- Email: [email protected]
Association of Body Mass Index, Metabolic Syndrome, and Leukocyte Count
Association of Body Mass Index, Metabolic Syndrome, and Leukocyte Count Milind Y. Desai, MDa, Darshan Dalal, MD, MPHa, Raul D. Santos, MD, PhDb,c, Jose A.M. Carvalho, MDb, Khurram Nasir, MD, MPHa, and Roger S. Blumenthal, MDa,* Obesity and metabolic syndrome (MS), which often co-exist, are associated with an increased cardiovascular risk. An increased leukocyte count is also associated with an increased cardiovascular risk. However, the role of obesity, independent of MS, has been debated. We sought to assess the influence of MS on the association of obesity and leukocyte count in asymptomatic patients. The data from 431 asymptomatic Brazilian men (mean age 46 ⴞ 7 years), who presented for cardiovascular risk assessment, were analyzed. MS was defined as the presence of >3 of the following risk factors: hypertension (>130/85 mm Hg), truncal obesity (>102 cm or 40 in), hypertriglyceridemia (>150 mg/dl), high-density lipoprotein cholesterol (<40 mg/dl), and hyperglycemia (glucose >110 mg/dl). Obesity was defined as a body mass index of >30 kg/m2. Confounding variables (age, smoking, lipid-lowering therapy, and physical activity) and leukocyte count (109/L) were recorded. The patients were divided into 4 groups: group 1, no obesity and no MS; group 2, obesity but no MS; group 3, no obesity but MS; and group 4, obesity and MS. The mean leukocyte count increased from groups 1 to 4 (6.10 ⴞ 0.09, 6.42 ⴞ 0.28, 6.71 ⴞ 0.21, and 6.96 ⴞ 0.22 ⴛ 109/L, p <0.001 for trend). Multivariate regression analysis demonstrated that the leukocyte count was significantly higher in groups 3 (coefficient 0.61, p ⴝ 0.007) and 4 (coefficient 0.86, p <0.001) compared with group 1. However, no significant difference was found in the leukocyte count between groups 1 and 2 (coefficient 0.29, p ⴝ 0.42) and groups 3 and 4 (coefficient 0.25, p ⴝ 0.41). The association between obesity and leukocyte count was highly dependent on the presence of MS. © 2006 Elsevier Inc. All rights reserved. (Am J Cardiol 2006;97:835– 838) An elevated (even within the normal range) leukocyte count is associated with cardiovascular risk factors (e.g., hypertension, smoking, elevated lipids, abnormal glucose metabolism, and increased body mass index [BMI])1–3 and with an increased risk of metabolic syndrome (MS).4 It is not clear whether the association between leukocyte count (also a risk factor for cardiovascular disease5,6) and obesity is independent or significantly dependent on the presence of concomitant MS. The present study evaluated the association among BMI, MS, and leukocyte count and assessed the influence of MS on the association between obesity and leukocyte count. •••
This was a cross-sectional study of a consecutive sample of 559 previously asymptomatic men with a mean age of 46 ⫾ 7 years. These men were company executives who had
a Ciccarone Preventive Cardiology Center, Johns Hopkins University School of Medicine, Baltimore, Maryland; bInstituto de Ensino e Pesquisa Israelita, Albert Einstein-Preventive Medicine Center, Albert Einstein Hospital, São Paulo, Brazil; and cLipid Clinic Heart Institute (InCor), University of São Paulo Medical School Hospital, São Paulo, Brazil. Manuscript received May 30, 2005; revised manuscript received and accepted October 3, 2005. This study was supported in part by an unrestricted educational grant from Maryland Athletic Club Charitable Foundation, Timonium, Maryland. * Corresponding author: Tel: 410-955-7376; fax: 410-614-9190. E-mail address: [email protected] (R.S. Blumenthal).
0002-9149/06/$ – see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.amjcard.2005.10.021
presented for a mandatory routine annual medical evaluation, paid for by their employers, that consisted of a clinical consultation with laboratory analysis between July 1999 and June 2003 at the Preventive Medicine Center of the Albert Einstein Hospital (São Paulo, Brazil). The local institutional review board approved the study, which received a waiver of patient consent. All patients provided detailed information of their demographics, medical history, and medication use at the clinical consultation. Hyperglycemia was defined as a fasting blood glucose level of ⱖ110 mg/dl. The presence of hypertension was defined as blood pressure ⬎130/85 mm Hg or the use of antihypertensive medications. A history of cigarette smoking was defined as those who were current smokers. The patients were considered physically active if they engaged in moderate physical activity ⱖ3 times a week. Weight (in kilograms) and height (in meters) was measured with a standard physician’s scale and stadiometer, respectively. Waist circumference (in centimeters) was measured at the narrowest diameter between the costal margin and the iliac crest using a plastic anthropometric tape. Blood pressure was obtained with a mercury sphygmomanometer (with the patient in a sitting position after approximately 5 minutes) using the auscultatory methods recommended by the American Heart Association.7 Blood specimens were collected after an overnight fast. Total cholesterol and triglycerides were estimated by enzymatic methods (cholesterol oxidase/peroxidase-aminophenwww.AJConline.org
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Table 1 Demographics of study population divided according to presence of obesity and metabolic syndrome (MS) Variable Age (yrs) Smokers Physically active* Lipid-lowering therapy † Systolic blood pressure (mm Hg) BMI (kg/m2)† Abdominal girth (cm)* Serum glucose (mg/dl)* Serum total cholesterol (mg/dl) Serum low-density lipoprotein cholesterol (mg/dl) Serum triglycerides (mg/dl)† Serum HDL cholesterol (mg/dl)†
⫺Obesity/⫺MS (n ⫽ 292)
⫹Obesity/⫺MS (n ⫽ 31)
⫺Obesity/⫹MS (n ⫽ 56)
⫹Obesity/⫹MS (n ⫽ 52)
Total (n ⫽ 431)
46 ⫾ 7 48% 54% 7% 123 ⫾ 12 26 ⫾ 2 92 ⫾ 7 86 ⫾ 8 204 ⫾ 36 132 ⫾ 32 140 ⫾ 86 48 ⫾ 13
47 ⫾ 7 68% 40% 6% 123 ⫾ 11 32 ⫾ 3 106 ⫾ 7 91 ⫾ 10 215 ⫾ 56 146 ⫾ 56 128 ⫾ 55 50 ⫾ 11
48 ⫾ 7 50% 46% 7% 136 ⫾ 15 27 ⫾ 2 100 ⫾ 6 93 ⫾ 12 213 ⫾ 32 135 ⫾ 31 249 ⫾ 97 35 ⫾ 7
46 ⫾ 7 46% 33% 8% 134 ⫾ 11 34 ⫾ 4 111 ⫾ 8 89 ⫾ 9 211 ⫾ 50 131 ⫾ 46 251 ⫾ 90 36 ⫾ 7
46 ⫾ 7 49% 50% 7% 126 ⫾ 13 27 ⫾ 4 96 ⫾ 10 88 ⫾ 9 207 ⫾ 39 133 ⫾ 36 166 ⫾ 75 45 ⫾ 13
Obesity, BMI ⬎30 kg/m2. * p ⬍0.05 for group; † p ⬍0.001 for group.
azone for cholesterol, glycerol phosphate oxidase/peroxidase-aminophenazone for triglycerides) on an automated system using standard kits (Johnson & Johnson Clinical Diagnostics, Rochester, New York). High-density lipoprotein (HDL) cholesterol was estimated using a precipitation method, and low-density lipoprotein cholesterol was calculated (total cholesterol ⫺ HDL cholesterol ⫺ triglycerides/5) for triglyceride levels ⱕ400 mg/dl. Fasting blood glucose was measured with a glucose oxidase method using a colorimetric assay on the Vitros automated platform (Johnson & Johnson Clinical Diagnostics). The leukocyte count was measured automatically using Sysmex XE 2100 equipment (Roche Diagnostics, Kobe, Japan) (normal range 6 to 10 ⫻ 109/L). The following metabolic risk factors were recorded for every patient, and those with ⱖ3 metabolic risk factors were considered to have MS8: high blood pressure (ⱖ130/85 mm Hg), truncal obesity (ⱖ102 cm or 40 in), hypertriglyceridemia (ⱖ150 mg/dl), low HDL (ⱕ40 mg/dl), and hyperglycemia (fasting blood glucose ⱖ110 mg/dl). The patients were also separated into 3 categories on the basis of their BMI: normal (BMI ⬍25 kg/m2), overweight (BMI 25 to 29.9 kg/m2), and obese (BMI ⱖ30 kg/m2).9 The distribution of values was assessed by the Kolmogorov-Smirnov test for homogeneity of variances. Baseline demographics, risk factors, and clinical variables are descriptively summarized. Continuous variables are expressed as means ⫾ SDs. Categorical data are presented as the percent frequency. The relation between leukocyte count and different groups of BMI and MS risk factors was assessed using univariate and multivariate linear regression analyses. Logistic regression analysis was used to determine the association between a high or low leukocyte count (higher or lower than the median) and groups of MS and obesity. The confounding variables used for the analysis included age, smoking history, lipid-lowering therapy, and physical activity. The difference in the means of the variables among different groups was tested with analysis of variance using the Bonferroni correction.
Table 2 Multivariate model demonstrating association between leukocyte count versus body mass index (BMI) and increasing metabolic risk factors
BMI (kg/m2) ⬍25 25–29 ⱖ30 Intercept Metabolic risk factors None 1 2 ⱖ3 Intercept
Unadjusted Coefficient (95% CI)
Adjusted Coefficient* (95% CI)
Reference 0.27 (⫺0.62 to 0.59)† 0.78 (0.36 to 1.19)†‡ 6.03 (5.77 to 6.30)‡
Reference 0.22 (⫺0.14 to 0.57)† 0.76 (0.31 to 1.22)†§ 5.43 (4.19 to 6.67)‡
Reference 0.19 (⫺0.21 to 0.59) 0.65 (0.24 to 1.06)§ 0.98 (0.57 to 1.39)‡ 5.86 (5.57 to 6.15)‡
Reference 0.10 (⫺0.33 to 0.53) 0.43 (0.03 to 0.89)§ 0.87 (0.43 to 1.30)‡ 5.40 (4.20 to 6.60)‡
* Data adjusted for age, smoking, lipid-lowering therapy, and physical activity; smoking was the only significant confounding variable. † Coefficient significantly higher for obese compared with overweight men (p ⬍0.01). ‡ p ⬍0.001; § p ⬍0.01. CI ⫽ confidence interval.
STATA Statistical Software, release 8.0 (Stata, College Station, Texas) was used for the analyses. A p value ⬍0.05 was considered statistically significant. Of the 559 patients in the original study population, a complete set of data was available for 431, which constituted our final study population. The demographic data of the entire group are listed in Table 1. The mean leukocyte count, adjusted for confounding variables, demonstrated a significant incremental increase with increasing BMI (normal 6.05 ⫾ 0.15 ⫻ 109/L, overweight 6.27 ⫾ 0.11 ⫻ 109/L, and obese 6.82 ⫾ 0.18 ⫻ 109/L, p ⬍0.01 for trend). A significant association was found between BMI and leukocyte count on univariate and multivariate analyses. These results are given in Table 2. Similarly, the mean leukocyte count, adjusted for confounding variables, was lowest in the patients with no MS risk factors and demonstrated a significant incremental in-
Preventive Cardiology/BMI, Metabolic Syndrome, and Leukocyte Count Table 3 Multivariate models demonstrating association among obesity, metabolic syndrome (MS), and leukocyte count Group ⫺Obesity/⫺MS ⫹Obesity/⫺MS ⫺Obesity/⫹MS ⫹Obesity/⫹MS Intercept
Unadjusted Coefficient (95% CI)
Adjusted Coefficient* (95% CI)
Reference 0.46 (⫺0.11 to 1.04) 0.62 (0.18 to 1.07)† 0.92 (0.46 to 1.37)§ 6.08 (5.91 to 6.26)§
Reference 0.33 (⫺0.26 to 0.91) 0.61‡ (0.17 to 1.06)† 0.86‡ (0.39 to 1.34)§ 5.43 (4.22 to 6.64)§
* Data adjusted for age, smoking, lipid-lowering therapy, and physical activity; smoking was the only significant confounding variable. † p ⬍0.01; § p ⬍0.001. ‡ Coefficient, although higher for group 4 compared with group 3, was not statistically significant (p ⫽ 0.41). Abbreviation as in Table 2. Table 4 Logistic regression analysis demonstrating association among obesity, metabolic syndrome (MS), and elevated leukocyte count (ⱖ50th percentile) Group
⫺Obesity/⫺MS ⫹ Obesity/⫺MS ⫺Obesity/⫹MS ⫹ Obesity/⫹MS
Model 1 (unadjusted) (95% CI)
Model 2 (adjusted for all confounding factors) (95% CI)
Reference 1.67 (0.79–3.51) 1.83 (1.03–3.27)* 2.83 (1.52–5.28)‡
Reference 1.42 (0.65–3.12) 1.93† (1.06–3.51)* 2.76† (1.43–5.35)‡
Data are presented as odds ratios. * p ⬍0.05; ‡ p ⬍0.001; confounding variables included age, smoking status, lipid-lowering therapy, and physical activity. † Odds ratio, although higher for group 4 compared with group 3, was not statistically significant (p ⫽ 0.39). Abbreviation as in Table 2.
crease with an increasing number of MS risk factors (no risk factors 5.95 ⫾ 0.16 ⫻ 109/L, 1 risk factor 6.05 ⫾ 0.15 ⫻ 109/L, 2 risk factors 6.39 ⫾ 0.16 ⫻ 109/L, and ⱖ3 risk factors 6.82 ⫾ 0.15 ⫻ 109/L, p ⬍0.05 for trend). The association between an increasing number of MS risk factors and leukocyte count on univariate and multivariate analyses is given in Table 2. Subsequently, the study cohort was divided into 4 groups, according to the presence of obesity and MS: group 1, no obesity and no MS; group 2, obesity and no MS; group 3, no obesity but MS; and group 4, obesity plus MS. The demographic data of the study cohort divided into 4 groups are also listed in Table 1. Significant expected differences were found between groups that maintained their significance after Bonferroni’s correction. Also, some discordance was found in the prevalence of obesity and MS in the study population, such that 7% of patients were obese without MS, 14% had MS without obesity, and 12% had evidence of obesity and MS. The mean leukocyte count, adjusted for confounding variables, demonstrated a significant incremental increase from groups 1 to 4 (group 1 6.09 ⫾ 0.09 ⫻ 109/L, group 2 6.42 ⫾ 0.28 ⫻ 109/L, group 3 6.71 ⫾ 0.21 ⫻ 109/L,
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and group 4 6.96 ⫾ 0.22 ⫻ 109/L, p ⬍0.01 for trend). The association between leukocyte count and groups 1 to 4 (on univariate and multivariate analyses) is given in Table 3. Finally, the patients were also divided into 2 groups on the basis of their leukocyte counts: high (ⱖ50th percentile) and low (⬍50th percentile). The results of the logistic regression analysis are listed in Table 4. •••
The results of this study of asymptomatic middle-aged men support the findings from previous studies that there is a linear independent association between the leukocyte count and increasing BMI10 and increasing MS risk factors.11 However, the plausible mechanisms through which an increasing BMI and obesity are associated with leukocyte count, and in turn, cardiovascular disease risk, are not clearly defined. It has been suggested that the association could be mediated, in part, by the co-existence of MS.12 Thus, to test whether the association of obesity and leukocyte count is independent or dependent on the presence of MS, we divided the study cohort into 4 groups according to the presence or absence of obesity and MS. The present findings indicate that the leukocyte count in obese patients without MS was not significantly different from that in the reference group, However, nonobese patients with MS had a significantly higher leukocyte count than the reference group. As expected, obese patients with MS also had a significantly higher leukocyte count. Importantly, we found no significant difference in the leukocyte count in patients with MS who were obese compared with those who were not obese (Table 3). These data suggest 1 of the major determinants of the significant association of obesity and leukocyte count is the concomitant presence of MS. Routine assessment of obesity (by measuring the BMI) does not separately quantify the amount of subcutaneous and visceral fat in a patient. It has been suggested that visceral adiposity, and not subcutaneous fat (which most likely reflects total body weight, measured as the BMI), is independently associated with MS risk factors such as insulin resistance,13 low HDL cholesterol,13,14 high triglycerides,13,14 and high blood pressure.15 It is conceivable that the current clinical measures of obesity might misclassify patients in terms of whether they have excess visceral adiposity. Doing so might lead to a situation in which a “lean” patient, albeit with an increased visceral/subcutaneous fat ratio, might have evidence of MS and vice versa. The present study, along with others,12 has demonstrated that a small, but significant, group of patients have evidence of MS and insulin resistance without concomitant obesity. This study should be interpreted in light of several limitations. The study population consisted exclusively of middleage Brazilian men. As a result, it is uncertain whether these findings can be extrapolated to women, older patients, or other ethnic groups. Also, any study dealing with intercorrelated variables can potentially be affected by overadjustment and
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multicollinearity in the statistical analysis. The cross-sectional nature of the study permitted only associations, rather than causality. We had only 1 measure of the leukocyte count, and other markers associated with increased cardiovascular disease risk (e.g., C-reactive protein) were not measured. Finally, the present study did not include measurements of visceral adiposity; hence, it was not possible to know the amount of visceral fat present in each group and derive assumptions of its potential association with the leukocyte count. However, a good correlation was demonstrated between the waist measurement and visceral adipose tissue.16
9.
10. 11.
12. 1. Friedman GD, Tekawa I, Grimm RH, Manolio T, Shannon SG, Sidney S. The leucocyte count: correlates and relationship to coronary risk factors—the CARDIA study. Int J Epidemiol 1990;19:889 – 893. 2. Gray RS, Fabsitz RR, Cowan LD, Lee ET, Howard BV, Savage PJ. Risk factor clustering in the insulin resistance syndrome: the Strong Heart Study. Am J Epidemiol 1998;148:869 – 878. 3. Madjid M, Awan I, Willerson JT, Casscells SW. Leukocyte count and coronary heart disease: implications for risk assessment. J Am Coll Cardiol 2004;44:1945–1956. 4. Rader DJ. Inflammatory markers of coronary risk. N Engl J Med 2000;343:1179 –1182. 5. Weijenberg MP, Feskens EJ, Kromhout D. White blood cell count and the risk of coronary heart disease and all-cause mortality in elderly men. Arterioscler Thromb Vasc Biol 1996;16:499 –503. 6. Danesh J, Collins R, Appleby P, Peto R. Association of fibrinogen, C-reactive protein, albumin, or leukocyte count with coronary heart disease: meta-analyses of prospective studies. JAMA 1998;279:1477–1482. 7. Perloff D, Grim C, Flack J, Frohlich ED, Hill M, McDonald M, Morgenstern BZ. Human blood pressure determination by sphygmomanometry. Circulation 1993;88:2460 –2470. 8. The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In
13.
14.
15.
16.
Adults. Executive summary of the third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 2001;285:2486 –2497. National Institutes of Health. Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults—the evidence report. Obes Res 1998;6(suppl 2):51S–209S. Pradhan AD, Skerrett PJ, Manson JE. Obesity, diabetes, and coronary risk in women. J Cardiovasc Risk 2002;9:323–330. Nakanishi N, Sato M, Shirai K, Nakajima K, Murakami S, Takatorige T, Suzuki K, Tatara K. Associations between white blood cell count and features of the metabolic syndrome in Japanese male office workers. Ind Health 2002;40:273–277. Kip KE, Marroquin OC, Kelley DE, Johnson BD, Kelsey SF, Shaw LJ, Rogers WJ, Reis SE. Clinical importance of obesity versus the metabolic syndrome in cardiovascular risk in women: a report from the Women’s Ischemia Syndrome Evaluation (WISE) study. Circulation 2004;109:706 –713. Ribeiro-Filho FF, Faria AN, Azjen S, Zanella MT, Ferreira SR. Methods of estimation of visceral fat: advantages of ultrasonography. Obes Res 2003;11:1488 –1494. Pascot A, Despres JP, Lemieux I, Bergeron J, Nadeau A, Prud’homme D, Tremblay A, Lemieux S. Contribution of visceral obesity to the deterioration of the metabolic risk profile in men with impaired glucose tolerance. Diabetologia 2000;43:1126 –1135. Nagaretani H, Nakamura T, Funahashi T, Kotani K, Miyanaga M, Tokunaga K, Takahashi M, Nishizawa H, Kishida K, Kuriyama H, et al. Visceral fat is a major contributor for multiple risk factor clustering in Japanese men with impaired glucose tolerance. Diabetes Care 2001;24:2127–2133. Pouliot MC, Despres JP, Lemieux S, Moorjani S, Bouchard C, Tremblay A, Nadeau A, Lupien PJ. Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Cardiol 1994;73:460 – 468.
This was a cross-sectional study of a consecutive sample of 559 previously asymptomatic men with a mean age of 46 ⫾ 7 years. These men were company executives who had
a Ciccarone Preventive Cardiology Center, Johns Hopkins University School of Medicine, Baltimore, Maryland; bInstituto de Ensino e Pesquisa Israelita, Albert Einstein-Preventive Medicine Center, Albert Einstein Hospital, São Paulo, Brazil; and cLipid Clinic Heart Institute (InCor), University of São Paulo Medical School Hospital, São Paulo, Brazil. Manuscript received May 30, 2005; revised manuscript received and accepted October 3, 2005. This study was supported in part by an unrestricted educational grant from Maryland Athletic Club Charitable Foundation, Timonium, Maryland. * Corresponding author: Tel: 410-955-7376; fax: 410-614-9190. E-mail address: [email protected] (R.S. Blumenthal).
0002-9149/06/$ – see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.amjcard.2005.10.021
presented for a mandatory routine annual medical evaluation, paid for by their employers, that consisted of a clinical consultation with laboratory analysis between July 1999 and June 2003 at the Preventive Medicine Center of the Albert Einstein Hospital (São Paulo, Brazil). The local institutional review board approved the study, which received a waiver of patient consent. All patients provided detailed information of their demographics, medical history, and medication use at the clinical consultation. Hyperglycemia was defined as a fasting blood glucose level of ⱖ110 mg/dl. The presence of hypertension was defined as blood pressure ⬎130/85 mm Hg or the use of antihypertensive medications. A history of cigarette smoking was defined as those who were current smokers. The patients were considered physically active if they engaged in moderate physical activity ⱖ3 times a week. Weight (in kilograms) and height (in meters) was measured with a standard physician’s scale and stadiometer, respectively. Waist circumference (in centimeters) was measured at the narrowest diameter between the costal margin and the iliac crest using a plastic anthropometric tape. Blood pressure was obtained with a mercury sphygmomanometer (with the patient in a sitting position after approximately 5 minutes) using the auscultatory methods recommended by the American Heart Association.7 Blood specimens were collected after an overnight fast. Total cholesterol and triglycerides were estimated by enzymatic methods (cholesterol oxidase/peroxidase-aminophenwww.AJConline.org
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Table 1 Demographics of study population divided according to presence of obesity and metabolic syndrome (MS) Variable Age (yrs) Smokers Physically active* Lipid-lowering therapy † Systolic blood pressure (mm Hg) BMI (kg/m2)† Abdominal girth (cm)* Serum glucose (mg/dl)* Serum total cholesterol (mg/dl) Serum low-density lipoprotein cholesterol (mg/dl) Serum triglycerides (mg/dl)† Serum HDL cholesterol (mg/dl)†
⫺Obesity/⫺MS (n ⫽ 292)
⫹Obesity/⫺MS (n ⫽ 31)
⫺Obesity/⫹MS (n ⫽ 56)
⫹Obesity/⫹MS (n ⫽ 52)
Total (n ⫽ 431)
46 ⫾ 7 48% 54% 7% 123 ⫾ 12 26 ⫾ 2 92 ⫾ 7 86 ⫾ 8 204 ⫾ 36 132 ⫾ 32 140 ⫾ 86 48 ⫾ 13
47 ⫾ 7 68% 40% 6% 123 ⫾ 11 32 ⫾ 3 106 ⫾ 7 91 ⫾ 10 215 ⫾ 56 146 ⫾ 56 128 ⫾ 55 50 ⫾ 11
48 ⫾ 7 50% 46% 7% 136 ⫾ 15 27 ⫾ 2 100 ⫾ 6 93 ⫾ 12 213 ⫾ 32 135 ⫾ 31 249 ⫾ 97 35 ⫾ 7
46 ⫾ 7 46% 33% 8% 134 ⫾ 11 34 ⫾ 4 111 ⫾ 8 89 ⫾ 9 211 ⫾ 50 131 ⫾ 46 251 ⫾ 90 36 ⫾ 7
46 ⫾ 7 49% 50% 7% 126 ⫾ 13 27 ⫾ 4 96 ⫾ 10 88 ⫾ 9 207 ⫾ 39 133 ⫾ 36 166 ⫾ 75 45 ⫾ 13
Obesity, BMI ⬎30 kg/m2. * p ⬍0.05 for group; † p ⬍0.001 for group.
azone for cholesterol, glycerol phosphate oxidase/peroxidase-aminophenazone for triglycerides) on an automated system using standard kits (Johnson & Johnson Clinical Diagnostics, Rochester, New York). High-density lipoprotein (HDL) cholesterol was estimated using a precipitation method, and low-density lipoprotein cholesterol was calculated (total cholesterol ⫺ HDL cholesterol ⫺ triglycerides/5) for triglyceride levels ⱕ400 mg/dl. Fasting blood glucose was measured with a glucose oxidase method using a colorimetric assay on the Vitros automated platform (Johnson & Johnson Clinical Diagnostics). The leukocyte count was measured automatically using Sysmex XE 2100 equipment (Roche Diagnostics, Kobe, Japan) (normal range 6 to 10 ⫻ 109/L). The following metabolic risk factors were recorded for every patient, and those with ⱖ3 metabolic risk factors were considered to have MS8: high blood pressure (ⱖ130/85 mm Hg), truncal obesity (ⱖ102 cm or 40 in), hypertriglyceridemia (ⱖ150 mg/dl), low HDL (ⱕ40 mg/dl), and hyperglycemia (fasting blood glucose ⱖ110 mg/dl). The patients were also separated into 3 categories on the basis of their BMI: normal (BMI ⬍25 kg/m2), overweight (BMI 25 to 29.9 kg/m2), and obese (BMI ⱖ30 kg/m2).9 The distribution of values was assessed by the Kolmogorov-Smirnov test for homogeneity of variances. Baseline demographics, risk factors, and clinical variables are descriptively summarized. Continuous variables are expressed as means ⫾ SDs. Categorical data are presented as the percent frequency. The relation between leukocyte count and different groups of BMI and MS risk factors was assessed using univariate and multivariate linear regression analyses. Logistic regression analysis was used to determine the association between a high or low leukocyte count (higher or lower than the median) and groups of MS and obesity. The confounding variables used for the analysis included age, smoking history, lipid-lowering therapy, and physical activity. The difference in the means of the variables among different groups was tested with analysis of variance using the Bonferroni correction.
Table 2 Multivariate model demonstrating association between leukocyte count versus body mass index (BMI) and increasing metabolic risk factors
BMI (kg/m2) ⬍25 25–29 ⱖ30 Intercept Metabolic risk factors None 1 2 ⱖ3 Intercept
Unadjusted Coefficient (95% CI)
Adjusted Coefficient* (95% CI)
Reference 0.27 (⫺0.62 to 0.59)† 0.78 (0.36 to 1.19)†‡ 6.03 (5.77 to 6.30)‡
Reference 0.22 (⫺0.14 to 0.57)† 0.76 (0.31 to 1.22)†§ 5.43 (4.19 to 6.67)‡
Reference 0.19 (⫺0.21 to 0.59) 0.65 (0.24 to 1.06)§ 0.98 (0.57 to 1.39)‡ 5.86 (5.57 to 6.15)‡
Reference 0.10 (⫺0.33 to 0.53) 0.43 (0.03 to 0.89)§ 0.87 (0.43 to 1.30)‡ 5.40 (4.20 to 6.60)‡
* Data adjusted for age, smoking, lipid-lowering therapy, and physical activity; smoking was the only significant confounding variable. † Coefficient significantly higher for obese compared with overweight men (p ⬍0.01). ‡ p ⬍0.001; § p ⬍0.01. CI ⫽ confidence interval.
STATA Statistical Software, release 8.0 (Stata, College Station, Texas) was used for the analyses. A p value ⬍0.05 was considered statistically significant. Of the 559 patients in the original study population, a complete set of data was available for 431, which constituted our final study population. The demographic data of the entire group are listed in Table 1. The mean leukocyte count, adjusted for confounding variables, demonstrated a significant incremental increase with increasing BMI (normal 6.05 ⫾ 0.15 ⫻ 109/L, overweight 6.27 ⫾ 0.11 ⫻ 109/L, and obese 6.82 ⫾ 0.18 ⫻ 109/L, p ⬍0.01 for trend). A significant association was found between BMI and leukocyte count on univariate and multivariate analyses. These results are given in Table 2. Similarly, the mean leukocyte count, adjusted for confounding variables, was lowest in the patients with no MS risk factors and demonstrated a significant incremental in-
Preventive Cardiology/BMI, Metabolic Syndrome, and Leukocyte Count Table 3 Multivariate models demonstrating association among obesity, metabolic syndrome (MS), and leukocyte count Group ⫺Obesity/⫺MS ⫹Obesity/⫺MS ⫺Obesity/⫹MS ⫹Obesity/⫹MS Intercept
Unadjusted Coefficient (95% CI)
Adjusted Coefficient* (95% CI)
Reference 0.46 (⫺0.11 to 1.04) 0.62 (0.18 to 1.07)† 0.92 (0.46 to 1.37)§ 6.08 (5.91 to 6.26)§
Reference 0.33 (⫺0.26 to 0.91) 0.61‡ (0.17 to 1.06)† 0.86‡ (0.39 to 1.34)§ 5.43 (4.22 to 6.64)§
* Data adjusted for age, smoking, lipid-lowering therapy, and physical activity; smoking was the only significant confounding variable. † p ⬍0.01; § p ⬍0.001. ‡ Coefficient, although higher for group 4 compared with group 3, was not statistically significant (p ⫽ 0.41). Abbreviation as in Table 2. Table 4 Logistic regression analysis demonstrating association among obesity, metabolic syndrome (MS), and elevated leukocyte count (ⱖ50th percentile) Group
⫺Obesity/⫺MS ⫹ Obesity/⫺MS ⫺Obesity/⫹MS ⫹ Obesity/⫹MS
Model 1 (unadjusted) (95% CI)
Model 2 (adjusted for all confounding factors) (95% CI)
Reference 1.67 (0.79–3.51) 1.83 (1.03–3.27)* 2.83 (1.52–5.28)‡
Reference 1.42 (0.65–3.12) 1.93† (1.06–3.51)* 2.76† (1.43–5.35)‡
Data are presented as odds ratios. * p ⬍0.05; ‡ p ⬍0.001; confounding variables included age, smoking status, lipid-lowering therapy, and physical activity. † Odds ratio, although higher for group 4 compared with group 3, was not statistically significant (p ⫽ 0.39). Abbreviation as in Table 2.
crease with an increasing number of MS risk factors (no risk factors 5.95 ⫾ 0.16 ⫻ 109/L, 1 risk factor 6.05 ⫾ 0.15 ⫻ 109/L, 2 risk factors 6.39 ⫾ 0.16 ⫻ 109/L, and ⱖ3 risk factors 6.82 ⫾ 0.15 ⫻ 109/L, p ⬍0.05 for trend). The association between an increasing number of MS risk factors and leukocyte count on univariate and multivariate analyses is given in Table 2. Subsequently, the study cohort was divided into 4 groups, according to the presence of obesity and MS: group 1, no obesity and no MS; group 2, obesity and no MS; group 3, no obesity but MS; and group 4, obesity plus MS. The demographic data of the study cohort divided into 4 groups are also listed in Table 1. Significant expected differences were found between groups that maintained their significance after Bonferroni’s correction. Also, some discordance was found in the prevalence of obesity and MS in the study population, such that 7% of patients were obese without MS, 14% had MS without obesity, and 12% had evidence of obesity and MS. The mean leukocyte count, adjusted for confounding variables, demonstrated a significant incremental increase from groups 1 to 4 (group 1 6.09 ⫾ 0.09 ⫻ 109/L, group 2 6.42 ⫾ 0.28 ⫻ 109/L, group 3 6.71 ⫾ 0.21 ⫻ 109/L,
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and group 4 6.96 ⫾ 0.22 ⫻ 109/L, p ⬍0.01 for trend). The association between leukocyte count and groups 1 to 4 (on univariate and multivariate analyses) is given in Table 3. Finally, the patients were also divided into 2 groups on the basis of their leukocyte counts: high (ⱖ50th percentile) and low (⬍50th percentile). The results of the logistic regression analysis are listed in Table 4. •••
The results of this study of asymptomatic middle-aged men support the findings from previous studies that there is a linear independent association between the leukocyte count and increasing BMI10 and increasing MS risk factors.11 However, the plausible mechanisms through which an increasing BMI and obesity are associated with leukocyte count, and in turn, cardiovascular disease risk, are not clearly defined. It has been suggested that the association could be mediated, in part, by the co-existence of MS.12 Thus, to test whether the association of obesity and leukocyte count is independent or dependent on the presence of MS, we divided the study cohort into 4 groups according to the presence or absence of obesity and MS. The present findings indicate that the leukocyte count in obese patients without MS was not significantly different from that in the reference group, However, nonobese patients with MS had a significantly higher leukocyte count than the reference group. As expected, obese patients with MS also had a significantly higher leukocyte count. Importantly, we found no significant difference in the leukocyte count in patients with MS who were obese compared with those who were not obese (Table 3). These data suggest 1 of the major determinants of the significant association of obesity and leukocyte count is the concomitant presence of MS. Routine assessment of obesity (by measuring the BMI) does not separately quantify the amount of subcutaneous and visceral fat in a patient. It has been suggested that visceral adiposity, and not subcutaneous fat (which most likely reflects total body weight, measured as the BMI), is independently associated with MS risk factors such as insulin resistance,13 low HDL cholesterol,13,14 high triglycerides,13,14 and high blood pressure.15 It is conceivable that the current clinical measures of obesity might misclassify patients in terms of whether they have excess visceral adiposity. Doing so might lead to a situation in which a “lean” patient, albeit with an increased visceral/subcutaneous fat ratio, might have evidence of MS and vice versa. The present study, along with others,12 has demonstrated that a small, but significant, group of patients have evidence of MS and insulin resistance without concomitant obesity. This study should be interpreted in light of several limitations. The study population consisted exclusively of middleage Brazilian men. As a result, it is uncertain whether these findings can be extrapolated to women, older patients, or other ethnic groups. Also, any study dealing with intercorrelated variables can potentially be affected by overadjustment and
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multicollinearity in the statistical analysis. The cross-sectional nature of the study permitted only associations, rather than causality. We had only 1 measure of the leukocyte count, and other markers associated with increased cardiovascular disease risk (e.g., C-reactive protein) were not measured. Finally, the present study did not include measurements of visceral adiposity; hence, it was not possible to know the amount of visceral fat present in each group and derive assumptions of its potential association with the leukocyte count. However, a good correlation was demonstrated between the waist measurement and visceral adipose tissue.16
9.
10. 11.
12. 1. Friedman GD, Tekawa I, Grimm RH, Manolio T, Shannon SG, Sidney S. The leucocyte count: correlates and relationship to coronary risk factors—the CARDIA study. Int J Epidemiol 1990;19:889 – 893. 2. Gray RS, Fabsitz RR, Cowan LD, Lee ET, Howard BV, Savage PJ. Risk factor clustering in the insulin resistance syndrome: the Strong Heart Study. Am J Epidemiol 1998;148:869 – 878. 3. Madjid M, Awan I, Willerson JT, Casscells SW. Leukocyte count and coronary heart disease: implications for risk assessment. J Am Coll Cardiol 2004;44:1945–1956. 4. Rader DJ. Inflammatory markers of coronary risk. N Engl J Med 2000;343:1179 –1182. 5. Weijenberg MP, Feskens EJ, Kromhout D. White blood cell count and the risk of coronary heart disease and all-cause mortality in elderly men. Arterioscler Thromb Vasc Biol 1996;16:499 –503. 6. Danesh J, Collins R, Appleby P, Peto R. Association of fibrinogen, C-reactive protein, albumin, or leukocyte count with coronary heart disease: meta-analyses of prospective studies. JAMA 1998;279:1477–1482. 7. Perloff D, Grim C, Flack J, Frohlich ED, Hill M, McDonald M, Morgenstern BZ. Human blood pressure determination by sphygmomanometry. Circulation 1993;88:2460 –2470. 8. The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In
13.
14.
15.
16.
Adults. Executive summary of the third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 2001;285:2486 –2497. National Institutes of Health. Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults—the evidence report. Obes Res 1998;6(suppl 2):51S–209S. Pradhan AD, Skerrett PJ, Manson JE. Obesity, diabetes, and coronary risk in women. J Cardiovasc Risk 2002;9:323–330. Nakanishi N, Sato M, Shirai K, Nakajima K, Murakami S, Takatorige T, Suzuki K, Tatara K. Associations between white blood cell count and features of the metabolic syndrome in Japanese male office workers. Ind Health 2002;40:273–277. Kip KE, Marroquin OC, Kelley DE, Johnson BD, Kelsey SF, Shaw LJ, Rogers WJ, Reis SE. Clinical importance of obesity versus the metabolic syndrome in cardiovascular risk in women: a report from the Women’s Ischemia Syndrome Evaluation (WISE) study. Circulation 2004;109:706 –713. Ribeiro-Filho FF, Faria AN, Azjen S, Zanella MT, Ferreira SR. Methods of estimation of visceral fat: advantages of ultrasonography. Obes Res 2003;11:1488 –1494. Pascot A, Despres JP, Lemieux I, Bergeron J, Nadeau A, Prud’homme D, Tremblay A, Lemieux S. Contribution of visceral obesity to the deterioration of the metabolic risk profile in men with impaired glucose tolerance. Diabetologia 2000;43:1126 –1135. Nagaretani H, Nakamura T, Funahashi T, Kotani K, Miyanaga M, Tokunaga K, Takahashi M, Nishizawa H, Kishida K, Kuriyama H, et al. Visceral fat is a major contributor for multiple risk factor clustering in Japanese men with impaired glucose tolerance. Diabetes Care 2001;24:2127–2133. Pouliot MC, Despres JP, Lemieux S, Moorjani S, Bouchard C, Tremblay A, Nadeau A, Lupien PJ. Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Cardiol 1994;73:460 – 468.