Download Low Energy Density Diets Are Associated with

The Journal of Nutrition
Nutritional Epidemiology
Low Energy Density Diets Are Associated with
Favorable Nutrient Intake Profile and Adequacy
in Free-Living Elderly Men and Women1,2
Helmut Schröder,3,4* Joan Vila,5 Jaume Marrugat,5,6,7 and Maria-Isabel Covas3,4
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; 4CIBER de Fisiopatologia de la Obesidad y Nutrición (CIBEROBN),
Spain; 5Cardiovascular 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 7CIBER de Epidemiologia y
Salud Pública (CIBERESP), Spain
Abstract
Nutrient adequacy in the diet is of paramount importance to physical and mental health. The aim of this study was to
characterize the dietary pattern associated with a low energy density diet and determine its nutrient adequacy in elderly
men and women. The subjects were men (n ¼ 1150) and women (n ¼ 1094) .65 y, examined in 2 population-based crosssectional surveys (2000 and 2005) in northeast Spain (Girona). Dietary data were recorded using a 165-item FFQ. Reduced
rank regression (RRR) analysis was used to identify an energy density-associated dietary pattern. A nutrient adequacy
score (NAS) and Mediterranean diet score (MDS) were computed to estimate the association of diet adequacy with
energy density. The RRR-derived factor (dietary pattern) predicted 75.4% of the variance in energy density of the diet.
Vegetables, fruits, legumes, cooked potatoes, and low-fat milk and yogurt were key to the low energy density of the diet.
Higher proportions of men and women consuming low energy density diets met dietary recommendations for total fat,
saturated fat, cholesterol, total fiber, vitamin C, vitamin E, thiamin, riboflavin, vitamin B-6, folate, calcium, and magnesium
than their peers on high energy density diets. Multivariate linear regression analysis revealed an inverse association (P ,
0.001) of the NAS and MDS with energy density and energy density-related patterns. A low energy density diet has a
higher capacity to prevent nutrient deficiency, despite lower energy content, than a high energy density diet in the elderly
population studied. J. Nutr. 138: 1476–1481, 2008.
Introduction
As the age of the world’s population rapidly increases, it has
been estimated that by 2050 the median age in Spain will be .51 y
and 44% of Spaniards will be .60 y (1). Sustaining good health
in the elderly is a major challenge to public health (2). Nutrition
plays a major role in protecting health and slowing disease
progression. Nutrients are essential to brain structure and
function (3,4). Adverse alteration of the nutritional status,
particularly in the elderly, has been associated with impairment
in cognitive function (5). Aging is accompanied by physiological
changes, such as loss of appetite and taste sensitivity, that can
influence nutrition status (6–8). Therefore, it is not surprising
that older adults are generally at greater risk for nutritional
deficiencies than younger adults. Promoting diets for the older
adult that provide appetizing food with adequate nutrient intake
is of paramount importance. Lower energy consumption in the
1
Supported by grant 2FD097-0297-CO2-01 from Fondo Europeo de Desarrollo
Regional and by parts of grants from Spain’s Ministerio de Sanidad y Consumo,
Instituto de Salud Carlos III, Red HERACLES RD06/0009, and Fondo de
Investigación Sanitaria ISCIII CP 03/00115.
2
Authors disclosure: H. Schröder, J. Vila, J. Marrugat, and M.I. Covas, no
conflict of interest.
* To whom correspondence should be addressed: E-mail: [email protected].
1476
elderly can be accompanied by a general decrease in nutrient
intake or by an increase in nutrient density such as a selective
decrease in consumption of the ‘‘non-nutritive energy’’ in sugarsweetened beverages. The energy density of a diet, defined as the
amount of available dietary energy per unit of weight, seems to
play a role in regulating food intake (9–12). Lower energy density is associated with lower energy consumption; individuals
consuming a greater amount of energy are more likely to meet
their nutrient needs. Although, theoretically, restricting energy
intake could lead to nutrient imbalance, several recent reports
have suggested that low energy density diets are associated with
higher diet quality (13–15) and favorable health outcomes such
as prevention of obesity and diabetes in the general population
(16,17). However, we lack targeted population-based estimates
of the dietary adequacy of low energy density food patterns. This
study used reduced rank regression (RRR)8 analysis, a recently
developed method, to derive dietary patterns that predict a
specific response (18).
The aim of the present study was to characterize the dietary
pattern associated with low energy density diets through RRR
8
Abbreviations used: RRR, reduced rank regression; NAS, nutrient adequacy
score; MDS, Mediterranean diet score.
0022-3166/08 $8.00 ª 2008 American Society for Nutrition.
Manuscript received 26 February 2008. Initial review completed 15 March 2008. Revision accepted 23 May 2008.
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.
Methods
Study design and participants. Men (n ¼ 1251) and women (n ¼
1247) aged .65 y who participated in 2 population-based cross-sectional
surveys conducted in Girona (Spain) in 2000 and 2005 were included in
the present study. The analysis excluded subjects who reported energy
consumption corresponding to a physical activity level of .2.4 from
analysis (n ¼ 254). A physical activity level (PAL) of .2.4 is unrealistic for
our population, because it exceeds the established upper limit for
strenuous work or highly active leisure (19).
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) ¼
2.04 1 0.0565 3 weight (kg); for women .60 y, BMR ¼ 2.49 1 0.0439 3
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,
9
I kcal ¼ 4.184 kJ.
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 ¼ 1, and up to 20 g of alcohol ¼ 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 6 SEM or (95% CI).
Results
More men smoked and were involved in leisure time physical
activities than women. Men consumed more alcohol but had a
lower BMI compared with women (Table 1). Energy density and
the MDS were higher in men than in women. The opposite was
observed for the NAS. Dietary energy densities differed significantly between men and women. However, the results of dietary
pattern analysis were largely similar. For this reason, we presented unstratified results, adjusted for sex when appropriate.
Low energy density diet in the elderly
1477
TABLE 1
Characteristics of the study participants1
TABLE 3
Men
Women
1150
71.7 6 4.4
28.2 6 3.6
1.20 6 0.27
28.9 (26.3, 31.5)
461.5 6 427.8
1094
71.5 6 4.4
28.7 6 4.9
1.10 6 0.27
35.4 (32.7, 38.3)
244.1 6 222.3
0.323
0.003
,0.001
0.001
,0.001
17.1 (14.9, 19.3)
12.0 6 14.3
18.9 6 2.8
10.8 6 3.4
20.004 (26.2, 2.8)
2.3 (1.5, 3.2)
3.3 6 6.5
18.6 6 2.9
12.8 6 3.2
20.039 (24.8, 3.1)
,0.001
,0.001
0.017
,0.001
,0.001
Nutrient intake according to tertile distribution
of energy density1
P-value
Energy density
n
Age, y
BMI, kg/m 2
Energy density,2,3 kcal/g
Low energy reporters,4 %
Leisure time physical
activity,5 MET min/d
Current smoker, %
Alcohol consumption, g/d
MDS, U
NAS, U
Dietary pattern factor, U
1
Values are means 6 SEM, means (range), or % (95% CI).
Total energy density of the diet was calculated by dividing total energy intake (kcal)
from food for each day by the total weight of the reported food intake (g).
3
1 kcal ¼ 4.184 J.
4
Energy intake divided by basal metabolic rate ,1.2.
5
Metabolic equivalent (MET) level.
2
Leisure time physical activity showed linear (P ¼ 0.035) and
quadratic (P ¼ 0.031) trends across tertile distribution of energy
density [1st tertile: 365 (340, 390); 2nd tertile: 375 (350, 400);
and 3rd tertile: 328 (303, 352), adjusted for sex]. The dietary
pattern score (factor) derived through RRR analysis predicted
75.4% of the variance in the energy density of the diet. Inverse
loadings on this factor were found for fruits, vegetables, potatoes,
legumes, white meat, and low-fat milk and yogurt (Table 2). In
contrast, the factor was directly associated with loadings of refined bread, pastry, vegetable oils, animal fat, French fries, wholefat milk and yogurt, chocolate, soft drinks (sugar-sweetened
carbonated beverages), and red meat and sausages. Fruit and
vegetable intake explained the 2 highest proportions of factor
variation (Table 2).
TABLE 2
Factor loadings and explained proportion of factor
variation of food groups1,2
Bread (refined wheat)
Pastry
Olive oil
Other oils
Animal fat
French fries
Red meat and sausages
Chocolate
High-fat milk and yogurt
Nuts
Soft drinks
Fruits
Cruciferous vegetables
Cooked potatoes
Legumes
Other vegetables
White meat
Low-fat milk and yogurt
1
2
Explained proportion of
factor variation, %
Loadings
11.3
7.3
4.8
1.2
1.8
4.7
4.0
1.6
1.4
1.8
1.2
40.0
4.1
2.7
2.1
31.1
1.3
4.2
0.29
0.24
0.19
0.10
0.12
0.19
0.17
0.12
0.10
0.10
0.10
20.55
20.18
20.14
20.13
20.48
20.10
20.17
RRR-derived factor (dietary pattern) associated with energy density.
Factor loadings between 20.09 and 10.09 are not shown.
1478
Schröder et al.
1st tertile
2nd tertile
3rd tertile
n
744
748
752
0.88 6 0.10 1.12 6 0.08 1.49 6 0.20
Energy density3
Energy,4 kcal/d
2146 6 578 2171 6 542 2234 6 565
46.7 6 7.6
42.4 6 6.0
38.8 6 7.1
Carbohydrates,5 %
18.5 6 3.2
17.7 6 3.0
16.6 6 2.9
Protein,5 %
Total fat,5 %
35.7 6 6.6
40.2 6 6.0
44.1 6 6.6
9.9 6 2.3
11.3 6 2.2
12.4 6 2.4
Saturated fat,5 %
18.0 6 4.3
20.3 6 4.2
22.2 6 4.8
Monounsaturated fat,5 %
Polyunsaturated fat,5 %
5.3 6 1.5
6.1 6 1.7
6.7 6 2.0
Fiber, g/1000 kcal
17.3 6 4.3
13.0 6 2.6
9.9 6 2.4
Thiamin, mg/1000 kcal
0.9 6 0.2
0.8 6 0.2
0.7 6 0.2
Riboflavin, mg/1000 kcal
1.0 6 0.2
0.9 6 0.2
0.8 6 0.2
Niacin, mg/1000 kcal
13.8 6 4.4
12.1 6 3.1
10.3 6 2.6
Vitamin B-6, mg/1000 kcal
1.3 6 0.3
1.1 6 0.2
0.9 6 0.2
Folate, mg/1000 kcal
293 6 75
217 6 45
161 6 42
Vitamin C, mg/d /1000 kcal 168 6 70
109 6 38
70 6 30
Vitamin E, mg/1000 kcal
7.0 6 2.4
5.9 6 2.0
5.2 6 2.1
3.9 6 3.7
2.4 6 1.9
1.4 6 1.1
b-Carotene, mg/1000 kcal
Lycopene, mg/d/1000 kcal
2.6 6 2.1
1.6 6 1.3
1.1 6 1.0
TAC,6 U/1000 kcal
6.6 6 2.9
4.9 6 2.1
3.7 6 2.1
Calcium, mg/1000 kcal
627 6 221
532 6 163
480 6 157
Magnesium, mg/1000 kcal
211 6 40
183 6 35
158 6 36
Iron, mg/1000 kcal
8.2 6 1.8
7.1 6 1.1
6.5 6 1.2
Zinc, mg/1000 kcal
5.1 6 0.9
4.8 6 0.8
4.5 6 0.8
1
2
3
4
5
6
P-trend2
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
0.001
Values are means 6 SEM.
Adjusted for sex.
Energy density: energy intake from all foods consumed (kcal)/weight of foods consumed (g).
1 kcal ¼ 4.184 J.
Percentage of energy consumption.
Total antioxidant capacity expressed as ferric-reducing antioxidant power (mmol Fe21).
Energy density was directly associated with energy consumption and intakes of total, saturated, monounsaturated, and
polyunsaturated fat. In contrast, carbohydrate and protein intake
decreased across tertile distribution of energy density (Table 3).
The intakes of thiamin, riboflavin, vitamin B-6, vitamin E,
vitamin C, folate, niacin, b-carotene, lycopene, flavonoids, calcium, magnesium, iron, and zinc decreased across tertile distribution of energy density (Table 3). The total antioxidant capacity
of the diet was inversely associated with energy density (Table 3).
A low energy density diet was associated with a higher
proportion of meeting recommended intakes for total fat,
saturated fat, cholesterol, total fiber, vitamin C, vitamin E,
thiamin, riboflavin, niacin, vitamin B-6, folate, iron, calcium, and
magnesium (Table 4). Multivariate regression analysis revealed
an inverse (P , 0.001) association of energy density with the NAS
and MDS (Table 5). Magnitude and direction of this association
was similar for the factor derived with RRR as the predictor
variable of NAS and MDS.
Results were not essentially different for energy density
calculated by dividing total energy intake from food and energy
containing beverages by the total weight of the reported food
and beverage intake (data not shown).
Discussion
The main finding of the present study was that low energy
density diets were associated with a higher overall diet adequacy
compared with high energy density diets. Specifically, a greater
TABLE 4
Proportion of men and women meeting
recommended nutrient intake according to
tertile distribution of energy density1
Energy density
n
Carbohydrates
Protein
Total fat
Saturated fat
Cholesterol
Fiber
Vitamin C
Vitamin E
Thiamin
Riboflavin
Niacin
Vitamin B-6
Folic acid
Vitamin A
Vitamin D
Iron
Calcium
Magnesium
Zinc
1
1st tertile
2nd tertile
3rd tertile
P-trend
744
94.0 (92.1–95.8)
95.3 (93.3–96.7)
18.7 (16.7–20.7)
52.2 (49.0–55.3)
59.5 (56.0–63.1)
73.7 (70.3–77.0)
98.1 (96.2–100)
40.7 (37.5–44.0)
85.0 (82.1–87.8)
89.3 (86.8–91.8)
91.7 (89.5–93.9)
92.8 (90.4–95.5)
82.8 (79.3–85.7
12.8 (10.2–15.3)
58.8 (55.4–62.2)
96.6 (95.2–98.0)
50.7 (47.2–54.3)
64.2 (60.7–67.8)
59.9 (56.4–63.5)
748
94.4 (92.5–96.2)
96.6 (95.1–98.0)
5.1 (3.1–7.1)
28.0 (24.9–31.2)
49.2 (45.6–52.8)
57.4 (54.0–60.8)
95.7 (93.8–97.6)
29.4 (26.1–32.7)
81.0 (78.1–83.9)
87.0 (84.5–89.6)
91.1 (88.9–93.3)
88.7 (86.2–91.2)
65.8 (62.5–69.0)
14.4 (11.8–17.0)
68.7 (65.3–72.1)
97.1 (95.7–98.5)
40.9 (37.3–44.5)
53.0 (49.5–56.6)
55.7 (52.1–59.3)
752
91.4 (89.6–93.2)
95.5 (94.0–97.0)
2.2 (0.1–4.2)
12.6 (9.9–15.7)
44.5 (40.9–48.1)
34.7 (31.2–38.1)
81.3 (80.4–84.2)
19.6 (16.4–22.9)
73.9 (71.0–76.8)
80.5 (78.0–83.0)
85.5 (83.3–87.7)
75.3 (72.8–77.8)
34.0 (30.7–37.2)
17.3 (14.8–19.9)
72.5 (69.1–75.9)
94.5 (93.1–95.9)
34.7 (31.1–38.2)
40.1 (36.6–43.7)
51.8 (48.3–55.4)
0.053
0.842
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
,0.001
0.014
0.001
0.039
,0.001
,0.001
0.002
Dietary reference intakes (26).
proportion of the elderly population consuming low energy
density diets met recommended dietary intakes for essential
micronutrients compared with their peers with high energy
density diets.
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
TABLE 5
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 b-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
Regression coefficients and 95% CI of the association of energy density
and the corresponding factor extracted with RRR with the NAS
and the MDS in elderly men and women1
Energy density2
NAS
MDS
1
2
3
Dietary factor score3
Regression coefficient
95% CI
P
Regression coefficient
95% CI
P
25.722
22.652
26.095, 25.348
23.116, 22.189
,0.001
,0.001
21.398
20.604
21.501, 21.296
20.727, 20.480
,0.001
,0.001
Adjusted for sex, age, leisure time physical activity, educational level, smoking, alcohol consumption, and low energy reporting.
Calculated as total energy (kcal) of solid foods consumed divided by total weight (g) of solid foods consumed.
Dietary factor score derived by RRR, with energy density as the dependent and 19 foods and food groups as the predictor variables.
Low energy density diet in the elderly
1479
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 selfreported 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.
Acknowledgment
We appreciate the English revision made by Dr. Elaine M. Lilly
(Writers First Aid).
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