# Platelet Counts and Platelet Activation Markers   in Obese Subjects

Authors: Dorit Samocha-Bonet; Dan Justo; Ori Rogowski; Nili Saar; Subchi Abu-Abeid; Galina Shenkerman; Itzhak Shapira; Shlomo Berliner; Aaron Tomer
Journal: Mediators of Inflammation
 (2008)

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## Abstract

Objective. In this work we studied the correlation between platelet count, platelet activation, and systemic inflammation in overweight, obese, and morbidly obese individuals.
Methods and subjects. A total of 6319 individuals participated in the study. Complete blood counts, high sensitivity C-reactive protein (hs-CRP) serum levels, and body mass index (BMI) were measured during routine checkups. Platelet activation markers were studied among 30 obese (BMI = 41 ± 8 kg/m2) and 35 nonobese (BMI = 24 ± 3 kg/m2) individuals. Platelet activation status was evaluated by flow cytometry using specific antibodies against the activated platelet membrane glycoprotein IIb/IIIa, p-selectin (CD-62 p), and binding of Annexin-V to platelet anionic phospholipids.
Results. Overweight, obese, and morbidly obese females had significantly elevated platelet counts (P<.0001) compared with normal-weight females. No significant elevation of platelet counts was observed in the male subgroups. A significant age adjusted correlation between BMI and platelet counts (P<.0001) was found among females. This correlation was attenuated (P=.001) after adjustment for hs-CRP concentrations. The flow cytometry analysis of platelets showed no significant differences in activation marker expression between nonobese and obese individuals.
Discussion. Obesity may be associated with elevated platelet counts in females with chronic inflammation. Obesity is not associated with increased platelet activation.


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## Body

## 1. Introduction

Obesity, a major risk factor for cardiovascular disease, is
associated with an accelerated atherothrombotic process, resulting in increased
morbidity and mortality [1]. Cytokines, such as Interleukin-6 (IL-6),
originating from adipose tissue, have a fundamental role in the pathogenesis of
atherothrombosis [2]. IL-6 acts synergistically with other interleukins,
growth factors, and thrombopoietin in megakaryocytopoiesis; in-vivo
administration of IL-6 to both monkeys and humans reportedly increased
circulating platelet counts [3]. Higher platelet counts are
associated with adverse clinical outcome in patients with ST-elevation
myocardial infarction [4]. The association between increased platelet
counts and platelet activation is unclear; in patients with carotid stenosis,
increased platelet counts, and leukocyte-platelet complex formation, that is,
platelet activation, was associated with the risk of stroke [5]; platelet
count and platelet activation are associated in patients with chronic
inflammation, such as patients with essential thrombocytosis [6],
and patients with inflammatory bowel diseases [7]. Obesity is a
chronic inflammation state [2], yet the association between platelet
count and platelet activation has never been studied in obese subjects, to the
best of our knowledge.There is an ongoing debate on
whether obesity is accompanied by platelet activation. The findings that the
leptin receptor is expressed in platelets [8] and that leptin
potentiates platelet aggregation by agonists [8, 9] shed light on a possible direct link between obesity and thrombotic
complication. Davi et al. demonstrated platelet activation in obese individuals
by measuring urine excretion of a thromboxane B2 (TxB2)
metabolite (11-dehydrothromboxane B2, 11-dehydro TxB2) [10]. In contrast, other studies found no evidence of increased platelet
activation in obese individuals [11], in obese and overweight women [12], in overweight prediabetics [13], and further reported
a negative correlation between plasma β-thromboglobulin (BTG), a marker of platelet release,
and body weight [14]. In the
present study, we studied platelet count and platelet activation in obese
subjects.


## 2. Materials and Methods

### 2.1. Patients

Participants were all part
of the Tel-Aviv Medical Center Inflammation Survey (TAMCIS). The TAMCIS is a cross-sectional
study performed on a group of apparently healthy individuals, consisting of
employees of the Tel-Aviv Sourasky Medical Center (TASMC) and the Tel-Aviv Municipality. The study was conducted
between September 2002 and July 2006. All patients gave their informed consent
to participate in the survey in writing, according to the local ethics
committee instructions. Recruitment for the study was performed through
announcements in the monthly salary slips of the Tel-Aviv Medical Center
personnel. Platelet count was studied
initially in 9115 subjects. Excluded were individuals with an underlying
chronic inflammatory disease (n = 1677), such as arthritis, inflammatory bowel disease, individuals with an acute or recent inflammatory condition, and individuals with a
history of myocardial infarction or
surgery during the past six months. Also excluded were individuals treated with
antiplatelets, anticoagulants, hormone replacement therapy, oral contraception,
systemic steroids, immunosuppressive agents, and nonsteroidal anti-inflammatory
drugs (n = 1119). Medical conditions were self-reported, but were confirmed by a
physician who took history and examined each individual. Platelet activation
was studied in 65 randomly assigned patients: 30 obese subjects from the
obesity clinic and 35 nonobese subjects from the general study. In this group,
subjects taking antiplatelets, hormone replacement therapy, and oral
contraception were not excluded.


### 2.2. Cardiovascular Risk Factors

The
diagnosis of diabetes mellitus was consistent with the guidelines of the
American Diabetic Association. Diabetic individuals were those with fasting
plasma glucose level of 126 mg/dL or higher, or those who were taking
hypoglycemic agents [15]. The diagnosis of essential hypertension was
consistent with the
seventh
report of the Joint National Committee on prevention, detection,
evaluation, and treatment of high blood pressure (JNC 7). Hypertensive
individuals were those with systolic blood pressure (BP) of 140 mmHg or higher
and/or a diastolic BP of 90 mmHg or higher repeatedly, or individuals who were
taking antihypertensive agents [16].


### 2.3. Metabolic Syndrome Definition

Metabolic syndrome was
defined as having at least three of the following: men with high-density
lipoprotein (HDL) cholesterol ≤40 mg/dL, women with HDL cholesterol ≤50 mg/dL,
triglyceride ≥150 mg/dL for both genders, blood pressure ≥130/85 mmHg for both
genders, fasting plasma glucose (FBG) ≥110 mg/dL for both genders, and waist circumference
≥102 cm for men and ≥88 cm for women [15]. Fasting glucose levels and
lipid profiles were measured by routine biochemical determinations. Blood
samples for plasma glucose levels and lipid profile were drawn after an
overnight fast from all individuals.


### 2.4. Platelet Counts and Inflammatory Markers

Complete blood counts were
performed using the Coulter STKS (Beckman Coulter, Nyon, Switzerland)
automatic cell analyzer. High-sensitivity C-reactive protein (hs-CRP) was
measured using the Boering BN II nephelometer (DADE Boering, Marburg, Germany)
according to Rifai et al. [17]. Blood samples for complete blood
counts and systemic inflammation markers were drawn after an overnight fast
from all individuals.


### 2.5. Platelet Activation Markers

Platelet activation was
studied as previously described [18, 19]. Briefly, blood was collected in
citrate-containing syringes (1 : 10 volume of 3.8% citrate) and processed
immediately to prevent possible in-vitro activation of platelets. Platelet-rich
plasma was prepared immediately by standard slow centrifugation (150× g for 12
minutes) and used for flow cytometry analysis of platelet activation markers. A
5 μL platelet suspension (250×109 platelets/L) was immediately
incubated with Ca++- and Mg++-free phosphate-buffered
saline (PBS) and monoclonal antibodies (MoAb) in saturating concentrations,
in a total volume of 50 μL. An HBSS-HEPES buffer containing 2.5 mM CaCl2 was
used for the incubation with Annexin-V. Following 30 minutes incubation at 4°C,
the samples were diluted 1 : 10 with the same buffer and analyzed by a FACSCalibur flow
cytometer (Becton Dickinson Biosciences, Calif, USA). The
samples were not lysed or fixed and were analyzed immediately. MoAbs included
phycoerythrin (PE)-labeled CD41 against resting and activated glycoprotein
IIb/IIIa (Immunotech, Marseille, France), fluorescein-isothiocyanate
(FITC)-labeled PAC-1 against the activated conformation of glycoprotein
IIb/IIIa (Becton Dickinson Biosciences, CA), FITC-labeled P-selectin (CD62p),
an α-granule membrane glycoprotein expressed on the
platelet surface during secretion (Immunotech, Marseille, France), and
FITC-labeled annexin-V, which is known to react with platelet
anionic-phospholipids, such as phosphatydilserine (PS) exposed on platelets
following activation (R&D Systems, Minneapolis, MN). Isotype-matched MoAbs
(Immunotech, France; DAKO, DK) were used as negative controls, and platelets
treated in-vitro with adenosine diphosphate (ADP) and Ca++-Ionophore
A23817 (final concentrations 20 μmol/L and 5 μmol/L, resp.) were used as
positive controls. Platelets were identified by light-scatter properties and
CD41 expression and analyzed for binding of the specific Ab. All measurements
were performed on list mode using logarithmic scales and 10,000 platelets were
analyzed in each sample using the Cellquest Pro software (Becton Dickinson).
The results are expressed as mean fluorescence intensity (MFI) units for the
studied markers of activation.


### 2.6. Statistical Analysis

For platelet count analysis, subjects were divided
into four groups based on their BMI: normal weight (BMI < 25), overweight
(25 < BMI < 29.9), obese (30 < BMI < 39.9), and morbidly obese (BMI
≥ 40). Data was analyzed separately for males and females due to gender
differences in baseline inflammatory profiles and platelet counts [20].
Differences in prevalence of cardiovascular risk factors and cardiovascular disease
between the various BMI subgroups were analyzed using the chi-square test for
discrete variables, and analysis of variance (ANOVA) with the general linear
model for continuous variables. Since hs-CRP had a non-normal distribution to
begin with, a logarithmic transformation of hs-CRP was used for all statistical
procedures. Differences between the BMI subgroups in terms of platelet counts,
hemoglobin levels, and hs-CRP levels were analyzed using ANOVA. Platelet
counts, hemoglobin levels and hs-CRP levels for overweight, obese, and morbidly
obese subgroups were compared with those of the normal-weight subgroup using
the general linear model with post hoc multiple comparisons by the method of
Scheffe. The student’s t test was used to evaluate differences in platelet
counts between subjects with or without the metabolic syndrome. ANOVA was used
to evaluate the association between BMI and platelet counts after age- and
hs-CRP-adjustments. For platelet activation analysis, 65 subjects were divided
into two groups based on their BMI: nonobese (BMI < 30) and obese (BMI ≥ 30).
The student’s t test and Mann-Whitney test were used to evaluate differences in
the studied markers between the two groups. BMI and waist-to-hip ratio were
normally distributed in both groups. The SPSS statistical package was used
(SSPS Inc., Chicago, IL, USA).


## 2.1. Patients

Participants were all part
of the Tel-Aviv Medical Center Inflammation Survey (TAMCIS). The TAMCIS is a cross-sectional
study performed on a group of apparently healthy individuals, consisting of
employees of the Tel-Aviv Sourasky Medical Center (TASMC) and the Tel-Aviv Municipality. The study was conducted
between September 2002 and July 2006. All patients gave their informed consent
to participate in the survey in writing, according to the local ethics
committee instructions. Recruitment for the study was performed through
announcements in the monthly salary slips of the Tel-Aviv Medical Center
personnel. Platelet count was studied
initially in 9115 subjects. Excluded were individuals with an underlying
chronic inflammatory disease (n = 1677), such as arthritis, inflammatory bowel disease, individuals with an acute or recent inflammatory condition, and individuals with a
history of myocardial infarction or
surgery during the past six months. Also excluded were individuals treated with
antiplatelets, anticoagulants, hormone replacement therapy, oral contraception,
systemic steroids, immunosuppressive agents, and nonsteroidal anti-inflammatory
drugs (n = 1119). Medical conditions were self-reported, but were confirmed by a
physician who took history and examined each individual. Platelet activation
was studied in 65 randomly assigned patients: 30 obese subjects from the
obesity clinic and 35 nonobese subjects from the general study. In this group,
subjects taking antiplatelets, hormone replacement therapy, and oral
contraception were not excluded.


## 2.2. Cardiovascular Risk Factors

The
diagnosis of diabetes mellitus was consistent with the guidelines of the
American Diabetic Association. Diabetic individuals were those with fasting
plasma glucose level of 126 mg/dL or higher, or those who were taking
hypoglycemic agents [15]. The diagnosis of essential hypertension was
consistent with the
seventh
report of the Joint National Committee on prevention, detection,
evaluation, and treatment of high blood pressure (JNC 7). Hypertensive
individuals were those with systolic blood pressure (BP) of 140 mmHg or higher
and/or a diastolic BP of 90 mmHg or higher repeatedly, or individuals who were
taking antihypertensive agents [16].


## 2.3. Metabolic Syndrome Definition

Metabolic syndrome was
defined as having at least three of the following: men with high-density
lipoprotein (HDL) cholesterol ≤40 mg/dL, women with HDL cholesterol ≤50 mg/dL,
triglyceride ≥150 mg/dL for both genders, blood pressure ≥130/85 mmHg for both
genders, fasting plasma glucose (FBG) ≥110 mg/dL for both genders, and waist circumference
≥102 cm for men and ≥88 cm for women [15]. Fasting glucose levels and
lipid profiles were measured by routine biochemical determinations. Blood
samples for plasma glucose levels and lipid profile were drawn after an
overnight fast from all individuals.


## 2.4. Platelet Counts and Inflammatory Markers

Complete blood counts were
performed using the Coulter STKS (Beckman Coulter, Nyon, Switzerland)
automatic cell analyzer. High-sensitivity C-reactive protein (hs-CRP) was
measured using the Boering BN II nephelometer (DADE Boering, Marburg, Germany)
according to Rifai et al. [17]. Blood samples for complete blood
counts and systemic inflammation markers were drawn after an overnight fast
from all individuals.


## 2.5. Platelet Activation Markers

Platelet activation was
studied as previously described [18, 19]. Briefly, blood was collected in
citrate-containing syringes (1 : 10 volume of 3.8% citrate) and processed
immediately to prevent possible in-vitro activation of platelets. Platelet-rich
plasma was prepared immediately by standard slow centrifugation (150× g for 12
minutes) and used for flow cytometry analysis of platelet activation markers. A
5 μL platelet suspension (250×109 platelets/L) was immediately
incubated with Ca++- and Mg++-free phosphate-buffered
saline (PBS) and monoclonal antibodies (MoAb) in saturating concentrations,
in a total volume of 50 μL. An HBSS-HEPES buffer containing 2.5 mM CaCl2 was
used for the incubation with Annexin-V. Following 30 minutes incubation at 4°C,
the samples were diluted 1 : 10 with the same buffer and analyzed by a FACSCalibur flow
cytometer (Becton Dickinson Biosciences, Calif, USA). The
samples were not lysed or fixed and were analyzed immediately. MoAbs included
phycoerythrin (PE)-labeled CD41 against resting and activated glycoprotein
IIb/IIIa (Immunotech, Marseille, France), fluorescein-isothiocyanate
(FITC)-labeled PAC-1 against the activated conformation of glycoprotein
IIb/IIIa (Becton Dickinson Biosciences, CA), FITC-labeled P-selectin (CD62p),
an α-granule membrane glycoprotein expressed on the
platelet surface during secretion (Immunotech, Marseille, France), and
FITC-labeled annexin-V, which is known to react with platelet
anionic-phospholipids, such as phosphatydilserine (PS) exposed on platelets
following activation (R&D Systems, Minneapolis, MN). Isotype-matched MoAbs
(Immunotech, France; DAKO, DK) were used as negative controls, and platelets
treated in-vitro with adenosine diphosphate (ADP) and Ca++-Ionophore
A23817 (final concentrations 20 μmol/L and 5 μmol/L, resp.) were used as
positive controls. Platelets were identified by light-scatter properties and
CD41 expression and analyzed for binding of the specific Ab. All measurements
were performed on list mode using logarithmic scales and 10,000 platelets were
analyzed in each sample using the Cellquest Pro software (Becton Dickinson).
The results are expressed as mean fluorescence intensity (MFI) units for the
studied markers of activation.


## 2.6. Statistical Analysis

For platelet count analysis, subjects were divided
into four groups based on their BMI: normal weight (BMI < 25), overweight
(25 < BMI < 29.9), obese (30 < BMI < 39.9), and morbidly obese (BMI
≥ 40). Data was analyzed separately for males and females due to gender
differences in baseline inflammatory profiles and platelet counts [20].
Differences in prevalence of cardiovascular risk factors and cardiovascular disease
between the various BMI subgroups were analyzed using the chi-square test for
discrete variables, and analysis of variance (ANOVA) with the general linear
model for continuous variables. Since hs-CRP had a non-normal distribution to
begin with, a logarithmic transformation of hs-CRP was used for all statistical
procedures. Differences between the BMI subgroups in terms of platelet counts,
hemoglobin levels, and hs-CRP levels were analyzed using ANOVA. Platelet
counts, hemoglobin levels and hs-CRP levels for overweight, obese, and morbidly
obese subgroups were compared with those of the normal-weight subgroup using
the general linear model with post hoc multiple comparisons by the method of
Scheffe. The student’s t test was used to evaluate differences in platelet
counts between subjects with or without the metabolic syndrome. ANOVA was used
to evaluate the association between BMI and platelet counts after age- and
hs-CRP-adjustments. For platelet activation analysis, 65 subjects were divided
into two groups based on their BMI: nonobese (BMI < 30) and obese (BMI ≥ 30).
The student’s t test and Mann-Whitney test were used to evaluate differences in
the studied markers between the two groups. BMI and waist-to-hip ratio were
normally distributed in both groups. The SPSS statistical package was used
(SSPS Inc., Chicago, IL, USA).


## 3. Results

### 3.1. Platelet Counts and BMI Status

Platelet counts were
studied among 6319 individuals,
4352 males and 1967 females. The mean age of the cohort was44.6±10.4 years. Overall,
1234 (19.5%) subjects had hypertension, 246 (3.9) subjects had diabetes
mellitus, 1923 (30.4%) had dyslipidemia, 85 (1.3%) had history of ischemic
heart disease, and 9 (0.1%) had history of ceberovascular accident. Overall, 2463
(39.0%) were normal weight, 2749 (43.5%) were overweight, 1058 (17%) were obese,
and 49 (0.8%) were morbidly obese. The prevalence of hypertension, diabetes
mellitus, and history of myocardial infarction significantly increased with BMI
category (Table 1).Table 1
Clinical characteristics of subjects for platelet count analysis stratified by BMI.
BMI<2525–29.930–39.9>40P-valuen24602749105749—Age (mean ± SD)41.8±10.6 yrs45.9±10.1 yrs47.7±9.0 yrs45.6±7.6 yrs<.0001Hypertension246 (10.0%)596 (21.7%)370 (35%)22 (44.9%)<.0001Diabetes mellitus38 (1.5%)91 (3.3%)106 (10.0%)11 (22.4%)<.0001Dyslipidemia451 (18.3%)911 (33.1%)451 (51.1%)20 (40.8%)<.0001Ischemic heart disease30 (1.2%)35 (1.3%)19 (1.8%)1 (2.0%).53Past CVA4 (0.2%)1 (0.0%)3 (0.3%)1 (0.02%).001BMI = body mass index; MI =
myocardial infarction; CVA = cerebrovascular accident.Platelet counts increased with BMI in both genders. However, only among females, the
platelet counts were significantly elevated in the overweight (P = .015),
obese (P < .0001), and morbidly obese (P < .0001) subgroups
compared with the normal-weight subgroup after adjustment for age, diabetes
mellitus, and hypertension. Using ANCOVA, platelet counts were still associated
with BMI among females after adjustment for age and hs-CRP (P = .034).  Platelet counts were elevated, though not statistically
significant, in the overweight, obese, and morbidly obese male subgroups
compared with the normal-weight subgroup. The association between obesity and
inflammation was apparent by an increment in hs-CRP concentrations with BMI categories
in both males and females (Table 2). Mean platelet counts were significantly
higher in obese females with the metabolic syndrome compared with obese females
without the metabolic syndrome (P = .032). In the overweight and morbidly
obese female subgroups, the platelet counts tended to be higher in women with
the metabolic syndrome, however, this tendency did not reach statistic
significance (Table 3). Finally, there was a significant age-adjusted
correlation between BMI and platelet counts (P < .0001) in females.
This correlation was attenuated but remained significant (P = .001) after
adjustment for hs-CRP concentrations.Table 2
Age, hypertension, and diabetes mellitus adjusted estimated marginal mean (standard error of the
mean) of platelets count, hemoglobin concentration, and CRP stratified by BMI.
Body mass index<2525–29.930–39.9>40P-valueFemalesN101558534126—Platelet count (×109/L)260 (4)270 (4)*281 (4)*307 (11)*<.0001Hemoglobin (g/L)13.2 (0.08)13.2 (0.08)13.2 (0.08)13.2 (0.21).893hs-CRP1.2 (1.1)2.5 (1.1)*5.2 (1.1)*9.3 (1.2)*<.0001MalesN1448216471723—Platelet count (×109/L)241 (3)242 (2)246 (3)260 (11).11Hemoglobin (g/L)14.8 (0.04)15.0 (0.04)*15.0 (0.05)*14.8 (0.20)<.0001hs-CRP1.1 (1.0)1.7 (1.0)*2.6 (1.0)*6.2 (1.2)*<.0001*P < .05
compared with the BMI <25 group.Table 3
Platelet counts in overweight, obese, and morbidly obese females with and without the metabolic syndrome.
Metabolic syndromeNo metabolic syndromeP-valueBMI 25–29.9 (n = 585)270 ± 67 (n = 73)260 ± 56 (n = 512).23BMI 30–39.9 (n = 341)283 ± 62 (n = 119)268 ± 60 (n = 222).032BMI > 40 (n = 26)314 ± 53 (n = 14)292 ± 52 (n = 12).29BMI = body mass index.


### 3.2. Platelet Activation and BMI Status

Platelet activation was
studied in 65 individuals, 49
females, and 16 males. They were divided according to their BMI levels into nonobese
(BMI < 30,n = 35) and obese (BMI ≥ 30, n = 30) subjects. Their clinical
characteristics and laboratory findings are summarized in Table 4. Nine of the
obese subjects were on antihypertensive drugs and 3 were on low-dose aspirin
therapy. In the nonobese subgroup, 7 subjects were on estrogen treatment (oral
contraceptives or hormone replacement therapy), and 4 were on low-dose aspirin
therapy. The obese group differed from the nonobese group in fat distribution,
as evidenced by larger waist circumferences and larger waist-to-hip ratios.
Significant differences between obese and nonobese individuals were also found
in the metabolic markers FBG, HDL-cholesterol and serum triglyceride
concentrations. Table 4 displays the platelet activation status as measured by
the expression of the MoAb studied. No significant differences were found in platelet activation status
between the groups. Additionally, no statistically significant correlation was
found between BMI or waist-to-hip ratios and indices of platelet activation (Table 5).Table 4
Clinical data, laboratory findings, and platelet activation markers for subjects in
the platelet activation analysis.
NonobeseObeseP-valuen3530—Age (years)43 ± 1241 ± 13.2Body mass index (kg/m2)24 ± 2.741 ± 7.7<.0001Waist circumference (cm)83 ± 12120 ± 14<.0001Hip circumference (cm)96 ± 6131 ± 19<.0001Waist/hip ratio0.86 ± 0.130.93 ± 0.15.06Blood glucose (mg/dl)82 ± 8105 ± 26.003HDL cholesterol (mg/dl)62 ±1746 ± 12<.0001Triglyceride levels (mg/dl)119 ± 63170 ± 87.01Total cholesterol (mg/dl)232 ± 49216 ± 47.2LDL cholesterol (mg/dl)148 ± 47136 ± 41.3Platelet count (×109/L)259 ± 55263 ± 55.7Platelet activation
markers (MFI, range given in parentheses)PAC-1
binding8.4±5.9 (1.8–24.6)6.5±4.7 (1.7–22.7).09P-selectin (CD-62p)2.1±0.8 (1.3–4.4)2.2±0.6 (1.5–3.6).3Annexin-V binding4.4±2.3 (2.1–12.2)4.7±1.5 (2.4–8.0).15HDL = high-density lipoprotein;
LDL = low-density lipoprotein; MFI = mean fluorescence intensity.Table 5
Correlations between platelet activation markers, body mass index, and waist-to-hip ratios for subjects in
the platelet activation analysis.
Platelet activation markersBMI (kg/m2)Waist-to-hip ration6565PAC-1 bindingr = −0.2r = 0.004P = .1P = .9CD62p expressionr = −0.05r = −0.12P = .7P = .4Annexin-V bindingr = 0.06r = −0.15P = .6P = .3BMI = body mass index; MFI
= mean fluorescence intensity.


## 3.1. Platelet Counts and BMI Status

Platelet counts were
studied among 6319 individuals,
4352 males and 1967 females. The mean age of the cohort was44.6±10.4 years. Overall,
1234 (19.5%) subjects had hypertension, 246 (3.9) subjects had diabetes
mellitus, 1923 (30.4%) had dyslipidemia, 85 (1.3%) had history of ischemic
heart disease, and 9 (0.1%) had history of ceberovascular accident. Overall, 2463
(39.0%) were normal weight, 2749 (43.5%) were overweight, 1058 (17%) were obese,
and 49 (0.8%) were morbidly obese. The prevalence of hypertension, diabetes
mellitus, and history of myocardial infarction significantly increased with BMI
category (Table 1).Table 1
Clinical characteristics of subjects for platelet count analysis stratified by BMI.
BMI<2525–29.930–39.9>40P-valuen24602749105749—Age (mean ± SD)41.8±10.6 yrs45.9±10.1 yrs47.7±9.0 yrs45.6±7.6 yrs<.0001Hypertension246 (10.0%)596 (21.7%)370 (35%)22 (44.9%)<.0001Diabetes mellitus38 (1.5%)91 (3.3%)106 (10.0%)11 (22.4%)<.0001Dyslipidemia451 (18.3%)911 (33.1%)451 (51.1%)20 (40.8%)<.0001Ischemic heart disease30 (1.2%)35 (1.3%)19 (1.8%)1 (2.0%).53Past CVA4 (0.2%)1 (0.0%)3 (0.3%)1 (0.02%).001BMI = body mass index; MI =
myocardial infarction; CVA = cerebrovascular accident.Platelet counts increased with BMI in both genders. However, only among females, the
platelet counts were significantly elevated in the overweight (P = .015),
obese (P < .0001), and morbidly obese (P < .0001) subgroups
compared with the normal-weight subgroup after adjustment for age, diabetes
mellitus, and hypertension. Using ANCOVA, platelet counts were still associated
with BMI among females after adjustment for age and hs-CRP (P = .034).  Platelet counts were elevated, though not statistically
significant, in the overweight, obese, and morbidly obese male subgroups
compared with the normal-weight subgroup. The association between obesity and
inflammation was apparent by an increment in hs-CRP concentrations with BMI categories
in both males and females (Table 2). Mean platelet counts were significantly
higher in obese females with the metabolic syndrome compared with obese females
without the metabolic syndrome (P = .032). In the overweight and morbidly
obese female subgroups, the platelet counts tended to be higher in women with
the metabolic syndrome, however, this tendency did not reach statistic
significance (Table 3). Finally, there was a significant age-adjusted
correlation between BMI and platelet counts (P < .0001) in females.
This correlation was attenuated but remained significant (P = .001) after
adjustment for hs-CRP concentrations.Table 2
Age, hypertension, and diabetes mellitus adjusted estimated marginal mean (standard error of the
mean) of platelets count, hemoglobin concentration, and CRP stratified by BMI.
Body mass index<2525–29.930–39.9>40P-valueFemalesN101558534126—Platelet count (×109/L)260 (4)270 (4)*281 (4)*307 (11)*<.0001Hemoglobin (g/L)13.2 (0.08)13.2 (0.08)13.2 (0.08)13.2 (0.21).893hs-CRP1.2 (1.1)2.5 (1.1)*5.2 (1.1)*9.3 (1.2)*<.0001MalesN1448216471723—Platelet count (×109/L)241 (3)242 (2)246 (3)260 (11).11Hemoglobin (g/L)14.8 (0.04)15.0 (0.04)*15.0 (0.05)*14.8 (0.20)<.0001hs-CRP1.1 (1.0)1.7 (1.0)*2.6 (1.0)*6.2 (1.2)*<.0001*P < .05
compared with the BMI <25 group.Table 3
Platelet counts in overweight, obese, and morbidly obese females with and without the metabolic syndrome.
Metabolic syndromeNo metabolic syndromeP-valueBMI 25–29.9 (n = 585)270 ± 67 (n = 73)260 ± 56 (n = 512).23BMI 30–39.9 (n = 341)283 ± 62 (n = 119)268 ± 60 (n = 222).032BMI > 40 (n = 26)314 ± 53 (n = 14)292 ± 52 (n = 12).29BMI = body mass index.


## 3.2. Platelet Activation and BMI Status

Platelet activation was
studied in 65 individuals, 49
females, and 16 males. They were divided according to their BMI levels into nonobese
(BMI < 30,n = 35) and obese (BMI ≥ 30, n = 30) subjects. Their clinical
characteristics and laboratory findings are summarized in Table 4. Nine of the
obese subjects were on antihypertensive drugs and 3 were on low-dose aspirin
therapy. In the nonobese subgroup, 7 subjects were on estrogen treatment (oral
contraceptives or hormone replacement therapy), and 4 were on low-dose aspirin
therapy. The obese group differed from the nonobese group in fat distribution,
as evidenced by larger waist circumferences and larger waist-to-hip ratios.
Significant differences between obese and nonobese individuals were also found
in the metabolic markers FBG, HDL-cholesterol and serum triglyceride
concentrations. Table 4 displays the platelet activation status as measured by
the expression of the MoAb studied. No significant differences were found in platelet activation status
between the groups. Additionally, no statistically significant correlation was
found between BMI or waist-to-hip ratios and indices of platelet activation (Table 5).Table 4
Clinical data, laboratory findings, and platelet activation markers for subjects in
the platelet activation analysis.
NonobeseObeseP-valuen3530—Age (years)43 ± 1241 ± 13.2Body mass index (kg/m2)24 ± 2.741 ± 7.7<.0001Waist circumference (cm)83 ± 12120 ± 14<.0001Hip circumference (cm)96 ± 6131 ± 19<.0001Waist/hip ratio0.86 ± 0.130.93 ± 0.15.06Blood glucose (mg/dl)82 ± 8105 ± 26.003HDL cholesterol (mg/dl)62 ±1746 ± 12<.0001Triglyceride levels (mg/dl)119 ± 63170 ± 87.01Total cholesterol (mg/dl)232 ± 49216 ± 47.2LDL cholesterol (mg/dl)148 ± 47136 ± 41.3Platelet count (×109/L)259 ± 55263 ± 55.7Platelet activation
markers (MFI, range given in parentheses)PAC-1
binding8.4±5.9 (1.8–24.6)6.5±4.7 (1.7–22.7).09P-selectin (CD-62p)2.1±0.8 (1.3–4.4)2.2±0.6 (1.5–3.6).3Annexin-V binding4.4±2.3 (2.1–12.2)4.7±1.5 (2.4–8.0).15HDL = high-density lipoprotein;
LDL = low-density lipoprotein; MFI = mean fluorescence intensity.Table 5
Correlations between platelet activation markers, body mass index, and waist-to-hip ratios for subjects in
the platelet activation analysis.
Platelet activation markersBMI (kg/m2)Waist-to-hip ration6565PAC-1 bindingr = −0.2r = 0.004P = .1P = .9CD62p expressionr = −0.05r = −0.12P = .7P = .4Annexin-V bindingr = 0.06r = −0.15P = .6P = .3BMI = body mass index; MFI
= mean fluorescence intensity.


## 4. Discussion

It has been well established
that obesity is associated with low-grade subclinical and smoldering
inflammation [2, 21, 22]. Both BMI and body fat
mass have been shown to correlate with total leukocyte counts [23].
The Atherosclerosis Risk in Community (ARIC) study demonstrated a positive
correlation between leukocyte and platelet counts [24], but no
significant correlation between obesity and platelet counts. Moreover, it has
not yet been established whether elevated platelet counts in obese individuals
are associated with platelet activation. The answer to this question is
especially relevant when primary prevention by antiplatelet therapy is
considered for individuals at risk.The results of the present study demonstrate an association between
obesity and platelet counts in females probably due to higher body fat mass.
Yudkin et al. [21] have demonstrated an association between obesity
and IL-6 levels. A large proportion of IL-6 in the circulation originates from the
adipose tissue that in turn may contribute to atherogenesis and thrombosis, by
promoting inflammation. One of the proposed mechanisms for IL-6 contribution to
atherogenesis and thrombosis is its effect on platelets, fibrinogen concentrations,
and coagulation [2]. IL-6, in addition to other interleukins, plays a
crucial role in the proliferation of megakaryocyte progenitors and acts
synergistically with thrombopoietin and stem cell factor in stimulating
megakaryocytopoiesis [3, 25, 26]. Thus it is
conceivable that the elevated platelet counts found in obese females in the
present study are secondary to the presence of a chronic inflammation as
evident by elevated hs-CRP levels. In fact, the correlation between BMI and
platelet counts found in females was attenuated once adjusted for hs-CRP
levels.The ambiguity found in the literature regarding the role
of platelet activation in obese individuals stems from employment of different
markers to evaluate platelet activation in previous studies. Tangorra et al. [27] and Meade et al. [28] used aggregating agents to test platelet
susceptibility to aggregation. Both groups showed no correlation between
obesity and the susceptibility of platelets to aggregating agents. Davi et al.
and Licata et al. used 11-dehydro-TxB2 excreted in urine to evaluate
platelet activation. In an earlier (relatively small) study, they found no
differences in 11-dehydro-TxB2 excretion between obese and nonobese
individuals [11], yet later [10], the same group reported
increased levels of excreted 11-dehydro-TxB2 in obese compared to
nonobese women associated with android obesity. Interestingly, both β-thromboglobulin and the metabolite 11-dehydro-TxB2 correlate positively with platelet counts [29, 30], and increased concentrations of these,
found in obese individuals, may simply reflect their, already discussed,
elevated counts. Dogru et al. and De Pergola et al. used soluble P-selectin (sP-selectin)
plasma levels as a marker of platelet activation and found either similar level
in overweight prediabetic and healthy individuals [13] or increased
sP-selectin in overweight and obese compared to normal-weight individuals [12]. The latter finding failed to maintain significant associations
in a multiple regression that accounted for various anthropometric, metabolic
and inflammation markers [12].
In the present study, we used a panel of sensitive flow cytometry
determinants not previously reported in apparently healthy obese and nonobese
individuals. Flow cytometry is particularly well suited for discriminating
between various early activation events. Flow cytometry is highly sensitive in
its ability to demonstrate expression of active membrane epitopes preceding
early activation-induced platelet morphological changes. This method is also
capable of detecting activated platelets in the circulation despite their rapid
clearance [18, 19, 31]. The present study demonstrates no
evidence of increased platelet activation in obese subjects despite the fact
that obese and morbidly obese subjects exhibited chronic inflammation. Our
results are consistent with those of Dogru et al. and De Pergola et al. [12, 13] and with the study published by Marquardt et al. [32] that found no correlation between inflammation markers including
leukocyte count, CRP and fibrinogen levels, and platelet membrane p-selectin
(CD62p) expression in patients after an ischemic stroke.



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*Source: 834153-2008-03-12.xml*