ARTICULO ESTADISTICA

Appetite 135 (2019) 137–145
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Appetite
journal homepage: www.elsevier.com/locate/appet
Exploring the consumption of ultra-processed foods and its association with
food addiction in overweight children
T
Andrea Rocha Filgueirasa,∗, Viviane Belucci Pires de Almeidaa, Paulo Cesar Koch Nogueirab,
Semíramis Martins Alvares Domenec, Carlos Eduardo da Silvaa, Ricardo Sessod,
Ana Lydia Sawayaa
a
Department of Physiology, Federal University of São Paulo, UNIFESP, Brazil
Department of Pediatrics, Federal University of São Paulo, UNIFESP, Brazil
c
Department of Public Policies and Collective Health, Federal University of São Paulo, UNIFESP, Brazil
d
Department of Medicine, Federal University of São Paulo, UNIFESP, Brazil
b
ARTICLE INFO
ABSTRACT
Keywords:
Food addiction
Food intake
Children
Overweight
Behavioral addictions
Yale food addiction scale
The present study explored the consumption of ultra-processed foods and its association with food addiction in
overweight children. The prevalence of food addiction was investigated using the Yale Food Addiction Scale for
Children in overweight 9-11 year-old children (BMI/age ≥1 Z score) of both sexes from two schools (n = 139).
Food intake was estimated by a food frequency questionnaire and the food items were classified into 4 categories: minimally processed, culinary ingredients, processed foods and ultra-processed foods (UPF), based on
their degree of processing. Among the children, 95% showed at least one of the seven symptoms of food addiction and 24% presented with a diagnosis of food addiction. In analysis of covariance adjusted for age and sex,
a tendency of higher consumption of added sugar (refined sugar, honey, corn syrup) and UPF was found among
those diagnosed with food addiction. Multiple logistic regression adjusted for sugar, sodium and fat ingestion
showed that consumption of cookies/biscuits (OR = 4.19, p = 0.015) and sausages (OR = 11.77, p = 0.029)
were independently associated with food addiction. The identification of foods that may be associated with
addictive behavior is very important for correctly treating and preventing childhood obesity, which continues to
be one of the greatest health problems in the world.
1. Introduction
The term food addiction, which refers to an eating behavior that
involves excessive consumption of specific foods in a way similar to any
other addiction, has been used in the scientific community for decades
(Bruinsma & Taren, 1999; Burrows & Meule, 2015; Meule, 2017). The
first association of addictive behavior with some food was reported in a
scientific journal in 1890 when chocolate was described as a food with
potential trigger of addictive behaviors (Randolph, 1956).
As the global prevalence of overweight increased, the concept of
food addiction was once again valued for its possible contributions to
explaining part of the serious and psychopathological consequences of
this disease (Meule, 2015). This concept has become particularly relevant due to a number of studies indicating that continual access to
diets an eating pattern based on foods rich in fat, sugars and salt causes
behavioral signs of addiction, such as increased food craving, desire,
and motivation, that are associated with neurochemical adaptations in
∗
reward systems, similar to other chemical dependencies (Kelley, Schiltz,
& Landry, 2005; Mattes & Foster, 2014; Rogers & Brunstrom, 2015;
Schulte, Avena, & Gearhardt, 2015; Stice et al., 2008).
The Yale Food Addiction Scale – Children (YFAS-C) is the only existing validated measure designed to assess food addiction in children
(Gearhardt, Roberto, Seamans, Corbin, & Brownell, 2013). The YFAS-C
applies the criteria for substance abuse based on the Diagnostic and
Statistical Manual of Mental Disorders (DSM) IV (American Psychiatric
Association, 2013). Using this instrument some studies have pointed
out the prevalence of the diagnosis of food addiction in children
(Burrows et al., 2017; Kaisari, Dourish, & Higgs, 2017; Sinha, 2018) and
investigated associations with attention deficit disorder, obesity, eating
disorders and stress.
There is still an important debate about the food addiction hypothesis: some authors discuss a lack of evidence in the literature that
pinpoints the exact substance that is responsible for the neuroaddictive
responses (Blumenthal & Gold, 2010; Cameron, Chaput, Sjödin, &
Corresponding author.
E-mail address: [email protected] (A.R. Filgueiras).
https://doi.org/10.1016/j.appet.2018.11.005
Received 5 August 2018; Received in revised form 8 November 2018; Accepted 10 November 2018
Available online 12 November 2018
0195-6663/ © 2018 Elsevier Ltd. All rights reserved.
Appetite 135 (2019) 137–145
A.R. Filgueiras et al.
Goldfield, 2017; Ziauddeen & Fletcher, 2013). Other researchers emphasize the need to identify which foods would be associated with
addiction, not just a nutrient or a substance, e.g., sugar, since we consume foods rather than isolated nutrients (Leigh, Lee & Morris, 2018;
Lindgren et al., 2017; Pursey et al., 2014). In particular, advances in
food processing and technology have resulted in the greater availability, affordability, and marketing of ultra-processed foods (UPF)
(Floros et al., 2010; Poti et al., 2017; Swinburn et al., 2011; Zobel et al.,
2016). Moreover, UPFs are engineered in ways that appear to surpass
the rewarding properties of traditional foods (e.g., vegetables, fruits,
nuts) by increasing fat, sugar, salt, flavors, and food additives (Poti
et al., 2017; Wahlqvist, 2016; Slimani et al., 2009).
A new edition of the Dietary Guidelines for the Brazilian Population
was recently published (Martins et al., 2014) and one of the innovations
in this guideline was the classification of foods according to the degree
of processing, named ‘NOVA,’ which brought a new perspective to the
approach of studying a diet's quality. The NOVA classification categorizes foods, according to the extent and purpose of the processing to
which they are submitted, into four groups: unprocessed or minimally
processed foods, i.e., fruits, leaves, roots, or animal products (group 1);
processed culinary ingredients extracted directly from unprocessed
foods, i.e., oils and butter (group 2); processed foods with added processed culinary ingredients for greater preservation, i.e., cheeses and
breads (group 3); and UPF with modifications resulting in enhanced
amounts of salt, sugar, and fat as well as the use of additives in an
attempt to make this food category highly palatable (group 4)
(Monteiro et al., 2016).
With this change in the global food supply and the increasing incidence of childhood obesity, it is essential to study the possible types of
foods that may be associated with addictive behaviors in order to help
prevent and treat obesity. At the moment, to our knowledge, no study
has explored the association between the diagnosis of food addiction in
overweight children and the types of food, especially UPF. Therefore,
the present study investigated the prevalence of the diagnosis of food
addiction in children (primary aim) as well as their food intake characteristics according to the degree of food processing, based on the
NOVA system (secondary aim). Finally, this study also investigated the
association between UPF and current food addiction (tertiary aim).
such as answering questionnaires. In addition, we excluded those with
motor limitations, twins, those taking medications known to affect body
weight management, and those with any known family issues that could
affect the overall compliance and participation in the Program.
Children below 9-years-of-age that were part of the longitudinal study
were also excluded as they were too young to respond the Food
Addiction Scale.
All participants had weight (kg) and height (cm) measured at the
school, had blood tests, pubertal staging evaluation, and answered the
Yale Food Addiction Scale for Children questionnaire to identify the
presence of food addiction, and a Brazilian Food Frequency
Questionnaire.
2.2. Questionnaires
2.2.1. Yale Food Addiction Scale for Children (YFAS-C)
The YFAS-C scale includes 25 items based on the Manual of
Diagnostic and Statistical of Mental Disorders (DSM) (Gearhardt et al.,
2013) to investigate seven diagnostic criteria that identify the presence
of substance addiction and commitment or clinical loss related to
feeding behavior. Frequency scoring was used to assess behaviors that
could plausibly occur occasionally in non-problem eaters (i.e., criteria
associated with excess consumption, emotional eating, or dieting). Dichotomous scoring was used for questions that were considered more
critical and therefore likely to indicate eating problems (i.e., continuing
to consume foods in a certain way in the face of emotional or physical
problems). The scale contains instructions for completing the measure
that refer specifically to the consumption of high-fat and high-sugar
foods. For the symptom count scoring option, the number of symptoms
endorsed was summed (range 0–7). For the dichotomous measure,
participants which endorsed three or more symptoms and clinically
significant impairment or distress (which is analogous to the substance
dependence diagnostic requirements in the DSM-IV) (American
Psychiatric Association, 2013) were considered to have met the diagnostic criteria for food addiction. The questionnaire was simultaneously
applied to two children by a single interviewer. The children were separated so that they were not able to see one another and were instructed not to read their answers aloud. The children received the
questionnaires and a pen to mark their responses. The lines were alternated with white and gray to make it easier for the children to follow
the line and not mark the answer to the wrong question. The interviewer read each question while the children followed the reading in
their own questionnaires. Each application lasted about 15 min.
The food addiction scale was adapted transculturally for Brazilian
children and translated into Portuguese language. The psychometric
properties, such as the reliability and construct validity were determined. The results of the validation showed a Cronbach's
alpha = 0.83.
2. Methods
2.1. Participants and study design
This study is part of a longitudinal investigation called
“Effectiveness of a 16-month multi-component and environmental
school-based intervention for recovery of poor income overweight/
obese children and adolescents: The Health Multipliers Program”. A
power analysis was conducted for this longitudinal study based on the
changes in standardized body mass index. Assuming an alpha error of
0.05, power of 0.80, and mean difference of the delta (post-intervention
vs. baseline measurements) of standardized BMI of 0.12 relative to the
control group, with a standard error of 0.30, the estimated number of
participants needed to be studied per group was 98 (8–11-year-old).
The estimated difference of 0.12 in the standardized BMI was based on
previous intervention studies in obese children (Patriota et al., 2017).
The present cross-sectional study was conducted in a convenience
sample of 139 children with 9 to 11-year-old of both sexes (65 girls)
that were enrolled in two low-income public schools in the city of São
Paulo, Southeastern Brazil. All the parents/legal guardians and children
signed the Free and Informed Consent Form, and the study was approved by the Research Ethics Committee of the Federal University of
São Paulo (CAAE: 34,304,714.40000.5505).
The criterion for inclusion in the study was the presence of excess
weight, evaluated by the body mass index (BMI) Z score for age ≥1.
Subjects were excluded from the data collection if they were reported to
have cognitive delay, which could limit their involvement in activities
2.2.2. Dietary assessment
Dietary intake was estimated using the Semi-quantitative Food
Frequency Questionnaire (SFFQ) with 88 food items: 41 items represented UPF; 12 items belonged to the category of processed foods,
and 35 items were in natura or minimally processed foods (Araujo,
Yokoo, & Pereira, 2010). The frequency of consumption was measured
as one to ten times a day, once a week, or once a month. Each food
group was representative of Brazilian foods an composed the core foods
for school children. To make the questionnaire quantitative, the portion
sizes of the food consumed were estimated using a photographic
manual with images that depicted the sizes of the portions of each item
to assist in the correct choice (Brito, Araujo, Guimarães, & Pereira,
2017). The manual was made with children serving sizes. Participants
could select one of the following portion sizes: small, medium and
large.
The questionnaire was applied individually by a single interviewer.
The child sat next to the interviewer to view the photographic manual
138
Appetite 135 (2019) 137–145
A.R. Filgueiras et al.
and the questionnaire questions on the computer. The interviewer read
item by item and showed the picture of the food and portions. The
interviewer only went to the next item when the participant had already answered the question. The mean time of administration of the
questionnaire was 42 min.
products attractive sensory properties). Ultra-processed foods include
sweet and salty biscuits, chips, granola bars, confectionary in general,
instant noodles, various types of ready or semi-ready meals like sausages and soft drinks.
2.2.4. Pubertal staging
The evaluation of the pubertal stage was made by the children's selfevaluation method from a table with images of the five pubertal stages
of Tanner, according to sex (Tanner, 1957). For girls, the board was
presented with staging for breasts (M) and pubic hair (P). For boys, the
plank contained staging of the external genitalia (G) and pubic hair (P).
The evaluation was done individually, and the child indicated his/her
stage according to his/her perception. This evaluation was carried out
in a quiet and reserved environment, solely in the presence of the
evaluator.
2.2.3. Quantification and classification of foods
Data on food intake were converted to energy and nutrient data
using Nutrition Data System Research (NDS-R version 2014 - University
of Minnesota, USA) according to the standard protocol for conversion
and food pairing recommended by Fisberg and Marchioni for Brazilian
food surveys (Fisberg & Marchioni, 2012). The portion sizes were
converted to grams, milliliters or liters with the support of the Brazilian
table of home measures (Pinheiro et al., 2009). Considering that the
NDS-R has the US Department of Agriculture (USDA) table as the main
database, Brazilian typical foods that were not included in the software
had their nutritional values estimated and included, according to the
national data of the food composition table (TACO) (Filho et al., 2011).
Thus, nutritional information was obtained regarding the intake of
energy and nutrients for each participant.
Total sugars according to the NDS-R comprise six different types of
mono- and disaccharides (glucose, fructose, galactose, sucrose, lactose,
maltose) (Huong Duong, 2018). Their values were presented in grams.
The variable “added sugars” (by total sugars) included those sugars and
syrups added to foods during food preparation in home-prepared recipes or commercial or industrial food processing. They did not include
mono- and disaccharides occurring naturally in foods, such as lactose in
milk or fructose in fruits. The Nutrition Coordinating Center-NCC database designates as “added sugar”: white sugar (sucrose), brown sugar,
powdered sugar, honey, molasses, pancake syrup, corn syrups, high
fructose corn syrups, invert sugar, invert syrup, malt extract, malt
syrup, fructose, glucose (dextrose), galactose, and lactose.
The variable fructose was chosen because of its high content in UPfoods in the form of corn syrup. Its excessive intake has been linked to
the development of obesity and some non-transmissible chronic diseases (Lakhan & Kirchgessner, 2013; Stanhope et al., 2011; Suganami &
Ogawa, 2010). The FFQ has 19 foods with fructose: industrialized juice,
soda, bread form, ice cream, yogurt, ketchup, natural juice, tomato,
apple, banana, açaí, watermelon, cabbage, grape, mango, orange, papaya, pear and strawberry. The NDS-R calculates the amount of fructose
according to the consumption of these foods.
After the quantitative step of the questionnaire, the qualitative step
was started in order to classify the consumption items according to the
NOVA system, which classifies the foods according to the extent and
purpose of the processing to which they are submitted, dividing them in
four groups (Monteiro et al., 2016). The first group included foods that
had been directly obtained from plants or animals (such as seeds, fruits,
leaves, roots or animal products such as muscles, eggs and milk), those
acquired for consumption without having undergone any alteration
following their harvest (natural foods) and natural foods that, prior to
having been acquired, were cleaned, had their inedible or unwanted
parts removed, and had been subjected to drying, packing, pasteurization, freezing, refinement, fermentation and other processes that do
not include substances being added to the original food (minimally
processed foods). The second group included refined sugar, oils, fats,
salt, and other substances extracted from foods or from nature, and used
in kitchens to make culinary preparations. The third group is essentially
made up of industrial products produced in which salt or sugar (and
eventually oil or vinegar) had been added to a natural or minimally
processed food, including canned vegetables, fruits in syrup, cheeses
and breads made with flour, water, and salt. The fourth group is composed of industrial products that are entirely or mostly made from
substances extracted from food (oils, fats, sugar, proteins), those that
are derived from food constituents (hydrogenated fats, modified starch)
or foods synthesized in laboratory based on organic materials (colorants, flavorings, flavor enhancers and other additives used to give the
2.2.5. Socioeconomic data collection
Information on the children socioeconomic profiles was collected at
the Center for Nutritional Recovery and Education - CREN during
outpatient visits in order to gain insight into the children's family
conditions and the environment at their place of residence. Information
on the children's family composition, parents' level of education, and
family income were also registered. Two variables were considered for
socioeconomic classification: domicile density and type of domicile.
Table 1
Sociodemographic, anthropometric, biochemical and hemodynamic characteristics (n = 139) by group.
FA (n =
33)
Sex, N (%)ᵃ
Boys
14 (42)
Girls
19 (58)
Age (years), Mean (SD)ᵇ
9.6 (0.66)
Pubertal Stage, N (%)ᵃ
Prepuberty
9 (7)
Puberty
24 (18)
Anthropometry, Mean (SD)ᵇ
Height-for-age z-score
0.44 (1.02)
BMI-for-age z-score
1.94 (0.67)
Weight status N (%)ᵃ
Overweight (BMI-for-age
17 (52)
z-score ≥ 1–1.99)
Obese (BMI-for-age z-score ≥ 2–2.99)
15 (45)
Severely obese (BMI-for-age
1 (3)
z-score ≥ 3)
Socioeconomic profile, N (%)ᵃ
Household Density
Inadequate
22 (21)
Type of domicile
Adequate
13 (12)
Years of schooling of parents/guardians
<5
14 (13)
5–9
12 (11)
>9
0
Biochemical and hemodynamic parameters Mean (SD) ᵇ
Glucose (mg/dL)
91 (8.00)
Total cholesterol (mg/dL)
151 (34.23)
HDL (mg/dL)
LDL (mg/dL)
Triglycerides (mg/dL)
Insulin (ulU/mL)
HOMA IR
43 (9.30)
88 (30.71)
103 (45.84)
11.9 (7.28)
2.77 (1.83)
NFA (n =
106)
P value
50 (47)
56 (53)
9.6 (0.67)
0.636
0.998
30 (22)
74 (53)
0.718
0.66 (0.89)
1.94 (0.67)
0.246
0.985
61 (58)
0.348
36 (34)
8 (8)
61 (58)
0.322
38 (36)
0.532
32 (30)
42 (40)
7 (6)
0.280
93.16 (7.11)
150.06
(23.79)
44.79 (8.79)
87.05 (20.88)
90.94 (42.57)
11.64 (6.11)
2.69 (1.43)
0.270
0.848
0.257
0.848
0.200
0.172
0.270
a
Significant differences between groups were determined using the χ2 test
(p < 0.05). Chi-square tests showed a degree of freedom = 1 for the comparison of the variables: sex, pubertal stage, household density and type of domicile; and degree of freedom = 2 for the comparisons of: weight status and years
of schooling of parents/guardians.
b
Significant differences between groups were determined using Student's ttest (p < 0.05).
139
Appetite 135 (2019) 137–145
A.R. Filgueiras et al.
Table 2
Comparison of daily energy, nutrient intake and degree of processing of foods consumed between FA and NFA groups.
Total diet
Total energy (kJ/day)
Carbohydrates (g/day)
Total Protein (g/day)
Vegetable Protein (g/day)
Animal Protein (g/day)
Total Fat (g/day)
Fat Trans (g/day)
Sodium (g/day)
Fiber (g/day)
Total sugar (g/day)
Added sugars (by Total Sugars) (g/day)
Fructose (g/day)
Unprocessed/Minimally processed foods
Total energy (kJ/day)
Carbohydrates (g/day)
Total Protein (g/day)
Vegetable Protein (g/day)
Animal Protein (g/day)
Total Fat (g/day)
Sodium (g/day)
Fiber (g/day)
Total sugar (g/day)
Fructose (g/day)
Ultra-processed food
Total energy (kJ/day)
Carbohydrates (g/day)
Total Protein (g/day)
Vegetable Protein (g/day)
Animal Protein (g/day)
Total Fat (g/day)
Fat Trans (g/day)
Sodium (g/day)
Fiber (g/day)
Total sugar (g/day)
Fructose (g/day)
Energy of ultra-processed foods (kJ)
Soft drinks (kJ/day)
Sweetened Juices (kJ/day)
FA (n = 33) Mean, 95% CI
NFA (n = 106) Mean, 95% CI
df
Mean Square
F
P value
Critical P valueb
8896
8266–9526
274
254–294
80.6
74–86
30
28–32
49
45–54
79
73–86
2.0
1.81–2.28
3.3
3.03–3.58
17.7
16.23–19.04
106
93–119
69
58–79
20
16–23
8438
7978–8897
257
244–270
79.92
75–84
29
28–31
50
46–54
75
70–80
1.9
1.77–2.09
3.2
3.06–3.42
17.8
16.98–18.65
95
88–101
55
50–59
18
16–19
1135
342519.589
1.185
0.274
0.021
1135
7968.691
0.164
0.164
0.013
1135
27.894
0.051
0.818
0.042
1135
26.222
0.560
0.450
0.033
1135
0.026
0.000
0.993
0.05
1135
588.619
0.317
0.317
0.029
1135
0.387
0.317
0.425
0.025
1135
0.139
0.174
0.671
0.038
1135
0.846
0.046
0.828
0.046
1135
3595.074
3.190
0.077
0.008
1135
5168.355
8.006
0.008
0.004
1135
85.295
1.27
0.255
0.017
3482
3242–3723
98
90–106
43
40–47
11
10–12
32
29–35
21
19–24
0.9
0.87–1.02
7.5
6.46–8.52
25
21–29
0.12
0.16–0.20
3684
3500–3862
103
98–108
47
43–51
11
11–12
35
31–39
22
20–24
0.9
0.93–1.00
8.2
7.59–8.74
28
25–31
0.2
0.18–0.21
1135
51428.028
1.164
0.275
–
1135
641.453
1.118
0.287
–
1135
357.348
1.261
0.254
–
1135
12.243
1.207
0.268
–
1135
237.302
0.885
0.339
–
1135
10.765
0.115
0.729
–
1135
0.008
0.214
0.638
–
1135
12.087
1.368
0.241
–
1135
252.466
1.111
0.290
–
1135
0.004
0.670
0.404
–
3441
3018–3865
112
96–127
13
11–16
4.6
3.62–5.62
8.8
6.69–10.93
28
23–32
0.6
0.03–0.15
1.1
0.94–1.24
1.4
0.97–1.89
63
52–74
11
8–14
2905
2665–3146
93
85–101
10
9–11
3.9
3.37–4.55
6.3
5.58–7.05
23
20–26
0.1
0.02–0.19
0.9
0.86–1.01
1.2
0.94–1.49
50
45–55
9
8–10
1135
431820.400
5.006
0.030
0.023
1135
9118.141
5.292
0.025
0.018
1135
248.146
6.589
0.013
0.009
1135
11.116
1.241
0.263
0.041
1135
158.023
8.080
0.007
0.005
1135
626.795
3.408
0.070
0.036
1135
0.034
0.210
0.638
0.050
1135
0.628
4.165
0.045
0.027
1135
1.237
0.627
0.424
0.045
1135
4536.410
6.312
0.016
0.014
1135
173.628
3.981
0.054
0.032
262
167–357
118
87–149
178
145–211
116
91–141
1135
10859.045
4.960
0.036
0.011
1135
13.021
0.015
0.899
0.044
(continued on next page)
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Table 2 (continued)
FA (n = 33) Mean, 95% CI
NFA (n = 106) Mean, 95% CI
df
Mean Square
F
P value
Critical P valueb
189
148–229
216
168–265
335
251–419
996
730–1263
445
357–143
121
60–183
307
242–371
165
144–186
212
177–247
295
250–340
688
599–778
385
349–421
162
116–207
270
209–331
1135
825.168
1.170
0.280
0.022
1125
17.550
0.011
0.914
0.050
1135
1659.510
0.595
0.437
0.028
1135
150697.641
8.900
0.005*
0.006
1135
12697.344
4.303
0.046
0.017
1119
2297.996
0.140
0.703
0.039
1135
342.365
0.143
0.701
0.033
Sauces and condiments (kJ/day)
Sweetened milk drinks (kJ/day)
Desserts (kJ/day)
Cookies and savory biscuits (kJ/day)
Sausages (kJ/day)
Corn Chips (kJ/day)
Instant noodles (kJ/day)
ANCOVA test was used to evaluate the mean differences between the two groups: food addiction - FA and non-food addiction – NFA, adjusted for age and sex.
b
Critical P values corrected for multiplicity.
The household density consisted of the number of people in the
household in relation to the number of dormitories; it was considered
adequate when there were up to two persons per dormitory. The type of
domicile was considered inadequate if the households were classified as
any of the following: slum or community, tenement, tent, or clandestine
occupation of any nature.
status of the children was evaluated by calculating the Z score of the
BMI-for-age, using WHO AnthroPlus (version 1.0.4, 2009).
2.3.2. Biochemical analyses
Blood samples were collected by a nurse at the school in the
morning after an 8-h fast. The samples were centrifuged and the plasma
was stored at −22 °C. The concentration of insulin was determined by
chemiluminescence (ARCHITECT assay, Abbott). The concentration of
glucose was determined by the colorimetric enzymatic method with
ADVIA reading equipment at a wavelength of 340 nm (Siemens). The
total cholesterol and fractions were determined using the peroxidase
method (AU5800 analyzer, Beckman Coulter). The Homeostasis Model
Assessment (HOMA) calculator© for Microsoft Excel (The University of
Oxford) was used to evaluate the data.
2.3. Assessment and measures
2.3.1. Anthropometry
Weight was measured using a digital portable scale (Plenna® brand
MEA 07400, São Paulo, Brazil) with a maximum capacity of 200 kg and
a precision of 50 g. Participants were weighed using light clothing,
without shoes and accessories. Height was measured using a portable
stadiometer for field research with a precision of 1 mm (Height Exata®,
São Paulo, Brazil); subjects were measured in the vertical position while
wearing light clothes without head garments, with an undone hairstyle,
and the head positioned was in the Frankfurt plane. The nutritional
2.4. Statistical analyses
The Shapiro-Wilk test was used to evaluate normality and the
homogeneity of the continuous variables was verified using the Levene
test. The continuous variables were presented using means and standard deviations. For the categorical variables, χ2 tests were used. The
variables tested were sex, pubertal stage, household density and type of
domicile The ANCOVA test was used to evaluate the mean difference
between the two groups: food addiction - FA and non-food addiction –
NFA, adjusted for age and sex (Table 2). Statistical results of Table 2
were corrected to account for the multiple comparisons in the main
analysis, and the calculation of the multiplicity was performed according to Curran-Everett, 2010 and Appetite Guideline, 2015, to define the critical p-value for each comparison.
The univariate logistic regression analysis was performed among
dietary, sociodemographic and anthropometric variables to identify
those to be subsequently tested in multivariate logistic regression
models. We defined, a priori, that the variables with a p-value < 0.10 in
the univariate analysis would be selected for multivariate testing. Ultraprocessed foods were expressed in 100 g (Table 3) similar to the US and
Brazilian food composition tables that provide information about carbohydrates, lipids, fiber, proteins, minerals, and vitamins in amounts of
nutrients per 100 g of food (Filho et al., 2011; USDA, 2016).
Multivariate logistic regression models were constructed to verify
whether the consumption of selected ultra-processed foods might be
associated with food addiction. The models were adjusted for the
consumption of three nutritional components already recognized in the
scientific literature to be associated with addictive behaviors: sugar, salt
and fat (Schulte et al., 2015; Hone-Blanchet & Fecteau, 2014; Cocores &
Gold, 2009). All models were manually built and evaluated for
“goodness-of-fit” using the Hosmer-Lemeshow, Nagelkerke, and log
likelihood tests. The statistical analysis was executed with the
Table 3
Univariate logistic regression results of the association between food addiction
and anthropometric and dietary variables.
Variables
Odds
Ratio
Wald test
95% CI
P Value
Weight (kg)
0.99
0.16
0.94–1.03
0.687
Height (cm)
0.97
0.82
0.91–1.03
0.366
BMI z-score by age
1.00
1.82
0.55–1.80
0.984
Height z-score by age
0.77
1.35
0.50–1.18
0.245
Dietary variables (estimates per 418 kJ or 100 Kcal)
Total of all diet
1.04
1.03
0.97–1.11
0.310
Minimally processed
0.89
1.27
0.73–1.09
0.261
foods
Ultra-processed Food
1.15
4.44
1.01–1.31
0.035
Amount of ultra-processed foods (estimates per 100 g or 100 ml)
Soft drinks (100 ml)
1.39
3.89
1.00–1.92
0.049
Sweetened Juices
1.24
0.46
0.08–197.98
0.934
(100 ml)
Sauces and
2.77
1.18
0.34–44,83
2.770
condiments
(100 g)
Sweetened milk
0.98
0.02
0.35–2.75
0.973
drinks (100 ml)
Desserts (100 g)
1.16
0.60
0.39–3.43
0.786
Cookies and savory
3.43
7.89
1.46–8.06
0.005*
biscuits (100 g)
Sausages (100 g)
5.90
3.56
0.93–37.34
0.059
Corn Chips (100 g)
0.71
0.17
0.14–3.48
0.619
Instant noodles
2.01
1.57
0.06–63.90
0.693
(100 g)
b
Critical P
valueb
–
–
–
–
0.050
0.033
0.017
0.039
0.011
0.050
0.006
0.017
0.044
0.033
0.028
0.022
Critical P values corrected for multiplicity.
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A.R. Filgueiras et al.
Of the 139 children, 24% had a diagnosis for FA. There were no
significant statistical differences between the FA and NFA groups in
relation to sex, age, socioeconomic level or biochemical parameters
(Table 1). The whole sample had a mean number of food addiction
symptoms of 3 (95% CI: 3.1–3.8). Ninety-five percent of the participants were positive for at least one symptom. Symptom: abstinence was
the most prevalent (71%), followed by symptom: “a decrease or abandonment of important social, occupational or recreational activities due
to the desire to consume the substance or problems related to such”,
69%.
association of consumption of certain ultra-processed foods and current
FA (Table 4).
In model 1, the three selected variables were inserted into the model
together with the pre-defined adjustment variables (sugar, salt and fat
consumption) (Table 4). Current food addiction showed a tendency to
be associated with consumption of cookies/biscuits (after correction for
multiplicity the critical p value for cookies/biscuits was p = 0.017). A
positive association was found between food addiction and consumption of sausages (after correction for multiplicity the critical p value was
p = 0.033, therefore the significance was maintained). No association
was found between soft drinks (OR: 1.26, 95% CI: 0.83–1.92,
p = 0.278) and food addiction in this model.
In model 2, the variable ‘soft drinks’ was excluded, and frequent
consumption of cookies/biscuits and sausages were significantly and
independently associated with FA (critical p-values = 0.025 and 0.05,
respectively) (Table 4).
3.2. Secondary outcomes
4. Discussion
Table 2 shows the comparison of daily energy, nutrient intake and
degree of processing of foods consumed between the groups FA and
NFA. The food-addicted group exhibited, on average, 14 g higher intake
of added sugar per day than the non-food-addicted group, but after
correction for multiplicity this difference was not significant. In the
same way, UPF energy consumption was 535 kJ higher among children
with FA, but after correction for multiplicity this difference was not
significant. A similar result was found with sodium and fructose consumption. There was no statistically significant difference in the
average intake of foods classified as fresh/minimally processed between
the FA and NFA groups. Among the UPFs, soft drinks, sausages and
cookies were consumed more often by children with FA, but after
correction for multiplicity, only the consumption of cookies remained
statistically significant, while the other two showed a significance
tendency.
The abstinence symptom was positively association with the total
energy consumption of the diet (r = 0.23, p = 0.006). The intake of
added sugar was also positively correlated (r = 0.27, p = 0.001) with
continued use of the substance despite the knowledge that it was
causing or leading to a physical or psychological problem.
Univariate logistic regression analysis revealed that the anthropometric variables were not associated with current FA (Table 3), however, BMI z-score by age was associated with increased intake of added
sugar (OR: 6.77, 95% CI: 1.58–28.97, p = 0.010), sodium (OR: 2.71,
95% CI: 1.00–7.32, p = 0.050), carbohydrates and proteins from ultraprocessed foods (OR: 2.74, 95% CI: 1.10–6.79, p = 0.030; and OR:
3.24, 95% CI: 1.36–7.72, p = 0.008, respectively).
In the univariate analyses, using the pre-established selection criterion (p value < 0.10), three variables were linked with food addiction: soft drinks, cookies/biscuits and sausages (Table 3). Based on
these results, multivariate models were constructed to evaluate the
The current study is the first to explore the association of food addiction and food intake according to the degree of food processing and
to investigate this relationship in low-income children. The main
finding of the study was that the frequent consumption of UPFs, specifically cookies/biscuits and sausages, were associated with current
food addiction in overweight children. This result is consistent with
findings in animals and humans that suggest that highly processed
foods with additional amounts of fat, refined carbohydrates, and/or salt
are closely associated with food addiction indicators (Schulte et al.,
2015; Hone-Blanchet & Fecteau, 2014; Frank et al., 2012; Manabe,
Matsumura, & Fushiki, 2010; Kelley et al., 2005). Increased consumption of foods rich in fats, sugars, and salt foods causes changes in carbohydrate and fat metabolism, insulin sensitivity, and appetite hormone function (Leigh, Lee, & Morris, 2018, pp. 1–13; Wahlqvist, 2016).
It is known that these alterations can generate changes in the neural
control of reward, increase the release of dopamine (among other
hormones), and consequently reinforce the importance of and motivation for their ingestion (Alsiö et al., 2012; Dossat, Lilly, Kay, & Williams,
2011; Malik, McGlone, Bedrossian, & Dagher, 2008; Sinha & Jastreboff,
2013). This hyperstimulation of neural reward pathways resembles that
described for drug abuse and appears to be capable of generating
learning or conditioning mechanisms (Alsiö et al., 2012).
Recent studies have pointed to the increase in UPF consumption as a
determinant factor for increased risk to develop chronic diseases (Fiolet
et al., 2018; Aguayo-Patrón & Calderón de la Barca, 2017; Deus
Mendonça et al., 2016). Developed countries such as the United
Kingdom and the United States, for example, have high rates of obesity
(24.5% and 39.8%, respectively) and are also the largest consumers of
UPFs. In contrast, countries with low UPF consumption, such as France
and Italy, have much lower rates of obesity (7.1% and 8.2%, respectively) (Monteiro et al., 2018).
Statistical Package for the Social Sciences (SPSS), version 21.
3. Results
3.1. Primary outcome
Table 4
Multivariate logistic regression models with adjusted for sugar, sodium and fat.
Model 1
OR
Soft drinks
Cookies/biscuits
Sausages
1.26
4.04
11.01
Model 2
Wald test
95% CI
P value
1.18
5.60
4.58
0.83–1.92
1.27–12.85
1.22–99.01
0.278
0.018
0.032
Critical P value
0.05
0.017
0.033
b
Soft drinks
Cookies/biscuits
Sausages
OR
Wald test
95% CI
P value
Critical P valueb
–
4.19
11.77
–
5.93
4.77
–
1.32–13.26
1.29–107.42
–
0.015
0.029
–
0.025
0.05
The Models were adjusted for the amount of sugar, salt and fat consumed.
OR = odds ratio; CI = confidence interval.
Sugar, Fat, Sausages and Cookies/biscuit in 100 g; Sodium in 1 g; Soft drinks in 100 ml.
Model 1 – χ2 = 14.996 Nagelkerke = 0.154; Log Likelihood = 137.37; Hosmer-Lemeshow χ2 = 11.31 p = 0.185.
Model 2 – χ2 = 13.785 Nagelkerke = 0.142; Log Likelihood = 138.58; Hosmer-Lemeshow χ2 = 6.94 p = 0.543.
b
Critical P values corrected for multiplicity.
142
Appetite 135 (2019) 137–145
A.R. Filgueiras et al.
National analyses have shown a significant increase of UPF in the
Brazilian diet, especially in the last 30 years (Consumer Expenditure
Survey - POFs - Search Family Budget - 1987–1988, 1995–1996,
2002–2003 and 2008–2009) from 20.8% to 25.4% accompanied by an
increase of 23.0%–27.8% in energy intake (IBGE, 2010; Martins, Levy,
Claro, Moubarac, & Monteiro, 2013). In the present study UPFs consumption was observed in 36% of the children; a higher prevalence
than that reported in the Brazilian adolescent population (30%)
(Louzada et al., 2015). Among the FA group the consumption of UPFs
corresponded to 39% of the energy of the diet and in the NFA group it
was 34%. The National Food Survey (INA, 2008–2009) (Souza et al.,
2013) and the Study of Cardiovascular Risks in Adolescents - ERICA
(2013–2014) that evaluated 71,791 Brazilian adolescents identified an
increased consumption of sweet or savory biscuits and soft drinks, and
associated reduction in the consumption of milk and fruits. More specifically, the intake of soft drinks increased 28% (INA, 2008–2009) to
45% (ERICA, 2013–2014), whereas in the INA study milk consumption
was cited by 12.9% (18th position) of adolescents. In ERICA, milk was
not among the 20 most prevalent foods (Moura Souza et al., 2016).
Studies in humans and rats showed that sugar consumption increases dopamine in the nucleus accumbens, and after cessation of
sucrose or glucose intake, signs of withdrawal were described in both
humans and rats (Hone-Blanchet & Fecteau, 2014; Markus, Rogers,
Brouns, & Schepers, 2017). The present study showed an association
between sugar consumption and positive responses to withdrawal
symptoms and persistent use/compulsion. These findings contrast with
studies that claim that there is no support for the assumption that a
specific macronutrient, such as sugar, causes excessive intake, far more
than any other food source (Benton & Young, 2016; Te; Markus et al.,
2017; Morenga et al., 2013).
In the studied population the average daily energy consumption was
8502 kJ/day. This average is slightly higher in comparison to another
study in a similar population of overweight low-income schoolchildren
of the state of São Paulo (Vieira et al., 2014), which exhibit a daily
average energy consumption of 7372 kJ/day. The percentage contribution of macronutrients to total energy intake was 51.2% for carbohydrates, 15.5% for proteins and 33.3% for total fats; all percentages
are within the recommendations of the Dietary Reference Intakes (DRI)
(Food and Nutrition Board, 2003).
Recent estimates show that children and adolescents in the United
States consume, on average, 16% of their daily calories from added
sugars (Reicheltal et al., 2018). In the present study, the mean intake of
added sugars comprised 11% of total daily energy in all subjects evaluated. According to the WHO recommendations, the consumption of
added sugars should not exceed 10% of the total daily energy, and
ideally it should be 5% per day (WHO, 2016).
Excessive sugar consumption has been associated with an increased
risk of obesity, diabetes mellitus, and cardiovascular disease (Reicheltal
et al., 2018). Data from the POF national survey showed that the consumption of free sugar from UPF increased from 18.2% to 36% between
1987 and 2009, and that this increase is related to the contribution,
especially, of the soft drinks in the Brazilian diet (Monteiro et al.,
2018). In comparison to these findings, the BMI of both FA and NFA
groups was positively associated with the consumption of added sugar,
salt, and carbohydrate and protein of UPF foods. In relation to soft
drink consumption, FA children showed a trend towards a higher intake.
The FA group presented an average sugar intake of 13% of their
total daily energy, which represents a daily 14 g (1 dessert spoon)
higher consumption of sugar than the group without addiction; but the
difference was small between the two groups and did not remain significant after correction for multiple comparisons. On the other hand,
the difference between consumption of cookies and biscuits, after
controlling for sugar, salt and fat intake, remained significantly associated with food addiction. This result may mean that some attribute
present in the cookies/biscuits, such as addictives for example, other
than sugar, salt and fat accounted for this effect. In any case, this result
indicates that more important than a specific ingredient, food addiction
was associated with the type of food.
It is known that many sweet UPFs also contain high concentrations
of added salt (Pursey et al., 2017). Cookies, for example, have an
average of 4 g of sodium per package, which is twice the tolerable limit
intake recommended by the WHO (2000 mg) (WHO, 2003). A portion
of 50 g of sausage has, on average, 0.6 g of sodium. Although the difference in salt intake was not significant between the study groups, the
consumption of sausages was higher in children with food addiction.
Just as with the consumption of sugar, it is possible that food addiction
is more associated with a type of food than a specific ingredient like
salt.
The primary aim of this study was to investigate the prevalence of
food addiction, as there are no other studies in Brazil that explored this
eating disorder thus far. The sample studied showed a prevalence of
food addiction of 24%. Although, it is within the range of 7% and 38%
found in some studies with children in other countries that used the
same scale (Tracy Burrows et al., 2017; Meule et al., 2015; Gearhardt
et al., 2013), this prevalence is quite high and points out the importance
of further studies in this area for the adequate treatment of obesity in
children.
In terms of anthropometric or biochemical variables, there were no
significant differences in mean BMI Z scores or in any of the biochemical parameters between the groups with or without food addiction. These findings can be explained by the sample being composed of
young children aged 9–11 years that have a mild excess of weight with
an average mean BMI Z score lower than 2. Despite this, it is known that
even without obvious anthropometric and biochemical alterations, the
brain of these children is still under development and particularly
vulnerable to addiction (Burrows et al., 2017; Pretlow et al., 2015).
Finally, this is a cross-sectional study and it is important to consider
its limitations. The study design is not adequate to establish a causeand-effect relationship with the present findings, but raises hypotheses
for future investigations, such as whether the excessive consumption of
UPF increases the likelihood of developing addiction or whether children who are likely to have food addiction eat more UPF than those
who do not. Several associations did not remain statistically significant
after correction for multiple comparisons. This may be due to the limited statistical power for these comparisons as the sample size was
originally calculated for the longitudinal study and not for the present
UPF analysis. Consequently, longitudinal studies with larger sample
size are needed to better understand how the association between UPF
consumption and the development of food addiction unfold. In this
sense, our research group is developing a longitudinal survey to better
understand food addiction, as well as the context and conditions of its
occurrence and treatment. Another limitation is the use of food frequency questionnaires (FFQ) to determine food consumption in children. Although the methods for assessing food consumption are rather
unprecise and limit the impact of the result, especially when the child is
the respondent, the choice of this method was unavoidable to accomplish the study objectives. Moreover, the answering of the FFQ by
parent/guardians was not possible since the children ate most of their
meals outside their homes and not in the presence of their parents. In
any case, the results of the FFQ were similar to those of other Brazilian
studies with the same age group that used 24-h recall or had the FFQ
answered by parent/guardians (Assis, Guimarães, Calvo, Barros, &
Kupek, 2007; Matos et al., 2012; Scagliusi et al., 2011). Another limitation of the present study was the lack of information on physical
activity in this sample of children.
5. Conclusion
The present study provides evidence that frequent consumption of
UPFs are associated with food addiction in overweight children. In the
sample of Brazilian children studied, the UPF foods positively
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A.R. Filgueiras et al.
associated with FA were cookies/biscuits and sausages. These findings
show that food addiction is present in overweight young children and,
for this reason, has important social, clinical, and public health implications. More studies are needed to develop new approaches to map,
prevent and treat food addiction in this age group.
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Financial support
This work was supported by FAPESP - Foundation for Research
Support of the State of São Paulo and CNPq - National Council for
Scientific and Technological Development. Both institutes had no role
in the design, analysis, or writing of this manuscript.
Conflicts of interest
None of the authors has any conflicts of interest to declare.
CRediT authorship contribution statement
Andrea Rocha Filgueiras: Formal analysis, Writing – original
draft. Viviane Belucci Pires de Almeida: Formal analysis, Writing –
original draft. Semíramis Martins Alvares Domene: Supervision. Ana
Lydia Sawaya: Funding acquisition, Writing – original draft.
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