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June 06, 2011

Use of a DASH Food Group Score to Predict Excess Weight Gain in Adolescent Girls in the National Growth and Health Study

Author Affiliations

Author Affiliations: Sections of General Internal Medicine (Dr Berz) and Preventive Medicine and Epidemiology (Ms Singer and Dr Moore), Boston University Medical Center, Boston, and Outcome Sciences Inc, Cambridge (Ms Guo), Massachusetts; and Department of Pediatrics, University of Colorado School of Medicine, Anschutz Medical Campus, Denver (Dr Daniels).


Copyright 2011 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.2011

Arch Pediatr Adolesc Med. 2011;165(6):540-546. doi:10.1001/archpediatrics.2011.71

Objective  To study the effects of selected dietary patterns, particularly a DASH (Dietary Approach to Stop Hypertension) eating pattern, on body mass index (BMI) throughout adolescence.

Design  Prospective National Growth and Health Study.

Setting  Washington, DC; Cincinnati, Ohio; and Berkeley, California.

Participants  A total of 2327 girls with 10 annual visits starting at age 9 years.

Main Exposures  Individual DASH-related food groups and a modified DASH adherence score.

Main Outcome Measure  The BMI value from measured yearly height and weight over 10 years.

Results  Longitudinal mixed modeling methods were used to assess the effects of individual DASH food groups and a DASH adherence score on BMI during 10 years of follow-up, adjusting for race, height, socioeconomic status, television viewing and video game playing hours, physical activity level, and total energy intake. Girls in the highest vs lowest quintile of the DASH score had an adjusted mean BMI of 24.4 vs 26.3 (calculated as weight in kilograms divided by height in meters squared) (P < .05). The strongest individual food group predictors of BMI were total fruit (mean BMI, 26.0 vs 23.6 for <1 vs ≥2 servings per day; P < .001) and low-fat dairy (mean BMI, 25.7 vs 23.2 for <1 vs ≥2 servings per day; P < .001). Whole grain consumption was more weakly but beneficially associated with BMI.

Conclusions  Adolescent girls whose diet more closely resembled the DASH eating pattern had smaller gains in BMI over 10 years. Such an eating pattern may help prevent excess weight gain during adolescence.

Obesity is a major public health problem, with 17% of American children overweight and 67% of adults either overweight or obese.13 Excess weight during childhood leads to numerous health problems and is even associated with premature death as an adult.4,5 Few studies have examined the relation of food-based dietary patterns with weight gain, especially in children.

The examination of food-based dietary patterns acknowledges the synergistic effects on health that food nutrients may have when eaten together.6 One example is the DASH (Dietary Approach to Stop Hypertension) diet pattern. It emphasizes increased intakes of low-fat dairy products; fish, chicken, and lean meats (to decrease saturated fat and increase calcium levels); and nuts, fruits, whole grains, vegetables, and legumes (to increase potassium, magnesium, and dietary fiber levels).7 The DASH eating pattern was originally studied in clinical trials of adults as a treatment for hypertension8; these clinical trials assessed the effects of increased fruit and vegetable intake, with or without increasing the intake of low-fat dairy products. In these studies,911 the combined diet (rich in fruit, vegetables, and low-fat dairy products) led to the greatest reductions in blood pressure. The DASH pattern has also been studied in relation to the metabolic syndrome and selected cardiovascular end points.814 Little has been done, however, to examine the effects of a DASH eating pattern on measures of excess weight, frequently a precursor of the aforementioned conditions. In addition, the DASH eating pattern has been infrequently studied in children, and the American Academy of Pediatrics states that there is no reason to suggest that using DASH would not be safe as long as protein and calories are consumed in quantities adequate to support child and adolescent growth and development needs.15 In this study, we examined the effects of adherence to a DASH-style eating plan and its components on the change in body mass index (BMI) in a racially diverse sample of adolescent girls.


The National Growth and Health Study was initiated by the National Heart, Lung, and Blood Institute to investigate racial differences in dietary, physical activity, family, and psychosocial factors associated with the development of obesity in black and white girls. The National Growth and Health Study enrolled 2379 girls aged 9 and 10 years in 3 cities (Washington, DC; Berkeley, California; and Cincinnati, Ohio) in 1987-1988 and observed them for 9 years. Data were collected in a longitudinal manner on 10 occasions via follow-up at annual examinations. Height and weight were measured by trained study staff using standardized assessment protocols at each examination. Additional details of the methods used for ascertaining data are described elsewhere.16 Almost 90% of the girls originally enrolled were observed through study year 10. This study was approved by the Boston University institutional review board.


The main outcome of interest was BMI (calculated as weight in kilograms divided by height in meters squared) at each age from 9 to 19 years.


Dietary data were collected using 3-day diet records; the collection included 2 weekdays and 1 weekend day during each of 8 examination years. Participants were trained by a study nutritionist to record detailed dietary information using standard household measuring instruments for the estimation of portion sizes. Standardized debriefing was performed, and diet records that were considered unreliable by the research dietitian were excluded.

Dietary data were entered into the Nutrition Data System of the University of Minnesota, Minneapolis, to estimate the intake of total calories, macronutrients, and micronutrients.17 The Nutrition Data System also outputs food codes for each food and each ingredient from composite foods (eg, from lasagna, macaroni and cheese, and even condiments). The Nutrition Data System food code data were combined with the US Department of Agriculture's survey food code database, the Food and Nutrient Database for Dietary Studies, version 2.0.18 By matching these food codes, the child's average daily intake was derived in each of the 5 major food groups and in all the subgroups as defined by Nutrition and Your Health: Dietary Guidelines for Americans by the US Department of Agriculture.19 Thus, we derived total servings for each group and subgroup. For example, fruit servings included fruit from all sources, such as whole fruit, fruit-based desserts, 100% fruit juice, and that portion of fruit drinks derived from fruit juice.


We created a modified DASH food group score based on a previous publication by Levitan et al.20 This original score was designed to reflect adherence to a DASH eating pattern as described in the 2005 Dietary Guidelines for Americans.21 Levitan et al20 compared DASH scores for this scale with those of another DASH score by Fung et al13 and found them to be moderately well correlated (r = 0.61). Because the Dietary Guidelines for Americans differs across levels of energy intake, we used energy-specific standards for intake in each food group. The score contained 10 food groups or subgroups, 3 of which were excluded from the modified score: added sugars, discretionary fats and oils, and alcohol. Added sugars were excluded because the high intake of sugar in this population led to almost all the participants having a score of zero for this component. Discretionary fats and oils contributed nothing to the prediction of BMI in this analysis, and the alcohol component was excluded because of the ages of the girls. Therefore, we focused on the 7 DASH-related food groups in these analyses: fruits, vegetables, low-fat dairy products, total and whole grains, lean meats, and nuts, seeds, and legumes.

Low-fat dairy products were defined as those containing 2% fat or less. Lean meat was defined as fish, eggs, beef, and poultry that was 85% lean or greater. To obtain more stable estimates of intake, we included only girls with 2 or more sets of 3-day diet records collected between ages 9 and 17 years (2330 of the original 2379 participants). One girl with an average intake of less than 1000 kcal/d and 1 with an average of more than 3500 kcal/d at ages 9 to 17 years were excluded from the study, as well as 1 girl with absent physical activity data, leaving a final sample of 2327 girls with available data who were included in these analyses.

We followed the scoring protocol of Levitan et al.20 Each food group was assigned a score ranging from 0 to 1. For total grains, meats, low-fat dairy products, and nuts, seeds, and legumes, participants with intake meeting the guidelines were assigned 1 point. Those with intake above the recommended levels were assigned partial points as follows: 1 minus the percentage of intake over the guideline. For intake below the guideline level, partial points were assigned by dividing the actual intake by the recommended intake. For fruits, vegetables, and whole grains where optimal intake was deemed to be at or greater than recommended, a full point was assigned to those who consumed the recommended level of intake or higher. Partial points were given only for those who had less than the recommended intake. Because DASH recommends that most grains be whole, we used 50% of the total grain recommended as the goal for whole grain intake in accord with American Heart Association guidelines.15 The total DASH score was, thus, a sum of the scores for each individual food group.


Potential confounding factors that were evaluated for inclusion in these analyses included race, height at each age, socioeconomic status (SES), physical activity level, television viewing and video game playing (hours per day), total energy intake, and other dietary factors.22 The SES was classified as low, moderate, or high by combining information about parental income and education. Low-SES families were those with incomes of less than $10 000 per year or parental education level of less than high school; high-SES families included those with incomes of at least $40 000 per year and at least a high school education. All the other participants were classified as moderate SES. Physical activity was measured at each visit using a validated questionnaire that asked the participants to report the frequency and duration per week (during the school year and summer) of participation in a variety of structured physical activities in the past year.23 These data were combined with published information on metabolic equivalent levels to obtain a final score estimating daily energy expenditure.2426


Confounders were evaluated using a forward selection method. Factors determined to be confounders and those that were independent predictors of the outcome were retained in the final models (ie, race, height, SES, physical activity level, television viewing and video game playing, and total energy).

Categories of average intake in each of the DASH food groups were determined by balancing information about the distributions of the intake population (which affects study power) with the recommended intake levels. For example, DASH recommended intake level of fruits is 4 to 5 servings per day, but the category cutoff values used in the analysis were less than 1, 1 to less than 2, and 2 or more servings per day because few participants actually consumed 4 to 5 servings. Additional analyses were conducted to evaluate the sensitivity of the results to subtle changes in category definitions.

To determine the association between level of DASH food group intake and BMI over time, we used longitudinal data analysis methods, accounting for correlated observations from the repeated-measures data. In separate models, each categorical food group variable was entered as the main exposure variable, with age as an interaction factor, while controlling for fixed and changing potential confounders as previously described. This allowed us to estimate the adjusted mean BMI at each age in each category of intake for each food group. Analyses were conducted using an unstructured covariance matrix in Proc Mixed with the repeated option in SAS (SAS Institute Inc, Cary, North Carolina).22 The same longitudinal mixed modeling methods were used to estimate the adjusted mean BMI at each age according to quintile of DASH score.

In each model, the interaction of age and food group was examined first. When a significant interaction was found, further testing was performed to evaluate differences between the slopes, intercepts, and BMIs at the end of follow-up. When there was no significant age–food group interaction (P > .05), the statistical significance of the fixed effects for the main dietary exposure variable was examined (using type III sums of squares). Approximate linearity of the relationship between age and BMI was assumed. All analyses were performed using a commercially available statistical software program (SAS, version 9.1).


Dietary and population characteristics by quintile of DASH score are presented in Table 1. The overall mean DASH adherence score was 3.1, with a median of 3.1 (range, 1.3-5.2). Food group means in each quintile show that higher DASH scores were associated with higher intake in most food groups. Higher DASH scores were also associated with higher total energy intake. Black participants and those with lower SES were more likely to be in a lower quintile of DASH scores. In addition, there was higher mean physical activity and lower mean television viewing and video game playing hours in the highest quintile of the DASH score.

Table 1. 
Image not available
Characteristics of the Study Population by Quintile of DASH Adherence Scorea

The distribution of actual intakes for each food component of the DASH score is given in Table 2, along with the recommended DASH intakes. Even study participants in the 95th percentile of intake had relatively low intake of fruits, vegetables, whole grains, and low-fat dairy products compared with the DASH recommendations. The average intake of added sugars was approximately 10 times higher than recommended. Discretionary fat intake was also relatively high, although no direct equivalent DASH recommendation applies.

Table 2. 
Image not available
Actual and Recommended Intakes of DASH Food Groupsa

Figures 1, 2, 3, and 4 show the adjusted mean BMI at each age associated with average intake in 4 DASH food groups: fruits, vegetables, whole grains, and low-fat dairy products. Participants who consumed 2 or more servings of fruit per day had the smallest gain in BMI over time (P < .001) and the lowest BMI at the end of follow-up (23.6, 25.0, and 26.0 for low, moderate, and higher intakes of fruit, respectively) (Figure 1 and Table 3). No differences were noted in BMI according to intake of vegetables (Figure 2 and Table 3). Highest (vs lowest) whole grain intake conferred lower BMI increases over time (P = .01) and a lower BMI at the end of follow-up (Figure 3 and Table 3). Higher intake of low-fat dairy products led to lower BMI gains over time (Figure 4 and Table 3). In data not shown, we compared models including and excluding total energy and total and saturated fat as a percentage of energy intake. No substantive differences in the BMI trajectory were observed.

Figure 1.
Image not available

Body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared) over 10 years associated with fruit consumption (mean servings per day). Adjusted for race, height, socioeconomic status, physical activity level, television viewing and video game playing hours per day, added sugar, total dairy, vegetables, total grains, nuts/seeds/legumes, processed and nonprocessed meat, and total energy intake. Slopes: P < .001 for differences between each line. Difference in BMI at end of follow-up: less than 1 vs 1 to less than 2 servings, less than 1 vs 2 to less than 8 servings, and 1 to less than 2 vs 2 to less than 8 servings, P < .001 for all.

Figure 2.
Image not available

Body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared) over 10 years associated with vegetable consumption (mean servings per day). Adjusted for race, height, socioeconomic status, physical activity level, television viewing and video game playing hours per day, added sugar, total dairy, fruit, total grains, nuts/seeds/legumes, processed and nonprocessed meat, and total energy intake. For the overall difference between age and total grain interaction, P = .16. For the overall difference between intake categories, P > .99. The BMI at the end of follow-up did not statistically differ significantly among the 3 consumption groups.

Figure 3.
Image not available

Body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared) over 10 years associated with whole grain intake (mean servings per day). Adjusted for race, height, socioeconomic status, physical activity level, television viewing and video game playing hours per day, added sugar, total dairy, fruit, vegetables, nuts/seeds/legumes, processed and nonprocessed meat, and total energy intake. Slopes: less than 0.25 vs 0.25 to less than 1 serving, P = .62; less than 0.25 vs 1 to less than 7 servings, P = .05; and 0.25 to less than 1 vs 1 to less than 7 servings, P = .01. The BMI at the end of follow-up: less than 0.25 vs 0.25 to less than 1 serving, P = .51; and less than 0.25 vs 1 to less than 7 servings, P = .002.

Figure 4.
Image not available

Body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared) over 10 years associated with low-fat dairy products intake (mean servings per day). Adjusted for race, height, socioeconomic status, physical activity level, television viewing and video game playing hours per day, added sugar, fruit, vegetables, total grains, nuts/seeds/legumes, processed and nonprocessed meat, and total energy intake. Slopes: less than 1 vs 1 to less than 2.25 servings, P = .001; less than 1 vs 2.25 to less than 5 servings, P < .001; and 1 to less than 2.25 vs 2.25 to less than 5 servings, P = .02. The BMI at the end of follow-up: less than 1 vs 1 to less than 2.25 servings, P = .007; and less than 1 vs 2.25 to less than 5 servings, P < .001.

Table 3. 
Image not available
BMI at Baseline and End of Follow-up for 4 DASH Food Groups and DASH Adherence Scorea

Figure 5 shows adjusted mean BMIs at each age associated with quintiles of the DASH score, averaged over ages 9 to 17 years. Girls in the highest quintile had the smallest gains in BMI over time and the lowest BMIs at the end of follow-up (Table 3). In addition, at age 19 years, those in the lowest quintile of the DASH score (compared with those in the highest quintile) had a mean BMI that was greater than the threshold for overweight as defined by the 85th percentile for age.27

Figure 5.
Image not available

Body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared) over 10 years associated with DASH (Dietary Approach to Stop Hypertension) adherence score (quintile). Adjusted for race, height, socioeconomic status, physical activity level, television viewing and video game playing hours per day, and total energy intake. Slopes: quintiles 1 to 4 vs quintile 5, P < .05. The BMI at the end of follow-up: quintiles 1, 3, and 4 vs quintile 5, P < .05.


In this longitudinal cohort of adolescent girls, we found that higher adherence to a DASH-style diet resulted in a consistently lower BMI between the ages of 9 and 19 years. These findings were stable over a 10-year follow-up and after controlling for nondietary factors associated with eating patterns and excess weight gain.

Few studies have examined dietary patterns in children or used longitudinal data to examine their effects on weight gain. A cross-sectional study28 of Korean preschool children found that a diet pattern that shares components of the DASH eating pattern was not associated with measured weight status. One longitudinal study29 of women showed that a pattern of intake lower in fruit, vegetables, and low-fat foods was associated with a greater chance of becoming overweight, and another study30 showed that a diet consisting of many components present in the DASH pattern resulted in smaller gains in BMI over time.

The present findings for the DASH score were mirrored by the effects of some of the individual food group components. In particular, higher consumption of fruits, whole grains, and low-fat dairy products led to less weight gain. The observed fruit intake in this study was well below the DASH recommendation of 4 servings per day; on average, at 9 to 17 years of age, only 15% of girls reached this goal. In addition, higher vegetable consumption was not associated with decreased weight gain over time. It is possible that relatively low intakes of vegetables and the narrow range of types of vegetables consumed (ie, a predominance of starchy vegetables) may explain the absence of a protective effect. Indeed, in a subanalysis, a higher intake of nonstarchy vegetables was associated with a lower BMI at the end of follow-up (data not shown).

The diet records used in the present study may provide more precise ascertainment of total fruit intake in children than do Frequency Food Questionnaire methods because we extracted fruit servings from composite dishes, as previously described. Thus, the present study is likely to have less nondifferential misclassification of diet exposures and a greater ability to detect meaningful associations between diet and BMI.

Higher low-fat dairy product consumption resulted in smaller increases in BMI during adolescence. Data on dairy consumption and excess weight change during adolescence show mixed results in the larger literature. Two small studies31,32 found no effect of dairy intake on weight gain, and another study33 showed an adverse effect of higher dairy consumption on weight gain, even for low-fat milk, although the effect seemed to be mediated by excess energy intake. In contrast, 2 other studies,34,35 one using diet records and another using the Frequency Food Questionnaire, found that higher dairy intake protected young adults from excess weight gain.

The present study may be the largest long-term study using diet records that has shown a protective effect of dairy intake on weight gain. Dairy may act to decrease weight gain through a variety of possible mechanisms, including an association with a healthier diet in general; its protein content has been shown to increase satiety.36

Higher intakes of whole grains were associated with decreased BMI gains during study follow-up. Although there are few studies on grain intake and weight in children, the present finding is in line with other studies3740 showing grain to be protective. Although the level of whole grain intake across this study population was low and well below the target threshold of 50% of total grains, it was nonetheless protective. Whole grain intake may result in less weight gain via its higher fiber content and, thus, higher satiety or as a marker for a healthier lifestyle, something we may not have been able to completely capture in the multivariate models.

The study strengths include use of a large socioeconomically and geographically diverse sample that incorporates more than 50% black girls, a population particularly beset by the obesity epidemic. An additional strength is the availability of detailed dietary information that allows us to examine the change in BMI in relation to food group exposure, a method that has seen little study in the adolescent literature so far. Finally, the use of repeated measures collected in a longitudinal manner over 9 years of study increased power substantially and likely decreased random variation in exposure and covariate data.

A limitation of this study is the low level of intake in certain food groups that may have restricted our ability to detect true beneficial effects of these food groups. In addition, there are food groups that other studies have found to be important predictors of obesity, such as sugar-sweetened beverages that are not a part of the DASH eating pattern and unavailable in this analysis, that may be important predictors of excess weight gain.41,42 Finally, it is possible that there is biased reporting for some dietary factors, especially in obese individuals, that could affect the results of these analyses. However, the longitudinal nature of the study and the multiple measures of dietary intake beginning in preadolescence suggest that this explanation for these findings is unlikely. In conclusion, greater consistency with the DASH eating plan resulted in lower excess weight gains in girls from early adolescence to young adulthood.

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Article Information

Correspondence: Jonathan P. B. Berz, MD, MSc, Section of General Internal Medicine, Boston University Medical Center, 801 Massachusetts Ave, Second Floor, Boston, MA 02118 (

Accepted for Publication: December 23, 2010.

Author Contributions: Drs Berz and Moore had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Berz and Moore. Acquisition of data: Singer, Daniels, and Moore. Analysis and interpretation of data: Berz, Guo, and Moore. Drafting of the manuscript: Berz and Guo. Critical revision of the manuscript for important intellectual content: Berz, Singer, Daniels, and Moore. Statistical analysis: Guo and Moore. Obtained funding: Daniels and Moore. Administrative, technical, and material support: Singer and Moore. Study supervision: Moore.

Financial Disclosure: None reported.

Funding/Support: This work was supported by grant 5R21DK075068 from the National Institute of Diabetes and Digestive and Kidney Diseases.

Role of the Sponsor: No sponsors or funders had any role in the design and conduct of the study; in the collection, analysis, and interpretation of the data; and in the preparation, review, or approval of the manuscript.

Additional Contributions: Di Gao, AS, Caroline Apovian, MD, Gheorghe Doros, PhD, and M. Loring Bradlee, MS, assisted with statistical analysis, data presentation preparation, and insight.

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