hays  JHunt  JHubbell  F  et al.  The Women’s Health Initiative recruitment methods and results. Ann Epidemiol 2003;13S18- S77
PubMed Link to Article
Langer  RWhite  ELewis  CKotchen  JHendrix  STrevisan  M The Women’s Health Initiative Observational Study: baseline characteristics of participants and reliability of baseline measures. Ann Epidemiol 2003;13S107- S121
PubMed Link to Article
Block  GWoods  MPotosky  AClifford  C Validation of a self-administered diet history questionnaire using multiple diet records. J Clin Epidemiol 1990;431327- 1335
PubMed Link to Article
Patterson  RKristal  ATinker  LCarter  RBolton  MAgurs-Collins  T Measurement characteristics of the Women's Health Initiative food frequency questionnaire. Ann Epidemiol 1999;9178- 187
PubMed Link to Article
Ainsworth  BHaskell  WLeon  A  et al.  Compendium of physical activities: classification of energy costs of human physical activities. Med Sci Sports Exerc 1993;2571- 80
PubMed Link to Article
Ives  DFitzpatrick  ABild  D Surveillance and ascertainment of cardiovascular events: the Cardiovascular Health Study. Ann Epidemiol 1995;5278- 285
PubMed Link to Article
Pradhan  AManson  JRossouw  J  et al.  Inflammatory biomarkers, hormone replacement therapy, and incident coronary heart disease: prospective analysis from the Women's Health Initiative Observational Study. JAMA 2002;288980- 987
PubMed Link to Article
Danesh  JCollins  RAppleby  PPeto  R Association of fibrinogen, c-reactive protein, albumin, or leukocyte count with coronary heart disease. JAMA 1998;2791477- 1482
PubMed Link to Article
Sabatine  MMorrow  DCannon  C  et al.  Relationship between baseline white blood cell count and degree of coronary artery disease and mortality in patients with acute coronary syndromes: a TACTICS-TIMI 18 substudy. J Am Coll Cardiol 2002;401761- 1768
PubMed Link to Article
Bickel  CRupprecht  HBlankenberg  S  et al.  Relation of markers of inflammation (C-reactive protein, fibrinogen, von Willenbrand factor, and leukocyte count) and statin therapy to long-term mortality in patients with angiographically proven coronary artery disease. Am J Cardiol 2002;89901- 908
PubMed Link to Article
Kervinen  HPalosuo  TManninen  VTenkanen  LVaarala  OMänttäri  M Joint effects of C-reactive protein and other risk factors on acute coronary events. Am Heart J 2001;141580- 585
PubMed Link to Article
Libby  P Inflammation in atherosclerosis. Nature 2002;420868- 874
PubMed Link to Article
Dinerman  JMehta  J Endothelial, platelet and leukocyte interactions in ischemic heart disease: insights into potential mechanisms and their clinical relevance. J Am Coll Cardiol 1990;16207- 222
PubMed Link to Article

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