How to evaluate Ultra-processed Food Intake in pediatrics? UPF Score: an easy alternative

doi:10.21203/rs.3.rs-4909438/v1

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Research Article

How to evaluate Ultra-processed Food Intake in pediatrics? UPF Score: an easy alternative

https://doi.org/10.21203/rs.3.rs-4909438/v1

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Background

The rising intake of ultra-processed foods (UPFs) among children and adolescents poses challenges due to the lack of precise and accessible tools to quantify such intake. This complicates understanding its effects on dietary quality and hampers the design of effective preventive interventions across diverse socioeconomic and geographic contexts. Objective: This paper aims to determine the relationship between the UPF score and dietary characteristics.

Methods

This cross-sectional study involved 353 participants aged 3 to 17. The methods included (1) a three-day food intake record; (2) an evaluation of intake across five food groups; (3) categorization of intake frequency (0, 1, 2, or up to 3 times weekly); and (4) scoring by cumulative intake. Principal component analysis and regression models were utilized to assess diet associations with UPF score.

Results

Higher UPF score groups showed increased intake of energy, fats, and sodium (p < 0.01); decreased fiber intake (p = 0.03), vegetables, fruits, and cereals (p < 0.05); higher sugar intake (p < 0.01); and reduced intake of vitamins A and C (p < 0.05). Four principal components, representing over 65% of sample variability, identified two healthy and two less healthy dietary patterns. Linear regression indicated higher fiber intake at lower UPF scores, declining with higher UPF scores.

Conclusions

The UPF score represents a useful tool for efficiently and swiftly assessing UPF intake and dietary characteristics in pediatric population.

Ultra-Processed Foods

Dietary Intake

Child Nutrition

Dietary Patterns

Fiber

The NOVA classification system defines ultra-processed food (UPF) ¹ as industrial formulations of multiple ingredients manufactured from substances derived from foods, additives, or ingredients not typically used in home food preparation. ^2,3 These products contain added sugars, unhealthy fats such as saturated and trans fats, and sodium that extend shelf life, ³ making them highly palatable and addictive ⁴ due to their high energy content. ⁵

The dietary pattern of the pediatric population has been characterized by a high energy intake, with a prevalence of UPF intake, ⁶ leading to weight gain that can result in increased adiposity from childhood to adulthood. ⁷ The intake of these products causes a lower intake of certain nutrients, such as fiber and vitamins, ⁸ and a higher risk of cardiometabolic diseases, ⁹ impacting nutrition and health throughout life. ¹⁰ Easy availability, aggressive marketing toward young audiences, ¹¹ convenience, palatability, ³ and socioeconomic factors influence UPF intake, ⁸ which remains alarmingly high among children and adolescents (C&A) worldwide, ¹² with Mexico being the fourth country with the highest per capita purchases of these products. ¹³

While there are strategies to measure UPF intake, most adaptations provide the total calories that UPFs contribute to the daily diet rather than the number of times or portions consumed habitually. ^14,15 Therefore, a tool is proposed for use during a three-day food log that facilitates classification based on the frequency of intake and its relationship with the diet. ¹⁶ This study aims to determine the relationship between a UPF intake score (UPF score) and diet quality in the pediatric population.

Study Population

A cross-sectional study was conducted between March and November 2022, involving participants aged 3 to 17. Participants with incomplete dietary records (less than three days) or who reported extreme energy intake (< 800 kcal or > 3500 kcal) were excluded. The research protocol was approved by the Research Ethics Committee in our Institution with registration number 2021/070, and informed assent from participants and consent from their parents or legal guardians were obtained.

Dietary Energy Intake

The technique for collecting daily dietary and nutritional intake data was obtained before assessment through a three-day food recall (two weekdays and one weekend day). Parents or guardians were given detailed instructions on how to measure and record food quantities using household measures or portion sizes, with the aid of printed food replicas for guidance. Solid and liquid foods, including water and other beverages, were recorded, and the cooking techniques used were specified. This instrument included a list of commonly forgotten ingredients (e.g., water, salt, tortilla, sugar, sauce, and oil); if any were consumed, they were noted as missing.

Food records were captured using NutriKcal VO® v.1 software (CONSINFO, S.C., Mexico City, Mexico), which provides the energy and nutritional content per food item. Macronutrient intake was expressed as a percentage of energy, other nutrients compounds in grams, and portions belonging to the same food group per 1000 kcal. Average Daily Energy Intake (DEI) was also calculated in kcal/kg/day, along with Average Daily Protein Intake (DPI) in g/kg/day.

UPF score

For the development of the UPF score, five groups of UPF foods (i.e., cereals and tubers, processed meats, dairy products, oils and fats, and sugars) were considered. According to the three-day food record, the number of times each participant consumed each of these groups was summed (at least ½ serving daily was classified as significant intake), as described in Table 1.

Table 1

Ultra-processed foods and the creation of the UPF Score.
Food Group	Foods	UPF score
Food Group	Foods	No consume	1 time	2 times	3 times
Cereals and tubers	Boxed sugary cereal, cereal bar, bakery products, cake, industrialized sliced bread, sweet filled pastry, packaged sweet bread, loaf bread, potato chips, cookies, toast.	0	1	2	3
Processed meats	Sausage, blood sausage, ham, nugget, pâté, salted/cured/smoked meats, pepperoni, salami, crab sticks, yellow cheeses, industrialized sliced cheese, cream cheese. High-fat content: Pork rinds, offal.	0	1	2	3
Dairy products	Sweetened milks, sweetened yogurt (skimmed or whole), petit suisse, ice creams.	0	1	2	3
Oils and fats	Margarine, cream, mayonnaise	0	1	2	3
Sugar	Soda, carbonated beverages, soft drinks, tomato sauces, ketchup, gummy candies, nougat, condensed milk, evaporated milk, marshmallow, fruit-flavoured beverages, chocolate bars or pieces of candy with and without chocolate, chocolate powder, jellies.	0	1	2	3
Score (0–15), represents the number of times per week reported in a 3-day food record Based on Monteiro CA³, García-Blanco L¹², Gonzalez-Ortiz A⁴³

The scores obtained were divided into tertiles to classify UPF intake, where the lowest tertile represented low intake, the second tertile moderate intake, and the third tertile high intake of UPF.

Covariates

Anthropometric measurements were recorded at the beginning of the study. Weight and height were taken using a SECA® scale and stadiometer. Nutrition status was determined using Body Mass Index (BMI), utilizing percentiles from the Centers for Disease Control and Prevention (CDC) and the PediTools calculator.

Moreover, sociodemographic information was collected through direct interviews with the children’s parents or legal guardians.

Statistical Analysis

Categorical variables were presented as frequencies and percentages. Continuous variables with normal distribution were expressed as mean and standard deviation (SD), whereas non-normally distributed variables were expressed as median and interquartile range (IQR).

Group differences were assessed using the Chi-square test (Chi2) for categorical variables and analysis of variance (ANOVA) for tertile evaluation.

Subsequently, a principal component analysis was performed to evaluate the relationship between patterns of food portion intake and UPF intake using a polychoric matrix. The number of components was determined based on the elbow plot (Figure S1). Logistic regression models were used to evaluate the relationship between the components (Table S1) and the UPF score.

Finally, a restricted linear spline of the UPF score with knots chosen so that data are divided into three groups of equal size was performed to assess the association between fiber intake and the UPF score. The covariates selected for the regression models were those with biological plausibility, potential confounders, and a p-value of < 0.20 in bivariate analysis. A p-value of < 0.05 was considered statistically significant using STATA software.

From a total of 353 participants, a median UPF score of 6 (IQR 4–7), representing the frequency of UPF intake, was obtained. The median age was 11 years (range 7–15), with nearly half being adolescents (n = 164, 47%) and a minority being female (n = 147, 42%). Approximately 60% of the population had a normal nutritional status (n = 208, 58.9%), as detailed in Table 2. Participants’ characteristics were presented in tertiles to represent UPF intake: “low” (3–5 points), “moderate” (6–7 points), and “high” (8–10 points).

Table 2

Sociodemographic characteristics according to the UPF Score
Characteristics	All participants	Intake, UPF Score points			P-value
	All participants	Low	Moderate	High
	n = 353	n = 170	n = 111	n = 72
UPF score (points), median (IQR)	6 (4–7)	4 (3–5)	6 (6–7)	9 (8–10)	< 0.001
Age (years), median (IQR)	11 (7–15)	11 (7–15)	11 (8–14)	10 (6–15)	1.00
Age category, n (%)
Preschool	57 (16)	26 (15)	17 (15)	14 (19)	0.84
School-age	132 (37)	66 (39)	43 (39)	23 (32)
Adolescent	164 (47)	78 (46)	51 (46)	35 (49)
Female, n (%)	147 (42)	80 (47)	43 (39)	24 (33)	0.11
Weight (kg), median (IQR)	37 (23–54)	35.8 (21–54)	37.1 (26.1–54.4)	36.7 (24–53.3)	0.72
Height (cm), median (IQR)	141 (120–158)	141 (118–158)	142 (122–157)	138 (117–161.5)	0.85
BMI (kg/m²), median (IQR)	18.2 (15.7–22.9)	18.2 (15.6–22.9)	18.1 (15.7–22.4)	18.3 (15.9–23.3)	0.68
Nutritional status, n (%)
Malnutrition	36 (10.2)	22 (12.9)	8 (7.2)	6 (8.3)	0.07
Well nourished	208 (58.9)	100 (58.8)	67 (60.4)	41 (56.9)
Overweight	65 (18.4)	30 (17.6)	26 (23.4)	9 (12.5)
Obesity	44 (12.5)	18 (10.6)	10 (9)	16 (22.2)
Data are expressed as median (25th percentile, 75th), or number (%), as appropriate. cm: Centimeters; BMI: Body Mass Index; kg: Kilograms; MEDIAN (IQR): Median (interquartile range).

Table 3 enumerates the dietary characteristics according to the UPF. The group with the highest UPF score also had the highest energy intake (kcal), fat intake (%), grams of saturated fat, and the lowest fiber intake (p < 0.05 for all). Other variables showed no statistical significance. Additionally, the group with the highest UPF score reported the lowest intake of vegetables and fruits and the highest intake of sugar. The other groups did not exhibit statistically significant differences.

Table 3

Dietary characteristics according to the UPF Score
Characteristics	All participants	Intake, UPF Score points			P-value
	All participants	Low, 4 (3–5 points)	Moderate, 6 (6–7 points)	High, 9 (8–10 points)
	n = 353	n = 170	n = 111	n = 72
Energy (kcal/day), median (IQR)	1609 (1366–1922)	1478 (1228–1730)	1714 (1439–2005)	1873 (1575–2242)	< 0.01
DEI (kcal/kg/day), median (IQR)	47 (31–71)	43 (28–66)	46 (33–68)	55 (36–75)	0.05
DPI (g/kg/día), median (IQR)	1.9 (1.3–2.8)	1.7 (1.2–2.8)	1.9 (1.3–2.8)	2.3 (1.3–2.9)	0.29
Percent Daily Macronutrients (%)
Proteins, median (IQR)	16 (14–18)	17 (14–18)	16 (14–18)	15 (14–17)	0.08
Fats, mean ± SD	34 ± 7	32 ± 7	35 ± 7	35 ± 6	0.02
Carbohydrate, mean ± SD	50 ± 7	51 ± 7.5	49 ± 7	50 ± 6	0.06
Nutrients/1000 kcal, median (IQR)
Saturated Fats, g	9.4 (7.3–11.2)	9.1 (6.6–10.7)	9.3 (7.5–11.5)	10.2 (8.3–11.6)	0.02
Polyunsaturated Fats, g	12.1 (9.4–15.1)	11.6 (8.7–14.4)	12.1 (10.0–15.2)	12.8 (9.8–15.3)	0.08
Monounsaturated Fats, g	5.4 (4.3–6.7)	5.3 (4.1–6.6)	5.5 (4.4–6.8)	5.7 (4.3–6.8)	0.44
Cholesterol, mg	175.7 (127.8–231.1)	181.8 (132.5–234.5)	176.4 (123.6–242.0)	167.2 (118.9–209.9)	0.21
Sugar, g	26.5 (19.9–36.1)	27.1 (18.4–37.2)	25.8 (20.1–34.8)	27.6 (20.9–36.8)	0.76
Fiber, g	8.2 (5.9–10.5)	9.2 (7.1–12.2)	7.7 (5.9–9.5)	6.2 (4.9–8.5)	< 0.01
Liquids, ml	539.5 (392.1–758.3)	576.5 (395.8–837.4)	512.5 (401.0–728.2)	521.4 (352.3–665.5)	0.14
Daily servings/1000 kcal, median (IQR)
Vegetables	1.2 (0.7–1.8)	1.4 (0.8–2.1)	1.2 (0.8–1.7)	0.7 (0.5–1.3)	< 0.01
Fruits	1.0 (0.5–1.6)	1.1 (0.6–1.9)	0.9 (0.4–1.6)	0.9 (0.4–1.4)	0.02
Cereals	4.6 (3.8–5.3)	4.6 (3.8–5.6)	4.5 (3.9–5.3)	4.7 (3.8–5.1)	0.54
Legumes	0.1 (0.0–0.3)	0.1 (0.0–0.4)	0.1 (0.0–0.3)	0.0 (0.0–0.2)	0.06
Animal Products	3.0 (2.4–3.8)	3.0 (2.4–3.8)	3.1 (2.4–3.9)	2.9 (2.3–3.6)	0.41
Dairy	0.7 (0.4–1.0)	0.7 (0.4–1.1)	0.7 (0.5–1.0)	0.8 (0.5–1.0)	0.96
Fats	2.6 (1.7–3.7)	2.5 (1.5–3.6)	2.7 (1.8–3.9)	2.7 (2.0–3.8)	0.24
Sugars	1.6 (0.7–2.4)	1.0 (0.4–2.2)	1.6 (1.0–2.3)	1.9 (1.4–3.1)	< 0.01
Data are expressed as mean ± SD, median (25th percentile, 75th), or number (%), as appropriate. DEI: Average Daily Energy Intake; DPI: Daily Protein Intake

Regarding vitamin intake (Table 4), the group with the lowest UPF score had higher intake of vitamins A and C, as well as iron and potassium (p < 0.05). The rest of the variables demonstrated no statistically significant differences.

Table 4

Micronutrient intake, categorized by UPF Score.
Nutrients/1000 kcal, median (IQR)	All participants	Intake, UPF Score points			P-value
	All participants	Low, 4 (3–5 points)	Moderate, 6 (6–7 points)	High, 9 (8–10 points)
	n = 353	n = 170	n = 111	n = 72
Vitamin A, mcg	464.7 (299.3–787.0)	499.9 (333.2–847.5)	462.8 (327.3–709.6)	340.7 (245.7–605.7)	0.009
Vitamin B1, mg	0.6 (0.5–0.7)	0.6 (0.5–0.7)	0.6 (0.5–0.7)	0.6 (0.5–0.7)	0.720
Vitamin B2, mg	0.8 (0.6–0.9)	0.8 (0.6–1.0)	0.7 (0.6–0.9)	0.8 (0.6–0.9)	0.840
Vitamin B6, mg	0.7 (0.5–1.0)	0.8 (0.6–1.0)	0.7 (0.5–0.9)	0.7 (0.5–0.9)	0.075
Vitamin B12, mcg	1.2 (0.9–1.8)	1.3 (0.8–1.8)	1.2 (0.9–1.8)	1.2 (1.0–1.8)	0.870
Vitamin C, mg	43.3 (25.8–72.4)	48.4 (30.0–81.8)	40.6 (25.0–69.5)	41.5 (22.3–54.1)	0.026
Folic Acid, mcg	96.1 (68.8–131.9)	99.1 (71.6–135.2)	90.4 (69.1–133.5)	91.7 (62.6–128.7)	0.310
Pantothenic Acid, mg	1.5 (1.1–2.0)	1.6 (1.1–2.2)	1.5 (1.1–1.9)	1.5 (1.0–1.9)	0.230
Niacin, mg	7.6 (6.1–10.1)	7.9 (5.9–10.7)	7.5 (6.1–9.7)	7.5 (6.2–8.9)	0.290
Vitamin E, mg	2.3 (1.5–3.2)	2.3 (1.6–3.1)	2.4 (1.5–3.4)	2.1 (1.4–3.1)	0.380
Calcium, mg	473.1 (360.5–591.8)	480.8 (373.1–614.0)	474.4 (351.5–581.3)	467.5 (360.5–567.0)	0.510
Iron, mg	7.3 (6.0–8.6)	7.4 (6.5–8.8)	7.3 (6.2–8.5)	6.3 (5.3–8.2)	0.008
Potassium, mg	1052.6 (840.3–1297.5)	1135.2 (863.2–1403.8)	1027.4 (798.3–1223.2)	1012.5 (771.8–1203.8)	0.003
Magnesium, mg	114.5 (83.9–160.2)	120.6 (86.9–162.7)	115.7 (79.0–153.8)	110.1 (84.2–172.1)	0.460
Sodium, mg	1203.5 (998.8–1431.2)	1147.6 (973.2–1473.2)	1238.3 (1026.3–1405.6)	1240.1 (1047.4–1380.3)	0.760
Phosphorus, mg	368.8 (274.4–466.0)	376.6 (271.1–482.3)	354.6 (277.3–435.8)	385.2 (275.5–473.1)	0.390
Selenium, mg	27.0 (21.8–33.2)	27.6 (22.6–33.4)	26.4 (20.6–33.3)	26.2 (19.9–30.8)	0.250
Zinc, mg	3.3 (2.6–4.2)	3.3 (2.5–4.3)	3.2 (2.5–4.4)	3.3 (2.9–3.9)	0.920
mcg/1000 kcal: Micrograms per 1000 kilocalories; mg/1000 kcal: Milligrams per 1000 kilocalories; IQR: interquartile range.

Supplementary Table 1 specifies the relationships between different types of foods and their intake patterns. Component 1 (Comp1) identifies a positive association with the intake of cereals, animal products, and fats. Component 2 is positively associated with legume intake and negatively associated with sugar and fats. Component 3 is positively associated with fruit and vegetable intake as well as animal products and negatively associated with sugar. Finally, Component 4 is positively associated with fruits, sugar, milk, and dairy products (Table S1).

Table 5 presents the multinomial logistic regression models that evaluate the main components associated with the UPF score, where components 2 and 3 are protective factors for UPF intake, and components 1 and 4 are risk factors.

Table 5

Multinomial logistic regression models to assess the main components associated with the UPF score.
COMPONENTS
	1 + Cereal + Fat + Animal Origin Product	2 -Sugar -Grease + Legumes	3 -Sugar + Fruits + Vegetables + Animal Origin Product	4 + Fruits + Milk and derivatives + Sugar
Unadjusted models
UPF score	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)
Low	REF	REF	REF	REF
Moderate	1.4 (1.2–1.7)	0.7 (0.5–0.9)	0.8 (0.7–1.1)	1.3 (1.0–1.7)
High	1.8 (1.4–2.3)	0.5 (0.4–0.7)	0.6 (0.5–0.8)	1.8 (1.5–2.5)
Model 1
UPF score	OR (95% CI)	OR (95% CI)	OR (95% CI)	OR (95% CI)
Low	REF	REF	REF	REF
Moderate	1.4 (1.1–1.8)	0.7 (0.5–0.9)	0.9 (0.7–1.1)	1.3 (0.99–1.8)
High	1.8 (1.4–2.4)	0.6 (0.4–0.8)	0.6 (0.4–0.8)	1.8 (1.3–2.4)
Model 1: adjusted for gender, age, body mass index, and residence.

Finally, using the linear regression model, an inverse association was observed between fiber intake and the UPF score (Fig. 1).

This study proposes using a scoring system to identify low, moderate, or high intake of UPF. The results indicate that lower UPF intake (low frequency of consumption) is associated with better diet quality in C&A. Additionally, high UPF intake is positively associated with unhealthy eating patterns but inversely associated with a healthy diet. The role of UPF in children’s diets extends beyond immediate health effects, ^18–20 influencing long-term food preferences ¹⁸ and the development of healthy dietary patterns in the future. ^20,21 In Mexico, an increase in UPF intake of approximately 5 percentage points was reported among C&A from 2006 ²² to 2016, ²³ reflecting growths in the food industry and a radical transformation in the Mexican diet. ^24,25

Although the complications associated with high UPF intake, such as obesity and chronic diseases, ^26,27 are well known, it is essential to explore how these foods affect the intake of healthy nutrients. However, no specific amount of UPF that can be considered safe for the pediatric population has been determined. ²⁸

Information regarding UPF and various outcomes has gained significant relevance recently, from observational studies to systematic reviews of clinical trials. ^29,30 Despite the available information, conducting meta-analyses has been challenging due to data heterogeneity. A recent systematic review mentioned that nutritional interventions aimed at eliminating UPF intake reported no changes in intake. ³⁰ This heterogeneity limits the extrapolation and comparison of results, as well as short- or medium-term goals for its intake.

Several studies and health organizations, such as the World Health Organization, ³¹ emphasize the importance of limiting UPF intake to improve diet quality and prevent chronic diseases, ^32,33 as a crucial early intervention, especially given the negative impact of UPF. ^31–35

Energy and macronutrient requirements for C&A are significantly lower than those for adults and should be adjusted according to their stages of growth and development. ^36,37 However, caloric intake in the high UPF intake group resembles that of a high school teenager, despite the study population having a median age of 11 years. This suggests that energy intake observed in C&A, especially in the moderate and high UPF intake groups, exceeds the expected energy requirements for their age group, ³⁸ which could have negative implications regarding unhealthy daily dietary habits. ^33,39–42

Significant associations between different intake patterns and UPF scores were identified through principal component analysis. For instance, component 2, characterized by lower sugar and fat intake and higher legume intake, acts as a protective factor against high UPF intake. In contrast, component 4, which includes fruits, dairy, and sugar, represents a risk factor. An example of a meal that may appear healthy but includes UPF is chocolate milk with added sugar accompanied by an apple, illustrating how certain healthy foods are paired with high UPF intake due to a lack of awareness of UPF content. Evidence supports the use of principal components for analyzing other diets, enabling the development of indices tailored to specific populations and dietary contexts. ^43,44

Identifying protective and risk factors associated with UPF intake through dietary patterns provides a basis for future longitudinal research to establish temporal trends and population-specific parameters for more precise guidelines on acceptable UPF intake, contributing to child health.

Our findings reveal that a higher UPF score is associated with lower fiber intake and higher intake of fats and sugars. This pattern suggests that children consuming more UPF tend to have lower nutritional quality diets. ^12,45–47 Specifically, participants with “high intake” of UPF (8–10 points) showed significantly higher energy and saturated fat intake and lower fiber intake compared to those with “low intake” (3–5 points).

Our results also showed that even within moderate ranges of UPF intake, similar effects were observed across other groups, impacting overall diet quality. ^10,12,32

Our tool adapts and strengthens the NOVA system, which aids in understanding food quality and its health impact, for the pediatric population, considering the magnitude of UPF intake and classifying participants into low, moderate, and high intake tertiles. ^14,48

Furthermore, this study underscores the importance of promoting healthy eating habits from an early age. Our results find UPF intake to be a global public health issue, primarily affecting vulnerable and low-income populations due to the low cost and high availability of these foods. ^49–51

The strengths of the UPF score lie in its simplicity as a tool for assessing UPF intake in C&A, and of the main strength of using the UPF score is that being a three-day food record, it helps assess the frequency and quantity of UPF consumed; however, the main limitation is the nature of the study does not allow establishing causal associations, and the exact number of daily servings of UPF consumed was not identified.

The study’s findings underscore the importance of limiting UPF intake in children’s diets to promote better nutritional health. Accordingly, using the UPF score can guide nutritionists and healthcare professionals in clinically assessing and monitoring children.

The study indicates that ultra-processed food (UPF) intake in C&A is associated with poorer diet quality, characterized by higher energy and saturated fat intake and lower fiber intake. The UPF score is a valuable tool for quantifying this intake, adapting to dietary characteristics, and predicting overall diet quality.

ANOVA

Analysis of Variance

BMI

Body Mass Index

CDC

Centers for Disease Control and Prevention

Comp1

Component 1 (in Principal Component Analysis)

DEI

Daily Energy Intake

DPI

Daily Protein Intake

IQR

Interquartile Range

UPF

Ultra-Processed Foods

Ethical approval and consent to participate

The research protocol was approved by the Research Ethics Committee in our Institution with registration number 2021/070, and informed assent from participants and consent from their parents or legal guardians were obtained.

Consent for publication

Consent for the publication of the results of this study was obtained in accordance with established ethical requirements.

Availability of data and materials

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Competing interests

The authors declare that they have no competing interests.

Funding statement

This work was financially supported by Instituto Nacional de Pediatría through the E022 Program.

Authors' contributions

LFRA Participated in study conception and design the research generation, analysis of the data and writing the paper.

AGO Participated in study conception, design, revision, and analysis of the data, writing the paper and approval of the final version of the manuscript.

AVG Participated in study conception and design the research generation, analysis of the data and writing the paper.

CAG, EVA, RSR, BPG, JOGZ participated in interpretation of the data and/or critical revision of the manuscript to its final form. All authors read and approved the final manuscript.

Acknowledgements

No aplicable.

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How to evaluate Ultra-processed Food Intake in pediatrics? UPF Score: an easy alternative

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UPF score

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