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American Journal of Clinical Nutrition, Vol. 87, No. 2, 303-309, February 2008
© 2008 American Society for Nutrition

ORIGINAL RESEARCH COMMUNICATION

A novel interaction between dietary composition and insulin secretion: effects on weight gain in the Quebec Family Study1,2,3

Jean-Philippe Chaput, Angelo Tremblay, Eric B Rimm, Claude Bouchard and David S Ludwig

1 From the Division of Kinesiology, Department of Social and Preventive Medicine, Faculty of Medicine, Laval University, Quebec City, Canada (J-PC and AT); the Department of Nutrition, Harvard School of Public Health, Boston, MA (EBR); the Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA (CB); and the Division of Endocrinology, Department of Medicine, Children's Hospital, Boston, MA (DSL)

2 Supported by the Medical Research Council of Canada through several grants for the Quebec Family Study and other agencies from the governments of Quebec and Canada. DSL was supported by grants 1R01 DK59240, 1R01 DK72428, and 1R01 DK73025 from the US National Institutes of Diabetes and Digestive and Kidney Diseases and from the Charles H Hood Foundation. JPC was supported by a studentship from the Hospital Laval Research Center. AT was partly supported by the Canada Research Chair in Physical Activity, Nutrition, and Energy Balance. EBR was supported by grants from the National Institutes of Health. CB was partially supported by the George A Bray Chair in Nutrition.

3 Reprints not available. Address correspondence to DS Ludwig, Division of Endocrinology, Department of Medicine, Children's Hospital, 300 Longwood Avenue, Boston, MA 02115. E-mail: david.ludwig{at}childrens.harvard.edu.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: Clinical trials of low-fat diets characteristically produce small mean long-term weight loss but a large interindividual variation in response. This variation has been attributed to psychological and behavioral factors, although biological differences may also play a role.

Objective: The objective was to determine whether physiologic differences in insulin secretion explain differences in weight gain among individuals consuming low- and high-fat diets.

Design: Of 276 individuals followed in the Quebec Family Study for a mean of 6 y, we compared those in the lowest with those in the highest dietary fat tertiles. We performed oral-glucose-tolerance tests at baseline and examined the insulin concentration at 30 min (insulin-30) as a proxy measure of insulin secretion. Six-year changes in body weight and waist circumference were the primary endpoints. We determined the associations between insulin-30 and the primary endpoints by linear regression analysis, with adjustment for potentially confounding factors.

Results: Mean changes in body weight and waist circumference did not differ significantly between the lowest- and highest-fat diet groups. However, these endpoints were strongly associated with insulin-30, especially among individuals consuming the lowest-fat diet. Insulin-30 at baseline was significantly associated with 6-y weight gain (r = 0.51, P < 0.0001) and change in waist circumference (r = 0.55, P < 0.0001) in the lowest diet fat, group (r = 0.18, P = 0.086), but not in the highest diet fat group (r = 0.20, P = 0.058). Individuals in the highest insulin-30 and lowest dietary fat group gained 1.8 kg more than did those in the highest insulin-30 and highest dietary fat group (51%; P = 0.034); they gained 4.5 kg more than did those in the lowest insulin-30 and lowest dietary fat group (6.5-fold; P = 0.0026).

Conclusion: A proxy measure of insulin secretion strongly predicts changes in body weight and waist circumference over 6 y in adults, especially among those consuming lower-fat diets, which demonstrates the existence of a novel diet-phenotype interaction.

Key Words: Obesity • low-fat diet • dietary carbohydrate • glycemic index • insulin • glucose homeostasis • oral-glucose-tolerance test


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
For much of the last half century, a reduction in dietary fat has been the primary approach for preventing and treating obesity and diabetes. Although some studies have shown benefits (1, 2), recent prospective observational studies suggest that dietary fat is not a major determinant of body weight, and clinical trials of low-fat diets have been disappointing (3-5). Recently, the Women's Health Initiative reported the largest clinical trial of diet and body weight ever conducted (6). Almost 50 000 women were randomly assigned to a low-fat intervention or control group. Individuals in the low-fat group showed a statistically significant but small (0.5 kg) weight loss compared with the controls.

Despite the small mean long-term weight loss characteristic of low-fat-diet studies, some individuals do lose a considerable amount of weight when they consume low-fat diets. Dansinger et al (7) conducted a 12-mo randomized controlled trial of 4 popular weight-loss diets. Of those assigned to the Ornish very-low-fat diet, mean weight loss was again small, {approx}2 kg, but individual weight change ranged from almost –30 kg to >10 kg. This interindividual variation is commonly attributed to differences in motivation and compliance, but biological factors may also be contributory.

One biological factor that might affect weight loss during a low-fat diet is the early insulin response to carbohydrate. Sigal et al (8) conducted intravenous-glucose-tolerance tests on 107 glucose-tolerant offspring of parents with type 2 diabetes. They reported that first-phase insulin secretion strongly predicted weight gain over a mean of 16.7 y, especially among individuals with high insulin sensitivity. Le Stunff et al (9) identified alleles of the variable number of tandem repeat locus of the insulin gene that are associated with increased insulin secretion and body fatness in children. Octreotide, a drug that blocks insulin secretion, caused weight loss in adults who were characterized as insulin hypersecreters (10). Moreover, diazoxide, another drug that blocks insulin secretion, produced weight loss in some (11) but not all (12) clinical trials.

Low-fat diets are inherently high in carbohydrate because, for most people, the third major nutrient, protein, remains within a fairly narrow range. Carbohydrate has the most potent effect on insulin secretion of the major nutrients. Therefore, individuals with high insulin secretion consuming a low-fat diet might be especially susceptible to weight gain. To test this hypothesis, we examined the associations between insulin concentration at 30 min (insulin-30) during an oral-glucose-tolerance test (OGTT) and change in body weight or waist circumference in the Quebec Family Study (QFS). Insulin-30 (or derivatives of that value) comprises a good measure of insulin secretion, better suited for population studies than more time-consuming and costly methods, such as the intravenous-glucose-tolerance test or hyperglycemic clamp (13-15).


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Subjects
The QFS was initiated at Laval University in 1978. The primary objective of this study was to investigate the role of genetics in the etiology of obesity and related cardiovascular disease risk factors. In phase 1 of the study (1978 to 1981), a total of 1650 individuals from 375 families were recruited and measured. Recruitment was conducted irrespective of body weight during phase 1, which resulted in a cohort with a wide range of body mass index (BMI; in kg/m2), 13.8–64.9. In phases 2 (1989–1994) and 3 (1995–2001), 100 families from phase 1 were remeasured, and an additional 123 families with at least one parent and one offspring with a BMI of ≥32 were added to the cohort. Families were recruited through the media and were all French Canadians from the greater Quebec City area. From the pool of 223 white 2-parent families (totaling 951 subjects involved in phases 1, 2, and 3), 163 men and 199 women were potentially eligible for longitudinal analyses between phases 2 and 3 because OGTT data were not available for some individuals from phase 1. Additional details about the QFS were previously published (16). The mean (±SD) duration of follow-up between phases 2 and 3 was 6.0 ± 0.9 y. Exclusion criteria were as follows: 1) age < 21 y or > 64 y (27 men and 26 women were excluded); 2) diabetes, defined as the use of insulin or a hypoglycemic agent, a fasting plasma glucose concentration of ≥126 mg/dL (≥7.0 mmol/L), or a 2-h postload plasma glucose concentration of ≥200 mg/dL (≥11.1 mmol/L) (8 men and 3 women were excluded); and 3) body weight change >2 kg during the 6 mo before baseline testing (5 men and 7 women were excluded). In addition, subjects with missing data on 1 or more of the variables investigated in 1 of the 2 testing sessions (baseline and 6 y later) were excluded (6 men and 4 women). The final sample consisted of 276 individuals, and we restricted our analyses to the 92 subjects in the lowest tertile and the 92 subjects in the highest tertile of dietary fat. All subjects gave written informed consent. The project was approved by the Medical Ethics Committee of Laval University and was in accordance with the Helsinki II Declaration.

Anthropometric and body-composition data
Height was measured to the nearest 0.1 cm with a standard stadiometer, and body weight was measured to the nearest 0.1 kg with a digital panel indicator scale (model 610/612; Beckman Industrial Ltd, Fife, United Kingdom). BMI was calculated as body weight divided by height squared (kg/m2). Waist circumference was measured at the line between the lower border of the last rib and the upper border of the iliac crest. These anthropometric data were measured in the same way at both baseline and after 6 y and were performed according to standardized procedures recommended at The Airlie Conference (17).

Oral-glucose-tolerance test
A baseline OGTT was performed in the morning after a 12-h overnight fast. Participants consumed 75 g glucose (Glucodex; Ratiopharm Inc, Mississauga, Canada) at time 0, and blood samples were collected via an indwelling venous catheter at –15, 0, 15, 30, 45, 60, 90, 120, 150, and 180 min. Blood samples were collected in tubes containing EDTA and Trasylol (Miles Pharmaceutics, Rexdale, Ontario, Canada). Plasma glucose concentration was measured enzymatically (18) and plasma insulin concentration was measured by radioimmunoassay with polyethylene glycol separation (19). Fasting glucose and insulin concentrations were calculated as the mean of the –15- and 0-min concentrations. In addition, we calculated the glucose minimum (GMIN; the lowest measured glucose concentration during the OGTT minus fasting glucose concentration) as a marker of reactive hypoglycemia after glucose ingestion. According to the glucostatic theory of appetite control as originally proposed (20) and more recently interpreted (21), decreases in blood glucose concentration may stimulate hunger and food intake.

Measurement of diet
Diet was assessed with a 3-d record, including 2 weekdays and 1 weekend day, at baseline and year 6. Participants were shown how to complete this record by a dietitian who provided instruction about measuring the quantities of ingested foods. This method of dietary assessment has been shown to provide a reliable measure of diet in this population (22). Nutrient intake was estimated by a dietitian using a computerized version of the Canadian Nutrient File (23).

Measurement of covariates
Numerous potentially confounding variables were measured via self-reported questionnaires at baseline and year 6: age, sex, smoking habits [nonsmoker or ex-smoker, light smoker (≤10 cigarettes/d), and heavy smoker (>10 cigarettes/d)], employment status (student, paid employment, looking for work, home duties, retired, and disabled), highest educational level (high school, college, and university), total annual family income (categorized into 5 groups ranging from <$10 000 to ≥$70 000 Canadian dollars), alcohol intake (g/d), protein intake (%), saturated fat intake (%), and fiber intake (g/g total carbohydrate intake). The last 4 covariates were obtained from the 3-d dietary record. A mean of baseline and year 6 was calculated for statistical adjustment because these variables might change over time.

Statistical analysis
Participants were stratified into tertiles of dietary fat (mean of baseline and 6-y intake), expressed as a proportion of total energy. We used the average of both fat intake measures (at baseline and at year 6) to better estimate the habitual diet of the participants. However, we also repeated the analyses using only baseline dietary fat, and the results were not materially different. Baseline characteristics of participants in the lowest and highest dietary fat tertiles were compared by independent Student's t test (continuous variables) and chi-square test for comparison of frequencies (categorical variables). Multiple linear regression analysis was used to assess the relations between plasma insulin-30 at baseline (independent variable) and weight gain or weight change in waist circumference 6 y later (dependent variables) within tertile of dietary fat. The model was adjusted for age, sex, baseline body weight or waist circumference, length of follow-up, smoking habits, employment status, educational level, total annual family income, alcohol intake, protein intake, saturated fat intake, and fiber intake. The general linear model (GLM) procedure was used to compare the slopes obtained between the highest and lowest dietary fat tertiles. The effects of insulin-30 tertiles according to lowest versus highest dietary fat tertiles on weight gain or change in waist circumference were compared with analysis of covariance (ANCOVA) using the abovementioned variables as covariates. In the presence of a significant effect, a Tukey's post hoc test was performed to determine which groups were significantly different. To determine the best insulin measure for change in weight or waist circumference, partial correlation analyses were performed to assess the independent contributions of insulin at different time points during the OGTT (fasting insulin concentration and insulin at 30, 60, 90, and 120 min) while adjusting for the abovementioned covariates as well as the other insulin time points. A multiple regression analysis was then performed to determine the overall contribution of the 5 predicting variables. The correlation coefficients were subsequently compared between the 2 dietary fat groups by using a test of homogeneity of correlation coefficients (24). An independent Student's t test was used to compare mean GMIN values between the lowest and highest dietary fat tertiles. Because some individuals in this family study are biologically related, we adjusted for clustering in the analyses to avoid underestimation of SD using the generalized estimating equations statistical method (25). Insulin concentrations were log transformed because of the lack of normal distribution. Data are given as means ± SDs unless otherwise noted. Statistical significance was set at a P value <0.05. All statistical analyses were performed by using JMP software (version 3.2.2; SAS Institute, Cary, NC).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
The baseline characteristics of the subjects according to diet group are shown in Table 1Go. Those in the highest dietary fat group had greater baseline body weight, BMI, and abdominal circumference. These differences became insignificant (P > 0.15 for all) after adjustment for covariates (age, sex, smoking habits, employment status, educational level, total annual family income, alcohol intake, protein intake, saturated fat intake, and fiber intake). Mean change in body weight over 6 y (2.60 ± 4.88 kg compared with 2.52 ± 4.56 kg; P = 0.61) and waist circumference (2.70 ± 5.56 cm compared with 2.72 ± cm; P = 0.92) did not differ between the lowest and the highest dietary fat groups, respectively. Adjusted baseline glucose and insulin concentrations from the OGTT are shown in Table 2Go. As shown in Figure 1Go, insulin-30 at baseline strongly predicted changes in body weight and waist circumference among participants in the lowest dietary fat group but not among those in the highest dietary fat group after adjustment for many potential confounding factors. A comparison of the 2 dietary fat groups showed that the slopes of the linear regression lines were significantly different for weight gain (P = 0.0072) and change in waist circumference (P = 0.0013). The association of insulin-30 and dietary fat with weight gain and change in waist circumference are shown in Figure 2Go. Individuals in the highest insulin-30 and lowest dietary fat group gained 1.8 kg more than did those in the highest insulin-30 and highest dietary fat group (P = 0.034) and 4.5 kg more than did those in the lowest insulin-30 and lowest dietary fat group (P = 0.0026). Similar relations were observed with change in waist circumference. In addition, reactive hypoglycemia, as assessed on the basis of GMIN, was related to weight gain overall (r = –0.22, P = 0.0012) and was more severe among individuals in the lowest (–1.21 ± 0.11 mmol/L) than in the highest (–0.80 ± 0.15 mmol/L) dietary fat groups (P = 0.039).


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  		TABLE 1. Baseline characteristics of subjects according to lowest and highest dietary fat tertiles1

 

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  		TABLE 2. Baseline data from the oral-glucose-tolerance test according to the lowest and highest dietary fat tertiles1

 

Figure 1
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  		FIGURE 1.. Association between plasma insulin 30 min after oral glucose at the beginning of the study and 6-y changes in weight gain and waist circumference according to dietary fat tertile. A multiple linear regression analysis was used, and the model was adjusted for age, sex, baseline body weight or waist circumference, length of follow-up, smoking habits (nonsmoker or ex-smoker, light smoker, and heavy smoker), employment status (student, paid employment, looking for work, home duties, retired, and disabled), highest educational level (high school, college, and university), total annual family income (categorized into 5 groups ranging from <$10 000 to ≥$70 000 Canadian dollars), alcohol intake (g/d), protein intake (%), saturated fat intake (%), and fiber intake (g/g total carbohydrate intake). The slopes of the lines were significantly different for weight gain (P = 0.0072) and for the change in waist circumference (P = 0.0013).

 

Figure 2
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  		FIGURE 2.. Joint associations between tertiles of insulin 30 min after oral glucose at the beginning of the study (insulin-30) and tertiles of dietary fat with 6-y weight gain and change in waist circumference. The effects of insulin-30 tertiles were compared by ANCOVA. The model was adjusted for age, sex, baseline body weight or waist circumference, length of follow-up, smoking habits (nonsmoker or ex-smoker, light smoker, and heavy smoker), employment status (student, paid employment, looking for work, home duties, retired, and disabled), highest educational level (high school, college, and university), total annual family income (categorized into 5 groups ranging from <$10 000 to ≥$70 000 Canadian dollars), alcohol intake (g/d), protein intake (%), saturated fat intake (%), and fiber intake (g/g total carbohydrate intake) as covariates. In the presence of a significant effect, a Tukey's post hoc test was performed to determine which groups were significantly different. The fat tertile x insulin tertile interaction was significant for both panels (P < 0.048 for weight gain and P = 0.043 for change in waist circumference). *Significantly different from low insulin-30 and low dietary fat tertile (P = 0.0026 for weight gain and P = 0.0018 for change in waist circumference). **Significantly different from low insulin-30 and high dietary fat tertile (P = 0.011 for weight gain and P = 0.0082 for change in waist circumference). {dagger}Significantly different from middle insulin-30 and low dietary fat tertile (P = 0.011 for weight gain and P = 0.0016 for change in waist circumference). §Significantly different from high insulin-30 and high dietary fat tertile (P = 0.034 for weight gain and P = 0.039 for change in waist circumference). Values are means ± SEMs; n = 31 for each bar.

 
Finally, we examined the independent contributions of insulin at various time points during the OGTT to the overall associations with the endpoints using partial correlation analyses (Table 3Go). Insulin-30 showed the strongest associations among individuals in the lowest dietary fat group, which explained significantly more variance (r2) than did the other time points. Insulin at 120 min showed moderately strong associations in this diet group, although in the opposite (inverse) direction.


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  		TABLE 3. Partial correlation analyses of the independent contributions of insulin at different time points during the oral-glucose-tolerance test with the overall associations with change in body weight or waist circumference among individuals in low- and high-fat tertiles1

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
The primary findings of this study are that a proxy measure of insulin secretion strongly predicted weight gain and change in waist circumference over 6 y in adult whites, especially among those consuming lower-fat diets. Of particular interest, insulin-30 explained a considerable amount of the individual variance in these endpoints in the lowest dietary fat group, but little of the variance in the highest dietary fat group.

These findings have direct relevance to research in the area of glycemic index (GI). The GI is a system used to classify carbohydrate-containing foods according to how they affect blood glucose concentrations in the postprandial period (26). Short-term studies have reported greater hunger or food intake among individuals consuming macronutrient-controlled high-GI meals than among those consuming low-GI meals (21, 27). Some prospective observational studies and clinical trials have found a relation between GI and body weight (28-32). However, other clinical trials have been negative (33-35). As with low-fat diets, the effects of dietary GI may vary according to individual differences in insulin secretion, a possibility previously raised by Ludwig et al (36). Experimental support for this hypothesis comes from a translational study by Pawlak et al (37), who performed an OGTT on rats and then fed them macronutrient-controlled low- or high-GI diets for 18 wk. They found that insulin-30 at the beginning of the study predicted most of the variation in weight gain among the high GI–fed animals (r2 = 0.84, P < 0.0001) but none of the variation among the low-GI–fed animals (r2 = 0.003, P = 0.94), analogous to findings from the present study.

Taken together, our results and those involving GI suggest the existence of a unique physiologic phenotype that responds poorly to high insulin-stimulating diets, regardless of whether these diets are high in carbohydrate or have a high GI. Moreover, the combination of a high-carbohydrate and high-GI diet—that is, a diet high in glycemic load (GL) (38, 39)—may produce especially great weight gain among individuals with this phenotype. Indeed, a recent randomized controlled trial involving 73 obese young adults showed that individuals with high insulin-30 lost substantially more weight with a low-GL diet than with a low-fat diet over 18 mo (5.8 compared with 1.2 kg; P = 0.004); whereas no significant effect of diet on body weight was found among individuals with low insulin-30 (group x time x insulin-30 interaction: P = 0.02) (40).

With regard to physiologic mechanisms, the high insulin:glucagon ratio observed after consumption of a high-GL meal is thought to create a powerful anabolic stimulus that suppresses the concentrations of metabolic fuels several hours after a meal (21). From this perspective, increased hunger and food intake represent the body's attempt to restore concentrations of metabolic fuels to normal. The demonstration of more severe reactive hypoglycemia during the OGTT among those who consumed the lowest-fat diet (and therefore the highest GL) than in those who consumed the highest-fat diet (and therefore the lowest GL) is consistent with this mechanism, in that glucose comprises 1 of 2 key metabolic fuels (in addition to nonesterified fatty acids). Indeed, GMIN was significantly associated with weight gain among the entire cohort.

An additional finding was the inverse association between insulin concentration later after glucose consumption and the study endpoints. In contrast to insulin-30, a measure of primary hyperinsulinemia, insulin at 120 min likely reflects systemic insulin resistance and compensatory hyperinsulinemia. This inverse association may be attributable to the protective effects of insulin resistance against weight gain (41-44). Alternatively, compensatory hyperinsulinemia itself may prevent weight gain, as previously proposed (45, 46). Remarkably, the combined contributions of all insulin concentrations in the OGTT explained more than half the variance in weight gain and change in waist circumference among individuals in the lower dietary fat group after adjustment for conventional covariates.

Because of the observational nature of this study, definitive conclusions about causality cannot be made. Nevertheless, several feature of the study provide a high level of confidence in the validity of the data, including its prospective design, availability of relevant covariates for statistical modeling, and use of comparison groups differing in dietary fat to explore relations involving insulin and weight gain—an approach that should serve to minimize confounding. Errors in the measurement of diet could affect our findings if these errors affected the classification of subjects into dietary groups. However, misclassification is likely nondifferential with respect to weight gain, so any resulting bias should be toward the null hypothesis, leading to underestimation of the true magnitude of effect. In addition, we used a proxy measure of insulin secretion (insulin-30) instead of a direct measure. However, the insulin concentration 30 min after glucose consumption, the time point of particular interest, has been shown to be a good measure of insulin secretion in humans (13-15), and this time point was examined in our previous studies (37, 40). Because data on dietary GI are not available in this cohort, we could not directly explore possible interactions between GL and insulin with regard to body weight. We recognize that other dietary factors affect insulin secretion and could therefore mediate the observed diet-phenotype interaction to some degree. However, the postprandial rise in blood glucose constitutes the most potent stimulus for insulin secretion, and GL is the best available dietary predictor of how blood glucose changes in the postprandial period (47). Moreover, dietary protein—the second most potent insulin secretagogue after carbohydrate of all the macronutrients—was not significantly different between dietary groups.

In summary, the insulin response to carbohydrate consumption explained a substantial amount of variance in 6-y weight gain among individuals consuming a lower-fat diet. Consistent with other investigations, insulin secretion (as measured by insulin concentration soon after glucose consumption) predicted weight gain, whereas insulin resistance (as measured by insulin concentration later after glucose consumption) appears to protect against weigh gain. Of particular interest, we identified a subgroup of individuals, characterized by high insulin secretion, who gained a great deal of weight while consuming a lower-fat diet. Further research is needed to explore the genetic, perinatal, and acquired factors that regulate insulin secretion among individuals without diabetes and how these factors might affect the response to weight-loss treatment. If these findings are confirmed in other populations, insulin concentrations from an OGTT may help clinicians individualize dietary prescription to improve long-term effectiveness.


   ACKNOWLEDGMENTS
 
We express our gratitude to the subjects for their participation and the staff of the Physical Activity Sciences Laboratory at Laval University for their contribution to this study. We especially thank Germain Thériault, Guy Fournier, Monique Chagnon, Lucie Allard, and Claude Leblanc for their help with the collection and analysis of the data.

The authors' responsibilities were as follows—J-PC and DSL: designed the study, conducted the analyses, and wrote the manuscript; CB and AT: designed and created the Quebec Family Study and helped revise the manuscript; and EBR: provided statistical advice and helped revise the manuscript. DSL is author of a book on childhood obesity entitled Ending the Food Fight: Guide your Child to a Health Weight in a Fast Food/Fake Food World. None of the other authors had a personal or financial conflict of interest.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication May 8, 2007. Accepted for publication September 18, 2007.




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