Content uploaded by Ana Amélia Freitas Vilela
Author content
All content in this area was uploaded by Ana Amélia Freitas Vilela on May 16, 2020
Content may be subject to copyright.
1 3
Eur J Nutr
DOI 10.1007/s00394-015-1022-y
ORIGINAL CONTRIBUTION
Dietary patterns are associated with excess weight and abdominal
obesity in a cohort of young Brazilian adults
Soraia Pinheiro Machado Arruda1 · Antônio Augusto Moura da Silva2 ·
Gilberto Kac3 · Ana Amélia Freitas Vilela3 · Marcelo Goldani4 · Heloisa Bettiol5 ·
Marco Antônio Barbieri5
Received: 3 November 2014 / Accepted: 13 August 2015
© Springer-Verlag Berlin Heidelberg 2015
body mass index (BMI ≥ 25 kg/m2), abdominal obesity
as waist circumference (WC > 80 cm for women; >90 cm
for men) and waist/hip ratio (WHR > 0.85 for women;
>0.90 for men). Poisson regression with robust variance
adjustment was used to estimate the prevalence ratio (PR)
adjusted for socio-demographic and lifestyle variables.
Four dietary patterns were identified by principal compo-
nent analysis: healthy, traditional Brazilian, bar and energy
dense.
Results In the adjusted analysis, the bar pattern was asso-
ciated with a higher prevalence of excess weight (PR 1.46;
95 % CI 1.23–1.73) and abdominal obesity based on WHR
(PR 2.19; 95 % CI 1.59–3.01). The energy-dense pattern
was associated with a lower prevalence of excess weight
(PR 0.73; 95 % CI 0.61–0.88). Men with greater adherence
to the traditional Brazilian pattern showed a lower preva-
lence of excess weight (PR 0.65; 95 % CI 0.51–0.82), but
no association was found for women. There was no asso-
ciation between the healthy pattern and excess weight/
abdominal obesity.
Conclusions In this sample, the bar pattern was associ-
ated with higher prevalences of excess weight and abdomi-
nal obesity, while the energy-dense (for both genders) and
traditional Brazilian (only for men) patterns were associ-
ated with lower prevalences of excess weight.
Keywords Dietary patterns · Excess weight · Abdominal
obesity · Young adults
Introduction
Excess weight and obesity currently represent two of the
biggest health problems in developed and developing coun-
tries, being considered important risk factors for various
Abstract
Purpose The objective of the present study was to
investigate whether dietary patterns are associated with
excess weight and abdominal obesity among young adults
(23–25 years).
Methods A cross-sectional study was conducted on 2061
participants of a birth cohort from Ribeirão Preto, Brazil,
started in 1978–1979. Twenty-seven subjects with caloric
intake outside ±3 standard deviation range were excluded,
leaving 2034 individuals. Excess weight was defined as
Electronic supplementary material The online version of this
article (doi:10.1007/s00394-015-1022-y) contains supplementary
material, which is available to authorized users.
* Soraia Pinheiro Machado Arruda
soraia.arruda@uece.br
1 Department of Nutrition, State University of Ceará, Av. Dr.
Silas Munguba, 1700, Coordination of Nutrition Course,
Itapery, Fortaleza, CE 60740-000, Brazil
2 Department of Public Health, Federal University
of Maranhão, Rua Barão de Itapary, 155, Centro, São Luís,
MA 65.020-070, Brazil
3 Nutritional Epidemiology Observatory, Graduate Program
in Nutrition, Department of Social and Applied Nutrition,
Institute of Nutrition Josué de Castro, Federal University
of Rio de Janeiro, Avenida Carlos Chagas Filho, 367, CCS
– Bloco J – 2º andar, sala 29, Cidade Universitária – Ilha do
Fundão, 21941-590 Rio de Janeiro, RJ, Brazil
4 Department of Pediatrics and Puericulture, Faculty
of Medicine, Federal University of Rio Grande do Sul,
Rua Ramiro Barcellos, 2350, Bom Fim, Porto Alegre, RS
90035-003, Brazil
5 Department of Pediatrics and Puericulture, Faculty
of Medicine of Ribeirão Preto, University of São Paulo,
Av. Bandeirantes, 3900, Monte Alegre, Ribeirão Prêto, SP
14049-900, Brazil
Eur J Nutr
1 3
non-communicable chronic diseases (NCCD) [1–3]. It is
estimated that 1.46 billion adults are overweight all over
the world, of whom 205 million men and 297 million
women are obese [4]. In Brazil, 49.0 % of the adult popula-
tion is overweight and 14.8 % of all individuals are obese
[5].
The importance of diet in the genesis of obesity has been
well established in the literature, although the pathways
through which diet produces obesity remain controversial
[6]. Studies on the dietary factors associated with excess
weight and obesity conducted over the last decades have
given priority to the determination of specific dietary com-
ponents such as macronutrients and fiber [7, 8]. However,
the variety of foods in a diet results in a complex combi-
nation of chemical compounds that may be antagonistic
and that may compete with or alter the bioavailability of
other chemical compounds or nutrients, a fact that is taken
into account only when dietary patterns are considered [9].
The analysis of food consumption based on dietary patterns
enhances the understanding of the processes of prevention
and treatment of obesity and other NCCD, representing a
fundamental tool for nutritional interventions [9, 10].
Over the last decade, the association of dietary patterns
with obesity has been investigated in various studies [11–
16]. Dietary patterns consisting of red meat, whole milk
dairy products, processed foods and refined grains, usually
denoted as “Western” [11, 13, 15], appear to be positively
associated with general and abdominal obesity [13, 15].
In contrast, dietary patterns denoted “prudent or healthy,”
involving the consumption of fruits, vegetables, whole
grains and fish, are inversely associated with obesity [14,
17]. Most of these studies were conducted in developed
countries on populations older than 30 years of age. Few
studies have been specifically conducted [16] on young
adults who are at risk of obesity or even for excess weight
gain during this period that marks the transition between
adolescence and the early stages of adult life [18, 19].
The objective of the present study was to investigate
whether dietary patterns are associated with excess weight
and abdominal obesity in young adults from the 1978/79
Ribeirão Preto birth cohort, São Paulo, Brazil. As a second-
ary objective, we aimed to describe daily portion sizes of
each food group for those in the lowest and highest quar-
tiles of adherence to each dietary pattern stratifying by sex.
Methods
Study design and setting
This was a cross-sectional study nested in the first Ribeirão
Preto birth cohort, São Paulo, Brazil, started in 1978/79,
which investigated live births that took place at hospitals
to mothers residing in the municipality. The study used
data collected during the fourth phase of the research, con-
ducted from 2002 to 2004. A total of 9396 children born
at the eight maternities of Ribeirão Preto from June 1978
to May 1979 participated in the original study, correspond-
ing to 98 % of live-born infants. Losses due to refusal or
early hospital discharge amounted to 3.5 % (329). After the
exclusion of children whose families do not reside in the
municipality (2094) and of twins (146), 6827 of the ini-
tial sample were left. Of these, 343 died before complet-
ing 20 years of age, with 6484 eligible subjects remaining.
Based on the records of the Unified Health System and of
private health plans, and on contacts made during the sec-
ond and third phase of the study, it was possible to locate
5665 individuals. Based on the geoeconomic characteriza-
tion of the city, divided into four regions according to head
of family income, one in three individuals was contacted.
The losses due to refusal to participate (209 cases), death
after 20 years of age (34 cases), imprisonment (31 cases)
and failure to attend the interview (431 cases) resulted in a
total of 705 individuals. Thus, a total of 2063 adults aged
23–25 years, corresponding to 31.8 % of the 6484 subjects,
participated in the fourth phase of the Ribeirão Preto cohort
study [20]. Two subjects were excluded because they did
not fill out the Food Frequency Questionnaire (FFQ)
and 27 because they reported energy intake outside the
range ± 3SD, leaving 2034 cases for analysis.
The study was conducted according to the directives
established in the Declaration of Helsinki, and all pro-
cedures involving human beings were approved by the
Research Ethics Committee of the University Hospital,
Faculty of Medicine of Ribeirão Preto, University of São
Paulo, Brazil. All persons gave their informed consent prior
to their inclusion in the study.
Study variables
The participants responded to a questionnaire containing
socioeconomic, demographic and lifestyle data in which
the following variables were considered: sex, self-reported
skin color, marital status, monthly family income, educa-
tion, level of physical activity, and current smoking habit.
Skin color was investigated as a proxy for race, according
to the Brazilian census criterion that considers the self-
reported skin color. Educational status was obtained by the
completed years of study. The monthly household income
was calculated summing the income of all family members
residing in the household in the last month and was subse-
quently converted into Brazilian minimum wages, which is
the most used approach to assess monthly income in Brazil.
The conversion of the Brazilian minimum wage was essen-
tially equivalent to US$ 89.8 in 2003. Currently, the Brazil-
ian monthly household income is equivalent to US$ 273.9
Eur J Nutr
1 3
per month. The level of physical activity was assessed
based on the International Physical Activity Questionnaire
protocol [21]. Physical activity was then classified as low,
moderate and high, based on the metabolic cost or unit of
resting metabolic rate (MET) following IPAQ guidelines.
Tobacco consumption during the last month preceding the
interview was considered to be current smoking habit; how-
ever the amount of tobacco consumed was not investigated.
Dietary data were obtained from a FFQ adapted from an
instrument validated for the Japanese–Brazilian community
of São Paulo, which was later used in programs of preven-
tion of NCCD diseases for adults after the exclusion of
foods of Japanese origin [22]. The adapted FFQ contained
75 items, the respective option of frequency of consump-
tion during the last year (number of times per day, week, or
month) and the size of the mean reference portion, so that
the individual would estimate whether the portion usually
consumed was small (smaller than the reference), medium
(equal to the reference) or large (larger than the reference),
representing the 25th, 50th and 75th percentiles, respec-
tively. A nutritionist administered the questionnaire and a
photo album was used to help the subject estimates the por-
tions consumed. The methodology of this study has been
described in detail by Molina et al. [23].
The nutritional composition of the foods was obtained
from the Dietsys software, version 4.0 (National Can-
cer Institute, Bethesda, MD, USA), which estimates the
chemical composition of the foods by their daily portion
sizes described in the FFQ. Some Brazilian foods and
food preparations that were not available in the Dietsys
program were added to this software when complete data
about them were available from Brazilian tables of food
composition [24]. Foods with incomplete or doubtful infor-
mation regarding its chemical composition were replaced
with foods of similar composition. We opted to use mainly
the US food composition dataset, because when the data
of this study were collected, although there was a Brazil-
ian food composition dataset available, it was incomplete
with missing information about nutritional composition of
some foods. The caloric contributions of proteins, carbohy-
drates and fats were calculated by dividing the caloric value
of each of these nutrients by the total caloric value of the
diet and multiplying by 100 in order to express the values
as percentages.
In order to identify the dietary patterns, the daily por-
tion consumption of each food in grams or milliliters was
the dietary variable used for analysis. The 75 items of the
FFQ were divided into 48 groups according to similar-
ity of nutritional composition and frequency of intake.
The food items with high intake that were cited by at
least 80 % of the subjects, such as rice, beans, banana,
chicken, beef, eggs and non-diet sodas, were kept sepa-
rate [25, 26].
Excess weight was measured by the body mass index
(BMI), and abdominal obesity was measured according to
waist circumference (WC) and the waist/hip ratio (WHR),
which assess fat accumulated in the central or abdominal
region. For this purpose, body weight, height [27] and waist
and hip circumferences [28] were measured in the young
adults according to the techniques standardized. Trained
doctors and nurses made these measurements. Weight was
measured with a Filizola® scale (São Paulo, SP, Brazil)
with 100 g graduations and with a 140-kg capacity, and
height was measured with a wood stadiometer with 0.5 cm
markings. WC was obtained as the smallest circumference
between the ribs and the iliac crest at the end of a normal
expiration, with the subjects standing up with a relaxed
abdomen. When there was no natural waist, the measure-
ment was made at the level of the navel. The values were
recorded with 0.1 cm precision. Height and waist and hip
circumferences were measured in duplicate. When these
two measurements differed by more than the amount rec-
ommended in the protocol, a third measurement was per-
formed. The average of the two more similar measures was
considered. BMI was calculated as weight in kg divided by
height in meters squared (kg/m2), and WHR was calculated
as the measure of WC in cm divided by hip circumference
in cm.
Statistical analysis
To determine the dietary patterns, we used the method of
principal components analysis (PCA) followed by orthogo-
nal varimax rotation. The fit of the data was confirmed by
the coefficient of Kaiser–Meyer–Olkin (KMO) and the
Bartlett’s test of sphericity [29, 30]. The number of factors
retained was defined according to the following criteria:
components with eigenvalues higher than 1.0, Cattel graph
(scree plot) and conceptual meaning of the patterns identi-
fied. Each principal component was interpreted on the basis
of foods with a factorial load ≥0.3 or ≤−0.3, considered to
make an important contribution to the pattern [10]. Within
a component, negative loads indicate inverse association
of the food item and positive loads indicate a direct asso-
ciation [31]. The dietary patterns were labeled according
to the nutritional composition of the foods that the factors
included. More methodological details of the dietary pat-
terns have been published elsewhere [32].
Four principal dietary patterns were identified: healthy
(positive factorial loads for vegetables, fruits, peas and
other legumes, fish, non-fried potatoes, manioc and polenta,
chicken and breakfast cereals); traditional Brazilian (posi-
tive factorial loads for beans, rice, margarine and beef and a
negative factor loading for low-fat dairy foods, whole grain
bread and diet sodas); bar (positive factorial loads for alco-
holic beverages, salty snacks, pork meat, sausages, eggs,
Eur J Nutr
1 3
bacon, seafood and mayonnaise); and energy dense (posi-
tive factorial loads for desserts, white bread, cookies, choc-
olates, popcorn/chips, fried potatoes, manioc and polenta
and whole milk dairy products). Usually, the studies that
have identified a dietary pattern composed of a combina-
tion of bar and energy-dense foods are labeled as processed
pattern [25, 33, 34]. In our study, the bar and energy-dense
dietary patterns were representative of the processed pat-
tern and labeled as bar pattern because it was composed of
alcoholic beverages and foods commonly served in bars or
pubs, and like the energy-dense pattern, this dietary pattern
was characterized by highly caloric foods.
Together, the four patterns explained 20.9 % of the total
variance, with the healthy and traditional Brazilian patterns
explaining the greatest proportion of variance (6.6 and
5.1 %, respectively).
Each individual received a score for each factor cal-
culated by the sum of the standardized value of the items
(food groups), weighted with their absolute scoring coef-
ficients (factor loadings). In the PCA, the dietary patterns
are not mutually exclusive. On the contrary, the individuals
adhere to all dietary patterns (in a higher or lower degree).
Thus, to verify the adherence, the dietary patterns were cat-
egorized into quartiles, with the upper quartile of the distri-
bution representing those with the highest adherence to the
pattern.
The main variables were analyzed descriptively accord-
ing to sex divided in quartiles. To determine the differ-
ences between the characteristics of the individuals within
the lower quartile (Q1) and those of the individuals within
the highest quartile (Q4) of consumption in each pattern,
we used analysis of variance for continuous variables and
the Chi-square test for categorical variables. The Q2 and
Q3 quartiles were omitted from this analysis. The Shap-
iro–Wilk test was used to assess normal distribution of the
quantitative variables.
Daily portion intakes of each food group were described
for those in the lowest and highest quartiles of adherence
to each dietary pattern stratified by sex. The ANOVA was
applied to compare the means of the quartiles.
To determine the association of the dietary patterns
with excess weight and abdominal obesity, we used Pois-
son regression with robust estimate of variance in bivari-
ate analysis and in multivariable analysis in order to esti-
mate the prevalence ratios (PR). The dependent variables
(excess weight/obesity according to the three indicators
investigated) were classified as dichotomous, i.e., with
excess weight/obesity (BMI ≥ 25 kg/m2 [35], WHR > 0.85
for women and >0.90 for men [36], and WC > 0.80 cm for
women and >0.90 cm for men [37]) and without excess
weight/obesity. In multivariable analysis, the adjust-
ment was performed for the socio-demographic vari-
ables (self-reported skin color, monthly family income
and marital status) and variables associated with lifestyle
(current smoking habit, level of physical activity and total
energy intake). All dietary patterns were inserted in the
analysis, i.e., simultaneous adjustment was made between
the dietary patterns. Interaction terms between sex and
excess weight/abdominal obesity were tested and when sig-
nificant models were presented stratified by sex. Since the
independent variables were categorized into quartiles, the
P value for trend was calculated. Point estimate and 95 %
confidence intervals were calculated. The level of signifi-
cance was set at 0.05. Since power to test for interaction
was lower than the power to test for main effects, signifi-
cance level was set at 0.10 for interaction terms to increase
sensitivity, although at the cost of increasing the risk of
type I error (false positive) [38]. The statistical analyses
were carried out with the aid of the Stata software, version
10.0.
Results
Individuals with greater adherence (Q4) to the healthy pat-
tern were less sedentary (30.2 % of men and 44.5 % of
women) than those with the lowest adherence (Q1) (53.1
and 65.9 % of men and women, respectively). Among men,
the percentage of smokers was lower in the highest quar-
tile (Q4) (18.5 vs. 28.0 %). Individuals in the Q4 of the
healthy dietary pattern had more energy from protein and
less energy from fats compared to individuals in Q1. There
was no significant difference between Q1 and Q4 regarding
excess weight and/or abdominal obesity among men and
women (Table 1).
For the traditional Brazilian pattern, men in the upper
quartile of adherence showed a lower prevalence of excess
weight (35.9 %) than those in the lowest quartile (53.7 %),
while women who more adhered to this pattern showed
higher frequency of excess weight/obesity when compared
to Q1 on the basis of the three indicators assessed: BMI
(31.1 vs. 24.1 %), WC (18.5 vs. 9.0 %) and WHR (12.1 vs.
5.3 %). The diet consumed by men and women with greater
adherence to this pattern showed a greater caloric contribu-
tion from carbohydrates and a lower contribution from pro-
teins and fats compared to the diet of Q1 subjects (Table 1).
Considering the bar pattern, men in the Q4 showed a
higher frequency of sedentary (44.0 vs. 42.1 %) and excess
weight/obesity than Q1 men on the basis of the three indi-
cators assessed: BMI (52.1 vs. 39.4 %), WC (18.1 vs.
7.4 %) and WHR (26.7 vs. 15.8 %). Current smoking habit
was more frequent among men and women who adhere
more to this pattern. The diet consumed by Q4 individuals
showed a lower caloric contribution of carbohydrates and
a higher contribution of proteins and fats compared to that
consumed by Q1 individuals (Table 1).
Eur J Nutr
1 3
Table 1 Characteristics of 23- to 25-year-old adults of the Ribeirão Preto birth cohort (4th phase: 2002–2004) according to quartile categories of dietary patterns
Healthy pattern Traditional Brazilian pattern
Males Females Males Females
Q1 Q4 PQ1 Q4 PQ1 Q4 PQ1 Q4 P
N 243 243 – 266 265 – 243 243 – 266 265 –
% Excess weight (BMI)a44.6 49.8 0.312 30.7 26.0 0.503 53.7 35.9 0.015 24.1 31.1 0.013
BMI (kg/m2) 25.2/4.6 25.5/4.8 0.058 23.7/4.8 23.4/5.1 0.894 25.6/4.1 24.5/4.7 0.039 23.0/4.1 24.0/5.7 0.047
% Central obesity (WC)b14.1 11.9 0.180 15.1 12.1 0.434 12.4 10.3 0.888 9.0 18.5 0.001
WC (cm) 88.2/12.1 88.7/12.3 0.175 77.0/11.1 76.1/11.9 0.572 88.7/11.0 86.4/12.6 0.125 74.4/10.0 78.5/12.5 <0.001
% Central obesity (WHR)c23.5 24.7 0.387 9.8 8.3 0.926 20.7 20.6 0.736 5.3 12.1 0.001
WHR 0.86/0.07 0.86/0.06 0.311 0.77/0.06 0.75/0.06 0.194 0.85/0.06 0.85/0.06 0.394 0.74/0.06 0.78/0.06 <0.001
% Smokers 28.0 18.5 0.006 15.0 16.2 0.113 21.0 21.4 0.501 15.0 14.0 0.857
% Sedentary 53.1 30.2 <0.001 65.9 44.5 <0.001 42.0 41.3 0.713 51.3 60.8 0.240
Food intake
% Carbohydrate calories 47.7/8.1 48.3/6.8 0.488 47.0/7.4 48.3/5.9 0.136 44.8/6.8 50.0/6.8 <0001 45.7/6.7 49.9/7.1 <0.001
% Protein calories 14.9/2.7 16.9/3.6 <0.001 14.7/3.1 17.2/3.3 <0.001 17.5/3.7 15.1/2.7 <0.001 17.3/3.7 14.9/3.3 <0.001
% Fat calories 37.1/6.5 34.5/5.2 <0.001 37.8/5.7 34.3/4.9 <0.001 37.3/5.9 34.5/5.2 <0.001 36.9/6.0 34.7/5.1 <0.001
Bar food pattern Energy-dense pattern
Males Females Males Females
Q1 Q4 PQ1 Q4 PQ1 Q4 PQ1 Q4 P
N 243 243 – 266 265 – 243 243 – 266 265 –
% Excess weight (BMI)a39.4 52.1 0.033 26.8 28.7 0.441 50.8 39.3 0.074 32.6 25.7 0.052
BMI (kg/m2) 24.3/4.1 26.1/4.7 <0.001 23.4/5.5 23.7/4.7 0.250 25.5/4.7 24.5/4.1 0.074 23.8/4.8 23.3/5.2 0.657
% Central obesity (WC)b7.4 18.1 0.001 13.6 14.7 0.200 11.9 11.2 0.979 14.7 15.9 0.643
WC (cm) 85.3/10.2 90.5/12.3 <0.001 76.0/12.3 76.8/10.7 0.095 88.9/12.1 86.7/10.9 0.175 76.9/11.5 76.5/11.8 0.980
% Central obesity(WHR)c15.8 26.7 0.032 9.4 7.9 0.822 24.7 18.6 0.445 9.8 8.3 0.879
WHR 0.85/0.05 0.87/0.06 0.004 0.76/0.06 0.76/0.06 0.627 0.86/0.06 0.85/0.06 0.758 0.76/0.06 0.76/0.06 0.727
% Smokers 7.0 35.8 <0.001 3.0 30.2 <0.001 25.9 19.7 0.034 19.5 10.6 0.009
% Sedentary 42.1 44.0 0.034 54.2 50.2 0.057 45.9 41.6 0.180 57.4 53.0 0.402
Food intake
% Carbohydrate calories 50.9/6.3 44.4/6.9 <0.001 50.9/5.9 44.4/7.3 <0.001 49.1/8.6 46.7/6.1 <0.001 48.5/8.0 46.9/6.5 0.053
% Protein calories 15.2/3.2 16.5/3.3 <0.001 14.6/3.1 16.8/3.7 <0.001 16.7/4.1 15.0/2.4 <0.001 17.3/4.1 14.7/2.8 <0.001
% Fat calories 33.6/4.8 38.7/5.7 <0.001 34.4/4.7 38.3/6.1 <0.001 33.6/5.9 38.2/5.5 <0.001 33.6/5.3 38.3/5.3 <0.001
Values reported as means/standard deviation for the continuous variables and as percentages for the categorical variables
Q1, the lower quartile of the distribution represents the lowest adherence to the pattern; Q4, the upper quartile of the distribution represents the highest adherence to the pattern
ANOVA for continuous variables and Chi-square test for categorical variables
a Excess weight: body mass index—BMI ≥ 25 kg/m2
b Central obesity, waist circumference WC > 80 cm for women and >90 cm for men
c Central obesity, waist hip ratio WHR > 0.85 for women and >0.90 cm for men
Eur J Nutr
1 3
Individuals in the highest quartile of consumption of
the energy-dense dietary pattern showed a lower percent-
age of smokers (men: 19.7 vs. 25.9 % and women: 10.6
vs. 19.5 %) than those in the lowest quartile. The caloric
contribution of protein was lower, while the contribution
of fats was higher among individuals adhering to this pat-
tern compared to Q1 individuals. Among men, the diet con-
sumed by Q4 individuals showed a lower caloric contribu-
tion of carbohydrates. Prevalences of excess weight did not
differ comparing those in the lower with those in the upper
quartile of adherence to the energy-dense pattern (Table 1).
We observed that daily portion sizes of foods that com-
posed the healthy pattern were higher for women as compared
to men in the highest quartile, except for fish, chicken, banana
and fruit juice (Online Resource Table 1). The daily portion
intakes of those in the highest quartile of the traditional Brazil-
ian pattern were higher for men when compared to women for
rice, beans and low-fat dairy foods (Online Resource Table 2).
Beer and salty snacks were the two items with the highest
daily portion sizes for the bar pattern, independently of the
sex. These items were followed by cold cuts and pork meat
(Online Resource Table 3). Whole dairy products, white bread
and desserts were the food items with the highest daily portion
sizes in the highest quartile of adherence for the energy-dense
pattern (Online Resource Table 4).
A significant interaction between sex and excess
weight/obesity (BMI: P = 0.005; WC: P = 0.054; WHR:
P = 0.018) was detected only for the traditional Brazil-
ian dietary pattern (data not shown in table). We tested an
interaction term between the bar pattern and sex, and it
turned out to be nonsignificant, so the bar pattern is similar
between both sexes.
In the adjusted analysis, a greater adherence to the bar
pattern was associated with a higher prevalence of excess
weight (PR 1.46; CI 95 % 1.23–1.73) and abdominal obe-
sity as measured by WHR (PR 2.19; CI 1.59–3.01). The
energy-dense pattern was found to be associated with lower
prevalences of excess weight (PR 0.73; 95 % CI 0.61–0.88).
After adjustment in multivariable analysis, there was no
association between the healthy pattern and excess weight/
abdominal obesity (Table 2). The traditional Brazilian pat-
tern seemed to protect against excess weight among men
(PR 0.63; CI 95 % 0.50–0.79). However, among women,
the association between more adherence to this pattern and
higher excess weight/abdominal obesity detected in the
unadjusted analysis was no longer present after adjustment
(Table 3).
Discussion
The traditional Brazilian (among men) and energy-dense
patterns were associated with a lower prevalence, whereas
the bar pattern was associated with a higher prevalence of
excess weight and/or abdominal obesity.
The dietary pattern labeled healthy was not associated
with excess weight/abdominal obesity. These findings dif-
fer from those reported in most studies, in which dietary
patterns considered healthy were inversely associated with
obesity [14, 17, 39, 40] in cross-sectional studies and with
lower BMI and WC gains in longitudinal studies [41, 42].
In a study on adult women from southern Brazil, different
dietary patterns consisting of healthy foods were identified,
in which the pattern denoted “fruits” was inversely associ-
ated with obesity, as measured by BMI, while the “vegeta-
bles” pattern was directly associated with obesity [7].
The composition of the healthy dietary pattern identified
in the present study is similar to that reported in other stud-
ies [12, 13, 31, 43], composed of vegetables, fruits, peas
and other legumes, fish and chicken, and characterized by
foods rich in vitamins, minerals, fibers, unsaturated fats,
containing low amounts of simple sugars and trans and
saturated fats [15]. However, we hypothesize that the pres-
ence of foods with high starch content in this pattern may
increase the glycemic content of such foods. However, we
were unable to specify the way preparations were cooked.
High consumption of energy-dense foods such as break-
fast cereals may explain why we did not find an associa-
tion between this pattern and lower prevalences of excess
weight/abdominal obesity. In addition, some studies sug-
gest that a wide variety of food items in the diet may play a
role in the increase in BMI [44, 45].
Individuals of both sexes with higher scores for the
healthy pattern (Q4) were found to be less sedentary and to
consume a diet with a greater contribution of proteins and
a lower contribution of fats compared to Q1 individuals,
supporting the notion that a healthy diet is associated with
other healthy habits [11, 14, 15, 25, 46, 47].
The second pattern identified was called traditional
Brazilian, consisting of traditional foods of the Brazilian
diet (rice and beans), and was found to protect against
excess weight among men. Other Brazilian studies have
also identified patterns similar to this [14, 25, 31, 44, 47,
48] and have observed a similar protective effect of the
traditional pattern against excess weight measured by
the BMI [16, 44, 45]. The protective effect of this pat-
tern may be explained by: (1) a low glycemic index of
the rice and beans combination; (2) the high fiber content
of beans; (3) the reduced variety of the foods included
in this pattern [44, 45]; and (4) a lower consumption of
processed foods habitually consumed outside the home,
since the consumption of rice and beans suggested a diet
prepared at home [16, 45]. On the other hand, Neumann
et al. [47] also observed a positive association of the “tra-
ditional” pattern with obesity, although measured accord-
ing to the BMI. Among women, after adjustment, there
Eur J Nutr
1 3
Table 2 Non-adjusted and adjusted prevalence ratios (PR) and 95 % confidence intervals (95 % CI) for the association of excess weight and abdominal obesity with the healthy, bar and
energy-dense food dietary patterns identified among 23- to 25-year-old adults of the Ribeirão Preto cohort (4th phase: 2002–2004)
The dietary patterns were categorized into quartiles—Q1 (the lower quartile of the distribution represents the lowest adherence to the pattern), Q2, Q3 and Q4 (the upper quartile of the distribu-
tion represents the highest adherence to the pattern)
* P value referring to the maximum likelihood ratio obtained by Poisson regression (P value for trend)
a Excess weight: body mass index—BMI ≥ 25 kg/m2
b Central obesity, waist circumference WC > 80 cm for women and >90 cm for men
c Central obesity, waist hip ratio WHR > 0.85 for women and >0.90 cm for men
d Adjusted for skin color, schooling, monthly family income, marital status, smoking habit, level of physical activity, and total caloric intake (kcal/day) and the other dietary patterns
Dietary patterns Excess weight (Body Mass Index—BMI)aAbdominal obesity (waist circumference—WC)bAbdominal obesity (waist hip ratio—WHR)c
% Non-adjusted PR
(95 % CI)
Adjusted PRd (95 %
CI)
% Non-adjusted PR
(95 % CI)
Adjusted PRd (95 % CI) % Non-adjusted PR
(95 % CI)
Adjusted PRd
(95 % CI)
Healthy P = 0.719 P = 0.866 P = 0.558 P = 0.800 P = 0.778 P = 0.455
Q1 38.1 Reference Reference 14.4 Reference Reference 16.6 Reference Reference
Q2 35.2 0.92 (0.79–1.09) 0.95 (0.81–1.12) 12.6 0.87 (0.64–1.19) 0.89 (0.65–1.22) 14.4 0.87 (0.65–1.16) 0.89 (0.67–1.19)
Q3 35.0 0.92 (0.78–1.08) 0.97 (0.82–1.14) 11.8 0.81 (0.59–1.13) 0.86 (0.62–1.19) 14.7 0.89 (0.69–1.18) 0.94 (0.71–1.26)
Q4 36.0 0.95 (0.81–1.11) 1.01 (0.85–1.20) 11.8 0.82 (0.60–1.13) 0.89 (0.63–1.24) 15.5 0.94 (0.71–1.24) 1.13 (0.84–1.51)
Bar P < 0.001 P < 0.001 P = 0.085 P = 0.149 P < 0.001 P < 0.001
Q1 31.6 Reference Reference 12.2 Reference Reference 16.6 Reference Reference
Q2 32.1 1.01 (0.85–1.21) 1.03 (0.87–1.23) 10.0 0.82 (0.58–1.16) 0.84 (0.59–1.19) 14.4 1.14 (0.81–1.60) 1.16 (0.83–1.63)
Q3 35.8 1.13 (0.95–1.34) 1.15 (0.97–1.37) 13.0 1.06 (0.77–1.47) 1.13 (0.82–1.58) 14.7 1.39 (1.01–1.91) 1.47 (1.06–2.04)
Q4 44.9 1.42 (1.21–1.67) 1.46 (1.23–1.73) 15.3 1.25 (0.92–1.71) 1.25 (0.89–1.76) 15.5 2.01 (1.50–2.71) 2.19 (1.59–3.01)
Energy dense P = 0.004 P = 0.004 P = 0.673 P = 0.464 P = 0.315 P = 0.720
Q1 42.4 Reference Reference 13.2 Reference Reference 16.6 Reference Reference
Q2 34.1 0.80 (0.69–0.94) 0.83 (0.71–0.97) 12.2 0.93 (0.67–1.28) 0.95 (0.69–1.32) 14.4 0.92 (0.70–1.22) 0.99 (0.76–1.30)
Q3 35.8 0.84 (0.72–0.98) 0.86 (0.73–1.01) 11.4 0.87 (0.62–1.20) 0.82 (0.59–1.15) 14.7 0.85 (0.64–1.13) 0.92 (0.70–1.22)
Q4 32.1 0.76 (0.64–0.89) 0.73 (0.61–0.88) 13.8 1.04 (0.77–1.43) 0.79 (0.56–1.10) 15.5 0.76 (0.57–1.02) 0.84 (0.61–1.16)
Eur J Nutr
1 3
was no association between the traditional Brazilian
pattern and excess weight/abdominal obesity in the pre-
sent study. Gimeno et al. [14], in a study of adults aged
30 years or more in Ribeirão Preto, also did not observe
an association between the “popular Brazilian” pattern
and BMI or WC. In general, the association of dietary
patterns with BMI or with obesity has been less evident
in women than in men [7].
The bar and the energy dense were the other two iden-
tified patterns. Patterns with similar compositions have
been designated in other ways in other studies: bar—
“atherogenic” [47], “alcohol” [34] and “processed foods”
[25] and energy dense—“obesogenic” [14], “energy
dense” [12], “snacks” [31], “dairy products and desserts”
[25] and “cafeteria” [47]. However, in many other studies,
a type of pattern was identified that was called in most
cases “Western” [11, 15, 44, 45, 48, 49] or “unhealthy”
[33], representing a combination of foods included in the
bar and energy-dense patterns presented in our study.
Combined or separate, these patterns included foods rich
in sugars and saturated fats and were positively associated
with indicators of excess weight/obesity [12, 13, 45, 47,
48]. An inverse association of the “Western” pattern with
BMI was observed only in the study by Sánchez-Villegas
et al. [11].
In the present study, in agreement with data reported in
previous investigations [12, 13, 15, 16, 45], the bar pattern
was directly associated with excess weight/abdominal obe-
sity. On the other hand, the energy-dense pattern was associ-
ated with lower prevalence of excess weight, which may be
explained by the cross-sectional design of our study. This is
a limitation for the interpretation of the associations between
dietary patterns and anthropometric characteristics, because
of the potential reverse causality. There is also a possibility
of misreporting. It is known that obese individuals tend to
misreport their dietary habits more than the others [7]. In the
association under study, reverse causality may have contrib-
uted to the discordant findings, since obese individuals may
opt to consume healthier foods that do not belong to the
pattern defined as energy dense. The hypothesis of reverse
causality is supported by the lower frequency of unhealthy
behaviors (current smoking habit in both sexes) among the
individuals with more adherence to this pattern (Q4) com-
pared to those in the category of lower consumption (Q1), a
fact that also disagrees with data from other studies, which
show that unhealthy dietary patterns are related to other
unhealthy lifestyle behaviors [11, 25, 33]. This may indicate
a reversal of some unhealthy behaviors associated with obe-
sity among obese individuals that are trying to change their
lifestyle to lose weight. Another possible explanation for
Table 3 Non-adjusted and adjusted prevalence ratios (PR) and 95 %
confidence intervals (95 % CI) for the association of excess weight
and abdominal obesity with the traditional Brazilian pattern identi-
fied among 23- to 25-year-old adults of the Ribeirão Preto cohort (4th
phase: 2002–2004)
The dietary patterns were categorized into quartiles—Q1 (the lower quartile of the distribution represents the lowest adherence to the pattern),
Q2, Q3 and Q4 (the upper quartile of the distribution represents the highest adherence to the pattern)
* P value referring to the maximum likelihood ratio obtained by Poisson regression (P value for trend)
a Excess weight: body mass index—BMI ≥ 25 kg/m2
b Central obesity, waist circumference WC >80 cm for women and >90 cm for men
c Central obesity, waist hip ratio WHR >0.85 for women and >0.90 cm for men
d Adjusted for skin color, schooling, monthly family income, marital status, smoking habit, level of physical activity, and total caloric intake
(kcal/day) and the other dietary patterns
Sex Excess weight (Body Mass Index—BMI)aAbdominal obesity
(waist circumference—WC)bAbdominal obesity (waist hip ratio—WHR)c
% Non-adjusted PR
(95 % CI)
Adjusted PRd
(95 % CI)
% Non-adjusted PR
(95 % CI)
Adjusted PRd
(95 % CI)
% Non-adjusted PR
(95 % CI)
Adjusted PRd
(95 % CI)
Male P = 0.001 P = 0.003 P = 0.888 P = 0.727 P = 0.736 P = 0.545
Q1 53.9 Reference Reference 12.4 Reference Reference 20.7 Reference Reference
Q2 41.6 0.77 (0.64–0.93) 0.80 (0.65–0.97) 10.7 0.86 (0.53–1.42) 0.86 (0.51–1.45) 24.3 1.17 (0.84–1.64) 1.26 (0.89–1.78)
Q3 42.8 0.79 (0.66–0.96) 0.79 (0.65–0.97) 11.5 0.93 (0.57–1.51) 0.86 (0.51–1.45) 21.9 1.06 (0.75–1.49) 1.12 (0.77–1.63)
Q4 36.2 0.67 (0.55–0.82) 0.65 (0.51–0.82) 10.3 0.83 (0.50–1.37) 0.71 (0.40–1.27) 20.6 0.99 (0.70–1.41) 1.05 (0.70–1.59)
Female P = 0.111 P = 0.587 P = 0.001 P = 0.464 P = 0.001 P = 0.267
Q1 24.1 Reference Reference 9.0 Reference Reference 5.3 Reference Reference
Q2 28.3 1.18 (0.88–1.57) 0.95 (0.71–1.28) 10.2 1.13 (0.67–1.91) 0.93 (0.54–1.59) 6.4 1.22 (0.61–2.43) 0.89 (0.44–1.81)
Q3 33.1 1.37 (1.05–1.81) 0.93 (0.69–1.25) 18.1 2.00 (1.26–3.17) 1.30 (0.78–2.17) 13.5 2.57 (1.42–4.66) 1.51 (0.78–2.95)
Q4 31.3 1.30 (0.99–1.72) 0.81 (0.58–1.13) 18.5 2.04 (1.30–3.24) 1.55 (0.67–1.99) 12.1 2.29 (1.25–4.20) 1.16 (0.58–2.34)
Eur J Nutr
1 3
this finding is that those who consumed more energy-dense
foods ate fewer overall calories. However, the models were
adjusted for total energy intake. Thus, since the reverse asso-
ciation between the energy-dense pattern and excess weight/
obesity is independent of the amount of calories consumed,
this explanation seems unlikely.
Contrary to expectations, those who adhered more to the
healthy pattern did not present lower prevalence of excess
weight. This may be explained by the cross-sectional
design of the study. Another possibility is that those in
the upper quartile of adherence to the healthy pattern may
be consuming more calories derived from healthy foods.
However, the models were adjusted for total energy intake.
Thus, the lack of association between the healthy dietary
pattern and excess weight/obesity is independent of the
amount of calories consumed.
The results that show the daily portion sizes of the food
items that composed each of the four dietary patterns pro-
vide interesting data for comparisons. We observed that
women tended to consume higher portion sizes of food
items that compose the healthy pattern when compared to
men. It is interesting to note that rice and beans were the
two most important food items of the traditional Brazil-
ian pattern in comparison with the other food groups that
compose this pattern, as has been shown previously by their
factor loading [32]. We also found that the food items that
account to the alcohol content of the bar pattern were highly
consumed by both men and women. Finally we observed an
overall trend of a higher consumption of whole dairy prod-
ucts in comparison with low-fat ones, independently of the
dietary pattern.
Some limitations should be considered when interpret-
ing the findings of the present study. First, its cross-sectional
design did not permit us to observe the effect of dietary pat-
terns on the indicators of excess weight/obesity, but only to
establish associations between independent and dependent
variables, which may be due to reverse causality. Second, the
subjectivity of the method of principal component factor anal-
ysis, which requires the investigators to make various deci-
sions, includes how to group the food items and what to call
the patterns [15, 30]. Third, the limitations inherent to dietary
surveys such as errors in the estimate of food portions and
memory bias [30]. Moreover, the US food composition dataset
was used as a reference for almost all FFQ items. However,
factors such as seasons, climate, soil nutrients, food prepara-
tion and processing can make the nutritional composition of
Brazilian foods somewhat different from those included in the
US food composition table. It should also be pointed out that
selective losses occurred when the group of individuals fol-
lowed up in the fourth stage of the cohort study was compared
to the individuals who were not followed up. The follow-up
rates were slightly higher for women and for subjects whose
mothers had a higher schooling at the time of their birth.
However, although statistically significant, these differences
were small [20].
Considering the strengths, it should be pointed out that
the reproducibility and validity of the method used to
define the dietary patterns in the present investigation, i.e.,
PCA with data from the FFQ, were confirmed in a previ-
ous study [50]. Nutritionists with the aid of a photo album
applied the FFQ in order to facilitate estimation of portion
sizes. The consumption period investigated was the last
year, considering that longer recall periods may reduce the
likelihood of reverse causality [7]. In addition, all the sub-
jective decisions typical of the PCA method were based on
scientific knowledge and on an extensive literature review.
In conclusion, the traditional Brazilian (only among men)
and energy-dense (for both genders) patterns were associated
with a lower prevalence, whereas the bar pattern was associated
with a higher prevalence of excess weight and/or abdominal
obesity. The recommendation is to reduce consumption of alco-
holic beverages and foods composing the bar dietary pattern
and to encourage consumption of traditional foods of the Brazil-
ian diet (rice and beans) to reduce obesity among young adults.
Acknowledgments Financial support for the present study was
provided by Conselho Nacional de Desenvolvimento Científico e
Tecnológico (CNPq), by the University of São Paulo and by the
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
(Grant Number 00/09508-7).
Author contributions Machado Arruda and Soraia Pinheiro par-
ticipated in the stages of data analysis and was responsible for writ-
ing the manuscript; Silva, Antônio Augusto Moura da participated in
the stage of data analysis and critical review of the manuscript; Kac,
Gilberto, Vilela, Ana Amélia Freitas, and Goldani, Marcelo contrib-
uted to the discussion and critical revision of the manuscript; Bettiol,
Heloisa and Barbieri, Marco Antônio were responsible for the origi-
nal design of the project and participated in the discussion and critical
revision of the manuscript.
Compliance with ethical standards
Conflict of interest All authors declare that there was no conflict of
interest.
Ethical aspects The study was conducted according to the directives
established in the Declaration of Helsinki, and all procedures involving
human being were approved by the Research Ethics Committee of the
University Hospital, Faculty of Medicine of Ribeirão Preto, University
of São Paulo, Brazil. All persons gave their informed consent prior to
their inclusion in the study.
References
1. Pou KM, Massaro JM, Hoffmann U et al (2009) Patterns of
abdominal fat distribution. Diabetes Care 32(3):481–485
2. Huxley R, Mendis S, Zheleznyakov E et al (2010) Body mass
index, waist circumference and wais:hip ratio as predictors of
cardiovascular risk—a review of the literature. Eur J Clin Nutr
64:16–22
Eur J Nutr
1 3
3. Bergman RN, Stefanovski D, Buchana TA et al (2011) A better
index of body adiposity. Obesity 19(5):1083–1089
4. Finucane MM, Stevens GA, Cowan MJ et al (2011) National,
regional, and global trends in body-mass index since 1980: sys-
tematic analysis of health examination surveys and epidemiolog-
ical studies with 960 country-years and 9.1 million participants.
Lancet 377:557–567
5. Brazil (2010) Pesquisa de Orçamentos Familiares (POF 2008–
2009)—Antropometria e estado nutricional de crianças, adoles-
centes e adultos no Brazil. Disponível em: http://www.ibge.gov.
br/home/estatistica/populacao/condicaodevida/pof/2008_2009_
encaa/pof_20082009_encaa.pdf. Acesso em 26 de janeiro de 2011
6. Acheson KJ (2010) Carbohydrate for weight and metabolic con-
trol: Where do we stand? Nutrition 26:141–145
7. Perozzo G, Olinto MTA, Dias-da-Costa JS et al (2008) Asso-
ciação dos padrões alimentares com obesidade geral e abdomi-
nal em mulheres residentes no Sul do Brazil. Cad Saude Publica
24:2427–2430
8. Du H, Van der ADL, Boshuizen HC et al (2010) Dietary fiber
and subsequent changes in body weight and waist circumference
in European men and women. Am J Clin Nutr 91:329–336
9. Hu FB (2002) Dietary pattern analysis: a new direction in nutri-
tional epidemiology. Curr Opin Lipidol 13:3–9
10. Hearty AP, Gibney MJ (2008) Comparison of cluster and princi-
pal component analysis techniques to derive dietary patterns in
Irish adults. Br J Nutr 101:598–608
11. Sánchez-Villegas A, Martínez-González MA, Delgado-Rod-
ríguez MA et al (2003) Gender, age, socio-economic and life-
style factors associated with major dietary patterns in the Span-
ish Project SUN (Sguimento Universidad de Navarra). Eur J Clin
Nutr 57:285–292
12. Langsetmo L, Poliquin S, Hanley DA et al (2010) Dietary pat-
terns in Canadian men and women ages 25 and older: relation-
ship to demographics, body mass index and bone mineral den-
sity. BMC Musculoskelet Disord. doi:10.1186/1471-2474-11-20
13. Denova-Gutiérrez E, Castañón S, Talayera JO et al (2011) Die-
tary patterns are associated with different indexes of adiposity
and obesity in an urban. J Nutr 141:921–927
14. Gimeno SGA, Mondini L, Moraes AS et al (2011) Padrões de
consumo de alimentos e fatores associados em adultos jovens
de Ribeirão Preto, São Paulo, Brazil: Projeto OBEDIAPR. Cad
Saude Publica 27:533–545
15. Naja F, Nasreddine L, Italini L et al (2011) Dietary patterns
and their association with obesity and sociodemographic fac-
tors in a national sample of Lebanese adults. Public Health Nutr
14:1570–1578
16. Olinto MTA, Gigante DP, Horta B et al (2012) Major dietary
patterns and cardiovascular risk factors among young Brazilian
adults. Eur J Nutr 51:281–291
17. Maskarinec G, Novotny R, Tasaki K (2000) Dietary patterns are
associated with body mass index in multiethnic women. J Nutr
130:3068–3072
18. Gordon-Larsen P, Adair LS, Nelson MC et al (2004) Five-year
obesity incidence in the transition period between adolescence
and adulthood: the National Longitudinal Study of Adolescence
Health. Am J Clin Nutr 80(3):569–575
19. Singh AS, Mulder C, Twisk JWR et al (2008) Tracing of child-
hood overweight into adulthood: a systematic review of the lit-
erature. Obes Rev 9(5):474–488
20. Barbieri MA, Bettiol H, Silva AAM et al (2006) Health in early
adulthood: the contribution of the 1978/79 Ribeirão Preto birth
cohort. Braz J Med Biol Res 39:1041–1055
21. Guidelines for data processing and analysis of the International
Physical Activity Questionnaire (IPAQ) [November 23, 2011]
IPAQ web site http://www.ipaq.ki.se/dloads/IPAQ%20LS%20
Scoring%20Protocols_Nov05.pdf. Published Nov 2005
22. Ribeiro AB, Cardoso MA (2002) Construção de um ques-
tionário de frequência alimentar como subsídio para programas
de prevenção de doenças crônicas não transmissíveis. Rev Nutr
15:239–245
23. Molina MC, Bettiol H, Barbieri MA et al (2007) Food consump-
tion by young adults living in Ribeirão Preto, SP, 2002/2004.
Braz J Med Biol Res 40:1257–1266
24. Instituto Brasileiro de Geografia e Estatística—IBGE (1999)
Tabelas de Composição de Alimentos/IBGE—Rio de Janeiro/
RJ—5th edn, p 137
25. Olinto MTA, Willett WC, Gigante DP et al (2010) Sociode-
mographic and lifestyle characteristics in relation to dietary
patterns among young Brazilian adults. Public Health Nutr
14:150–159
26. Eshriqui I, Vilela AAF, Rebelo F et al (2014) Gestational dietary
patterns are not associated with blood pressure changes dur-
ing pregnancy and early postpartum in a Brazilian prospective
cohort. Eur J Nutr. doi:10.1007/s00394-014-0819-4
27. Callaway CW, Chumlea WC, Bouchard C, Himes JH, Lohman
TG, Martin AD et al (1988) Circumferences. In: Lohman TG,
Roche AF, Martorell R (eds) Anthropometric standardization ref-
erence manual. Human Kinetics Books, Champaign, pp 39–54
28. Gordon CC, Chumlea WC, Roche AF (1988) Stature, recum-
bent length, and weight. In: Lohman TG, Roche AF, Martorell R
(eds) Anthropometric standardization reference manual. Human
Kinetics Books, Champaign, pp 3–8
29. Hair JF, Black WC, Babin BJ et al (2010) Multivariate data anal-
ysis, 7th edn. Prentice Hall, Englewood Cliffs
30. Newby PK, Tucker KL (2004) Empirically derived eating pat-
terns using factor or cluster analysis: a review. Nutr Rev
62:177–203
31. Marchioni DML, Latorre MRDO, Eluf-Neto J et al (2005) Iden-
tification of dietary patterns using factor analysis in an epide-
miological study in São Paulo. Sao Paulo Med J 123:124–127
32. Arruda SPM, Silva AAM, Kac G et al (2014) Socioeconomic
and demographic factors are associated with dietary patterns in
cohort of Young Brazilian adults. BMC Public Health 14:654
33. Rezazadeh A, Rashidkhani B, Omidvar N (2010) Association of
major dietary patterns with socioeconomic and lifestyle factors
of adult women living in Tehran, Iran. Nutrition 26:337–341
34. Shulze MB, Hoffmann K, Kroke A et al (2001) Dietary pat-
terns and their association with food and nutrient intake in the
European Prospective Investigation into Cancer and Nutrition
(EPIC)—Postdam study. Br J Nutr 85:363–373
35. WHO (World Health Organization) (1998) Report of a WHO
consultation on obesity. Obesity: preventing and managing the
global epidemic. WHO, Geneva
36. Coutinho W (1999) Consenso latino-americano de obesidade.
Arq Bras Endocrinol Metab 43:21–67
37. Alberti KG, Zimmet P, Shaw J (2005) The metabolic syn-
drome—a new worldwide definition. Lancet 366:1059–1062
38. Peace KE, Chen D (2010) Clinical trial methodology. Chapman
& Hall/CRC, Boca Raton
39. Esmaillzadeh A, Azadbakht L (2008) Major dietary patterns in
relation to general obesity and central adiposity among Iranian
women. J Nutr 138:358–363
40. Eilat-Adar S, Mete M, Fretts A et al (2013) Dietary patterns and
their association with cardiovascular risk factors in a populations
undergoing lifestyle changes: the Strong Hearty Study. Nutr
Metab Cardiovas 23:528–535
41. Newby PK, Muller D, Hallfrisch J et al (2004) Food patterns
measured by factor analysis and anthropometric changes in
adults. Am J Clin Nutr 80(2):504–513
42. Mendez MA, Popkin BM, Jakszyn P et al (2006) Adherence to a
Mediterranean diet is associated with reduced 3-year incidence
of obesity. J Nutr 136:2934–2938
Eur J Nutr
1 3
43. Aekplakorn W, Satheannoppakao W, Putwatana P et al (2015)
Dietary patterns and metabolic syndrome in Thai adults. J Nutr
Metab. doi:10.1155/2015/468759
44. Sicheiri R (2002) Dietary patterns and their associations
with obesity in the Brazilian city of Rio de Janeiro. Obes Res
10:42–48
45. Cunha DB, Almeida RMVR, Sichieri R et al (2010) Association
of dietary patterns with BMI and waist circumference in a low-
income neighborhood in Brazil. Br J Nutr 104:908–913
46. Lenz A, Olinto MTA, Dias-da-Costa JS et al (2009) Socioeco-
nomic patterns of women living, demographic and lifestyle fac-
tors associated with dietary of women living in Southern Brazil.
Cad Saude Publica 25:1297–1306
47. Neumann AICP, Martins IS, Marcopito LF et al (2007)
Padrões alimentares associados a fatores de risco para doenças
cardiovasculares entre residentes de um município Brasileiro.
Rev Panam Salud Publica 22:329–339
48. Sun J, Buys NJ, Hills AP et al (2014) Dietary pattern and its
association with the prevalence of obesity, hypertension and
other cardiovascular risk factors among Chinese older adults. Int
J Environ Res Public Health 11:3956–3971
49. Hsiao PY, Mitchell DC, Coffman DL et al (2013) Dietary pat-
terns and relationship to obesity-related health outcomes and
mortality in adults 75 years of age or greater. J Nutr Health
Aging 17(6):566–572
50. Hu FB, Rimm E, Smith-Warner SA et al (1999) Reproducibility
and validity of dietary pattern assessed with a Food-Frequency
Questionnaire. Am J Clin Nutr 69:243–249
