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3 Cardiovascular Risk and Nutrition Research Group (CARIN-ULEC), IMIM-Hospital del Mar, Biomedical Research Park-Parc de Recerca Biomèdica de Barcelona-PRBB, 08003 Barcelona, Spain; 4 CIBER de Fisiopatologia de la Obesidad y Nutrición (CIBEROBN), Spain; 5 Cardiovascular Epidemiology and Genetics Research Group (EGEC-ULEC) IMIM-Hospital del Mar, 08003 Barcelona, Spain; 6 Program of Research in Inflammatory and Cardiovascular Disorders (RICAD), 08003 Barcelona, Spain; and 7 CIBER de Epidemiologia y Salud Pública (CIBERESP), Spain
* To whom correspondence should be addressed: E-mail: hschroeder{at}imim.es.
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ABSTRACT |
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Introduction |
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TOPABSTRACTIntroductionMethodsResultsDiscussionLITERATURE CITED |
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The aim of the present study was to characterize the dietary pattern associated with low energy density diets through RRR analysis and to determine the degree to which low energy density diets meet recommended nutrient intakes in the older adult at population scale. Furthermore, we analyzed the association of low energy density diets with overall diet adequacy in this segment of society.
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Methods |
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The study was conducted according to the guidelines of the Helsinki declaration and the study protocol was approved by an Ethics Committee (CEIC-IMAS, Barcelona). All participants gave their written informed consent.
Dietary assessment.
Food consumption and nutrient intake were measured by a validated FFQ administered by trained interviewers (20). The FFQ asked for the usual intake over the preceding year of 165 foods and alcoholic and nonalcoholic beverages. For each food item, participants were asked to indicate their usual consumption using 9 frequency categories, ranging from never or less than once a month to 
6 times a day. Each food item was assigned a portion size in commonly used units wherever possible (for example, a slice of bread or teaspoon of sugar); alternatively, a standard average portion size was specified. Servings of food groups were calculated by multiplying the frequency of consumption of each food by the reported portions and totaling consumption of each food group (in grams). Nutrient intakes were computed on the basis of the frequency of consumption of each unit of food and the nutrient content of the specified portions. Nutrient composition of the foods was obtained from Spanish food tables (21,22). Total antioxidant capacity of foods consumed by study participants was calculated using recently published data on the ferric-reducing antioxidant power of foods (23,24).
Energy density refers to the amount of energy (kcal)9 in a given weight of food (g). Total energy density of the diet was calculated by dividing total energy intake from food for each day by the total weight of the reported food intake. All beverages were excluded from this calculation. In a recent validation/calibration study, we performed 1 unannounced 24-h recall (in 150 participants) each month for 1 y (unpublished data). For data analysis, we excluded participants with <9 dietary recalls (11%). The Pearson correlation coefficient for energy density (food and energy-containing beverages) between the reference method (24-h recalls) and the FFQ was 0.43, indicating that the FFQ provides a reasonable estimate of energy density.
Energy underreporting was determined by the quotient of reported energy intake to the predicted basal metabolic rate (BMR) (<1.2). The BMR was calculated using predictive equations of the FAO/WHO/UNU (25) based on sex, age, and body weight [for men >60 y, BMR (MJ/d) = 2.04 + 0.0565 x weight (kg); for women >60 y, BMR = 2.49 + 0.0439 x weight (kg)].
Measurement of diet adequacy. The Spanish Nutrition Society has not established specific dietary recommendations for elderly Spaniards. Therefore, we used the most recent age- and gender-specific United States dietary reference intakes to calculate the proportion of the population meeting adequate dietary intake of carbohydrates, protein, total fat, saturated fat, cholesterol, total fiber, vitamin C, vitamin E, thiamin, riboflavin, niacin, vitamin B-6, folate, vitamin A, vitamin D, iron, calcium, magnesium, and zinc (26).
We computed 2 indices, a nutrient adequacy score (NAS) and a Mediterranean Diet Score (MDS), to calculate overall diet quality. The nutrient adequacy ratios, defined as average daily intake of a nutrient divided by the age- and sex-specific recommended intake of that nutrient, of the 19 aforementioned nutrients were included in the NAS. The nutrient adequacy ratio of each nutrient included in the NAS was expressed as 0 (ratio <1.0) or 1 (ratio 1.0). The final score ranged from 0 to 19 (population score range, 1–19).
With the exception of red wine, the MDS was calculated according to the tertile distribution of energy-adjusted food consumption (11). The lowest tertile was coded as 1, medium as 2, and highest as 3, for cereals, fruits, vegetables, legumes, fish, olive oil, and nuts. The highest tertile was coded as 1, medium as 2, and lowest as 3 for meat and diary products. Red wine consumption was computed as alcohol intake proceeding from red wine (0 g and >20 g of alcohol = 1, and up to 20 g of alcohol = 3). The distribution values of all dietary components were calculated. The resulting MDS was energy-adjusted through the residual method (20) and ranged from 10 to 30 (population score range, 10–28).
Other variables. Leisure-time physical activity was measured by the Minnesota Leisure Time Physical Activity Questionnaire, which had been previously validated for Spanish men and women (27,28). Information on smoking was obtained by a structured open-ended questionnaire. Never smoked, former smoker, and current smoker were the categories for smoking habits. Weight and height were measured. We recorded subjects' maximum level of education attained as primary school, secondary school, or higher than secondary school.
Statistical analyses. Differences in continuous variables were compared using the Student's t test. We used the Mann-Whitney U test for nonparametric variables. Categorical variables were tested using the chi-square goodness-of-fit test. Different methods have been used to study dietary pattern in epidemiological studies. RRR analysis was developed to derive dietary patterns that predict a specific response, such as energy density (18). The advantage of this analysis is that the extracted dietary pattern factor, a linear function of food groups, explains as much response variation as possible. Furthermore, loadings of foods or food groups, derived by RRR analysis, illustratively describe the magnitude and direction of these variables with respect to the response variable of interest. RRR analysis used the partial least-squares option in SAS (PROCPLS; SAS version 9.1) to extract a dietary pattern related to energy density. RRR identifies linear functions of predictors (e.g. food and/or food groups) that explain as much response (e.g. energy density) variation as possible. RRR analysis starts from a linear function of responses, called response score, that is projected against the space of predictors to produce a factor score. Factor loadings indicate the magnitude and direction of each item's contributions to the factor score (dietary pattern score). We used dietary intake data of 19 food groups as the predictor and energy density as the response variable. We obtained 1 dietary pattern score, because the number of the extracted RRR dietary pattern scores cannot be higher than the number of selected responses. A detailed description of this method is provided elsewhere (18).
General linear modeling procedures (PROC GLM; SAS Institute; version 8.0) were used to estimate continuous variables according to the quartile distribution of energy density. Polynomial contrast was used to determine P for linear trend. Linear regression models, adjusted for sex, age, leisure time physical activity, educational level, smoking, alcohol consumption, and low energy reporting, were fitted (PROC REG, SAS Institute; version 8.0) to determine the magnitude and strength of the association of energy density and related dietary pattern (factor) with the NAS and MDS indices. Differences were considered significant at P < 0.05. Values in the text are means ± SEM or (95% CI).
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Results |
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TOPABSTRACTIntroductionMethodsResultsDiscussionLITERATURE CITED |
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Discussion |
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TOPABSTRACTIntroductionMethodsResultsDiscussionLITERATURE CITED |
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Our findings on energy density and diet quality for elderly men and women are consistent with previously published results on the general population, cited below, and contribute to our understanding of the dietary patterns that may specifically address the needs of the elderly (13–15). Evidence indicates that low energy density diets were associated with good diet quality and a lower risk of obesity and diabetes than high energy density diets in the general population (13–15). In the elderly, the low energy density dietary pattern, derived from RRR analysis, comprised high consumption of fruits, vegetables, legumes, potatoes, white meat, and low-fat milk and yogurt. It is of interest to note that dietary patterns based on plant foods are also associated with favorable health outcomes in the general population (29).
In the present study, the adherence to a low energy density diet was characterized by lower total energy consumption than consuming a high energy density diet. Furthermore, nutrient density of protein (expressed in percentage of total energy intake) and nearly all minerals and vitamins (expressed as mg or g crude nutrient intake per 1000 kcal) was higher among elderly men and women consuming a low energy density diet. This finding is consistent with previously published results on the degree of nutrient density by tertile distribution of energy density in the general population older than 19 y of age (13).
Low energy reporting can seriously distort the interpretation of results from dietary surveys. In the present population, 22.4% reported low energy intakes. Furthermore, the proportion of low energy reporters was higher among those consuming a low energy density diet. However, in the present elderly population, neither the magnitude nor direction of the association between energy density and nutrient adequacy changed when the analysis was stratified by low energy reporters and plausible energy reporters (data not shown). Although it has been shown that low energy reporter adult men and women aged 20 y selectively underreport food intake (30), data from the present elderly population did not confirm this finding.
Aging has been associated with altered sensations of thirst, hunger, and satiety, and a loss of appetite is generally observed in the elderly (6–8). In this context, obtaining sufficient amounts of micronutrients becomes a challenge within the complex human aging process. Indeed, inadequate nutrient intake is not unusual among the elderly (31–34). Data from the present study indicate that elderly men and women consuming a low energy density diet had a generally favorable micronutrient intake pattern. These low energy density diets were characterized by higher intakes of antioxidant vitamins, B vitamins, and a higher total antioxidant status compared with high energy density diets. This finding is of importance because of the particular importance of B vitamins and antioxidants for cognitive function (35,36), a major determinant of quality of life in older adults. Furthermore, findings of a recently published prospective study indicate that high intakes of vitamin C, vitamin E, and β-carotene may delay cognitive impairment in the elderly (37). Hence, low energy density diets in the present population may contribute to strengthening cognitive function in the elderly.
The inverse association of nutrient density with energy density observed in several populations does not necessarily imply that individuals consuming a low energy density diet meet their nutrient needs. To address this question, we analyzed the proportion of the elderly population in our study that met the RDA in terms of the energy density of their diet. Diet adequacy measured as compliance with RDA for 19 nutrients was remarkably higher among elderly men and women on low energy density diets compared with their peers consuming a high energy density diet.
Low energy density diets in elderly men and women were more strongly associated with the healthy eating pattern of the traditional culture, the Mediterranean diet, than with high energy density diets. It has been shown that this eating pattern protects against cardiometabolic risk factors (38–40) and premature mortality in the general (41) and elderly population (42). However, it should be underlined that food distribution among tertiles of energy density is not identical with food distribution across MDS tertiles (data not shown). In the present elderly population, low energy density diets were positively associated with high consumption of fruits, vegetables, cooked potatoes, white meats, and low-fat milk and yogurt. Therefore, nutritional counseling that recommends a balanced diet emphasizing these foods may help to maintain and/or improve the health status of the health status of the elderly. Our results demonstrate that low energy density diets, at least with the present characteristics, exhibit a good overall diet quality.
The present study has several limitations. Causality between energy density and diet quality cannot be drawn because of the study's cross-sectional design. Furthermore, all dietary instruments measuring past food intake are vulnerable to random and systematic measurement errors. Finally, misreporting of self-reported food intake is an acknowledged source of measurement error in prospective or retrospective methods of dietary assessment.
In conclusion, adherence to a low energy density diet was characterized by a favorable nutrient intake profile and good overall diet adequacy in the free-living elderly population studied.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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2 Authors disclosure: H. Schröder, J. Vila, J. Marrugat, and M.I. Covas, no conflict of interest.
8 Abbreviations used: RRR, reduced rank regression; NAS, nutrient adequacy score; MDS, Mediterranean diet score.
Manuscript received 26 February 2008. Initial review completed 15 March 2008. Revision accepted 23 May 2008.
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LITERATURE CITED |
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