OBJECTIVE—To assess the prevalence and relationships of insulin resistance syndrome (IRS) with inflammatory and hemostatic markers in a representative sample of the population of Southwestern France aged 35–64 years.
RESEARCH DESIGN AND METHODS—In this cross-sectional study, data were collected from 597 men and 556 women and were assessed regarding BMI, blood pressure, total and HDL cholesterol levels, triglyceride level, glucose level, plasma insulin level, white blood cell count, fibrinogen level, factor VII level, von Willebrand factor, C-reactive protein level, soluble intercellular adhesion molecule, soluble vascular cell adhesion molecule-1, and soluble CD14. Insulin resistance was defined by homeostasis model assessment ≥3.8.
RESULTS—Prevalence of IRS was higher in men than in women (23 vs. 12%, respectively; P < 0.001) and increased with age in both sexes (9, 24, and 34% for age groups 35–44, 45–54, and 55–64 years, respectively, for men and 4, 10, and 21% for women). After adjusting for age, alcohol consumption, tobacco smoking, and also for menopause in women, subjects (men and women) with IRS had significantly higher white blood cell count, factor VII levels, coagulating factor VII levels, and C-reactive protein levels than the other subjects. In men, further increases in soluble intercellular adhesion molecule and soluble vascular cell adhesion molecule-1 were noted, whereas in women, the differences were borderline significant. Conversely, no differences were found in fibrinogen, von Willebrand factor, and soluble CD14 in both sexes.
CONCLUSIONS—IRS is relatively common in residents of Southwestern France and is related to a deleterious increase in hemostatic and inflammatory parameters.
Insulin resistance syndrome (IRS) is characterized by the clustering of several cardiovascular risk factors, such as increased insulin levels, hypertension, obesity, and dyslipidemia (1), and is related to an increased cardiovascular risk (2) and an increased incidence of coronary heart disease (3). Although the effects of IRS on atherosclerosis and coronary heart disease are believed to be exerted via the different risk factors that compose this clinical entity (4), less is known regarding the levels of inflammatory, hemostatic, and endothelial cell activation (5). Furthermore, although the prevalence of IRS has been assessed in several populations (6–9), only very few studies have focused on its prevalence in France. Therefore, we used data from the third MONICA survey to assess the prevalence of IRS and the levels of inflammatory and hemostatic factors in patients of both sexes with IRS in Southwestern France, a region characterized by a low level of cardiovascular disease (10).
RESEARCH DESIGN AND METHODS
Populations
The World Health Organization MONICA Project is a study that monitors deaths due to coronary heart disease and myocardial infarction, coronary care, and risk factors in men and women aged 35–64 years (11). It consists of 39 MONICA Collaborative Centers in 26 countries. Each MONICA Collaborative Center is in charge of performing two or three population surveys on cardiovascular risk factors in the beginning and at the end of the 10th year and possibly in the middle of the study period. The sampling strategy was to have representative probability samples within each sex and 10-year age group, at least for subjects aged 35–64 years. The number of eligible subjects asked to participate was 50% higher than the required number to obtain the necessary quota of 200 persons for each sex and 10-year age group (allowing for subjects refusing to participate or not participating for any other reason). The survey was conducted in Southwestern France (Department of Haute-Garonne, with a population of ∼360,000 subjects aged 35–64 years) between December 1994 and July 1997 and included subjects of both sexes. Polling lists available in each town hall and a list of foreigners living in the survey area were used for sampling. Informed consent to participate in the study was obtained from the subjects before the survey. Participation rates were 67 and 59% for men and women, respectively.
Data collection
Subjects were advised to refrain from physical exercise, smoking, eating, and drinking anything other than water for at least 10 h before the screening visit (12). Screening included standardized questionnaires on personal data and clinical measurements. Height and weight were measured according to a standardized protocol (12); BMI was assessed as kg/m2. Professional activity was based on current status (employed/unemployed/sick leave/retired), and educational level was based on the number of years spent in school/university. Women were also questioned about use of oral contraceptives. Subjects were considered current smokers if they answered yes to the question “do you currently smoke cigarettes?”. Subjects were considered drinkers if they reported drinking any amount of alcohol during the week.
Alcohol consumption was assessed by a questionnaire on which the subject reported his mean consumption (in units) of wine, beer, cider, and spirits for each day of the week. Intake of alcohol (expressed in milliliters of pure ethanol/week) was estimated from the average number of milliliters of ethanol in a serving of each type of alcoholic beverage and multiplying by 0.8 (density of ethanol): wine, 12-cl serving, 10 or 12% alcohol (vol/vol); beer, 12-cl serving, 5% alcohol; beer, 25- or 33-cl serving, 6 or 8% alcohol; cider, 12-cl serving, 5% alcohol; spirits, 2- or 6-cl serving, 20% or 40% alcohol.
Blood pressure measurement
Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured twice on the right arm of subjects who had been resting for at least 5 min in a comfortable position. Two consecutive measurements of SBP and DBP were recorded to the nearest 2 mmHg, and the mean values of these were used for the present analysis. Hypertension was defined as SBP ≥140 mmHg and/or DBP ≥90 mmHg and/or presence of antihypertensive treatment.
Biological measurements
Lipids were measured on plasma EDTA plasma samples using automated enzymatic assays (Boehringer, Mannheim, Germany). External quality control by the MONICA reference laboratory indicated no relevant deviation. Non-HDL cholesterol was calculated as the difference between total cholesterol and HDL cholesterol.
Glucose levels were measured by the glucose oxidase method (Du Pont Dimension; Du Pont, Wilmington, DE). Total plasma insulin was measured by radioimmunoassay (BiInsuline, Eria Pasteur, France). Previously diagnosed diabetes was defined by current oral hypoglycemic treatment and newly diagnosed diabetes was defined as fasting plasma glucose (FPG) ≥7.0 mmol/l. Impaired fasting glucose (IFG) was defined by 6.1 ≤ FPG ≤ 6.9 mmol/l (13). Insulin resistance was estimated by the homeostasis model assessment (HOMA) defined as fasting serum insulin (μU/ml) × FPG (mmol/l)/22.5. Subjects with HOMA ≥3.8 (lower level of the population upper quartile) were considered insulin resistant.
Fibrinogen was assessed by the method of Clauss using an automated device (Diagnostica Stago, Asnières, France). Factor VII coagulant (FVIIc) was assessed by chronometric assay (Diagnostica Stago), and the results were expressed as percentage of a standardized pool of normal subjects. Activated factor VII (FVIIa) was assessed by a chronometric assay with a recombinant tissue factor (Statclot-rTF; Diagnostica Stago), and results were expressed in mU/ml. Assessment of both FVIIc and FVIIa was performed on the same automated device (STA; Diagnostica Stago). von Willebrand factor activity levels were assessed using a platelet aggregation technique on a BCT (Behring coagulation timer) device (Dade Behring Marburg, Marburg, Germany). C-reactive protein (CRP) levels were assessed by immunonephelemetric method (Dade Behring Marburg); the lower limit of detection was 0.175 mg/l. Levels of soluble vascular cell adhesion molecule-1 (sVCAM-1) and soluble intercellular adhesion molecule (sICAM) were assessed by immunoenzymatic methods (Immunotech, Marseilles, France). Levels of soluble CD14 (sCD14) were assessed by immunoenzymatic methods (IBL, Hamburg, Germany). Between-series coefficients of variation (maximum values) were as follows: fibrinogen 3.6%, FVIIc 5.3%, von Willebrand factor 8%, CRP 4.4%, sCD14 11%, sVCAM-1 8.7%, and sICAM 6.9%.
IRS
Presence of IRS was determined by HOMA ≥3.8 and/or presence of antidiabetic drug treatment and/or fasting blood glucose ≥6.1 mmol/l, as well as at least two of the following conditions (14): 1) anthropometrical data: BMI ≥30 kg/m2 or a waist-to-hip ratio (WHR) ≥0.95 (for men) or ≥0.85 (for women); 2) blood pressure: SBP >140 mmHg or DBP >90 mmHg or presence of antihypertensive treatment; 3) dyslipidemia: triglyceride level >1.70 mmol/l or HDL level <0.90 mmol/l or presence of triglyceride-lowering drug therapy.
Statistical analysis
Subjects on insulin therapy (two men, one woman) or subjects who had fasted <10 h (15 men, 12 women) were excluded from the analysis. Statistical analysis was conducted using SAS statistical software (version 8.1; SAS Institute, Cary, NC). Quantitative data were expressed as mean ± SD or as adjusted mean ± SE; qualitative data were expressed as number of subjects and percentage. Univariate comparisons were performed using χ2 or Student’s t test; multivariate adjustments were performed using a generalized linear model (Proc GLM; SAS Institute) or by stepwise multiple linear regression. Significance was established for P < 0.05.
RESULTS
Subject characteristics
Overall, data from 1,153 subjects were assessed (597 men and 556 women). Their clinical characteristics are summarized in Table 1. BMI, WHR, blood pressure levels, total cholesterol and triglyceride levels, HOMA, insulin, white blood cell count, sICAM, and sVCAM-1 were lower and HDL cholesterol, fibrinogen, FVIIc, and von Willebrand factor levels were higher in women than in men, whereas no differences were found regarding age, FPG level, FVIIa, CRP, and sCD14. Also, prevalence of ex-smokers, IFG, and diabetes (diagnosed + FPG ≥7.0 mmol/l) was higher in men.
Prevalence of insulin resistance
The prevalence of the main components of IRS is shown in Table 2. Prevalence of WHR, hypertension, hypertriglyceridemia, low HDL cholesterol, dyslipidemia, and insulin resistance (defined by a HOMA ≥3.8 or antidiabetic treatment) was higher in men than in women, whereas no differences were found regarding obesity or antihypertriglyceridemic, antihypertensive, or antidiabetic drug therapy (Table 2). Prevalence of IRS was higher in men than in women (Table 2), but this difference decreased with increasing age. Prevalence of IRS also increased with age in both sexes: in men, prevalence of IRS was 9, 24, and 34% for age groups 35–44, 45–54, and 55–64 years, respectively (P < 0.001); in women, the corresponding values were 4, 10, and 21% (P < 0.001).
Levels of inflammatory and hemostatic parameters in insulin-resistant patients
For each sex, the levels of inflammatory and hemostatic parameters were assessed between IRS and non-IRS subjects, after adjusting for age group (three groups), drinking status (yes/no), and smoking status (yes/no). In women, a further adjustment on menopausal status was performed. Results are summarized in Table 3. In both men and women, subjects with IRS had significantly higher white blood cell count and levels of FVIIa, FVIIc, and CRP than the other subjects (Table 3). In men, further significant increases in sICAM and sVCAM were noted; the corresponding increases in women were borderline significant (P < 0.07). Conversely, no differences were found for fibrinogen, von Willebrand factor, and sCD14 in both men and women.
Relationships between IRS components and inflammation and coagulation biomarkers
The relationships were assessed independently for men and women, by stepwise multiple regression, using each inflammatory and coagulation marker as a dependent variable and age, alcohol consumption, smoking, BMI, WHR, HOMA, HDL and non-HDL cholesterol, triglycerides (as Log), and blood pressure as independent variables. The results are expressed in Tables 4 and 5. In men, age, smoking, WHR, HOMA, HDL cholesterol, and triglycerides were related to most of the inflammatory and coagulation markers, whereas in women, the most important determinants were age, BMI, and triglycerides. Furthermore, significant sex/covariate interactions were found for several inflammatory and hemostatic markers, namely age (for FVIIa, FVIIc, CRP, and sCD14), alcohol consumption (for fibrinogen and sCD14), smoking (for white blood cell count and fibrinogen), and triglycerides (for fibrinogen, FVIIc, and CRP). Finally, when HOMA was replaced by insulin and plasma glucose, only insulin was selected for all variables tested (not shown).
CONCLUSIONS
Most studies using factor analysis to characterize insulin resistance led to similar results, i.e., that three to four main factors could be drawn: a factor related to anthropometrical data, another related to blood pressure, and another related to lipid values. However, a factor related to insulin levels has not been consistently found (3, 15,16). In this study and to allow for further comparisons, we chose to define IRS according to the World Health Organization recommendations (1,14). Furthermore, although its reliability among older obese subjects has been questioned (17), HOMA provides a useful model to assess insulin resistance and β-cell function in epidemiological studies in which only fasting samples are available (18). It is also a reliable marker of insulin resistance for assessment and follow-up of patients with diabetes (19). In this study, we chose to use the lower limit of the upper quartile of the HOMA distribution in the overall population to define insulin resistance.
The main objective of this study was to assess the prevalence of IRS in the general population of Southwestern France (20). Therefore, subjects who were treated for hypertension, hypertriglyceridemia, or diabetes were not excluded from the analysis, because this would create a selection bias leading to an underestimation of the true prevalence of IRS in this population. Indeed, excluding treated subjects from the analysis led to a much lower prevalence of IRS (12% in men and 4% in women), because a considerable number of subjects were treated for hypertension, hypertriglyceridemia, or diabetes. Furthermore, it should be stressed that most studies focusing on the metabolic interrelationships of IRS did not indicate whether treated subjects were excluded from the statistical analysis (15). Notwithstanding, our data indicate that excluding subjects with treated cardiovascular risk factors leads to a considerable underestimation of the true prevalence of IRS in the general population and might also bias the metabolic interrelationships, although this latter hypothesis deserves further investigation.
The prevalence of IRS reported in this study was much higher than that in studies of Mexicans (6) and native Americans (21) but was in relative agreement with another study conducted in Caucasians (22). Possible explanations for this discrepancy are the fact that the authors of one of the studies (6) did not include obesity in their definition of IRS, as well as differences regarding nutritional intake, physical activity, and socioeconomic status. However, our data indicate that prevalence of IRS in the population of Southwestern France is high and increases with age.
Subjects with IRS had significantly higher levels of CRP, an inflammatory marker that has been shown to be predictive of cardiovascular disease (23). Those findings are in agreement with previously published studies (5), and although the increase in CRP levels among patients with IRS might be due to obesity or high insulin levels (24), our data suggest that part of the cardiovascular effect of insulin resistance might be mediated by inflammation. This hypothesis is further confirmed by an increase in white blood cell count in patients with IRS. Conversely, no differences were found for fibrinogen between IRS and non-IRS subjects, although significant relationships were found between fibrinogen levels and age, alcohol consumption, smoking, HDL, and triglycerides, in agreement with previous findings (25). A possible explanation for this result is the intricate and differential relationships between fibrinogen levels and components of IRS; for instance, fibrinogen levels are positively related to BMI but negatively related to WHR.
sICAM and sVCAM are molecular markers for endothelial cell activation, arteriosclerosis, and development of coronary heart disease (26). In this study, both sICAM and sVCAM-1 levels were significantly increased in men with IRS, with borderline significant differences in women. Those findings were further confirmed by the fact that IRS parameters explained ∼10% of the overall variation of sICAM, whereas their effect in variation of sVCAM-1 was much lower (Tables 4 and 5). von Willebrand factor is also synthesized by endothelial cells, and its increased plasma level is usually considered a marker for endothelial lesion (27) and predictive of cardiovascular mortality in diabetic and nondiabetic subjects (28). Conversely, in this study, no increase in von Willebrand factor was noted with IRS, contrary to other studies conducted in patients with type 2 diabetes (29). Also, in both men and women, most of the IRS parameters explained only a small part of the total variance of von Willebrand factor levels, further confirming the lack of relationship between IRS and von Willebrand factor. Still, our findings indicate a possible association between IRS and increased endothelial cell activation, namely mediated by sICAM and sVCAM levels, which could also mediate the development of atherosclerosis and coronary heart disease in this group.
FVIIa is produced after activation of FVII by tissue factor and is a marker of the activation status of this tissue factor coagulation pathway when detected in vivo; increased levels of FVIIc also have been shown to be a risk factor for coronary heart disease (30). In this study, increased FVIIc and FVIIa levels were found in both men and women with IRS, indicating a possible procoagulant status associated with IRS. Those findings are in agreement with previously published studies (5) and indicate that the increased levels of FVII might partly be due to several components of IRS, such as increased BMI (31), lipid levels (25), or insulin resistance (32). Therefore, our data indicate that the relationship between IRS, atherosclerosis, and coronary heart disease might be more complex than initially believed because of IRS exerting its effects not only through its components (insulin resistance, obesity, high blood pressure, and dyslipidemia) but also via several other metabolic pathways such as inflammation, coagulation, or endothelial cell activation (33).
The association of CD14 with bacterial lipopolysaccharide stimulates endothelial and smooth muscle cells, and some polymorphisms of CD14, have been associated with myocardial infarction (34). Nevertheless, in this study, no increase in the levels of sCD14 was found in patients with IRS. Therefore, according to our data, it seems unlikely that IRS exerts its effects through a modification of sCD14 levels.
Stepwise multiple regression showed that age, BMI, WHR, HDL cholesterol, and triglycerides were the IRS components that were most frequently related with the hemostatic, inflammatory, and endothelial cell markers studied. Notwithstanding, the magnitude of the effect of each IRS component differed between sexes: in men, it was WHR (not BMI) and HDL cholesterol (not triglycerides) that were significantly related to the inflammatory and hemostatic variables, whereas in women, it was BMI (not WHR) and mostly triglycerides. Those findings were further confirmed by the assessment of significant interactions between sex and triglycerides in relationship with hemostatic, inflammatory, and endothelial cell markers. Therefore, our data indicate that the effect of at least one of the components of the IRS (triglycerides) differs according to sex.
The mechanisms by which insulin resistance influences hemostatic, inflammatory, and endothelial cell activation are complex and multiple, with the specific effect of insulin on selected gene polymorphisms (35,36) or via other metabolic pathways such as BMI or lipid levels (32). In this study, HOMA and insulin levels were frequently associated with hemostatic, inflammatory, and endothelial cell markers in men but not in women and, contrary to findings of other authors (37), no significant interactions were found between sex and HOMA levels regarding those markers. A possible explanation is the higher prevalence of increased HOMA levels and IRS in men (Table 2), which would lead to stronger relationships in men than in women. Notwithstanding, further research is needed to assess the differential metabolic relationships between IRS and hemostatic, inflammatory, and endothelial cell markers in men and women.
In summary, our data indicate that the prevalence of IRS is relatively high in middle-aged subjects living in Southwestern France. Our data also indicate that this clinical status is related to increases in several hemostatic, inflammatory, and endothelial cell markers and that those increases seem to differ according to sex.
Characteristics of the subjects, by sex
. | Men . | Women . | P . |
---|---|---|---|
n | 597 | 556 | |
Age (years) | 49.8 ± 8.6 | 49.7 ± 8.6 | 70.84 |
BMI (kg/m2) | 26.2 ± 3.7 | 24.6 ± 4.5 | <0.001 |
WHR | 0.94 ± 0.06 | 0.82 ± 0.07 | <0.001 |
Smoking status (%) | |||
Current smoker | 148 (25) | 118 (21) | |
Ex-smoker | 244 (41) | 118 (21) | <0.001 |
Never smoker | 205 (34) | 320 (58) | |
Drinkers (%) | 480 (78) | 300 (53) | <0.001 |
Systolic blood pressure (mmHg) | 135 ± 16 | 127 ± 19 | <0.001 |
Diastolic blood pressure (mmHg) | 82 ± 10 | 77 ± 10 | <0.001 |
Total cholesterol (mmol/l) | 5.90 ± 1.01 | 5.78 ± 1.01 | 0.04 |
HDL cholesterol (mmol/l) | 1.29 ± 0.33 | 1.65 ± 0.45 | <0.001 |
Triglycerides (mmol/l) | 1.34 ± 0.82 | 1.00 ± 0.49 | <0.001 |
FPG (mmol/l) | 6.06 ± 4.04 | 5.61 ± 4.07 | 0.06 |
Fasting insulin (μU/ml) | 13.0 ± 7.8 | 11.0 ± 6.5 | <0.001 |
HOMA | 3.57 ± 2.93 | 2.86 ± 3.78 | <0.001 |
Known diabetes (%) | 22 (4) | 13 (2) | 0.18 |
Diabetes (known + FPG ≥7.0 mmol/l) | 48 (8) | 23 (4) | 0.006 |
IFG (%) | 99 (17) | 43 (8) | <0.001 |
White blood cell count (× 103/mm3) | 6.37 ± 1.75 | 6.17 ± 1.62 | 0.05 |
Fibrinogen (g/l) | 2.78 ± 0.57 | 2.99 ± 0.79 | <0.001 |
FVIIa (IU/l) | 69.5 ± 29.9 | 67.3 ± 28.1 | 0.26 |
FVIIc (%) | 103 ± 21 | 107 ± 24 | 0.01 |
von Willebrand factor (%) | 113 ± 49 | 120 ± 52 | 0.02 |
CRP (ng/ml) | 1.64 ± 2.55 | 1.68 ± 3.82 | 0.81 |
sCD14 (μg/ml) | 3.55 ± 1.00 | 3.60 ± 1.00 | 0.40 |
sICAM (ng/ml) | 269 ± 95 | 257 ± 85 | 0.02 |
sVCAM-1 (ng/ml) | 685 ± 278 | 648 ± 263 | 0.03 |
. | Men . | Women . | P . |
---|---|---|---|
n | 597 | 556 | |
Age (years) | 49.8 ± 8.6 | 49.7 ± 8.6 | 70.84 |
BMI (kg/m2) | 26.2 ± 3.7 | 24.6 ± 4.5 | <0.001 |
WHR | 0.94 ± 0.06 | 0.82 ± 0.07 | <0.001 |
Smoking status (%) | |||
Current smoker | 148 (25) | 118 (21) | |
Ex-smoker | 244 (41) | 118 (21) | <0.001 |
Never smoker | 205 (34) | 320 (58) | |
Drinkers (%) | 480 (78) | 300 (53) | <0.001 |
Systolic blood pressure (mmHg) | 135 ± 16 | 127 ± 19 | <0.001 |
Diastolic blood pressure (mmHg) | 82 ± 10 | 77 ± 10 | <0.001 |
Total cholesterol (mmol/l) | 5.90 ± 1.01 | 5.78 ± 1.01 | 0.04 |
HDL cholesterol (mmol/l) | 1.29 ± 0.33 | 1.65 ± 0.45 | <0.001 |
Triglycerides (mmol/l) | 1.34 ± 0.82 | 1.00 ± 0.49 | <0.001 |
FPG (mmol/l) | 6.06 ± 4.04 | 5.61 ± 4.07 | 0.06 |
Fasting insulin (μU/ml) | 13.0 ± 7.8 | 11.0 ± 6.5 | <0.001 |
HOMA | 3.57 ± 2.93 | 2.86 ± 3.78 | <0.001 |
Known diabetes (%) | 22 (4) | 13 (2) | 0.18 |
Diabetes (known + FPG ≥7.0 mmol/l) | 48 (8) | 23 (4) | 0.006 |
IFG (%) | 99 (17) | 43 (8) | <0.001 |
White blood cell count (× 103/mm3) | 6.37 ± 1.75 | 6.17 ± 1.62 | 0.05 |
Fibrinogen (g/l) | 2.78 ± 0.57 | 2.99 ± 0.79 | <0.001 |
FVIIa (IU/l) | 69.5 ± 29.9 | 67.3 ± 28.1 | 0.26 |
FVIIc (%) | 103 ± 21 | 107 ± 24 | 0.01 |
von Willebrand factor (%) | 113 ± 49 | 120 ± 52 | 0.02 |
CRP (ng/ml) | 1.64 ± 2.55 | 1.68 ± 3.82 | 0.81 |
sCD14 (μg/ml) | 3.55 ± 1.00 | 3.60 ± 1.00 | 0.40 |
sICAM (ng/ml) | 269 ± 95 | 257 ± 85 | 0.02 |
sVCAM-1 (ng/ml) | 685 ± 278 | 648 ± 263 | 0.03 |
Data are means ± SD or n (%). Statistical analysis by χ2, Student’s t test, or Wilcoxon’s test (for triglycerides).
Presence of the main components of the insulin resistance syndrome, by sex
. | Men . | Women . | P . |
---|---|---|---|
n | 597 | 556 | |
Obesity | 79 (13) | 59 (11) | 0.17 |
Increased WHR | 272 (46) | 173 (31) | <0.001 |
Obesity or increased WHR | 283 (47) | 190 (34) | <0.001 |
Antihypertensive drug treatment | 69 (12) | 80 (14) | 0.15 |
Hypertension | 232 (39) | 161 (29) | <0.001 |
Hypertriglyceridemia | 108 (18) | 39 (7) | <0.001 |
Low HDL cholesterol | 56 (9) | 16 (3) | <0.001 |
Drug treatment against hypertriglyceridemia | 46 (8) | 30 (5) | 0.11 |
Dyslipidemia | 177 (30) | 77 (14) | <0.001 |
Antidiabetic drug treatment | 24 (4) | 14 (2) | 0.16 |
HOMA ≥3.8 or antidiabetic treatment | 193 (32) | 107 (19) | <0.001 |
IRS | 135 (23) | 65 (12) | <0.001 |
. | Men . | Women . | P . |
---|---|---|---|
n | 597 | 556 | |
Obesity | 79 (13) | 59 (11) | 0.17 |
Increased WHR | 272 (46) | 173 (31) | <0.001 |
Obesity or increased WHR | 283 (47) | 190 (34) | <0.001 |
Antihypertensive drug treatment | 69 (12) | 80 (14) | 0.15 |
Hypertension | 232 (39) | 161 (29) | <0.001 |
Hypertriglyceridemia | 108 (18) | 39 (7) | <0.001 |
Low HDL cholesterol | 56 (9) | 16 (3) | <0.001 |
Drug treatment against hypertriglyceridemia | 46 (8) | 30 (5) | 0.11 |
Dyslipidemia | 177 (30) | 77 (14) | <0.001 |
Antidiabetic drug treatment | 24 (4) | 14 (2) | 0.16 |
HOMA ≥3.8 or antidiabetic treatment | 193 (32) | 107 (19) | <0.001 |
IRS | 135 (23) | 65 (12) | <0.001 |
Data are n (%). Statistical analysis by χ2.
Inflammatory and hemostatic parameters in subjects with and without IRS, by sex
. | Men (n = 597) . | . | P . | Women (n = 556) . | . | P . | ||
---|---|---|---|---|---|---|---|---|
. | IRS (n = 135) . | Other (n = 462) . | . | IRS (n = 65) . | Other (n = 491) . | . | ||
White blood cell count (× 103/mm3) | 7.34 ± 0.16 | 6.63 ± 0.10 | <0.001 | 7.23 ± 0.20 | 6.27 ± 0.09 | <0.001 | ||
Fibrinogen (g/l) | 2.98 ± 0.05 | 2.88 ± 0.03 | 0.08 | 3.16 ± 0.10 | 3.03 ± 0.05 | 0.21 | ||
FVIIa (IU/l) | 77.8 ± 2.9 | 68.8 ± 1.7 | 0.003 | 75.9 ± 3.6 | 67.6 ± 1.6 | 0.03 | ||
FVIIc (%) | 112 ± 2 | 101 ± 1 | <0.001 | 119 ± 3 | 105 ± 1 | <0.001 | ||
von Willebrand factor (%) | 116 ± 5 | 114 ± 3 | 0.69 | 123 ± 7 | 120 ± 3 | 0.59 | ||
CRP (ng/ml) | 2.61 ± 0.24 | 1.85 ± 0.15 | 0.003 | 3.54 ± 0.49 | 1.86 ± 0.23 | 0.001 | ||
sCD14 (μg/ml) | 3.60 ± 0.10 | 3.55 ± 0.06 | 0.64 | 3.71 ± 0.14 | 3.67 ± 0.06 | 0.77 | ||
sICAM (ng/ml) | 313 ± 9 | 283 ± 5 | 0.001 | 293 ± 11 | 272 ± 5 | 0.07 | ||
sVCAM-1 (ng/ml) | 729 ± 27 | 671 ± 17 | 0.04 | 703 ± 34 | 634 ± 16 | 0.06 |
. | Men (n = 597) . | . | P . | Women (n = 556) . | . | P . | ||
---|---|---|---|---|---|---|---|---|
. | IRS (n = 135) . | Other (n = 462) . | . | IRS (n = 65) . | Other (n = 491) . | . | ||
White blood cell count (× 103/mm3) | 7.34 ± 0.16 | 6.63 ± 0.10 | <0.001 | 7.23 ± 0.20 | 6.27 ± 0.09 | <0.001 | ||
Fibrinogen (g/l) | 2.98 ± 0.05 | 2.88 ± 0.03 | 0.08 | 3.16 ± 0.10 | 3.03 ± 0.05 | 0.21 | ||
FVIIa (IU/l) | 77.8 ± 2.9 | 68.8 ± 1.7 | 0.003 | 75.9 ± 3.6 | 67.6 ± 1.6 | 0.03 | ||
FVIIc (%) | 112 ± 2 | 101 ± 1 | <0.001 | 119 ± 3 | 105 ± 1 | <0.001 | ||
von Willebrand factor (%) | 116 ± 5 | 114 ± 3 | 0.69 | 123 ± 7 | 120 ± 3 | 0.59 | ||
CRP (ng/ml) | 2.61 ± 0.24 | 1.85 ± 0.15 | 0.003 | 3.54 ± 0.49 | 1.86 ± 0.23 | 0.001 | ||
sCD14 (μg/ml) | 3.60 ± 0.10 | 3.55 ± 0.06 | 0.64 | 3.71 ± 0.14 | 3.67 ± 0.06 | 0.77 | ||
sICAM (ng/ml) | 313 ± 9 | 283 ± 5 | 0.001 | 293 ± 11 | 272 ± 5 | 0.07 | ||
sVCAM-1 (ng/ml) | 729 ± 27 | 671 ± 17 | 0.04 | 703 ± 34 | 634 ± 16 | 0.06 |
Data are adjusted means ± SE. Data were adjusted on smoking status, alcohol consumption, and age group. In women, a further adjustment on menopausal status was performed.
Relationships between hemostatic and inflammatory markers and components of IRS men
. | White blood cell count (× 103/mm3) . | Fibrinogen (gr/l) . | FVIIa (IU/l) . | FVIIc (%) . | von Willebrand factor (%) . | CRP (ng/ml) . | sCD14 (μg/mL) . | sICAM (ng/ml) . | sVCAM-1 (ng/ml) . |
---|---|---|---|---|---|---|---|---|---|
Age | −0.026 ± 0.008 | 0.016 ± 0.003 | — | −0.218 ± 0.097 | 1.410 ± 0.234 | 0.040 ± 0.012 | 0.014 | — | — |
Alcohol consumption | — | −0.0002 ± 0.0001 | 0.011 ± 0.005 | — | — | — | 0.000 | — | — |
Smoking | 1.437 ± 0.149 | 0.275 ± 0.053 | 5.736 ± 2.801 | — | — | 1.001 ± 0.232 | 0.260 | 62.1 ± 8.6 | — |
BMI | — | — | — | — | — | — | — | −2.56 ± 1.19 | — |
WHR | 3.158 ± 1.308 | — | 87.8 ± 21.6 | 83.5 ± 15.5 | — | — | — | — | — |
HOMA | 0.064 ± 0.024 | 0.017 ± 0.008 | — | — | 1.748 ± 0.681 | 0.162 ± 0.034 | — | 4.549 ± 1.453 | 9.981 ± 3.983 |
Non-HDL cholesterol | — | — | — | 1.806 ± 0.858 | — | — | — | — | −33.74 ± 12.30 |
HDL cholesterol | −0.593 ± 0.205 | −0.180 ± 0.069 | 10.17 ± 3.66 | 10.8 ± 2.5 | — | −0.979 ± 0.303 | — | — | — |
Triglycerides (Log) | 0.315 ± 0.154 | — | — | 14.00 ± 2.01 | — | — | — | 33.78 ± 8.49 | 94.07 ± 28.0 |
SBP | 0.011 ± 0.004 | — | — | — | — | — | — | — | — |
DBP | — | — | — | — | −0.455 ± 0.204 | — | — | — | — |
R2 | 0.23 | 0.12 | 0.06 | 0.19 | 0.07 | 0.11 | 0.08 | 0.13 | 0.04 |
. | White blood cell count (× 103/mm3) . | Fibrinogen (gr/l) . | FVIIa (IU/l) . | FVIIc (%) . | von Willebrand factor (%) . | CRP (ng/ml) . | sCD14 (μg/mL) . | sICAM (ng/ml) . | sVCAM-1 (ng/ml) . |
---|---|---|---|---|---|---|---|---|---|
Age | −0.026 ± 0.008 | 0.016 ± 0.003 | — | −0.218 ± 0.097 | 1.410 ± 0.234 | 0.040 ± 0.012 | 0.014 | — | — |
Alcohol consumption | — | −0.0002 ± 0.0001 | 0.011 ± 0.005 | — | — | — | 0.000 | — | — |
Smoking | 1.437 ± 0.149 | 0.275 ± 0.053 | 5.736 ± 2.801 | — | — | 1.001 ± 0.232 | 0.260 | 62.1 ± 8.6 | — |
BMI | — | — | — | — | — | — | — | −2.56 ± 1.19 | — |
WHR | 3.158 ± 1.308 | — | 87.8 ± 21.6 | 83.5 ± 15.5 | — | — | — | — | — |
HOMA | 0.064 ± 0.024 | 0.017 ± 0.008 | — | — | 1.748 ± 0.681 | 0.162 ± 0.034 | — | 4.549 ± 1.453 | 9.981 ± 3.983 |
Non-HDL cholesterol | — | — | — | 1.806 ± 0.858 | — | — | — | — | −33.74 ± 12.30 |
HDL cholesterol | −0.593 ± 0.205 | −0.180 ± 0.069 | 10.17 ± 3.66 | 10.8 ± 2.5 | — | −0.979 ± 0.303 | — | — | — |
Triglycerides (Log) | 0.315 ± 0.154 | — | — | 14.00 ± 2.01 | — | — | — | 33.78 ± 8.49 | 94.07 ± 28.0 |
SBP | 0.011 ± 0.004 | — | — | — | — | — | — | — | — |
DBP | — | — | — | — | −0.455 ± 0.204 | — | — | — | — |
R2 | 0.23 | 0.12 | 0.06 | 0.19 | 0.07 | 0.11 | 0.08 | 0.13 | 0.04 |
Analysis by stepwise multiple regression. Dependent variables are shown in columns and fixed covariates in rows. Only significant coefficients ± SE are presented. R2 indicates the percentage of the variance of the dependent variable explained by the covariates. Smoking is used as (yes = 1, no = 0). — = not retained in the model.
Relationships between hemostatic and inflammatory markers and components of IRS in women
. | White blood cell count (× 103/mm3) . | Fibrinogen (gr/l) . | FVIIa IU/l) . | FVIIc (%) . | von Willebrand factor (%) . | CRP (ng/ml) . | sCD14 (μg/ml) . | sICAM (ng/ml) . | sVCAM-1 (ng/ml) . |
---|---|---|---|---|---|---|---|---|---|
Age | −0.038 ± 0.008 | 0.008 ± 0.004 | 0.596 ± 0.138 | — | 1.643 ± 0.251 | −0.039 ± 0.020 | 0.031 | — | 5.144 ± 1.301 |
Alcohol consumption | — | −0.0008 ± 0.0002 | — | — | — | — | — | — | — |
Smoking | 0.724 ± 0.159 | — | — | — | — | 0.907 ± 0.393 | — | 43.69 ± 8.57 | — |
BMI | 0.034 ± 0.016 | 0.038 ± 0.008 | 1.484 ± 0.269 | 1.112 ± 0.217 | — | 0.185 ± 0.038 | — | — | — |
WHR | — | −1.158 ± 0.446 | — | — | — | — | — | — | — |
HOMA | 0.044 ± 0.017 | — | — | — | — | — | — | — | 7.722 ± 2.928 |
Non-HDL cholesterol | −0.169 ± 0.071 | 0.094 ± 0.033 | — | 3.074 ± 0.980 | — | — | — | — | — |
HDL cholesterol | — | — | 11.53 ± 2.58 | 13.25 ± 2.10 | — | — | — | −23.75 ± 8.08 | — |
Triglycerides (Log) | 1.298 ± 0.192 | 0.225 ± 0.090 | — | 23.19 ± 2.79 | — | 1.862 ± 0.457 | — | 21.16 ± 10.38 | — |
SBP | — | — | — | — | — | — | — | 0.576 ± 0.192 | — |
DBP | — | — | — | — | — | — | — | — | — |
R2 | 0.19 | 0.16 | 0.13 | 0.25 | 0.07 | 0.11 | 0.06 | 0.10 | 0.05 |
. | White blood cell count (× 103/mm3) . | Fibrinogen (gr/l) . | FVIIa IU/l) . | FVIIc (%) . | von Willebrand factor (%) . | CRP (ng/ml) . | sCD14 (μg/ml) . | sICAM (ng/ml) . | sVCAM-1 (ng/ml) . |
---|---|---|---|---|---|---|---|---|---|
Age | −0.038 ± 0.008 | 0.008 ± 0.004 | 0.596 ± 0.138 | — | 1.643 ± 0.251 | −0.039 ± 0.020 | 0.031 | — | 5.144 ± 1.301 |
Alcohol consumption | — | −0.0008 ± 0.0002 | — | — | — | — | — | — | — |
Smoking | 0.724 ± 0.159 | — | — | — | — | 0.907 ± 0.393 | — | 43.69 ± 8.57 | — |
BMI | 0.034 ± 0.016 | 0.038 ± 0.008 | 1.484 ± 0.269 | 1.112 ± 0.217 | — | 0.185 ± 0.038 | — | — | — |
WHR | — | −1.158 ± 0.446 | — | — | — | — | — | — | — |
HOMA | 0.044 ± 0.017 | — | — | — | — | — | — | — | 7.722 ± 2.928 |
Non-HDL cholesterol | −0.169 ± 0.071 | 0.094 ± 0.033 | — | 3.074 ± 0.980 | — | — | — | — | — |
HDL cholesterol | — | — | 11.53 ± 2.58 | 13.25 ± 2.10 | — | — | — | −23.75 ± 8.08 | — |
Triglycerides (Log) | 1.298 ± 0.192 | 0.225 ± 0.090 | — | 23.19 ± 2.79 | — | 1.862 ± 0.457 | — | 21.16 ± 10.38 | — |
SBP | — | — | — | — | — | — | — | 0.576 ± 0.192 | — |
DBP | — | — | — | — | — | — | — | — | — |
R2 | 0.19 | 0.16 | 0.13 | 0.25 | 0.07 | 0.11 | 0.06 | 0.10 | 0.05 |
Analysis by stepwise multiple regression. Dependent variables are shown in columns and fixed covariates in rows. Only significant coefficients ± SE are presented. R2 indicates the percentage of the variance of the dependent variable explained by the covariates. Smoking is used as (yes = 1, no = 0). — = not retained in the model.
Article Information
We thank all the investigators of the Toulouse MONICA center for their invaluable contribution in the careful collection and validation of the data.
References
Address correspondence and reprint requests to Jean Ferrières, INSERM U558, Faculté de Médecine, Département d’ Epidémiologie, 1er étage, 37, Allées Jules Guesde, 31073 Toulouse cedex, France. E-mail: [email protected].
Received for publication 15 October 2001 and accepted in revised form 20 April 2002.
A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.