Family Practice 2012; 29:i36–i43
doi:10.1093/fampra/cmr093
Ó The Author 2012. Published by Oxford University Press. All rights reserved.
For permissions, please e-mail: [email protected].
Nutrient-rich foods in relation to various measures
of anthropometry
Martinette T Streppel*, Lisette C P G M de Groot
and Edith J M Feskens
Division of Human Nutrition, Wageningen University, part of Wageningen UR, Wageningen, The Netherlands.
*Correspondence to Martinette T Streppel, Division of Human Nutrition, Wageningen University, Part of Wageningen UR, PO
Box 8129, 6700 EV Wageningen, The Netherlands; E-mail: [email protected]
Received 19 April 2011; Revised 21 September 2011; Accepted 26 September 2011.
Background. Nutrient quality systems, for example the nutrient-rich foods (NRF) index, measure
the nutrient quality of individual foods and may be used to assess the nutrient density of the
overall diet. It is not yet known whether the NRF index is helpful in weight management. We hypothesize that a nutrient-dense diet is associated with a lower body weight and waist circumference.
Objective. The objective of the present study was to examine the association between the NRF
index and various measures of anthropometry.
Methods. This study was carried out in a sample of 2044 men and 2925 women, aged >55 years,
participating in a community-based prospective cohort study in Rotterdam, The Netherlands.
The NRF9.3 algorithms were used to estimate the nutrient density of the subjects’ diets. Linear
regression was used to examine the association between the NRF index scores and body mass
index (BMI), body weight, waist circumference, wait-to-hip ratio and waist-to-height ratio.
Results. Subjects with a high NRF9.3 index score had a lower energy intake (EI) as compared to
those with low NRF9.3 index score. However, after adjustment for age, gender and other confounders, the NRF9.3 index score as well as the Nutrient Rich 9 index score were positively associated with BMI, body weight, waist circumference and waist-to-height ratio.
Conclusions. Although subjects with a high NRF9.3 index score had a lower EI than those subjects with a low index score, their BMI, body weight, waist circumference and waist-to-height ratio was higher. The association between nutrient quality and body composition is therefore
complex.
Keywords. Anthropometry, cross-sectional study, nutrient density.
Various nutrient quality models have been developed.3,4 One of them is the NRF index.5 The NRF index has two components: (i) the nutrient-rich (NR)
component which is based on a variable number of
beneficial nutrients and (ii) the limiting nutrients
(LIM) component, which is based on saturated fat,
added sugar and sodium. The simplest algorithm subtracts the LIM score from the NR score. Nutrient quality models can be applied to individual food items, but
they may also be used to measure nutrient density of
the overall diets of individuals and populations.6 Fulgoni et al.7 validated the NRF algorithms against the
Healthy Eating Index (HEI) and the best results were
obtained for the NFR9.3 model composed of a positive
subscore based on: protein; dietary fibre; vitamins A, C
and E; calcium; iron; potassium and magnesium and
the negative subscore based on saturated fat, added
sugar and sodium. Often, data on added sugar are less
Introduction
The Dietary Guidelines for Americans, 2010, recommend eating foods that are low in calories and to focus
on consuming more nutrient-dense foods and beverages.1 Nutrient-dense or nutrient-rich foods are those
that provide substantial amount of vitamins and minerals and relatively few calories. Nutrient-rich foods
(NRF) include whole- and fibre-rich grain foods, vibrantly coloured vegetables and fruits (including 100%
fruit juice), low fat and fat-free milk, cheese and yogurt
and lean meats, skinless poultry, fish, eggs, beans and
nuts.1 Nutrient quality models can be used to measure
the nutrient quality of individual food items by ranking
foods based on their nutrient composition. According
to Drewnowski,2 an important application of the nutrient quality models is to help consumers identify foods
that provide optimal nutrition at an affordable cost.
i36
NRF and anthropometric measures
readily available and total sugar is used instead. Fulgoni
et al.7 found lower correlations with the HEI when
added sugars were replaced by total sugar. However,
as the NRF9.3 index score with total sugar still explained >35% of the variation in HEI,7 including total
sugar, seems a reasonable alternative. Nutrient quality
models can be calculated based on 100 kcal, 100 g or
on serving sizes. Although each method has its advantages and disadvantages, quality models based on 100
kcal are preferable for the positive subscores.8
Studies have shown that subjects with a high HEI
had a lower odds of (abdominal) obesity.9,10 It is, however, not yet clear whether the NRF index is helpful in
weight management. We hypothesize that a nutrientdense diet is associated with lower body weight and
waist circumference. Therefore, we examined the
cross-sectional association between the NRF index
and various anthropometric measures like body mass
index (BMI), body weight and waist-to-hip ratio.
Subjects and methods
The Rotterdam Study
The present study was carried out as part of the Rotterdam Study, a prospective cohort study among 7983 persons who live in one defined geographic area in
Rotterdam, The Netherlands. The rationale and design
of the study have been described previously by Hofman
et al.11 In brief, all inhabitants of the Rotterdam district
Ommoord, aged >55 years, were invited to participate.
The baseline examinations started in August 1990 and
continued until June 1993. To collect data on current
health and medical history, use of medication, lifestyle
and risk indicators for chronic diseases, the subjects
were visited at home by trained interviewers. The subjects subsequently visited the research centre twice for
clinical examinations and the assessment of their diet.
Assessment of dietary habits and nutrient intake
Non-institutionalized subjects who visited the study
centre at baseline (n = 6521, 82% of the cohort) were
eligible for a dietary interview. Baseline dietary data
were collected with a semiquantitative food frequency
questionnaire (SFFQ), containing 170 food items in
13 food groups, covering the habitual food intake during the preceding year. The questionnaire was left
with the subjects with careful instructions from the
home interviewers. The subjects were asked to indicate which food items they used on a regular basis (at
least twice a month). During a visit to the research
centre, frequencies and estimates of intake quantities
of selected foods were specified during a 20-minute interview with a trained dietician. In addition, consistency checks of the completed dietary questionnaire
were performed and questions were asked about dietary habits, the use of supplements (vitamin or other
i37
health pills) and medically prescribed diets. Thereafter, the average daily intake of all food items and food
groups was estimated for each person. The intake of
vitamin and mineral supplements was not included in
the calculations of nutrient intake since brand labels
of these supplements had not been recorded with sufficient accuracy.12 Foods were converted to energy and
nutrient intake with a computerized version of the
Dutch food composition table from 1993.13 To estimate dietary fibre intake, the Dutch food composition
table from 1996 was used.14 The ratio of energy intake
(EI) to basal metabolic rate (BMR) was calculated to
identify individuals who may have underreported their
EI.15 BMR was predicted from the standard equation
from Henry et al.16
Subjects’ dietary data was excluded for subjects of
the pilot phase (n = 271), subjects suspected of dementia (n = 122), due to logistic reasons (n = 481) and for
subjects whose dietary reports were considered unreliable by the dietician (n = 212). Dietary data were thus
available for 5435 subjects12,17 of which 2225 men and
3210 women.
Calculation of the NRF index scores
In the present study, the NRF9.3 score was used to derive dietary patterns as this score explained most of
the variation in the HEI compared to other NRF algorithms.7 The NRF9.3 score is based on the sum of the
percentage of reference daily values (DVs) for nine
beneficial nutrients—protein, dietary fibre, vitamin A,
vitamin C, vitamin E, calcium, magnesium, iron and
potassium— minus the sum of the percentage of reference DVs for three nutrients to limit—saturated fat,
total sugar, i.e. mono- and disaccharides, and sodium.7
Originally, the Limited Nutrient (LIM) score included
added sugars. However, as data on added sugars were
not available, we including total sugars in the score.
Firstly, we scored all foods consumed by each subject using the NRF9.3 algorithms (Table 1). This resulted in a NRF9.3 score (per 100 kcal) for every food
item, i.e. a NRF9.3 food score. The recommended
daily allowances as set by the European Union18 and
the labelling reference intake values as set by the European Food Safety Authority were used as reference
DVs19–22 (Table 2). The percentage of reference DV
for each nutrient was capped at 100% DV to avoid
overvaluing of food items that provide very large
amounts of a single nutrient.5
Secondly, the NRF9.3 food scores were converted to
individual NRF9.3 index scores by multiplying the
amount of kcal consumed of each food item, in
100-kcal units, by the NRF9.3 food scores and then
summing these scores for each subject. Nextly, the
NRF9.3 index scores were divided by the number of
100-kcal units of the subjects’ total EI to provide
a ‘weighted average’ diet quality score. Higher
NRF9.3 index scores indicate higher nutrient density
i38
Family Practice—The International Journal for Research in Primary Care
TABLE 1
Model
NRF algorithms5
Algorithm
NR9100 kcal
P
LIM3100 kcal
P
NRF9.3100 kcal
NR9-LIM3
Comment
i = 1–9(Nutrienti/RDVi)
i=1–3(Nutrienti/MDVi)
100
100
Nutrienti: content of nutrient i in 100-kcal edible
portion; RDVi: DVs for nutrient i
Li: content of limiting nutrient i in 100-kcal
edible portion; MDVi: maximum MDVi for
nutrient i
Difference between sums
NR consisting of nine beneficial nutrients: protein, dietary fibre, vitamin A, vitamin C, vitamin E, calcium, magnesium, iron and potassium; LIM
consisting of three nutrients to limit: saturated fat, total sugar and sodium.
TABLE 2 Recommended and maximum DVs for selected nutrients
Nutrient
RDV
Protein (g)
Dietary fibre (g)
Vitamin A (RE)
Vitamin E (mg)
Vitamin C (mg)
Calcium (mg)
Magnesium (mg)
Iron (mg)
Potassium (mg)
Saturated fat (g)
Total sugar (g)
Sodium (mg)
5720
2521
80018
1218
8018
80018
37518
1418
200018
MDV
2019,22
9019
240019
RDV, recommended DV; MDV, maximum DV; based on an intake
of 2000 kcal/day.
per 100 kcal and thus, subjects with a high NRF9.3 index score were considered to have a healthier dietary
pattern than those with a low NRF9.3 index score.
Assessment of anthropometric measures and other
health risk factors
Physical height and body weight were measured with
the participants standing without shoes and heavy outer garments. BMI was calculated as weight divided by
height squared (kg/m2). Waist circumference was measured at the level midway between the lower rib margin and the iliac crest with participants in standing
position without heavy outer garments and with emptied pockets, breathing out gently. Hip circumference
was recorded as the maximum circumference over the
buttocks and waist-to-hip ratio was calculated as the
ratio of waist circumference over the hip circumference.23 Furthermore, waist-to-height ratio was calculated as the ratio of waist circumference over physical
height.
Smoking history was assessed during the home interview, and subjects were categorized into never, former and current smokers. Alcohol consumption was
assessed with the SFFQ. Subjects’ intake of alcohol
was categorized into no alcohol intake, >0 to 20 g and
>20 g of alcohol per day. Socio-economic status was
determined by the level of education attended and
subjects were categorized into three groups: primary;
intermediate general and lower vocational; higher general, intermediate and higher vocational and university.12 Serum total cholesterol level (mmol/l) was
determined by an automated enzymatic procedure.24
High-density lipoprotein (HDL) cholesterol (mmol/l)
was measured similarly after precipitation of the nonHDL fraction. A trained research assistant measured
sitting systolic and diastolic blood pressure twice with
a random zero sphygmomanometer after a 5-minute
rest, and values were averaged. Hypertension was
defined as a systolic blood pressure >160 mmHg or
diastolic blood pressure >95 mmHg or use of
anti-hypertensive medication. Diabetes mellitus was
considered present when the subject reported antidiabetic treatment or when random or post-load
plasma glucose levels were >11.1 mmol/l.
Subjects with missing data on waist circumference (n
= 350), BMI (n = 36), total cholesterol (n = 32), HDL
cholesterol (n = 41), systolic and diastolic blood pressure (n = 29), diabetes mellitus (n = 7), education (n =
27), hypertension (n = 22) and smoking (n = 32) were
excluded from the analysis. Thus, complete data on dietary intake and health risk indicators were available
from 4969 subjects.
Statistical analysis
The baseline characteristics of the subjects were compared between quintiles of the NRF9.3 index score using analysis of variance for continuous variables and
the X2 statistic for categorical variables.
Linear regression analysis was used to study the association between the NRF9.3, NR9 and LIM3 index
scores and anthropometric measures like BMI, body
weight and waist-to hip ratio. The regression models
were adjusted for age and gender and additionally for
total EI (kcal), smoking history (current, former,
never), medically prescribed diet (yes, no), alcohol
consumption (no, yes: <20 g, yes: >20 g) and education
level (primary school, lower general and vocational
education, higher general and intermediate vocational education, higher vocational education and
university). In additional analyses, the multivariable
i39
NRF and anthropometric measures
quintiles of the NRF9.3 index score. Subjects in the
highest quintile of the NRF9.3 index score consumed
less bread, potatoes, pastry, fats and oils, sugar and
sweets and meat but consumed more fruit and vegetables (including legumes), cereal products, fish and milk
products as compared to those in the lowest quintile.
regression models were additionally adjusted for the
EI-to-BMR ratio.
All statistical analyses were carried out using SAS
Statistical analysis computer package (version 9.2).
Two-sided P-values <0.05 were considered statistically
significant.
NRF9.3 index and anthropometric measures
Subjects in the highest quintile of the NRF9.3 index
score had higher BMI compared to those in the lowest
quintile (P for trend: <0.0001). In contrast, waist circumference (P-value for trend: <0.001), waist-to-hip
ratio (P-value for trend: <0.0001) and waist-to-height
ratio (P-value for trend: <0.005) were lower in subjects
in the higher NRF9.3 quintile (Table 3).
Figure 2 shows the relationship of the NRF9.3 index
score with BMI (Fig. 2A), body weight (Fig. 2B), waist
circumference (Fig. 2C), waist-to-hip ratio (Fig. 2D)
and waist-to-height ratio (Fig. 2E) among the subjects
included in the present study. In crude linear regression analyses, the NRF9.3 index score was positively
associated with BMI (b coefficient for each unit increase in the NRF9.3 index: 0.055, P-value: <0.0001)
and inversely associated with waist circumference (beta
coefficient: –0.053, P-value: 0.001) and waist-to-hip ratio
(beta coefficient: –0.001, P-value: <0.0001 Table 5). After
adjustment for age and gender, the NRF9.3 index score
Results
Population characteristics
The baseline characteristics of subjects from the Rotterdam Study are shown in Table 3. Subjects in the
highest quintile of the NRF9.3 index score (range:
41.0–107.1) were younger and had a higher percentage
of females relative to those in the lowest quintile
(range: 4.7–25.9). Furthermore, the subjects in the
highest quintile had a higher percentage of never
smokers and moderate alcohol consumers (>0 to 20 g/
day). The level of education was not different between
subjects with a high or low NRF9.3 index score.
Subjects in the highest quintile of the NRF9.3 index
score had a lower total EI as compared to those in the
lowest quintile (Table 3). In linear regression analysis,
the NRF9.3 index score was inversely associated with
total EI (b coefficient: –24.7, P-value: <0.0001; Fig. 1).
Table 4 shows the difference in food intake among
TABLE 3
Baseline characteristics of subjects from the Rotterdam Study according to quintiles of the NRF9.3 index
Characteristic
All (n = 4969)
Q1
Q2
n
NRF9.3 index score
NR9 index score
LIM3 index score
993
21.4 ± 3.7
41.0 ± 3.9
19.6 ± 2.0
994
28.6 ± 1.4
47.4 ± 2.4
18.8 ± 2.0
Age (years)
Number of males (%)
BMI (kg/m2)
Body weight (kg)
Waist circumference (cm)
Waist-to-hip ratio
Waist-to-height ratio
Total EI (kcal)
Medically prescribed diet (%)
Smoking history (%)
Current
Former
Never
Alcohol consumption (%)
No
Moderate
High
Education level (%)
Primary school
Lower general and vocational
Higher general, intermediate and higher
vocational and university
69 ± 8
588 (59)
25.7 ± 3.5
73.8 ± 11.7
90.9 ± 10.8
0.92 ± 0.09
0.54 ± 0.06
2289 ± 550
58 (6)
67 ± 8
478 (48)
25.7 ± 3.4
73.5 ± 11.8
89.9 ± 11.2
0.91 ± 0.10
0.53 ± 0.06
2108 ± 841
95 (10)
339 (34)
430 (43)
224 (23)
261 (26)
411 (41)
322 (32)
199 (20)
465 (47)
329 (33)
353 (36)
266 (27)
374 (38)
Q3
Q4
Q5
P trend
994
33.1 ± 1.3
51.7 ± 2.2
18.6 ± 1.9
994
38.1 ± 1.6
56.4 ± 2.3
18.4 ± 1.8
994
47.1 ± 6.6
65.1 ± 6.9
18.1 ± 1.8
<0.0001
<0.0001
<0.0001
68 ± 7
440 (44)
26.3 ± 3.6
74.2 ± 11.8
90.3 ± 11.0
0.90 ± 0.09
0.54 ± 0.06
1994 ± 426
123 (12)
67 ± 8
334 (34)
26.6 ± 3.5
73.6 ± 11.2
90.0 ± 10.8
0.90 ± 0.09
0.54 ± 0.07
1861 ± 416
154 (15)
67 ± 8
204 (21)
27.0 ± 3.9
73.2 ± 11.9
89.0 ± 11.3
0.88 ± 0.09
0.54 ± 0.07
1631 ± 372
234 (24)
197 (20)
481 (48)
316 (32)
183 (18)
428 (43)
383 (39)
173 (17)
392 (39)
429 (43)
170 (17)
516 (52)
308 (31)
197 (20)
528 (53)
269 (27)
192 (19)
556 (56)
246 (25)
257 (26)
547 (55)
190 (19)
349 (35)
275 (28)
370 (37)
326 (33)
290 (29)
378 (38)
322 (32)
311 (31)
361 (36)
353 (36)
290 (29)
351 (35)
<0.0001
<0.0001
<0.0001
0.40
0.001
<0.0001
0.005
<0.0001
<0.0001
<0.0001
<0.0001
0.72
Values are presented as means ± SD, unless indicated otherwise.
i40
Family Practice—The International Journal for Research in Primary Care
remained positively associated with BMI (b coefficient:
0.048, P-value: <0.0001), while the NRF9.3 index score
was no longer associated with waist-to-hip ratio (b coefficient: 0.000, P-value: 0.91). Moreover, the NRF9.3 index score became positively associated with body
weight (b coefficient: 0.113, P-value: <0.0001), waist
circumference (b coefficient: 0.065, P-value: <0.0001)
and waist-to-height ratio (b coefficient: 0.0005, P-value:
<0.0001). Additional adjustment for total EI, medically
prescribed diet, smoking history, alcohol consumption
and education level did not change these associations
(Table 5). Furthermore, the NR9 subscore was also
positively associated with BMI, body weight, waist circumference and waist-to-height ratio after adjustment
for age, gender and other potential confounders (Table
5). In contrast, the LIM3 index score was not associated
with measures of anthropometry (Table 5).
BMI was significantly and inversely associated with
EI-to-BMR ratio (b coefficient for each unit increase
in BMI: –0.03; P-value < 0.0001). After additional adjustment for EI-to-BMR ratio in the multivariable regression models, the positive association between the
NRF9.3 index score and BMI was no longer statistically
significant (Table 5). In addition, the NRF9.3 index score
became inversely associated with waist circumference.
Discussion
FIGURE 1 Crude association between the NRF9.3 index and
total EI (b coefficient: –24.7, P-value: <0.0001, R2: 21.1%) 211
141 mm (72 72 DPI)
Nutrient quality models, like the NRF index, measure
the nutrient quality of individual foods and can be used
to rank foods based on their nutrient composition. These
models may also be used to assess the nutrient density of
the overall diets of individuals and populations. It is not
yet known whether the NRF index is helpful in weight
management. We hypothesized that a nutrient-dense diet, assessed using the NRF index, is associated with
lower body weight and waist circumference. Therefore,
we examined the cross-sectional association between the
NRF9.3 index and various anthropometric measures.
We observed that subjects with a high NRF9.3 index
score had a lower EI as compared to those with low
NRF9.3 index score. However, after adjustment for age,
TABLE 4 Average daily food intake from subjects of the Rotterdam Study according to quintiles of the NRF9.3 index
Food group (gram)
n
Potatoes
Alcoholic and non-alcoholic beverages
Bread
Eggs
Fruit
Pastry, cake and biscuits
Cereal products and binding agents
Vegetables
Savoury sandwich filling
Cheese
Milk and milk products
Soya products and vegetarian products
Nuts, seeds and snacks
Legumes
Composite dishes
Soups
Sugar, confectionary, sweet fillings and sweet
sauces
Fats, oils and savoury sauces
Fish
Meat, meat products and poultry
Values are presented as means ± SD.
All (n = 4969)
Q1
Q2
Q3
Q4
Q5
P trend
993
130 ± 67
1326 ± 489
144 ± 57
14 ± 9
165 ± 105
46 ± 42
13 ± 22
168 ± 71
1.3 ± 4.4
36 ± 28
340 ± 245
0.2 ± 2.3
9 ± 17
1.4 ± 5.7
9 ± 16
62 ± 69
72 ± 45
994
133 ± 70
1320 ± 464
144 ± 57
14 ± 9
204 ± 114
43 ± 30
16 ± 30
192 ± 73
1.4 ± 4.3
37 ± 25
371 ± 244
0.1 ± 1.8
7 ± 14
1.4 ± 5.6
10 ± 17
63 ± 70
46 ± 32
994
135 ± 71
1350 ± 487
140 ± 52
14 ± 8
236 ± 125
40 ± 29
18 ± 27
207 ± 73
1.6 ± 5.0
37 ± 22
387 ± 240
0.7 ± 5.4
8 ± 13
2.1 ± 7.2
10 ± 19
58 ± 63
34 ± 25
994
131 ± 71
1339 ± 495
133 ± 53
12 ± 8
263 ± 124
36 ± 25
20 ± 26
218 ± 82
2.0 ± 5.7
37 ± 22
428 ± 262
0.7 ± 5.6
7 ± 12
2.1 ± 7.2
9 ± 14
58 ± 64
26 ± 21
994
115 ± 64
1455 ± 544
117 ± 46
12 ± 8
304 ± 154
27 ± 20
23 ± 30
271 ± 187
1.3 ± 4.7
34 ± 19
464 ± 290
1.7 ± 9.4
4±8
3.4 ± 18.3
7 ± 12
58 ± 72
16 ± 17
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
0.25
0.24
<0.0001
<0.0001
<0.0001
<0.0001
0.0002
0.06
<0.0001
47 ± 20
13 ± 18
116 ± 53
44 ± 19
15 ± 18
116 ± 46
41 ± 19
16 ± 19
111 ± 51
38 ± 18
16 ± 18
105 ± 45
31 ± 16
19 ± 21
95 ± 44
<0.0001
<0.0001
<0.0001
NRF and anthropometric measures
i41
FIGURE 2 (A) Crude association between the NRF9.3 index and BMI (R2: 2.04%) 211 141 mm (72 72 DPI). (B) Crude
association between the NRF9.3 index and body weight (R2: -0.01%) 211 141 mm (72 72 DPI). (C) Crude association between
the NRF9.3 index and waist circumference (R2: 0.19%) 211 141 mm (72 72 DPI). (D) Crude association between the NRF9.3
index and waist-to-hip ratio (R2: 2.06%) 211 141 mm (72 72 DPI). (E) Crude association between the NRF9.3 index and waistto-height ratio (R2: 0.16%) 211 141 mm (72 72 DPI)
gender and other potential confounders, the NRF9.3 index score as well as the NR9 index score were positively
associated with various measures of anthropometry.
There are several methodological issues with regard
to nutrient quality models like the NRF index. We selected the NRF9.3 index for the present study as it explained most of the variation in HEI. However, some
nutrients included in the NRF9.3 index may be less
important for weight management, while other important nutrients may be missing. We included total sugar
in our NRF9.3 index, as added sugar was not available. This, of course, has consequences for the LIM index scores. When using total sugar, the intake of milk
and milk products contributed substantially to the
i42
Family Practice—The International Journal for Research in Primary Care
TABLE 5
Variable
NRF9.3 index score
BMI (kg/m2)
Body weight (kg)
Waist circumference (cm)
Waist-to-hip ratio
Waist-to-height ratio
NR9 index score
BMI (kg/m2)
Body weight (kg)
Waist circumference (cm)
Waist-to-hip ratio
Waist-to-height ratio
LIM3 index score
BMI (kg/m2)
Body weight (kg)
Waist circumference (cm)
Waist-to-hip ratio
Waist-to-height ratio
The association between the NRF9.3 index and anthropometric measures in the Rotterdam Study
Adjusteda
Crude
Adjustedb
Adjustedc
b coefficient
P-value
b coefficient
P-value
b coefficient
P-value
b coefficient
P-value
0.055
–0.014
–0.053
–0.001
0.000
<0.0001
0.42
0.001
<0.0001
0.003
0.048
0.113
0.065
0.000
0.000
<0.0001
<0.0001
<0.0001
0.91
<0.0001
0.034
0.111
0.046
–0.000
0.000
<0.0001
<0.0001
0.01
0.63
0.044
0.008
0.002
–0.030
–0.000
–0.000
0.11
0.83
0.04
0.02
0.32
0.060
–0.043
–0.072
–0.002
0.000
<0.0001
0.02
<0.0001
<0.0001
0.001
0.050
0.121
0.065
–0.000
0.000
<0.0001
<0.0001
0.0002
0.98
<0.0001
0.037
0.124
0.053
–0.000
0.000
<0.0001
<0.0001
0.005
0.88
0.03
0.007
–0.001
–0.033
0.000
0.000
0.16
0.95
0.03
0.04
0.28
0.017
–0.586
–0.324
–0.004
0.000
0.52
<0.0001
<0.0001
<0.0001
0.37
–0.042
–0.065
–0.115
–0.000
–0.001
0.11
0.41
0.14
0.53
0.08
–0.005
0.030
0.051
0.001
0.000
0.85
0.74
0.55
0.11
0.75
–0.026
–0.057
–0.008
0.001
–0.000
0.24
0.20
0.90
0.18
0.85
a
Adjusted for age and gender.
Adjusted for age, gender, total EI, smoking history, alcohol consumption, medically prescribed diet and education level.
c
Additionally adjusted for the ratio of EI to BMR.
b
LIM3 index score (14%), while the contribution of
sugar and sweets is 10%. By replacing total sugar
with added sugar, we expect that sugars and sweets
will contribute much more to the LIM3 index. This
may affect our results. In the present study, the NRF
scores were calculated using country-specific food
composition tables and European reference DVs. As
other studies may use other food composition tables
and reference DVs, the NRF scores for each food item
and every individual may be different from country to
country. However, for all studies, a higher NRF index
indicates a higher nutrient density of the diet and the
association with various health outcomes is expected
to be the same.
The present study has its limitations. Firstly, our
study has a cross-sectional design as only baseline data
on food intake and anthropometric measures were
available. Cross-sectional studies provide a snapshot of
the study population with respect to exposure or outcome variables, or both, at a specific point in time.25
Cross-sectional studies can be used to examine associations between exposure, in this case the NRF9.3 index
score, and an outcome of interest, e.g. various anthropometric measures. It is, however, impossible to infer
causality. In other words, the present study gives no indication of whether a high NRF9.3 index resulted in
high BMI, body weight and waist circumference levels
or that subjects consumed nutrient-dense diets because
of their body composition. Secondly, the calculation of
the NRF9.3 index scores was based on self-reported
food intake data. Every dietary assessment method is
subject to a number of errors, such as reporting errors,
that may result is a systematic bias or random variations
in the true intake.26 Studies have shown that underreporting occurs often in obese subjects due to changes in
true intake as a function of recording or being observed,
lack of awareness concerning foods and amounts consumed or reluctance to disclose food or amounts consumed.27,28 This may have major consequences for
studies that examine the association between diet- and
obesity-related outcomes.
In contrast with the study by Drewnowski et al.,29
we observed a positive association between the
NRF9.3 index score and various measures of anthropometry. As indicated earlier, the observed positive
association may be the result of underreporting of
food intake, especially by obese subjects. The SFFQ
used to collect the dietary intake data was considered
a valid and time-efficient dietary assessment tool that
can be used in epidemiological studies in the elderly.30
They also evaluated the occurrence of underreporting
on group level by calculating EI-to-BMR ratio as suggested by Goldberg et al.15 Comparison of this ratio
for men and women combined with a predetermined
cut-off (1.48) did not indicate the presence of underreporting (EI-to-BMR = 1.49). However, there was a significant and inverse association between BMI and
EI-to-BMR indicating more underreporting by obese
subjects.30 In the present study, we repeated these
analyses and also observed a significant and inverse association between BMI and EI-to-BMR ratio (b coefficient for each unit increase in BMI: –0.03; P-value:
<0.0001). Furthermore, we additionally adjusted our
multivariable model for EI-to-BMR ratio. The results
NRF and anthropometric measures
of this analysis suggest that the positive association between the NRF9.3 index score and anthropometry
may indeed be due to underreporting of food intake.
In conclusion, subjects with a high NRF9.3 index
score had a higher BMI, body weight, waist circumference and waist-to-height ratio as compared to those
with a low NRF9.3 index score in spite of the inverse
association between the NRF9.3 index score and total
EI. The association between nutrient density and body
composition is therefore complex.
9
10
11
12
13
14
Declaration
Funding: The Rotterdam Study is supported by the
Erasmus Medical Center and Erasmus University Rotterdam, the Netherlands Organisation of Scientific Research (NWO), the Netherlands Organisation for
Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE),
the Ministry of Education, Culture and Science, the
Ministry of Health, Welfare and Sports and the European Commission (DG XIII). The current analyses
were supported by the Dutch Dairy Foundation. The
sponsors had no role in the design or conduct of the
study; in the collection, management, analysis or interpretation of the data or in the preparation, review or
approval of the paper.
Ethical approval: The medical ethics committee of
Erasmus University approved the protocol of the Rotterdam Study. Written informed consent was obtained
from all subjects.
Conflict of interest: none.
15
16
17
18
19
20
21
References
1
2
3
4
5
6
7
8
U.S. Department of Agriculture and U.S. Department of Health
and Human Services. Dietary Guidelines for Americans. 7th
Edn, Washington, DC: U.S. Government Printing Office, 2010.
Drewnowski A. The Nutrient Rich Foods Index helps to identify
healthy, affordable foods. Am J Clin Nutr 2010; 91: 1095S–01S.
Drewnowski A. Concept of a nutritious food: toward a nutrient
density score. Am J Clin Nutr 2005; 82: 721–32.
Stockley L, Rayner M, Kaur A. Nutrient Profiles for Use in Relation to
Food Promotion and Children’s Diet: Update of 2004 Literature
Review. Oxford, UK: British Heart Foundation Health Promotion Research Group, Department of Public Health, University
of Oxford, 2007.
Drewnowski A. Defining nutrient density: development and validation
of the nutrient rich foods index. J Am Coll Nutr 2009; 28: 421S–6S.
Miller GD, Drewnowski A, Fulgoni V et al. It is time for a positive
approach to dietary guidance using nutrient density as a basic
principle. J Nutr 2009; 139: 1198–202.
Fulgoni VL III, Keast DR, Drewnowski A. Development and validation of the nutrient-rich foods index: a tool to measure nutritional quality of foods. J Nutr 2009; 139: 1549–54.
Drewnowski A, Maillot M, Darmon N. Should nutrient profiles be
based on 100 g, 100 kcal or serving size? Eur J Clin Nutr 2009;
63: 898–04.
22
23
24
25
26
27
28
29
30
i43
Guo X, Warden BA, Paeratakul S et al. Healthy eating index and
obesity. Eur J Clin Nutr 2004; 58: 1580–6.
Tande DL, Magel R, Strand BN. Healthy Eating Index and abdominal obesity. Public health Nutr 2010; 13: 208–14.
Hofman A, Grobbee DE, de Jong PT et al. Determinants of disease
and disability in the elderly: the Rotterdam Elderly Study. Eur
J Epidemiol 1991; 7: 403–22.
van Rossum CT, van de Mheen H, Witteman JC et al. Education
and nutrient intake in Dutch elderly people. The Rotterdam
Study. Eur J Clin Nutr 2000; 54: 159–65.
Dutch food composition tables. Nederlands voedingsstoffenbestand
1993. Den Haag, Nederlands: Voorlichtingsbureau voor de
Voeding, 1993.
Stichting Nederlands Voedingsstoffenbestand. Nederlands voedingsstoffenbestand 1996. Den Haag, Nederlands: Voorlichtingsbureau voor de Voeding, 1996.
Goldberg GR, Black AE, Jebb SA et al. Critical evaluation of
energy intake data using fundamental principles of energy
physiology: 1. Derivation of cut-off limits to identify underrecording. Eur J Clin Nutr 1991; 45: 569–81.
Henry CJ. Basal metabolic rate studies in humans: measurement
and development of new equations. Public Health Nutr 2005;
8: 1133–52.
Geleijnse JM, Vermeer C, Grobbee DE et al. Dietary intake of menaquinone is associated with a reduced risk of coronary heart
disease: the Rotterdam Study. J Nutr 2004; 134: 3100–5.
European Union. Commission Directive 2008/100/EC of 28 October
2008 amending Council Directive 90/496/EEC on nutrition labelling for foodstuffs as regards recommended daily allowances, energy conversion factors and definitions. Official Journal of the
European Union, Vol. L285, 2008, 9–12. http://eur-lex.europa.eu/
LexUriServ/LexUriServ.do?uri=OJ:L:2008:285:0009:0012:EN:PDF.
Scientific Opinion of the Panel on Dietetic products, Nutrition and
Allergies on a request from the European Commission on the
review of labelling reference intake values for selected nutritional elements. EFSA J 2009; 1008: 1–14.
EFSA Panel on Dietetic Products, Nutrition, and Allergies (NDA);
Scientific Opinion on Dietary Reference Values for protein released for public consultation. EFSA J pp. 63. doi:10.2903/j.
efsa.20NN.NNNN. www.efsa.europa.eu/efsajournal
EFSA Panel on Dietetic Products, Nutrition and Allergies. Scientific Opinion on Dietary Reference Values for carbohydrates
and dietary fibre. EFSA J 2010; 8: 1462.
EFSA Panel on Dietetic Products, Nutrition and Allergies. Scientific Opinion on Dietary Reference Values for fats, including
saturated fatty acids, polyunsaturated fatty acids, trans fatty
acids, and cholesterol. EFSA J 2010; 8: 1461.
Visscher TL, Seidell JC, Molarius A et al. A comparison of body
mass index, waist-hip ratio and waist circumference as predictors of all-cause mortality among the elderly: the Rotterdam
Study. Int J Obes Relat Metab Disord 2001; 25: 1730–5.
van Gent CM, van der Voort HA, de Bruyn AM et al. Cholesterol
determinations. A comparative study of methods with special reference to enzymatic procedures. Clin Chim Acta 1977; 75: 243–51.
Rothman KJ. Epidemiology—An Introduction. New York, NY:
Oxford University Press, 2002.
Bingham SA. Limitations of the various methods for collecting dietary intake data. Ann Nutr Metab 1991; 35: 117–27.
Lissner L. Measuring food intake in studies of obesity. Public
Health Nutr 2002; 5: 889–92.
Lissner L, Heitmann BL, Bengtsson C. Population studies of diet
and obesity. Br J Nutr 2000; 83 (suppl 1): S21–4.
Drewnowski A, Fulgoni VL, Young MK et al. Nutrient-rich foods:
applying nutrient navigation systems to improve public health.
J Food Sci 2008; 73: H222–8.
Klipstein-Grobusch K, den Breeijen JH, Goldbohm RA et al. Dietary assessment in the elderly: validation of a semiquantitative
food frequency questionnaire. Eur J Clin Nutr 1998; 52: 588–96.