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American Journal of Clinical Nutrition, Vol. 87, No. 6, 1695-1701, June 2008
© 2008 American Society for Nutrition

ORIGINAL RESEARCH COMMUNICATION

Familial influences and obesity-associated metabolic risk factors contribute to the variation in resting energy expenditure: the Kiel Obesity Prevention Study1,2,3

Anja Bosy-Westphal, Andreas Wolf, Frederike Bührens, Britta Hitze, Norbert Czech, Heiner Mönig, Oliver Selberg, Uta Settler, Maria Pfeuffer, Jürgen Schrezenmeir, Michael Krawczak and Manfred J Müller

1 From the Institut für Humanernährung und Lebensmittelkunde, Christian-Albrechts-Universität, Kiel, Germany (AB-W, FB, BH, US, and MJM); the Institut für Medizinische Informatik und Statistik, Universitäts klinikum Schleswig-Holstein, Kiel, Germany (AW and MK); the Klinik für Diagnostische Radiologie, Abt Nuklearmedizin, Universitäts klinikum Schleswig-Holstein, Kiel, Germany (NC); the Klinik für Allgemeine Innere Medizin, Universitäts-klinikum Schleswig-Holstein, Kiel, Germany (HM); the Städtisches Klinikum Braunschweig, Abt Klinische Chemie, Braunschweig, Germany (OS); and the Bundesforschungsanstalt für Ernährung und Lebensmittel, Kiel, Germany (MP and JS)

2 Supported by a grant from the Bundesministerium für Forschung und Technologie, Netzwerk Molekulare Ernährung, Nahrungsfette und Genvariabilität, Teilprojekt 6.1.2.

3 Reprints not available. Address correspondence to MJ Müller, Institut für Humanernährung und Lebensmittelkunde, Agrar-und Ernährungswissenschaftliche Fakultät, Christian-Albrechts-Universität zu Kiel, Düsternbrooker Weg 17-19, D-24105 Kiel, Germany. E-mail: mmueller{at}nutrfoodsc.uni-kiel.de.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: A low metabolic rate may be inherited and predispose to obesity, whereas a higher metabolic rate in obesity may be acquired by obesity-associated cardiometabolic risk.

Objective: We aimed to explain the interindividual variation in resting energy expenditure (REE) by assessing 1) the association between REE and body composition, thyroid hormones, and obesity-related cardiometabolic risk factors, and 2) the familial (genetic and environmental) contribution to REE.

Design: REE and metabolic risk factors (ie, blood pressure and plasma insulin, glucose, and C-reactive protein concentrations) were assessed in 149 two- or three-generation families, including at least one overweight or obese member. Heritability of REE, respiratory quotient (RQ), thyroid hormones [thyrotropin (TSH), free triiodothyronine (FT3) and free thyroxine (FT4)], and body composition (fat-free mass and fat mass) were estimated by using variance components–based quantitative genetic models.

Results: REE adjusted for body composition, sex, and age (REEadj) significantly correlated with systolic and diastolic blood pressure, plasma insulin and glucose concentrations, and the homeostasis model assessment (HOMA) (r = 0.14–0.31, P < 0.05). Thyroid hormones had a modest influence on REE variance only. Heritability was 0.30 ± 0.07 for REEadj and 0.29 ± 0.08 for REE after additional adjustment for thyroid hormones and metabolic risk. Furthermore, heritability was estimated to be 0.22 ± 0.08 for RQ, 0.37 ± 0.08 for TSH, 0.68 ± 0.06 for FT4, and 0.69 ± 0.05 for FT3 (all significantly larger than zero).

Conclusions: Obesity-related cardiometabolic risk factors contribute to interindividual variation in REE, with hypertension and insulin resistance being associated with a higher REE. REE was moderately heritable, independent of body composition, sex, age, thyroid function, and cardiometabolic risk.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The genetic basis of the main component of daily energy expenditure, namely resting energy expenditure (REE), and substrate utilization [respiratory quotient (RQ)—the relation of carbon dioxide production to oxygen consumption] may also predispose to obesity. Several candidate genes and chromosomal loci have been shown to be associated with metabolic rate and lipid or carbohydrate oxidation (1), but the results of most genetic epidemiologic studies in this field remain contradictory. Correlations in REE and RQ have been shown to be higher in monozygotic than in dizygotic twins (2, 3). On the basis of these findings, ≥40% of the population variance in REE (adjusted for age, sex, body mass, and body composition) was estimated to be genetic. In contrast, the significant familial component of REE (coefficient of heritability of 42%) observed in 80 pairs of mono- and dizygotic twins was entirely accounted for by body weight (4). Similarly, results from overfeeding studies do not support that the inherited susceptibility to obesity is explained by REE (5, 6).

In contrast with twin studies, family studies have consistently suggested that REE is a modestly heritable trait. After adjustment for body size, {approx}11% of the observed variance of REE in Pima Indians was due to familial aggregation (7). Path analysis of {approx}80 families of the Quebec Family Study has shown that the heritability of REE (adjusted for body composition, sex, and age) is {approx}30% and that the heritability of the RQ is {approx}20% (8). Segregation analysis of REE provided evidence for a major gene effect independent of body composition (9). In 1064 Nigerian individuals from 153 families, the heritability for REE was also estimated to be 30% after adjustment for sex, age, age2, fat mass (FM), and fat-free mass (FFM) (10).

The results of family studies therefore suggest a genetic predisposition to obesity that results from a relatively low metabolic rate. One explanation for the discrepant findings in twin studies may be a higher mean age of individuals in the family studies. Age-related comorbidities of obesity may contribute to a higher REE and thus may lead to an overestimation of the familial influences on REE. REE was reported to be associated with several metabolic risk factors, such as blood pressure (11), central or visceral obesity (12-14), plasma insulin concentrations, insulin resistance, or glycemia (15, 16). In addition, REE was 10% higher in subjects with the metabolic syndrome than in healthy adults (17). The thermic effect of thyroid hormones is well known, but the effects of hormonal variations in the euthyroid range remains unclear, and free thyroxine (FT4) may show a closer association with REE than free triiodothyronine (FT3) (18).

The present study set out to 1) analyze the contribution of obesity-related metabolic risk factors to interindividual variation in REE (adjusted for body composition, sex, and age), and 2) calculate the familial contribution to REE variance independent of metabolic risk (adjusted for body composition, sex, age, and metabolic risk) and thyroid hormone concentrations.


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Study population and design
Between July 2003 and April 2006, 149 families were recruited through the Kiel Obesity Prevention Study (KOPS; 19, 20). The main objective of this 3-generation trial was to assess the contribution of genetic factors to obesity and the metabolic syndrome. The study comprised 815 subjects (age range: 4-84 y), including 223 grandparents (mean age: 67.4 ± 7.0 y), 296 parents (43.5 ± 6.4 y), and 296 children (13.0 ± 4.6 y). Participants were recruited through advertisements in local newspapers, through notice-board postings, and by mail contact to families who are continuously followed up in a KOPS subcohort. All participants were white northern Europeans. Inclusion criteria were at least one overweight or obese family member and the study participation of ≥2 grandparents. The study protocol was approved by the ethics committee of the Medical Faculty of the Christian-Albrechts-Universität, Kiel. All subjects provided informed written consent before participation. Parents assented for underage children.

Resting energy expenditure
Indirect calorimetry with a ventilated hood system (Vmax 29n and Vmax software, version 12-1A; SensorMedics, GmbH, Höchberg, Germany) was performed between 0730 and 0900 after an overnight fast in a metabolic ward at constant temperature and humidity. The minimum duration of measurement was 30 min. The first 10 min of each measurement were discarded. Before each measurement, flow calibration was performed with a 3L syringe, and gas analyzers were calibrated by using 2 standard gases (gas 1: 16% O2, 4% O2; gas 2: 20% O2 and 0.75% CO2). Recalibration of the gas analyzers was undertaken every 5 min during the measurements. Data were collected every 20 s, and the acquired oxygen and carbon dioxide uptake values were converted to REE (kcal/24 h) by using the abbreviated equation of Weir (21). Data from nonphysiologic measurements (RQ > 1.0 or < 0.73) were discarded and excluded from further analyses of REE and RQ (n = 92). The CV for between-day repeated measurements of REE was 5.0% (22).

Anthropometric measurements and body-composition analysis
Body weight was measured to the nearest 0.1 kg on an electronic scale coupled to the BOD-POD Body Composition System (Life Measurement Instruments, Concord, CA). Height was measured on a stadiometer to the nearest 0.5 cm. Waist circumference was measured to the nearest 0.5 cm midway between the lowest rib and the iliac crest while the subject was at minimal respiration. Air-displacement plethysmography was performed by using the BOD-POD device as described in detail elsewhere (23). The BOD-POD software was used to calculate body density as body weight divided by body volume and FM% using Siri's equation (24). Child-specific corrections of air-displacement plethysmography results were used (23). FFM (kg) was calculated accordingly as weight (kg) – FM (kg).

Overweight and obesity were determined by use of corresponding actual German BMI percentiles (>90th and >97th percentiles) for children and adolescents (25) and by World Health Organization criteria for adults (26).

Clinical and metabolic variables
Blood pressure measurements were obtained while the subject was in a seated position with a standard manual sphygmomanometer. Blood samples were taken after the subjects fasted for 8 h overnight and were analyzed following standard procedures. Briefly, plasma glucose was assayed by using a hexokinase enzymatic method. Plasma insulin was measured by radioimmunoassay (RIA), which showed no cross-reactivity with C-peptide and only 14% with proinsulin (Adaltis, Rome, Italy). The homeostasis model assessment (HOMA) was used to calculate insulin resistance (IR) as follows: HOMA-IR = fasting insulin (µU/mL) x fasting glucose (mmol/L)/22.5 (27). HOMA-IR was not calculated for subjects who had a fasting glucose concentration >7.0 mmol/L or were receiving insulin treatment or oral antidiabetics. Serum concentrations of thyrotropin (TSH), FT3, and FT4 were measured by RIA (DiaSorin, Dietzenbach, Germany); the intra- and interassay CVs were 2.5% and 5.7% (TSH), 4.6% and 6.5% (FT3), and 2.4% and 6.8% (FT4), respectively. The sensitivity limits were 0.02 mIU/mL (TSH), 0.35 pg/mL (FT3), and 1 pg/mL (FT4). RIA test kits from LINCO Research (St Charles, MO) using the doubly-antibody/polyethylene glycol technique were used for determination of plasma leptin and adiponectin. Analytic sensitivity and intra- and interassay CVs were 0.5 ng/mL, 3–8%, and 4–8% for leptin and 1 ng/mL, 2–6%, and 7–9% for adiponectin, respectively. C-reactive protein (CRP) was measured turbidimetrically by using a latex-agglutination test with a sensitivity of 0.1 mg/L and a between-day precision CV < 1.9% (CRP-Dynamik/-Hit917; BIOMED Labordiagnostik GmbH, Oberschleiβheim, Germany). CRP concentrations >5 mg/L were excluded from the analysis. Nonesterified fatty acids (NEFAs) were determined by an enzymatic colorimetric method (Wako NEFA test kit, NEFA C, ACS-ACOD method; Wako Chemicals GmbH, Neuss, Germany). Participants who were taking antihypertensive (14.5%; n = 118), antidiabetic (insulin or oral agents) (2.8%; n = 23), or thyroid hormone (4.8%; n = 39) drugs were excluded from the respective analysis.

Statistical analyses
Means ± SDs were used as descriptive statistics. Relations between variables were analyzed by partial correlation taking age and family identity into account. Plasma concentrations of insulin and CRP, blood pressure, and HOMA-IR were log transformed for correlation analysis. REE was adjusted for the effects of FFM, FM, sex, and age, with covariates retained at {alpha}<0.1 (REE adj1) in a stepwise multiple linear regression analysis. REE adj1 was further adjusted for metabolic risk factors (blood pressure, fasting plasma glucose, insulin, and HOMA-IR) and thyroid hormones (REE adj2). In this analysis, parameters for the computation of standardized residuals were obtained by applying regression models to 4 sex-by-age groups, with an age cutoff of 18 y. For each thyroid trait, we estimated the effects of sex and age (linear and quadratic). RQ was adjusted for sex-specific effects of NEFA concentrations, age, and the interaction term NEFA x age. Regression coefficients were assessed for statistical significance by using a Wald test, and a P value < 0.05 was considered statistically significant. All analyses were performed by using SPSS 13.0 for WINDOWS (SPSS Inc, Chicago, IL).

After the significant covariates were determined and their effects adjusted for, the familial contribution to the variance in each trait was estimated by using a pedigree-based likelihood approach. Univariate genetic analyses were carried out by using SOLAR (Sequential Oligogenic Linkage Analysis Routines; 23). A total of 149 families, ranging in size from 3 to 10 individuals, were included in the heritability estimation. Heritability (H2) is defined as the proportion (VG) of the observed phenotypic variance (VP) of a particular trait that is attributable to genetic causes, ie, H2= VG/VP. In the case of FFM and FM, mean univariate H2 ± SE were calculated from Z-transformed trait values to adjust for age and sex effects.

The variance component method used in the above calculations is based on the fact that relatives share a certain amount of genes identical-by-descent (IBD). The expected genetic variance is then specified as a function of the IBD relation between relatives while the phenotypic variance is estimated from the data. The statistical significance of the estimated heritabilities was assessed by means of a likelihood ratio test comparing the log likelihood including the estimated genetic variance to the log likelihood with the additive genetic variance component constrained to zero.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
The descriptive characteristics of the study group, stratified by sex and generation number, are presented in Table 1Go. REE data were available for 388 females and 335 males. The selection criteria used resulted in a high prevalence of overweight (41.3%) and obesity (24.1%) and a mean BMI (in kg/m2) of 27.7 ± 5.1 in adults. %FM, systolic and diastolic blood pressure, and FPG and CRP concentrations all increased with age, whereas RQ and FT3 tended to decrease. REE was highest in the group of parents. Plasma leptin and adiponectin concentrations were assessed in a subgroup of 347 subjects (218 adults and 129 children and adolescents). Mean plasma concentrations of leptin and adiponectin were 23.4 ± 16.3 and 16.5 ± 9.1 ng/mL in women, 7.5 ± 6.2 and 10.0 ± 5.0 ng/mL in men, 13.4 ± 9.7 and 14.6 ± 5.5 ng/mL in girls, and 6.6 ± 12.0 and 11.8 ± 4.1 ng/mL in boys, respectively.


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  		TABLE 1 Characteristics of the study population, stratified by sex and generation1

 
The major determinants of REE were FFM and age, which accounted for 63% of the trait variance (R2). Additional effects of FM and sex increased total R2 to 70% for the full model (Table 2Go). No associations were found between REE and either leptin or adiponectin concentrations, which correlated with FM (R = 0.70, P < 0.001 for leptin) and with HOMA-IR for leptin (R = 0.42, P < 0.001) and for adiponectin (R = –0.14, P < 0.05). The correlation between REE, adjusted for body composition, age, and sex (REEadj1) and waist circumference and metabolic risk factors was analyzed across 4 sex-by-age groups to identify covariates for further adjustment of REEadj1 (= REEadj2; Table 3Go). REEadj1 was positively correlated with systolic blood pressure in adults of both sexes, whereas diastolic blood pressure was a significant determinant of REEadj1 in males only. In men, REEadj1 was also significantly correlated with loginsulin concentrations, logHOMA-IR, and waist circumference. In addition, both groups of children showed a positive association between REEadj1 and FPG concentration that was not observed among adults. Thyroid hormones were not significantly correlated with REEadj1 in any sex-by-age group, with the exception of TSH (R = –0.34, P < 0.001) and FT4 (R = –0.20, P < 0.05) in boys. Possible covariates affecting the variance of REEadj1 were analyzed in 4 sex-by-age groups, and the results are shown in Table 4Go. Together with the interaction term age x FT4, systolic blood pressure in women and diastolic blood pressure in men were independent predictors of REE adjusted for body composition, sex, and age in adults. In underage subjects, FPG concentrations together with age x FT4 in females, and TSH concentrations in males were the main independent predictors of REEadj1. Seventy percent of the interindividual variance in REE was explained by body composition, sex, and age (Table 2Go). Metabolic risk factors and thyroid hormone concentrations explained between 9% and 20% (Table 4Go) of the residual variance in REE (= REEadj1). Thus the total variance in REE that could be explained by body composition, age, sex, thyroid hormones, and metabolic risk factors was calculated to be 70% plus 9–20% of the 30% variance unexplained by body composition, age, and sex, which resulted in a total of 73–76%.


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  		TABLE 2 Regression analysis of resting energy expenditure (REE, in MJ/d), used to adjust REE for potential covariates (REEadj1)

 

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  		TABLE 3 Age- and relatedness-corrected correlation coefficients between resting energy expenditure, adjusted for body composition, age and sex (REEadj1), waist circumference, and metabolic risk factors1

 

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  		TABLE 4 Covariate effects on the phenotype of resting energy expenditure, adjusted for fat-free mass (FFM), fat mass (FM), sex, and age (REEadj1)1

 
RQ was not dependent on body composition or body size, but was significantly associated with age (R = –0.15, P < 0.001) and NEFA concentration (R = –0.18, P < 0.001). RQ showed no relation with measures of glucose metabolism or insulin resistance (FPG, loginsulin concentrations, and logHOMA-IR). Because no difference in covariate association was observed between underage subjects and adults, the adjustment of RQ was carried out for the whole group of males and females. Stepwise multiple regression analysis identified the interaction term age x NEFA concentration as the only significant predictor of RQ, explaining 2–6% of the interindividual variation in RQ. As revealed by the significant age x NEFA interactions, RQ concentrations significantly decreased with increasing age at high NEFA (NEFA > 0.5 mmol/L; R = –0.27, P = 0.001) concentrations but not at low NEFA concentrations (NEFA ≤ 0.5 mmol/L; R = 0.07, P = 0.172).

Of the thyroid hormone traits, TSH was adjusted for age and age2, FT4 was adjusted for age, and FT3 was adjusted for age and sex. FT4 and FT4adj were inversely associated with logHOMA-IR or loginsulin concentrations in adults (R = –0.15 and –0.21; P < 0.01), whereas in children and adolescents there was a weak and positive correlation between FT3adj and logHOMA-IR or loginsulin concentrations (R = 0.13 and 0.13, P < 0.05).

Univariate residual heritabilities for REE, after adjustment for body composition, age, and sex (REEadj1) and metabolic risk factors and thyroid hormone concentrations (REEadj2), are shown in Table 5Go. The heritabilities of RQ and the physiologic determinants of REE are also given. REEadj1 showed a modest heritability of 30% that was only slightly lower after additional adjustment for thyroid hormone concentrations and metabolic risk factors. The heritability of RQ was even lower, amounting to only 17% and 22% for RQ and RQadj, respectively. In contrast, the heritability of FT3 and FT4 was high and reached 68–69% for the age- and sex-adjusted traits.


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  		TABLE 5 Univariate heritabilities (H2) of resting energy expenditure (REE), respiratory quotient (RQ), thyroid hormones, and body composition1

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Association between REE or RQ and obesity-related metabolic risk factors
There were significant associations of REE with systolic and diastolic blood pressure, FPG, insulin concentrations, and HOMA-IR (Table 3Go). Our results agree with those of previous studies, showing a higher REE in type 2 diabetes (3, 7, 15, 28). Similarly, hyperglycemia and glycemic intolerance were associated with an increase in REE (16, 29, 30). REE was also positively correlated with fasting insulin concentrations in nondiabetic schizophrenia patients (31) and with insulin resistance (insulin clamp method) in both diabetic and nondiabetic patients with liver cirrhosis (32). Contradictory results were reported by De Luis et al (33), who found no association between REE and systolic blood pressure or insulin resistance in 87 obese nondiabetic patients. REE was also not found to differ between mildly hyperglycemic and diabetic obese patients (34). It is tempting to speculate that the discrepant results are explained by the possibility that the effects of insulin resistance on REE are dependent on whether insulin resistance is primarily caused by a genetic disposition or is mainly caused by obesity and a sedentary lifestyle associated with a minor genetic contribution. The former idea is supported by the finding of a lower REE (35) and an impaired mitochondrial function (36, 37) in nondiabetic relatives of patients with type 2 diabetes. A genetic predisposition to type 2 diabetes may also confer a higher risk for low REE (hypometabolism) and thus weight gain. However, longitudinal studies are needed to prove this hypothesis and to prospectively investigate the impact of low REE in relatives of patients with type 2 diabetes.

In contrast, acquired insulin resistance as a consequence of obesity may lead to a higher REE by increasing protein turnover, futile cycling, gluconeogenesis, and the activity of the sympathetic nervous system (for a review see 38). This agrees with the idea that an increased REE at an impaired glucose tolerance is a metabolic consequence of obesity that is directed against further weight gain (6, 39).

However, the relation between acquired insulin resistance in obesity and REE may be time-dependent. Mitochondrial degeneration has been shown to occur in obesity, independent of type 2 diabetes (40). In that study, smaller mitochondria were associated with lower insulin sensitivity. Thus, a lower REE may result from mitochondrial dysfunction induced by oxidative stress from the chronic overload of metabolic pathways, a mechanism that may also be involved in the lower REE at the acquired insulin resistance of aging (41).

Both fasting plasma insulin and free fatty acids are regarded as determinants of fat and glucose oxidation (42, 43). However, there were inverse correlations between RQ and NEFA concentrations or age only. This finding indicates an increase in fat oxidation with increasing body fat, lipolysis, or fat intake.

Heritability of REE and RQ
We estimated the heritability of REE to be {approx}30% (Table 5Go). The familial (genetic and environmental) contribution to REE was only slightly lower after adjustment for blood pressure and FPG concentrations and was therefore independent of obesity-associated metabolic risk factors.

The impact of inherited reduction in metabolic rate on weight gain and, thus, obesity has been questioned by a number of publications but was confirmed by other authors (for a review see 44). One possible explanation of discrepant results is that a considerable weight gain over time results only from a small daily surplus of calories. Because of methodologic limitations, the origin of a few calories of positive energy balance cannot be sufficiently detected by current measurement methods of energy intake (eg, dietary records) or of energy expenditure (eg, 24-h heart rate monitoring). Genetic research offers yet another perspective to the subject. Thus far, all monogenetic causes of obesity have been found to be associated with increased appetite and food intake but with nearly unchanged energy expenditure. These findings render energy expenditure unimportant but favor excess energy intake as the main cause of weight gain. However, the heritability of REE, the main component of daily energy expenditure, was {approx}30% in our studies and other family studies (10, 8). This implies that the interaction between genes and the environment (eg, adherence to a healthy lifestyle or addiction to an obese environment) determines the phenotype. The finding of a similarly specific metabolic rate in obese and lean subjects (45-47) at first sight contradicts the idea that a low REE is a main cause of obesity. One reason for the undetected association between low REE and obesity may be that obesity-related metabolic risk factors mask the lower metabolic rate that initially contributed to weight gain.

One limitation of our study was that our heritability estimates do not allow differentiation between shared environmental (household) effects and genotype x environment interactions. Such effects can mimic additive genetic effects and, if existent, would thus be included in our estimates of H2.

The heritability estimate for RQ was 0.17 and increased by 5% after adjustment for variance arising from age and NEFA concentrations (Table 5Go). Our data thus confirm data from the Quebec Family Study, which showed the heritability of RQ to be {approx}20% (8). In our study, the highest heritability estimates were obtained for thyroid hormones (Table 5Go). Considering the associations between FT4 and HOMA-IR in adults and between FT3 and HOMA-IR in children and adolescents and a heritability estimate of 0.39 ± 0.12 for HOMA-IR that was previously published from the KOPS Family Study (20), there might be a small contribution of shared familial (genetic and environmental) variance between these 2 traits.

In conclusion, obesity-related cardiometabolic risk factors contribute to interindividual variation in REE, with hypertension and insulin resistance being associated with a higher REE. Our findings provide further evidence that REE and RQ are moderately heritable, independent of body composition, sex, age, thyroid function, and cardiometabolic risk.


   ACKNOWLEDGMENTS
 
The authors' responsibilities were as follows—AB-W and MJM: study design; AB-W, FB, and BH: data collection; AB-W, HM, NC, US, MP, JS, AW, and OS: data analysis; AB-W, MK, and MJM: discussion of data; AB-W, AW, and MK: statistics; and AB-W and MJM: writing of the manuscript. There are no conflicts of interest.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication October 5, 2007. Accepted for publication January 20, 2008.





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