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Whole Grain Consumption and Body Mass Index in Adult Women: An Analysis of NHANES 1999-2000 and the USDA Pyramid Servings Database

General Mills Bell Institute of Health and Nutrition (C.K.G., A.M.A., A.L.E.)
Statistics Department (N.H.), General Mills, Inc., Minneapolis, MN USA

Address correspondence to: Carolyn K. Good PhD, RD, Senior Nutrition Research Scientist, General Mills Bell Institute of Health and Nutrition, 9000 Plymouth Avenue North, Minneapolis, MN 55427. E-mail: Carolyn.Good{at}genmills.com

	ABSTRACT

TOP FOOTNOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS CONCLUSIONS ACKNOWLEDGMENTS REFERENCES

 
Objective: To examine the relationship between whole grain consumption and body mass index (BMI) in a sample of American adult women.

Methods: Dietary intake data from the National Health and Nutrition Examination Survey 1999-2000 were linked to the USDA Pyramid Servings Database. Women 19 years of age and older (n = 2,092) were classified into groups based on their average whole grain (WG) intake: 0 servings, more than 0 but less than 1 serving, and 1 servings per day. Within these classifications, mean BMI, mean waist circumference and percent overweight/obese (BMI 25) were identified as primary dependent variables. Regression and logistic regression analyses were used to assess associations between BMI, waist circumference and percent of the population overweight/obese (BMI 25) and WG consumption.

Results: Women consuming at least one serving of WG had a significantly lower mean BMI and waist circumference than women with no WG consumption (p < 0.05). Multiple regression analysis showed a significant inverse relationship between BMI and whole grain intake after adjustment for age, energy intake, dietary fiber and alcohol intake (p = 0.004). This effect was mildly attenuated but remained significant after further adjustment for level of physical activity, smoking status, ethnicity and education (p = 0.018). The odds ratio for having a BMI 25 was 1.47 (95% CI 1.12–1.94; p for trend 0.013) for women consuming no WG compared to those consuming at least one serving, after adjustment for all covariates.

Conclusions: These data support other research suggesting increased WG intake may contribute to a healthy body weight in adult women.

Key words: NHANES, whole grain, body mass index

Abbreviations: BMI = body mass index • CIs = confidence intervals • EAR = estimated average requirement • HHS = Health and Human Services • kcals = kilocalories • NHANES = National Health and Nutrition Examination Survey • OR = odds ratio • USDA = United States Department of Agriculture • WG = whole grain

	INTRODUCTION

TOP FOOTNOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS CONCLUSIONS ACKNOWLEDGMENTS REFERENCES

 
The complex etiology of the American obesity epidemic has led to increased interest in the identification of lifestyle factors that promote healthy weight. In addition, national dietary guidance has an increased focus on body weight reduction and regulation. The recent USDA/HHS Dietary Guidelines report for the first time included guidelines for whole grain intake, recommending that "at least 3 servings of whole grain per day can reduce risk of coronary heart disease and helps with weight maintenance" [1].

Previous epidemiological studies have reported a relationship between consumption of whole grain foods and a lower body mass index (BMI) [2–6]. Prospective, cohort studies in adult men and women reported that those who ate more whole grain foods weighed less at baseline, gained less weight over time, and had an overall lower risk of obesity [2,6]. These studies suggest whole grains may play a role in body weight regulation due to potential effects on appetite regulation, hormonal factors, and carbohydrate absorption [7].

Additionally, consumption of whole grain foods is associated with a lower risk of heart disease, cancer, type 2 diabetes and other chronic diseases [8–11]. However, the exact mechanisms underlying the beneficial effects of whole grain remain unclear. It is proposed that the disease-preventing ability of chronic whole grain consumption is through synergies among naturally occurring phytonutrients, antioxidants and other nutrients that provide benefits beyond dietary fiber alone [12].

Beyond epidemiological evidence, whole grain consumption in the population is difficult to study, as the majority of intake data available do not contain quantified whole grain intake measures. The United States Department of Agriculture (USDA), Pyramid Servings Database is the only national database that provides quantified measures of whole grain foods [13]. The National Health and Nutrition Examination Survey (NHANES) provides national probability estimates of food and nutrient consumption along with limited measures of physical activity [14]. Combination of the two data sets allows the study of associations between intake of whole grain foods and markers of overweight among adult women.

	MATERIALS AND METHODS

TOP FOOTNOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS CONCLUSIONS ACKNOWLEDGMENTS REFERENCES

 
Study Design and Population
Data used in this study are from NHANES 1999-2000 [15] and the corresponding USDA Pyramid Servings Database for USDA Survey Food Codes [16]. NHANES is a national survey conducted by the National Center for Health Statistics of the Centers for Disease Control and Prevention. NHANES 1999-2000 was a stratified, multistage probability sample of the civilian, non-institutionalized population of the United States collected between March 1999 and December 2000. The 1999-2000 survey over-sampled adolescents, older people, pregnant women, non-Hispanic blacks, Hispanics and low-income individuals. The NHANES 1999-2000 dataset included a total of 5,082 females. Of those, 684 had unreliable dietary intake records, 2,013 were less than 19 years of age, and 293 were either pregnant or breast feeding. Thus, the exclusion of a total of 2,990 subjects lead to a final sample size of 2,091. Within those subjects, data on BMI and waist circumference were available for 1,943 and 1,930 subjects, respectively, after excluding those who were also missing one or more of the covariate values.

Diet Assessment
Dietary intake data were collected from each respondent through a home-based, computer-assisted, trained interviewer-administered standardized dietary recall. Total intake of energy and nutrients were computed from foods and beverages consumed during a 24-hour period from midnight to midnight. Anthropometric and other health data were assessed at mobile examination centers where participants were asked to complete additional questionnaires and provide blood samples. The examination included standing height (in) and weight (lbs).

The Pyramid Servings Database for USDA Survey Food Codes Version 1.0 (PyrServDB_v1) includes servings data for codes included in the USDA food coding databases that were used to process the dietary components of NHANES 1999-00 [16]. This database provides servings data using recommended sizes from the USDA Food Guide Pyramid, Pyramid Servings food data files containing the number of Pyramid servings per 100 grams of food by 30 Food Pyramid Groups (including whole grain), and Pyramid Servings intake data files for NHANES 1999-2000. A full description of how whole grain content was determined for each of the USDA food codes and which food ingredients are classified as whole grain was described previously [17].

BMI and Waist Circumference
Anthropometric measures were assessed at mobile examination centers. Body weight and height were measured to the nearest 0.1 kg and 0.1 cm, respectively, by using standardized equipment and procedures [18]. Individual heights and weights were used to calculate BMI using the formula: BMI = weight (lbs.)/height (in.)² x 704.5. Overweight is defined as at or above a BMI of 25 and obese is defined as at or above a BMI of 30 [19]. Waist circumference, as an indicator of abdominal adiposity, was measured to the nearest 0.1 cm at the level of the ileac crest while the subject was at minimal respiration [18].

Statistical Analysis
Intake of whole grain was considered as a categorical variable. Due to the fact the distribution of whole grain is not symmetrical and almost 30% of the population had no whole grain intake, we did not use quartiles to categorize the data. Rather, women were categorized into semi-balanced consumption groups based on mean daily servings consumed. The population was classified into three groups based on ranges of intake: 0 servings of whole grain (WG = 0), greater than 0 but less than 1 serving of whole grain (0 < WG < 1), and 1 or more servings (WG 1). Within these classifications, mean BMI, mean waist circumference, and mean and proportion of women overweight/obese (BMI 25) were estimated. Baseline dietary intakes of macronutrients (expressed as total and percent of calories), total dietary fiber and select vitamins and minerals (vitamin A, vitamin E, vitamins B₆ and B₁₂, folate, calcium, iron, zinc, magnesium) typically associated with whole grain intakes were assessed. The percentage of women below their Estimated Average Requirement (EAR) [20] for those nutrients assigned an EAR was also evaluated.

All statistical analyses were performed using SUDAAN® software, (Release 8.0.2, Research Triangle Institute, Research Triangle Park, NC), a specialized statistical program which accounts for multi-stage sample designs. Sample weights were applied to the data to provide national probability estimates adjusted for differential rates of selection and non-response. Mean and standard errors for all variables were calculated by the Jackknife procedure and tested for statistical significance by t-test. P values 0.05 were used as a criterion for statistical significance.

Multiple logistic and linear regression analyses were used to explore the extent to which measures of overweight in adult women could be attributed to whole grain intake. Regression coefficients and standard errors for mean log of BMI and mean log of waist circumference were estimated after multivariate adjustment for dietary and lifestyle covariates which could influence those endpoints. Two regression models were developed. The first included energy intake (kcals), age (years), total dietary fiber (g), and alcohol consumption (kcals). The second model was extended to include other lifestyle and demographic factors, such as weekly minutes of physical activity (0 minutes/week; <150 minutes/week; >150 minutes per week), current smoking status (yes/no), education level (high school; high school or GED; >high school), and ethnicity (Caucasian; African-American; Mexican-American; Other).

Similarly, logistic regression was used to estimate the odds ratios (ORs) and associated 95% confidence intervals (CIs) for the effect of whole grain intake on having a BMI 25. The reference whole grain group (thus, OR = 1.0 by definition) in the regression model was women consuming 1 serving of whole grain. The analysis included the same covariates as described above.

	RESULTS

TOP FOOTNOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS CONCLUSIONS ACKNOWLEDGMENTS REFERENCES

 
Approximately 30% of the final sample consumed zero servings of whole grain. The remaining sample was classified into two consumption groups based on semi-balanced ranges of intake (Table 1). Mean whole grain intake for the entire sample was 0.76 servings, with a range from zero to more than 7.8 servings per day. Over 70% of women consumed less than one daily serving of whole grain, and only 6% of women consumed the recommended three servings of whole grain per day (data not shown). Women who consumed at least one serving of whole grain also tended to be Caucasian, more educated, reported higher weekly minutes of physical activity, and smoked less.

View this table: [in this window] [in a new window]   Table 1. Baseline Characteristics of Women 19 Years Old by Whole Grain (WG) Intake Category in NHANES 1999-2000a

Mean energy intake was positively associated with whole grain consumption, with women who consumed no whole grain having the lowest mean energy intake compared to the other consumption groups (p < 0.05). Relative to women who consumed no whole grain, women with intakes higher than one serving also consumed significantly more dietary fiber, had a higher percentage of calories from carbohydrate and lower percentage of calories from fat (Table 2). There were no differences among the groups in terms of protein intake as a percentage of total calories, or alcohol consumption. Overall, women who consumed whole grain had better nutrient intakes. Consuming any amount of whole grain was associated with significantly higher intakes of vitamins A, E, and B₆, as well as calcium, iron, folate and magnesium. The percent of women consuming below their EAR was also examined by whole grain intake (Table 3). The percent of women not meeting the recommended daily intake levels of vitamin B₆, vitamin C, iron, magnesium and zinc was significantly higher in those consuming no whole grain (p < 0.05). Of note, these percentages are not adjusted for the measurement variation in estimating individual intakes.

View this table: [in this window] [in a new window]   Table 2. Baseline Nutrient Intakes of Women 19 Years Old by Whole Grain (WG) Intake Category in NHANES 1999-2000a

View this table: [in this window] [in a new window]   Table 3. Percent of Women 19 Years Old Not Meeting their Estimated Average Requirement (EAR) by Whole Grain (WG) Intake Category in NHANES 1999-2000a

View this table: [in this window] [in a new window]   Table 4. Regression Coefficients and their Associated Standard Errors for Log-Transformed Body Mass Index and Waist Circumference of Women 19 Years Old by Whole Grain (WG) Consumption Group Based on Data from NHANES, 1999-2000a

View this table: [in this window] [in a new window]   Table 5. Odds Ratio (OR) and Confidence Interval (CI) for Body Mass Index (BMI) 25 kg/m2 of Women 19 Years Old by Whole Grain (WG) Consumption Group Based on Data from NHANES, 1999-2000a

	DISCUSSION

Several explanations can be proposed for the inverse association between whole grain consumption and measures of overweight. First, whole grain consumption may be a marker for other health behaviors practiced by this population of women that are associated with healthy weight, although attempts were made to adjust for potentially confounding lifestyle factors. Similar to previous studies, our findings illustrated that adults who consume more whole grain are less likely to smoke and are more likely to engage in physical activity [17]. Findings from the Nurses' Health Study reported that women with the highest whole grain intakes had a lower body weight and tended to gain less weight over time, compared to women who ate less whole grain [2]. These observations did take into account numerous dietary and lifestyle indices including change in exercise, smoking and use of hormone replacement therapy [2].

The inverse association between whole grain intake and body mass index may also be a reflection of eating patterns more favorable for the regulation of body weight. For example, frequent breakfast eating has been associated with lower BMI in adults [22,23] and lower fat intakes [24]. Over 20% of adults typically choose ready-to-eat cereals for breakfast, and breakfast cereals make a significant (30%) contribution to overall whole grain intake [17,25]. Song et al. examined the relationship between breakfast consumption, breakfast cereal consumption and BMI in adults from the same NHANES 1999-2000 dataset as the present study [25]. Despite higher energy intakes, women who regularly consumed breakfast, and breakfast cereals, had a significantly lower odds over overweight and lower BMI compared to women who did not consume breakfast or breakfast cereals after adjustment for sociodemographic and lifestyle variables [25]. The authors did not observe a significant relationship between breakfast or ready-to-eat breakfast cereal consumption and BMI in men. However, a recent prospective cohort study reported that adult men who regularly consumed breakfast cereal, including whole grain cereals, had a lower BMI at baseline and gained significantly less weight over 8- and 13 years of follow-up compared to men who consumed cereal less often [26]. Consumption of whole grain was also identified as part of a specific eating pattern related to smaller gains in BMI and waist circumference in a longitudinal cohort of older adults [27].

Whole grain is a natural source of dietary fiber, which may influence body weight regulation through effects on satiation, satiety and carbohydrate absorption [7]. Including more high-fiber foods is often encouraged as part of a weight management program as it can reduce the energy density of the diet, which can affect total energy intakes [28]. The influence of whole grain on body weight regulation may be partially mediated by the effects of dietary fiber, although the inverse relationship between whole grain and markers of obesity remained significant after controlling for total fiber intake in this population.

Although both BMI and waist circumference represent indicators of obesity, few studies have examined waist circumference in relation to whole grain intake. McKeown and colleagues investigated the association of whole grain intake and various metabolic risk markers, including BMI and waist circumference, in a cross-sectional analysis of the Framingham Offspring Study cohort [3]. They found that both baseline BMI and waist circumference were significantly lower comparing adults in the highest quintile of whole grain intake with those in the lowest quintile (p = 0.04; p = 0.01 respectively after adjustment for age, energy and sex). A recent cross-sectional analysis of a population of Iranian adults (n = 827) reported that subjects in the highest quartile of whole grain consumption (143 g/day) had a significantly lower waist circumference compared to those in the lowest quartile (<10g/day;p < 0.01) [29]. In contrast, results from the Insulin Resistance Atherosclerosis Study in middle-aged adults with normal or impaired glucose tolerance did not find a correlation between whole grain consumption and waist circumference [30]. Assessment of waist circumference in addition to BMI should be encouraged in future observational studies examining relationships between diet and body weight, as recent findings suggest waist circumference and/or waist-to-hip ratio may be stronger predictors of disease risk in overweight individuals [31–33]. Although an inverse relation of whole grain intake and waist circumference was observed in the present study, it should be noted that the mean waist circumference of this population was 92.2 cm, and the recommended waist circumference associated with decreased risk of chronic disease for adult women is <88 cm [19].

The present study contains several strengths and limitations. NHANES is designed to survey a nationally representative sample; therefore accordingly, our results are applicable to the US population of women 19 years of age and older. Furthermore, the present study is the first to report estimates of whole grain intake from the NHANES dataset. The relationship between whole grain intake and indicators of obesity remained significant after adjustment of the regression models for various dietary and lifestyle factors that may influence BMI. However, the potential effects of other relevant confounders not accounted for in the regression models must also be considered. The USDA Pyramid Servings Database was released for public use in 1997, and created from limited data supplied by food manufacturers and processors regarding whole grain content. Assumptions made for this dataset may have caused misclassification of certain whole grain foods, which could result in under- or over-estimating the effect of whole grain in the present study. A more accurate assessment of whole grain consumption cannot be achieved until this dataset is updated to reflect current product formulation and processing techniques, as it is the only nationally representative database with a measure of whole grain intake. Moreover, the present data may not necessarily reflect typical consumption habits as dietary intake information was only collected over one 24 hour period. Due to the cross-sectional nature of our study, limited causal conclusions can be drawn. Additional longitudinal and clinical studies are needed to further elucidate the role of whole grain in weight management.

	CONCLUSIONS

TOP FOOTNOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS CONCLUSIONS ACKNOWLEDGMENTS REFERENCES

 
In this cross-sectional analysis of adult women, consumption of whole grain foods was associated with lower BMI, lower waist circumference, and a reduced odds of overweight or obesity. These findings are consistent with prior studies, suggesting whole grain may contribute to maintenance of a healthy weight after adjustment for other health behaviors. Despite federal objectives to improve whole grain intakes, consumer awareness and consumption of whole grains remains low. Various factors have been cited as barriers to improving consumer preference for whole grain, including taste [34], availability [35] and confusion in identifying whole grain foods [36]. If the proposed recommendations for Americans to consume 3 servings per day of whole grain are to be reached, additional population-wide efforts are needed.

	ACKNOWLEDGMENTS

TOP FOOTNOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS CONCLUSIONS ACKNOWLEDGMENTS REFERENCES

 
The authors would like to gratefully acknowledge the statistical assistance of Jenny K. Fahrenholtz. Funding for this study was provided by General Mills, Inc.

	FOOTNOTES

TOP FOOTNOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS CONCLUSIONS ACKNOWLEDGMENTS REFERENCES

 
All coauthors are employees of General Mills, Inc. which provided funding for this study.

Presented in part at Experimental Biology, San Diego, CA, April, 2005

Received March 29, 2006. Accepted January 3, 2007.

	REFERENCES

TOP FOOTNOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS CONCLUSIONS ACKNOWLEDGMENTS REFERENCES

 

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This Article Abstract Full Text (PDF) Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Good, C. K. Articles by Eldridge, A. L. Search for Related Content PubMed PubMed Citation Articles by Good, C. K. Articles by Eldridge, A. L.