Blood Pressure Medication And Elevated White Blood Cell Count
Prognostic Value of Elevated White Blood Cell Count in Hypertension * :
Giuseppe Schillaci, 1 Unit of Internal Medicine, Angiology and Arteriosclerosis, University of Perugia , Italy Address correspondence and reprint requests to Prof. Giuseppe Schillaci, Unit of Internal Medicine, Angiology and Arteriosclerosis, University of Perugia Medical School, Hospital "S. Maria della Misericordia," piazzale Menghini , 1, IT-06132 Perugia , Italy E-mail: skill@unipg.it Search for other works by this author on: 1 Unit of Internal Medicine, Angiology and Arteriosclerosis, University of Perugia , Italy Search for other works by this author on: 1 Unit of Internal Medicine, Angiology and Arteriosclerosis, University of Perugia , Italy Search for other works by this author on: 1 Unit of Internal Medicine, Angiology and Arteriosclerosis, University of Perugia , Italy Search for other works by this author on: 1 Unit of Internal Medicine, Angiology and Arteriosclerosis, University of Perugia , Italy Search for other works by this author on: 1 Unit of Internal Medicine, Angiology and Arteriosclerosis, University of Perugia , Italy Search for other works by this author on: 2 Department of Cardiology, Perugia General Hospital , Perugia , Italy . Search for other works by this author on: 1 Unit of Internal Medicine, Angiology and Arteriosclerosis, University of Perugia , Italy Search for other works by this author on:
* This work was supported in part by the grant no. 2004060902 from the Italian Ministry for University.
Author Notes
Received:
12 September 2006
Accepted:
16 October 2006
Abstract
Background:
Chronic low-grade inflammation may contribute to vascular injury and atherogenesis, and has been described in association to high blood pressure (BP). However, as yet the prognostic significance of white blood cell (WBC) count in the setting of uncomplicated hypertension has not been investigated.
Methods:
In the Progetto Ipertensione Umbria Monitoraggio Ambulatoriale (PIUMA) study, 1617 white patients with essential hypertension (aged 49 ± 12 years, 55% men) without prevalent cardiovascular or renal disease underwent off-treatment baseline clinical evaluation and were then followed up for 11 years (average 4.9 years).
Results:
The WBC count had a direct association with smoking status, serum triglycerides, body mass index, and 24-h BP, and an inverse one with age (all P < .05). During follow-up, 146 patients developed a major fatal or nonfatal cardiovascular event (1.9 events per 100 patient-years). Patients who will develop a cardiovascular event had a higher WBC count (7.08 ± 1.6 v 6.68 ± 1.6 × 109 cells/L, P = .004). Event rate increased progressively from the first to the fourth quartile of WBC count distribution (1.2, 1.8, 1.9, and 2.3 events per 100 patient-years; P < .01 by log-rank test). After adjustment (Cox model) for the effect of age, gender, diabetes, serum cholesterol, glomerular filtration rate, smoking, left ventricular hypertrophy, and 24-h systolic BP, cardiovascular event risk increased by 24% (95% confidence interval +4% to +48%; P = .019) for each 2 × 109 cells/L increase in WBC.
Conclusions:
After adjustment for average 24-h BP, established risk factors and target organ damage, an elevated WBC count remains an independent predictor of cardiovascular morbidity in hypertensive patients. Am J Hypertens 2007;20: 364–369 © 2007 American Journal of Hypertension, Ltd.
Increasing evidence implicates the role for inflammation in the pathogenesis of atherosclerosis and its complications. 1 Several prospective studies have shown that an elevated circulating white blood cell (WBC) count, a widely available marker of systemic inflammation, is related to cardiovascular disease and mortality, independent of the traditional risk factors, in general populations, 2–8 dyslipidemic men, 9 patients with stable cardiovascular disease, 10,11 and patients with acute myocardial infarction. 12 However, this finding may be biased by confounding, owing to the strong link between WBC count and other cardiovascular risk factors, such as smoking, hypertension, obesity, elevated triglyceride levels, and insulin resistance. 13–16 In two community studies that adjusted for such coronary risk factors, the WBC count was no longer associated with elevated coronary risk. 17,18 Elevated WBC count has been associated with high blood pressure (BP), 14,16,19 and a pathogenetic link has been hypothesized between hypertension and inflammation. 20,21 To our knowledge, however, the relationship between WBC count and cardiovascular complications has not been examined in patients with essential hypertension.
In the setting of the Progetto Ipertensione Umbria Monitoraggio Ambulatoriale (PIUMA) study, 22,23 we had the opportunity to investigate the independent relation between WBC count and future cardiovascular morbidity in subjects with essential hypertension without prevalent cardiovascular disease at the baseline examination.
Methods
The PIUMA study is a prospective follow-up study of white adult subjects with essential hypertension. 22,23 Hypertensive subjects were referred to one of three participating centers (Perugia, Città della Pieve, and Castiglione del Lago) for baseline evaluation by a group of general practitioners practicing in Umbria, in central Italy. A total of 1629 white subjects enrolled between 1988 and 1998, for whom the WBC at baseline is available, were included in the present analysis. All study subjects fulfilled the following criteria: (1) office systolic BP of ≥140 mm Hg, diastolic BP of ≥90 mm Hg, or both on ≥3 visits at 1-week intervals; (2) no previous treatment for hypertension (70%), or withdrawal from antihypertensive drugs ≥4 weeks before the study; (3) no clinical or laboratory evidence of heart failure, coronary heart disease, previous stroke, valvular defects, secondary causes of hypertension, cancer, renal failure, or hepatic disease; and (4) ≥1 valid BP measurement per hour during the 24 h. All subjects gave informed consent to participate in the study, which was approved by the institutional review board.
Baseline Measurements
Office BP was measured by a physician in the hospital clinic with a mercury sphygmomanometer, after the subject sat for ≥10 min. The average of ≥3 measurements on each of ≥2 sessions was considered for the analysis. Ambulatory BP was recorded with an oscillometric device (models 90202 and 90207; SpaceLabs, Redmond, WA) that was set to take a reading every 15 min throughout the 24 h. 24 Electrocardiographic left ventricular hypertrophy was defined according to the Perugia criterion (S-wave in lead V3 + R-wave in lead aVL ≥2.4 mV in men and ≥2.0 mV in women, or typical LV strain, or a Romhilt-Estes score of ≥5 points), 25 which shows a greater attributable risk for cardiovascular morbidity and mortality than do other criteria. 26 Glomerular filtration rate was estimated using the simplified Modification of Diet in Renal Disease Study equation. 27 The WBC count was measured at the local laboratory at the time of blood collection using automated Coulter cell counters by standard techniques. Whole blood was collected in 5-mL EDTA anticoagulated tubes by a trained phlebotomist. The cell counter aspirated a sample from the collection tube, and after lysis of red blood cells and platelets, WBCs were counted by use of a standard direct current detection method.
Follow-Up Procedures and End Point Evaluation
All subjects were followed by their family physicians, in cooperation with the outpatient clinic of the referring hospital, and treated through the use of standard lifestyle and pharmacologic measures. At the follow-up visit, 67% of the study patients were taking antihypertensive drugs, and 33% were receiving lifestyle measures only. Contacts with family physicians and telephone interviews were periodically undertaken to determine the incidence of coronary heart disease. For the subjects who developed a cardiovascular event during follow-up, hospital record forms and other available original source documents were reviewed in conference by the investigators, who were unaware of the baseline clinical data of the subjects examined. Cardiovascular events included new-onset coronary artery disease (myocardial infarction, unstable angina with documentation of ischemic ECG changes, or sudden cardiac death), congestive heart failure that required hospitalization, stroke, transient cerebral ischemia, and symptomatic aortoiliac occlusive disease verified by angiography. The international standard criteria used to diagnose cardiovascular events in the PIUMA study have been described elsewhere. 22
Statistical Analysis
Parametric data are reported as mean ± SD. To describe participant characteristics across levels of WBC count, WBC was categorized using quartile divisions and cross-tabulations were examined. The partition values were 5.6, 6.5, and 7.6 × 109 cells/L. The rates of cardiovascular events are presented as the number of events per 100 patient-years. For those subjects who experienced multiple events, survival analysis was restricted to the first event. Survival curves were compared with the use of the Mantel (logistic-rank) test. The effect of prognostic factors on survival was evaluated with the use of the stepwise Cox semiparametric regression model. The assumption of linearity for the Cox model was tested through visual inspection and no violation of proportional hazards was found. We tested the variables of age, gender, body mass index, office and 24-h systolic and diastolic BP, serum cholesterol, serum triglycerides, diabetes, smoking habits (previous or never smokers, current smokers), left ventricular hypertrophy, glomerular filtration rate, antihypertensive treatment at follow-up (lifestyle measures alone, diuretics and β-blockers alone or combined, angiotensin-converting enzyme inhibitors and calcium antagonists alone or combined, other drug combinations), and WBC count. The WBC count was considered both as a continuous variable and after categorization into quartiles.
We considered as clinically relevant an absolute difference in the rate of events of 1.0 per 100 patient-years (ie, 1.3 per 100 person-years in the lowest WBC quartile versus 2.3 per 100 person-years in the highest WBC quartile). On this basis, a sample size of 400 subjects per quartile with an average follow-up time of 4.9 years per patient had 87% power to detect such clinically significant difference (two-tailed test) with a type I error of 5%.
Results
Follow-up data were available in 1617 of the 1629 subjects (99.3%), and only 0.7% were lost to follow-up. At entry (Table 1), the subjects with future cardiovascular events were older and had higher WBC count, office and 24-h BP values, serum cholesterol and triglycerides, and lower glomerular filtration rate. They were also more likely to be of male gender and to have diabetes and left ventricular hypertrophy. Table 2 reports the main clinical characteristics of the study subjects stratified by quartile of WBC count. Subjects with high WBC count were slightly younger and more frequently smokers, and had higher body mass index, higher average 24-h systolic and diastolic BPs, and lower glomerular filtration rate values, whereas office BP did not differ among WBC quartiles. The WBC count had a significant direct association with 24-h systolic (r = 0.13, P < .001) and diastolic BP (r = 0.14, P < .001), serum triglycerides (r = 0.11, P < .001), and body mass index (r = 0.09, P < .001), and an inverse one with age (r = −0.08, P < .001). No significant relation was found with serum glucose (r = 0.04, P = .09) and office BP (r = 0.03, P = .30 and r = 0.04, P = .11 for systolic and diastolic BP, respectively).
Table 1
Main characteristics of patients
| Data | All (n = 1617) | No Events (n = 1471) | Events (n = 146) | P |
|---|---|---|---|---|
| Age (y) | 49 (12) | 48.1 (12) | 58.3 (12) | <.001 |
| Men (%) | 55 | 54 | 65 | .02 |
| Body mass index (kg/m2) | 26.7 (4) | 26.7 (4) | 26.8 (3) | .78 |
| Current smokers (%) | 25 | 24 | 30 | .14 |
| Diabetes (%) | 5 | 4 | 19 | <.001 |
| Office systolic BP (mm Hg) | 153 (19) | 151 (18) | 165 (23) | <.001 |
| Office diastolic BP (mm Hg) | 96 (11) | 96 (10) | 97 (13) | .27 |
| 24-h systolic BP (mm Hg) | 135 (15) | 134 (14) | 145 (20) | <.001 |
| 24-h diastolic BP (mm Hg) | 86 (10) | 85 (10) | 89 (12) | .002 |
| Cholesterol (mmol/L) | 5.51 (1.1) | 5.50 (1.1) | 5.64 (1.3) | .15 |
| Triglycerides (mmol/L) | 1.61 (1.1) | 1.59 (1.1) | 1.86 (1.1) | .003 |
| White blood cell count (109 cells/L) | 6.72 (1.6) | 6.68 (1.6) | 7.09 (1.6) | .004 |
| Serum creatinine (μmol/L) | 87 (19) | 86 (17) | 98 (34) | <.001 |
| GFR (mL/min/1.73 m2) | 80 (19) | 81 (19) | 72 (19) | <.001 |
| Left ventricular hypertrophy (%) | 14 | 13 | 32 | <.001 |
| Data | All (n = 1617) | No Events (n = 1471) | Events (n = 146) | P |
|---|---|---|---|---|
| Age (y) | 49 (12) | 48.1 (12) | 58.3 (12) | <.001 |
| Men (%) | 55 | 54 | 65 | .02 |
| Body mass index (kg/m2) | 26.7 (4) | 26.7 (4) | 26.8 (3) | .78 |
| Current smokers (%) | 25 | 24 | 30 | .14 |
| Diabetes (%) | 5 | 4 | 19 | <.001 |
| Office systolic BP (mm Hg) | 153 (19) | 151 (18) | 165 (23) | <.001 |
| Office diastolic BP (mm Hg) | 96 (11) | 96 (10) | 97 (13) | .27 |
| 24-h systolic BP (mm Hg) | 135 (15) | 134 (14) | 145 (20) | <.001 |
| 24-h diastolic BP (mm Hg) | 86 (10) | 85 (10) | 89 (12) | .002 |
| Cholesterol (mmol/L) | 5.51 (1.1) | 5.50 (1.1) | 5.64 (1.3) | .15 |
| Triglycerides (mmol/L) | 1.61 (1.1) | 1.59 (1.1) | 1.86 (1.1) | .003 |
| White blood cell count (109 cells/L) | 6.72 (1.6) | 6.68 (1.6) | 7.09 (1.6) | .004 |
| Serum creatinine (μmol/L) | 87 (19) | 86 (17) | 98 (34) | <.001 |
| GFR (mL/min/1.73 m2) | 80 (19) | 81 (19) | 72 (19) | <.001 |
| Left ventricular hypertrophy (%) | 14 | 13 | 32 | <.001 |
BP = blood pressure; GFR = glomerular filtration rate.
Table 1
Main characteristics of patients
| Data | All (n = 1617) | No Events (n = 1471) | Events (n = 146) | P |
|---|---|---|---|---|
| Age (y) | 49 (12) | 48.1 (12) | 58.3 (12) | <.001 |
| Men (%) | 55 | 54 | 65 | .02 |
| Body mass index (kg/m2) | 26.7 (4) | 26.7 (4) | 26.8 (3) | .78 |
| Current smokers (%) | 25 | 24 | 30 | .14 |
| Diabetes (%) | 5 | 4 | 19 | <.001 |
| Office systolic BP (mm Hg) | 153 (19) | 151 (18) | 165 (23) | <.001 |
| Office diastolic BP (mm Hg) | 96 (11) | 96 (10) | 97 (13) | .27 |
| 24-h systolic BP (mm Hg) | 135 (15) | 134 (14) | 145 (20) | <.001 |
| 24-h diastolic BP (mm Hg) | 86 (10) | 85 (10) | 89 (12) | .002 |
| Cholesterol (mmol/L) | 5.51 (1.1) | 5.50 (1.1) | 5.64 (1.3) | .15 |
| Triglycerides (mmol/L) | 1.61 (1.1) | 1.59 (1.1) | 1.86 (1.1) | .003 |
| White blood cell count (109 cells/L) | 6.72 (1.6) | 6.68 (1.6) | 7.09 (1.6) | .004 |
| Serum creatinine (μmol/L) | 87 (19) | 86 (17) | 98 (34) | <.001 |
| GFR (mL/min/1.73 m2) | 80 (19) | 81 (19) | 72 (19) | <.001 |
| Left ventricular hypertrophy (%) | 14 | 13 | 32 | <.001 |
| Data | All (n = 1617) | No Events (n = 1471) | Events (n = 146) | P |
|---|---|---|---|---|
| Age (y) | 49 (12) | 48.1 (12) | 58.3 (12) | <.001 |
| Men (%) | 55 | 54 | 65 | .02 |
| Body mass index (kg/m2) | 26.7 (4) | 26.7 (4) | 26.8 (3) | .78 |
| Current smokers (%) | 25 | 24 | 30 | .14 |
| Diabetes (%) | 5 | 4 | 19 | <.001 |
| Office systolic BP (mm Hg) | 153 (19) | 151 (18) | 165 (23) | <.001 |
| Office diastolic BP (mm Hg) | 96 (11) | 96 (10) | 97 (13) | .27 |
| 24-h systolic BP (mm Hg) | 135 (15) | 134 (14) | 145 (20) | <.001 |
| 24-h diastolic BP (mm Hg) | 86 (10) | 85 (10) | 89 (12) | .002 |
| Cholesterol (mmol/L) | 5.51 (1.1) | 5.50 (1.1) | 5.64 (1.3) | .15 |
| Triglycerides (mmol/L) | 1.61 (1.1) | 1.59 (1.1) | 1.86 (1.1) | .003 |
| White blood cell count (109 cells/L) | 6.72 (1.6) | 6.68 (1.6) | 7.09 (1.6) | .004 |
| Serum creatinine (μmol/L) | 87 (19) | 86 (17) | 98 (34) | <.001 |
| GFR (mL/min/1.73 m2) | 80 (19) | 81 (19) | 72 (19) | <.001 |
| Left ventricular hypertrophy (%) | 14 | 13 | 32 | <.001 |
BP = blood pressure; GFR = glomerular filtration rate.
Table 2
Baseline characteristics of study subjects by quartile of white blood cell count
| Data | 1st Quartile (n = 409) | 2nd Quartile (n = 413) | 3rd Quartile (n = 402) | 4th Quartile (n = 394) | P (F) |
|---|---|---|---|---|---|
| Age (y) | 50 (12) | 49 (12) | 49 (12) | 48 (12) | .03 |
| Men (%) | 53 | 56 | 53 | 58 | .33 |
| Body mass index (kg/m2) | 26.3 (4) | 26.4 (4) | 27.0 (4) | 27.1 (4) | .005 |
| Current smokers (%) | 15 | 19 | 27 | 39 | <.001 |
| Diabetes (%) | 3 | 5 | 5 | 37 | .06 |
| Office systolic BP (mm Hg) | 152 (19) | 152 (18) | 152 (19) | 153 (20) | .55 |
| Office diastolic BP (mm Hg) | 95 (10) | 96 (10) | 96 (10) | 97 (11) | .27 |
| 24-h systolic BP (mm Hg) | 133 (13) | 134 (15) | 135 (14) | 138 (15) | <.001 |
| 24-h diastolic BP (mm Hg) | 84 (10) | 85 (10) | 85 (10) | 88 (11) | <.001 |
| Cholesterol (mmol/L) | 5.49 (1.1) | 5.46 (1.0) | 5.55 (1.1) | 5.55 (1.1) | .56 |
| Triglycerides (mmol/L) | 1.48 (1.0) | 1.50 (0.8) | 1.63 (1.0) | 1.83 (1.2) | <.001 |
| White blood cell count (×109 cells/L) | 4.9 (0.5) | 6.1 (0.3) | 7.1 (0.3) | 8.9 (1.3) | <.001 |
| Serum creatinine (μmol/L) | 87 (22) | 86 (17) | 87 (17) | 87 (21) | .72 |
| GFR (mL/min/1.73 m2) | 80 (19) | 81 (17) | 80 (17) | 81 (19) | .13 |
| Left ventricular hypertrophy (%) | 15 | 15 | 13 | 15 | .60 |
| Data | 1st Quartile (n = 409) | 2nd Quartile (n = 413) | 3rd Quartile (n = 402) | 4th Quartile (n = 394) | P (F) |
|---|---|---|---|---|---|
| Age (y) | 50 (12) | 49 (12) | 49 (12) | 48 (12) | .03 |
| Men (%) | 53 | 56 | 53 | 58 | .33 |
| Body mass index (kg/m2) | 26.3 (4) | 26.4 (4) | 27.0 (4) | 27.1 (4) | .005 |
| Current smokers (%) | 15 | 19 | 27 | 39 | <.001 |
| Diabetes (%) | 3 | 5 | 5 | 37 | .06 |
| Office systolic BP (mm Hg) | 152 (19) | 152 (18) | 152 (19) | 153 (20) | .55 |
| Office diastolic BP (mm Hg) | 95 (10) | 96 (10) | 96 (10) | 97 (11) | .27 |
| 24-h systolic BP (mm Hg) | 133 (13) | 134 (15) | 135 (14) | 138 (15) | <.001 |
| 24-h diastolic BP (mm Hg) | 84 (10) | 85 (10) | 85 (10) | 88 (11) | <.001 |
| Cholesterol (mmol/L) | 5.49 (1.1) | 5.46 (1.0) | 5.55 (1.1) | 5.55 (1.1) | .56 |
| Triglycerides (mmol/L) | 1.48 (1.0) | 1.50 (0.8) | 1.63 (1.0) | 1.83 (1.2) | <.001 |
| White blood cell count (×109 cells/L) | 4.9 (0.5) | 6.1 (0.3) | 7.1 (0.3) | 8.9 (1.3) | <.001 |
| Serum creatinine (μmol/L) | 87 (22) | 86 (17) | 87 (17) | 87 (21) | .72 |
| GFR (mL/min/1.73 m2) | 80 (19) | 81 (17) | 80 (17) | 81 (19) | .13 |
| Left ventricular hypertrophy (%) | 15 | 15 | 13 | 15 | .60 |
Table 2
Baseline characteristics of study subjects by quartile of white blood cell count
| Data | 1st Quartile (n = 409) | 2nd Quartile (n = 413) | 3rd Quartile (n = 402) | 4th Quartile (n = 394) | P (F) |
|---|---|---|---|---|---|
| Age (y) | 50 (12) | 49 (12) | 49 (12) | 48 (12) | .03 |
| Men (%) | 53 | 56 | 53 | 58 | .33 |
| Body mass index (kg/m2) | 26.3 (4) | 26.4 (4) | 27.0 (4) | 27.1 (4) | .005 |
| Current smokers (%) | 15 | 19 | 27 | 39 | <.001 |
| Diabetes (%) | 3 | 5 | 5 | 37 | .06 |
| Office systolic BP (mm Hg) | 152 (19) | 152 (18) | 152 (19) | 153 (20) | .55 |
| Office diastolic BP (mm Hg) | 95 (10) | 96 (10) | 96 (10) | 97 (11) | .27 |
| 24-h systolic BP (mm Hg) | 133 (13) | 134 (15) | 135 (14) | 138 (15) | <.001 |
| 24-h diastolic BP (mm Hg) | 84 (10) | 85 (10) | 85 (10) | 88 (11) | <.001 |
| Cholesterol (mmol/L) | 5.49 (1.1) | 5.46 (1.0) | 5.55 (1.1) | 5.55 (1.1) | .56 |
| Triglycerides (mmol/L) | 1.48 (1.0) | 1.50 (0.8) | 1.63 (1.0) | 1.83 (1.2) | <.001 |
| White blood cell count (×109 cells/L) | 4.9 (0.5) | 6.1 (0.3) | 7.1 (0.3) | 8.9 (1.3) | <.001 |
| Serum creatinine (μmol/L) | 87 (22) | 86 (17) | 87 (17) | 87 (21) | .72 |
| GFR (mL/min/1.73 m2) | 80 (19) | 81 (17) | 80 (17) | 81 (19) | .13 |
| Left ventricular hypertrophy (%) | 15 | 15 | 13 | 15 | .60 |
| Data | 1st Quartile (n = 409) | 2nd Quartile (n = 413) | 3rd Quartile (n = 402) | 4th Quartile (n = 394) | P (F) |
|---|---|---|---|---|---|
| Age (y) | 50 (12) | 49 (12) | 49 (12) | 48 (12) | .03 |
| Men (%) | 53 | 56 | 53 | 58 | .33 |
| Body mass index (kg/m2) | 26.3 (4) | 26.4 (4) | 27.0 (4) | 27.1 (4) | .005 |
| Current smokers (%) | 15 | 19 | 27 | 39 | <.001 |
| Diabetes (%) | 3 | 5 | 5 | 37 | .06 |
| Office systolic BP (mm Hg) | 152 (19) | 152 (18) | 152 (19) | 153 (20) | .55 |
| Office diastolic BP (mm Hg) | 95 (10) | 96 (10) | 96 (10) | 97 (11) | .27 |
| 24-h systolic BP (mm Hg) | 133 (13) | 134 (15) | 135 (14) | 138 (15) | <.001 |
| 24-h diastolic BP (mm Hg) | 84 (10) | 85 (10) | 85 (10) | 88 (11) | <.001 |
| Cholesterol (mmol/L) | 5.49 (1.1) | 5.46 (1.0) | 5.55 (1.1) | 5.55 (1.1) | .56 |
| Triglycerides (mmol/L) | 1.48 (1.0) | 1.50 (0.8) | 1.63 (1.0) | 1.83 (1.2) | <.001 |
| White blood cell count (×109 cells/L) | 4.9 (0.5) | 6.1 (0.3) | 7.1 (0.3) | 8.9 (1.3) | <.001 |
| Serum creatinine (μmol/L) | 87 (22) | 86 (17) | 87 (17) | 87 (21) | .72 |
| GFR (mL/min/1.73 m2) | 80 (19) | 81 (17) | 80 (17) | 81 (19) | .13 |
| Left ventricular hypertrophy (%) | 15 | 15 | 13 | 15 | .60 |
During a follow-up of 4.9 ± 2 years (range, 1 to 11 years), there were 146 first cardiovascular morbid events (1.9 events per 100 patient-years) at the cardiac (n = 82), cerebrovascular (n = 52), or peripheral vascular (n = 12) level. Specifically, there were 38 patients with myocardial infarction, 6 with sudden cardiac death, 25 with unstable angina, 13 with heart failure that required hospitalization, 39 with stroke, 13 with transient cerebral ischemia, and 12 with new-onset aortoiliac occlusive disease.
Cardiovascular event rate increased progressively from the first to the fourth quartile of WBC count distribution (1.2, 1.8, 1.9, and 2.3 events per 100 patient-years). Event-free survival curves in the four quartiles of WBC count distribution differed significantly (P < .01 by log-rank test).
Results of multivariate survival analysis are shown in Table 3. The association between WBC count and subsequent cardiovascular morbidity was maintained after adjustment for the confounding effect of age, gender, smoking, diabetes, office and 24-h ambulatory BP values, body mass index, cholesterol level, left ventricular hypertrophy, glomerular filtration rate, cholesterol concentrations, and treatment status. The observed excess risk was 1.24 (95% confidence interval 1.04–1.48; P = .019) for each 2 × 109/L increase in WBC count.
Table 3
Independent predictors of cardiovascular events (Cox model)
| Variable | Adjusted Hazard Ratio (95% confidence interval) | P |
|---|---|---|
| Age (10 y) | 1.73 (1.48–2.03) | <.001 |
| Diabetes (yes v no) | 3.03 (2.00–4.60) | <.001 |
| Sex (men v women) | 1.69 (1.19–2.40) | .003 |
| Cigarette smoking (yes v no) | 1.66 (1.14–2.43) | .009 |
| 24-h systolic blood pressure (10 mm Hg) | 1.26 (1.14–1.39) | <.001 |
| White blood cell count (2 × 109 cells/L) | 1.24 (1.04–1.48) | .019 |
| Left ventricular hypertrophy (yes v no) | 1.75 (1.20–2.55) | .004 |
| GFR (20 mL/min/1.73 m2) | 0.74 (0.61–0.90) | .004 |
| Serum cholesterol (mmol/L) | 1.15 (1.00–1.34) | <.05 |
| Variable | Adjusted Hazard Ratio (95% confidence interval) | P |
|---|---|---|
| Age (10 y) | 1.73 (1.48–2.03) | <.001 |
| Diabetes (yes v no) | 3.03 (2.00–4.60) | <.001 |
| Sex (men v women) | 1.69 (1.19–2.40) | .003 |
| Cigarette smoking (yes v no) | 1.66 (1.14–2.43) | .009 |
| 24-h systolic blood pressure (10 mm Hg) | 1.26 (1.14–1.39) | <.001 |
| White blood cell count (2 × 109 cells/L) | 1.24 (1.04–1.48) | .019 |
| Left ventricular hypertrophy (yes v no) | 1.75 (1.20–2.55) | .004 |
| GFR (20 mL/min/1.73 m2) | 0.74 (0.61–0.90) | .004 |
| Serum cholesterol (mmol/L) | 1.15 (1.00–1.34) | <.05 |
Office blood pressure, 24-h diastolic blood pressure, serum triglycerides, body mass index, and treatment status failed to enter the final equation.
Table 3
Independent predictors of cardiovascular events (Cox model)
| Variable | Adjusted Hazard Ratio (95% confidence interval) | P |
|---|---|---|
| Age (10 y) | 1.73 (1.48–2.03) | <.001 |
| Diabetes (yes v no) | 3.03 (2.00–4.60) | <.001 |
| Sex (men v women) | 1.69 (1.19–2.40) | .003 |
| Cigarette smoking (yes v no) | 1.66 (1.14–2.43) | .009 |
| 24-h systolic blood pressure (10 mm Hg) | 1.26 (1.14–1.39) | <.001 |
| White blood cell count (2 × 109 cells/L) | 1.24 (1.04–1.48) | .019 |
| Left ventricular hypertrophy (yes v no) | 1.75 (1.20–2.55) | .004 |
| GFR (20 mL/min/1.73 m2) | 0.74 (0.61–0.90) | .004 |
| Serum cholesterol (mmol/L) | 1.15 (1.00–1.34) | <.05 |
| Variable | Adjusted Hazard Ratio (95% confidence interval) | P |
|---|---|---|
| Age (10 y) | 1.73 (1.48–2.03) | <.001 |
| Diabetes (yes v no) | 3.03 (2.00–4.60) | <.001 |
| Sex (men v women) | 1.69 (1.19–2.40) | .003 |
| Cigarette smoking (yes v no) | 1.66 (1.14–2.43) | .009 |
| 24-h systolic blood pressure (10 mm Hg) | 1.26 (1.14–1.39) | <.001 |
| White blood cell count (2 × 109 cells/L) | 1.24 (1.04–1.48) | .019 |
| Left ventricular hypertrophy (yes v no) | 1.75 (1.20–2.55) | .004 |
| GFR (20 mL/min/1.73 m2) | 0.74 (0.61–0.90) | .004 |
| Serum cholesterol (mmol/L) | 1.15 (1.00–1.34) | <.05 |
Office blood pressure, 24-h diastolic blood pressure, serum triglycerides, body mass index, and treatment status failed to enter the final equation.
A significant risk gradient for adverse events was evident across the quartiles of WBC count distribution (Fig. 1). Compared with the first quartile, age- and risk factor-adjusted excess risk for cardiovascular morbidity was significant for the second (odds ratio 1.74, 95% confidence interval 1.02–3.13, P = .042), third (odds ratio 1.80, 95% confidence interval 1.08–3.04, P = .021), and fourth (odds ratio 2.08, 95% confidence interval 1.25–3.48, P = .005) quartile.
Cardiovascular event-free survival curves at mean of covariates (age, gender, diabetes, smoking, average 24-h systolic blood pressure, serum cholesterol, glomerular filtration rate, left ventricular hypertrophy) in 1617 hypertensive patients grouped by quartile of the distribution of white blood cell count.
Figure 1.
Figure 1.
Cardiovascular event-free survival curves at mean of covariates (age, gender, diabetes, smoking, average 24-h systolic blood pressure, serum cholesterol, glomerular filtration rate, left ventricular hypertrophy) in 1617 hypertensive patients grouped by quartile of the distribution of white blood cell count.
Cerebrovascular Events
The rate of cerebrovascular events was 0.4, 0.4, 0.8, and 0.9 in the four quartiles of WBC count distribution (log-rank test, P = .03). In a multivariate Cox analysis, the risk for cerebrovascular events was independently predicted by age, diabetes, average 24-h systolic BP, and WBC count. The observed excess risk was 1.35 (95% confidence interval 1.02–1.82; P = .039) for each 2 × 109/L increase in WBC count.
Cardiac Events
The rate of cardiac events was 0.6, 1.0, 0.9, and 1.1 in the four quartiles of WBC count distribution (log-rank test, P = .18). In a multivariate Cox analysis, age, male gender, diabetes, average 24-h systolic BP, smoking, left ventricular hypertrophy, and a low glomerular filtration rate were all independent predictors of events, whereas WBC count was associated with a nonsignificant 1.12 excess risk for each 2 × 109/L increase (95% confidence interval 0.87–1.43; P = .32).
Discussion
In an untreated hypertensive population, we found that elevated WBC count was associated with subsequent cardiovascular morbidity, independent of BP levels, smoking, diabetes, lipid levels, and established markers of target organ damage including electrocardiographic left ventricular hypertrophy and glomerular filtration rate. Compared to individuals in the first quartile (≤5.6 × 109 cells/L), individuals in the fourth quartile of WBC count (≥7.6 × 109 cells/L) had a more than twofold age- and risk factor-adjusted risk of cardiovascular events.
These findings are consistent with prior studies demonstrating an association between WBC count and cardiovascular morbidity and mortality among different age, gender, and ethnic groups. 28 In a meta-analysis that included 5337 patients from seven prospective studies, Danesh et al 29 reported a 1.4 risk ratio for future cardiac events in patients in the upper third of leukocyte counts compared with those in the lower third. However, little has been published about the ability of WBC count to predict future cardiovascular disease in the specific setting of hypertension. To our knowledge, this is the first report of an association between elevated WBC count and cardiovascular complications in a hypertensive population.
Many hypotheses explaining the association of leukocyte count and vascular disease have been proposed, including both direct and indirect effects of leukocytes. Recent evidence suggests that neutrophils are the leukocyte type most strongly associated with coronary risk, 30 and this might provide pathophysiologic clues linking leukocyte count to coronary events. It has been hypothesized that elevated WBC count is just a marker of the chronic inflammatory state and that other aspects of inflammation may be the direct cause of the vascular disease. On the other hand, leukocytes may contribute directly to acute and chronic ischemic vascular disease through a number of potential prothrombotic and vascular mechanisms. Recently, primed polymorphonuclear leukocytes have been suggested to induce endothelial dysfunction 31 and hypertension, possibly through an increase in oxidative stress. 32 Monocytes may contribute to atherogenesis by giving rise to foamy macrophages and reactive oxygen species. 28 Both macrophages and lymphocytes secrete proinflammatory cytokines, and mast cells secrete serine proteases that activate matrix metalloproteases. 33 Monocytes also participate in vascular thrombosis through interactions with platelets and are a rich source of highly thrombogenic tissue factor. 34
It is possible that the associations between WBC count and incident cardiovascular disease are simply confounded by some unmeasured covariate that reflects the severity of the initial insult. In the present study, however, we did collect information regarding office and average 24-h BP, major concomitant cardiovascular risk factors, and markers of target organ damage. Although WBC count had a weak direct relationship with 24-h systolic BP, smoking, and serum triglycerides, the association between WBC count and cardiovascular disease was independent of these and other markers of cardiovascular risk. Thus, although the association between WBC count and cardiovascular disease may be partly explained by the association with established risk factors, the present study clearly demonstrates that WBC count yields significant additional information to risk-stratify patients with uncomplicated essential hypertension.
There are several important limitations to the study. The sample size was insufficient to assess the relation between WBC count and cardiovascular death. C-reactive protein was not measured in our study to make comparisons with WBC count. The true importance of WBC count is likely to be underestimated in our study, which was based on a single examination, not taking into account the spontaneous variability of WBC count over time. Finally, our findings could be explained by unmeasured variations in health care among study participants and time-varying variables such as diabetes, smoking, and BP and lipid levels.
In conclusion, results from this study support the hypothesis of an independent association between high WBC count and cardiovascular complications in essential hypertension. These data provide new epidemiologic evidence regarding cardiovascular risk stratification in hypertensive subjects. The WBC count, a simple, inexpensive, almost universally obtainable test, may add valuable prognostic information to that obtainable from standard clinical cardiovascular risk factors and markers of target organ damage.
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Author notes
* This work was supported in part by the grant no. 2004060902 from the Italian Ministry for University.
© 2007 by the American Journal of Hypertension, Ltd.
American Journal of Hypertension, Ltd.
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