Introduction

Peripheral arterial disease (PAD)¹ coexists with other manifestations of atherosclerotic disease at other locations in the vasculature. The risk of a fatal or non-fatal myocardial infarction or stroke is high in patients with PAD, whereas the incidence of complications associated with ischemia of the lower extremities is rather limited (1). The 5-year mortality due to cardiovascular diseases in PAD patients is 30% . Moreover, these patients have a 3.1-fold increase in all-cause mortality compared with patients without PAD and a 6.6-fold increased risk of death from coronary artery disease (2)(3)(4).

Metabolic syndrome, the clustering of risk factors associated with central obesity, is prevalent in 58% of PAD patients (5) and is associated with increased vascular damage (6). In general, patients with metabolic syndrome are at increased risk of developing type 2 diabetes and of cardiovascular morbidity and mortality (7)(8)(9)(10). The high prevalence of metabolic syndrome in patients with PAD may contribute to the high incidence of cardiovascular events in these patients. Intra-abdominal fat is a major driver of insulin resistance and, therefore, plays, an important role in the development of metabolic disorders, including hyperglycemia, hypertension, hypertriglyceridemia, and low high-density lipoprotein (HDL)-cholesterol (11). Furthermore, intra-abdominal fat accumulation causes dysregulation of adipocyte function, leading to oversecretion of tumor necrosis factor-, free fatty acids, plasminogen activator inhibitor-1, interleukin-6, and growth factors, as well as hyposecretion of adiponectin, all of which may participate in the development of metabolic dysfunction (12). Patients with metabolic syndrome have a 3- to 4-fold increased risk of mortality due to coronary heart disease (CHD) (10), and intra-abdominal fat is an important determinant of the risk of CHD (13)(14)(15), but the relative importance of intra-abdominal fat in patients with PAD is unknown. In addition, little is known about which of the various measurements of obesity show the strongest relation with the risk of CHD in patients with an arterial disease. Previous studies were done primarily in healthy persons and provided conflicting results (16)(17)(18)(19)(20)(21). Therefore, in the present study, we investigated whether the presence of concomitant CHD can be explained by intra-abdominal fat accumulation and compared different measures of adiposity as predictors of CHD in patients with PAD.

Research Methods and Procedures

Study Population

In this study, we used data from patients enrolled in the Second Manifestations of ARTerial disease (SMART) study. The SMART study is an ongoing prospective cohort study of patients with manifest vascular disease or vascular risk factors. From 1996, patients 18 to 80 years old, newly referred to the University Medical Centre Utrecht with manifest vascular disease or a cardiovascular risk factor, underwent a vascular screening including a questionnaire, laboratory assessments, ankle-brachial pressure index (ABPI), duplex scan of the carotid arteries, and ultrasonography of the abdomen. All participants gave their informed consent, and the local Ethics Committee approved the study. Study design and definitions have been described in detail previously (22).

For this cross-sectional study, analyses were based on the inclusion period from May 2000 to April 2004 and were limited to patients with a qualifying diagnosis or medical history of PAD. For the current study, 315 consecutive patients with PAD, 18 to 80 years old, were enrolled.

PAD Category

The presence of PAD was based on referral diagnosis. Patients with typical symptoms of intermittent claudication [ cramping pain in the lower leg(s) during exercise] and a resting ABPI 0.90 or with rest pain, non-healing ulcers, or gangrene were referred by the general practitioner to the outpatient clinic of the Department of Vascular Surgery at the University Medical Center Utrecht, the Netherlands. If the vascular surgeon confirmed the diagnosis of PAD, patients were asked by their vascular surgeon to participate in the SMART study. Patients with a history of peripheral artery bypass surgery or confirmed intermittent claudication (Fontaine II and III) or rest pain/ulcer or amputation of the leg could also be included.

CHD

Patients with PAD were categorized according to the presence of CHD (past and current). The presence of a history of CHD was based on referral diagnosis and medical history. Patients who had one of the referral diagnoses, angina pectoris or myocardial infarction, and had an elective percutaneous transluminal coronary angioplasty or coronary bypass surgery or who stated a history of one of these diagnosis in the questionnaire were considered as having a history of CHD.

Angina pectoris was defined as chest pain with or without documented ischemia on the electrocardiogram and with documented stenoses on angiography. All patients had indication for percutaneous transluminal coronary angioplasty. Myocardial infarction was defined as having at least two of the following: chest pain for at least 20 minutes, not disappearing after administration of nitrates; sinus tachycardia elevation >1 m in two following leads or a left bundle branch block on the electrocardiogram; and/or creatine kinase (CK) elevation of at least 2 times the normal value of CK and a myoglobin fraction >5% of the total CK.

Anthropometric and Ultrasonography Measurements

Intra-abdominal fat thickness was estimated anthropometrically and ultrasonographically. The subjects' height and weight were measured while they wore indoor clothes and no shoes. BMI was calculated as weight in kilograms divided by the square of height in meters.

Waist circumference was measured halfway between the lower rib and the iliac crest, and hip circumference was measured at the level of the greater trochanter. Both measurements were taken in standing position. Sagittal diameter was measured at the level halfway between the lower rib and the iliac crest while the patient was in a supine position. Ultrasonographic measurements were taken in supine position with an HDI 3000 (Philips Medical Systems, Eindhoven, Netherlands) with a C 4-2 transducer. There was no bowel prep performed before the ultrasound measurement. For the ultrasound measurement of intra-abdominal fat, we used electronic calipers to measure the distance between the peritoneum and the lumbar spine or psoas muscles. For determining subcutaneous fat, the distance between the linea alba and the skin was measured. This means that the abdominal muscles were excluded for both measurements. A strict protocol, including the position of and pressure on the transducer, was used. For all images, the transducer was placed on a straight line drawn between the left and right midpoints of the lower rib and the iliac crest. All measurements were performed at the end of a quiet inspiration, applying minimal pressure without displacement or compression of the abdominal cavity. Each distance was measured three times at three positions (23).

Blood Pressure

Blood pressure was measured two times at the right and left upper arms, with a non-random sphygmomanometer, with the subject in a sitting position. The mean value of the two blood pressure measurements was taken as the blood pressure.

Vascular Screening

All elements of the vascular screening were conducted during 1 day at the University Medical Centre Utrecht. Patients were asked to complete a standardized health questionnaire covering medical history of vascular disease (CHD, PAD, abdominal aortic aneurysm, and cerebrovascular disease), symptoms of cardiovascular disease, risk factors (type 2 diabetes, hypertension, hyperlipidemia, alcohol consumption, physical activity, current and former smoking habits), family history, and current drug use. All patients underwent a standardized diagnostic protocol including physical examination (weight, height, waist circumference, systolic and diastolic blood pressure), non-invasive screening of asymptomatic atherosclerotic disease including ABPI, duplex scan of the carotid arteries, ultrasonography of the abdomen, and laboratory tests to determine the lipid profile (serum triglycerides, serum total cholesterol, serum HDL-cholesterol) and glucose and creatinine levels. Blood samples were collected after an overnight fast. The laboratory techniques and screening have been described previously (22).

Definitions

Metabolic syndrome was defined according to the Adult Treatment Panel III (ATP III) criteria. Three or more of the following metabolic abnormalities had to be present: abdominal obesity (waist circumference >102 cm in men and >88 cm in women), high blood pressure (130 mm Hg systolic or 85 mm Hg diastolic), hypertriglyceridemia [ serum triglycerides 1.70 mM (150 mg/dl)] , low HDL-cholesterol [ serum HDL-cholesterol <1.04 mM (40 mg/dl) in men and <1.29 mM (50 mg/dl) in women] , and high fasting glucose [ serum glucose 6.1 mM (110 mg/dl)] (24). When waist circumference was missing, we used BMI as a measure of obesity, with a cut-off point of 30 kg/m² (25).

Subjects who did not meet the ATP III criteria for high blood pressure or high fasting glucose but who were being treated with blood pressure-lowering agents or glucose-lowering agents or who had (self) reported type 2 diabetes were also considered to fulfill the criteria for high blood pressure or high fasting glucose, respectively. A fasting glucose 7.0 mM in a patient without a history of type 2 diabetes was considered as newly diagnosed type 2 diabetes.

Statistical Analysis

Values are given as percentages or as mean standard deviation (SD) for normally distributed variables. Differences between patients with and without CHD were tested with ² (categorical variables) or unpaired Student's t test (continuous normal distributed variables).

To adjust the mean of measures of adiposity for age and sex differences between patients with and without CHD, we used analysis of covariance (general linear model procedure). Multiple logistic regression analysis was performed to investigate the independent association between the different measures of adiposity and the presence of CHD. Results are expressed as adjusted odds ratios (ORs) with 95% confidence intervals (CIs). We estimated the ORs corresponding to a 1 SD increase in each measure of adiposity. Three models were used. The first was adjusted for age and sex. In the second model, additional adjustment was performed for systolic blood pressure, glucose, triglycerides, HDL-cholesterol, and ever smoking, and in the third model, final additional adjustment was performed for use of glucose-lowering agents, lipid-lowering agents, and blood pressure-lowering agents. The covariates were included as continuous variables. Only sex, smoking, and use of glucose-lowering agents, lipid-lowering agents, and blood pressure-lowering agents were included as categorical variable. The presence of CHD was taken as the dependent variable and age, sex, systolic blood pressure, glucose, triglycerides, HDL-cholesterol, ever smoking, and the different measures of adiposity as the independent variables. All statistical analyses were performed with SPSS for Windows 12.0.1 (SPSS, Inc., Chicago, IL).

Results

Study Population

The baseline characteristics of the study population are given in Table 1. Seventy-nine of the 315 patients had CHD, most patients were men (71% ), and the mean age was 59 years.

Table 1. - Baseline characteristics in 18- to 80-year-old patients with peripheral vascular disease.

Full table

The group with CHD were mostly men, consisted of more patients with the metabolic syndrome (63% ), and were slightly older than the patients without CHD. All components of the metabolic syndrome were more prevalent in the patients with CHD than in the patients without CHD, except for hypertension. Among all patients, 4% had no components of the metabolic syndrome, 21% had one component, 24% had two components, 27% had three components, and 25% had four or more components.

Intra-abdominal Fat in PAD Patients with and without CHD

Table 2 displays the measures of adiposity according to patients with and without CHD. All measures of adiposity were higher in patients with CHD than in patients without CHD, with the exception of the amount of subcutaneous fat (2.6 1.7 cm in patients with CHD vs. 2.7 1.8 cm in patients without CHD). In addition, more of the patients with CHD were obese (20% ). In both groups, with and without CHD, 9% of the waist and hip circumference, waist-to-hip ratio, and waist-to-height ratio measurements were missing. For BMI and subcutaneous fat, 2% of the measurements were missing in the group without CHD, and none were missing in the group with CHD. In both groups, no measurement of intra-abdominal fat was missing.

Table 2. - Measures of adiposity in patients with and without CHD.

Full table

Relation between Intra-abdominal Fat Measurements and CHD

The ORs for the presence of CHD in patients with PAD for each 1 SD increase in individual measures of adiposity, adjusted for age and sex, are shown in Table 3. Waist circumference and BMI showed the strongest association with the presence of CHD (OR, 1.64; 95% CI, 1.20 to 2.23) after adjustment for age and sex, whereas subcutaneous fat and hip circumference were not associated with the presence of CHD in these patients with PAD. The OR for intra-abdominal fat was 1.48 (95% CI, 1.13 to 1.94). To show whether there was an association between adiposity and CHD, we additionally adjusted for the components of the metabolic syndrome and smoking (Table 3). The OR for waist circumference was 1.61 (95% CI, 1.10 to 2.31) after additional adjustment for the components of the metabolic syndrome and smoking. The OR for intra-abdominal fat after adjustment for the metabolic syndrome factors and smoking was 1.40 (95% CI, 0.99 to 1.83). After additional adjustment for the use of glucose-lowering agents, lipid-lowering agents, and anti-hypertensive drugs, only waist circumference remained significantly associated with CHD (1.41; 95% , CI, 1.00 to 2.04).

Table 3. - The risk of the presence of coronary heart disease for each 1 standard deviation (SD) increase of different measurements of fat.

Full table

To investigate whether the location of fat in the abdomen was most important in the association with CHD or whether total body fat mainly determined the risk, we additionally adjusted for a quantitative measure of body weight (BMI). However, due to collinearity, these analyses were not interpretable.

Discussion

In this study, it is shown that each SD increase in waist circumference, an indirect indicator of intra-abdominal fat, was associated with a 61% increase in the risk of concomitant CHD in patients with PAD. Moreover, the metabolic syndrome was more prevalent among patients with CHD (63% ). Patients with recently established atherosclerotic arterial disease are at high risk of developing another vascular complication in the same or another part of the vascular system.

Patients with increased intra-abdominal fat are at increased risk for cardiovascular morbidity and mortality. Although most deaths among patients with PAD are due to CHD (4), little is known about the relative importance of intra-abdominal fat in patients with PAD. In the present study, we found that the presence of CHD in patients with PAD was associated with abdominal fat accumulation, as evidenced by the strong association with waist circumference.

Several factors may explain the increased cardiovascular risk of cardiovascular events associated with abdominal obesity. First, abdominal fat is associated with a number of metabolic disturbances, such as elevated blood pressure, hypertriglyceridemia, low serum HDL-cholesterol, and elevated plasma glucose, all established risk factors for the development of CHD (11). Second, visceral fat acts as an endocrine organ by secreting several hormones and cytokines, such as tumor necrosis factor-, interleukin-6, plasminogen activator inhibitor-1, and adiponectin (26). These adipokines are directly or indirectly involved in the process of atherosclerosis, thus contributing to an increased cardiovascular risk. Metabolic syndrome, the clustering of risk factors associated with central obesity (27)(28)(29), is associated with advanced vascular damage in patients who already have clinical manifestations of vascular diseases (6), indicating that metabolic syndrome may lead to more generalized atherosclerosis. Indeed, metabolic syndrome is highly prevalent among patients with atherosclerosis (5).

Several previous studies have examined the association between different measures of adiposity (BMI, waist-to-hip ratio, or waist circumference) and CHD (16)(17)(18)(19)(20)(21). The results of these studies are conflicting. Some suggest that BMI is a good predictor of CHD risk, whereas other studies suggest that waist-to-hip ratio or waist circumference is a better indicator of vascular risk. Most of these studies compared only a few measures of adiposity, and the strength of the present study is that we used several measures and investigated their relationship with CHD risk.

Our findings are in agreement with several studies (30) that found waist circumference to be more strongly associated with the risk of myocardial infarction than BMI. Some indicated that of the various anthropometric measures commonly used, waist circumference and waist-to-hip ratio showed the strongest relationship with the risk of CHD (20). Because waist circumference is considered to be a simple and crude measure of visceral obesity, we measured intra-abdominal fat by ultrasonography but found it to have a weaker association with CHD than waist circumference, waist-to-hip ratio, and waist- to-height ratio. Although waist circumference and BMI both predicted an increased risk of CHD, they measure different aspects of body fatness. BMI is a measure of overall fatness but does not provide information about the distribution of fat or distinguish between lean and fat mass, whereas waist circumference is a measure of abdominal adiposity. A previous study (31) reported that waist circumference and BMI independently contributed to the prediction of abdominal subcutaneous, visceral, and non-abdominal fat. The authors found that waist circumference was a better predictor of visceral fat than BMI but recommended that both should be used in clinical practice.

Hence, it is our view that the intra-abdominal fat accumulation that plays an important role in the development of metabolic syndrome favors the development of generalized atherosclerosis in patients with PAD, increasing the risk of CHD. Thus, interventions aimed at reducing weight, and especially focused on the waist area, may reduce the CHD risk. Such interventions should be considered in combination with conventional medical treatment of risk factors clustered in the metabolic syndrome. Indeed, several intervention studies have shown that weight reduction leads to a better cardiovascular risk profile in patients with abdominal adiposity (32)(33).

We acknowledge some limitations in our study. It had a cross-sectional design, which means that we can only make assumptions about possible etiologic relationship. Moreover, there were too few women in our sample to enable us to perform separate analyses for men and women, which would have been interesting because men and women have a different fat distribution (34). Additionally, the study population was comprised of a selected group of patients with symptomatic PAD referred to an academic center, and only patients who wished to participate were included. Additional adjustment for BMI, a quantitative measure, to investigate whether there was an association between localization of fat and CHD was difficult to interpret given the collinearity. Finally, computed tomography has been considered the most accurate and reproducible technique for measuring intra-abdominal fat (35). We determined intra-abdominal fat by ultrasonography because this method has been proposed as a suitable alternative technique to accurately measure intra-abdominal fat (23)(36). Some studies have shown that measuring intra-abdominal fat by ultrasonography has low reproducibility (37), but the method used in this article has been shown to be a good reproducible method to assess the amount of intra-abdominal fat using a strict protocol (23).

We concluded that, of the various measures of adiposity, waist circumference has the strongest association with CHD in patients with PAD. Reduction of abdominal adiposity may diminish the risk of vascular events in patients with PAD.

Notes

¹ Nonstandard abbreviations: PAD, peripheral arterial disease; HDL, high-density lipoprotein; CHD, coronary heart disease; SMART, Second Manifestations of Arterial Disease; ABPI, ankle-brachial pressure index; CK, creatine kinase; ATP III, Adult Treatment Panel III; SD, standard deviation; OR, odds ratio; CI, confidence interval.

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Acknowledgments

We gratefully acknowledge the contribution of the SMART research nurses, Michael Edlinger (data manager), and Harry G. Pijl (vascular manager), and the participants of the SMART Study Group: Ale Algra, Yolanda van der Graaf, Diderick E. Grobbee, and Guy E.H.M. Rutten (Julius Center for Health Sciences and Primary Care); Frank L.J. Visseren (Department of Internal Medicine); Hein A. Koomans (Department of Nephrology); Bert C. Eikelboom and Frans L. Moll (Department of Vascular Surgery); Jaap Kappelle (Department of Neurology); Willem P.T.M. Mali (Department of Radiology); and Pieter A. Doevendans (Department of Cardiology). There was no funding/outside support for this study.