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Nutritional Epidemiology

Dietary Energy Density Determined by Eight Calculation Methods in a Nationally Representative United States Population¹

Department of Nutritional Sciences, The Pennsylvania State University, State College, PA 16802 and ^* Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Public Health Promotion, Division of Nutrition and Physical Activity, Atlanta, GA 30341

²To whom correspondence should be addressed. E-mail: mvh111{at}psu.edu.

	ABSTRACT

TOP ABSTRACT SUBJECTS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
Dietary energy density [kcal/g (kJ/g)] influences energy intake under controlled laboratory conditions. Little is known about the energy density of the diets of free-living persons. Because energy density investigations are a relatively new endeavor, there are neither standard calculation methods nor published nationally representative values. This paper examines the calculation of energy density based on systematic exclusion of beverage categories, presents data on variability, and compares values by sex, age, and race/ethnicity in a representative sample of U.S. adults. Mean daily dietary energy density values for adults (aged >19 y) were calculated using two 24-h recalls from the Continuing Survey of Food Intakes by Individuals 1994–1996 based on food, food and liquid meal replacements, food and alcohol, food and juice, food and milk, food and juice and milk, food and energy-containing beverages, and food and all beverages. Energy density varied by calculation method, ranging from 0.94 to 1.85 kcal/g (3.93–7.74 kJ/g). Intraindividual-to-interindividual CV ratios were highest for the food and energy-containing beverages calculation. Men reported diets with a higher energy density than women for all calculation methods (P < 0.0001). There were differences by race/ethnicity and an inverse linear trend for age. These data indicate that beverage inclusion schemes should be clearly defined when reporting energy density values. In epidemiologic studies, calculations based on food and all beverages and food and energy-containing beverages may diminish associations with outcome variables. These nationally representative data, which provide an important frame of reference for other studies, indicate that dietary energy density differs by sex, age, and race/ethnicity.

KEY WORDS: • energy density • intraindividual variation • methodology • diet

With the majority of adults in the United States now classified as overweight or obese (1), the identification of modifiable environmental factors and behaviors is imperative. One factor known to influence short-term energy intake is dietary energy density [i.e., kcal/g (kJ/g)]. Laboratory feeding studies manipulating energy density indicated that individuals consistently consume more energy when presented with foods having a higher energy density than with similar foods having a lower energy density. This effect was shown regardless of whether the manipulation involved a meal preload (2), a portion of a meal (3), or meals over multiple days (4). Because of these laboratory findings, there is now interest in examining the relation between energy density and weight status in epidemiologic studies of free-living individuals.

Little is known, however, about dietary energy density among free-living persons. Nutrient analysis programs are not designed to calculate dietary energy density automatically nor is there a standard calculation method. Although several studies have reported dietary energy density values for free-living persons, these values have often been difficult to interpret and compare, in part because of methodological differences in the treatment of beverages (5). Beverages tend to have a lower energy density than most foods and may disproportionately influence dietary energy density values. Furthermore, different types of beverages may have different influences on food intake, hunger, and thirst. Energy density values reported in the literature have been calculated by a variety of different methods that include only food (5–10), as well as food and various combinations of beverages, such as all beverages, all beverages excluding water, energy-containing beverages, milk, juice, and so forth (5–7,9–14). Reported daily dietary energy density values range from 1.36 kcal/g (5.69 kJ/g) to 1.82 kcal/g (7.61 kJ/g) when based on consumption of food only, and they have been as low as 0.76 kcal/g (3.18 kJ/g) when consumption included beverages (5,6). Assuming that adults consume 1400 g of food and 2600 g of food and beverages a day (7), a 0.10 kcal/g (0.42 kJ/g) difference in energy density would represent a difference of 140 kcal (586 kJ) if food only is examined and a difference of 260 kcal (1089 kJ) if food and beverages are both examined.

There is much that can be learned about the role that dietary energy density plays in energy balance. There is, however, a need for clearly defined calculation methods and nationally representative values. Information on day-to-day variance in dietary energy density is required for planning and interpreting studies investigating energy density. This paper presents a rationale for the inclusion of various beverages based on influences on food intake, hunger, and thirst, describes how energy density can be calculated, and illustrates how the resulting values vary depending on the inclusion of beverages. Intra- and interindividual CV are presented, which were used to calculate the number of days of group intake data required to obtain a given correlation between observed and usual dietary energy density. This paper also presents dietary energy density values by sex, age, and race/ethnicity in a representative sample of U.S. adults.

	SUBJECTS AND METHODS

TOP ABSTRACT SUBJECTS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
    Sample population. This analysis is based on the 1994–1996 Continuing Survey of Food Intakes by Individuals (CSFII). CSFII employed a complex, multistage probability sampling design to obtain a nationally representative sample of noninstitutionalized persons in the United States (15). Briefly, dietary intake data were collected through interviewer-administered 24-h recalls with a multiple-pass technique on 2 nonconsecutive days, 3–10 d apart. The response rates for d 1 and 2 were 80.0 and 76.1%, respectively. Adults aged >19 y who completed both diet recalls were considered for inclusion in these analyses. After exclusion of subjects who reported consumption of no foods on either day (n = 35), 9288 persons remained in the analytic sample. Energy, nutrient, and food weight estimations were based on the dietary intake reported by the respondents. Because water intake was not assessed during the 24-h recall, intake of tap or bottled water is not included in these analyses. Although one questionnaire item was administered asking participants to estimate the ounces of water consumed on the previous day, these values were not included because they were not collected with the well-validated rigor of a 24-h recall with portion size estimation aids and multiple passes (16).

    Calculation of energy density. The energy density of a single food is the ratio of energy [kcal (kJ)] to weight (g); this ratio remains constant regardless of the amount consumed. In the present study, total energy intake for each day was divided by the total weight of the food/beverages reported to determine daily energy density values; d 1 and 2 values were averaged to derive a mean dietary energy density value for each subject.

Several different energy density values were calculated for each person based on the inclusion of specific groups of beverages (^Appendix). The CSFII 94–96 Food Coding Database contains 7300 unique food codes that represent all of the foods and beverages reported. Food/beverage codes³ assigned to items in the Food Coding Database were merged with the Individual Food File dataset, which contains line by line data for each individual food reported by the respondents. The Individual Food File dataset was then subset based on the 8 beverage inclusion methods and energy densities were calculated. Energy density values were normally distributed for each of the 8 calculation methods.

    Statistical methods. Variance components were calculated with the use of the SAS (version 8.1, SAS Institute) VARCOMP procedure by the MIVQUE method. The variance components were used to calculate the intraindividual (within person) CVs (CV_W = SD_W/mean) and the interindividual (between person) CVs (CV_B = SD_B/mean) for each beverage calculation method. The number of days of dietary intake data required to obtain a specified level of correlation between observed dietary energy density and true values was calculated with the following formula from Nelson et al. (17):

where n is the number of days, r² is the unobservable correlation between the observed and true mean dietary energy densities, and s_w² and s_b² are the observed intraindividual and interindividual variance, respectively. All other analyses were done using SUDAAN (release 8.02, 2003, Research Triangle Institute). Estimated means, SEM, pairwise comparisons, and linear trend analysis were based on weighted observations that reflect the probability of selection, nonresponse, and poststratification adjustments. Pairwise comparisons were made to test for differences in energy density by sex and race/ethnicity. Polynomial contrasts were used to test for a linear trend with age. P-values 0.05 were considered to be significant.

	RESULTS

TOP ABSTRACT SUBJECTS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
Eight different calculation methods were used to assess dietary energy density values based on the inclusion of various types of beverages for adults > 19 y old ^(Appendix). The calculation method based on food only or food and liquid meal replacements yielded the highest value, 1.85 kcal/g (7.74 kJ/g). Including beverages other than liquid meal replacements in the calculation produced lower energy density values. The lowest value, 0.94 kcal/g (3.93kJ/g), was based on the food and all beverages calculation method.

The dietary intake reported by men had a higher energy density than the intake reported by women for each of the 8 calculation methods (Table 1). For most calculation methods, the difference between men and women was 0.07–0.11 kcal/g. The sex difference for the food and alcohol calculation method was smaller [0.03 kcal/g (0.13 kJ/g)]. Including alcohol had less effect on energy density values in women than men: values were 1.79 kcal/g (7.49 kJ/g) and 1.91 kcal/g (7.99kJ/g) for food only, respectively, and 1.75 kcal/g (7.32 kJ/g) and 1.78 kcal/g (7.45 kJ/g) for food and alcohol, respectively. For the food only calculation method, the mean daily energy densities for the 25th and 90th percentiles were 1.56 kcal/g (6.53 kJ/g) and 2.54 kcal/g (10.63 kJ/g) for men and 1.43 kcal/g (5.98 kJ/g) and 2.47 kcal/g (10.33 kJ/g) for women, respectively.

	DISCUSSION

TOP ABSTRACT SUBJECTS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
Because the investigation of energy density in free-living persons is a relatively new endeavor, there is no standard calculation method. Given that the data presented in these analyses were based on a nationally representative sample of U.S. adults, the data provide an important frame of reference for dietary energy density values obtained in other samples. The data reported here were derived from 8 clearly described methods for calculating mean daily dietary energy density values using several beverage inclusion schemes. Attention to the treatment of beverages in calculations is particularly important because beverages, which have a high water content, tend to have a lower energy density than most foods and may disproportionately influence dietary energy density values. Each of the beverage calculation schemes had a unique rationale and addressed the complexities involved in defining certain items as a food or a beverage. The rationale for these calculation methods took into account the potential influences of the beverages within each category on food intake, hunger, and thirst.

Energy density values varied widely depending on the beverage inclusion method. Although insufficient data are available to state definitively which beverage calculation method is superior, several important conclusions can be drawn from this study. First, studies investigating energy density should clearly define the treatment of beverages. The best method may depend on the purpose of the analysis, the outcome of interest, and the study population. As reported here and by others (5), the inclusion or exclusion of beverages can have a substantial effect on energy density values. Decisions in this regard are critical in investigations of energy density among free-living persons because beverages may influence associations between energy density and other variables.

Calculating energy density based on food and all beverages excluding water may be easy and convenient, requiring no special manipulation of the dietary intake dataset, but values based on this method should be interpreted with caution. These values may not provide meaningful measures of dietary energy density because water is not included whereas other energy-free beverages such as diet cola, coffee, and tea, are included with this calculation method. Thus, individuals drinking primarily water will have relatively high energy density values.

This study quantified components of variation, which can be used to make judgments on the utility of the beverage calculation methods. The food and energy-containing beverages calculation method yielded intraindividual-to-interindividual CV ratios that were relatively higher than the ratios from other calculation methods. This was a result of more day-to-day variation within individuals as well as less variation between individuals, compared with the values for other calculation methods. The higher variance ratio for the food and energy-containing beverages calculation method indicates that relative to the other methods, the within-person variation is greater than the between-person variation. This is not desirable because large within-person variation relative to between person variation can decrease the strength of associations with outcome variables (18). Although energy density values based on food and energy-containing beverages may be found to be an important measure of energy density, null results from studies based solely on this measure should be interpreted with caution because associations with other dependent variables possibly will be weakened or missed.

Conclusions can also be drawn about the utility of various beverage inclusion methods in different populations. The choice of a particular beverage calculation method may depend upon the study population. For the general U.S. population, it appears that the inclusion of liquid meal replacements compared with food has only a small influence on energy density values regardless of sex, age, or race/ethnicity, which is likely because these items are consumed less frequently than solid foods in the general population. Including liquid meal replacements with food may have a greater effect on energy density values in populations that consume these items more frequently or if these items become more widely used as weight control strategies. Similarly, including alcohol in calculations of energy density may be a more important consideration in samples with younger adults given that the difference in energy density values calculated based on food only and food and alcohol appeared to be larger for younger than older persons. Consideration should also be given to ethnic differences, population differences, and cultural norms regarding alcohol intake because intake is likely to differ by population. For example, the difference in energy density values calculated using food only and food and alcohol was quite small for Asian/Pacific Islander respondents compared with other race/ethnic groups.

Substantial differences in energy density were found between the diets of men and women. Differences were also found by race/ethnicity category. Although little has been reported in the literature about race/ethnic differences in dietary energy density, sex differences were reported previously in a Mediterranean sample of adults (12). These data indicate that the higher energy intake generally reported by men is not merely a product of the consumption of larger amounts of food. Apparently, men in the U.S. are also selecting diets with a higher energy density than women. Selective underreporting by women of foods with a high energy density, such as fats, sweets, and alcohol, could contribute to this finding (19,20).

In this study, dietary energy density values were inversely associated with age. Data reported by Marti-Hennenberg et al. (12) also indicated that energy density values decrease with age. In that study, however, dietary energy density values for women declined only after age 40 y. Because of the cross-sectional nature of the data used in these analyses, it cannot be determined whether the decline in dietary energy density values with age was an actual age effect, a cohort effect, or a combination of these 2 factors.

Further work is warranted to understand the potential influences of misreporting on estimated energy density values. If individuals consistently underreport or overreport across all types of foods, there will likely be little influence on energy density values. However, underreporting of high-energy-dense-foods or overreporting of low-energy-dense-foods could cause energy density estimations to be lower than actual values and could mask associations with health outcomes. This may be especially problematic among individuals who are older, overweight, dieting, or have a low level of education because they have been identified as being likely to misreport their food intakes (19,20). Another potential limitation of these data is that the food coding database may not have fully accounted for water loss during cooking, which may influence the energy density of a food.

Analyses from this study indicate that energy density values differ not only by method of calculation, but also by sex, age, and race/ethnicity. The number of days of intake data required to accurately assess dietary energy density was similar to that reported by others for the estimation of macronutrient intakes (21). Until more is known about the influence of different types of beverages on energy intake, investigators examining dietary energy density may have to use several calculation methods. At a minimum, methods should include a calculation based on food only. Presenting data based solely on values calculated from food and all beverages or food and energy-containing beverages should be avoided. As more is learned about energy density and the influence of different foods and beverages on hunger and satiety, additional calculation methods may be warranted. Given that energy-containing beverages can contribute substantially to overall energy intake for some people, future work is required to determine the best way to deal with items such as energy-containing carbonated beverages and juice when investigating energy density. When comparing studies examining energy density, it is important to consider the method of energy density calculation because it may provide insight for differing results. Care should be taken to adjust for potential confounding factors such as sex, age, and race/ethnicity when examining associations between dietary energy density and outcome variables among free-living individuals. Although calculating dietary energy density can be challenging, it is a worthwhile endeavor because reducing dietary energy density may be an effective strategy for weight management. More research focused on this area is required to understand better the variability of energy density within the population and the potential influences of energy density on health and weight status.

	ACKNOWLEDGMENTS

	FOOTNOTES

 
¹ Supported in part by an appointment to the Research Participation Program at the Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Division of Nutrition and Physical Activity administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and CDC (J.H.L.). Additional support provided by National Institutes of Health grants R37DK039177 and R01DK059853 (B.J.R.). These data were not presented previously.

³ The specific food codes from the CSFII 94–96 Food Coding Database that were used for each beverage calculation method are available with the online posting of this paper at www.nutrition.org.

Manuscript received 26 July 2004. Initial review completed 11 September 2004. Revision accepted 10 November 2004.

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