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American Journal of Clinical Nutrition, Vol. 83, No. 4, 803-808, April 2006
© 2006 American Society for Nutrition

ORIGINAL RESEARCH COMMUNICATION

Carbohydrate balance predicts weight and fat gain in adults1,2,3

Robert H Eckel1, Teri L Hernandez1, Melanie L Bell1, Kathleen M Weil1, Trudy Y Shepard1, Gary K Grunwald1, Teresa A Sharp1, Coni C Francis1 and James O Hill1

1 From the Division of Endocrinology, Metabolism, and Diabetes (RHE, TLH, KMW, and TYS), the Department of Preventive Medicine and Biometrics (MLB and GKG), the Department of Pediatrics, Center for Human Nutrition (TAS and JOH), and the Adult General Clinical Research Center (CCF), University of Colorado at Denver and Health Sciences Center, Denver, CO

2 Supported by grant R01DK-46881 from the National Institute of Diabetes and Digestive and Kidney Diseases, grant P30 DK-48520-01 from the Colorado Clinical Nutrition Research Unit (Metabolic and Energy Balance Laboratories), and grant M01-RR00051 from the Adult General Clinical Research Center by the NIH Division of Research Resources.

3 Address reprint requests to RH Eckel, Division of Endocrinology, Metabolism, and Diabetes, Division of Cardiology, University of Colorado at Denver and Health Sciences Center, PO Box 6511, MS 8106, Aurora, CO 80045. E-mail: robert.eckel{at}uchsc.edu.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: The prevention and treatment of obesity is a public health challenge.

Objective: We investigated the effects of dietary composition, insulin sensitivity (SI), and energy balance on predicted changes in body composition.

Design: In a randomized crossover design study, 39 normal-weight (n = 23), overweight (n = 8), and obese (n = 8) men and women (aged 25–36 y) each followed a 15-d isocaloric high-fat (HF; 50% fat) and high-carbohydrate [HC; 55% carbohydrate (CHO)] diet with a 4–6-wk washout period during the first year. During each treatment, energy balance was measured while the subjects were inactive by using indirect calorimetry on day 15, and SI was measured by using a euglycemic clamp study (40 mU · m–2 · min–1) on day 16. Weight and body composition were then measured annually for 4 y. The outcomes for fat mass, percentage body fat, and weight were measured by using a linear 2-stage mixed model.

Results: CHO balance (day 15) and SI (day 16) on the HC diet were highly and significantly correlated (r = 0.55, P < 0.001). On the HC diet, the subjects who had a higher positive CHO balance (day 15) gained less fat mass (P < 0.001), percentage body fat (P = 0.006), and weight (P = 0.024) over time. When adjusted for SI, CHO balance remained a significant predictor of changes in fat mass (P = 0.021) and percentage body fat (P = 0.025).

Conclusions: On a HC diet, the subjects who had a higher positive CHO balance on day 15 while they were inactive gained less fat mass during 4 y, a predictive effect independent of SI. As suggested in rodents, the capacity to expand the glycogen pool might reduce energy intake and protect against fat and weight gain.

Key Words: Carbohydrate balance • dietary fat • dietary carbohydrate • metabolic predictor • weight gain • fat mass gain • body composition • energy balance • insulin sensitivity • obesity • indirect calorimetry


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Because obesity now affects >44 million adults in the United States alone (1), its control creates a public health challenge. Although most of the population is overweight (2), some persons seem more resistant to weight gain than others. Persons can be susceptible to weight gain because of either behavioral or metabolic factors (3). Consequently, finding effective prevention strategies has become crucial.

Although genetics plays a role in weight gain (4), it cannot independently explain the dramatic increase in the development of obesity over the past several decades (5). During the past 20 y, several metabolic predictors of obesity, such as lower basal metabolic rates (6, 7), reductions in nonexercise activity thermogenesis (NEAT) (8), increases in carbohydrate (CHO) oxidation (9, 10), insulin sensitivity (SI) (11–13), low concentrations of leptin (14), and reduced levels of sympathetic nervous system activity (15, 16), were reported. A better understanding of these metabolic factors could be useful in identifying obesity prevention interventions for those at greater risk.

Exposing persons to an interval of positive energy balance could elucidate metabolic differences in the propensity for weight gain. The standard energy balance equation, by definition, indicates equality between energy intake (EI) and energy expenditure (EE; EI = EE). Conversely, positive energy balance indicates that EI exceeds EE (EI > EE), which sets the stage for weight gain. Energy balance can also be quantified for each of the macronutrients (protein, fat, and CHO), such that CHO balance is the rate of CHO intake minus the rate of CHO oxidation, reflecting a change in CHO stores. Thus, the term "positive CHO balance" reflects storage of CHO in the body (17).

The purpose of the present study was to investigate the effect of diets on the degree to which the components of energy balance and SI predict long-term changes in weight and body composition. Here, we address the question of whether prospective differences in energy balance on a high-CHO diet compared with a high-fat diet during 1 day of physical inactivity could predict weight or adipose tissue gain over 4 y.


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Subjects
The Colorado Multiple Institutional Review Board at the University of Colorado at Denver and Health Sciences Center approved the protocol. Thirty-nine normal-weight [body mass index (BMI; in kg/m2): ≤25; n = 23), overweight (BMI: 25 to <30; n = 8), and obese (BMI: ≥30; n = 8) white men and women (aged 25–36 y; BMI range: 18.7–50.2) (Table 1Go) were recruited and granted their written informed consent. The subjects were at their maximum BMI and were weight stable for ≥3 mo. The subjects' usual dietary patterns were assessed by using The Diet Habit Survey (18). Eight subjects dropped out of the study: 2 completed only the initial diet phases, 1 completed through year 1, 2 completed through year 2, and 3 completed through year 3. Two subjects missed the year 4 time point but did complete a measurement at year 5. Three subjects missed the year 3 time point but completed their participation. Because of the statistical method used, it was possible to use all of the data collected.


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  		TABLE 1 Baseline characteristics of subjects1

 
Screening laboratory tests included urinalysis; complete blood count; serum glucose, electrolytes, liver function, and renal function; and thyroid-stimulating hormone. All values were within normal limits for each subject. None of the subjects were taking medications that would affect lipid or CHO metabolism.

Procedures
On day 0, fasting glucose and insulin were measured. At breakfast on day 0, each subject began 1 of 2 randomized diets, either a 15-d isocaloric high-fat (HF) diet or a 15-d isocaloric high-CHO (HC) diet. Initial estimates of individual daily energy requirements were made based on the Diet Habit Survey (18) and an ensuing measurement of resting metabolic rate (RMR). Actual energy consumption was adjusted daily during the first 7 d to achieve weight stability and maintenance of admission weight; minimal adjustments were made during the second 7 d. The subjects were weight-stable (within 1 kg) during the 14-d feeding period.

Each diet phase lasted 14 d and was followed by a 23-h stay in the whole-room indirect calorimeter, during which the subjects were fed the same diet as the preceding 14 d. EI was not reduced for the stay in the calorimeter. Because confinement to the calorimeter limited physical activity, the subjects had a positive energy balance on day 14. Although positive energy balance can be achieved through overfeeding, it is likely that a substantial amount of weight gain in the population results from reductions in physical activity. Thus, the positive energy balance observed in the calorimeter could mimic such a population phenomenon. Physical activity was not controlled per se during each feeding period; subjects were asked to abstain from rigorous exercise for ≥24 h before metabolic measurements. SI was measured on day 16 of each phase by using the hyperinsulinemic euglycemic clamp technique (40 mU · m–2 · min–1) (19). The subjects were discharged for a 4–6-wk washout phase under free-living conditions but returned weekly for weight measurements to ensure weight stability. Results of the response to the 14-d diet periods and short-term results of the calorimeter studies were previously reported (20).

Diets
The HC diet provided a macronutrient content of 55% of energy as CHO, 25% as fat, and 20% as protein. The HF diet provided 30% of energy as CHO, 50% as fat, and 20% as protein. During the calorimeter stay, the subjects consumed 3 meals and 2 snacks. All foods were weighed, and the diets were analyzed by using a computer program (FOOD PROCESSOR PLUS; ESHA Research, Salem, OR).

Whole-room indirect calorimeter
The whole-room calorimeter was described previously (20, 21). This is a small room (2.6 x 3.4 m). All oxygen consumption and carbon dioxide production is continuously measured while the subjects are inside. EE and oxidation of protein, CHO, and fat are determined from these measurements (22). This information, combined with measured nutrient intakes, can be used to determine daily balances for total energy and for each macronutrient. The accuracy and precision of this whole-room indirect calorimeter system was previously established (21).

Resting metabolic rate
For the purposes of estimating EI and monitoring the effect of dietary composition on respiratory quotient (RQ), it was necessary to obtain RMR measurements before the subjects were admitted to the whole-room calorimeter. In these instances, the RMR was measured by using indirect calorimetry (Sensormedics Metabolic Cart, Model 2900; Sensormedics, Yorba Linda, CA). Measurements were made in the morning after a 12-h fast and 24-h abstention from exercise. After 30 min of rest, RMR was measured for 15–20 min with the use of a ventilated hood. Oxygen consumption and carbon dioxide production were used to calculate RMR according to the formula of Weir (23). Criteria for valid RMR was a minimum of 15 min of steady state, determined as <10% fluctuation in minute ventilation and oxygen consumption and <5% fluctuation in RQ.

Insulin sensitivity
On day 16 of each diet phase, a 3-h 40 mU · m–2 · min–1 hyperinsulinemic euglycemic clamp study was performed to measure SI (19). The euglycemic goal was measured as the fasting blood glucose concentration measured on the morning of day 16 of diet phase 1. The individual SI with each diet was then measured as the mean glucose infusion rate (in mg · m–2 · min–1) over the last hour of the infusion study (120–180 min). Therefore, the glucose infusion rate is an expression of SI. Fasting serum insulin concentrations were also considered to be an indicator of relative SI (24). The female subjects were each studied in the early follicular phase of their menstrual cycle.

Physical activity index
Physical activity level was estimated by calculating the physical activity index (PAI) (25). This index is the ratio of total EE to basal EE. In the present study design, actual EE was not measured during each 14-d feeding phase before the calorimeter stay. However, each feeding period was highly controlled and monitored; the subjects were weight-stable within 1 kg and were therefore in energy balance. PAI was calculated as follows:

Formula 1(1)

Nonexercise activity thermogenesis
NEAT, the thermogenesis that accompanies nonvolitional exercise (8), was calculated by using the following formula:

Formula 2(2)
The thermic effect of food was estimated as 10% RMR (26). As with PAI, EE outside the chamber (14 d) was estimated as the EI fed to the subjects to achieve weight stability.

Body weight and body fat mass
To power the present study, the expected weight change over 4 y was 1.2 kg, on the basis of the following formula obtained from data from the second National Health and Nutrition Examination Survey (27):

Formula 3(3)
Body weight and body composition were measured annually for 4 y after first beginning the study. However, some variability was observed in the timing of these measures, as previously described. Body composition was measured by hydrodensitometry, with residual volume measured simultaneously by using the open-circuit nitrogen-dilution technique (28). Nitrogen was measured by using a Med-Science 505-D Nitralizer (St Louis, MO). Percentage body fat was estimated from body density (average of 7–10 repeat measurements) by using the revised equation of Brozek et al (29).

Laboratory procedures
Serum insulin concentrations were measured by radioimmunoassay (30). Measurements of total urinary nitrogen were done by pyrochemiluminescence (31) with the use of the Antek (Houston, TX) nitrogen analyzer system (21).

Data analysis
The goal of the analysis was to estimate the degree to which baseline responses to the HC and HF diets predicted changes in outcomes of body weight, fat mass, and percentage fat during the next several years. Candidate predictor variables were 24-h energy balance, CHO and fat oxidation and balance, and SI on each diet. Associations of candidate variables individually with changes in outcomes separately were estimated by using a 2-stage mixed model (32). All mixed models included a fixed-effect intercept, a fixed effect for time (which represented the population mean change in outcome), and random effects for subject-specific intercepts and slopes. Each model also included a fixed effect for one candidate predictor as well as an interaction between the predictor and time. The interaction estimates and tests the association of the predictor with change in outcome. This analysis is similar to first regressing each subject's outcome on time to obtain a slope (rate of change of outcome), then regressing this slope on the predictor, but it is preferred in the presence of missing data. Two of the 39 subjects had only initial measurements of weight and fat mass, so these measurements were omitted from all analyses of changes in weight and fat mass. Results were similar with the regression approach. Fixed effects for sex and baseline BMI and their interactions with time were included in all models to adjust for these characteristics. Analyses were also repeated with adjustment for estimated baseline physical activity outside the calorimeter. Two forms were considered: PAI (25) and NEAT (8).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Weight remained stable during the 14 d of isocaloric HC and HF feeding (data previously reported) (20). During the calorimeter stay of both diets, the subjects had a positive energy balance (P < 0.001), with mean (±SD) energy storage of 859 ± 138 kcal/d on the HC diet and 944 ± 176 kcal/d on the HF diet. No significant difference in energy balance was observed between the HC and HF diets (P = 0.50). However, total energy balance was significantly related to PAI during the HC calorimeter stay (r = 0.85, P < 0.0001), as was CHO balance (r = 0.54, P = 0.0007). During the HF calorimeter stay, CHO balance (r = 0.38, P = 0.02) and total energy balance (r = 0.57, P = 0.003) were also correlated with PAI.

By design, CHO balance was higher on the HC diet (P < 0.005), and fat balance was higher on the HF diet (P < 0.005). The differences in macronutrient expenditures were consistent with observed differences in the 24-h RQ. For all subjects, the mean (±SEM) 24-h RQ from the HC diet was 0.815 ± 0.010, whereas that from the HF diet was 0.775 ± 0.009 (P < 0.01). No significant differences in protein balance or expenditure were noted between the diet groups. Additionally, no significant difference in SI was observed between the HC and HF diets (P = 0.17) (Table 2Go).


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  		TABLE 2 Results from the indirect calorimeter stay on both the high-carbohydrate (HC) and high-fat (HF) diets1

 
During the 4 y of follow-up, the mean (±SEM) increase in body weight was 0.29 ± 0.15 kg/y (P = 0.0628). Fat mass increased by 0.31 ± 0.15 kg/y (P = 0.0508). Candidate predictor variables were examined individually as predictors of changes over time in the 3 outcomes, with adjustment for sex and baseline BMI. No significant relation was observed between CHO or fat oxidation and any of the 3 outcomes on either diet (P > 0.10). Borderline inverse relations were observed between total energy balance and weight gain for both diets (P = 0.053 for HC diet; P = 0.058 for HF diet). Fat balance on the HF diet was also inversely related to weight gain (P = 0.038) but was otherwise not a significant predictor for any of the outcomes on either diet (P > 0.25). CHO balance and SI on the HF diet were not predictive of future gains in any of the 3 outcomes (P > 0.30 for all). Adjustment for usual physical activity as quantified by PAI or NEAT did not substantially change any of these results; additionally, no relation was observed between either measure of physical activity and weight or fat gain over 4 y for either diet (P > 0.1 for all). Specifically, no relation was observed between the PAI at the beginning of year 1 and the change in fat mass (r = –0.2, P = 0.2) (Figure 1Go), weight (r = –0.2, P = 0.3), or percentage body fat (r = –0.12, P = 0.4) over 4 y.


Figure 1
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  		FIGURE 1. Relation between physical activity index (year 1) and the change in fat mass over 4 y (r = –0.2, P = 0.2; 2-stage mixed model, described in Methods).

 
The strongest and most consistent predictors of future weight and fat change were CHO balance and SI after the HC diet. On the HC diet, the subjects who had a higher positive CHO balance (day 15) gained less fat mass (P < 0.001), percentage body fat (P = 0.006), and weight (P = 0.024) over time. When adjusted for SI, CHO balance remained a significant predictor of changes in fat mass (P = 0.021) and percentage body fat (P = 0.025). The effects of CHO balance and SI on each of the 3 outcomes, adjusted for sex and baseline BMI, are shown in Table 3Go. Both predictors were consistently and inversely related to the outcomes (with the exception of SI for fat mass change, P = 0.051), and all were significant except SI for percentage body fat. For example, for each additional 100 kcal/d in CHO balance, the subjects gained a mean (±SEM) 0.081 ± 0.037 kg/y less weight (P = 0.038) and 0.097 ± 0.033 kg/y less fat (P = 0.007). For each additional 100 mg · m–2 · min–1 in SI, the subjects gained, on average, 0.30 ± 0.10 kg/y less weight (P = 0.008) and 0.22 ± 0.11 kg/y less fat (P = 0.051). R2 values indicate that CHO balance and SI explained 9% and 19% of the variation in weight change, and 21% and 19% of the variation in fat mass change, respectively.


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  		TABLE 3 . Effects of carbohydrate (CHO) balance and insulin sensitivity (SI) with a high-CHO diet at baseline measurement on changes in fat and weight over 4 y

 
The significant inverse relation between CHO balance after the HC diet had been consumed for 15-d in year 1 and the change in fat mass during 4 y is shown in Figure 2Go (n = 37, r = –0.46, P = 0.007). The pattern of the relation (slope of each regression line) between CHO balance and change in fat mass was similar in all initial BMI categories (P = 0.79 for interaction between CHO balance and BMI category).


Figure 2
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  		FIGURE 2. Carbohydrate (CHO) balance after 15 d of a high-CHO diet in year 1 at baseline as a predictor of change in fat mass over 4 y (r = –0.46, P = 0.007; 2-stage mixed model, described in Methods).

 
The effects of CHO balance and SI after the HC diet when the other variable was added to the model (still adjusting for sex and baseline BMI) are also shown in Table 3Go. CHO balance remained inversely related to changes in fat mass and percentage fat (P = 0.037 and P = 0.029, respectively) and SI was inversely related to weight change (P = 0.027), but the significance of each and the effects were attenuated because of the strong relation between the 2 variables (r = 0.55, P < 0.001). Thus, these effects do not completely substitute for each other, but they are difficult to completely separate.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
We identified a strong prospective metabolic predictor of long-term gains in body weight and body fatness. This marker may be useful in identifying persons who are susceptible to weight gain, so that interventions for weight gain prevention can be more efficiently tested. As a model, it may be useful to study subjects on a day of relative inactivity, when their EI remains constant for the normal level of activity. Thus, they would be studied on a day of positive energy and CHO balance. The duration of HC feeding before, as well as the period of inactivity during, testing would require additional study.

These results also provide useful information about the mechanisms by which differences in substrate metabolism affect susceptibility to a gain in weight and fat mass. Evidence has implicated differences in fat oxidation and fat balance in weight gain (9, 33), and we were surprised to find that CHO balance was so strongly predictive of long-term gain in weight and fat mass compared with fat balance. Although fat balance on the HF diet was predictive of change in weight only, and energy balance was predictive of weight gain on both diets, we were less enthusiastic about these seemingly isolated and only mildly significant values in comparison to the strength of the relations between CHO balance on the HC diet and changes in body weight and composition. Clearly, those persons who had the greatest degree of positive CHO balance gained the least weight and fat mass over time, even when SI was accounted for in the model.

Possible explanations exist as to why CHO balance on the HC diet most predicted gain in weight and fat mass. First, those persons who showed more positive CHO balance on the HC diet could be ones with an enhanced ability to store glycogen or have a lesser tendency to deplete glycogen stores. Flatt (34) suggested the critical role of CHO balance in the regulation of body weight. Specifically, glycogen stores may be related to EI such that persons with a greater tendency to deplete glycogen stores may experience more hunger, thereby causing ingestion of more total energy. Persons with a lower tendency to deplete glycogen stores, then, do not consume as much energy. However, the studies by Flatt (35, 36) were mostly conducted in rodents, and short-term studies conducted in humans, in which glycogen stores were manipulated by dietary means, have produced inconsistent effects on EI (37–39). In particular, the findings of Shetty et al (39) may not be relevant to our observations because of substantial differences in the study designs, that is, sample size [n = 6, all men; Formula 3 (±SD) BMI: 23.0 ± 2.0], short-term dietary manipulation (total of 5 d), and no longitudinal follow-up. Of interest, evidence shows that short-term glycogen storage could be increased more after exercise-induced glycogen depletion than by high CHO feeding (40). If a long-term relation does exist between glycogen stores and EI, this may partly explain why continuance of physical activity promotes weight maintenance. It is also possible that some of the subjects changed their lifestyle during the 4 y of follow-up; that is, they decreased or increased their habitual physical activity. However, the relations between CHO balance, glycogen stores, and the regulation in long-term EI remain to be tested.

The strong relations between energy and CHO balance in the chamber and PAI in the setting of 1 d of physical inactivity on the HC diet suggests that the protection from increases in gain of body weight and fat mass over 4 y could be explained by the level of physical activity, not the response to HC feeding. However, the lack of any relation between the PAI and NEAT and the gain in weight and fat mass indicates a separate mechanism is operational.

In the present study, the RQ remained <1.0, and mean (±SEM) fat balance on the HC diet was –4 ± 112 kcal/24 h. Thus, de novo lipogenesis from glucose was not the predominant fate of excess dietary CHO during the period of physical inactivity. However, because it often takes days for fat balance to adjust to changes in diet composition (33, 41), and the effect of sudden changes in physical activity may also be important in the long-term regulation of energy balance, the ability of changes in fat balance over 1 d to predict changes in weight or body composition over 4 y would less likely be observed. Although fat oxidation during HC feeding was not a significant predictor of gain in weight and fat mass, it could still be an important part of the explanation for our results. Persons who continue to rely to a greater extent on fat oxidation when eating a HC diet would be more likely to show more positive CHO balance when they become physically inactive. Moreover, because the same could be said about athletes or even persons who are highly physically active, it is intriguing that, even when PAI and NEAT were accounted for in the model, CHO balance remained a strong predictor of changes in body composition over time.

Although the present study was highly controlled, a limitation of these data is that total EE was not measured in the subjects while they were outside of the indirect calorimeter. Yet, because their food intakes were adjusted to sustain their weights, the method of calculation proved highly accurate. Moreover, during the 4-y follow-up, the subjects were in their free-living environment in which neither their diet nor their physical activity levels were controlled. This is perhaps a strength, rather than a limitation, of the study, in that the baseline data predicted the response in a free-living environment.

These results do not necessarily implicate a HC diet in the development of obesity. The role of diet composition in the development of obesity is controversial (42, 43), and our results do not directly address the question. They do, however, suggest that differences in handling of excess CHO during inactive periods could be important for gain of weight and fat mass. The results are significant because they provide both a model for studying differences in substrate utilization and a predictor of future weight and fat gain. Many factors could have contributed to the differences in weight gain, making it even more remarkable that a single metabolic predictor could be identified. Overall, the long-term results of the present highly controlled, short-term diet and energy balance experiment require confirmation, and mechanisms need to be pursued.


   ACKNOWLEDGMENTS
 
We thank the nurses, CORE laboratory technicians, and kitchen personnel of the General Clinical Research Center (GCRC) for their professional assistance and the personnel of the Energy Balance Laboratory of the Colorado Clinical Nutrition Research Unit and of the GCRC, who skillfully managed the whole-room indirect calorimeter.

RHE was the principal investigator and was responsible for the original design of study, data collection, analysis and interpretation of the data, and manuscript construction. TLH was responsible for monitoring of study conduct, data compilation, analysis and interpretation of the data, and manuscript construction. MLB was responsible for the data analysis design, analysis and interpretation of the data, and manuscript construction. KMW was responsible for monitoring of study conduct, data collection, analysis and interpretation of the data, and manuscript construction. TYS was responsible for the original design of the study, monitoring of study conduct, data collection, and manuscript construction. GKG was responsible for the data analysis design, analysis and interpretation of the data, and manuscript construction. TAS was responsible for the original design of the study, data collection and compilation, analysis and interpretation of the data, and manuscript construction. CCF was responsible for data collection and compilation, data interpretation, and manuscript construction. JOH was responsible for the original study design, data interpretation, and manuscript construction. None of the authors had a conflict of interest.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication July 26, 2005. Accepted for publication January 4, 2006.




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