Artigo original
Association of fat intake and socioeconomic status
on anthropometric measurements of adults
Associação do consumo de gorduras e do nível socioeconômico sobre
as medidas antropométricas de adultos
Katia Cristina Portero-McLellan1, Gustavo Duarte Pimentel2, Jose Eduardo Corrente3, Roberto
Carlos Burini4
ABSTRACT
We aimed to identify the influence of dietary fat profile on body mass index (BMI) and waist circumference (WC) in a middleclass general population sample. A cross-sectional study of 448 adults aged 35-85 years was carried out from January 2004
to December 2007. Patients were divided in two groups according to family income: Group 1 (G1) with higher income, and
Group 2 (G2) with lower income. Demographic and socioeconomic status were identified, along with anthropometric data,
health eating index (HEI) and dietary profile. The groups were similar with respect to gender, age, BMI and WC. HEI was
higher in G1 due to a higher intake of protein (+12.8%), dairy products (p<0.001), higher intake of vegetables (p<0.01), fruit
(p<0.001), and less dietary fat (-9.8%). The main contribution of fats was saturated fat for G1 (+5.0%) and polyunsaturated
fat for G2 (+14.4%). Besides differences in socioeconomic status the groups had similar BMI and abdominal fatness. Only
differences in fat profile were correlated with the anthropometric measures mostly explained by the lower vegetable oil intake
in higher income participants.
Key words: Dietary fats, anthropometry, food consumption, diet
RESUMO
O objetivo do estudo foi identificar a influência das gorduras dietéticas sobre o índice de massa corporal (IMC) e a circunferência abdominal (CA) em uma amostra populacional de adultos. O estudo tranversal foi realizado com 448 adultos, entre 35
e 85 anos, de janeiro de 2004 a dezembro de 2007. Os indivíduos foram divididos em dois grupos de acordo com a renda
familiar: Grupo 1 (G1) com maior renda, e Grupo 2 (G2) com menor renda. Foram levantadas variáveis socioeconômicas e
demográficas, juntamente com variáveis antropométricas, índice de alimentação saudável (IAS) e perfil dietético. Os grupos
foram semelhantes quanto ao gênero, à idade, ao IMC, e à CA. O IAS foi maior no G1 em decorrência do maior consumo
de proteínas (+12,8%), laticínios (p<0.001), maior consumo de hortaliças (p<0,01), frutas (p<0,001), e menor de gordura
(-9,8%). A maior contribuição das gorduras foi a saturada para o G1 (+5,0%) e a polinsaturada para o G2 (+14,4%). Apesar
das diferenças socioeconômicas os grupos foram similares quanto ao IMC e à adiposidade abdominal. As diferenças no
perfil do consumo de gorduras foram correlacionadas com as medidas antropométricas e puderam ser explicadas pelo
menor consumo de óleo entre os indivíduos de renda mais elevada.
Palavras-chave: Gorduras na dieta, antropometria, consumo de alimentos, dieta
Doutora em Ciências. Professora do Departamento de Saúde Pública da Faculdade de Medicina de Botucatu da Universidade Estadual Paulista (Unesp). End.:
Rua Taiaçu, 77 - Piracicaba (SP) - CEP: 13432-506 - E-mail: [email protected]
2
Mestrando em Nutrição pela Universidade Federal de São Paulo (Unifesp).
3
Livre Docente. Professor Adjunto da Unesp.
4
Livre Docente. Professor Titular da Unesp.
1
266 Cad. Saúde Colet., 2010, Rio de Janeiro, 18 (2): 266-74
Association of fat intake and socioeconomic status on anthropometric measurements of adults
Introduction
Nutrition transition is a widely studied global phenomenon that is characterized by an abandonment of traditional
diets that are high in fiber, grains, fruits, and vegetables; and
increasing modern diets that are high in fat, sugar, and salt
(Popkin, 2002). Effects of the nutrition transition include increase in obesity rates and other non-communicable chronic
diseases. The most dramatic effects of this transition are
found in developing countries because of the impact it has on
nutrition deficiencies and the rapid growth of the population
(Popkin, 2002).
The ongoing increase in adult obesity in Brazil has been
occurring among all groups of men and women with a higher
proportion of increase among lower income families (Monteiro
et al., 1995). A very noticeable change in the income-obesity relationship is indicated by the following facts: (1) income and body
mass index (BMI) are inversely related among the 30% richest
women; (2) a higher prevalence of female obesity (15.4%) occurs
in 40% of the middle-income group; and (3) 30% of the poorest
Brazilian women (9.7% prevalence) can no longer be considered
to be protected from obesity (Monteiro et al., 1995).
In recent years the dietary pattern of the Brazilian population has been changing (Levy-Costa et al., 2005; Molina et al.,
2007). Levy-Costa et al. (2005) examined data from household
budget surveys from 1974 to 2003 that showed time-trends in
metropolitan areas indicating a decline in the consumption
of traditional food (rice and beans); noticeable increases in
the consumption of processed items such as cookies and soft
drinks (Mondini & Monteiro, 1994; Monteiro et al., 2000); a
continued excessive consumption of sugar; and a continued
increase in total fat and saturated fat content in the diet. These
changes represent an important negative dietary pattern trend
in the country and may represent an important health risk factor (Monteiro et al., 2000). Patterns and trends regarding food
availability in Brazilian households are consistent with the
increasing presence of chronic non-communicable diseases
in morbidity and mortality, and with the continuous increase
in the prevalence of obesity (Levy-Costa et al., 2005).
Studies on Brazilian food consumption, specifically involving adults, have been conducted preferentially in state capitals
and their metropolitan regions (Fornes et al., 2000; 2002; Salvo
& Gimeno, 2002; Sichieri et al., 2003; Bonomo et al., 2003;
Chor et al., 2003; Newmann et al., 2007). Moreover, only a few
diet studies have been performed within the Brazilian population to observe the relationship between food consumption
and body composition (Fornes et al., 2002; Bonomo et al.,
2003; Castanheira et al., 2003; Fisberg et al., 2006; Peixoto et
al., 2007; Molina et al., 2007). For this reason, we aimed to
identify the influence of dietary fat intake on BMI and WC
in a Brazilian middle-town population, and the demographic
and socioeconomic effects of these associations.
Research design and methods
The Lifestyle Changing Program (LISC) that was offered
to patients with non-communicable chronic diseases consisted of regular physical exercise and nutritional counseling.
TheSão Paulo State University Medical School of Botucatu,
SP, Brasil, Metabolism, Exercise and Nutrition Center (CeMENutri), has been conducting this LISC program since
1992, in Botucatu, which is a city located in the center of São
Paulo State, about 230 km west of the capital city of São Paulo
and has a population of 121,274 (IBGE, 2006). Crops (including corn, sugarcane, beans, and fruits) grown in the region
are processed in Botucatu, which also has foundries, textile
mills, bakeries, and factories producing buses, auto parts, and
agricultural and industrial machinery. Goods are shipped
by railroad and highway to the city of São Paulo. The City
Hall conducts four major food-supply programs for the lowincome population: food-kit fellowship, community garden,
community bakery, and life-milk program (for every child in
a public school).
The inclusion criteria for LISC participants are people
over the age of 35, of both genders, with at least one of the
metabolic syndrome components and/or comorbities, and
without metabolic or motor disabilities that would limit
physical exercise.
The subject data came from the baseline of people who
joined the program between 2004 and 2007. Subjects gave
their written consent to participate in this study, which was
approved by the Medical Ethics Committee of São Paulo
State University (Comitê de Ética em Pesquisa da Faculdade
de Medicina de Botucatu da Universidade Estadual Paulista
“Júlio de Mesquita Filho” – Unesp).
Subjects and study design
A cross-sectional observational study of 448 adults, aged
35-85 years (predominantly Caucasian) was carried out from
January 2004 to December 2007. Patients were divided in two
groups according to family income: Group 1 (G1) with higher
income (n=285), and Group 2 (G2) with lower income (163).
We excluded subjects with diabetes, renal, liver, or heart diseases; or chronic alcohol intake before starting this study.
Demographic data and socioeconomic status
Participants were classified by gender, range of age (< and
≥ 60 years), family income (measured in monthly minimal income (MI), 1 MI=U$ 250.00) and education (years of school).
Cad. Saúde Colet., 2010, Rio de Janeiro, 18 (2): 266-74 267
Katia Cristina Portero-McLellan, Gustavo Duarte Pimentel, Jose Eduardo Corrente, Roberto Carlos Burini
Dietary intake
Usual dietary intake data was determined using a 24hour recall. The diet was documented by trained personnel,
and to obtain precise information, the subjects were asked
how often they usually ate during the day, what variety
of food was consumed, how the food was prepared, what
the serving size was, and what the brand of the food/meal
was. The diets were analyzed with the software NutWin®
(2002) version 1.5, and the principal nutrients of interest
were energy, protein, fat (saturated, mono and polyunsaturated), cholesterol, carbohydrates, and dietary fiber. Mean
individual nutrient intakes per day were computed using
the NutWin database and Brazilian food tables (IBGE,
1999; Phillippi, 2002; Nepa, 2004). The Healthy Eating
Index (HEI) modified for the Brazilian population was
used to assess the quality of the participants’ diet (Mota et
al., 2008). The original HEI was developed based on a 10component system of five food groups with a total possible
index score of 100 (Kennedy et al., 1995). This method was
adapted for the Brazilian population based on the Brazilian
food guide (Philippi et al., 1999) that has 8 food groups
and 12 components to measure the variety of food intake.
Each of the 12 components has a score ranging from 0 to
10, so the total possible index score is 120.
Body composition
Weight and height were measured with a Filizola® electronic scale (capacity and precision in grams), and a stadiometer (precision in millimeters). WC was measured halfway between the lower rib region and the iliac crest in the recumbent
position after a normal exhale. BMI and WC were evaluated
according to The World Health Organization (1998a).
Statistical analysis
Statistical analyses were conducted with SAS software for
windows (SAS version 9.1.3., SAS Institute, Inc., Cary, NC).
Descriptive statistics were performed for the study and continuous variables are presented as mean±standard deviation
(SD). Categorical variables are presented as absolute numbers
and percentages. Continuous variables were compared by
the independent test t. The percentage of total energy intake
attribuited to fat: total, saturated (SFA), monounsaturated
(MUFA), and polyunsaturated (PUFA), was calculated by
multiplying each individual’s fat intake in grams by 9 kcal/g,
dividing the product by individual energy intake, and then
multiplying by 100. The nutrients of the diet were adjusted
for total energy intake to control for confounding as recommended in published literature (Willett et al., 1997). Fat intake
values were categorized as quintiles after adjusting for age,
268 Cad. Saúde Colet., 2010, Rio de Janeiro, 18 (2): 266-74
gender, BMI and WC and were analyzed by chi-square test.
ANOVA and Tukey tests were used to compare the differences
between quintiles. Simple logistic regression analyses were
used to determine the association of dietary fat intake, and
body composition using two models: the first was adjusted for
demographic data such as age, gender, income, and education;
and the second was additionally adjusted for energy intake
and HEI. Results from the logistic regression are presented,
and odds ratios (ODs) with 95% confidence interval (CI) were
calculated. The results are discussed based on a significance
level of 5% (p<0.05).
Results
Groups were similar statistically by gender, age, BMI, and
WC (Table 1). Subjects from G1 had a higher educational
level (p<0.001) and higher income (p<0.001) than subjects
from G2. They also had a higher HEI (p<0.001) along with
higher concentration of protein (% energy), lower total fat (%
energy), and lower PUFA (% energy).
The distribution of normal and elevated values for BMI
between groups showed similar prevalence for overweight and
obesity, and regarding WC, a slightly higher (6.2%) prevalence
of values elevated in G2. Subjects from G1 showed better HEI
than G2 as a result of eating more energy as protein (+12.8%)
and carbohydrates (5.5%) and less as lipid (-9.8%). The main
contribution for fat intake was SFA for G1 (+5.0%) and PUFA
for G2 (+14.4%). Both groups exhibited poor intake of MUFA
and similarly lower amounts of fibers (Table 2).
Table 3 shows the distribution of the HEI components in
both groups. HEI was higher in G1 due to a higher intake
of protein from dairy products (1.5 servings/day versus 0.8,
p<0.001), higher intake of vegetables (1.5 servings/day versus
1.0, p<0.01), and higher intake of fruit (2.5 servings/day versus 0.0, p<0.001).
The lower educated and lower income group had a poor
quality diet characterized by higher caloric intake as total
fat and PUFA reflecting a higher vegetable oil intake. MUFA
as well as fiber intake were similarly lower in both groups
(Table 4).
Relative risks of BMI>25 and WC>88 (female) and >102
(male) according to the quintiles of oil intake are shown in
Table 5. After adjusting for age, gender, education, and income
individuals from the G2 in the third quintile were 0.07 (95%
CI 0.007-0.07) times more likely to have a BMI>25. Additionally adjusting for energy intake and HEI, the same group of
individuals were 0.06 (95% CI 0.006-0.06) times more likely
to have a BMI>25. The higher intake of oil in G1 and G2 was
not significantly associated with BMI and WC.
Association of fat intake and socioeconomic status on anthropometric measurements of adults
Table 1 - Sociodemographic, anthropometric and dietary characteristics of the studied groups
Sociodemographic
Gender
Male
Female
Age, years
Education
Income
Anthropometric
BMI
WC
Dietary
HEI
Energy, kcal
Carbohydrates, %
Carbohydrates, g/kg
Protein, %
Protein, g/kg
Total Fat, % energy
Total Fat, g/kg
SFA, % energy
SFA, g/kg
MUFA, % energy
MUFA, g/kg
PUFA, % energy
PUFA, g/kg
Cholesterol, mg
Vegetable Oil, serving
Fiber, g
G1
Groups
G2
P-value*
75 (26.3)
210 (73.7)
52.47 (9.93)
3.44 (2.05)
2.58 (1.03)
41 (25.2)
122 (74.8)
53.93 (10.70)
1.39 (1.06)
1.67 (0.72)
0.7876
29.74 (6.05)
96.28 (15.02)
28.91 (5.20)
96.08 (12.42)
0.1407
0.8815
82.93 (14.60)
1624.60 (611.81)
52.28 (9.26)
3.27 (1.27)
18.68 (5.37)
1.15 (0.47)
29.03 (8.43)
0.83 (0.45)
7.99 (3.73)
0.23 (0.14)
8.82 (3.67)
0.25 (0.14)
7.08 (3.50)
0.20 (0.13)
180.28 (119.92)
2.33 (2.07)
15.15 (8.63)
77.98 (13.21)
1539.25 (609.13)
51.64 (9.29)
3.15 (1.13)
17.70 (6.18)
1.09 (0.56)
30.69 (8.04)
0.86 (0.41)
7.83 (3.38)
0.22 (0.14)
9.25 (3.53)
0.26 (0.15)
9.14 (4.20)
0.25 (0.15)
169.87 (121.68)
2.19 (1.53)
15.84 (8.67)
0.0004
0.1555
0.4863
0.2773
0.0456
0.1416
0.0418
0.4671
0.6504
0.6317
0.2214
0.3871
0.0000
0.0001
0.4140
0.4437
0.7231
0.1483
0.0000
0.0000
*student’s t-test.
Values are mean (SD) or n and %. G1: Group 1 – higher income; G2: Group 2 – lower income; BMI: Body Mass Index; WC: waist circumference; HEI-ad: healthy
eating index adapted; SFA: saturated fatty acids; MUFA: monounsaturated fatty acids; PUFA: polyunsaturated fatty acids.
Discussion
The changes in percentage of energy from dietary fat over
the past 30 years in Brazil, as assessed by household food availability (1974-2003), went from 25.8% in 1974/1975 to 30.5% in
2002/2003, representing an increase in 4.7% of fat in the Brazilian diet (Levy-Costa et al., 2005). This increase is consistent with
the observed rise in the prevalence of obesity in Brazil (IBGE,
2008). The role of dietary fat in weight gain and obesity has been
widely studied in developed countries but remains highly controversial (Lissner & Heitmann, 1995; Willett & Leibel, 2002;
Pirozzo et al., 2003) as evidenced from epidemiological studies
and long term randomized trials linking fat intake and weight
gain or obesity being weak (Willettt & Leibel, 2002; Pirozzo et
al., 2003) and inconsistent (Sheppard et al., 1991; Heitmann et
al., 1995; Carmichael et al., 1998; Westerterp-Plantenga et al.,
1998; Willet & Leibel, 2002).
The results of this study suggest that educational level and
income may be important determinants of quality of diet but
not body fatness. Total fat intake (% energy) was higher among
subjects with lower educational level and lower income. This
fact could be related to the intake of vegetable oil. Lower
income subjects receive monthly food supplies from their
employers which consist basically of vegetable oil, salt, sugar,
and refined carbohydrates. The oil they receive may be used
to cook food and deep fry vegetables or meat/poultry which
contributes to the high percentage of fat in the diet. Dietary
fat is the most energy-dense nutrient and could lead to over
consumption of calories. Increased dietary energy density is
associated with obesity (Mendonza et al., 1997). In our study,
the amount of fat was related to energy intake. Subjects with
higher dietary fat intake had higher energy intake.
The higher intake of protein and SFA among subjects with
higher educational levels and higher income can be explained
by the high consumption of meat (important source of protein
and SFA) among these individuals. Protein rich foods (meat,
poultry, and milk and dairy products) are more expensive
than carbohydrates and vegetable oil, and are consumed more
among subjects with higher income.
The rapid increase of obesity among the world’s population has become a major public health problem affecting both
Cad. Saúde Colet., 2010, Rio de Janeiro, 18 (2): 266-74 269
Katia Cristina Portero-McLellan, Gustavo Duarte Pimentel, Jose Eduardo Corrente, Roberto Carlos Burini
developed and developing countries. In Brazil, according to
data from the Family Budget Survey (POF 2002-2003), 40.6%
of adults were overweight. The increase of obesity occurred
in all socioeconomic strata but was notacibly higher in lowincome families (Coitinho, 1991; IBGE, 2008).
Several studies have indicated that overweight and obesity
are inversely correlated with socioeconomic status in developed countries (Wamala et al., 1997; Lahti-Koski et al., 2002;
Liebman et al., 2003) and have shown a negative association
between educational level and BMI (Rissanen et al., 1991;
Gutiérrez-Fisac et al., 1996; Mokdad et al., 2001). In Brazil,
some studies show a positive relationship between income
and BMI (Fisberg et al., 2006; Peixoto et al., 2007). Our study
failed to show an association between body fatness (BMI
and WC) and socioeconomic status (educational level and
income) which may indicate that this association depends on
the degree of development of the population (Gigante et al.,
1997; Martorell et al., 2000; Kruger et al., 2000).
WC values were not different between the groups studied
but they were higher when compared to subjects from the
South of Brazil (Castanheira et al., 2003). The study in the
South of Brazil found a linear association of WC and income
for both genders but inverse among women, indicating higher
risk among men with higher income and women with lower
income. The educational levels were inversely related to the
central fat only among women (Castanheira et al., 2003).
Socioeconomic profile and its relationship with poverty and obesity remain controversial. Monteiro et al., (2001)
showed an inverse relationship between obesity and educational levels in Brazilian women in both wealthy and poor
part of the country. The study also found a direct relationship
with income in the poor area of the country.
Our study probably underestimates the effects of dietary
fat on body fatness because we used a single 24-hour dietary
Table 2 - Obesity, adiposity, and dietary intake distribution between
the groups
G1
BMI (Kg/m2)
<25
25 – 30
≥30
WC
Normal
Elevated
HEI
<70
70 – 100
≥100
Protein, % energy
≤15
>15
Total fat, % energy
≤30
>30
SFA, % energy
<10
≥10
MUFA, % energy
<10
≥10
PUFA, % energy
<10
≥10
Vegetable Oil, serving
<2
≥2
Fiber, g
<20
≥20
Groups
G2
53 (20.0)
115 (40.3)
113 (39.7)
35 (21.5)
66 (40.5)
62 (38.0)
112 (39.3)
173 (60.7)
54 (33.1)
109 (66.9)
57 (20.1)
195 (68.7)
32 (11.2)
41 (25.2)
113 (69.3)
9 (5.5)
70 (24.6)
215 (75.4)
61 (37.4)
102 (62.6)
222 (77.9)
63 (22.1)
111 (68.1)
52 (31.9)
206 (72.3)
79 (27.7)
126 (77.3)
37 (22.7)
200 (72.2)
85 (29.8)
99 (60.7)
64 (39.3)
228 (80.0)
57 (20.0)
107 (65.6)
56 (34.4)
135 (83.3)
27 (16.7)
74 (47.1)
83 (52.9)
215 (75.7)
69 (24.3)
127 (77.9)
36 (22.1)
Values are n and %. G1: Group 1 – higher income; G2: Group 2 – lower
income; BMI: Body Mass Index; WC: waist circumference; HEI-ad: healthy
eating index adapted; SFA: saturated fatty acids; MUFA: monounsaturated
fatty acids; PUFA: polyunsaturated fatty acids.
Table 3 - Health eating index and food group of the studied groups
HEI
Bread, cereal, rice, pasta
Fruits
Vegetables
Legumes, beans
Milk, dairy
Meat, poultry, fish
Sugar
Oils
Variety
G1
Mean (SD) / Median
82.9 (14.6) / 84.3
3.5 (1.5) / 3.5
3.4 (3.2) / 2.5
2.4 (2.4) / 1.5
1.2 (1.5) / 1.0
1.7 (1.3) / 1.5
1.8 (1.4) / 1.5
1.7 (2.2) / 1.0
2.4 (2.2) / 2.0
13.7 (3.9) / 13.0
Groups
G2
Mean (SD) / Median
78.0 (13.2) / 78.3
3.5 (1.6) / 3.3
1.8 (5.5) / 0.0
1.7 (2.2) / 1.0
2.2 (2.1) / 1.6
1.0 (1.2) / 0.8
1.9 (1.4) / 1.5
1.6 (2.1) / 1.0
2.2 (1.5) / 2.0
10.3 (8.2) / 9.0
*student’s t-test.
G1: Group 1 – higher income; G2: Group 2 – lower income; HEI-ad: healthy eating index adapted.
270 Cad. Saúde Colet., 2010, Rio de Janeiro, 18 (2): 266-74
P-value*
<0.0001
0.705
0.001
0.003
<0.0001
<0.001
0.957
0.731
0.145
<0.0001
Association of fat intake and socioeconomic status on anthropometric measurements of adults
Table 4 - Characteristics and dietary intakes of the subjects according quintiles of total fat (% energy)
Age, years
Income
Education
Weight, kg
BMI, kg/m2
WC, cm
HEI
Energy, kcal
CHO, %
PROT, %
FAT, %
SFA, %
MUFA, %
PUFA, %
Cholesterol, mg
Vegetable oil, serving
Fiber, g
1 (lowest)
55.45 (11.04)
2.87 (1.17)
3.63 (2.18)
75.95 (17.16)
28.80 (5.11)
92.93 (16.30)
88.87 (12.95)
1411.88 (445.68)
61.93 (6.87)
19.64 (5.63)
18.41 (2.72)
4.69 (1.83)
5.15 (1.43)
4.47 (1.70)
129.43 (105.62)
1.63 (1.69)
17.28 (10.40)
Quintiles of dietary fat intake (% energy)
G1
G2
3
5 (highest)
1 (lowest)
3
54.02 (9.86)
48.50 (7.74)
58.35 (11.88)
50.61 (9.20)
2.20 (0.77)
2.38 (0.98) §
1.53 (0.74)
1.80 (0.65)
3.67 (1.93)
2.81 (1.92)
1.20 (0.77)
1.92 (1.38)
76.64 (18.55)
81.31 (20.71)
70.80 (12.90)
74.00 (15.69)
29.71 (6.33)
30.84 (7.36)
29.15 (5.27)
29.42 (6.04)
96.74 (14.96)
99.32 (15.11)§
95.72 (12.89)
97.20 (12.34)
83.78 (12.47)
73.83 (11.86)*
83.58 (13.94)
81.48 (14.01)
1603.68 (596.68) 1708.63 (702.06) 1293.14 (392.72) 1557.48 (669.47)
51.17 (5.43)
39.88 (6.33)*
63.87 (9.65)
52.70 (5.91)
19.52 (5.16)
17.37 (4.52)
17.89 (8.18)
18.16 (5.73)
29.31 (1.22)
42.71 (5.66)*
18.25 (3.55)
29.14 (1.25)
8.62 (3.19)
11.13 (3.72)
4.45 (2.07)
7.62 (2.16)
9.09 (3.19)
13.36 (3.60)*
4.93 (1.65)
8.62 (2.02)
7.06 (2.76)
10.26 (4.07)*
5.15 (2.08)
8.67 (2.59)
191.65 (124.63)
207.78 (118.71)
139.89 (111.23)
167.01 (116.89)
2.00 (1.14)
3.68 (2.84)*
1.32 (0.72)
2.24 (1.02)
15.55 (8.98)
14.45 (7.53)
13.03 (7.62)
17.21 (10.53)
5 (highest)
54.55 (11.60)
1.64 (0.74)
1.24 (0.99) §
71.84 (14.79)
28.70 (4.88)
96.07 (12.97)
69.62 (9.95)*
1718.76 (690.13)
43.44 (5.73)*
16.20 (5.74)
40.48 (3.39)*
10.53 (3.87)
12.48 (3.27)*
12.45 (4.62)*
223.24 (142.00)
3.09 (1.53)*
13.93 (7.79)
§ p<0.05; * p<0.001 (chi-square test; ANOVA and Tukey test); G1: Group 1 – higher income; G2: Group 2: lower income; BMI: Body Mass Index; WC: waist
circumference; HEI-ad: healthy eating index adapted; SFA: saturated fatty acids; MUFA: monounsaturated fatty acids; PUFA: polyunsaturated fatty acids.
Table 5 - Relative risks of BMI>25 and WC>88 (female) and >102 (male) according to the quintiles of oil intake (serving)
Quintiles of oil intake (serving)
3
1
2
OR for BMI
G1
Crude
Model I
Model II
1.00
1.00
1.00
1.59 (0.52-4.87)
0.76 (0.13-4.25)
0.84 (0.14-4.83)
G2
Crude
Model I
Model II
1.00
1.00
1.00
OR for WC
G1
Crude
Model I
Model II
G2
Crude
Model I
Model II
P-value
4
5
1.80 (0.59-5.44)
1.75 (0.35-8.71)
1.92 (0.37-9.85)
1.23 (0.37-4.09)
0.76 (0.12-4.81)
1.34 (0.19-9.26)
0.86 (0.26-2.84)
0.96 (0.16-5.77)
2.57 (0.32-20.06)
0.50
0.04
0.09
0.85 (0.22-3.20)
0.65 (0.12-3.58)
0.51 (0.08-3.01)
0.26 (0.06-1.13)
0.07 (0.007-0.07)
0.06 (0.006-0.06)
1.14 (0.31-4.12)
0.92 (0.17-4.85)
0.77 (0.13-4.46)
0.97 (0.26-3.57)
0.84 (0.17-4.08)
0.81 (0.14-4.71)
0.22
0.01
0.01
1.00
1.00
1.00
0.82 (0.33-2.04)
0.35 (0.80-1.56)
0.38 (0.08-1.70)
1.81 (0.75-4.38)
1.85 (0.47-7.22)
2.11 (0.53-8.42)
1.23 (0.48-3.15)
0.90 (0.21-3.87)
0.99 (0.21-4.58)
1.04 (0.42-2.56)
0.88 (0.20-3.77)
1.55 (0.28-8.47)
0.26
0.03
0.02
1.00
1.00
1.00
0.85 (0.25-2.85)
0.35 (0.06-1.98)
0.34 (0.06-1.92)
0.47 (0.14-1.52)
0.22 (0.04-1.00)
0.21 (0.04-1.00)
1.08 (0.33-3.52)
0.36 (0.06-2.09)
0.35 (0.06-2.12)
1.08 (0.33-3.52)
0.63 (0.14-2.83)
0.61 (0.12-3.12)
0.46
0.30
0.31
Data are OR (95% IC). Model I: adjusted for age, gender, education and income; Model II: additionally adjusted for energy intake and healthy eating index.
recall to obtain dietary data and this type of measurement
limits the information about usual dietary intake. It is possible that a stronger relationship between dietary fat intake
and body fatness exists with the use of a 7-day weighted
intake, but this was not possible in our study. Although the
assessment of dietary intake was based on one-day only, this
method’s validity for assessing intakes of energy, protein,
carbohydrates, and fat has been documented (Conway et al.,
2004). Food intake is hard to measure, especially in obese
subjects. Goris et al., (2000) observed in their study that 37%
Cad. Saúde Colet., 2010, Rio de Janeiro, 18 (2): 266-74 271
Katia Cristina Portero-McLellan, Gustavo Duarte Pimentel, Jose Eduardo Corrente, Roberto Carlos Burini
of the obese men studied underreported food intake, mainly
energy-dense food with high fat content. The degree of obesity influences the dietary reporting both quantitatively and
qualitatively (Heitmann & Lissner, 1995).
Subjects with higher education and higher income had
a higher intake of energy and protein, and a lower intake of
total fat. This data differs from published literature as studies
show that total fat intake may increase with higher income
(Bonomo et al., 2003).
Energy dense foods, often high in refined grains, added
sugar, and added fat, are palatable, inexpensive and highly
consumed among lower income and less educated populations. These poor quality food supplies are provided by the
government. Thus, the inadequate diet lower income individuals consume may be related to the food supplies they
receive and the food that they buy. They cannot afford to
buy high quality food and the food they receive is not high
quality either.
On the other hand, while the higher income population
could afford good quality food, they also have poor dietary
habits as indicated by restaurant/fast food consumption, deep
fried meals (fats contribuite to greater flavor and palatability
of foods, which could lead to greater consumption of them),
consumption of beverages with sugar added, and whole meat
habits (barbecue). These behaviors are positively associated
with overweight and obesity (Greenwood & Stanford, 2008).
McCrory et al., (1999) showed that dietary variety, and the
specific food groups that provide it, may be an important
determinant of body fatness. A main difference between the
foods that were positively associated with increased body fat
(sweets, snacks, condiments, entrees and carbohydrates) and
foods negatively associated with body fat (vegetables) is their
energy density.
Our study showed, both in higher and lower income
groups, an adequate relative consumption of fat (29.0 and
30.7%, respectively) and a higher intake of protein (18.7
and 17.7%, respectively). Subjects with better education and
higher income had higher energy and protein intake, lower
SFA, and lower PUFA when compared with the Brazilian national data from the Family Budget Survey (2002-2003). Total
fat intake of the subjects studied was similar to the results of
the Family Budget Survey (2002-2003). The main difference
between the groups studied was total fat intake and PUFA
which were higher among subjects with lower educational
level and income.
Most of the epidemiological studies about dietary intake are based on the analysis of energy intake, macronutrients, and micronutrients (Willett, 2000; Sichieri et al.,
2003). Recently the World Health Organization (1998b)
suggested that studies should analyze the dietary profile
instead of the nutrients. Nutrients and foods are consumed in combination, and their effects can be observed
only when the entire eating pattern is considered. Dietary
patterns may suggest a more comprehensive approach to
disease prevention or treatment because the focus is on
the entire diet and not just one food or an isolated nutrient (Fung et al., 2001).
Conclusion
Besides differences in socioeconomic status, educational
level and (deficient) HEI the groups were similar by presenting high BMI and abdominal fatness. Only differences in fat
profile were correlated with the anthropometric measures
mostly explained by the lower vegetable oil intake in higher
income participants.
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Aceito em: 13/4/2010