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Journal of Clinical Endocrinology & Metabolism, doi:10.1210/jc.2004-1292
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The Journal of Clinical Endocrinology & Metabolism Vol. 90, No. 1 190-197
Copyright © 2005 by The Endocrine Society

Hemostatic Risk Factors and Insulin Sensitivity, Regional Body Fat Distribution, and the Metabolic Syndrome

I. F. Godsland, D. Crook, A. J. Proudler and J. C. Stevenson

Endocrinology and Metabolic Medicine, Faculty of Medicine (I.F.G., A.J.P., J.C.S.), and Wynn Department of Metabolic Medicine, Division of Medicine (I.F.G., D.C., A.J.P., J.C.S.), Imperial College London, London, United Kingdom W2 1NY; and Division of Primary Care and Public Health, Brighton and Sussex Medical School (D.C.), Brighton, United Kingdom BN1 9PH

Address all correspondence and requests for reprints to: Dr. Ian F. Godsland, Wynn Reader in Human Metabolism, Endocrinology, and Metabolic Medicine, Imperial College London, St. Mary’s Hospital, Mint Wing Second Floor, Praed Street, London, United Kingdom W2 1NY. E-mail: i.godsland{at}imperial.ac.uk.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Disturbances in the thrombotic and fibrinolytic systems are a feature of insulin resistance, obesity, and the metabolic syndrome. However, there are few studies in which these relationships have been explored in mainly asymptomatic individuals using sophisticated measures of insulin sensitivity and regional adiposity. Variables of the hemostatic system were measured in 106 men (aged 32–68 yr; body mass index, 20–34 kg/m2). Insulin sensitivity was measured by minimal model analysis and regional adiposity by dual energy x-ray absorptiometry. Clustering of intercorrelated variables was assessed by the statistical technique of factor analysis. Plasma levels of procoagulant factors VII and X, anticoagulant proteins C and S, and plasminogen activator inhibitor-1 correlated positively with total and percent central body fat (r = 0.25–0.38; P < 0.05) and negatively with insulin sensitivity (except protein S; r = –0.24 to –0.35; P < 0.05). On factor analysis, procoagulant factors VII and X, proteins C and S, and plasminogen activator inhibitor-1 were components of the cluster of variables that explained the greatest proportion of the variance in the data (39.2%). Other variables included in this cluster were those typical of the metabolic syndrome and also serum {gamma}-glutamyl transferase activity. These results suggest that factors VII and X and proteins C and S are features of the intercorrelated disturbances of the metabolic syndrome. Associations with adiposity and liver enzyme activity suggest the involvement of hepatic fat deposition.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
CORONARY HEART DISEASE has a multifactorial origin, and recent attention has focused on the possible role of the metabolic syndrome, the syndrome of metabolic disturbances constellated around insulin resistance. Since its recognition during the 1980s (1), the core metabolic syndrome of hyperinsulinemia, impaired glucose homeostasis, dyslipidemia, and hypertension has been expanded to include among its features visceral or central obesity (2), hyperuricemia (3), impaired postprandial triglyceride metabolism (4), microalbuminuria (5), endothelial dysfunction (6), subclinical inflammation (7), and adverse changes in fibrinolytic and thrombotic factors (8). Awareness of the potential importance of the metabolic syndrome has been heightened with the publication of working clinical definitions by the World Health Organization and the National Cholesterol Education Program (9, 10). Additional investigations are still needed, however, to quantify and characterize fully and reliably the basic features of the syndrome.

The working clinical definitions of the metabolic syndrome do not include any hemostatic system measure despite significant associations having been reported between such measures and the insulin sensitivity, body fat, inflammation and lipid components of the syndrome. Studies in which a direct measure of insulin sensitivity has been related to hemostatic system measures are rare (11) as are studies incorporating both a broad range of hemostatic factors and metabolic syndrome components (12, 13). The Heart Disease and Diabetes Risk Factors in a Screened Cohort Study (HDDRISC) is an open, occupational cohort study, which has included an unusually detailed range of risk factor measurements. In the present analysis, we have employed a validated measure of insulin sensitivity, derived from minimal model analysis of the iv glucose tolerance test (IVGTT), as well as direct measures of total and regional body fat. Relationships have been investigated between these variables and a range of measures of the thrombotic and fibrinolytic systems in 106 consecutively studied men, free of diabetes mellitus, who were attending a company health screening program. Clustering of hemostatic measures with a range of lipid, lipoprotein, liver function, and subclinical inflammation measures has been explored using factor analysis.


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
Design

The HDDRISC is a cohort study of metabolic risk factors for the development of coronary heart disease and diabetes (14, 15). The study began in 1971 and derives from a company health program, in the course of which participants received a range of metabolic, clinical, and laboratory measurements. From 1986 on, participants in the program underwent IVGTT. The present analysis concerns the 106 consecutively studied male recruits who, between 1992 and 1995, underwent IVGTT and also had measurements of a range of hemostatic factors. Written, informed consent to the study was obtained in each case, and local research ethics committee approval was given.

Procedures

Participants were instructed to consume more than 200 g/d carbohydrate in their diet for the previous 3 d as preparation for the IVGTT, to have fasted overnight (>12 h), and to have taken only water and refrained from cigarette smoking on the morning of their test. Height and weight were measured, and a clinical history was taken. An indwelling cannula was inserted into an antecubital vein in each arm. With the volunteer semirecumbent, blood samples were taken for fasting plasma and serum measurements. All samples were kept on ice before separation of plasma or serum, which took place within 1 h of the sample being taken. Samples for routine biochemical measurements were stored at 4 C before analysis. Plasma samples for measurement of insulin and hemostatic factors were frozen immediately. An iv glucose injection was then given [0.5 g glucose/kg body weight as a 50% (wt/vol) solution of dextrose, given over 3 min] via the cannula in the opposite arm to the sampling arm. Blood samples (10 ml) were then taken at 3, 5, 7, 10, 15, 20, 30, 45, 60, 75, 90, 120, 150, and 180 min for measurement of plasma glucose, insulin, and C peptide. Regional distribution of fat and lean tissue was measured by dual energy x-ray absorptiometry (DXA) using a whole body scanner (DPX; Lunar Radiation Corp., Madison, WI). Total body fat mass was recorded and central and peripheral fat masses were measured using manually determined regions of interest defined by anatomical bone landmarks, as previously described (16). The precision of total fat mass, based on repeated measurements in volunteers, was 2.9% (17).

Laboratory measurements

Plasma levels of fibrinogen, factors VII and X, and proteins C and S were measured by prothrombin time-based nephelometry on an automated coagulation analyzer (ACL 100, Instrumentation Laboratory, Lexington, MA). Antithrombin III and plasminogen were measured by enzymatic methods (Chromogenix, Mölndal, Sweden) using a discrete clinical analyzer (Cobas MIRA, Roche, Basel, Switzerland). Tissue plasminogen activator (tPA) and plasminogen activator inhibitor-1 (PAI-1) activities were measured by manual enzymatic methods (Chromogenix), and fibrinopeptide A was determined by RIA (Byk-Sangtec, Dietzenbach, Germany).

Plasma glucose and insulin, and serum total cholesterol, triglycerides, high density lipoprotein (HDL) and low density lipoprotein (LDL) cholesterol concentrations were measured as described previously (14). Participants also received a full blood count and liver function test profile, including white cell count, erythrocyte sedimentation rate (ESR), serum uric acid, globulin and albumin concentrations, and serum {gamma}- glutamyl transferase (GGT) activity.

Quality control was continuously monitored with commercially available lyophilized sera and by participation in national schemes. Between-batch assay coefficients of variation were: fibrinogen, 7%; factor VII, 7%; factor X, 4%; protein C, 8%; protein S, 11%; antithrombin III, 8% plasminogen, 5%; PAI-1, 22%; tPA, 17%; fibrinopeptide A, 13%; plasma glucose, 3%; plasma insulin, 6%; serum cholesterol and triglycerides, 2%; HDL cholesterol, 4%; serum uric acid, 4%, globulin and albumin concentrations, 5%; and serum GGT activity, 3%.

IVGTT modeling analysis

Insulin sensitivity was determined using the minimal model of glucose disappearance described by Bergman et al. (18). The relatively high glucose dose (0.5 g/kg) we employed provides for a high rate of model identification (19), and we have validated our procedure, without recourse to augmentation of insulin concentrations by tolbutamide or insulin injection, against the reference euglycemic clamp technique (r = 0.92) (20). ß-Cell function was estimated according to the minimal model of posthepatic insulin delivery described by Toffolo et al. (21), which returns measures of the sensitivity of first phase ({phi}1) and late phase ({phi}2) plasma insulin delivery to glucose. For a model analysis to be acceptable, parameter estimates were required to be positive and have parameter coefficients of variation less than 100%.

Data analysis

Fasting plasma glucose and insulin concentrations were expressed as the mean of two fasting measurements made within 10 min of each other before commencement of the IVGTT. Mean fasting glucose (MFG) and insulin (MFI) concentrations were used to estimate insulin resistance and ß-cell function according to the homeostasis model assessment (HOMA) method (22). The HOMA index of insulin resistance (HOMA-IR) was estimated as (MFG x MFI)/22.5, and the HOMA index of ß-cell function (HOMA-B) was estimated as (20 x MFI)/(MFG – 3.5) (units of glucose concentration, millimoles per liter; units of insulin concentration, milliunits per liter). The glucose elimination rate during the IVGTT was expressed as the k value (i.e. the slope of the regression line for the natural log of the IVGTT glucose concentrations between 20 and 60 min). The net increment in postload insulin concentrations above the fasting insulin level was calculated as the area under the curve using the trapezium rule. Cigarette smoking was categorized as: never smoked, ex-smoker, or less than five, 5–14, or 15–24 cigarettes/d. Alcohol intake was expressed as units consumed per week [a unit of alcohol approximates 10 ml or 8 g pure ethanol and is the amount contained in a half-pint (284 ml) of beer, a single glass (125 ml) of table wine, or a single measure (25 ml) of spirits]. Alcohol intake was also expressed categorically as never, light irregular, or less than 28, 28–56, or more than 56 U/wk. Exercise habit was expressed as none, moderate, or aerobic. DXA-derived central and peripheral fat masses were expressed as a percentage of total fat mass. Statistical analyses were carried out using STATA 8 (Stata Corp., College Station, TX). For subsequent parametric statistical analysis, insulin sensitivity measures and HOMA-B were square root-transformed; otherwise, measures were log-transformed, as appropriate, to normalize their distributions. One-way ANOVA was used to detect significant variation in hemostatic variables according to cigarette smoking, alcohol intake, and exercise habit. Pearson correlation was used to explore univariate associations between hemostatic and other continuous variables, and multiple linear regression was used to confirm the independence of significant associations detected.

Factor analysis was used to detect clustering of intercorrelated variables. Factor analysis supposes that the existence of a large number of highly intercorrelated variables reflects variation in a more limited number of underlying variables or factors. Measured variables are related to the resulting factors by their so-called loading, which is equivalent to the correlation coefficient between the variable and the factor. The variables with the highest loadings are then the measures that are of greatest importance in interpreting the nature of the underlying factor responsible for their covariation. A number of different procedures are available for factor analysis. In the present study factor analysis employed a principal factors analysis, followed by varimax rotation, as previously described for this cohort (23). Blood pressure was included in factor analysis as mean arterial pressure (calculated as: [(2 x diastolic blood pressure) + systolic blood pressure]/3) to avoid the emergence of a single, uninformative factor consisting of only the highly correlated systolic and diastolic blood pressure measurements. Only factors with eigen values greater than 1 were considered. Potential factor components were considered to be features of a given factor if their loading on that factor was 0.30 or more (15).


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
The characteristics of the study group are presented in Table 1Go. One subject had a fasting plasma glucose concentration above the limit for a diagnosis of diabetes mellitus, but this was less than 7 mmol/liter on subsequent retesting, and the subject was classified as having impaired fasting glycemia. Four other individuals had impaired fasting glycemia. None of those included in the present analysis had a diagnosis of arterial disease. Four participants were taking blood pressure-lowering medication, one of whom was also taking uric acid-lowering medication. One participant was taking lipid-lowering medication. Exclusion of those with impaired fasting glycemia or those who were taking medication made no difference in the findings reported below, and they were, therefore, retained in the analysis. Forty-five percent of subjects had never smoked, and 86% were nonsmokers at the time of testing. Mean alcohol intake was 16.9 U/wk, with 19% consuming 28 or more units/wk. Seventeen percent took regular aerobic exercise, and 40% engaged in moderate exercise.


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  		TABLE 1. Study group characteristics for 106 men

 
Statistically significant relationships between cigarette smoking and plasma fibrinogen (P < 0.0001) and factor X (P = 0.018) were detected. The association with fibrinogen was independent of body mass index (BMI), whereas that with factor X was not. Mean plasma fibrinogen levels in never, 5–14 cigarettes/d, and 15–24 cigarettes/d smoking categories were 293, 307, and 342 mg/dl, respectively. Positive or borderline positive (P < 0.1) associations were seen between alcohol intake, and factors VII and X and protein S, but these associations were not independent of BMI. Statistically significant variation with exercise habit was seen with plasma fibrinogen (P = 0.018) independently of BMI; mean plasma fibrinogen levels in never, moderate, and aerobic exercise categories were 314, 301, and 269 mg/dl, respectively.

Two IVGTT modeling analyses employing the minimal model of glucose disappearance were unsuccessful, resulting in a sample size of 104 for analyses involving SI. For the minimal model of posthepatic insulin delivery, seven analyses were unsuccessful. Measures of ß-cell function, HOMA-B, {phi}1, and {phi}2 were not significantly associated with hemostatic system measures, with the exception of {phi}2, which was positively associated with PAI-1 (r = 0.21; P < 0.05). HOMA-IR was significantly associated with PAI-1 (r = 0.21; P < 0.05) and tPA (r = –0.28; P < 0.01).

Associations between hemostatic factors, SI, and measures of adiposity are shown in Table 2Go. Plasma levels of hemostatic factor VII, protein C, and PAI-1 were significantly positively correlated with SI, BMI, total body fat mass, and central and peripheral fat as a percentage of total fat. Factor X was significantly positively correlated with SI, BMI, total fat mass, and central fat as a percentage of total fat. Protein S was significantly positively correlated with BMI and total fat mass.


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  		TABLE 2. Univariate correlations between hemostatic system variables, SI, and body fat measures in 106 men: Pearson correlation coefficients and significances

 
The dependency of the associations between SI, factors VII and X, protein C, and PAI-I on measures of adiposity was explored in multiple linear regression analysis (Table 3Go). Significant associations between SI, factor X, and PAI-1 were diminished, but not eliminated, by inclusion of measures of adiposity. Associations between SI and factor VII were diminished to a greater extent by inclusion of measures of adiposity, with elimination of the association altogether by inclusion of total body fat. Associations between SI and protein C were eliminated altogether by inclusion of measures of adiposity.


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  		TABLE 3. Multiple linear regression analysis of the effects of adiposity on significant associations between hemostatic system variables and SI: regression coefficients for SI in each model and their significances are shown

 
On factor analysis, six factors with eigen values greater than 1 emerged. Two had eigen values less than 1.1 and were not considered further. The factor that explained the greatest proportion of the variance in the data (39.2%; Table 4Go) included, in descending order of factor loading: percentage peripheral fat (negative), percentage central fat, total fat, triglycerides, IVGTT incremental insulin response, SI, PAI-1, GGT activity, factor X, HDL cholesterol (negative), total serum cholesterol, factor VII, protein C, protein S, and fasting plasma insulin. This factor was termed the metabolic syndrome factor. Relationships between plasma levels of hemostatic factors VII and X and the score for the metabolic syndrome factor are shown in Fig. 1Go. The second factor (13.1% of the variance explained) comprised, again in descending order of factor loading: serum globulin, ESR, fibrinogen, GGT activity, protein S, and triglycerides. A third factor (11.8% of the variance explained) included antithrombin III, plasminogen, protein C, hemostatic factors VII and X, tPA (negative), albumin, cholesterol, and PAI-1. A fourth factor (9.2% of the variance explained) included age, fasting plasma glucose, IVGTT k value (negative), SI (negative), and mean arterial pressure.


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  		TABLE 4. Factor analysis in 106 men

 


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  		FIG. 1. Scattergrams of factor VII and X coagulant activities on metabolic syndrome factor scores in 106 men. Factor loadings were 0.38 and 0.42 for factors VII and X, respectively.

 
To explore these associations further, the 11 nonhemostatic system components of the metabolic syndrome factor were used in multiple linear regression analysis as predictors of each of the four hemostatic system components. Significant or borderline significant independent predictors (P < 0.1) were as follows: factor VII: percentage central fat (P = 0.07) and total fat (P = 0.02); factor X: percentage of central fat (P = 0.09), percentage of peripheral fat (P = 0.07), and IVGTT insulin area under the curve (P = 0.07); protein C: percentage of central fat (P = 0.03) and total cholesterol (P = 0.01); protein S: GGT activity (P = 0.05); and PAI-1: percentage of central fat (P = 0.006).

The association between hemostatic factors and metabolic variables other than SI and adiposity were explored (Table 5Go). Factors VII and X, proteins C and S, antithrombin III, and plasminogen were positively associated with fasting serum cholesterol and triglyceride concentrations. Proteins C and S were also positively associated with LDL cholesterol concentrations. Fibrinopeptide A was negatively associated with serum cholesterol and LDL cholesterol concentrations. There were no associations between hemostatic system measures and HDL cholesterol concentrations. All hemostatic factors except fibrinopeptide A and tPA correlated significantly (and positively) with GGT activity. On multivariate analysis, the positive associations between GGT activity and the various hemostatic system measures were independent of alcohol intake. There was a strong positive association between fibrinogen and ESR (r = 0.54; P < 0.001).


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  		TABLE 5. Univariate correlations between hemostatic system variables and lipids, LDL cholesterol, and GGT in 106 men: Pearson correlation coefficients and significances (n = 106)

 
Hemostatic variables measured in the present study, their interrelations with other hemostatic variables, and hemostatic variables we found to be increased with decreasing insulin sensitivity, increasing adiposity, and the metabolic syndrome factor are illustrated in Fig. 2Go.



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  		FIG. 2. Simplified diagram of pro- and anticoagulatory and pro- and antifibrinolytic variables of the hemostatic system. Continuous lines show positive influences. Dashed lines show inhibitory influences. Variables measured in the present study are highlighted. Black arrows mark those variables that increased with decreasing insulin sensitivity and were part of the metabolic syndrome factor on factor analysis.

 

   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
Our understanding of the relationships between hemostatic system variables, insulin sensitivity, and the metabolic syndrome has previously focused mainly on high PAI-1 activity (8, 24). In accordance with this, we found strong negative associations between PAI-1 and insulin sensitivity, and in factor analysis, PAI-1 was a prominent component of the factor that included the classic metabolic syndrome features of low insulin sensitivity, increased central adiposity, and high triglyceride and low HDL cholesterol concentrations. Evidence for associations among factor VII, insulin resistance, and the metabolic syndrome is growing (13, 25, 26, 27). In addition to PAI-1 and factor VII, we found evidence for negative associations among factor X, protein C, and SI. Moreover, on factor analysis, these hemostatic system variables emerged as features of the metabolic syndrome, with the additional inclusion of protein S. These findings suggest that in addition to PAI-1, elevated levels of the procoagulant factors VII and X and anticoagulant proteins C and S are features of the intercorrelated disturbances of the metabolic syndrome.

In such a condition as the metabolic syndrome, where each disturbance may itself augment other disturbances, the technique of factor analysis is well suited to identifying those variables that are significantly interdependent. Identifying any primary disturbance in such clusters is problematic, not the least because the statistical significance of relationships identified in multivariate analysis may be influenced not only by mechanistic relationships, but also by the precision with which the variables concerned can be measured. Nevertheless, in several different analyses, we identified increased central adiposity as an important correlate of the hemostatic system variables of the metabolic syndrome. Moreover, other associations we report accord with an additional dimension to the effects of adiposity involving the liver. Hemostatic factors VII and X, proteins C and S, and PAI-1 were strongly positively associated with GGT activity. GGT activity has rarely been reported in studies of the metabolic syndrome, although in a previous factor analysis, we noted that it clustered with other components of the syndrome (15), and others have suggested that it should be included as a feature of the syndrome (28, 29, 30). Variation in GGT activity might be expected to be determined by alcohol intake, and nine men had GGT levels above the upper limit of normal (60 U/liter). On regression analysis, however, we found that self-reported alcohol intake explained only 11% of the variance in GGT activity, and associations between hemostatic factors and alcohol intake were relatively weak (results not shown). Moreover, associations between hemostatic system measures and GGT activity were independent of alcohol intake. GGT activity might be providing a better measure of true alcohol intake than the self-reported quantity. Nevertheless, we have previously demonstrated the expected associations between self-reported alcohol intake and other metabolic variables in the HDDRISC study cohort (14), and previous studies have reported that associations between GGT activity and variables of the metabolic syndrome are independent of alcohol intake (29, 31). Independently of alcohol intake, GGT activity can also be raised in association with hepatic fat deposition, as exemplified by nonalcoholic fatty liver disease (32). Nonalcoholic fatty liver disease has recently received attention in relation to its possible links with the metabolic syndrome and is itself closely associated with obesity and visceral fat deposition. The pathological significance of a combined elevation in procoagulant factors VII and X and anticoagulant proteins C and S is uncertain. However, hepatic fat deposition could be accompanied by an overproduction of both pro- and anticoagulant factors synthesized in the liver and could be an important determinant of the metabolic syndrome factor we report. Moreover, if liver health is related to the metabolic syndrome, this could contribute to new therapeutic approaches for the treatment of heart disease and diabetes.

Few previous studies have explored hemostatic system measures other than PAI-1 as potential features of the metabolic syndrome. In the Cardiovascular Health Study (CHS), factor analysis revealed no evidence of clustering between insulin resistance-related variables and hemostatic variables (12). In accordance with our findings, however, a positive association was found between postglucose load insulin concentrations (a surrogate for low insulin sensitivity) and factor X. Nevertheless, in the CHS, factors VII and X were unrelated to measures of adiposity (weight and waist circumference), and factor VII was unrelated to postload insulin concentrations. Possible reasons for these discrepancies with our findings could include the advanced age of the CHS population and the use of indirect measures of insulin sensitivity and regional adiposity.

In the study by Agewall et al. (11), insulin sensitivity was measured using the euglycemic, hyperinsulinemic clamp reference method. In accordance with our findings, there were significant negative relationships between insulin sensitivity and protein C activity, and protein C and S activities were significantly higher in those with the metabolic syndrome, defined according to World Health Organization criteria. Also in accordance with our findings, Marques-Vidal et al. (13) found elevated factor VII levels in subjects with the metabolic syndrome defined according to World Health Organization criteria. A recent family study, in which fasting insulin provided a surrogate measure of insulin sensitivity, demonstrated a significant genetic component to covariation between fasting insulin and factor VII levels (25)

Relationships between hemostatic and other metabolic variables have generally been explored in relation to lipid and lipoprotein concentrations. Our findings in this respect are largely in accordance with previous studies and include associations between factors VII and X, protein C, plasminogen, cholesterol, and triglycerides (33, 34, 35, 36, 37). It is noteworthy that in our study cholesterol and triglycerides, but neither LDL nor HDL cholesterol, were significant positive correlates of factor VII and factor X. This would accord with the association previously reported between triglyceride-rich lipoproteins and procoagulant activities (38). We also observed that total and LDL cholesterol were positively associated with proteins C and S and plasminogen and were negatively associated with fibrinopeptide A. This suggests an independent complex of associations between LDL cholesterol and anticoagulant activity, which, to the best of our knowledge, has not been reported previously.

Some studies have suggested links between low insulin sensitivity and high fibrinogen concentrations (8, 39), although the study by Agewall et al. (11), which, like ours, employed a sophisticated measure of insulin sensitivity, found no such association. We found an inverse relationship between exercise and fibrinogen levels, so it is possible that previous reports of high fibrinogen levels in states consistent with low insulin sensitivity might have been confounded by the well established positive association between insulin sensitivity and exercise. Studies have also suggested associations between fibrinogen levels and other cardiovascular risk factors of the metabolic syndrome (35, 40, 41, 42). We found fibrinogen levels to be uncorrelated with other risk factors, including measures of adiposity. We did, however, observe the expected associations among fibrinogen, exercise, and smoking, and we also noted a strong positive association with ESR. Studies reporting associations between fibrinogen and other risk factors have generally concerned groups of individuals already at risk of arterial disease, for example, the obese (41, 42) or the elderly (40), and it has been suggested that associations between features of the metabolic syndrome and fibrinogen may be secondary to an accompanying acute phase reaction (43). We studied asymptomatic, mainly nonobese, individuals in whom the acute phase reaction would not have been prominent.

In factor analysis, the factor that explained the greatest proportion of the variance in the data showed the majority of the characteristics typical of the metabolic syndrome. Fasting plasma insulin was only a weak feature of this factor, and fasting plasma glucose concentration was not a feature, which accords with the relative lack of association between hemostatic system variables and HOMA-IR. It is also noteworthy that in univariate analysis there were no associations between hemostatic system variables and measures of ß-cell function, which suggests that insulin resistance is the primary locus for associations between hemostatic system variables and insulin metabolism. A second factor included globulin, ESR, and fibrinogen as its major components and accords with a profile that might be expected of subclinical inflammation. A third factor included the majority of the hemostatic factors we measured, with tPA as a negative component, as well as cholesterol and albumin. A fourth factor demonstrated the typical profile expected from aging, with age itself as its major component, along with fasting plasma glucose, decreasing IVGTT k value (i.e. deteriorating glucose tolerance), decreasing SI (i.e. insulin resistance), and mean arterial pressure.

In summary, we have demonstrated in a group of asymptomatic men significant positive associations among insulin sensitivity, central body fat, the procoagulant factors VII and X, and the anticoagulant proteins C and S and have shown that these hemostatic system measures cluster as part of the metabolic syndrome complex. We hypothesize that hepatic fat deposition is responsible for many of these associations.


   Acknowledgments
 
Prof. Victor Wynn initiated and established the HDDRISC study. We also extend our thanks to the many clinical, scientific, technical, nursing, and administrative staff who contributed to the study, in particular Mandeep Sidhu for the hemostatic factor measurements and Belinda Lees, Ph.D., for the DXA measurements. We are also grateful to Prof. Desmond Johnston for his critical reading of the manuscript.


   Footnotes
 
This work was supported by the Heart Disease and Diabetes Research Trust and the Rosen Foundation. I.F.G. is supported by the Heart Disease and Diabetes Research Trust.

First Published Online October 19, 2004

Abbreviations: BMI, Body mass index; CHS, Cardiovascular Health Study; DXA, dual energy x-ray absorptiometry; ESR, erythrocyte sedimentation rate; GGT, {gamma}-glutamyl transferase; HDDRISC, Heart Disease and Diabetes Risk Factors in a Screened Cohort Study; HDL, high-density lipoprotein; HOMA, homeostasis model assessment; HOMA-B, HOMA index of ß-cell function; HOMA-IR, HOMA index of insulin resistance; IVGTT, iv glucose tolerance test; LDL, low-density lipoprotein; MFG, mean fasting glucose; MFI, mean fasting insulin; PAI-1, plasminogen activator inhibitor-1; SI, minimal model insulin sensitivity; tPA, tissue plasminogen activator.

Received July 9, 2004.

Accepted October 10, 2004.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

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