- Email: [email protected]
Dietary Fiber and Fat Are Associated with Excess Weight in Young and Middle-Aged US Adults
RESEARCH Current Research
Dietary Fiber and Fat Are Associated with Excess Weight in Young and Middle-Aged US Adults NANCY C. HOWARTH, PhD; TERRY T.-K. HUANG, PhD, MPH; SUSAN B. ROBERTS, PhD; MEGAN A. MCCRORY, PhD
ABSTRACT Objective To examine relative associations of dietary composition variables with body mass index (BMI; calculated as kg/m2) among young and middle-aged US adults. We hypothesized that in subjects with physiologically plausible reported energy intakes, fiber intake would be inversely associated with BMI, independent of other dietary composition and sociodemographic variables. Subjects and design Data from adults age 20 to 59 years in the Continuing Survey of Food Intakes by Individuals (CSFII) 1994-1996 were used. Exclusions were pregnancy or lactation, food insecurity, missing weight or height data, or having only one dietary recall. Based on our previously published methods, a physiologically plausible reported energy intake was calculated as being within ⫾22% of predicted energy requirements for the mean of two 24-hour recalls. Results Reporting plausibility ([reported energy intake ⫼predicted energy requirements]⫻100) averaged 83% in the total sample (N⫽4,539) and increased to 96% in the plausible sample (n⫽1,932). Only approximately 5% of the plausible sample consumed the Adequate Intake for N. C. Howarth is a postdoctoral fellow, University of Hawaii Cancer Research Center of Hawaii, Honolulu. T. T.-K. Huang is a research assistant professor, Gerald J. and Dorothy R. Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA. S. B. Roberts is a senior scientist, Energy Metabolism Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA. M. A. McCrory is a research associate professor, School of Nutrition and Exercise Science, Bastyr University, Kenmore, WA. At the time of the study, N. C. Howarth was a doctoral candidate, T. T.-K. Huang was a postdoctoral research associate, and M. A. McCrory was a scientist II, Energy Metabolism Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA. Address correspondence to: Megan A. McCrory, PhD, Research Associate Professor, School of Nutrition and Exercise Science, Room 464, Bastyr University, 14500 Juanita Dr NE, Kenmore, WA 98028. E-mail: [email protected] Copyright © 2005 by the American Dietetic Association. 0002-8223/05/10509-0002$30.00/0 doi: 10.1016/j.jada.2005.06.001
© 2005 by the American Dietetic Association
fiber. In plausibly reporting women, fiber, its interaction with percentage energy from fat, and energy density were independently associated with BMI. Only percentage energy from fat was associated with BMI in men reporting plausibly. Compared with the total sample, stronger relationships between diet and BMI were observed among the plausible reporters. In women, a low-fiber (⬍1.5 g/MJ*), high-fat (ⱖ35% energy) diet was associated with the greatest increase in risk of overweight or obesity compared with a high-fiber, low-fat diet. Conclusions Weight control advice for US women should place greater emphasis on consumption of fiber. J Am Diet Assoc. 2005;105:1365-1372.
S
ixty-five percent of US adults are overweight or obese (body mass index [BMI; calculated as kg/m2] ⱖ25) (1). Dietary contributors to obesity are not well understood. Dietary fat was considered a leading cause of overeating and weight gain (2,3) until other dietary composition factors began to emerge as potentially important modifiers of energy intake. These include carbohydrate, protein, fiber, energy density, and glycemic index (4,5). However, their relative importance to the development of obesity remains uncertain. Low intake of dietary fiber may be one cause of excess energy intake. Increasing dietary fiber may help reduce energy intake by decreasing overall energy density and absorption of energy-yielding nutrients, and enhancing satiety (6-9). The importance of fiber to weight regulation relative to other dietary variables, however, is difficult to ascertain from previous studies. High fiber intake is often correlated with other key dietary factors such as low fat, low energy density, and low glycemic index (8). Of the observational studies that have statistically controlled for at least some of these confounding dietary factors, most (10-13), though not all (14,15), have shown that higher fiber intake is independently associated with lower BMI or less weight gain. Even so, uncertainty remains about the relative importance of fiber because of dietary reporting bias (16). Energy intake is typically underreported by 10% to 50% on average (17-19), with overweight and obese individuals underreporting to a greater extent than normal-weight individuals (18,20). The specificity of underreporting of energy-dense foods and beverages (20-22), which are also
*To convert MJ to kcal, multiply MJ by 238.8.
Journal of the AMERICAN DIETETIC ASSOCIATION
1365
likely to be low in fiber, may result in an underestimation of the association of fiber with BMI (16). Intervention studies suggest that fiber may help promote weight loss; however, many of these studies have not controlled for the potentially confounding influence of other dietary factors such as food form, palatability, energy density, and variety (4,8,23). Furthermore, adding 1 to 30 g/day of fiber to ad libitum diets had only modest effects on weight loss (8,24). One reason for this may be that fiber types—such as functional vs dietary and soluble vs insoluble— differ in their effectiveness (8). Alternatively, higher fiber intakes than those previously studied may be necessary to confer substantial reductions in energy intake and/or body weight (25). Also, the effects of fiber on energy regulation may be stronger when coupled with other nutrients. In support of the latter suggestion, weight loss is substantially greater when an increase in fiber intake is coupled with a decrease in fat intake, compared with when either dietary component is changed alone (8,9). The purpose of this study was to examine, using US national survey data, the relative associations of BMI with dietary factors, specifically fiber, energy density, and macronutrients. We hypothesized that high-fiber intake is associated with a low BMI, independent of macronutrients and energy density, and that a high-fiber, lowfat diet is more strongly associated with low BMI than is either a high-fiber or low-fat diet alone. Because our previous analyses showed stronger dietary associations with BMI when physiologically implausible energy intake reports were excluded (16), we compared associations in the total sample and in the plausible sample. Our analysis was limited to young and middle-aged adults because of the age-related decrease in BMI after approximately age 60 years (26). METHODS Study Population Data from the US Department of Agriculture (USDA) Continuing Survey of Food Intakes by Individuals (CSFII) 1994-1996 (27) were used. This survey of 16,103 noninstitutionalized individuals age 2 to 90 years residing in the United States contains information about dietary intake (by one or two nonconsecutive, multiple-pass 24-hour recalls); socioeconomic, demographic, and health parameters; and self-reported height and weight. From 8,219 respondents age 20 years or older, we excluded those who were 60 years of age or older, pregnant or lactating, food insecure, on medically related diets, completed only one 24-hour dietary recall, or did not provide both height and weight. The number in our total sample for analysis was 4,539 (2,374 men and 2,165 women). Data Analysis Two-day average intakes were used for all analyses. Physiologically plausible reports of energy intake were determined by comparing reported energy intake with predicted energy requirements determined by using the new Dietary Reference Intake (DRI) equations for predicted energy requirements (28). Detailed procedures are described elsewhere (16). Briefly, we calculated sex, age-group, and
1366
September 2005 Volume 105 Number 9
weight-class specific cutoffs for reported energy intake as a percentage of predicted energy requirements ([reported energy intake⫼predicted energy requirements]⫻100), taking into account measurement and biological intraindividual variation in reported energy intake and total energy expenditure as measured by doubly labeled water, and error in equations for predicted energy requirements (16,28-30). When different cutoffs levels were tested (⫾2 standard deviations [SD], ⫾1.5 SD, ⫾1 SD of [reported energy intake⫼predicted energy requirements]⫻100), only the ⫾1 SD cutoff resulted in a relationship between reported energy intake and body weight that did not differ significantly from the relationship between total energy expenditure and body weight. For the present analysis, we therefore used the ⫾1 SD cutoff (⫾22% for these data) to maximize the validity of reported energy intake. This required that an individual’s reported energy intake be within 78% to 122% of predicted energy requirements to be considered physiologically plausible. Fifty-seven percent of the total sample was detected as having an implausible reported energy intake, leaving 1,932 subjects in the plausible sample (1,108 men and 824 women). After initial analyses comparing the total and plausible samples (Tables 1 and 2), we present results only for the plausible sample. Descriptive data (mean⫾standard error of the mean [SEM]) were calculated, and independent t tests and 2 tests were used to compare sample characteristics. Multiple-regression analysis determined dietary associations with BMI. Specifically, relative associations of fiber density [g/MJ, herein referred to as “fiber” unless otherwise specified, in which fiber in the CSFII was determined using methods of the Association of Official Analytical Chemists (31)], energy density (MJ/g of all reported foods and beverages; ie, not water), and macronutrients (percentage of energy intake) with BMI were tested. Fiber by percentage of energy from fat, and fiber by sex interactions were also examined. Because the fiber by sex interaction was significant (P⬍.001), subsequent analyses were performed separately in men and women. Covariates included age, sex, education (high school or less vs beyond high school), current smoking (yes/no), selfreported chronic disease (yes/no for at least one of the following: diabetes, hypertension, heart disease, cancer, hypercholesterolemia, and/or stroke), ethnicity (white vs nonwhite), annual household income (0% to 130%, 131% to 350%, and ⬎350% of poverty threshold), urbanicity (urban, suburban, rural), geographic region (Northeast, Midwest, South, West), and television viewing (hours per day) (as a proxy for inactivity). Results were similar when vegetarians or individuals with self-reported chronic disease were excluded (data not shown). Results were also similar when models using fiber (g), fat (g), their interaction, and controlling for total energy to predict BMI were tested to assess the effect that ratios might have on the outcome (data not shown). We therefore retained fiber density to be consistent with the epidemiologic literature. Linear regression analysis to compare BMIs and logistic regression analysis to assess the relative risk of overweight and obesity (ⱖ25) from consuming different combinations of fiber and fat were performed, controlling for the covariates described earlier. Low- and high-fiber intakes were defined, respectively, as less than 1.5 g/MJ and 1.5 g/MJ or more. A fiber intake of 1.5 g/MJ approx-
Table 1. Demographic and dietary characteristics of total, plausible, and implausible reporters of dietary intake in the Continuing Survey of Food Intake by Individuals 1994-96a Women Total (nⴝ2,165)
Plausible (nⴝ824)
Men Implausible (nⴝ1,341)
Total (nⴝ2,374)
Plausible (nⴝ1,108)
Implausible (nⴝ1,266)
4™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™ mean⫾SEM b ™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™3 Demographic Age (y) Height (cm) Weight (kg) BMIc TV/day (h) Overweight/obese (BMI ⱖ25) Current smoker High school or less Urban White With diseasec ⬍130% of poverty level September 2005 ● Journal of the AMERICAN DIETETIC ASSOCIATION
Dietary Energy intake (MJ/d)d Predicted energy requirement (MJ)d Energy intake⫼predicted energy requirement⫻100 (%) Carbohydrate (% energy) Protein (% energy) Fat (% energy) Energy density (kJ/g)e Fiber (g/d) Fiber (g/MJ)d a
39.0⫾0.3 38.6⫾0.5 39.2⫾0.3 38.2⫾0.3 38.8⫾0.4 37.8⫾0.3 163.5⫾0.2 163.2⫾0.3 163.8⫾0.3** 178.2⫾0.2 177.8⫾0.3 178.6⫾0.3 67.6⫾0.4 64.6⫾0.6 69.5⫾0.5*** 84.2⫾0.4 82.3⫾0.5 85.8⫾0.6*** 25.3⫾0.2 24.3⫾0.2 26.0⫾0.2*** 26.5⫾0.1 26.0⫾0.1 26.9⫾0.2*** 2.1⫾0.1 2.1⫾0.1 2.1⫾0.1 2.4⫾0.1 2.3⫾0.1 2.5⫾0.1 4™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™ % ™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™3 63.4 59.4 67.0*** 46.1 37.4 51.5*** 24.0 22.6 25.0 28.9 26.0 31.4 42.1 36.2 45.5** 42.0 42.3 41.7 33.1 36.7 31.2 31.4 27.6 34.5** 80.3 83.7 78.1* 81.0 84.3 78.2* 23.9 21.5 25.4 24.3 22.9 25.4 11.0 9.2 12.1* 9.9 7.1 10.4 4™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™ mean⫾SEM b ™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™ 3 7.0⫾0.1
8.4⫾0.1
6.0⫾0.1***
10.6⫾0.2
11.3⫾0.1
10.1⫾0.4**
9.1⫾0.0
8.9⫾0.0
9.2⫾0.0***
11.7⫾0.0
11.6⫾0.0
11.9⫾0.0***
76.7⫾0.6 51.8⫾0.3 15.5⫾0.1 32.5⫾0.2 3.7⫾0.0 13.5⫾0.2 2.0⫾0.0
94.5⫾0.4 51.4⫾0.4 14.7⫾0.2 33.3⫾0.3 4.0⫾0.1 15.8⫾0.3 1.9⫾0.0
65.8⫾0.7*** 52.0⫾0.4 16.0⫾0.2*** 32.0⫾0.3** 3.5⫾0.0*** 12.1⫾0.2*** 2.1⫾0.0***
88.3⫾0.7 48.3⫾0.3 16.0⫾0.1 33.9⫾0.2 4.1⫾0.0 18.2⫾0.3 1.8⫾0.0
96.7⫾0.4 48.0⫾0.3 15.6⫾0.1 34.8⫾0.3 4.2⫾0.0 19.4⫾0.3 1.7⫾0.0
80.9⫾1.1*** 48.6⫾0.3 16.4⫾0.1*** 33.2⫾0.3*** 3.9⫾0.1** 17.2⫾0.3*** 1.8⫾0.0
Respondents were age 20-59 y during the Continuing Survey of Food Intakes by Individuals (1994-1996). SEM⫽standard error of the mean. c BMI⫽body mass index (calculated as kg/m2); self-reported condition of ⱖ one of the following: diabetes, high blood pressure, heart disease, cancer, high blood cholesterol, stroke. d To convert MJ to kcal, multiply MJ by 238.8. e To convert kJ/g to kcal/g, multiply kJ/g by 4.184. *P⬍.05, differing significantly from plausible reporters of the same sex. **P⬍.01. ***P⬍.001. b
1367
Table 2. Multiple linear regression models for dietary and lifestyle factor associations with BMIab in the total sample and the subsample with plausible energy intake reports Total Sample (nⴝ2,165 women and 2,374 men)
ⴞSEc Women Constant Fiber (g/MJ)d Fat (% energy) Fiber (g/MJ)d⫻fat (%energy) Energy density (kJ/g)e TV (h/d) Current smoker (yes/no) Model R 2 Men Constant Fiber (g/MJ) Fat (% energy) Fiber (g/MJ)⫻fat (%energy) Energy density (kJ/g)e TV (h/d) Current smoker (yes/no) Model R 2
24.01⫾1.64 ⫺1.32⫾0.39 ⫺.03⫾0.03 .04⫾0.01 ⫺.11⫾0.14 .25⫾0.08 1.13⫾0.30 23.04⫾1.25 .56⫾0.42 .09⫾0.03 ⫺.02⫾0.01 ⫺.27⫾0.09 .28⫾0.05 1.02⫾0.19
Plausible Sample (nⴝ824 women and 1,108 men)
Partial R 2
P value
ⴞSE
0.001 0.006 0.005 0.000 0.006 0.006 0.130
⬍.001 ⬍.001 .293 .005 .409 .003 ⬍.001 ⬍.001
23.03⫾1.64 ⫺2.46⫾0.72 ⫺.04⫾0.05 .06⫾0.02 .33⫾0.16 .13⫾0.12 1.12⫾0.41
0.001 0.006 0.000 0.005 0.014 0.006 0.103
⬍.001 .190 .003 .143 .005 ⬍.001 ⬍.001 ⬍.001
21.77⫾1.69 .66⫾0.77 .13⫾0.05 ⫺.03⫾0.02 ⫺.21⫾0.16 .24⫾0.06 .79⫾0.30
Partial R 2
P value
0.012 0.031 0.005 0.008 0.004 0.004 0.190
⬍.001 .001 .402 .008 .04 .168 .050 ⬍.001
0.001 0.020 0.003 0.002 0.012 0.004 0.102
⬍.001 .396 .019 .224 .179 ⬍.001 .013 ⬍.001
a
BMI⫽body mass index. Models also controlled for age, race, education, US region, urbanicity, income, and self-reported chronic disease (data not shown). c SE⫽standard error. d To convert MJ to kcal, multiply MJ by 238.8. e To convert kJ to kcal, multiply kJ by 4.184. b
imates the national average (27). A higher cutoff was also examined initially (ⱖ3.0 g/MJ, roughly the Adequate Intake (AI) established in the new DRI, for an 8.4 MJ/day (2,000 kcal/day) energy intake (28), but too few individuals consumed this amount of fiber to provide adequate statistical power. Low-, medium-, and high-fat intakes were defined as less than 25%, 25% to 35%, and more than 35% of total energy intake, respectively. For the logistic regression model, the low-fat/high-fiber category was designated the reference with a relative risk of 1.0. The unweighted mean variation of ([reported energy intake⫼predicted energy requirements]⫻100) (samplespecific) and weighted partial R2 from regression analyses were calculated using SAS (version 8.2, 2003, SAS Institute, Cary, NC). t tests, R2, and 2 tests, linear and logistic regressions, and least-squared means tests were performed using SUDAAN (v.8, 2001, Research Triangle Institute, Research Triangle Park, NC) and weighted for sampling design, with ␣ set at .05. SUDAAN was used to incorporate sample design (stratified, multistage area probability sample) for variance estimation. Failure to account for sample design is known to underestimate standard errors of parameters, hence increasing the risk of rejecting true null hypotheses. RESULTS Demographic and Dietary Characteristics As shown in Table 1, mean BMI and the percentage of overweight or obese individuals were significantly higher in the implausible reporters compared with the plausible re-
1368
September 2005 Volume 105 Number 9
porters. In addition, implausible reporters were more likely to be nonwhite, and among women less educated and poorer. As expected, reporting plausibility ([reported energy intake⫼predicted energy requirements]⫻100) was higher in the plausible sample, approaching 100% in both sexes, but was much lower in the implausible sample, especially in women. The implausible sample also reported lower percentage energy intake from fat, energy density, and fiber (g), but higher percentage of energy from protein and a more fiber-dense diet (the latter significant only in women). Even in the plausible sample, fiber intake was particularly low (Figure 1), averaging approximately half the AI of 25 g/day for women and 38 g/day for men (28). Additionally, only 8% of women and 2% of men in the plausible sample consumed the AI or more. Dietary Associations with BMI in the Total vs Plausible Samples of Women Regression models showing the associations of diet with BMI in the total and plausible samples are shown in Table 2. In the total sample of women, fiber and the fiber by percentage energy from fat interaction were significantly associated with BMI, but percentage energy from fat alone was not significantly associated. In the plausible sample, fiber, its interaction with percentage energy from fat, and energy density were significantly associated with BMI. Note that dietary factors were more strongly associated with BMI in the plausible sample, as indicated by the steeper regression coefficients.
Figure 1. Fiber intake of participants age 20 to 59 years in the Continuing Survey of Food Intakes by Individuals 1994-1996 whose reported energy intakes were physiologically plausible (n⫽824 women and 1,108 men; see text for explanation) compared with recommended intake levels. Symbols indicate mean, bars indicate range (minimum and maximum values), and bars in bold indicate the 25th, 50th, and 75th percentiles. Dotted line shows the Adequate Intake (AI) for fiber according to the recent Dietary Reference Intake recommendations (28). 8.6% of women and 2.2% of men consumed the AI or more (not shown). Also shown in Table 2 are the relative associations of dietary composition vs lifestyle factors with BMI. Partial R2 indicate that in the total sample, dietary composition accounted for 1.2% of the between-subject variation in BMI, whereas in the plausible sample dietary composition accounted for four times or more of this amount, or 4.8% to 5.6%, of the between-subject variation in BMI. Furthermore, in the plausible sample, diet accounted for a much larger between-subject variation in BMI than did the combination of the other modifiable lifestyle factors modeled (television viewing and smoking, 0.8%). Dietary Associations with BMI in the Total vs Plausible Samples of Men In both the total and plausible samples of men (Table 2), percentage energy from fat was positively associated with BMI, and this association was stronger in the plausible sample. Energy density was inversely associated with BMI in the total but not the plausible sample. In both the total or plausible samples, fiber was not significantly associated with BMI. The models showed that in the total sample, percentage energy from fat accounted for 0.6% of the between-subject variation in BMI, whereas in the plausible sample it accounted for 2.0%. Other modifiable lifestyle factors modeled accounted for 2.0% of the between-subject variation in BMI in the plausible sample, and 1.6% in the total sample. Fiber by Fat Interactions and Relative Risk of Overweight and Obesity in Women Figure 2 illustrates the fiber by percentage energy from fat interaction associations with BMI seen in the regression model for the plausibly reporting women. BMI at high-, medium-, and low-fat intakes are shown at both low- and high-fiber intakes. Mean BMI was the highest and did not differ significantly among women with lowfiber intakes at any level of fat intake, or high-fiber intake
Figure 2. Adjusted mean (⫾standard error of the mean) body mass index (BMI) in relation to intakes of fiber and percentage energy from fat in women age 20 to 59 years participating in the Continuing Survey of Food Intakes by Individuals 1994-96. Only women whose reported energy intakes were physiologically plausible are shown (see text for explanation). Low- and high-fiber intakes were defined as ⬍1.5 g/MJ vs ⱖ1.5 g/MJ, respectively. (To convert MJ to kcal, multiply MJ by 238.8.) Low, medium, and high fat intakes were defined as ⬍25%, 25%-35%, and ⬎35% of energy intake, respectively. Values are adjusted for age, sex, education, current smoking, self-reported chronic disease, ethnicity, household income, urbanicity, geographic region, and television viewing. Bars with the same letter are not significantly different. n⫽145, 89, 24, 219, 252, 95 (left to right).
coupled with high-fat intake (25.1⫾0.6 for the four groups combined). Mean BMI was significantly lower among women consuming high-fiber, medium-fat (23.6⫾0.4) diets, and even lower among women consuming high-fiber, low-fat diets (22.2⫾0.4). Using the high-fiber/low-percentage energy from fat category as the reference, the relative risk of consuming the other fiber/percentage energy from fat combinations is shown in women (Figure 3). The relative risk of having excess weight was significantly elevated among all other fiber/percentage energy from fat combinations compared with consuming a high-fiber, low-fat diet. DISCUSSION This study is the first to examine relationships between dietary fiber and BMI in a national data set using a rigorous approach to screen for physiologically implausible energy intake reports. In women, higher fiber intake was strongly associated with a lower BMI: those with fiber intakes more than the US average had a lower BMI than those who consumed the average or less than average. We also found that among women, dietary fat intake had no relationship with BMI unless it was coupled with a high-fiber diet. In men, percentage energy from fat was the only dietary variable independently associated with BMI. These relationships were more apparent when implausible dietary reports were excluded, and furthermore accounted for a greater portion of the between-subject
September 2005 ● Journal of the AMERICAN DIETETIC ASSOCIATION
1369
Figure 3. Relative risk of overweight or obesity by fiber and percent energy from fat categories of intake in women age 20 to 59 years participating in the Continuing Survey of Food Intakes by Individuals 1994-96. Only women whose reported energy intakes were physiologically plausible are shown (see text for explanation). Low- and high-fiber intakes were defined as ⬍1.5 g/MJ vs ⱖ1.5 g/MJ, respectively. (To convert MJ to kcal, multiply MJ by 238.8.) Low, medium, and high fat intakes were defined as ⬍25%, 25%-35%, and ⬎35% of energy intake, respectively. The high-fiber/low-percentage energy from fat category was defined as the reference (relative risk 1.0). Relative risks are adjusted for age, sex, education, current smoking, self-reported chronic disease, ethnicity, household income, urbanicity, geographic region, and television viewing. Values in parentheses are 95% confidence intervals. The relative risk of having excess weight was significantly elevated among all fiber/percentage fat combinations compared with consuming a high-fiber, low-fat diet. n⫽95, 252, 219, 24, 89, 145 (left to right).
variation in BMI of both men and women than did other modifiable lifestyle factors. Our findings highlight the importance of diet in weight control and strongly suggest that fiber should be considered along with fat as a key modulator of weight status in women. Physiological explanations for the role of fiber in energy regulation include reducing energy density; decreasing nutrient absorption rate, thereby slowing the return of hunger; up-regulating satiety hormones such as glucagon-like peptide-1; and sequestering energy-providing nutrients, causing them to be excreted (8). Given the very low fiber intakes observed in our study, this latter mechanism alone could theoretically be responsible for the substantial fat gain through young and middle-age that occurs in most individuals in Westernized societies (32,33). Intake-balance studies indicate that 3% to 6% less energy is absorbed when fiber intake is at least 34 g/day (almost double the mean fiber intake in our study), with an increasing amount of energy not absorbed with increasing fiber intake (7,34,35). On a 10 MJ/d diet, this approximates 300 to 600 kJ (70 to140 kcal) not absorbed. If this level of fiber intake were maintained daily without any other changes, the reduced absorption of energy would amount to roughly 3 to 7 kg of body weight either lost or not gained during 1 year. However, to affect body weight, fiber may need to be an integral part of food, because 30 g of supplemental functional fiber over 3 weeks had no effect (24). Another possibility is that certain fiber types are more effective than others for weight control, but this remains inconclusive (8,24).
1370
September 2005 Volume 105 Number 9
It is unclear why fiber was inversely associated with BMI in women but not in men. Experimental studies of fiber effects on ad libitum energy intake and body weight change have either not reported results by sex or, when conducted in either men or women, have not adequately controlled for confounding dietary factors (8). Some observational studies have reported significant inverse relationships between fiber and BMI or percentage body fat in men (10,36,37), but these studies also did not control for confounding dietary factors. Interestingly, in our study, in plausibly reporting men, fiber was also significantly inversely related to BMI (⫽⫺.54, P⫽.018), but only when other dietary factors were not controlled. Thus, the previously reported inverse relationship between fiber and BMI in men may be an artifact due to lack of control for confounding. Alternatively, a relationship between fiber and BMI may not be detectable in our sample of men because of their exceedingly low-fiber intakes. Possibly because of the lower fat-free mass and larger fat mass among women for a given BMI (38,39), their higher fiber intake relative to energy requirements may have a greater impact on energy regulation and BMI than among men. Clearly, more research is needed on the potentially different effects of dietary fiber on overeating and obesity in men and women. We found a significant fiber-by-fat interaction association with BMI in women. Dietary fat was not by itself significantly associated with BMI; however, a low-fat intake combined with a high-fiber intake was significantly associated. In addition, the relative risk of overweight or obesity was significantly higher in women consuming all combinations of fiber and percentages of energy from fat relative to a high-fiber, low-fat diet. These findings agree with results of several previous intervention studies in which weight loss was threefold greater when recommendations were made to reduce fat and increase fiber intakes, compared with recommending either change alone (9). In the present study, mean BMI was 1.5 less in women consuming a high- vs low-fiber diet, and this difference increased to 2.75 when a low-fat diet was consumed with a high-fiber diet. Although only limited conclusions can be drawn from our cross-sectional study about the role of fiber in weight control, our results combined with those of previous intervention studies (8,9) imply that a high-fiber diet is much more effective than a low-fat diet at preventing weight gain in women, and the combination of high-fiber and low-fat may be even more effective in maintaining normal weight than either one alone. Given the substantial associations between fiber intake and BMI noted even at the low levels consumed in this study, increased fiber consumption should be more widely promoted for potential weight control, at least in women, and in both sexes for known effects on reducing the risk for several chronic diseases such as coronary heart disease, type 2 diabetes, and some cancers (28,40). Our study suggests that previous observational studies, due to dietary reporting bias, may have underestimated the importance of fiber in energy regulation. Consistent with most previous studies (17,20,22), the implausibly reporting group had a higher percentage of overweight and obese individuals compared with the plausibly reporting group. Although absolute fiber intake (g) was higher in the total
sample than in the plausible sample, fiber density (g/MJ) was slightly less. This is probably due to the exclusion of implausible reporters who, based on previous studies as well as other analyses in this dataset (16,41,42), likely underreported foods high in energy density and hence low in fiber. We found that associations between BMI and fiber were relatively low in the total sample but were much stronger after excluding implausible reporters. We also observed that, in the plausible reporters, dietary factors accounted for greater between-subject variation in BMI compared with the other modifiable lifestyle factors we were able to take into account. This was especially true for women, in whom dietary variables accounted for seven times the between-subject variation in BMI than did smoking and inactivity. Our results are corroborated by a recent study in Beijing, China (43), in which energy intake reporting plausibility was very high (96%), and activity levels are much higher than in the United States. In that study, both dietary factors and physical activity accounted equally for the between-subject variation in percentage body fat. Although we were unable to account for other important lifestyle factors such as physical activity due to limitations in the survey design, our results suggest that diet may play a stronger role in weight control, relative to other lifestyle factors, than is often attributed to it (44,45); however, this deserves further study. The primary strengths of this study are that, unlike most previous studies on dietary relationships with BMI, we simultaneously modeled several dietary composition variables, thus assessing relative associations with BMI. Additionally, we objectively identified implausible dietary reports, and compared results in the total sample with those in the plausible sample. However, there were some limitations. Few subjects in the survey consumed diets high enough in fiber for an adequate evaluation of high intakes (eg, the AI or higher). Weight and height were self-reported. We did not have a measure of physical activity and, therefore, used the amount of time viewing television per day as a proxy for inactivity, which may not represent the same construct. Finally, exclusion of more than half of the total sample to obtain a physiologically plausibly reporting sample could be considered a potential weakness. However, the tradeoff for excluding implausible reports was a substantial gain in the validity of the data for representing usual dietary intake of the subjects studied. Due to the exclusion of a large number of subjects for reporting implausibly, we have no way of knowing whether our findings are applicable to the US population as a whole. Nonetheless, we can think of no physiological reason to suspect that the results would differ in the implausible reporters had they reported plausibly. CONCLUSIONS Our results, combined with those of previous intervention studies, strongly suggest that among women a high-fiber diet seems to be more effective than a low-fat diet for preventing weight gain, and the combination of a highfiber and low-fat eating plan may be even more effective than either one alone. We did not find an association between fiber and excess weight in men; however, fiber intakes were extremely low in this study, with only approximately 5% of individuals consuming the AI or more.
This suggests inadequate fiber intake in the US population as a whole. However, given that in women associations between fiber intake and BMI were found even at low levels, and the known benefit of fiber in the prevention of several chronic diseases, increased fiber consumption should be more widely promoted. This study was funded in part by US Department of Agriculture/Economic Research Service/Food and Nutrition Research Program grant #43-3AEM-2-80088. References 1. Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among US adults, 1999-2000. JAMA. 2002;288:1723-1727. 2. Roberts SB, Pi-Sunyer FX, Dreher M, Hahn R, Hill JO, Kleinman RE, Peters JC, Ravussin E, Rolls BJ, Yetley E, Booth SL. Physiology of fat replacement and fat reduction: Effects of dietary fat and fat substitutes on energy regulation. Nutr Rev. 1998;56(5 Pt 2):S29-S49. 3. Willett WC. Dietary fat plays a major role in obesity: No. Obes Rev. 2002;3:59-68. 4. Roberts SB. High-glycemic index foods, hunger, and obesity: Is there a connection? Nutr Rev. 2000;58:163170. 5. Roberts SB, McCrory MA, Saltzman E. The influence of dietary composition on energy intake and body weight. J Am Coll Nutr. 2002;21(suppl):S140-S145. 6. Bonfield CT. Dietary fiber and body weight management. In: Kritchevsky D, Bonfield C, eds. Dietary Fiber in Health and Disease. St Paul, MN: Eagan Press; 1995:459-465. 7. Baer DJ, Rumpler WV, Miles CW, Fahey GCJ. Dietary fiber decreases the metabolizable energy content and nutrient digestibility of mixed diets fed to humans. J Nutr. 1997;127:579-586. 8. Howarth NC, Saltzman E, Roberts SB. Dietary fiber and weight regulation. Nutr Rev. 2001;59:129-139. 9. Yao M, Roberts SB. Dietary energy density and weight regulation. Nutr Rev. 2001;59:247-258. 10. Appleby PN, Thorogood M, Mann JI, Key TJ. Low body mass index in non-meat eaters: The possible roles of animal fat, dietary fibre and alcohol. Int J Obes. 1998;22:454-460. 11. Ludwig DS, Pereira MA, Kroenke CH, Hilner JE, Van Horn L, Slattery ML, Jacobs DR Jr. Dietary fiber, weight gain, and cardiovascular disease risk factors in young adults. JAMA. 1999;282:1539-1546. 12. Liu S, Willett WC, Manson JE, Hu FB, Rosner B, Colditz G. Relation between changes in intakes of dietary fiber and grain products and changes in weight and development of obesity among middleaged women. Am J Clin Nutr. 2003;78:920-927. 13. Sasaki S, Katagiri A, Tsuji T, Shimoda T, Amano K. Self-reported rate of eating correlates with body mass index in 18-y-old Japanese women. Int J Obes. 2003; 27:1405-1410. 14. Lovejoy JC, Champagne CM, Smith SR, de Jonge L, Xie H. Ethnic differences in dietary intakes, physical activity, and energy expenditure in middle-aged, premenopausal women: The Healthy Transitions Study. Am J Clin Nutr. 2001;74:90-95.
September 2005 ● Journal of the AMERICAN DIETETIC ASSOCIATION
1371
15. Stookey JD. Energy density, energy intake and weight status in a large free-living sample of Chinese adults: Exploring the underlying roles of fat, protein, carbohydrate, fiber and water intakes. Eur J Clin Nutr. 2001;55:349-359. 16. McCrory MA, Hajduk CL, Roberts SB. Procedures for screening out inaccurate reports of dietary energy intake. Public Health Nutr. 2002;5:873-882. 17. Schoeller DA, Bandini LG, Dietz WH. Inaccuracies in self-reported intake identified by comparison with the doubly labelled water method. Can J Physiol Pharmacol. 1990;68:941-949. 18. Schoeller DA. Limitations in the assessment of dietary energy intake by self-report. Metabolism. 1995; 44:18-22. 19. Subar AF, Kipnis V, Troiana RP, Midthune D, Schoeller DA, Bingham S, Sharaugh CO, Trabulsi J, Runswick S, Ballard-Barbash R, Sunshine J, Schatzkin A. Using intake biomarkers to evaluate the extent of dietary misreporting in a large sample of adults: The OPEN study. Am J Epidemiol. 2003;158:1-13. 20. Livingstone MBE, Black AE. Markers of the validity of reported energy intake. J Nutr. 2003;133(suppl 3):S895-S920. 21. Briefel RR, Sempos CT, McDowell MA, Chien S, Alaimo K. Dietary methods research in the third National Health and Nutrition Examination Survey: underreporting of energy intake. Am J Clin Nutr. 1997;65(suppl 4):S1203-S1209. 22. Macdiarmid JI, Vail A, Cade JE, Blundell JE. The sugar-fat relationship revisited: Differences in consumption between men and women of varying BMI. Int J Obes Relat Metab Disord. 1998;22:1051-1061. 23. McCrory MA, Suen VM, Roberts SB. Biobehavioral influences on energy intake and adult weight gain. J Nutr. 2002;132:S3830-S3834. 24. Howarth NC, Saltzman E, McCrory MA, Greenberg AS, Dwyer J, Ausman L, Kramer DG, Roberts SB. Fermentable and nonfermentable fiber supplements did not alter hunger, satiety or body weight in a pilot study of men and women consuming self-selected diets. J Nutr. 2003;133:3141-3144. 25. Hays NP, Bathalon GP, McCrory MA, Roubenoff R, Lipman R, Roberts SB. Eating behavior correlates of adult weight gain and obesity in healthy women aged 55-65 y. Am J Clin Nutr. 2002;75:476-483. 26. Roberts SB, Williamson DF. Causes of adult weight gain. J Nutr. 2002;132(suppl):S3824-S3825. 27. US Department of Agriculture ARS, ARS Food Surveys Research Group. Data (CD-ROM) and documentation for the 1994-1996 Continuing Survey of Food Intakes by Individuals (CSFII)-Diet and Health Knowledge Survey. Washington, DC: National Technical Information Service; 1998. National Technical Information Service Accession No. PB98-500457. 28. Institute of Medicine. Dietary Reference Intakes Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids, Part 1. Washington, DC: Institute of Medicine; 2002:18-69. 29. Goldberg GR, Black AE, Jebb SA, Cole TJ, Murgatroyd PR, Coward WA, Prentice AM. Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off
1372
September 2005 Volume 105 Number 9
30.
31. 32. 33.
34. 35. 36. 37. 38. 39.
40.
41.
42.
43.
44.
45.
limits to identify under-recording. Eur J Clin Nutr. 1991;45:569-581. Black AE. Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations. Int J Obes Relat Metab Disord. 2000;24:1119-1130. Association of Official Analytical Chemists. Official methods of analysis. 16th ed. Gaithersburg, MD: AOAC International; 1995. Steen B. Body composition and aging. N Engl J Med. 1988;46:45-51. Shimokata H, Tobin JD, Muller DC, Elahi D, Coon PJ, Andres R. Studies in the distribution of body fat: I. Effects of age, sex, and obesity. J Gerontol. 1989; 44:M66-M73. Miles CW, Kelsay JL, Wong NP. Effect of dietary fiber on the metabolizable energy of human diets. J Nutr. 1988;118:1075-1081. Miles CW. The metabolizable energy of diets differing in dietary fat and fiber measured in humans. J Nutr. 1992;122:306-311. Miller WC, Niederpruem MG, Wallace JP, Lindeman AK. Dietary fat, sugar, and fiber predict body fat content. J Am Diet Assoc. 1994;94:612-615. Alfieri MAH, Pomerleau J, Grace DM, Anderson L. Fiber intake of normal weight, moderately obese and severely obese subjects. Obes Res. 1995;3:541-547. Pond CM. The fats of life. Cambridge, UK: Cambridge University Press; 1998. Pietrobelli A, Allison DB, Heshka S, Heo M, Wang ZM, Bertkau A, Laferrere B, Rosenbaum M, Aloia JF, Pi-Sunyer FX, Heymsfield SB. Sexual dimorphism in the energy content of weight change. Int J Obes Relat Metab Disord. 2002;26:1339-1348. Pereira MA, O’Reilly E, Augustsson K, Fraser GE, Goldbourt U, Heitmann BL, Hallmans G, Knekt P, Liu S, Pietinen P, Spiegelman D, Stevens J, Virtamo J, Willett WC, Ascherio A. Dietary fiber and risk of coronary heart disease: A pooled analysis of cohort studies. Arch Intern Med. 2004;164:370-376. Bingham SA, Cassidy A, Cole TJ, Welch A, Runswick SA, Black AE, Thurnham D, Bates C, Khaw KT, Key TJ. Validation of weighted records and other methods of dietary assessment using the 24 h urine nitrogen technique and other biological markers. Br J Nutr. 1995;73:531-550. Poppitt SD, Swann D, Black AE, Prentice AM. Assessment of selective under-reporting of food intake by both obese and non-obese women in a metabolic facility. Int J Obes Relat Metab Disord. 1998;22:303-311. Yao M, McCrory MA, Ma G, Tucker KL, Gao S, Fuss P, Roberts SB. Relative influence of diet and physical activity on body composition in urban Chinese adults. Am J Clin Nutr. 2003;77:1409-1416. Kromhout D, Bloemberg B, Seidell JC, Nissinen A, Menotti A. Physical activity and dietary fiber determine population body fat levels: The Seven Countries Study. Int J Obes Relat Metab Disord. 2001;25:301-306. Esparza J, Fox C, Harper IT, Bennett PH, Schulz LO, Valencia ME, Ravussin E. Daily energy expenditure in Mexican and USA Pima Indians: Low physical activity as a possible cause of obesity. Int J Obes Relat Metab Disord. 2000;24:55-59.
Dietary Fiber and Fat Are Associated with Excess Weight in Young and Middle-Aged US Adults NANCY C. HOWARTH, PhD; TERRY T.-K. HUANG, PhD, MPH; SUSAN B. ROBERTS, PhD; MEGAN A. MCCRORY, PhD
ABSTRACT Objective To examine relative associations of dietary composition variables with body mass index (BMI; calculated as kg/m2) among young and middle-aged US adults. We hypothesized that in subjects with physiologically plausible reported energy intakes, fiber intake would be inversely associated with BMI, independent of other dietary composition and sociodemographic variables. Subjects and design Data from adults age 20 to 59 years in the Continuing Survey of Food Intakes by Individuals (CSFII) 1994-1996 were used. Exclusions were pregnancy or lactation, food insecurity, missing weight or height data, or having only one dietary recall. Based on our previously published methods, a physiologically plausible reported energy intake was calculated as being within ⫾22% of predicted energy requirements for the mean of two 24-hour recalls. Results Reporting plausibility ([reported energy intake ⫼predicted energy requirements]⫻100) averaged 83% in the total sample (N⫽4,539) and increased to 96% in the plausible sample (n⫽1,932). Only approximately 5% of the plausible sample consumed the Adequate Intake for N. C. Howarth is a postdoctoral fellow, University of Hawaii Cancer Research Center of Hawaii, Honolulu. T. T.-K. Huang is a research assistant professor, Gerald J. and Dorothy R. Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA. S. B. Roberts is a senior scientist, Energy Metabolism Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA. M. A. McCrory is a research associate professor, School of Nutrition and Exercise Science, Bastyr University, Kenmore, WA. At the time of the study, N. C. Howarth was a doctoral candidate, T. T.-K. Huang was a postdoctoral research associate, and M. A. McCrory was a scientist II, Energy Metabolism Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA. Address correspondence to: Megan A. McCrory, PhD, Research Associate Professor, School of Nutrition and Exercise Science, Room 464, Bastyr University, 14500 Juanita Dr NE, Kenmore, WA 98028. E-mail: [email protected] Copyright © 2005 by the American Dietetic Association. 0002-8223/05/10509-0002$30.00/0 doi: 10.1016/j.jada.2005.06.001
© 2005 by the American Dietetic Association
fiber. In plausibly reporting women, fiber, its interaction with percentage energy from fat, and energy density were independently associated with BMI. Only percentage energy from fat was associated with BMI in men reporting plausibly. Compared with the total sample, stronger relationships between diet and BMI were observed among the plausible reporters. In women, a low-fiber (⬍1.5 g/MJ*), high-fat (ⱖ35% energy) diet was associated with the greatest increase in risk of overweight or obesity compared with a high-fiber, low-fat diet. Conclusions Weight control advice for US women should place greater emphasis on consumption of fiber. J Am Diet Assoc. 2005;105:1365-1372.
S
ixty-five percent of US adults are overweight or obese (body mass index [BMI; calculated as kg/m2] ⱖ25) (1). Dietary contributors to obesity are not well understood. Dietary fat was considered a leading cause of overeating and weight gain (2,3) until other dietary composition factors began to emerge as potentially important modifiers of energy intake. These include carbohydrate, protein, fiber, energy density, and glycemic index (4,5). However, their relative importance to the development of obesity remains uncertain. Low intake of dietary fiber may be one cause of excess energy intake. Increasing dietary fiber may help reduce energy intake by decreasing overall energy density and absorption of energy-yielding nutrients, and enhancing satiety (6-9). The importance of fiber to weight regulation relative to other dietary variables, however, is difficult to ascertain from previous studies. High fiber intake is often correlated with other key dietary factors such as low fat, low energy density, and low glycemic index (8). Of the observational studies that have statistically controlled for at least some of these confounding dietary factors, most (10-13), though not all (14,15), have shown that higher fiber intake is independently associated with lower BMI or less weight gain. Even so, uncertainty remains about the relative importance of fiber because of dietary reporting bias (16). Energy intake is typically underreported by 10% to 50% on average (17-19), with overweight and obese individuals underreporting to a greater extent than normal-weight individuals (18,20). The specificity of underreporting of energy-dense foods and beverages (20-22), which are also
*To convert MJ to kcal, multiply MJ by 238.8.
Journal of the AMERICAN DIETETIC ASSOCIATION
1365
likely to be low in fiber, may result in an underestimation of the association of fiber with BMI (16). Intervention studies suggest that fiber may help promote weight loss; however, many of these studies have not controlled for the potentially confounding influence of other dietary factors such as food form, palatability, energy density, and variety (4,8,23). Furthermore, adding 1 to 30 g/day of fiber to ad libitum diets had only modest effects on weight loss (8,24). One reason for this may be that fiber types—such as functional vs dietary and soluble vs insoluble— differ in their effectiveness (8). Alternatively, higher fiber intakes than those previously studied may be necessary to confer substantial reductions in energy intake and/or body weight (25). Also, the effects of fiber on energy regulation may be stronger when coupled with other nutrients. In support of the latter suggestion, weight loss is substantially greater when an increase in fiber intake is coupled with a decrease in fat intake, compared with when either dietary component is changed alone (8,9). The purpose of this study was to examine, using US national survey data, the relative associations of BMI with dietary factors, specifically fiber, energy density, and macronutrients. We hypothesized that high-fiber intake is associated with a low BMI, independent of macronutrients and energy density, and that a high-fiber, lowfat diet is more strongly associated with low BMI than is either a high-fiber or low-fat diet alone. Because our previous analyses showed stronger dietary associations with BMI when physiologically implausible energy intake reports were excluded (16), we compared associations in the total sample and in the plausible sample. Our analysis was limited to young and middle-aged adults because of the age-related decrease in BMI after approximately age 60 years (26). METHODS Study Population Data from the US Department of Agriculture (USDA) Continuing Survey of Food Intakes by Individuals (CSFII) 1994-1996 (27) were used. This survey of 16,103 noninstitutionalized individuals age 2 to 90 years residing in the United States contains information about dietary intake (by one or two nonconsecutive, multiple-pass 24-hour recalls); socioeconomic, demographic, and health parameters; and self-reported height and weight. From 8,219 respondents age 20 years or older, we excluded those who were 60 years of age or older, pregnant or lactating, food insecure, on medically related diets, completed only one 24-hour dietary recall, or did not provide both height and weight. The number in our total sample for analysis was 4,539 (2,374 men and 2,165 women). Data Analysis Two-day average intakes were used for all analyses. Physiologically plausible reports of energy intake were determined by comparing reported energy intake with predicted energy requirements determined by using the new Dietary Reference Intake (DRI) equations for predicted energy requirements (28). Detailed procedures are described elsewhere (16). Briefly, we calculated sex, age-group, and
1366
September 2005 Volume 105 Number 9
weight-class specific cutoffs for reported energy intake as a percentage of predicted energy requirements ([reported energy intake⫼predicted energy requirements]⫻100), taking into account measurement and biological intraindividual variation in reported energy intake and total energy expenditure as measured by doubly labeled water, and error in equations for predicted energy requirements (16,28-30). When different cutoffs levels were tested (⫾2 standard deviations [SD], ⫾1.5 SD, ⫾1 SD of [reported energy intake⫼predicted energy requirements]⫻100), only the ⫾1 SD cutoff resulted in a relationship between reported energy intake and body weight that did not differ significantly from the relationship between total energy expenditure and body weight. For the present analysis, we therefore used the ⫾1 SD cutoff (⫾22% for these data) to maximize the validity of reported energy intake. This required that an individual’s reported energy intake be within 78% to 122% of predicted energy requirements to be considered physiologically plausible. Fifty-seven percent of the total sample was detected as having an implausible reported energy intake, leaving 1,932 subjects in the plausible sample (1,108 men and 824 women). After initial analyses comparing the total and plausible samples (Tables 1 and 2), we present results only for the plausible sample. Descriptive data (mean⫾standard error of the mean [SEM]) were calculated, and independent t tests and 2 tests were used to compare sample characteristics. Multiple-regression analysis determined dietary associations with BMI. Specifically, relative associations of fiber density [g/MJ, herein referred to as “fiber” unless otherwise specified, in which fiber in the CSFII was determined using methods of the Association of Official Analytical Chemists (31)], energy density (MJ/g of all reported foods and beverages; ie, not water), and macronutrients (percentage of energy intake) with BMI were tested. Fiber by percentage of energy from fat, and fiber by sex interactions were also examined. Because the fiber by sex interaction was significant (P⬍.001), subsequent analyses were performed separately in men and women. Covariates included age, sex, education (high school or less vs beyond high school), current smoking (yes/no), selfreported chronic disease (yes/no for at least one of the following: diabetes, hypertension, heart disease, cancer, hypercholesterolemia, and/or stroke), ethnicity (white vs nonwhite), annual household income (0% to 130%, 131% to 350%, and ⬎350% of poverty threshold), urbanicity (urban, suburban, rural), geographic region (Northeast, Midwest, South, West), and television viewing (hours per day) (as a proxy for inactivity). Results were similar when vegetarians or individuals with self-reported chronic disease were excluded (data not shown). Results were also similar when models using fiber (g), fat (g), their interaction, and controlling for total energy to predict BMI were tested to assess the effect that ratios might have on the outcome (data not shown). We therefore retained fiber density to be consistent with the epidemiologic literature. Linear regression analysis to compare BMIs and logistic regression analysis to assess the relative risk of overweight and obesity (ⱖ25) from consuming different combinations of fiber and fat were performed, controlling for the covariates described earlier. Low- and high-fiber intakes were defined, respectively, as less than 1.5 g/MJ and 1.5 g/MJ or more. A fiber intake of 1.5 g/MJ approx-
Table 1. Demographic and dietary characteristics of total, plausible, and implausible reporters of dietary intake in the Continuing Survey of Food Intake by Individuals 1994-96a Women Total (nⴝ2,165)
Plausible (nⴝ824)
Men Implausible (nⴝ1,341)
Total (nⴝ2,374)
Plausible (nⴝ1,108)
Implausible (nⴝ1,266)
4™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™ mean⫾SEM b ™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™3 Demographic Age (y) Height (cm) Weight (kg) BMIc TV/day (h) Overweight/obese (BMI ⱖ25) Current smoker High school or less Urban White With diseasec ⬍130% of poverty level September 2005 ● Journal of the AMERICAN DIETETIC ASSOCIATION
Dietary Energy intake (MJ/d)d Predicted energy requirement (MJ)d Energy intake⫼predicted energy requirement⫻100 (%) Carbohydrate (% energy) Protein (% energy) Fat (% energy) Energy density (kJ/g)e Fiber (g/d) Fiber (g/MJ)d a
39.0⫾0.3 38.6⫾0.5 39.2⫾0.3 38.2⫾0.3 38.8⫾0.4 37.8⫾0.3 163.5⫾0.2 163.2⫾0.3 163.8⫾0.3** 178.2⫾0.2 177.8⫾0.3 178.6⫾0.3 67.6⫾0.4 64.6⫾0.6 69.5⫾0.5*** 84.2⫾0.4 82.3⫾0.5 85.8⫾0.6*** 25.3⫾0.2 24.3⫾0.2 26.0⫾0.2*** 26.5⫾0.1 26.0⫾0.1 26.9⫾0.2*** 2.1⫾0.1 2.1⫾0.1 2.1⫾0.1 2.4⫾0.1 2.3⫾0.1 2.5⫾0.1 4™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™ % ™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™3 63.4 59.4 67.0*** 46.1 37.4 51.5*** 24.0 22.6 25.0 28.9 26.0 31.4 42.1 36.2 45.5** 42.0 42.3 41.7 33.1 36.7 31.2 31.4 27.6 34.5** 80.3 83.7 78.1* 81.0 84.3 78.2* 23.9 21.5 25.4 24.3 22.9 25.4 11.0 9.2 12.1* 9.9 7.1 10.4 4™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™ mean⫾SEM b ™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™ 3 7.0⫾0.1
8.4⫾0.1
6.0⫾0.1***
10.6⫾0.2
11.3⫾0.1
10.1⫾0.4**
9.1⫾0.0
8.9⫾0.0
9.2⫾0.0***
11.7⫾0.0
11.6⫾0.0
11.9⫾0.0***
76.7⫾0.6 51.8⫾0.3 15.5⫾0.1 32.5⫾0.2 3.7⫾0.0 13.5⫾0.2 2.0⫾0.0
94.5⫾0.4 51.4⫾0.4 14.7⫾0.2 33.3⫾0.3 4.0⫾0.1 15.8⫾0.3 1.9⫾0.0
65.8⫾0.7*** 52.0⫾0.4 16.0⫾0.2*** 32.0⫾0.3** 3.5⫾0.0*** 12.1⫾0.2*** 2.1⫾0.0***
88.3⫾0.7 48.3⫾0.3 16.0⫾0.1 33.9⫾0.2 4.1⫾0.0 18.2⫾0.3 1.8⫾0.0
96.7⫾0.4 48.0⫾0.3 15.6⫾0.1 34.8⫾0.3 4.2⫾0.0 19.4⫾0.3 1.7⫾0.0
80.9⫾1.1*** 48.6⫾0.3 16.4⫾0.1*** 33.2⫾0.3*** 3.9⫾0.1** 17.2⫾0.3*** 1.8⫾0.0
Respondents were age 20-59 y during the Continuing Survey of Food Intakes by Individuals (1994-1996). SEM⫽standard error of the mean. c BMI⫽body mass index (calculated as kg/m2); self-reported condition of ⱖ one of the following: diabetes, high blood pressure, heart disease, cancer, high blood cholesterol, stroke. d To convert MJ to kcal, multiply MJ by 238.8. e To convert kJ/g to kcal/g, multiply kJ/g by 4.184. *P⬍.05, differing significantly from plausible reporters of the same sex. **P⬍.01. ***P⬍.001. b
1367
Table 2. Multiple linear regression models for dietary and lifestyle factor associations with BMIab in the total sample and the subsample with plausible energy intake reports Total Sample (nⴝ2,165 women and 2,374 men)
ⴞSEc Women Constant Fiber (g/MJ)d Fat (% energy) Fiber (g/MJ)d⫻fat (%energy) Energy density (kJ/g)e TV (h/d) Current smoker (yes/no) Model R 2 Men Constant Fiber (g/MJ) Fat (% energy) Fiber (g/MJ)⫻fat (%energy) Energy density (kJ/g)e TV (h/d) Current smoker (yes/no) Model R 2
24.01⫾1.64 ⫺1.32⫾0.39 ⫺.03⫾0.03 .04⫾0.01 ⫺.11⫾0.14 .25⫾0.08 1.13⫾0.30 23.04⫾1.25 .56⫾0.42 .09⫾0.03 ⫺.02⫾0.01 ⫺.27⫾0.09 .28⫾0.05 1.02⫾0.19
Plausible Sample (nⴝ824 women and 1,108 men)
Partial R 2
P value
ⴞSE
0.001 0.006 0.005 0.000 0.006 0.006 0.130
⬍.001 ⬍.001 .293 .005 .409 .003 ⬍.001 ⬍.001
23.03⫾1.64 ⫺2.46⫾0.72 ⫺.04⫾0.05 .06⫾0.02 .33⫾0.16 .13⫾0.12 1.12⫾0.41
0.001 0.006 0.000 0.005 0.014 0.006 0.103
⬍.001 .190 .003 .143 .005 ⬍.001 ⬍.001 ⬍.001
21.77⫾1.69 .66⫾0.77 .13⫾0.05 ⫺.03⫾0.02 ⫺.21⫾0.16 .24⫾0.06 .79⫾0.30
Partial R 2
P value
0.012 0.031 0.005 0.008 0.004 0.004 0.190
⬍.001 .001 .402 .008 .04 .168 .050 ⬍.001
0.001 0.020 0.003 0.002 0.012 0.004 0.102
⬍.001 .396 .019 .224 .179 ⬍.001 .013 ⬍.001
a
BMI⫽body mass index. Models also controlled for age, race, education, US region, urbanicity, income, and self-reported chronic disease (data not shown). c SE⫽standard error. d To convert MJ to kcal, multiply MJ by 238.8. e To convert kJ to kcal, multiply kJ by 4.184. b
imates the national average (27). A higher cutoff was also examined initially (ⱖ3.0 g/MJ, roughly the Adequate Intake (AI) established in the new DRI, for an 8.4 MJ/day (2,000 kcal/day) energy intake (28), but too few individuals consumed this amount of fiber to provide adequate statistical power. Low-, medium-, and high-fat intakes were defined as less than 25%, 25% to 35%, and more than 35% of total energy intake, respectively. For the logistic regression model, the low-fat/high-fiber category was designated the reference with a relative risk of 1.0. The unweighted mean variation of ([reported energy intake⫼predicted energy requirements]⫻100) (samplespecific) and weighted partial R2 from regression analyses were calculated using SAS (version 8.2, 2003, SAS Institute, Cary, NC). t tests, R2, and 2 tests, linear and logistic regressions, and least-squared means tests were performed using SUDAAN (v.8, 2001, Research Triangle Institute, Research Triangle Park, NC) and weighted for sampling design, with ␣ set at .05. SUDAAN was used to incorporate sample design (stratified, multistage area probability sample) for variance estimation. Failure to account for sample design is known to underestimate standard errors of parameters, hence increasing the risk of rejecting true null hypotheses. RESULTS Demographic and Dietary Characteristics As shown in Table 1, mean BMI and the percentage of overweight or obese individuals were significantly higher in the implausible reporters compared with the plausible re-
1368
September 2005 Volume 105 Number 9
porters. In addition, implausible reporters were more likely to be nonwhite, and among women less educated and poorer. As expected, reporting plausibility ([reported energy intake⫼predicted energy requirements]⫻100) was higher in the plausible sample, approaching 100% in both sexes, but was much lower in the implausible sample, especially in women. The implausible sample also reported lower percentage energy intake from fat, energy density, and fiber (g), but higher percentage of energy from protein and a more fiber-dense diet (the latter significant only in women). Even in the plausible sample, fiber intake was particularly low (Figure 1), averaging approximately half the AI of 25 g/day for women and 38 g/day for men (28). Additionally, only 8% of women and 2% of men in the plausible sample consumed the AI or more. Dietary Associations with BMI in the Total vs Plausible Samples of Women Regression models showing the associations of diet with BMI in the total and plausible samples are shown in Table 2. In the total sample of women, fiber and the fiber by percentage energy from fat interaction were significantly associated with BMI, but percentage energy from fat alone was not significantly associated. In the plausible sample, fiber, its interaction with percentage energy from fat, and energy density were significantly associated with BMI. Note that dietary factors were more strongly associated with BMI in the plausible sample, as indicated by the steeper regression coefficients.
Figure 1. Fiber intake of participants age 20 to 59 years in the Continuing Survey of Food Intakes by Individuals 1994-1996 whose reported energy intakes were physiologically plausible (n⫽824 women and 1,108 men; see text for explanation) compared with recommended intake levels. Symbols indicate mean, bars indicate range (minimum and maximum values), and bars in bold indicate the 25th, 50th, and 75th percentiles. Dotted line shows the Adequate Intake (AI) for fiber according to the recent Dietary Reference Intake recommendations (28). 8.6% of women and 2.2% of men consumed the AI or more (not shown). Also shown in Table 2 are the relative associations of dietary composition vs lifestyle factors with BMI. Partial R2 indicate that in the total sample, dietary composition accounted for 1.2% of the between-subject variation in BMI, whereas in the plausible sample dietary composition accounted for four times or more of this amount, or 4.8% to 5.6%, of the between-subject variation in BMI. Furthermore, in the plausible sample, diet accounted for a much larger between-subject variation in BMI than did the combination of the other modifiable lifestyle factors modeled (television viewing and smoking, 0.8%). Dietary Associations with BMI in the Total vs Plausible Samples of Men In both the total and plausible samples of men (Table 2), percentage energy from fat was positively associated with BMI, and this association was stronger in the plausible sample. Energy density was inversely associated with BMI in the total but not the plausible sample. In both the total or plausible samples, fiber was not significantly associated with BMI. The models showed that in the total sample, percentage energy from fat accounted for 0.6% of the between-subject variation in BMI, whereas in the plausible sample it accounted for 2.0%. Other modifiable lifestyle factors modeled accounted for 2.0% of the between-subject variation in BMI in the plausible sample, and 1.6% in the total sample. Fiber by Fat Interactions and Relative Risk of Overweight and Obesity in Women Figure 2 illustrates the fiber by percentage energy from fat interaction associations with BMI seen in the regression model for the plausibly reporting women. BMI at high-, medium-, and low-fat intakes are shown at both low- and high-fiber intakes. Mean BMI was the highest and did not differ significantly among women with lowfiber intakes at any level of fat intake, or high-fiber intake
Figure 2. Adjusted mean (⫾standard error of the mean) body mass index (BMI) in relation to intakes of fiber and percentage energy from fat in women age 20 to 59 years participating in the Continuing Survey of Food Intakes by Individuals 1994-96. Only women whose reported energy intakes were physiologically plausible are shown (see text for explanation). Low- and high-fiber intakes were defined as ⬍1.5 g/MJ vs ⱖ1.5 g/MJ, respectively. (To convert MJ to kcal, multiply MJ by 238.8.) Low, medium, and high fat intakes were defined as ⬍25%, 25%-35%, and ⬎35% of energy intake, respectively. Values are adjusted for age, sex, education, current smoking, self-reported chronic disease, ethnicity, household income, urbanicity, geographic region, and television viewing. Bars with the same letter are not significantly different. n⫽145, 89, 24, 219, 252, 95 (left to right).
coupled with high-fat intake (25.1⫾0.6 for the four groups combined). Mean BMI was significantly lower among women consuming high-fiber, medium-fat (23.6⫾0.4) diets, and even lower among women consuming high-fiber, low-fat diets (22.2⫾0.4). Using the high-fiber/low-percentage energy from fat category as the reference, the relative risk of consuming the other fiber/percentage energy from fat combinations is shown in women (Figure 3). The relative risk of having excess weight was significantly elevated among all other fiber/percentage energy from fat combinations compared with consuming a high-fiber, low-fat diet. DISCUSSION This study is the first to examine relationships between dietary fiber and BMI in a national data set using a rigorous approach to screen for physiologically implausible energy intake reports. In women, higher fiber intake was strongly associated with a lower BMI: those with fiber intakes more than the US average had a lower BMI than those who consumed the average or less than average. We also found that among women, dietary fat intake had no relationship with BMI unless it was coupled with a high-fiber diet. In men, percentage energy from fat was the only dietary variable independently associated with BMI. These relationships were more apparent when implausible dietary reports were excluded, and furthermore accounted for a greater portion of the between-subject
September 2005 ● Journal of the AMERICAN DIETETIC ASSOCIATION
1369
Figure 3. Relative risk of overweight or obesity by fiber and percent energy from fat categories of intake in women age 20 to 59 years participating in the Continuing Survey of Food Intakes by Individuals 1994-96. Only women whose reported energy intakes were physiologically plausible are shown (see text for explanation). Low- and high-fiber intakes were defined as ⬍1.5 g/MJ vs ⱖ1.5 g/MJ, respectively. (To convert MJ to kcal, multiply MJ by 238.8.) Low, medium, and high fat intakes were defined as ⬍25%, 25%-35%, and ⬎35% of energy intake, respectively. The high-fiber/low-percentage energy from fat category was defined as the reference (relative risk 1.0). Relative risks are adjusted for age, sex, education, current smoking, self-reported chronic disease, ethnicity, household income, urbanicity, geographic region, and television viewing. Values in parentheses are 95% confidence intervals. The relative risk of having excess weight was significantly elevated among all fiber/percentage fat combinations compared with consuming a high-fiber, low-fat diet. n⫽95, 252, 219, 24, 89, 145 (left to right).
variation in BMI of both men and women than did other modifiable lifestyle factors. Our findings highlight the importance of diet in weight control and strongly suggest that fiber should be considered along with fat as a key modulator of weight status in women. Physiological explanations for the role of fiber in energy regulation include reducing energy density; decreasing nutrient absorption rate, thereby slowing the return of hunger; up-regulating satiety hormones such as glucagon-like peptide-1; and sequestering energy-providing nutrients, causing them to be excreted (8). Given the very low fiber intakes observed in our study, this latter mechanism alone could theoretically be responsible for the substantial fat gain through young and middle-age that occurs in most individuals in Westernized societies (32,33). Intake-balance studies indicate that 3% to 6% less energy is absorbed when fiber intake is at least 34 g/day (almost double the mean fiber intake in our study), with an increasing amount of energy not absorbed with increasing fiber intake (7,34,35). On a 10 MJ/d diet, this approximates 300 to 600 kJ (70 to140 kcal) not absorbed. If this level of fiber intake were maintained daily without any other changes, the reduced absorption of energy would amount to roughly 3 to 7 kg of body weight either lost or not gained during 1 year. However, to affect body weight, fiber may need to be an integral part of food, because 30 g of supplemental functional fiber over 3 weeks had no effect (24). Another possibility is that certain fiber types are more effective than others for weight control, but this remains inconclusive (8,24).
1370
September 2005 Volume 105 Number 9
It is unclear why fiber was inversely associated with BMI in women but not in men. Experimental studies of fiber effects on ad libitum energy intake and body weight change have either not reported results by sex or, when conducted in either men or women, have not adequately controlled for confounding dietary factors (8). Some observational studies have reported significant inverse relationships between fiber and BMI or percentage body fat in men (10,36,37), but these studies also did not control for confounding dietary factors. Interestingly, in our study, in plausibly reporting men, fiber was also significantly inversely related to BMI (⫽⫺.54, P⫽.018), but only when other dietary factors were not controlled. Thus, the previously reported inverse relationship between fiber and BMI in men may be an artifact due to lack of control for confounding. Alternatively, a relationship between fiber and BMI may not be detectable in our sample of men because of their exceedingly low-fiber intakes. Possibly because of the lower fat-free mass and larger fat mass among women for a given BMI (38,39), their higher fiber intake relative to energy requirements may have a greater impact on energy regulation and BMI than among men. Clearly, more research is needed on the potentially different effects of dietary fiber on overeating and obesity in men and women. We found a significant fiber-by-fat interaction association with BMI in women. Dietary fat was not by itself significantly associated with BMI; however, a low-fat intake combined with a high-fiber intake was significantly associated. In addition, the relative risk of overweight or obesity was significantly higher in women consuming all combinations of fiber and percentages of energy from fat relative to a high-fiber, low-fat diet. These findings agree with results of several previous intervention studies in which weight loss was threefold greater when recommendations were made to reduce fat and increase fiber intakes, compared with recommending either change alone (9). In the present study, mean BMI was 1.5 less in women consuming a high- vs low-fiber diet, and this difference increased to 2.75 when a low-fat diet was consumed with a high-fiber diet. Although only limited conclusions can be drawn from our cross-sectional study about the role of fiber in weight control, our results combined with those of previous intervention studies (8,9) imply that a high-fiber diet is much more effective than a low-fat diet at preventing weight gain in women, and the combination of high-fiber and low-fat may be even more effective in maintaining normal weight than either one alone. Given the substantial associations between fiber intake and BMI noted even at the low levels consumed in this study, increased fiber consumption should be more widely promoted for potential weight control, at least in women, and in both sexes for known effects on reducing the risk for several chronic diseases such as coronary heart disease, type 2 diabetes, and some cancers (28,40). Our study suggests that previous observational studies, due to dietary reporting bias, may have underestimated the importance of fiber in energy regulation. Consistent with most previous studies (17,20,22), the implausibly reporting group had a higher percentage of overweight and obese individuals compared with the plausibly reporting group. Although absolute fiber intake (g) was higher in the total
sample than in the plausible sample, fiber density (g/MJ) was slightly less. This is probably due to the exclusion of implausible reporters who, based on previous studies as well as other analyses in this dataset (16,41,42), likely underreported foods high in energy density and hence low in fiber. We found that associations between BMI and fiber were relatively low in the total sample but were much stronger after excluding implausible reporters. We also observed that, in the plausible reporters, dietary factors accounted for greater between-subject variation in BMI compared with the other modifiable lifestyle factors we were able to take into account. This was especially true for women, in whom dietary variables accounted for seven times the between-subject variation in BMI than did smoking and inactivity. Our results are corroborated by a recent study in Beijing, China (43), in which energy intake reporting plausibility was very high (96%), and activity levels are much higher than in the United States. In that study, both dietary factors and physical activity accounted equally for the between-subject variation in percentage body fat. Although we were unable to account for other important lifestyle factors such as physical activity due to limitations in the survey design, our results suggest that diet may play a stronger role in weight control, relative to other lifestyle factors, than is often attributed to it (44,45); however, this deserves further study. The primary strengths of this study are that, unlike most previous studies on dietary relationships with BMI, we simultaneously modeled several dietary composition variables, thus assessing relative associations with BMI. Additionally, we objectively identified implausible dietary reports, and compared results in the total sample with those in the plausible sample. However, there were some limitations. Few subjects in the survey consumed diets high enough in fiber for an adequate evaluation of high intakes (eg, the AI or higher). Weight and height were self-reported. We did not have a measure of physical activity and, therefore, used the amount of time viewing television per day as a proxy for inactivity, which may not represent the same construct. Finally, exclusion of more than half of the total sample to obtain a physiologically plausibly reporting sample could be considered a potential weakness. However, the tradeoff for excluding implausible reports was a substantial gain in the validity of the data for representing usual dietary intake of the subjects studied. Due to the exclusion of a large number of subjects for reporting implausibly, we have no way of knowing whether our findings are applicable to the US population as a whole. Nonetheless, we can think of no physiological reason to suspect that the results would differ in the implausible reporters had they reported plausibly. CONCLUSIONS Our results, combined with those of previous intervention studies, strongly suggest that among women a high-fiber diet seems to be more effective than a low-fat diet for preventing weight gain, and the combination of a highfiber and low-fat eating plan may be even more effective than either one alone. We did not find an association between fiber and excess weight in men; however, fiber intakes were extremely low in this study, with only approximately 5% of individuals consuming the AI or more.
This suggests inadequate fiber intake in the US population as a whole. However, given that in women associations between fiber intake and BMI were found even at low levels, and the known benefit of fiber in the prevention of several chronic diseases, increased fiber consumption should be more widely promoted. This study was funded in part by US Department of Agriculture/Economic Research Service/Food and Nutrition Research Program grant #43-3AEM-2-80088. References 1. Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among US adults, 1999-2000. JAMA. 2002;288:1723-1727. 2. Roberts SB, Pi-Sunyer FX, Dreher M, Hahn R, Hill JO, Kleinman RE, Peters JC, Ravussin E, Rolls BJ, Yetley E, Booth SL. Physiology of fat replacement and fat reduction: Effects of dietary fat and fat substitutes on energy regulation. Nutr Rev. 1998;56(5 Pt 2):S29-S49. 3. Willett WC. Dietary fat plays a major role in obesity: No. Obes Rev. 2002;3:59-68. 4. Roberts SB. High-glycemic index foods, hunger, and obesity: Is there a connection? Nutr Rev. 2000;58:163170. 5. Roberts SB, McCrory MA, Saltzman E. The influence of dietary composition on energy intake and body weight. J Am Coll Nutr. 2002;21(suppl):S140-S145. 6. Bonfield CT. Dietary fiber and body weight management. In: Kritchevsky D, Bonfield C, eds. Dietary Fiber in Health and Disease. St Paul, MN: Eagan Press; 1995:459-465. 7. Baer DJ, Rumpler WV, Miles CW, Fahey GCJ. Dietary fiber decreases the metabolizable energy content and nutrient digestibility of mixed diets fed to humans. J Nutr. 1997;127:579-586. 8. Howarth NC, Saltzman E, Roberts SB. Dietary fiber and weight regulation. Nutr Rev. 2001;59:129-139. 9. Yao M, Roberts SB. Dietary energy density and weight regulation. Nutr Rev. 2001;59:247-258. 10. Appleby PN, Thorogood M, Mann JI, Key TJ. Low body mass index in non-meat eaters: The possible roles of animal fat, dietary fibre and alcohol. Int J Obes. 1998;22:454-460. 11. Ludwig DS, Pereira MA, Kroenke CH, Hilner JE, Van Horn L, Slattery ML, Jacobs DR Jr. Dietary fiber, weight gain, and cardiovascular disease risk factors in young adults. JAMA. 1999;282:1539-1546. 12. Liu S, Willett WC, Manson JE, Hu FB, Rosner B, Colditz G. Relation between changes in intakes of dietary fiber and grain products and changes in weight and development of obesity among middleaged women. Am J Clin Nutr. 2003;78:920-927. 13. Sasaki S, Katagiri A, Tsuji T, Shimoda T, Amano K. Self-reported rate of eating correlates with body mass index in 18-y-old Japanese women. Int J Obes. 2003; 27:1405-1410. 14. Lovejoy JC, Champagne CM, Smith SR, de Jonge L, Xie H. Ethnic differences in dietary intakes, physical activity, and energy expenditure in middle-aged, premenopausal women: The Healthy Transitions Study. Am J Clin Nutr. 2001;74:90-95.
September 2005 ● Journal of the AMERICAN DIETETIC ASSOCIATION
1371
15. Stookey JD. Energy density, energy intake and weight status in a large free-living sample of Chinese adults: Exploring the underlying roles of fat, protein, carbohydrate, fiber and water intakes. Eur J Clin Nutr. 2001;55:349-359. 16. McCrory MA, Hajduk CL, Roberts SB. Procedures for screening out inaccurate reports of dietary energy intake. Public Health Nutr. 2002;5:873-882. 17. Schoeller DA, Bandini LG, Dietz WH. Inaccuracies in self-reported intake identified by comparison with the doubly labelled water method. Can J Physiol Pharmacol. 1990;68:941-949. 18. Schoeller DA. Limitations in the assessment of dietary energy intake by self-report. Metabolism. 1995; 44:18-22. 19. Subar AF, Kipnis V, Troiana RP, Midthune D, Schoeller DA, Bingham S, Sharaugh CO, Trabulsi J, Runswick S, Ballard-Barbash R, Sunshine J, Schatzkin A. Using intake biomarkers to evaluate the extent of dietary misreporting in a large sample of adults: The OPEN study. Am J Epidemiol. 2003;158:1-13. 20. Livingstone MBE, Black AE. Markers of the validity of reported energy intake. J Nutr. 2003;133(suppl 3):S895-S920. 21. Briefel RR, Sempos CT, McDowell MA, Chien S, Alaimo K. Dietary methods research in the third National Health and Nutrition Examination Survey: underreporting of energy intake. Am J Clin Nutr. 1997;65(suppl 4):S1203-S1209. 22. Macdiarmid JI, Vail A, Cade JE, Blundell JE. The sugar-fat relationship revisited: Differences in consumption between men and women of varying BMI. Int J Obes Relat Metab Disord. 1998;22:1051-1061. 23. McCrory MA, Suen VM, Roberts SB. Biobehavioral influences on energy intake and adult weight gain. J Nutr. 2002;132:S3830-S3834. 24. Howarth NC, Saltzman E, McCrory MA, Greenberg AS, Dwyer J, Ausman L, Kramer DG, Roberts SB. Fermentable and nonfermentable fiber supplements did not alter hunger, satiety or body weight in a pilot study of men and women consuming self-selected diets. J Nutr. 2003;133:3141-3144. 25. Hays NP, Bathalon GP, McCrory MA, Roubenoff R, Lipman R, Roberts SB. Eating behavior correlates of adult weight gain and obesity in healthy women aged 55-65 y. Am J Clin Nutr. 2002;75:476-483. 26. Roberts SB, Williamson DF. Causes of adult weight gain. J Nutr. 2002;132(suppl):S3824-S3825. 27. US Department of Agriculture ARS, ARS Food Surveys Research Group. Data (CD-ROM) and documentation for the 1994-1996 Continuing Survey of Food Intakes by Individuals (CSFII)-Diet and Health Knowledge Survey. Washington, DC: National Technical Information Service; 1998. National Technical Information Service Accession No. PB98-500457. 28. Institute of Medicine. Dietary Reference Intakes Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids, Part 1. Washington, DC: Institute of Medicine; 2002:18-69. 29. Goldberg GR, Black AE, Jebb SA, Cole TJ, Murgatroyd PR, Coward WA, Prentice AM. Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off
1372
September 2005 Volume 105 Number 9
30.
31. 32. 33.
34. 35. 36. 37. 38. 39.
40.
41.
42.
43.
44.
45.
limits to identify under-recording. Eur J Clin Nutr. 1991;45:569-581. Black AE. Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations. Int J Obes Relat Metab Disord. 2000;24:1119-1130. Association of Official Analytical Chemists. Official methods of analysis. 16th ed. Gaithersburg, MD: AOAC International; 1995. Steen B. Body composition and aging. N Engl J Med. 1988;46:45-51. Shimokata H, Tobin JD, Muller DC, Elahi D, Coon PJ, Andres R. Studies in the distribution of body fat: I. Effects of age, sex, and obesity. J Gerontol. 1989; 44:M66-M73. Miles CW, Kelsay JL, Wong NP. Effect of dietary fiber on the metabolizable energy of human diets. J Nutr. 1988;118:1075-1081. Miles CW. The metabolizable energy of diets differing in dietary fat and fiber measured in humans. J Nutr. 1992;122:306-311. Miller WC, Niederpruem MG, Wallace JP, Lindeman AK. Dietary fat, sugar, and fiber predict body fat content. J Am Diet Assoc. 1994;94:612-615. Alfieri MAH, Pomerleau J, Grace DM, Anderson L. Fiber intake of normal weight, moderately obese and severely obese subjects. Obes Res. 1995;3:541-547. Pond CM. The fats of life. Cambridge, UK: Cambridge University Press; 1998. Pietrobelli A, Allison DB, Heshka S, Heo M, Wang ZM, Bertkau A, Laferrere B, Rosenbaum M, Aloia JF, Pi-Sunyer FX, Heymsfield SB. Sexual dimorphism in the energy content of weight change. Int J Obes Relat Metab Disord. 2002;26:1339-1348. Pereira MA, O’Reilly E, Augustsson K, Fraser GE, Goldbourt U, Heitmann BL, Hallmans G, Knekt P, Liu S, Pietinen P, Spiegelman D, Stevens J, Virtamo J, Willett WC, Ascherio A. Dietary fiber and risk of coronary heart disease: A pooled analysis of cohort studies. Arch Intern Med. 2004;164:370-376. Bingham SA, Cassidy A, Cole TJ, Welch A, Runswick SA, Black AE, Thurnham D, Bates C, Khaw KT, Key TJ. Validation of weighted records and other methods of dietary assessment using the 24 h urine nitrogen technique and other biological markers. Br J Nutr. 1995;73:531-550. Poppitt SD, Swann D, Black AE, Prentice AM. Assessment of selective under-reporting of food intake by both obese and non-obese women in a metabolic facility. Int J Obes Relat Metab Disord. 1998;22:303-311. Yao M, McCrory MA, Ma G, Tucker KL, Gao S, Fuss P, Roberts SB. Relative influence of diet and physical activity on body composition in urban Chinese adults. Am J Clin Nutr. 2003;77:1409-1416. Kromhout D, Bloemberg B, Seidell JC, Nissinen A, Menotti A. Physical activity and dietary fiber determine population body fat levels: The Seven Countries Study. Int J Obes Relat Metab Disord. 2001;25:301-306. Esparza J, Fox C, Harper IT, Bennett PH, Schulz LO, Valencia ME, Ravussin E. Daily energy expenditure in Mexican and USA Pima Indians: Low physical activity as a possible cause of obesity. Int J Obes Relat Metab Disord. 2000;24:55-59.