European Journal of Internal Medicine
Volume 19, Issue 2 , Pages 115-121, March 2008

The impact of metabolic syndrome and CRP on vascular phenotype in type 2 diabetes mellitus☆

  • Reza Alizadeh Dehnavi

      Affiliations

    • Vascular Medicine, Department of General Internal Medicine & Endocrinology, Leiden University Medical Centre, Leiden, The Netherlands
    • Corresponding Author InformationCorresponding author. Vascular Medicine C4-R, Department General Internal Medicine & Endocrinology, Leiden University Medical Centre, P.O. Box 9600, 2300 RC Leiden, The Netherlands. Tel.: +31 71 5264684; fax: +31 71 5248140.
    • ,
  • Edith D. Beishuizen

      Affiliations

    • Vascular Medicine, Department of General Internal Medicine & Endocrinology, Leiden University Medical Centre, Leiden, The Netherlands
    • ,
  • Marcel A. van de Ree

      Affiliations

    • Department of Internal Medicine, Diakonessenhuis, Utrecht, The Netherlands
    • ,
  • Saskia Le Cessie

      Affiliations

    • Department of Medical Statistics and Bio-Informatics, Leiden University Medical Centre, Leiden, The Netherlands
    • ,
  • Menno V. Huisman

      Affiliations

    • Vascular Medicine, Department of General Internal Medicine & Endocrinology, Leiden University Medical Centre, Leiden, The Netherlands
    • ,
  • Cornelis Kluft

      Affiliations

    • Gaubius Laboratory TNO-QoL, Leiden, The Netherlands
    • ,
  • Hans M.G. Princen

      Affiliations

    • Gaubius Laboratory TNO-QoL, Leiden, The Netherlands
    • ,
  • Jouke T. Tamsma

      Affiliations

    • Vascular Medicine, Department of General Internal Medicine & Endocrinology, Leiden University Medical Centre, Leiden, The Netherlands

Received 27 August 2006; received in revised form 20 April 2007; accepted 12 June 2007. published online 02 November 2007.

Article Outline

Abstract 

Background

The burden of cardiovascular disease in diabetes mellitus type 2 (DM2) patients is variable. We hypothesize that metabolic syndrome (MS) and low-grade systemic inflammation modify the extent of atherosclerosis in DM2.

Methods

Vascular phenotype was determined using the following endothelium-related, hemostatic, and sonographic endpoints in 62 DM2 patients with mild dyslipidemia: sVCAM, sE-selectin, von Willebrand factor (VWF), fibrinogen, s-thrombomodulin (sTM), tPA, PAI-1, flow-mediated dilation (FMD), and intima media thickness (IMT). The impact of MS load (number of criteria present), MS components, and CRP on these parameters was assessed.

Results

Serum sVCAM, sTM, and tPA levels significantly increased with increasing MS load. IMT also significantly increased from 0.602±0.034 (one MS criterion) to 0.843±0.145 (four MS criteria, p=0.007). LogCRP significantly correlated with fibrinogen, PAI-1, and IMT. In a multiple regression (MR) model with age and gender as covariates, MS load predicted sVCAM and sTM; CRP predicted PAI-1 and fibrinogen; MS load and CRP simultaneously predicted tPA and IMT. For each MS criterion present, IMT significantly increased by 0.04 mm. An increase in CRP from 1 to 3 mg/L resulted in a significant increase of 0.04 mm. Patients with four MS criteria and inflammation (CRP ≥3 mg/L) are predicted to have a 0.21 mm thicker IMT than those without. A second stepwise MR analysis based on gender, traditional risk factors, diabetes-related parameters, renal function, individual MS criteria, and LogCRP as explanatory variables showed a significant effect of systolic and diastolic blood pressure, HDL, and LogCRP on IMT(r2=0.36, p<0.001).

Conclusion

MS and low-grade chronic inflammation have an independent impact on vascular phenotype including IMT in DM2.

Keywords: Metabolic syndrome, C-reactive protein, Diabetes mellitus type 2, Intima media thickness, Endothelial function, PAI-1

 

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1. Introduction 

Patients with diabetes mellitus type 2 (DM2) are known to suffer from increased rates of cardiovascular disease [1], [2], [3], [4]. DM2 has even been regarded as a cardiovascular risk equivalent [5]. However, the burden of cardiovascular disease can vary considerably within this population. The metabolic syndrome (MS) is often present in DM2 patients and frequently coexists with elevated C-reactive protein (CRP) levels, a measure of chronic, low-grade inflammation [6]. Variability in MS load (a rough measure for the extent of metabolic dysregulation), defined as the number of MS criteria present, and the level of CRP has also been shown in DM2 patients. MS and CRP are both associated with increased cardiovascular morbidity and mortality but are not interchangeable and may even have additional predictive value [7]. CRP has been related to atherosclerotic vessel wall structural changes such as intima media thickness (IMT) in several non-diabetic cohorts [8], [9], [10], [11], [12]. Moreover, the regulation of CRP has been argued to be independent of MS and is thought to be mostly due to inherited traits [13]. Thus, low-grade systemic inflammation could have a potential independent impact on vascular phenotype in DM2 patients. On the other hand, MS load has been directly related to coronary calcium scores, reflecting total body atherosclerotic burden in non-diabetic subjects [14]. MS also modified coronary heart disease prevalence in DM2 patients in an epidemiological study [4]. We hypothesize that MS and low-grade systemic inflammation, as measured by CRP, modify the extent of atherosclerotic disease burden in DM2 patients. We set out to study the impact of MS components, MS load, and CRP levels on endothelial, hemostatic, and echographically assessed vascular wall parameters in DM2.

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2. Materials and methods 

2.1. Study design 

This study was a single-center sub-study of the previously reported DALI study [15]. The study was carried out in accordance with the principles of the Declaration of Helsinki. The local medical ethics committee approved the study and all patients gave informed consent.

2.2. Patients 

Male and female patients aged 45–75 years with DM2 (according to the American Diabetes Association classification) of at least 1-year duration were included in the study. Inclusion criteria were: fasting triglycerides between 1.5 and 6.0 mmol/L, total cholesterol between 4.0 and 8.0 mmol/L, and HbA1c <10%. Patients were assessed after a washout period of lipid-lowering medication of at least 8 weeks. Patients with manifest or previous cardiovascular disease were excluded from the study. Pre-menopausal women and patients with acute liver disease, hepatic dysfunction, or impaired renal function (plasma creatinine >150 μmol/L) were excluded. Patients consuming more than four alcoholic drinks per day or on systemic steroids, androgens, cyclosporine, other immunosuppressive drugs, erythromycin, or mibefradil were also excluded.

2.3. Definition of MS criteria cut-off values and low-grade chronic systemic inflammation 

The WHO cut-off values were used for the assessment of MS load [16]. The criteria were defined as follows: hypertension: systolic/diastolic ≥140/90 (mm Hg); triglyceride levels ≥1.7 mmol/L; and HDL-cholesterol <1.0 mmol/L (women) and <0.9 mmol/L (men). Furthermore, waist circumference was used as a measure for obesity with a cut-off value of ≥94 cm in men and ≥80 cm in women. MS load was defined by the number of factors present exceeding the thresholds defined above, in addition to DM2. Five groups with respectively 0, 1, 2, 3, or 4 additional MS criteria could be defined. As for CRP, patients with levels over 15 mg/L were excluded from the evaluations on the suspicion of being a temporary outlier not representing the continuous habitual level [17].

2.4. Endpoints 

The impact of MS and CRP on vascular phenotype in DM2 was assessed using sonographic vascular parameters, intima media thickness (IMT), and flow-mediated dilatation (FMD), as well as the following endothelium-related and hemostatic factors: fibrinogen, s-thrombomodulin (sTM), tissue type plasminogen-activator (tPA), plasminogen-activator inhibitor-1 (PAI-1), sVCAM, sE-selectin, and von Willebrand factor (VWF).

2.5. Laboratory assessments 

Blood sampling and plasma lipid measurements, according to standard protocols, have been described previously [15]. CRP was measured in citrate, theophyline, adenosine, dipyridamol (CTAD) plasma with an enzyme immunoassay (EIA) using polyclonal antibodies (Dako, Copenhagen, Denmark) showing an intra- and inter-assay coefficient of variation around 2 mg/L of 2.7 and 4.3%, respectively [17]. Functional fibrinogen was measured in citrated plasma using a clotting rate method, essentially according to Clauss [18]. PAI-1 antigen was determined in CTAD plasma using an EIA (Innotest PAI-1, Innogenetics, Temse, Belgium) [19]. VWF antigen was measured in CTAD plasma with an in-house EIA using polyclonal rabbit antibodies to human VWF (DAKO, Copenhagen, Denmark) [20]. tPA antigen was determined in citrated plasma using an EIA (Immulyse™; Biopool, Umea, Sweden). An EIA was also used to measure sVCAM (Quantikine human sVCAM-1 from R&D systems, Abingdon, UK), sTM (Asserachrom s-Trombomodulin, from Diagnostica Stago, Asnières, France), and sE-selectin (Quantikine human sE-selectin from R&D systems, Abingdon, UK).

2.6. Flow-mediated dilatation (FMD) and intima media thickness (IMT) 

These measurements were performed as described previously [21]. Patients were in a fasting state, did not use tobacco on the morning of the evaluation, and rested in the supine position for 10 min before the study. FMD of the brachial artery was obtained using three baseline images frozen on the R-wave of the electrocardiogram. After the induction of ischemia, R-wave-triggered brachial artery images were frozen and recorded every 15 s for 5 min (reactive hyperemic period). All images were analyzed offline in a standardized and blinded manner.

For the measurement of IMT, patients were examined in the supine position with the neck slightly extended and rotated in the opposite direction. A 7.5-MHz linear array transducer was used (Aloka SD1400). A three-lead ECG was attached for R-wave triggering. Subsequently, the right and left distal 10 mm of the common carotid artery (CCA) were visualized under the angle with the clearest image of near and far walls. Three images were frozen on the R-wave of the ECG (end-diastole) and stored on S-VHS videotapes for offline IMT analysis. Images from the videotape were displayed on a personal computer and the optimal images digitized. The mean carotid IMT of the distal 10 mm of the CCA was measured using the Artery Measurement System, which semi-automatically traced the trailing edges on the near wall and the leading edges of the far wall to provide for near and far wall IMT. The measurements of near walls and far walls, left and right, were averaged to provide an individual IMT.

2.7. Statistics 

Means between the groups were analyzed using ANOVA including an analysis for a linear trend. Percentages were compared using the chi-square test for trend. Since the distribution of CRP was skewed, a logarithmic transformation (log 10) was performed and used in correlation analysis and multiple regression analysis. Pearson's correlations were calculated for LogCRP and fibrinogen, sVCAM, sTM, tPA, PAI-1, sE-selectin, VWF, FMD, and IMT. A multiple regression analysis was performed with MS load and LogCRP as independent variables, age and gender as covariates, and the abovementioned parameters as dependent variables. In the case of sTM, renal clearance (calculated using the Cockcroft formula) was also added as a covariate as its plasma levels are known to be dependent on renal function [22], [23], [24]. The concept of MS load results in a binary response due to the fixed thresholds used for each parameter. Furthermore, the impact of traditional risk factors, diabetes-related factors, and renal function should be taken into account with regard to vascular phenotype. Therefore, a second stepwise multiple regression analysis was performed to examine the impact of the MS criteria used as continuous variables taking diabetes and cardiovascular risk factors into account. The following variables were used: traditional risk factors (age, gender, and cholesterol), waist circumference, systolic/diastolic blood pressure, HDL, triglycerides, fasting blood glucose, inflammation (LogCRP), DM2-related parameters (duration of DM2 and HbA1c) and, finally, renal function (Cockcroft clearance). Possible interactions were systematically assessed. As no relevant interactions were observed, none was taken into account in the final models. The level of significance was set at p<0.05. All analyses were performed using SPSS for Windows software, version 11.0.1.

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3. Results 

3.1. Patient characteristics 

Patient characteristics are given in Table 1. There were no patients without additional MS criteria. In addition to DM2, one other MS criterion was observed in 4 patients; two additional MS criteria were observed in 12 patients; three in 30 patients; and four in 16 patients. Hypertension, body mass index (BMI), waist circumference, weight, HDL-cholesterol, and triglycerides significantly differed between the groups with different MS loads. A trend to differences was observed for fasting blood glucose. Renal function was similar for the groups.

Table 1. Patient characteristics and laboratory results
All patientsDM+1 MS criterionDM+2 MS criteriaDM+3 MS criteriaDM+4 MS criteriap-value
(n=62)(n=4)(n=12)(n=30)(n=16)
Age (years)59.6±6.9855.5±3.7059.8±6.8659.7±6.6660.4±8.340.66
Male/female (n/n)38/244/07/516/1411/50.30
Present smoking (%)21.00.041.723.36.30.72
Weight (kg)90.9±16.568.8±8.091.2±19.989.6±12.398.8±17.50.008
BMI (kg/m2)30.8±5.222.86±1.831.1±6.430.9±4.032.5±5.40.007
Waist circumference (cm)105.486.0104.3105.2111.40.004
Glucose (mmol/L) a10.7±2.97.1±1.511.1±3.111.1±3.010.6±2.40.06
HbA1c (%)9.2±1.47.8±1.29.2±1.49.4±1.59.2±1.30.24
Total cholesterol (mmol/L)6.01±0.935.93±0.616.48±0.725.88±1.055.96±0.840.29
HDL-c (mmol/L)1.04±0.221.18±0.251.20±0.221.06±0.180.83±0.10<0.001
Triglycerides (mmol/L)2.50±0.882.22±0.681.98±0.562.32±0.653.31±0.99<0.001
Blood pressure (mm Hg) systolic/diastolic143.5±19.3112.3±16.2133.6±13.3145.5±13.6154.4±22.5<0.001
84.2±9.176.0±9.877.1±10.585.8±7.187.9±7.90.002
Cockcroft clearance (ml/min)97.96±3079.63±18103.30±3997.72±2998.98±280.61

Numbers given as mean value±SD except for gender and present smoking, which are given as percentage.

aGlucose and lipid levels are given as fasting concentrations on the day of examination after an overnight fast.

3.2. Effect of MS load and CRP on vascular parameters (Table 2) 

Mean sVCAM, fibrinogen, sTM, and tPA levels differed between the groups. The plasma levels of sVCAM, sTM, and tPA significantly increased with increasing MS load. LogCRP correlated with fibrinogen and PAI-1. Endothelial function as assessed by FMD was not significantly influenced by MS load or by inflammation. Imaging of the carotid artery yielded a mean IMT of 0.797±0.135 mm. IMT was different between the groups and significantly increased with increasing MS load. Furthermore, IMT significantly correlated with LogCRP (Table 2).

Table 2. MS load and CRP vs vascular parameters
FactorDM+1 MS criterionDM+2 MS criteriaDM+3 MS criteriaDM+4 MS criteriap-value difference between groupsp-value linearityCorrelation of factors with LogCRP
sVCAM (μg/L)427.9 (63.3)389.5 (74.4)453.8 (130.9)510.5 (89.7)0.040.01NS
sE-selectin (μg/L)54.1 (16.0)59.9 (23.6)59.8 (26.3)66.7 (20.6)NSNSNS
VWF-ag (%)105.3 (38.3)122.0 (52.3)128.9 (53.4)136.4 (47.7)NSNSNS
Fibrinogen (g/L)2.91 (0.22)3.90 (0.79)3.72 (0.56)3.91 (0.57)0.03NS (0.07)r=0.62, p<0.001
sTM (μg/L)27.85 (7.25)28.31 (9.13)25.98 (11.45)38.70 (13.75)0.0080.03NS
tPA (μg/L)9.55 (1.92)12.25 (2.34)12.70 (2.80)15.01 (4.37)0.010.002NS
PAI-1 (μg/L)69.1 (28.8)115.2 (81.9)123.7 (73.1)145.9 (83.3)NSNS (0.08)r=0.37, p=0.003
Sonography
FMD (%)1.37 (1.88)3.29 (1.31)3.28 (5.29)4.30 (3.43)NSNSNS
IMT (mm)0.602 (0.034)0.794 (0.086)0.800 (0.134)0.843 (0.145)0.010.007r=0.29, p=0.02

Data are given as mean (SD).

NS = not significant.

3.3. Multiple regression analysis 

3.3.1. Model including age, gender, MS load, and CRP (Table 3, model 1) 

A multiple regression analysis was performed to assess the impact of MS load and LogCRP on the endpoints of the study using age and gender as covariates. The significant models are summarized in Table 3, model 1. MS load was a significant explanatory variable in the model for: sVCAM (β: 48.56[16.76, 80.37]) and sTM (β: 4.39[1.13, 7.64]). CRP significantly predicted plasma levels of fibrinogen (β: 0.86[0.50, 1.22]) and PAI-1 (β: 65.67[14.94, 116.40]). MS and CRP predicted tPA and IMT. For each MS criterion present, IMT significantly increased by 0.04 mm (SE 0.02, p=0.03). In addition, an increase in CRP from 1 mg/L (log 1=0) to 3 mg/L (log 3=0.47) resulted in an increase of IMT by 0.04 mm (0.47⁎0.095). Thus, patients with four MS criteria and a hsCRP level of 3 mg/L are predicted to have a 0.21 mm thicker IMT than DM2 patients without MS and a hsCRP level of 1 mg/L. This implies a higher risk of reaching the upper limit of normal IMT (0.90 mm) in these patients [25].

Table 3. Multiple regression models
Dependent variableModel 1 r2 (p-value)Model 2 r2 (p-value)Explanatory variables model 2
sVCAM (μg/L)0.25 (0.002)0.20 (0.03)Gendera, age, cholesterol, diastolic blood pressure, and LogCRP
sE-Selectin (μg/L)NS0.30 (0.003)HbA1c a, gender, DM2 duration, systolic blood pressure, TG, Cockroft clearance
VWF-ag (%)NS0.13 (0.02)HbA1c a, LogCRP
Fibrinogen (g/L)0.41 (<0.001)0.46 (<0.001)Cholesterola, LogCRPa, and systolic blood pressure
sTM (μg/L)0.35b (<0.001)0.47 (<0.001)Gendera, waist circumferencea, age, cholesterol, HbA1c, and Cockroft clearance
tPA (μg/L)0.26 (0.001)0.29 (0.001)Gendera, TGa, LogCRPa, age, and cholesterol
PAI-1 (μg/L)0.16 (0.04)0.30 (<0.001)LogCRPa, DM2 durationa, HbA1c a, and HDL
FMD (%)NS0.10 (0.05)TGa, and gender
IMT (mm)0.24 (0.003)0.36 (<0.001)Systolica/diastolica blood pressure, HDLa, LogCRPa, and TG

Model 1: Age, gender, MS load, LogCRP.

Model 2: Stepwise model based on gender, traditional risk factors, renal function, MS criteria, inflammation, and diabetes-related parameters.

a: Significant explanatory variable; the other factors listed were found to have high betas, thus contributing to the model, but they did not reach the predefined level of significance.

b: Model using renal function as covariate, as described in text.

3.3.2. Model based on gender, traditional risk factors, renal function, MS criteria, inflammation, and diabetes-related parameters (Table 3, model 2) 

A stepwise, multiple regression approach was used including traditional risk factors, diabetes-related factors, and renal function as independent factors in addition to the individual MS criteria and LogCRP. Significant models were observed for all variables except FMD (p=0.05). The predictive power was comparable to model 1 for sVCAM, fibrinogen, and tPA. The model improved for PAI-1 (explanatory variables: LogCRP, diabetes duration and HbA1c) and IMT. LogCRP (β: 0.09[0.007, 0.164]) and the following MS criteria were the significant determinants of IMT: systolic (β: 0.003[0.001, 0.005]) and diastolic (β: −0.007[−0.011, −0.003]) blood pressure and HDL (β: −0.27[−0.44, −0.11]); triglycerides contributed non-significantly: (β: −0.03)[−0.068, 0.013]). The model explained 36% (p<0.001) of IMT. A decrease of 0.1 mmol/L HDL-cholesterol increased IMT by 0.27 mm. The impact of hsCRP on IMT is very similar in model 1 and model 2 (β: 0.095 vs 0.09, respectively). Traditional risk factors and diabetes-related factors did not significantly contribute to the model explaining IMT in these patients.

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4. Discussion 

In this study, we observed that MS load, the presence of the individual defining criteria, and chronic low-grade inflammation independently contributed to vascular phenotype in this DM2 population. The data support the hypothesis that, although partly interrelated, metabolic and inflammatory pathways both modify vascular phenotype.

MS is estimated to be present in 75–80% of DM2 patients and has been shown to influence cardiovascular risk [4], [26] and fibrinolytic function [27], [28]. In patients with MS but without diabetes, increased measures of atherosclerosis [14], [29], [30], [31] and a higher incidence of cardiovascular events have been observed [27], [32]. This has partly been attributed to impaired fibrinolytic function [27] due to altered regulation of PAI-1, tPA, and fibrinogen [28], [33], [34]. In patients with MS, it has been shown that most procoagulant features are associated with anthropometry [28], while fibrinogen is associated with inflammation [28]. High tPA and PAI-1 levels have previously been reported in DM2 [35]. In line with these studies, we observed that tPA levels depended on both MS load and CRP, while fibrinogen and PAI-1 levels were solely explained by CRP. For tPA, model 2 (Table 3) points to triglycerides as the most relevant MS-related predicting factor. Diabetes-related factors were significant predictors for PAI-1 in addition to CRP, while none of the MS-defining criteria seemed to contribute to fibrinogen and PAI-1 levels. Thus, the main inhibitor of fibrinolysis was found to be severely influenced by the state of diabetes per se in alliance with low-grade systemic inflammation as measured by CRP.

An increased IMT was observed with increasing MS load, compatible with an increased burden of atherosclerosis (Table 3, model 1). Model 2 revealed that systolic and diastolic blood pressure and HDL were the MS criteria significantly defining IMT. HDL-cholesterol levels had a strong inverse relationship with IMT, underlining the potential importance of HDL and reverse cholesterol transport pathways in mildly dyslipidemic DM2 patients. Previous studies reported on the positive relation between MS and IMT: in healthy children without any other risk factors [8]; in adult male dyslipidemic patients [9]; and in non-diabetic adults with cardiovascular disease or at high risk of cardiovascular disease [10], [11], [12]. Our findings extend these observations to mildly dyslipidemic patients with DM2. Furthermore, our data support previously published reports showing that the presence of MS is strongly associated with the prevalence of cardiovascular disease in subjects with DM2 [4].

MS has been found to be associated with low-grade systemic inflammation as assessed by CRP. CRP is thought to provide additional predictive information regarding cardiovascular risk in patients with MS [7]. The current study shows that, in the setting of DM2, CRP is directly related to vascular wall abnormalities and the abovementioned adverse changes in the fibrinolytic system. We observed that low-grade chronic systemic inflammation contributed to IMT in addition to the effects of MS and that, together, they could predict 36% of IMT (p<0.001).

All parameters studied could have been influenced by the state of DM2 per se. This was indeed found to be the case for PAI-1 (Table 3, model 2). However, the state of diabetes per se is also likely to be relevant for FMD. We and others [15], [36], [37], [38] have consistently observed low FMD in DM2. Endothelial dysfunction is most likely an early feature in the pathogenesis of atherosclerosis in patients with diabetes. If diabetes is present, the low FMD cannot deteriorate further by increasing MS load or low-grade inflammation. This hypothesis is, however, at variance with one other previously reported observation [39].

In summary, both MS and low-grade systemic inflammation modify vascular phenotype in patients with diabetes. Their impact is not mutually exclusive, nor is it synergistic for all parameters. Some hemostatic variables are especially influenced by inflammation (e.g., fibrinogen, PAI-1) while others are predicted by MS (e.g., sVCAM). Together, MS and inflammation significantly predict IMT, a direct measurement of the vascular wall and established surrogate marker for future cardiovascular events. Our study showed that, although MS load is a useful rough estimate for clinical practice, the power of the model to predict IMT improved when the MS criteria were used as individual, continuous variables. The use of individual MS factors, however, did not exclude systemic low-grade inflammation as an independent, contributing variable explaining vascular phenotype in DM2 patients.

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5. Learning points 


•MS criteria and CRP independently modified vascular phenotype in this DM2 population.

•This influence is, at times, additive, e.g., for IMT. Simultaneous and additive.

•Some 36% (p<0.001) of IMT was explained by LogCRP, systolic and diastolic blood pressure, and HDL.

•HDL-cholesterol had a strong atheroprotective potential in these mildly dyslipidemic DM2 patients.

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Acknowledgement 

The study was supported in part by an unconditional research grant from Pfizer BV, The Netherlands.

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Appendix A. 

Members of the DALI Study Group: Erasmus Medical Centre Rotterdam, Department of Internal Medicine (I. Berk-Planken, N. Hoogerbrugge, H. Jansen); Erasmus University Rotterdam, Departments of Biochemistry and Clinical Chemistry (H. Jansen); Gaubius Laboratory TNO-PG, Leiden (H. M. G. Princen); Leiden University Medical Center (M. V. Huisman, M. A. van de Ree); University Medical Centre Utrecht, Julius Centre for General Practice and Patient Oriented Research (R. P. Stolk, F. V. van Venrooij); University Medical Centre Utrecht, Division of Internal Medicine (J. D. Banga, G. Dallinga-Thie, F.V. van Venrooij).

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☆ Grant support: The study was supported in part by an unconditional research grant from Pfizer BV, The Netherlands.

PII: S0953-6205(07)00255-5

doi:10.1016/j.ejim.2007.06.011

European Journal of Internal Medicine
Volume 19, Issue 2 , Pages 115-121, March 2008

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