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Department of Nutritional Sciences, University of Connecticut, Storrs, CT 06269 and * Department of Medicine, Columbia University, New York, NY 10032
1To whom correspondence should be addressed. E-mail: maria-luz.fernandez{at}uconn.edu.
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ABSTRACT |
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 TOP ABSTRACT SUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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30 kg/m2 or WC > 88 cm had significantly higher leptin concentrations than women having a BMI < 30 kg/m2 or a WC 
88 cm; women with WC > 88 cm also had higher diastolic pressure (P < 0.05), and higher plasma TG (P < 0.05) and apo C-III (P < 0.05) concentrations than those with WC 88. In addition, subjects with the higher WC walked an average of 1000 fewer steps per day (P < 0.01). These results suggest that WC is a stronger predictor of CHD risk than BMI and is more closely associated with the level of exercise in premenopausal women.
KEY WORDS: • waist circumference • body mass index • coronary heart disease • overweight • premenopausal women
Despite the continuing effort to educate the public that excessive weight raises the risk for chronic disease, the prevalence of overweight and obesity continues to increase (1). Overweight has historically been defined as having a BMI of 25–29.9 kg/m2 and obesity as having a BMI 30 kg/m2 (2). The first National Health Examination Survey, covering 1960–1962, estimated the prevalence of obesity to be 13.4% (1). By 2000, the third National Health and Nutrition Examination Survey estimated that 64.5% of U.S. adults were overweight or obese, with the overall prevalence of obesity escalating to 30.5% (3). Research showed that persons who are overweight or obese have a higher risk of developing insulin resistance (IR),2 the metabolic syndrome, diabetes, hypertension, and coronary heart disease (CHD) (4,5). Currently, 
16 million people are estimated to have diabetes mellitus type II (DM2) in the United States. The prevalence of DM2 in those 30–40 y old rose 70% from 1990 to 1998 (6). CHD, the number one killer of men and women (7), affects 13 million people in the United States alone (8).
Scientists have utilized BMI as the primary tool in the research setting for linking body composition with other risk factors for chronic disease. For the general public, BMI is less practical for assessing risk because of the calculations that are required for its determination (9). Furthermore, interpretation of the BMI chart may be difficult for untrained individuals (9). In contrast, abdominal fat was found to be a stronger predictor for CHD and DM2 risk than total body adipose mass (4,5). The National Heart, Lung, and Blood Institute of the National Institutes of Health developed a classification system in 1998 that used BMI and waist circumference (WC) as screening tools for developing the risk profile of overweight and obese adults (10). WC is a proxy measurement of abdominal fat, and more specifically visceral fat (10,11). Because abdominal fat predicts a higher risk for CHD and DM2, WC provides more information on risk assessment than BMI alone (9,10).
WC may also be indicative of physical activity level. It could be assumed that individuals exercising regularly have less abdominal fat and therefore a smaller WC. Ross et al. (11) reported that subjects with higher levels of cardiorespiratory fitness, independent of BMI, have a lower risk of developing chronic diseases due to their lower concentrations of abdominal fat. These authors also reported that weight loss in their population was correlated with comparable decreases in WC, and abdominal and visceral fat (11).
The objective of this study was to measure select biomarkers for CHD risk in a group of overweight and obese premenopausal women to assess whether WC is a better predictor for CHD risk than BMI. The plasma variables measured were lipids, apolipoproteins (apo), LDL atherogenicity, glucose, leptin, and insulin levels. The number of steps taken per day was also recorded to determine the influence of physical activity in modulating these risk factors.
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SUBJECTS AND METHODS |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Subjects. Overweight or obese premenopausal women (n = 80; 74% Caucasians) were recruited from the University of Connecticut and surrounding communities. The Institutional Review Board of the University approved the experimental protocol and signed informed consent was obtained from all subjects. The subjects were all between the ages of 20 and 45 y and their BMIs ranged from 25 to 37 kg/m2. The exclusionary criteria included a history of cardiovascular, kidney and liver disease, or diabetes mellitus. Participants were also excluded if they were pregnant or lactating. Nine subjects were smokers and 61 consumed alcohol; 9 participants were taking a multivitamin, 6 were taking calcium as a separate supplement, and 1 participant was taking calcium, vitamins A and E, and folic acid also as separate supplements. Of the women, 26 were using some form of oral contraception, 8 were taking thyroid medication and had been stable for at least 2 y, and 1 participant had been taking a cholesterol-lowering medication for >5 y. Three participants reported to be anemic, but did not report the utilization of any prescription or nonprescription drugs for the condition. Using the International Physical Activity Questionnaire (12), the majority of participants considered themselves to be sedentary to moderately active.
Two blood samples were collected from each subject after a 12-h fast on different days into tubes containing 0.15 g/100 g EDTA to determine plasma lipids, plasma glucose, insulin, leptin, LDL susceptibility to oxidation, LDL size, and plasma apolipoproteins. Plasma was separated by centrifugation at 1500 x g for 20 min at 4°C, and placed into vials containing PMSF (0.05 g/100 g), sodium azide (0.01 g/100 g), and aprotinin (0.01 g/100 g).
Blood pressure, both diastolic and systolic, was measured on the right arm using a Welch Allyn, Tycos blood pressure cuff with the participant seated and following a 5 min rest. In addition, subjects were instructed on the proper use of an HJ-104BL pedometer (Omron Healthcare) to determine their baseline number of steps by averaging the number of steps taken per day for 1 wk.
Anthropometrics. Measurement of WC and calculation of BMI were done according to standard techniques and equipment (9,10). WC was measured at the midway point between the lowest rib and the iliac crest to the nearest 0.1 cm (9,10). The calculated CV for WC measurements was 0.30 cm. Weight was measured in pounds to the nearest 0.5 lb and height was measured in inches to the nearest 0.5 inch on a portable stadiometer (9). The weight and height were converted into metric measures to calculate the BMI (kg/m2).
Pedometer data. Each participant was given an Omron HJ-104 pedometer to determine the number of daily steps taken during 1 wk, and log sheets were provided for recording purposes. The pedometer functions by counting the number of steps taken over a 24-h period, after which it resets to 0. As an additional feature, the pedometer is equipped with a 7-d memory function. Stride length was adjusted for each participant to ensure accuracy. Subjects were selected at random for an inspection of the 7-d memory function to verify the accuracy of the reported number of steps taken by the participants. Subjects were asked to maintain their normal activity level.
Plasma lipids and apolipoproteins. Our laboratory has participated in the Centers for Disease Control-National Heart, Lung and Blood Institute (CDC-NHLBI) Lipid Standardization Program since 1989 for quality control and standardization for plasma total cholesterol (TC), HDL cholesterol (HDL-C), and TG assays. CVs assessed by the Standardization Program during the study period were 0.76–1.42 for TC, 1.71–2.72 for HDL-C, and 1.64–2.47 for TG.
TC was determined by enzymatic methods using Roche-Diagnostics standards and kits (13). HDL-C was measured in the supernatant after precipitation of apo B-containing lipoproteins (14), and LDL cholesterol was determined using the Friedewald equation (15). TG were determined using Roche-Diagnostics kits, which adjust for free glycerol (16). Apo B concentrations were measured by an immunoturbidimetric method using kits from Wako, and turbidity was determined at 340 nm (17); apo C-III (18) and apo E (19) were measured with a Hitachi Autoanalyzer 740 utilizing kits from Wako.
Plasma glucose. Plasma glucose was determined enzymatically using kits from Wako (20). Briefly, 3 mL of working solution was added to 0.20 mL of sample and mixed, transferred to cuvettes, incubated at 37°C for 5 min, and then read at 505 nm on a DU-640 UV spectrophotometer (Beckman Coulter).
Plasma insulin. Plasma insulin was measured using a Linco RIA kit that utilizes the double-antibody/polyethylene glycol technique (21). Briefly, 100 µL of plasma was incubated with 125I-labeled human insulin and guinea pig anti-human insulin antiserum. After an overnight incubation, a precipitating reagent containing goat anti-guinea pig IgG was added, and samples were mixed and incubated for 20 min. Samples were then centrifuged at 2500 x g for 20 min, the liquid was decanted, and tubes containing the pellet were counted for 1 min using a Cobra II-Auto Gamma Counting System (Packard Instruments).
Plasma leptin. Plasma leptin was analyzed using a Linco RIA kit. A similar protocol as for the measurement of insulin was used. Plasma samples (100 µL) were incubated with 125I-labeled human leptin and goat anti-human leptin antiserum. After an overnight incubation, samples were centrifuged at 2500 x g for 20 min, the supernatant discarded, and the radioactivity in the pellet counted in a Cobra II-Auto Gamma Counting System.
LDL oxidation. LDL oxidation was determined according to Abbey et al. (22). LDL was isolated by ultracentrifugation in an L8-M ultracentrifuge (Beckman Instruments). LDL was isolated at a density of 1.09 kg/L at 60,000 x g for 3 h in a VT5 65.5 rotor. Samples were dialyzed overnight in 10 mmol/L Na2HPO4, 0.15 mol/L NaCl, pH 7.4, and protein (23) was determined following dialysis.
LDL samples were diluted to 102 µg LDL/1.2 mL using the dialysis buffer. Samples were then transferred to cuvettes, which were placed in a DU-640 UV spectrophotometer (Beckman Coulter) to be read. CuSO4 was added to initiate oxidation. Kinetics of samples proceeded at 37°C for 180 min, and absorbance was plotted every 120 s.
Conjugated diene formation was determined from the differences between the intercept of the propagation and termination phases and absorbance at time zero. Conjugated diene concentrations were determined by using the extinction coefficient for conjugated dienes at 234 nm (29,500 L · mol–1 · cm–1). Lag time was determined from the intercept of the lag and propagation phases.
LDL size determination.
The Lipoprint LDL system (Quantimetrix) was used to identify the size of LDL using a nongradient high-resolution polyacrylamide gel electrophoresis system. Briefly, 25 µL of plasma was added to precast polyacrylamide gel tubes and overlayered with 200 µL of loading gel. Tubes were then photopolymerized for 30 min and placed into the electrophoresis chamber. Electrophoresis buffer (Tris-hydroxymethyl aminomethane 66.1 g/100 g, boric acid 33.9 g/100 g, pH 8.2–8.6) was added to the top and bottom portion of the chamber. The gel was run for 90 min at 72 mV or until the HDL fraction was 1 cm from the end of the gel. Gels were allowed to sit for 30 min and scanned with a densitometer.
Classification of subjects with IR or the metabolic syndrome.
Insulin resistance (IR) is defined as an impaired metabolic response to the body’s own insulin. IR is characterized by a decreased capacity of insulin to promote typical glucose disposal, which results in increased insulin production by pancreatic ß cells in an effort to overcome this deviation from normal metabolism (24,25). The homeostasis model assessment (HOMA) (26) was used to calculate IR according to the following equation: IR (HOMA IR) = fasting insulin (µU/mL) x fasting glucose (mmol/L)/22.5. The HOMA model was shown to be a reliable method of measuring IR in various populations when other more invasive methods are not feasible (26). On the basis of the equation, subjects were classified as having IR if the calculated value was 3.8 (27).
The subjects were classified as having the metabolic syndrome if they had 3 of the 5 risk factors delineated by the Adult Treatment Panel III (ATP III) criteria: a fasting plasma glucose > 110 mg/dL (>6.11 mmol/L), waist circumference > 88 cm for women, fasting TG > 150 mg/dL (> 1.70 mmol/L), fasting HDL-C < 50 mg/dL for women (< 1.30 mmol/L), and blood pressure 130 mm Hg (systolic) or 85 mm Hg (diastolic) (28)
Statistical analysis. Descriptive statistics (means ± SD) were used to evaluate the characteristics of the 80 subjects. Pearson correlations were used to determine significant relations of WC and BMI with the measured variables. Due to the nature of the nonparametric distribution of plasma TG, Spearman correlations were used to determine a relation between this variable and anthropometric measurements. Unpaired Student’s t test was used to compare subjects with lower and higher BMI and WC. A P-value < 0.05 was considered significant.
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RESULTS |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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30 kg/m2 (Table 3). Systolic blood pressure, insulin, and leptin were higher in subjects having a BMI 30 kg/m2 than in those with BMI < 30 kg/m2 (Table 3). We also classified our study population into two groups on the basis of WC (10) (Table 4); 29 subjects had WC 88 cm and 51 of the participants had WC > 88 cm. Plasma TG, apo B, apo C-III, leptin, and diastolic blood pressure were significantly higher in subjects with WC > 88 cm compared with those with WC 88 cm. In addition, the number of steps taken per day was greater in women with the lower WC (P < 0.01) (Table 4).
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30. |
DISCUSSION |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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A straightforward variable such as WC can be used in conjunction with BMI to monitor the success of diet and exercise-based preventive therapies, both of which have been demonstrated to reduce the risk of DM2 and CHD (5,9). Independent of WC, BMI was shown to predict chronic diseases associated with obesity (10,11). However, changes in body composition due to increased exercise may not alter BMI, and risk assessment remains unaffected (11). In contrast, WC was shown to change with increased exercise, indicating changes in abdominal obesity (11), which allows for a more accurate risk assessment for DM2 and CHD.
Risk factors for CHD in participants.
The study subjects were overweight and obese premenopausal women with a mean age of 29.2 y. The mean values for blood pressure, plasma lipids, apoproteins, glucose, insulin, and leptin were lower than the level that places a person at risk for IR, the metabolic syndrome, DM2, or CHD. Before subject stratification, one risk variable identified was the presence of pattern B LDL (29), which was observed in almost half of the study population. These small, dense LDL particles have been associated with elevated levels of TG, abdominal fat, and apo B, and low levels of HDL (30). In addition, the average concentration of plasma apo C-III in these subjects was higher than the values observed in premenopausal women (31,32) and men (33) of normal weight.
Other variables that indicated participants at risk were fasting glucose and insulin levels. The mean fasting plasma glucose (5.07 mmol/L) and insulin (114.8 pmol/L) concentrations were elevated compared with the normal range, which was reflected in the high percentage of participants (30%) who were classified as IR using the HOMA method (26). IR has been shown to be a strong predictor of DM2 and CHD in otherwise healthy individuals (26). Overweight and obese individuals have a higher risk for developing IR, which is considered an initial stage of DM2, and is detectable through measuring insulin and glucose levels, despite the absence of other metabolic abnormalities (34,35).
Differences between BMI and WC in association with risk factors.
Studies were conducted to determine whether BMI or WC are better predictors of chronic disease (5,9–11). In this study, it was primarily after the subjects were classified according to their BMI and WC status that the majority of risk factors for chronic disease were identifiable. Lean et al. (9) studied >2200 participants to identify the risk for chronic disease using WC as a tool (9). They reported that WC had stronger associations with metabolic functioning than the waist:hip ratio and better predicted myocardial events (9). Lean et al. (9) also found that BMI was not correlated with as many of the known risk factors for CHD, whereas WC was correlated.
In the present study, although a strong correlation between BMI and WC (r = 0.709, P < 0.01) was found, we observed that the associations of these two measurements with risk factors for chronic disease differed once the subjects were stratified on the bases of BMI and WC. The collection of risk factors that define the metabolic syndrome demonstrates that BMI alone will not determine those persons with dyslipidemia or irregular carbohydrate and lipid metabolism that lead to DM2 and CHD (36,37). We observed that 22 of the participants had BMIs < 30 kg/m2 (overweight but not obese), but had a WC > 88 cm (92.6 ± 3.7 cm). The higher WC places these 22 subjects at higher risk for chronic disease, independently of their BMI. This was clearly demonstrated when subjects were evaluated for the metabolic syndrome. Six of the 11 subjects who presented with the metabolic syndrome did not have a BMI 30 kg/m2, but did have a WC > 88 cm, suggesting that WC provides additional risk information that is not detected by classifying subjects as overweight or obese.
In this study, subjects who had a WC > 88 cm also had higher plasma TG and apo C-III concentrations, which are associated with other risk factors for DM2 and CHD including the presence of small, dense LDL particles. Small, dense LDL is also a prominent feature associated with IR and CHD and predisposes individuals to develop the metabolic syndrome (29). The pattern B LDL has a large percentage of TG compared with pattern A LDL. Plasma TG were also positively correlated with plasma LDL-C, apo B, apo E, apo C-III, and susceptibility of LDL to oxidation (conjugated diene formation), but was negatively correlated with LDL size (data not shown). As an inhibitor of lipoprotein lipase, apo C-III has a significant role in TG metabolism and in determining the concentration of potentially atherogenic lipoproteins (38). Thus, the higher plasma TG concentrations can be associated with the increased presence of specific lipoproteins and apolipoproteins, which have been identified as proatherogenic and play a role in the development of IR and CHD.
In our study, subjects with WC > 88 cm had higher diastolic blood pressure that those with the lower WC values, whereas subjects with the higher BMI (30 kg/m2) had a higher systolic blood pressure compared with those with lower BMI values. Speculation exists concerning whether diastolic or systolic blood pressure represents a higher risk for CHD. Using data from the Framingham Heart Study, Franklin et al. (39) found that in persons < 50 y old, diastolic blood pressure was a stronger predictor of CHD than systolic blood pressure. Therefore, because this population is <50 y old, we considered the diastolic blood pressure values to be the more influential marker for disease risk.
In this study, an HJ-104 pedometer was utilized to measure number of steps taken per day, and WC was found to be a predictor of daily steps. Increased physical activity has been recommended as a weight loss technique along with dietary modifications. Research has shown that lack of exercise is one of the primary causes of excess weight (40).
Other studies have shown that the pedometer is a valid tool for measuring physical activity (41,42), and it is also a noninvasive and relatively inexpensive technique (42). Scruggs et al. (43) utilized pedometers to quantify number of steps taken by children participating in elementary physical education and concluded that the pedometer was an accurate indicator of moderate-to-vigorous physical activity. Tudor-Locke et al. (41) also demonstrated the accuracy of the pedometer in young adults; however, this study found discrepancies in the measurement of daily steps when the pedometers were used by older individuals with a slower gait. Omron Healthcare reports the SD with the HJ-104 pedometer to be ± 5% (personal communication, Omron Healthcare). This low SD may be due to the presence of an option to set stride length.
The negative correlation between WC and number of daily steps suggests that more active participants have a smaller WC, which indicates lower abdominal fat. Lower abdominal fat places subjects at decreased risk for IR, the metabolic syndrome, DM2, and CHD. In the current study, subjects with a higher WC also walked fewer steps than those with a lower WC, which identifies the important role of exercise in decreasing risk factors for CHD. In the present study, although there was a correlation between BMI and level of activity, no significant differences were found between subjects in relation to lipid variables with lower or higher BMIs as was the case with WC measurements. The WC classification provided a measure of the level of exercise in this population of overweight women as indicated by the higher number of steps taken per day in those subjects with WC 88 cm.
Although neither BMI nor WC provides a complete picture of overall risk, the WC classification of the subjects from the present study revealed stronger associations with multiple risk factors for chronic disease. This finding suggests that WC can be used to screen the general population. Therefore, it is useful to combine both anthropometric measures for risk assessment with the knowledge that the inclusion of WC in the diagnosis will expand the information regarding risk factors that may be present. WC measurement appears to provide more comprehensive information on the potential occurrence of other risk factors for CHD, including diastolic blood pressure, the presence of the metabolic syndrome, and high plasma TG and apo C-III concentrations. Our data also suggest that WC can be used as a reliable measure of the level of physical activity.
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FOOTNOTES |
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Manuscript received 9 January 2004. Initial review completed 21 January 2004. Revision accepted 15 February 2004.
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  G. Mutungi, J. Ratliff, M. Puglisi, M. Torres-Gonzalez, U. Vaishnav, J. O. Leite, E. Quann, J. S. Volek, and M. L. Fernandez Dietary Cholesterol from Eggs Increases Plasma HDL Cholesterol in Overweight Men Consuming a Carbohydrate-Restricted Diet J. Nutr., February 1, 2008; 138(2): 272 - 276. [Abstract] [Full Text] [PDF]
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 S. Klein, D. B. Allison, S. B. Heymsfield, D. E. Kelley, R. L. Leibel, C. Nonas, and R. Kahn Waist Circumference and Cardiometabolic Risk: A Consensus Statement from Shaping America's Health: Association for Weight Management and Obesity Prevention; NAASO, The Obesity Society; the American Society for Nutrition; and the American Diabetes Association Diabetes Care, June 1, 2007; 30(6): 1647 - 1652. [Full Text] [PDF]
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 S. Klein, D. B Allison, S. B Heymsfield, D. E Kelley, R. L Leibel, C. Nonas, and R. Kahn Waist circumference and cardiometabolic risk: a consensus statement from Shaping America's Health: Association for Weight Management and Obesity Prevention; NAASO, The Obesity Society; the American Society for Nutrition; and the American Diabetes Association Am. J. Clinical Nutrition, May 1, 2007; 85(5): 1197 - 1202. [Full Text] [PDF]
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 B.M. Brooks, R. Rajeshwari, T. A. Nicklas, S.-J. Yang, and G. S. Berenson Association of Calcium Intake, Dairy Product Consumption with Overweight Status in Young Adults (1995-1996): The Bogalusa Heart Study J. Am. Coll. Nutr., December 1, 2006; 25(6): 523 - 532. [Abstract] [Full Text] [PDF]
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 L. J. Shaw, C. N. Bairey Merz, C. J. Pepine, S. E. Reis, V. Bittner, S. F. Kelsey, M. Olson, B. D. Johnson, S. Mankad, B. L. Sharaf, et al. Insights From the NHLBI-Sponsored Women's Ischemia Syndrome Evaluation (WISE) Study: Part I: Gender Differences in Traditional and Novel Risk Factors, Symptom Evaluation, and Gender-Optimized Diagnostic Strategies J. Am. Coll. Cardiol., February 7, 2006; 47(3_Suppl_S): S4 - S20. [Abstract] [Full Text] [PDF]
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I. E. Lofgren, K. L. Herron, K. L. West, T. L. Zern, R. A. Brownbill, J. Z. Ilich, S. I. Koo, and M. L. Fernandez Weight Loss Favorably Modifies Anthropometrics and Reverses the Metabolic Syndrome in Premenopausal Women J. Am. Coll. Nutr., December 1, 2005; 24(6): 486 - 493. [Abstract] [Full Text] [PDF]
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T. L. Zern, R. J. Wood, C. Greene, K. L. West, Y. Liu, D. Aggarwal, N. S. Shachter, and M. L. Fernandez Grape Polyphenols Exert a Cardioprotective Effect in Pre- and Postmenopausal Women by Lowering Plasma Lipids and Reducing Oxidative Stress J. Nutr., August 1, 2005; 135(8): 1911 - 1917. [Abstract] [Full Text] [PDF]
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 R. A Whitmer, E. P Gunderson, E. Barrett-Connor, C. P Quesenberry Jr, and K. Yaffe Obesity in middle age and future risk of dementia: a 27 year longitudinal population based study BMJ, June 11, 2005; 330(7504): 1360. [Abstract] [Full Text] [PDF]
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M. Patalay, I. E. Lofgren, H. C. Freake, S. I. Koo, and M. L. Fernandez The Lowering of Plasma Lipids following a Weight Reduction Program Is Related to Increased Expression of the LDL Receptor and Lipoprotein Lipase J. Nutr., April 1, 2005; 135(4): 735 - 739. [Abstract] [Full Text] [PDF]
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M. N. Ballesteros, R. M. Cabrera, M. d. S. Saucedo, D. Aggarwal, N. S. Shachter, and M. L. Fernandez High Intake of Saturated Fat and Early Occurrence of Specific Biomarkers May Explain the Prevalence of Chronic Disease in Northern Mexico J. Nutr., January 1, 2005; 135(1): 70 - 73. [Abstract] [Full Text] [PDF]
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 G. L. Bakris, E. Gaxiola, F. H. Messerli, G. Mancia, S. Erdine, R. Cooper-DeHoff, C. J. Pepine, and for the INVEST Investigators Clinical Outcomes in the Diabetes Cohort of the International Verapamil SR-Trandolapril Study Hypertension, November 1, 2004; 44(5): 637 - 642. [Abstract] [Full Text] [PDF]
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