(2024) 22:340
Zancheta et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03556-z

BMC Medicine

RESEARCH ARTICLE

Open Access

The consumption of ultra‑processed foods
was associated with adiposity, but not withmetabolic indicators in a prospective cohort
study of Chilean preschool children
Camila Zancheta1,2, Natalia Rebolledo2, Lindsey Smith Taillie3, Marcela Reyes2 and Camila Corvalán2*

Abstract
Background Increasing consumption of ultra-processed foods (UPF) has been identified as a risk factor for obesityand various diseases, primarily in adults. Nonetheless, research in children is limited, especially regarding longitudinalstudies with metabolic outcomes. We aimed to evaluate the longitudinal association between consumption of UPF,
adiposity, and metabolic indicators in Chilean preschool children.
Methods We conducted a prospective analysis of 962 children enrolled in the Food and Environment Chilean Cohort(FECHIC). Dietary data were collected in 2016 at age 4 years with 24-h recalls. All reported foods and beverages wereclassified according to the NOVA food classification, and the usual consumption of UPF in calories and grams was estimated using the Multiple Source Method. Adiposity (z-score of body mass index [BMI z-score], waist circumference[WC], and fat mass [in kg and percentage]) and metabolic indicators (fasting glucose, insulin, HOMA-IR, triglycerides,
total cholesterol, and cholesterol fractions) were measured in 2018, at the age of 6 years. Linear regression models((0) crude, (1) adjusted for covariables, and (2) adjusted for covariables plus total caloric intake) were used to evaluate
the association between UPF and outcomes. All models included inverse probability weights to account for the loss
to the follow-up.
Results At 4 years, usual consumption of UPF represented 48% of the total calories and 39% of the total foodand beverages grams. In models adjusted for covariables plus caloric intake, we found a positive association
between UPF and BMI z-score (for 100 kcal and 100 g, respectively: b = 0.24 [95%CI 0.16–0.33]; b = 0.21 [95%CI 0.10–
0.31]), WC in cm (b = 0.89 [95%CI 0.41–1.37]; b = 0.86 [95%CI 0.32–1.40]), log-fat mass in kg b = 0.06 [95%CI 0.03–0.09];
b = 0.04 [95%CI 0.01–0.07]), and log-percentage fat mass (b = 0.03 [95%CI 0.01–0.04]; b = 0.02 [95%CI 0.003–0.04]),
but no association with metabolic indicators.
Conclusions In this sample of Chilean preschoolers, we observed that higher consumption of UPF was associatedwith adiposity indicators 2 years later, but not with metabolic outcomes. Longer follow-up might help clarify the natural history of UPF consumption and metabolic risks in children.
Keywords Ultra-processed food , Adiposity, Metabolic markers, Children, Child diet

*Correspondence:
Camila Corvalánccorvalan@inta.uchile.cl
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
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Zancheta et al. BMC Medicine(2024) 22:340

BackgroundChildhood obesity has become an escalating health concern worldwide. According to the 2019 projections by
the World Obesity Federation, it is anticipated that by2025, approximately 206 million children and adolescents
aged 5–19 years will be affected by obesity, mainly due
to increasing rates in emerging countries [1]. In Latin
America, 7% of children under 5 years of age and 20–25%
of children and adolescents up to 19 years are estimatedto living with overweight or obesity [2]. In Chile, datafrom a survey including students in the public education
system in 2019 revealed that 26.5 and 24.9% of preschool
children (kinder) presented overweight and obesity,
respectively [3]. Childhood obesity tends to persist overtime and is associated with metabolic disturbances,which increasingly manifest at younger ages [4]. Several
determinants are associated with childhood obesity, withchanges in eating patterns being described as one of themain ones.
During the last decades, the food system has changed
in different countries, and traditional diets have been
increasingly replaced by ultra-processed foods (UPF)[5]. UPF are industrial formulations made mainly of substances extracted or derived from foods (e.g., sugar andfats), with little or no whole food in their composition
and which typically contain added additives such as flavorings, colorings, and other additives used to modify
the sensory attributes of the final product [6]. Children
and adolescents have been described as the primary consumers of UPF in national surveys from Australia [7],Canada [8], the USA [9], Mexico [10], and Chile [11]. In
developed countries such as the UK and the USA, UPF
represents more than 60% of the calories consumed inchildren’s and adolescents’ diets [12, 13]. In some Latin
American countries such as Chile and Mexico, it is morethan one-third of the total calories consumed by children
1–19 years old [10, 11]. Non-representative studies in
Brazil and Chile have reported that more than 40% of thetotal caloric intake comes from UPF in preschoolers at 4
years old [14, 15].
Nationally representative data from food purchases andconsumption from different countries showed that highamounts of UPF in diets are related to higher amounts ofsugar and sodium, high energy density, and lower quantities of protein, micronutrients, and fiber [5, 16–20]. In
adults, systematic reviews and meta-analyses indicated a
direct association between UPF consumption and overweight, obesity, metabolic syndrome, diabetes, and allcause mortality [21–24]. However, evidence regarding
health impacts in children is still scarce and inconsistent[25]. A recent systematic review of the effects of UPF,
as defined by NOVA, on obesity and cardiometabolic
comorbidities in children and adolescents showed that

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higher consumption of UPF was associated with greater
adiposity in most studies. In the case of metabolic indicators, studies available are only a few and mostly from
Brazil. Moreover, results have shown conflicting results[25]. For instance, prospective studies with children
aged between 3 and 6 years found a direct relationshipbetween the consumption of UPF and total cholesterol[26, 27], LDL cholesterol [26], and triglycerides [27], but
not with the glycemic profile [14].
Given the extent of the public health burden related topoor nutrition in children and the exponential increasein the consumption of UPF, a better understanding of
the effects of UPF on indicators of metabolic risk in children is crucial. To our knowledge, no previous study on
this topic has been conducted in Chile, so we aimed toprospectively evaluate the association between the consumption of ultra-processed foods, adiposity, and metabolic indicators in a sample of low-to-middle-income
Chilean preschool children after 2 years of follow-up.

Methods
Study design and subjects

We used data from the Food and Environment Chilean Cohort (FECHIC), a cohort of 962 Chilean low-tomiddle-income preschoolers from Southeast Santiago,
Chile, started in 2016. Mothers were recruited in publicschools to participate in the study with their 4- to 6-yearold children. Details on the recruitment and inclusion
criteria are available elsewhere [28]. Briefly, the inclusion
criteria were mothers as the primary caregivers for foodpurchases and childcare, absence of mental illness in themother and child, and of other diseases with an impact
on food consumption and child development, besides
children of non-twin gestation, born at term and withnormal birth weight. The present study included children
with dietary data at baseline (year 2016, average age: 4.9
years) and anthropometric, body composition, or metabolic indicators measured after 2 years (year 2018, average age: 6.1 years).
Dietary intake

At baseline, trained dietitians collected 24-h dietary
recalls (24HR) following the United States Departmentof Agriculture (USDA) Automated Multiple-Pass method[29]. They used a photographic atlas to help estimate portion sizes accurately [30], and recorded data on portion
size, type of preparation, type of food, and product brandand flavor in the case of packaged foods, as well as thesource of the food and eating location. This information
was entered into SER-24, a software developed by theCenter for Research in Food Environment and Prevention of Obesity and Non-Communicable Diseases (CIAPEC), INTA, that includes over 6000 foods and beverages

Zancheta et al. BMC Medicine(2024) 22:340

and 1400 standard recipes of traditional Chilean dishes
and estimates nutrient intake using the Food Composition Table of the USDA [28, 31]. The mother was theprimary respondent and reported 1 day of their child’s
food consumption in a face-to-face interview. Children
were present during the interview and complemented theinformation for the eating occasions when the respondent was absent (e.g., school time). In the case of receiving
meals from the School Feeding Program, these preparations were also recorded to link them to the recipes andnutrient contents of the food providers. A second dietary
recall was collected within 30 days in a random subsample of 20.1% of participants.
Food consumption according to the NOVA foodclassification system

Briefly, the NOVA classification considers the extent andpurpose of industrial processing and classifies all foods
and beverages into four groups: group 1—natural or minimally processed foods (MPF); group 2—processed culinary ingredients (PCI); group 3—processed foods (PF);and group 4—ultra-processed foods (UPF). Examples
of UPF include industrialized sodas, toddler milk, confectionaries, chocolates, ice cream, hamburgers, reconstituted meat products, pizzas and other frozen dishes,
instant soups, and packaged bakery products, among
others [6]. We identified UPF based on food descriptions, food categories and type of food, whether packaged or unpackaged, brand, and flavor, when available.
Simple preparations included in the software SER-24(e.g., cooked rice) were classified based on their main
component. Other homemade recipes were disaggregated into their components, and each of them was individually classified. Food classification was carried out
by a postgraduate dietitian at CIAPEC and reviewed bya second dietitian. Disagreements (0.4%) were discussed
and resolved by consensus. To verify the interrater agreement, a third dietitian independently classified a random subset of 5% of SER-24 records (n = 306). We foundan agreement of 97.4% and a kappa coefficient of 0.95,
indicating almost perfect agreement between the raters.
More details about the methodology applied were published elsewhere [32].
We calculated the consumption of UPF in calories andin grams for each participant. Most published studies
used the caloric share of UPF; however, presenting UPF
grams allowed us to consider the consumption of low or
non-calorie UPF, such as artificially sweetened beverages
commonly consumed by Chilean children at this age [33].

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Exclusion of outliers in dietary data

We identified outliers using two techniques: comparing
the total calories consumed and the energy requirementsof each participant and considering the extremes in the
distribution of UPF (both in calories and grams).
We estimated the energy requirements with the Dietary Reference Intake (DRI) equation according to age
and sex [34], using sedentary and very active levels ofphysical activity to calculate the lower and the higher
cutoff points, respectively [35]. We used the subsample
with two dietary recalls to calculate the standard deviation (SD) for the ratio (in %) between reported energy
intake (rEI) and predicted energy requirement (pER),
using the formula provided by Huang [36]. The formulaconsiders the pooled coefficient of variation (CV) of therEI (CVrEI = 32.6%, calculated for our sample [37]), thenumber of days of dietary assessment (d = 2), the CVof the pER (CVpER = 12.1%, calculated with the mean
and SD for the total energy of 3- to 18-year-old boys
and girls described in the DRI [34]), and the coefficientof variation in the measured total energy expenditure(CVmTEE = 8.2%, obtained from literature [36, 37]). The
value of SD for our sample was 27.3%, and we defined
implausible diets as those in which reported energy wasfrom <  − 3 or + 3 SD away from predicted energy requirements (i.e., < 18.1% or > 181.9% of the pER).
Additionally, diets under the 1st and above the 99th
percentile of UPF consumption in calories and grams
were excluded (UPF consumption < 42 kcal or > 1478.5
kcal and < 27 g or > 1554.5 g).
Of the 1154 records collected at the beginning of thestudy, 15 were considered implausible, and 30 were considered extreme UPF consumption. Then, the estimatesof usual consumption included 743 children with aunique and 183 with two measures of 24HR.
Usual consumption of UPF

We estimated the usual consumption of UPF using theMultiple Source Method (MSM). This method assumes
that the 24HR is not biased for the usual consumption and models the probability of consumption—with
logistic regression—and the amount consumed in a dayof consumption—with linear regression—allowing theincorporation of covariates and is based on the premisethat habitual consumption is equal to the probability of
consumption times the usual amount consumed. Usual
consumption can be estimated for dietary componentsthat have frequent or daily consumption (e.g., nutrients),
but also for those that have episodic consumption (e.g.,food categories), as long as at least two measurements
for a part of participants are available [38]. A minimumof 50 individuals with at least two 24HR is required toapply statistical methods to account for within- andZancheta et al. BMC Medicine(2024) 22:340

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between-person variation and estimate the usual consumption for food groups consumed almost every day[39].
The MSM was applied using free access online software developed by the Department of Epidemiology of
the German Institute of Human Nutrition PotsdamRehbrücke, available at https://​nugo.​dife.​de/​msm/.
Covariables included for the estimates were sex, age,baseline body mass index (BMI) z-score for sex and age,and maternal variables (age, BMI, work outside the home,and education level).

English for homeostatic model to assess insulin resistance). Triglycerides, total cholesterol, and HDL-c were
measured using enzymatic colorimetric assays. LDL-c
was calculated using the Friedewald formula [42]. All
lipid profile markers were expressed in mg/dl. Glycemia
was measured by the enzymatic colorimetric method andexpressed in mg/dL, and insulin by electrochemiluminescent immunoassay and expressed in μU/ml. HOMAIR was calculated as insulin (μU/ml) × glucose (mmol/l)/22.5. All metabolic outcomes were considered continuous variables in the analysis.

Outcomes

Covariables in the association models

All outcomes were measured after approximately 2 years
of follow-up when the children were, on average, 6.1
years old.

Directed acyclic graphs (DAGs) were used to represent
the structures of the causal networks that link exposure(consumption of UPF) and the outcomes of interest (adiposity and metabolic profile) and support the identification of confounding variables in the associations studied[43]. Given that we have two primary groups of outcomes(adiposity and metabolic indicators), we constructed two
separate DAGs using the online application DAGitty(Fig. 1) [44].
Considering the DAGs, to estimate the total effect of
the consumption of ultra-processed foods at 4 years on
adiposity and metabolic responses at 6 years of age, theminimally sufficient adjustment set of variables included
socioeconomic status (SES), maternal BMI and age, sex,age, and children’s television time (displayed in white in
Fig. 1).
To approximate SES, we considered in the models
mother’s educational level, categorized as “low” (less thanhigh school), “medium” (at least high school), or “high”(more than high school), and whether they worked outside the home (“yes” or “no”), considering that in Chile
the unemployment rate is higher in poor than in nonpoor [45] and women with higher educational levels
more often work outside the home [46]. We also included
other maternal variables such as maternal age (selfreported) and BMI (calculated using maternal weight andheight measurements collected by trained dietitians).
Among the variables for the children, we consideredsex (male or female), age (in months), and television time.
To estimate the total hours children spent watching television on weekdays, we summed the time spent watching
TV before and after school and in the evening based on
information provided by the mothers.
Full completeness was obtained for all covariates except
maternal BMI, for which data for 4.2% of the total sample were missing. All covariables included in the models
were measured at baseline.

Anthropometric indicators

We used data collected by trained dietitians following
standard procedures. Height was measured using a portable stadiometer (Seca 217, to the nearest 0.1 cm), andweight was measured using a digital electronic scale(Seca 803 or 813, precision of 0.1 kg). Weight and height
were taken in duplicate, and we used their average to calculate BMI. We compared the BMI of each child with theWorld Health Organization (WHO) growth references
specific for age and sex [40] to obtain their z-score value(BMI z-score). Waist circumference (WC) was measured
with a metal tape (Lufkin W 606 PM, USA, precision 0.1
cm) and taken in duplicate. A third measurement was
required if the difference between both measurements
was greater than 0.5 cm. We calculated the average WC
for each child in cm.
Body composition

Body composition was estimated using the bioelectrical
impedance (BIA) method using Tanita BC-418 (Tanita
Corp.) and following the manufacturer’s recommendations. The child’s age, sex, and height were entered manually. Children stood barefoot on the appliance while
holding the handles for approximately 30 s. We used predicted values of fat mass (kg) and percentage of fat masscalculated by the device using impedance, weight, height,
and age with standard calibrated equations based on datafrom dual-energy X-ray absorptiometry [41].
Metabolic indicators

A nursing team collected the blood samples from thechildren after 8 to 12 h of fasting. We used the serum
triglycerides, total cholesterol, high-density cholesterol(HDL-c), and low-density cholesterol (LDL-c) levels as
lipid profile variables. For the glycemic profile, we usedfasting glucose, insulin, and the HOMA-IR (acronym inZancheta et al. BMC Medicine(2024) 22:340

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Fig. 1 Conceptual framework for the relationship between UPF consumption at 4 years and adiposity (A) and metabolic indicators (B) at 6
years. Notes: UPF – ultra-processed foods, SES – socioeconomic status, BMI – body mass index, (i) – initial values at 4 years, (p) – other values
during the study period, (f ) – final values at 6 years

Zancheta et al. BMC Medicine(2024) 22:340

Statistical analysis

Descriptive analyses were presented using mean and SD
for quantitative variables and absolute and relative frequency for qualitative variables.
All participants whose dietary reports did not fulfill the above exclusion criteria and who provided data
for at least one or more health outcomes were includedin the association models. The proportion of loss to follow-up was 23.7% for anthropometric indicators, 33.5%
for body composition, and 39.9% for metabolic indicators. We compared the characteristics of participants
included and lost in the analysis by presenting the percentual difference between them and applying a T-test
for quantitative and chi-square for qualitative variables,and differential loss related to maternal educational level
was identified. Given the loss to follow-up and to address
the potential selection bias, we incorporated the stabilized inverse probability of censuring weights (SW) in allmodels. This method creates a pseudo-population withcharacteristics comparable to the initial population tosimulate random censuring of covariates of interest [47].
We calculated different SW for anthropometric, bodycomposition, and metabolic indicators since the numberof participants in each analysis differed. The calculationof SW uses as a numerator the probability of censuring(i.e., proportion of participants lost in the follow-up) andas a denominator the probability of censuring based on
the covariables included in the model [47, 48]. The probability of censorship was obtained with logistic regression
with loss of follow-up as the response variable (yes or no),and the covariates included were sex, age, and initial BMI
z-score of the child, and maternal age, BMI, work outside
the home, and educational level of the mother. Using SW
results in the same estimate as unstabilized inverse probability weights, but typically in narrower 95% confidence
intervals and increased statistical efficiency [47, 48]. SW
were included in all regression analysis using the option
pweight.
We used linear regression models to investigate theassociations between the consumption of UPF at 4 years(in 100 cal and grams), adiposity, and metabolic indicators at 6 years. We reported regression coefficients and
95% confidence intervals (95% CI) for crude and adjusted
models.
The model 1 was adjusted for covariables presented in
the DAG: socioeconomic status (represented by maternal
education and work outside the home), maternal BMI
and age, and sex, age, and television time of children.
The model 2 was adjusted for the same covariables plus
caloric intake. The coefficient is then interpreted as theeffect of substituting 1 unit of UPF with 1 unit of nonUPF, maintaining a constant caloric intake [49, 50].

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Given the low prevalence of missing data in the covariates (less than 5% and in only one variable), we assumed
that missing data were completely at random and performed regressions with complete case analysis [51]. The
goodness-of-fit of the models was evaluated via graphicalanalysis of the residuals and inflation factors of variance.
The distribution of residues was not random for insulin,
HOMA-IR, triglycerides, fat mass, and fat mass percentage, so the final models included the log-transformed
version of these variables. As sensibility analysis, we considered models without SW and models with quartiles
of UPF as the exposure variable. All analyses were conducted using Stata v18.0 (College Station, TX).

Results
The baseline characteristics of all FECHIC children andsub-samples with anthropometric, body composition,and metabolic indicators are presented in Table 1. The
characteristics of the children included in each evaluation were similar to those of the reference cohort. At
the start of the FECHIC cohort study, the children had
an average age of 4.9 ± 0.5 years old, were comparable
by sex (51.9% girls), and had a mean BMI z-score of 1.
The mothers were 31.4 ± 6.7 years old, and most had amedium education level (55.1%). Children lost in thefollow-up presented differences primarily related to theirmothers’ educational level; more children from mothersof low education level were lost for anthropometric indicators (p = 0.003) and body composition (p < 0.001), andmore children from mothers of high education level werelost for metabolic outcomes (p < 0.001).
Estimated usual consumption of UPF

Table 2 shows the estimated usual consumption of each
NOVA food group at baseline (4 years of age). Children
consumed approximately 48% of their diet by calories
from UPF and 39% of their diet by grams from UPF.
Among the NOVA groups, UPF contributed the highest
percentage of children’s calories, while MPF contributed
the highest percentage of grams to children’s diet (57.0%).
Adiposity and metabolic outcomes

A description of the outcomes included in the study isavailable in Table 3. After 2 years of follow-up, the mean
BMI z-score was 1.1 ± 1.3, and the mean fat mass percentage was 24.2 ± 5.3%. The mean fasting blood glucose
was 81.8 mg/dL.
Associations between consumption of UPF and adiposityand metabolic indicators

Tables 4 and 5 present the associations between the usual
consumption of UPF at 4 years and adiposity and metabolic indicators at 6 years, considering the three types

Reference(n = 962)

277 (28.8)

176 (18.3)  Overweight  Obesity

483 (50.2)

Work outside of home,n (%)

530 (55.1)

259 (26.9)  Medium  High191 (26.0)425 (57.9)

118 (16.1)
68 (29.8)

105 (46.1)

55 (24.1)

117 (51.3)

29.0 (5.1)

30.5 (6.6)

47 (20.6)

68 (29.8)

113 (49.6)

1.1 (1.2)

4.8 (0.5)120 (52.6)

108 (47.4)

14.6− 20.4

49.7

4.1

1.0

− 3.6

17.0

4.6

− 8.0

9.1

− 2.1

1.9

− 2.1

0.003

0.598

0.549

0.024

0.444

0.163

0.148

0.792

159 (24.9)

380 (59.5)

100 (15.6)

315 (49.3)

28.7 (5.6)

31.7 (6.7)

110 (17.2)

185 (29.0)

344 (53.8)

1.0 (1.2)

4.9 (0.5)334 (52.3)

305 (47.7)

Lost (n = 228) Diff. in % p-value Included(n = 639)

100 (31.0)

150 (46.4)

73 (22.6)

164 (50.8)

28.8 (5.3)

30.8 (6.5)

66 (20.4)

92 (28.5)

165 (51.1)

1.1 (1.2)

4.8 (0.5)165 (51.1)

158 (48.9)

24.5− 22.0

44.9

3.0

0.3

− 2.8

18.6

− 1.7

− 5.0

10.0

− 2.0

− 2.3

2.5

< 0.001

0.665

0.871

0.057

0.463

0.217

0.082

0.728

130 (22.5)

352 (60.9)

96 (16.6)

275 (47.6)

28.7(5.6)

31.6 (6.6)

101 (17.5)

169 (29.2)

308 (53.2)

1.0 (1.2)

4.9 (0.5)296 (51.2)

282 (48.8)

129 (33.6)

178 (46.3)

77 (20.1)

204 (53.1)

28.8 (5.3)

31.0 (6.8)

75 (19.6)

108 (28.1)

201 (52.3)

1.1 (1.2)

4.8 (0.5)203 (52.9)

181 (47.1)

49.3− 24.0

21.1

11.6

0.3

− 1.9

12.0

− 3.8

− 1.7

10.0

− 2.0

3.3

− 3.5

< 0.001

0.092

0.823

0.225

0.715

0.469

0.213

0.642

Lost (n = 384) Diff. in % p-value

Metabolic indicators

Lost (n = 323) Diff. in % p-value Included(n = 578)

Body composition(2024) 22:340

Children nutritional status was evaluated with WHO cutoffs for BMI for sex and age (BMI > 1 SD defined as overweight and BMI > 2 SD, as obesity) [40]. Maternal education level was categorized as low: less than highschool, medium: at least high school, and high: completed superior education. Maternal BMI was missing for 4.2% of the total sample. P-value for T-test is shown for age and BMI of children and mothers; and p-value forchi-square is showed for sex, weight status, work outside of home, and education level

Notes: BMI Body mass index

173 (18.0)  Low

Educationlevel, n (%)

28.7 (5.6)

28.8 (5.5)

BMI, mean(SD)

362 (49.3)

31.6 (6.7)

129 (17.6)

209 (28.5)

396 (53.9)

Age in years, 31.4 (6.7)
mean (SD)Maternal characteristics

509 (52.9)  NormalweightWeightstatus, n (%)

1.0 (1.2)

1.0 (1.2)

BMI z-score,mean (SD)379 (51.6)

4.9 (0.5)499 (51.9)  Female355 (48.4)

Included(n = 734)

Anthropometric indicators

Age in years, 4.9 (0.5)
mean (SD)

463 (48.1)  MaleSex, n (%)

Child characteristics

Variables

Table 1 Baseline description of the reference cohort and the participants evaluated and lost for each outcome. FECHIC, 2016

Zancheta et al. BMC Medicine
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Zancheta et al. BMC Medicine(2024) 22:340

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Table 2 Estimated usual dietary consumption at 4 y according to NOVA food system classification (n = 893). FECHIC, 2016
NOVA

Consumption in calories

Consumption in grams

Mean (SD)Min–Max%

Mean (SD)Min–Max%

Group 1. MPF

424.5 (122.9)

126.9–941.2

34.9

791.4 (250.1)

269.5–2002.7

57.0Group 2. PCI

103.4 (24.8)

55.0–207.0

8.5

14.5 (4.0)

7.4–30.6

1.0Group 3. PF

169.0 (43.6)

74.3–351.8

13.9

65.1 (21.5)

23.0–142.6

4.7

Group 4. UPF

577.2 (133.2)

263.3–972.6

47.5

539.0 (109.4)

261.3–933.9

38.8

Total

1214.5 (66.9)

1008.3–1407.4

100.0a1397.5 (159.1)

950.6–2040.3

100.0aNotes: MPF Minimally processed foods, PCI Processed culinary ingredients, PF Processed foods, UPF Ultra-processed foods, SD Standard deviation, Min Minimal value,
Max Maximal valuea

The usual consumption was estimated using the MSM for each food group and total diet; then, the sum of calories and grams for food groups is close but not
identical to the estimated values for total calories and total grams of diet

Table 3 Description of anthropometric, body composition, andmetabolic indicators at 6 years. FECHIC, 2018
Outcomes
Anthropometric indicators (n = 734)

Mean

SD

BMI z-score

1.1

1.3

WC, cm59.5

5.3

Fat mass, kg

6.4

2.8

Fat mass, %

24.2

5.3

Body composition (n = 690)

Metabolic indicators (n = 578)
Blood glucose, mg/dl

81.1

8.1

Insulinemia, μU/ml

7.5

3.5

HOMA-IR

1.5

0.7

Total cholesterol, mg/dl

164.3

26.0

HDL cholesterol, mg/dl

46.4

10.8

LDL cholesterol, mg/dl

99.4

21.5

Triglycerides, mg/dl

92.6

37.6

Notes: BMI Body mass index, WC waIst circumference, HOMA-IR Homeostasis
model assessment of insulin resistance, LDL Low-density lipoprotein, HDL Highdensity lipoproteinof models (crude, adjusted for covariables, adjusted forcovariables plus total caloric intake). We did not findan association between UPF and adiposity in crude andcovariable adjustment models. However, when UPF
was adjusted for covariables plus total caloric intake,we observed a positive association of small magnitudewith BMI z-score (respectively for 100 kcal and 100 g of
UPF: b = 0.24 [95% CI 0.16–0.33]; b = 0.21 [95% CI 0.10–
0.31]), WC (b = 0.89 [95% CI 0.41–1.37]; b = 0.86 [95% CI
0.32–1.40]), log-fat mass (b = 0.06 [95% CI 0.03–0.09];
b = 0.04 [95% CI 0.01–0.07]), and log-percentage fat mass(b = 0.03 [95% CI 0.01–0.04]; b = 0.02 [95% CI 0.003–
0.04]). For metabolic outcomes, the coefficients of UPF
and their 95% CI for both 100 cal and 100 g were close tonull values for all models.

Sensitivity analysis

The results obtained in models without SW (Additional
file 1: Tables S1 and S2) and in models with the consumption of UPF in quartiles (Additional file 1: Tables S3 andS4) were consistent with those obtained in main analysis.

Discussion
In this study, we found a high consumption of UPF interms of calories and grams in a sample of low- and middle-income preschoolers from Santiago, Chile. We also
found a positive association between the consumptionof UPF at the age of 4 years and several markers of adiposity measured at 6 years old. However, we did not findan association between UPF consumption and metabolic
indicators after 2 years of follow-up.
We remark that we found associations only in models
that included a total caloric intake adjustment. In nutritional epidemiology, an energy adjustment is used to
study the consumption of nutrients or foods in terms of
total energy. The underlying reason is that interventions
at the individual or population level usually aim to modify the consumption of certain nutrients or foods, withchanges in the composition of the diet, but not in theoverall amount of food consumed. The energy adjustment
also controls for the confounding effect resulting fromthe association between total energy intake with physical activity, differences in body size, and metabolic efficiency [49, 50]. On this basis, we consider the estimatesthat include the energy adjustment as the more reliable inour study. Analysis that takes into account the total calories by using the caloric share of UPF is the most prevalent in studies focused on investigating UPF and healthoutcomes [52]. The fact our results showed associations
between UPF and adiposity markers only when adjusting for total calories provides further support to suggestthat the relative contribution of UPF in the diet is more
important than their absolute amount, and the healthZancheta et al. BMC Medicine(2024) 22:340

Page 9 of 14

Table 4 Associations between the consumption of ultra-processed foods (in calories and grams) at 4 years, anthropometric indicators(n = 762), and body composition at 6 years (n = 690). FECHIC, 2018
Adiposity outcomes

Per 100 cal of UPF

Per 100 g of UPF

ββ

95% CI

95% CI

Anthropometric indicators
BMI z-score   Model 0 – Crude− 0.02− 0.090.05

0.07− 0.030.16   Model 1 – Covariables− 0.01− 0.090.06

0.03− 0.07

0.12   Model 2 – Covariables + caloric intake

0.24

0.16

0.33

0.21

0.10

0.31

WC, cm   Model 0 – Crude− 0.43− 0.84− 0.03− 0.03− 0.540.48   Model 1 – Covariables− 0.35− 0.750.06− 0.08− 0.590.43   Model 2 – Covariables + caloric intake

0.89

0.41

1.37

0.86

0.32

1.40

Body composition
Fat mass (log −), kg   Model 0 – Crude− 0.02− 0.040.01− 0.02− 0.050.01   Model 1 – Covariables− 0.01− 0.030.01− 0.01− 0.030.02   Model 2 – Covariables + caloric intake

0.06

0.03

0.09

0.04

0.01

0.07

Fat mass (log −), %   Model 0 – Crude− 0.01− 0.03− 0.001− 0.02− 0.03− 0.0005   Model 1 – Covariables− 0.01− 0.030.01− 0.005− 0.020.01   Model 2 – Covariables + caloric intake

0.03

0.01

0.04

0.02

0.003

0.04

Notes: UPF Ultra-processed foods, BMI Body mass index, WC Waist circumference. Stabilized inverse probability weights were included in all models. Model 1: adjusted
for children´s sex, age, and screen time, and mothers’ BMI, age, educational level, and work outside home status. Model 2: adjusted for all covariates from model 1 plus
usual total caloric intake

effects observed are a consequence of a displacement oftraditional dietary patterns [53].
In the present study, we found that almost half of thecalories of preschool children were derived from UPF,
in line with the findings of previous studies with similar
populations [14, 15]. We also found that consumptionof UPF during preschool years was positively associatedwith increases in BMI z-score and WC after 2 years of follow-up. Similarly, a study with 307 children of low socioeconomic status from Brazil found that the consumptionof UPF in 4 year-old children predicted a higher increase
in WC at 8 years old [14]. On the other hand, our findings do not align with the results of a previous study
conducted on 7-year-old children from Portugal. In Portuguese children, there was no association between UPF
and BMI z-score and WC z-score after 3 years of followup [54]. One potential explanation for the discrepancy inresults is the difference in the amount of UPF consumed
between both populations. Chilean children consumed
more UPF than did Portuguese children. The percentage of grams and calories from UPF in the diet of Chilean
children was 39 and 48%, while in Portuguese children,
UPF represented 25 and 31% of the total grams and calories consumed, respectively. Another potential explanation is the age difference of the participants between

studies. Our study followed children from 4 to 6 years,
when they were starting the adipose rebound [55], while
the study from Portugal followed children between 7 and10 years old. Age and duration of follow-up could be arelevant factor. For example, a prior study from the Avon
Longitudinal Study of Parents and Children that assessedlongitudinal associations between UPF and adiposity trajectories from 7 to 24 years old showed that differencesin BMI and fat mass by UPF consumption become more
accentuated starting adolescence, another critical period
for development [56].
Regarding metabolic indicators, we did not find an
association with any included indicator. A recent review
assessing the effect of UPF on metabolic syndrome
components in children and adolescents based on ninecohort studies found mixed results. Some longitudinalstudies have reported a positive association between
UPF and blood lipids, but not with blood glucose; it isimportant to highlight that only a few prospective studies on metabolic outcomes in children are available in theliterature to date [57]. Our results suggest that adiposity
indicators could be altered before we observe metabolic
marker alterations. However, metabolic alterations associated with adiposity during childhood are an increasingly common problem. A study with more than 26,000

Zancheta et al. BMC Medicine(2024) 22:340

Page 10 of 14

Table 5 Association between the consumption of ultra-processed foods (in calories and grams) at 4 years and metabolic indicators at
6 years (n = 628). FECHIC, 2018
Metabolic indicators

Per 100 cal of UPF

Per 100 g of UPF

ββ

95% CI

95% CI

Blood glucose, mg/dl
Model 0 – Crude− 0.30− 0.830.22

0.25− 0.370.87

Model 1 – Covariables− 0.34− 0.86

0.17

0.08

-0.57

0.72

Model 2 – Covariables + caloric intake− 0.28− 0.910.35

0.27− 0.430.97

Insulinemia (log −), μU/ml
Model 0 – Crude− 0.02− 0.040.01

0.02− 0.01

0.06

Model 1 – Covariables− 0.02− 0.050.01− 0.01− 0.040.03

Model 2 – Covariables + caloric intake

0.01− 0.020.05

0.02− 0.01

0.06

HOMA-IR (log −)Model 0 – Crude− 0.02− 0.050.004− 0.01− 0.040.02

Model 1 – Covariables− 0.02− 0.050.005− 0.004− 0.040.03

Model 2 – Covariables + caloric intake

0.01− 0.020.05

0.03− 0.01

0.06

Total cholesterol, mg/dl
Model 0 – Crude− 0.25− 1.881.37− 0.10− 2.13

1.93

Model 1 – Covariables− 0.23− 1.881.43

0.04− 2.06

2.15

Model 2 – Covariables + caloric intake

0.22− 1.902.35

0.44− 1.892.76

LDL cholesterol, mg/dl
Model 0 – Crude0.62− 0.041.27

0.70− 0.12

1.53

Model 1 – Covariables

0.52− 0.161.20

0.65− 0.25

1.55

Model 2 – Covariables + caloric intake

0.10− 0.801.01

0.31− 0.701.33

HDL cholesterol, mg/dl
Model 0 – Crude− 0.48− 1.790.83− 0.46− 2.101.18

Model 1 – Covariables− 0.41− 1.750.93− 0.57− 2.31

1.17

Model 2 – Covariables + caloric intake

0.18− 1.511.87− 0.15− 2.03

1.74

Triglycerides (log −), mg/dl
Model 0 – Crude− 0.01− 0.040.01− 0.01− 0.040.02

Model 1 – Covariables− 0.01− 0.03

0.01

0.001

− 0.030.03

Model 2 – Covariables + caloric intake

0.002− 0.030.03

0.02− 0.01

0.04

Notes: HOMA-IR Homeostasis model assessment of insulin resistance, LDL Low-density lipoprotein, HDL High-density lipoprotein. Stabilized inverse probability
weights were included in all models. Model 1: adjusted for children´s sex, age, and screen time, and mothers’ BMI, age, educational level, and work out of home status.
Model 2: adjusted for all covariates from model 1 plus usual total caloric intake

children with obesity (average: 12.6 ± 2.9 years) from
European countries found metabolic alterations in morethan half of the participants, the most prevalent being
high blood pressure (34%), dyslipidemias (32%), and lesscommon alterations in glucose metabolism (3.3%) [58].
Similar results were also described in Mexico [59]. The
results of these studies could indicate that alterations inblood pressure and lipid metabolism occur early in children with obesity. It is proposed that, with respect to glucose parameters, alterations may occur in the late stageof the development of metabolic alterations [60, 61].
There is also evidence of the role of diet in metabolic risk
from an early age. In a population-based cohort analyzing diet trajectories from ages 2–3 to 11–12 years, individuals who consistently adhered to an “unhealthy” diettrajectory showed compromised cardiovascular function
and poorer metabolic health when compared to children
consistently following a “healthy” diet, again suggestingthat adolescence would be a critical period for observing
the emergence of metabolic traits [62].
Various UPF characteristics have been examined to
explain their detrimental impact on health. The most
explored explanation revolves around the inadequate
nutritional profile of UPF, characterized by a higher
density of added sugars and saturated fats, and a lower
density of vitamins and minerals compared to non-ultraprocessed foods [16]. However, the nutritional imbalancein UPF seems incapable of fully explaining the observed
effects. Findings from different studies have shown thatthe association between consumption of UPF and healthZancheta et al. BMC Medicine(2024) 22:340

outcomes persists even after adjusting for the nutritional
profile of the diet [63, 64]. UPF manufacturing often
involves processed and refined ingredients that lackthe natural food matrix, leading to reduced satiety andheightened glycemic response [65]. Additionally, UPF
tend to have a higher energy density due to their ingredients and low water content, making them easy to consume rapidly in terms of volume and calories, facilitatingexcessive intake [66, 67]. Furthermore, UPF typicallyexhibit a lower protein density, and it has been hypothesized that this lower protein content could lead individuals to overconsume other foods and, consequently,
excess energy [68]. Another hypothesis considered to
explain these associations beyond the nutritional profileis that the widespread consumption of UPF may result inincreased intake of substances that are rare or absent innature, such as food additives [69].
The consumption of UPF by children is a matter ofconcern. We know that children are the main consumersof these products in several countries, with the percentage of consumption higher than that observed in adults[70]. In fact, media marketing that encourages increased
consumption of UPF targets children, given their highvulnerability. Additionally, eating habits built duringchildhood tend to persist throughout life [71]; therefore,
becoming accustomed to consuming high levels of sugars, sodium, and fats is worrisome. Moreover, children
have a lower body size; thus, they have a higher risk of
exposure to critical levels of substances found in UPF.
Thus, several countries, mostly in the Latin-American
region, have adopted food-based guidelines with messages advising against the consumption of UPF [72–75].
In Brazil, dietary guidelines for children under 2 yearsof age explicitly recommend offering MPF and avoiding UPF [76]. Additionally, in Brazil, the legislation of
the school feeding program prohibits the provisionof UPF for children under 3 years of age and mandates
that at least 75% of resources be allocated to the acquisition of MPF [77]. While not explicitly incorporating
the concept of UPF into its regulations, Chile has one of
the most comprehensive frameworks to protect children
from packaged foods and beverages high in nutrients ofconcern, such as sugar, salt, and saturated fats (mostly
UPF). The Chilean Food-Labeling and Advertising Law
implemented in 2016 (after our dietary data collection)
mandates the inclusion of warning labels “high in” on thefront of the package, restricts the marketing of regulated
foods to children under 14 years of age, and prohibits
selling or offering of these foods in schools [78]. These
measures should be reinforced and globally promoted
to create environments in which children have restricted
or no access to UPF given the risks associated with theirconsumption.

Page 11 of 14

Our study has several strengths, including its longitudinal design, detailed dietary information that includes
specific brand names of packaged foods, objective measurements of adiposity and metabolic profiles, and theestimation of usual consumption of UPF employing statistical methods to account for within-person variability
in food consumption. However, some limitations should
also be considered for interpreting our results. In observational studies, there is an inherent measurement error
in the dietary data, which refers to the difference between
the reported dietary intake and the true usual dietaryintake. However, we attempted to select only plausible
reports by excluding diets very far from the estimatedconsidered children’s sex and age, and we also excluded
diets with extreme values of UPF (< p1 and > p99). We
also gathered dietary information using the standardized 24-h dietary recall technique, deemed the methodwith the least misreporting in children [79], and included
children in the interviews which could reduce errors
due to lack of awareness of parents regarding children’s
dietary consumption. Additionally, we applied a statistical method to estimate the usual consumption of UPF;
however, our estimate could not represent the usual consumption over the entire follow-up period. Still, dietary
recalls can be subjected to social desirability bias, which
may lead to the underestimation of UPF and bias in theassociations toward the null. The proportion of loss on
the follow-up was significant, especially for metabolic
indicators, and we found differential losses related tomothers’ education. However, maternal education was
not associated with outcomes, except glycemia (datanot shown), so the estimates should not be importantly
modified with the observed differential loss to followup; besides, we applied inverse probability of censoring
weights to adjust all analyses to make more correct inferences considering the characteristics of our initial sample. Although we controlled for potential confounders,
we cannot rule out unmeasured or residual confounding as this was an observational study. Finally, the findings might lack broad generalizability because our sample
consisted of preschoolers attending public schools in
Santiago’s low- to middle-income region.

Conclusions
We observed that a higher consumption of UPF wasassociated with adiposity indicators in this sample ofChilean preschoolers. Our results suggest the need fora longer exposure time for metabolic effects to emerge,
so strategies to prevent the consumption of UPF aimed
at schoolchildren could still improve these trajectories. Therefore, policies promoting food environmentsthat facilitate the consumption of minimally processedZancheta et al. BMC Medicine(2024) 22:340

foods and make it difficult for children to access UPF
should be encouraged.
Abbreviations
24HR	24-H Diet Recall
BMI	Body mass index
CIAPEC	Center of Research in Food Environment and Prevention of Obesity and Non-Communicable Diseases
CV	Coefficient of variation
DAG	Directed acyclic graph
DRI	Dietary Reference Intake
FECHIC	Food and Environment Chilean Cohort
HDL-c	High-density cholesterol
HOMA-IR	Homeostatic Model to Assess Insulin Resistance
INTA	Institute of Nutrition and Food Technology
LDL-c	Low-density cholesterol
MPF	Minimally processed foods
MSM	Multiple Source Method
pER	Predicted energy requirement
PCI	Processed culinary ingredientsPF	Processed foodsrEI	Reported energy intake
SES	Socioeconomic status
SW	Stabilized inverse probability of censuring weights
UPF	Ultra-processed food
USDA	United States Department of AgricultureWC	Waist circumferenceWHO	World Health Organization

Page 12 of 14

Availability of data and materials
The datasets used and/or analyzed during the current study are available fromthe corresponding author on reasonable request.

Declarations
Ethics approval and consent to participate
The original study was approved by the ethics committee of the Instituteof Nutrition and Food Technology (INTA), University of Chile (Nº 7–2016, Nº
19–2017). All mothers signed an informed consent form on behalf of theirchildren. The ethics committee of the Faculty of Medicine, University of Chile,
also approved the current analyses (Nº 159–2021).
Consent for publication
All authors approve the publication of the final manuscript.
Competing interests
The authors declare that they have no competing interests. CC is a Guest Editor in BMC Medicine, for the article collection “Food Environments and Health”.
Author details
1
Doctoral Program in Public Health, School of Public Health, Universityof Chile, Santiago, Chile. 2 Center of Research in Food Environment and Prevention of Obesity and Non‑Communicable Diseases (CIAPEC), Institute of Nutrition and Food Technology (INTA), University of Chile, Santiago, Chile. 3 Carolina
Population Center, Department of Nutrition, Gillings School of Global Public
Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Received: 19 December 2023 Accepted: 12 August 2024

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03556-z.
Additional file 1: Tables S1-S4. Table S1 – Associations between the
consumption of UPF at 4 y, anthropometric indicators, and body composition at 6 y without considering SW. Table S2 – Associations between
the consumption of UPF at 4 y and metabolic indicators at 6 y without
considering SW. Table S3 – Associations between quartiles of consumption of UPF at 4 y, anthropometric indicators and body composition at 6y. Table S4 – Associations between quartiles of consumption of UPF at 4 y
and metabolic indicators at 6 y
Acknowledgements
We thank the participants of the Food and Environment Chilean Cohort. We
also thank the research teams at CIAPEC (Center of Research in Food Environment and Prevention of Obesity and Non-Communicable Diseases) at INTA
(Institute of Nutrition and Food Technology), University of Chile, and at the
Global Food Research Program, University of North Carolina at Chapel Hill.
Authors’ contributions
Conceptualization: CZ and CC. Methodology: CZ and CC. Investigation: CZ.
Funding acquisition: LST, MR and CC. Supervision: CC. Writing—original draft:
CZ. Writing—review and editing: CZ, NR, LST, MR, and CC. All authors read andapproved the final manuscript.
Funding
This work was supported by Bloomberg Philanthropies, and the ANID/Fondo
Nacional de Desarrollo Científico y Tecnológico-FONDECYT Regular (#1201633
and #1181370). CZ is supported by the National Agency for Research andDevelopment (ANID)/Scholarship Program/Doctorado Becas Chile #21200883.
NR is supported by the ANID/Fondo Nacional de Desarrollo Científico y
Tecnológico-FONDECYT Postdoctorado (#3230125). The funders had no role instudy design, data collection and analysis, decision to publish, or preparationof the manuscript.

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