Front. Nutr. Frontiers in Nutrition Front. Nutr. 2296-861X Frontiers Media S.A. 10.3389/fnut.2022.777384 Nutrition Clinical Trial “GENYAL” Study to Childhood Obesity Prevention: Methodology and Preliminary Results Marcos-Pasero Helena 1 2 Aguilar-Aguilar Elena 1 de la Iglesia Rocío 3 Espinosa-Salinas Isabel 4 Molina Susana 5 Colmenarejo Gonzalo 6 Martínez J. Alfredo 7 8 9 Ramírez de Molina Ana 10 Reglero Guillermo 11 12 Loria-Kohen Viviana 1 13 * 1Nutrition and Clinical Trials Unit, GENYAL Platform, IMDEA-Food Institute, CEI UAM + CSIC, Madrid, Spain 2Faculty of Health Sciences, Valencian International University (VIU), Valencia, Spain 3Departamento de Ciencias Farmaceúticas y de la Salud, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain 4Nutritional Genomics and Health Unit, GENYAL Platform, IMDEA-Food Institute, CEI UAM + CSIC, Madrid, Spain 5GenyalLab, GENYAL Platform, IMDEA-Food Institute, CEI UAM + CSIC, Madrid, Spain 6Biostatistics and Bioinformatics Unit, IMDEA-Food Institute, CEI UAM + CSIC, Madrid, Spain 7Precision Nutrition and Cardiometabolic Health, IMDEA-Food Institute, CEI UAM + CSIC, Madrid, Spain 8IdisNA, Navarra Institute for Health Research, Pamplona, Spain 9Center of Biomedical Research in Physiopathology of Obesity and Nutrition, Institute of Health Carlos III, Madrid, Spain 10Molecular Oncology and Nutritional Genomics of Cancer, IMDEA-Food Institute, CEI UAM + CSIC, Madrid, Spain 11Production and Development of Foods for Health, IMDEA-Food Institute, CEI UAM + CSIC, Madrid, Spain 12Department of Production and Characterization of Novel Foods, Institute of Food Science Research (CIAL), CEI UAM+CSIC, Madrid, Spain 13Departamento de Nutrición y Ciencia de los Alimentos, Facultad de Farmacia, Universidad Complutense de Madrid, Grupo de Investigación VALORNUT-UCM, Madrid, Spain

Edited by: Jorge Moreno-Fernandez, University of Granada, Spain

Reviewed by: Masahide Hamaguchi, Kyoto Prefectural University of Medicine, Japan; Margarita Torres-Tamayo, Instituto Nacional de Cardiologia Ignacio Chavez, Mexico; Francisco Jesús Llorente Cantarero, University of Cordoba, Spain

 *Correspondence: Viviana Loria-Kohen vloria@ucm.es

This article was submitted to Nutrition and Metabolism, a section of the journal Frontiers in Nutrition

 08 03 2022 2022 9 777384 15 09 2021 04 02 2022 Copyright © 2022 Marcos-Pasero, Aguilar-Aguilar, de la Iglesia, Espinosa-Salinas, Molina, Colmenarejo, Martínez, Ramírez de Molina, Reglero and Loria-Kohen. 2022 Marcos-Pasero, Aguilar-Aguilar, de la Iglesia, Espinosa-Salinas, Molina, Colmenarejo, Martínez, Ramírez de Molina, Reglero and Loria-Kohen

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 Objective

This article describes the methodology and summarizes some preliminary results of the GENYAL study aiming to design and validate a predictive model, considering both environmental and genetic factors, that identifies children who would benefit most from actions aimed at reducing the risk of obesity and its complications.

 Design

The study is a cluster randomized clinical trial with 5-year follow-up. The initial evaluation was carried out in 2017. The schools were randomly split into intervention (nutritional education) and control schools. Anthropometric measurements, social and health as well as dietary and physical activity data of schoolchildren and their families are annually collected. A total of 26 single nucleotide polymorphisms (SNPs) were assessed. Machine Learning models are being designed to predict obesity phenotypes after the 5-year follow-up.

 Settings

Six schools in Madrid.

 Participants

A total of 221 schoolchildren (6–8 years old).

 Results

Collected results show that the prevalence of excess weight was 19.0, 25.4, and 32.2% (according to World Health Organization, International Obesity Task Force and Orbegozo Foundation criteria, respectively). Associations between the nutritional state of children with mother BMI [β = 0.21 (0.13–0.3), p (adjusted) <0.001], geographical location of the school [OR = 2.74 (1.24–6.22), p (adjusted) = 0.06], dairy servings per day [OR = 0.48 (0.29–0.75), p (adjusted) = 0.05] and 8 SNPs [rs1260326, rs780094, rs10913469, rs328, rs7647305, rs3101336, rs2568958, rs925946; p (not adjusted) <0.05] were found.

 Conclusions

These baseline data support the evidence that environmental and genetic factors play a role in the development of childhood obesity. After 5-year follow-up, the GENYAL study pretends to validate the predictive model as a new strategy to fight against obesity.

 Clinical Trial Registration

This study has been registered in ClinicalTrials.gov with the identifier NCT03419520, https://clinicaltrials.gov/ct2/show/NCT03419520.

 pediatric obesity early intervention single nucleotide polymorphisms machine learning nutrition Introduction

Obesity is a complex, chronic and multifactorial disease, originated as an interaction between genetic and environmental factors (1). The prevalence of overweight and obese children is rising every year. Specifically, in compliance with the WHO, the number of overweight and obese children aged 0–5 years increased from 32 million globally in 1990 to 41 million in 2016. And it is expected to increase to 70 million by 2025 if these trends continue (2). The situation in Spain is also alarming, with a prevalence of 23.2% of overweight (22.4% boys and 23.9% girls), and 18.1% of obesity (20.4% boys and 15.8% girls) according to data from the ALADINO study carried out by the Spanish Agency for Consumer Affairs, Food Safety and Nutrition (3).

Childhood obesity usually leads to adulthood obesity, which increases the risk of developing certain diseases, such as hypertension, type 2 diabetes and cardiovascular diseases, prematurely (4–6). This early age has been identified as a key point for the implementation of healthy dietary and lifestyle patterns. Thus, the home and schools provide a useful environment to develop educational and lifestyle interventions for school-age children (7).

There is no doubt about the multifactorial etiology of obesity in which socio-cultural, dietetic, environmental and genetic factors are involved (8–11). However, current knowledge is still insufficient to determine the relative importance of these different factors, having a complex network of associations between them (12). In this regard, machine learning techniques represent a powerful prediction tool through their great ability to big data analysis. Thus, Machine learning represents a tool based on a set of algorithms that can characterize, adapt, learn, predict, and analyze data, increasing the knowledge of obesity and offering possibilities of predicting the disease with unprecedented precision. These techniques have been proposed as a potential tool to predict a future excess of body weight and its comorbidities. There are several predictive machine learning algorithms such as neural networks, decision tree analysis or random forest. Each of them should be used according to the purpose and nature of the study variables (13).

Considering all the above-mentioned aspects, the main objective of the GENYAL study is to design and validate a machine learning-based predictive model that identifies children who would benefit most from actions aimed at reducing the risk of obesity and its complications, considering both environmental and genetic factors, and applicable at the beginning of the school stage. The nutritional education developed in the intervention's schools will be also evaluated as part of the predictive model. This article describes the methods and analyses that will be applied. In addition, it summarizes some preliminary results obtained after the first year of the data collection.

 Methods Type of Study and Duration

The present study is a cluster randomized clinical trial with 5-year follow-up intervention based on nutritional education, annual anthropometric measurement evaluations and data collection from questionnaires. Saliva samples were collected for all the schoolchildren in the initial evaluation (2017) in order to obtain genetic information. The final evaluation will be carried out 4 years after the initial intervention, which corresponds to the end of the primary school (Figure 1). The study is therefore expected to last 5 years, from 2016 to 2017 academic year to 2021–2022. Table 1 provides a schedule of activities and interventions throughout the study.

Timetable of the study.

GENYAL study timeline.

Study period in years
Enrolment (T-1) Allocation (T0) T1 T2 T3 T4 T5
Enrolment
Study approval x
Schools recruitment x
Informed consent x
Group allocation x
Assessments
Sending of questionnaires x x x x x
Anthropometric measurements x x x x x
Saliva samples collection x
Questionnaires collection x x x x x
Machine learning model results x
Intervention group
Sending educational material for parents, teachers, and schoolchildren
Nutrition education workshops and talks

T-1 (enrolment) and T0 = 2016/2017; T1 = 2017/2018; T2 = 2018/2019; T3 = 2019/2020; T4 = 2021/2021; T5 = 2021/2022.

 Recruitment, Sample Size, and Sample Characteristics

Due to the nature of the study as a clinical trial, the large number of variables necessary for the design of the preventive model with machine learning (each of them of a very different nature), and the duration of 5 years, a statistically robust sample size could not be implemented. Furthermore, the Consejería de Educación e Investigación de la Comunidad de Madrid was responsible for the selection of six representative schools of the Autonomous Community of Madrid (ACM) (Spain) (two in the north, two in the center and two in the city's south zone), considering the number of students per center and the average socioeconomic level of the districts and neighborhoods. Therefore, the selection was representative of the average income of the ACM households (14). All the School Boards approved the participation in the study and included a total of 569 potential children participants from different districts of Madrid: Chamberí, Hortaleza, Carabanchel, Puente de Vallecas, and Moncloa-Aravaca.

 Inclusion and Exclusion Criteria

The inclusion criteria to participate in the study were: being in 1st or 2nd grade of primary school and having an informed consent signed by at least one of the parents.

Exclusion criteria were not attending school during the evaluation days or having planned not to stay at the school the following years.

 Randomization

In order to avoid cross-contamination between intervention and control subjects, randomization was carried out by school center instead of individually. Thus, participating schools were randomly and proportionally stratified into two groups: intervention schools and control schools, considering the number of participants per center, their geographic area and their socioeconomic status.

The randomization procedure was carried out with the statistical software R version 3.4 (www.r-project.org).

 Ethical Aspects and Data Processing

Protocols and methodology used in the present study comply with the ethical principles for research involving human subjects laid down in the Declaration of Helsinki (1964) and its modifications. The study was approved by the Research Ethics Committee of the IMDEA Food Foundation (PI:IM024; Approval date: March 29th, 2016) and it has been registered in ClinicalTrials.gov with the identifier NCT03419520. School centers and families were informed in detail about the different stages of the project both, orally and in writing. Signed informed consent from at least one of the parents were collected by the researchers prior to the first evaluation. This document included a specific consent to DNA extraction and the evaluation of polymorphisms from the saliva samples. In addition, it included a section on the storage of the remaining samples as a collection registered, according to Spanish legislation (Royal Decree 1716/2011, of November 18th).

Data compiled along the study are going to be processed using a web application that applies dissociation criteria making the volunteers' data anonymous, in compliance with the current Spanish legislation (Organic Law 15/1999 of December 13th, on the protection of Personal Data) and may be used for scientific purpose as publications and conferences. Only the researchers directly related to the study will be allowed to access data.

 Selection of Single Nucleotide Polymorphisms

A total of 26 single nucleotide polymorphisms (SNPs) associated with a higher risk of early-age onset of obesity and its comorbidities were selected. The selection was made considering the biological activity of each SNP, Caucasian allele frequencies and the scientific evidence that supports the association between the presence of the polymorphism and the risk of developing overweight, obesity or its complications. The sum of the risk alleles will further be used to design a genetic risk score.

Different databases such as 1,000 Genomes, HapMap, Pubmed, GWAS Central, GWAS Catalog or Ensembl were used. Table 2 shows the selected 26 SNPs, which will be included in the predictive model.

Single nucleotide polymorphisms selection.

Genea Name Function Locationb Alleles WT MAFb Frequencyb Association References
APOA5 rs662799 Upstream gene variant Chromosome 11:116792991 G A A 0.16 (G) G: 0.076/A: 0.924 G|G: 0.010; A|A: 0.859; A|G: 0.131 Overweight and obesity, lipid profile, CVD, MS (15–19)
BDNF rs925946 Intron variant Chromosome 11:27645655 T G G 0.25 (T) T: 0.338/G: 0.662 T|T: 0.141; G|T: 0.394; G|G: 0.465 Overweight and obesity, calcium consumption, MS, waist/hip index, abdominal obesity (20–25)
ETV5 rs7647305 Intron variant Chromosome 3:186116501 T C T 0.24 (T) T: 0.227/ C: 0.773 T|T: 0.101; C|T: 0.253; C|C: 0.646 Overweight and obesity, early menarche, BP (20, 24, 26, 27)
FTO rs3751812 Intron variant Chromosome 16:53784548 G T G 0.22 (T) G: 0.556/T: 0.444 G|G: 0.303; G|T: 0.505; T|T: 0.192 Overweight and obesity, lipid profile (28–31)
FTO rs7190492 Intron variant Chromosome 16:53794840 A G G 0.30 (A) A: 0.343/G: 0.657 A|A: 0.131; A|G: 0.424; G|G: 0.444 Overweight and obesity (28, 29, 32)
FTO rs8050136 Intron variant Chromosome 16:53782363 C A A 0.32 (A) C: 0.556/A: 0.444 C|C: 0.303; A|C: 0.505; A|A: 0.192 Overweight and obesity, T2DM (28, 29, 33–36)
FTO rs9939609 Intron variant Chromosome 16:53786615 T A A 0.34 (A) T: 0.556/A: 0.444 T|T: 0.303; A|T: 0.505; A|A: 0.192 Overweight and obesity, extreme childhood obesity, T2DM, MS, early menarche, fat mass (20, 27, 37–41)
GCKR rs1260326 Missense variant Chromosome 2:27508073 T C C 0.29 (T) T: 0.429/C: 0.571 T|T: 0.172; C|T: 0.515; C|C: 0.313 Overweight and obesity, MS, lipid profile, glycemic profile (42–45)
GCKR rs780094 a Intron variant Chromosome 2:27518370 T C C 0.30 (T) T: 0.409/C: 0.591 T|T: 0.162; C|T: 0.495; C|C: 0.343 Overweight and obesity, lipid profile, MS, glycemic profile, fatty liver, T2DM (42, 43, 46–50)
GNPDA2 rs10938397 Intergenic variant Chromosome 4:45180510 A G A 0.33 (G) A: 0.576/G: 0.424 A|A: 0.333; A|G: 0.485; G|G: 0.182 Overweight and obesity, extreme childhood obesity, early menarche, T2DM, glycemic profile, fat mass, waist circumference, waist-height ratio, central obesity (20, 27, 40, 41, 51)
KCTD15 rs368794 Intergenic variant Chromosome 19:33829547 T A A 0.42 (T) T: 0.298/A: 0.702 T|T: 0.081; A|T: 0.434; A|A: 0.485 Overweight and obesity, fat mass (20, 41, 52, 53)
LEPR rs1137101 Missense variant Chromosome 1:65592830 A G A 0.42 (A) A: 0.515/G: 0.485 A|A: 0.253; A|G: 0.525; G|G: 0.222 Obesity, lipid profile, glycemic profile, caloric intake, T2DM (54–58)
LPL rs328 Stop gained Chromosome 8:19962213 C G C 0.09 (G) C: 0.874/G: 0.126 C|C: 0.768; C|G: 0.212; G|G: 0.020 Lipid profile, CVD (59–63)
MC4R rs17782313 Intergenic variant Chromosome 18:60183864 T C T 0.24 (C) T: 0.742/C: 0.258 T|T: 0.515; C|T: 0.455; C|C: 0.030 Overweight and obesity, extreme childhood obesity, MS, T2DM, abdominal obesity, fat mass, waist circumference, waist-height ratio (22, 41, 64–69)
NEGR1 rs2568958 Upstream gene variant Chromosome 1:72299433 G A G 0.32 (G) G: 0.364/A: 0.636 G|G: 0.141; A|G: 0.444; A|A: 0.414 Overweight and obesity, extreme childhood obesity, fat mass. (20, 41, 70)
NEGR1 rs3101336 Upstream gene variant Chromosome 1:72285502 T C T 0.32 (T) T: 0.364/C: 0.636 T|T: 0.141; C|T: 0.444; C|C: 0.414 Overweight and obesity, severe early-onset obesity. (70–72)
NPY rs16147 Upstream gene variant Chromosome 7:24283791 T C T 0.48 (T) T: 0.485/C: 0.515 T|T: 0.273; C|T: 0.424; C|C: 0.303 Overweight and obesity (73–75)
PPARγ rs1801282 Missense variant Chromosome 3:12351626 C G C 0.07 (G) C: 0.904/G: 0.096 C|C: 0.828; C|G: 0.152; G|G: 0.020 Overweight and obesity, glycemic profile, CVD, T2DM, Skin folds (76–82)
SEC16B rs10913469 Intron variant Chromosome 1:177944384 T C C 0.23 (C) T: 0.758/C: 0.242 T|T: 0.576; C|T: 0.364; C|C: 0.061 Overweight and obesity, waist circumference, fat mass, early menarche (27, 28, 30, 55, 83, 84)
TCF7L2 rs7903146 Intron variant Chromosome 10:112998590 C T T 0.23 (T) C: 0.687/T: 0.313 C|C: 0.525; C|T: 0.323; T|T: 0.152 IMC, T2DM, MS, glycemic profile, height, lipid profile in DM patients (85–93)
TMEM18 rs2867125 Intergenic variant Chromosome 2:622827 T C C 0.14 (T) T: 0.162/C: 0.838 T|T: 0.051; C|T: 0.222; C|C: 0.727 Overweight and obesity (30, 94, 95)
TMEM18 rs4854344 Intergenic variant Chromosome 2:638144 G T G 0.15 (G) G: 0.167/T: 0.833 G|G: 0.051; G|T: 0.232; T|T: 0.717 Overweight and obesity, waist circumference, visceral fat and SBP (83, 96–98)
TMEM18 rs6548238 Regulatory region variant Chromosome 2:634905 T C C 0.12 (T) T: 0.167/C: 0.833 T|T: 0.051; C|T: 0.232; C|C: 0.717 Overweight and obesity, extreme childhood obesity, fat mass, early menarche, waist circumference, (20, 27, 41, 55, 65, 97, 99, 100)
TMEM18 rs7561317 Intergenic variant Chromosome 2:644953 A G A 0.16 (A) A: 0.162/G: 0.838 A|A: 0.040; A|G: 0.242; G|G: 0.717 Overweight and obesity (21, 22, 24, 33, 40, 101, 102)
UCP2 rs659366 Upstream gene variant Chromosome 11:73983709 C T C 0.41 (T) C: 0.636/T: 0.364 C|C: 0.414; C|T: 0.444; T|T: 0.141 Overweight and obesity, glycaemia (103–105)
UCP3 rs1800849 5 prime UTR variant Chromosome 11:74009120 G A G 0.19 (A) G: 0.768/A: 0.232 G|G: 0.586; A|G: 0.364; A|A: 0.051 Overweight and obesity, glycaemia, lipid profile, waist hip ratio (103, 106–110)

Approved name (111).

Ensembl (https://www.ensembl.org/index.html). Location, For forward strand.

WT, wild type; MAF, Minor allele frequency; Frequency, Allele and genotype; APOA5, apolipoprotein A5; BDNF, brain derived neurotrophic factor; ETV5, ETS variant 5; FTO, FTO, alpha-ketoglutarate dependent dioxygenase; GCKR, glucokinase regulator; GNPDA2, glucosamine-6-phosphate deaminase 2; KCTD15, potassium channel tetramerization domain containing 15; LEPR, leptin receptor; LPL, lipoprotein lipase; MC4R, melanocortin 4 receptor; NEGR1, neuronal growth regulator 1; NPY, neuropeptide Y; PPARγ, peroxisome proliferator activated receptor gamma; SEC16B, SEC16 homolog B, endoplasmic reticulum export factor; TCF7L2, transcription factor 7 like 2; TMEM18, transmembrane protein 18; UCP2, uncoupling protein 2; UCP3, uncoupling protein 3; CVD, cardiovascular disease; MS, metabolic syndrome; BP, blood pressure; T2DM, type 2 diabetes mellitus; SBP, systolic blood pressure.

 Questionnaires

Different questionnaires were designed based on other surveys used in similar studies to facilitate the comparison of the results. All of them are annually sent to families by email or in the paper format according to the parents' preference and are filled by at least one of the parents. The information collected is summarized in Table 3.

Content of the questionnaires used in the study.

Questionnaire Variables
Characteristics of parents and family
Social, health and demographic People living with the schoolchildren, number of siblings, family structure, level of education, employment situation, profession, country of birth, years living in Spain, income level, diagnosis of disease, smoking habit, body weight, height and date of birth.
Diet Daily intakes, frequency of breakfast per week, breakfast foods, reasons for stop breastfeeding, desire to extend the breastfeeding period, perception of their child's diet.
Physical activity Distance between school and home, weekly hours of physical activities, frequency of physical activity shared with their child, town conditions for doing outdoor activities.
Characteristics of children
Social, health and demographic Country of birth, years living in Spain, adopted or foster child, diagnosis of disease, body weight and height reported by their parents and date of birth.
Diet Diet changes over the last year and reasons, number of daily intakes, frequency of breakfast per week, place where they have breakfast, breakfast foods, place where they have lunch, frequency of eating in fast food restaurants, breastfeeding, beikost's start date.
48-h dietary record
KIDMED questionnaire
Physical activity Sleeping hours, type of transport used to go to school and back home, outdoor activities, time usually spend watching TV, playing video games or using the computer, presence of TV or computer games in their bedroom, weekly hours of physical activities and children's physical activity level according to their parents
48-h physical activity questionnaire

Regarding social, health and demographic data, parents annually complete a self-reported questionnaire that includes different personal questions based on the surveys used in the ALADINO and ELOIN studies (3, 112).

Dietary information is gathered using a 48-h food record of 2 non-consecutive days, a weekday and a weekend day, as recommended by the European Food Safety Authority guidelines (113). Afterwards, the data are tabulated and analyzed using the DIAL software (Alce Ingeniería, Madrid, Spain) (114) in order to obtain information about macro and micronutrients.

Moreover, the adherence to the Mediterranean diet pattern is assessed using the “KIDMED Mediterranean Diet Quality Index” in addition to general questions about the dietary habits of the children and their parents. The KIDMED questionnaire consists of a total of 16 dichotomous questions that must be answered affirmatively or negatively to obtain a score (115).

Physical activity and free time data about the children and their parents are gathered using a questionnaire with different sections adapted and modified from the ALADINO (3) and the ELOIN (112) studies. In addition, a 48-h physical activity record is collected, corresponding to 24 h of a weekday and a complete weekend day (116). In the physical activity record, parents had to specify the time that their children spent during 24 h of a week day and 24 h of a weekend day doing different activities, including resting hours and activities with a variable level of intensity (very light, light, moderate and intense). The time spent doing each activity is multiplied by the corresponding activity coefficient defined by the WHO (117), added and divided by 24, obtaining the Individual Physical Activity Coefficient (IPAC). Then, the IPAC corresponding to a weekday is multiplied by 5 and the weekend IPAC by 2, both results are added and divided by 7, thus, obtaining the median physical activity per individual. Afterwards, it is necessary to convert the IPAC into a Physical Activity Coefficient (PAC) according to sex, therefore an equivalence is made between the IPAC and the PAC proposed by the Institute of Medicine (118). Finally, participants are classified into sedentary, lightly active, active and very active in line with their PAC.

All these data and information are collected every year on equal terms.

 Anthropometric and Blood Pressure Measurements

These data are collected in the school centers, early in the morning, by previously trained nutritionists, following standardized protocols and WHO international instructions for this age group (117). For the anthropometric measurements, children had to wear a T-shirt and gym shorts. All measures are taken twice, and the average is used for the analyses.

Height is determined using a Leicester height rod with an accuracy of 1 mm (Biological Medical Technology SL, Barcelona, Spain). Body weight and fat mass percentage are assessed using a BF511 Body Composition Monitor (BF511- OMRON HEALTHCARE UK, LT, Kyoto, Japan). Furthermore, fat mass percentage is classified according to the tables offered by OMROM Healthcare (119). Waist and brachial circumferences measurements are taken using a non-elastic tape (KaWe Kirchner & Wilhelm GmbH, Asperg, Germany; range 0–150 cm, 1 mm of precision). The waist circumference measurements obtained are classified by percentiles in compliance with Fernández et al. (120). Triceps skinfolds are taken following the International Society for the Advancement of Kinanthropometry guidelines (121) using a mechanic caliper (HOLTAIN LTD. CRYMYCH UK 10 g/mm2 constant pressure; range 0–39 mm and 0.1 mm of precision) and the results obtained are ranked according to percentiles proposed by Frisancho AR (122).

Using these data, other variables of interest are calculated. In particular, BMI is calculated as the body weight divided by the squared height (kg/m2). There is not a universal technique to classify the BMI values in the pediatric collective, therefore, the results are ranked according to the percentiles of Faustino Orbegozo Eizaguirre Foundation, reviewed in 2011 (120), International Obesity Task Force reviewed in 2000 (123), and WHO reviewed in 2007 (124). The results of overweight and obesity rates are unified as a single category called excess weight (EW). The arm muscular and fat areas are obtained using the equations proposed by Mataix Verdú and López Jurado (125) and López-Sobaler and Quintas Herrero (126), respectively. The protein and caloric reserves are calculated by Frisancho AR equations (122). Waist/height ratio is calculated as waist circumference (cm)/height (cm) and it was classified according to Panjikkaran et al. and Ashwell investigations (127, 128). Height/age index is rated in percentiles according to Fernández et al. (120).

For blood pressure monitoring, an automatic digital monitor is used (OMRON M3-Intellisense) using a cuff suitable for children. The results are classified according to the percentiles established by the Spanish Association of Pediatrics (129).

All these measurements are repeated every year on equal terms.

 Compiling Saliva Samples, DNA Extraction and Genotyping

Buccal smears were collected for DNA extraction following standardized protocols. For this purpose, a sterile swab free of human RNAse, DNAse and DNA (300263DNA-Hisopos Deltalab polystyrene and polyester) was used. Children had to have their mouth clean and avoid eating or drinking 30 min prior to collection. Three samples were taken per children, each one identified with the number corresponding to the order of extraction, to ensure traceability. As the samples were collected, they were directly stored in refrigeration until all the children were evaluated. Immediately after, they were frozen at −80°C until their processing.

Genomic DNA was extracted from the buccal swabs using the INVISORB® SPIN TISSUE MINI KIT (Stratec), according to the manufacturer's instructions. Samples were lysed in the presence of proteinase K and a specific lysis buffer. The lysate was then purified and finally, it was eluted in a free EDTA solution.

For genotyping, the DNA samples were loaded in TaqMan® OpenArray® Real-Time PCR plates (Life Technologies Inc., Carlsbad, CA) already configured with the specific selected SNPs with specific waves for each allele, marked with a different fluorophore to determine the genotype. This process was made using the OpenArray® AccuFill™ System (Life Technologies Inc., Carlsbad, CA). Once it was charged, a PCR performed and the chips were read in the QuantStudio® 12K Flex Real-Time PCR Instrument (Life Technologies Inc., Carlsbad, CA). Results were analyzed using the TaqMan® Genotyper software (Life Technologies Inc., Carlsbad, CA), which automatically assigns the genotype to each sample according to the amount of detected signal for each fluorophore.

The duplicate analysis was used to validate the genotyping result.

 Design of Educational Tools and Implementation of the Nutritional Education Programme

For the implementation of the nutritional education programme in the “intervention schools”, three different kinds of guides were designed aimed at parents, children and teachers. All this information was developed and adapted to the participants' age by the nutritionists from the IMDEA Food Foundation. This material is sent to parents and educational centers in different modules adapted to parents, students, and teachers. The same modules include different activities and topics each year according to the children's growth. The sending strategy follows a protocol, and it will be maintained until the end of the study, through email or regular delivery, as the receiver may prefer.

Moreover, some workshops are being carried out and are summarized in Table 4.

Educational components of GENYAL study.

Format Module Objective
Children Notebook activities Food and nutrients Essential nutrients: definition, function and sources
The food pyramid To identify the different food categories and explore which foods are in each group
Workshops The food pyramid To draw a food pyramid and to create healthy dishes with the different food categories
Parents Nutritional guide Introduction To know the bases and objectives of the study
Food and nutrients Essential nutrients: definition, function and sources
The food pyramid To identify the different food categories and which foods are in each group
Workshops Preliminary results To know about the preliminary results of the study and the recommendations derived from them
Teachers Nutritional guide Introduction To know the bases and objectives of the study
Food and nutrients Essential nutrients: definition, function and sources
The food pyramid To identify the different food categories and which foods are in each group
Workshops Preliminary results To know about the preliminary results of the study and the recommendations derived from them

The validation of this tool is expected to be carried out through the impact generated over the years of the study, measured as the evolution of anthropometric variables and the dietary habits, between control and intervention schools. Moreover, parents and teachers along the study will evaluate all the material.

 Statistical Analysis

Descriptive analyses of the baseline data were performed by computing for the categorical variables the class's absolute and relative frequencies, and for the quantitative variables the mean, median, standard deviation, interquartile range, maximum and minimum. To check the homogeneity of the two groups in the case of quantitative variables, t-tests were used for normally-distributed variables, or Mann-Whitney U-test as non-parametric alternative. In the case of categorical variables, Chi-Square or Fischer exact tests were used. The association between anthropometric and dietary, social, health and SNP variables were performed by linear or logistic regressions. The Bonferroni correction was applied for multiple tests. In addition, for the SNPs variables, the Hardy-Weinberg equilibrium condition was tested by means of Chi-Square tests. All analyses were conducted with R Statistical Software version 3.41. Statistical tests used a 0.05 significance level, in two-tailed tests.

Regarding Machine Learning models, they will be derived to predict the BMI from all the analyzed variables after the 5-year follow-up. Both classification (after dichotomization of the BMI) and regression models will be considered, and Random Forest will be applied. It has been observed that the use of Random Forest improves the predictive model's performance, creating a more effective predictive model than the one that could be obtained using decision tree or logistic regression techniques (13). The predictive power of the models will be evaluated and internal cross-validation and external validations with external datasets will be implemented. Variable importance analyses will be performed in order to quantify the relative weights of the different variables in the prediction of BMI. The model will be iteratively improved by refitting with new data along with the successive yearly evaluations during the study.

 Results

Parents of 224 children (116 girls and 105 boys) accepted to participate in the study and signed the informed consent. It shows a collaboration rate of 39%. Finally, 221 children were evaluated, since three did not attend the initial evaluation (Figure 2).

Volunteers flow chart and final sample.

Among the total number of students enrolled in the study, 115 belonged to intervention schools while 106 were in control schools. Tables 5, 6 show the basal main characteristics of the study sample according to the corresponding control or intervention group.

Main socio-demographic and anthropometric characteristics of the schoolchildren by groups (intervention schools vs. control schools).

Intervention group(n = 115) Control group(n = 106) p
Socio-Demographic
Spanish (%)
– No 1.90 3.00 0.678
– Yes 98.10 97.00
Spanish mother (%)
– No 35.00 23.50 0.074
– Yes 65.00 76.50
Anthropometry
Age (years) 6.73 ± 0.76 6.77 ± 0.69 0.505
Sex (%)
– Girls 52.20 42.50 0.148
– Boys 47.80 57.50
Fat mass (%) 21.23 ± 7.56 19.84 ± 6.71 0.159
Muscle mass (%) 27.97 ± 3.00 28.01 ± 2.97 0.924
BMI (kg/m2) 17.21 ± 2.99 16.68 ± 2.24 0.319

Main dietary and physical activity characteristics of the schoolchildren by groups (intervention schools vs. control schools).

Intervention group(n = 115) Control group(n = 106) p
Physical activity
Physical activity rate 1.61 ± 0.11 1.55 ± 0.09 <0.001
Number of hours dedicated to moderate and vigorous activities 3.53 ± 1.72 3.96 ± 1.89 0.068
Diet
Total caloric value (kilojoules): 7,695.97 ± 1,962.52 7,678.59 ± 1,463.50 0.378
– Carbohydrates % 45.00 ± 5.15 43.94 ± 5.43 0.154
– Lipids % 38.40 ± 4.91 39.52 ± 5.09 0.116
– Proteins % 16.57 ± 2.25 16.52 ± 2.09 0.971
Total fiber (g) 18.54 ± 6.37 17.78 ± 5.19 0.481
KIDMED score 6.33 ± 1.98 6.67 ± 1.82 0.251

According to the preliminary results obtained after the first year of the evaluation, 32.2% of the students presented EW, taking into account the WHO criteria. These figures were higher when IOFT standard (25.4%) or the national criteria of the Orbegozo Foundation (19.0%) were applied.

A significative association between the nutritional state of children and mother BMI [β = 0.21 (0.13–0.3), p (adj) <0.001], geographical location of the school [OR = 2.74 (1.24–6.22), p (adj) = 0.06], dairy servings per day [OR = 0.48 (0.29–0.75), p (adj) = 0.05] and eight of the total SNPs studied [rs1260326, rs780094, rs10913469, rs328, rs7647305, rs3101336, rs2568958, rs925946; p (not adj) <0.05] were found (data adjusted for sex and age).

 Discussion

The GENYAL study to prevent child obesity is, to our knowledge, the first interventional trial in Spanish schoolchildren aiming to provide preventive and therapeutic approach based on a high degree of evidence for early obesity through machine learning.

The baseline data and the associations observed after the first analysis support the evidence that environmental and genetic factors play a role in the development of childhood obesity.

According with the results, for each point in the BMI of the mother, the BMI of the child was increased 0.21 kg/m2. It shows that among the multiple risk factors for the development of obesity in children, parental obesity is one of the most impactful as a result of both genetic and environmental interactions. Children imitate their parents, therefore, the parents' dietary habits and PA are more likely to be reproduced by their descendants (130).

With reference to the location of the school, that represent the socioeconomic level, presented a close relationship with the presence of EW, with a decreasing distribution of risk from south to north area. These results are consistent with other studies that have shown how the socioeconomic status of the school correlates with the prevalence of overweight and obesity as it increases the likelihood that schoolchildren will follow a diet rich in energy-dense, low-cost foods, as well as fewer opportunities to practice sport (131).

Regarding dietary aspects, dairy servings per day showed a protective effect against EW. It could be related to several factors such as if this food is a source of calcium, peptides, bioactive compounds, etc. They have been studied due to their relationship in the appetite control and other mechanisms involved in controlling weight (132, 133). These results highlight the important role that this group of foods could have for the prevention of weight overload.

The study of these factors in the child population and the social context of Madrid is minimal, having found a single study with similar sociodemographic characteristics (134). Nevertheless, no intervention was performed in this study, nor was genetic data collection, thus the GENYAL project is shown as a novel study in this regard.

In this study, a significant association (data adjusted for sex and age) between the nutritional status of schoolchildren and 8 SNPs was found (rs1260326, rs780094, rs10913469, rs328, rs7647305, rs3101336, rs2568958, rs925946). In previous studies, these polymorphisms have been associated with adiposity traits and their related comorbidities (Table 2). Genetic factors play an essential role in the development of obesity (135). Thus, knowing which ones are associated with excess weight early in life could contribute to obesity early detection.

Conversely, it is important to note that current knowledge is insufficient to determine the relative importance of these different factors. Therefore, new techniques are needed to be used as predictive instruments (12). Currently, machine learning is considered an extremely valuable tool in the medical field, since it is capable of providing diagnostic and early detection strategies for diseases through the analysis of large datasets (136). Prevention plays a crucial role in controlling the high obesity prevalence, so machine learning techniques have already been used for the prediction of the BMI in children (137). However, the current predictive model would be the first to include obesity-related SNPs as genetic information, as well as anthropometric, social and lifestyle variables.

Regarding the last results from the Commission on Ending Childhood Obesity, the implementation of integral programs that promote healthy environment in schools is recommended with the objective of ensuring that children grow well and develop healthy habits (138). Nevertheless, although Spain is one of the countries where more intervention studies to prevent obesity have been developed (139), the politic strategies to prevent chronic illnesses such as overweight and obesity are not defined, and even show very low evidence of efficiency, according to the last data revised by the Cochrane Database (140).

According to the latest scientific research, the intervention studies in schools which include family and community spheres, implementing actions to promote healthy food and physical activity, are the most effective (5, 46). This study has been designed to elaborate strategies and to work as a multidisciplinary team reinforcing the educational sphere of the participating children and their environment, school, and family.

One of the strong points of our study consists in the implementation, and subsequent validation, of educational tools for students, their parents and teachers, applying a nutritional education method that promotes healthy dietetic habits and physical exercise, both in schools and outside. The importance of the validation of these educational strategies lay on a large number of studies with contradictory results, which might be partly explained by the fact that many researches may have lacked statistical power to detect changes in the results of interest related to adiposity or children's nutritional status (5). In the present study, the educational approaches to the intervention schools will be held for 5 years, in line with the annual anthropometric assessments. Their utility will be evaluated taking into account the evolution of the anthropometric and dietetic results annually collected. Moreover, the presence or absence of the educational program (control and intervention schools) will be included as an input dichotomous variable in the predictive model, evaluating its influence on the predicted BMI. This will allow us to detect differences in the body composition between intervention and control schools, enabling us to assess the impact of educational support.

Another strength of the present study is the selection of 26 SNPs for early prevention of obesity, by making an extensive bibliographic research, as shown in Table 2. The nutritional genomic tools would be very useful in the research and prevention of obesity, and they would be an important support in public health applications. Obesity is a multifactorial illness, where the genetic variants involved are dispersed along the whole genome. Although SNPs have been cataloged as the best indicators to predict obesity risk (141), several studies suggest that much remains to be discovered. There is a lot of interest in predicting the appearance of chronic diseases at an early age (142). According to the last review about precision nutrition (143), the creation of a genetic risk score may let us determine the risk of developing obesity or other chronic pathologies related to the individual genetic component, and even be able to predict the expected weight gain as a consequence of exposure to different variables, such as specific diets.

On the other hand, we consider that the sample size used is one of the weak aspects in our study, pointing to the necessity to include new schools to increase the number of children. Nevertheless, from this first phase of the study, we expect to calculate the sample size needed to increase the statistical power and, consequently, to find solid associations between the studied variables. Furthermore, the classification of schools according to the area and the socioeconomic level widens the scope of the research, making it more representative of the city of Madrid. Similarly, the use of dietary and physical activity questionnaires may lead to reporting bias, but in the absence of better tools with low cost and high throughput, these records can offer valuable information, although it should be interpreted with caution.

After 5 years of follow-up, the GENYAL study aims to validate the machine learning predictive model that considers environmental and genetic factors in the obesity development, as well as the educational tools, to obtain new and potentially valuable data to increase our knowledge of the precipitants of childhood obesity and their relative importance to design preventive protocols at an early age based on machine learning models. With a view to the future perspective of continuity of this study, in addition to increasing the sample size to validate the results obtained, the possibility of implementing personalized nutritional education interventions is proposed to improve adherence and efficacy by applying the novel concepts provided by studies on precision nutrition.

 Data Availability Statement

The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author/s.

 Ethics Statement

The studies involving human participants were reviewed and approved by Ethics Committee of Fundación IMDEA-Food. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.

 Author Contributions

VL-K was the principal investigator and responsible for the study and protocol design. HM-P helped designing the protocol and drafting the manuscript. HM-P, EA-A, RI, and IE-S were responsible for data collection. GC conducted statistical analysis of the data. SM contributed to genetic samples management. JM, GR, and AR supervised the final compilation of the manuscript and provided scientific advice and consultation. All authors read and approved the final manuscript.

 Funding

This study was supported by Conserjería de Educación, Universidades y Ciencia de la Comunidad de Madrid, Dirección General de Educación Infantil, Primaria y Secundaria.

 Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

The authors would like to acknowledge the help of all the teachers and head teachers of Juan Zaragüeta, Fernando el Católico, Fernández Moratín, La Rioja, Concepción Arenal y Rosa Luxemburgo schools; and, especially, the families and children who participated in the study. We would like to thank all the IMDEA-Food students for their contribution in the nutritional education programme development.

 References Güngör NK. Overweight and obesity in children and adolescents. J Clin Res Pediatr Endocrinol. (2014) 6:129–43. 10.4274/jcrpe.147125241606 WHO. Facts and Figures on Childhood Obesity. Available online at: http://www.who.int/end-childhood-obesity/facts/en/ (accessed December 22, 2019). Agencia Española de Consumo, Seguridad Alimentaria y Nutrición. Estudio ALADINO 2015: Estudio de Vigilancia del Crecimiento, Alimentación, Actividad Física, Desarrollo Infantil y Obesidad en España 2015. Madrid: Ministerio de Sanidad, Servicios Sociales e Igualdad (2016) Kolotkin RL Andersen JR. A systematic review of reviews: exploring the relationship between obesity, weight loss and health-related quality of life. Clin Obes. (2017) 7:273–89. 10.1111/cob.1220328695722 Bleich SN Vercammen KA Zatz LY Frelier JM Ebbeling CB Peeters A. Interventions to prevent global childhood overweight and obesity: a systematic review. Lancet Diabetes Endocrinol. (2017) 4:332–46. 10.1016/S2213-8587(17)30358-329066096 Genoni G Menegon V Secco GG Sonzini M Martelli M Castagno M . Insulin resistance, serum uric acid and metabolic syndrome are linked to cardiovascular dysfunction in pediatric obesity. Int J Cardiol. (2017) 249:366–71. 10.1016/j.ijcard.2017.09.03128935462 Matwiejczyk L Mehta K Scott J Tonkin E Coveney J. Characteristics of effective interventions promoting healthy eating for pre-schoolers in childcare settings: an umbrella review. Nutrients. (2018) 10:293. 10.3390/nu1003029329494537 Iguacel I Fernández-Alvira JM Labayen I Moreno LA Samper MP Rodríguez G. CALINA study. Social vulnerabilities as determinants of overweight in 2-, 4- and 6-year-old Spanish children. Eur J Public Health. (2017) 28:289–95. 10.1093/eurpub/ckx09529020368 Paparo L di Costanzo M di Scala C Cosenza L Leone L Nocerino R . The influence of early life nutrition on epigenetic regulatory mechanisms of the immune system. Nutrients. (2014) 6:4706–19. 10.3390/nu611470625353665 Kumar S Kelly AS. Review of childhood obesity: from epidemiology, etiology, and comorbidities to clinical assessment and treatment. Mayo Clin Proc. (2017) 92:251–65. 10.1016/j.mayocp.2016.09.01728065514 Kostovski M Tasic V Laban N Polenakovic M Danilovski D Gucev Z. Obesity in childhood and adolescence, genetic factors. PRILOZI. (2017) 38:121–33. 10.2478/prilozi-2018-001329668472 Qasim A Turcotte M Souza RJ de Samaan MC Champredon D Dushoff J . On the origin of obesity: identifying the biological, environmental and cultural drivers of genetic risk among human populations. Obes Rev. (2018) 19:121–49. 10.1111/obr.1262529144594 DeGregory KW Kuiper P DeSilvio T Pleuss JD Miller R Roginski JW . A review of machine learning in obesity. Obes Rev. (2018) 19:668–85. 10.1111/obr.1266729426065 Renta neta media de los hogares (Urban Audit) - Ayuntamiento de Madrid. Available online at: http://www.madrid.es/portales/munimadrid/es/Inicio/El-Ayuntamiento/Estadistica/Areas-de-informacion-estadistica/Economia/Renta/Renta-neta-media-de-los-hogares-Urban-Audit-?vgnextfmt=default&vgnextoid=65e0c19a1666a510VgnVCM1000001d4a900aRCRD&vgnextchannel=ef863636b44b4210VgnVCM2000000c205a0aRCRD (accessed December 22, 2019). Suárez-Sánchez F Klunder-Klunder M Valladares-Salgado A Gómez-Zamudio J Peralta-Romero J Meyre D . APOA5 and APOA1 polymorphisms are associated with triglyceride levels in Mexican children. Pediatr Obes. (2017) 12:330–6. 10.1111/ijpo.1214727171122 Zhu W-F Wang C-L Liang L Shen Z Fu J-F Liu P-N . Triglyceride-raising APOA5 genetic variants are associated with obesity and non-HDL-C in Chinese children and adolescents. Lipids Health Dis. (2014) 13:93. 10.1186/1476-511X-13-9324903888 Au A Griffiths LR Irene L Kooi CW Wei LK. The impact of APOA5, APOB, APOC3 and ABCA1 gene polymorphisms on ischemic stroke: evidence from a meta-analysis. Atherosclerosis. (2017) 265:60–70. 10.1016/j.atherosclerosis.2017.08.00328865324 Ye H Zhou A Hong Q Tang L Xu X Xin Y . Positive association between APOA5 rs662799 polymorphism and coronary heart disease: a case-control study and meta-analysis. PLoS ONE. (2015) 10:e0135683. 10.1371/journal.pone.013568326309253 Xu C Bai R Zhang D Li Z Zhu H Lai M . Effects of APOA5−1131T>C (rs662799) on fasting plasma lipids and risk of metabolic syndrome: evidence from a case-control study in China and a meta-analysis. PLoS ONE. (2013) 8:e56216. 10.1371/journal.pone.005621623468858 Elks CE Loos RJF Sharp SJ Langenberg C Ring SM Timpson NJ . Genetic markers of adult obesity risk are associated with greater early infancy weight gain and growth. PLoS Med. (2010) 7:e1000284. 10.1371/journal.pmed.100028420520848 Dušátková L Zamrazilová H Aldhoon-Hainerová I Sedláčková B Včelák J Hlavatý P . common variant near BDNF is associated with dietary calcium intake in adolescents. Nutr Res. (2015) 35:766–73. 10.1016/j.nutres.2015.06.00426162542 Dušátková L Zamrazilová H Sedláčková B Včelák J Hlavatý P Aldhoon Hainerová I . Association of obesity susceptibility gene variants with metabolic syndrome and related traits in 1,443 Czech adolescents. Folia Biol. (2013) 59:123–33.23890480 Kvaløy K Kulle B Romundstad P Holmen TL. Sex-specific effects of weight-affecting gene variants in a life course perspective–The HUNT Study, Norway. Int J Obes. (2013) 37:1221–9. 10.1038/ijo.2012.22023318717 Sandholt CH Vestmar MA Bille DS Borglykke A Almind K Hansen L . Studies of metabolic phenotypic correlates of 15 obesity associated gene variants. PLoS ONE. (2011) 6:e23531. 10.1371/journal.pone.002353121912638 Akbarian S-A Salehi-Abargouei A Pourmasoumi M Kelishadi R Nikpour P Heidari-Beni M. Association of Brain-derived neurotrophic factor gene polymorphisms with body mass index: a systematic review and meta-analysis. Adv Med Sci. (2017) 63:43–56. 10.1016/j.advms.2017.07.00228818748 Lv D Zhou D Zhang Y Zhang S Zhu Y-M. Two obesity susceptibility loci in LYPLAL1 and ETV5 independently associated with childhood hypertension in Chinese population. Gene. (2017) 627:284–9. 10.1016/j.gene.2017.06.03028645872 Elks CE Perry JRB Sulem P Chasman DI Franceschini N He C . Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies. Nat Genet. (2010) 42:1077–85. 10.1038/ng.71421102462 Zhao J Bradfield JP Li M Wang K Zhang H Kim CE Annaiah K . The role of obesity-associated loci identified in genome-wide association studies in the determination of pediatric BMI. Obesity. (2009) 17:2254–7. 10.1038/oby.2009.15919478790 Grant SFA Li M Bradfield JP Kim CE Annaiah K Santa E . Association analysis of the FTO gene with obesity in children of Caucasian and African ancestry reveals a common tagging SNP. PLoS ONE. (2008) 3:e1746. 10.1371/journal.pone.000174618335027 Mitchell JA Hakonarson H Rebbeck TR Grant SFA. Obesity-susceptibility loci and the tails of the pediatric BMI distribution. Obesity. (2013) 21:1256–60. 10.1002/oby.2031923408508 Qureshi SA Mumtaz A Shahid SU Shabana NA. rs3751812, a common variant in fat mass and obesity-associated (FTO) gene, is associated with serum high- and low-density lipoprotein cholesterol in Pakistani individuals. Nutrition. (2017) 39–40:92–5. 10.1016/j.nut.2016.04.00827324062 Wang K Li W-D Zhang CK Wang Z Glessner JT Grant SFA . Genome-wide association study on obesity and obesity-related traits. PLoS ONE. (2011) 6:e18939. 10.1371/journal.pone.001893933640203 Namjou B Keddache M Marsolo K Wagner M Lingren T Cobb B . EMR-linked GWAS study: investigation of variation landscape of loci for body mass index in children. Front Genet. (2013) 4:268. 10.3389/fgene.2013.0026824348519 Dwivedi OP Tabassum R Chauhan G Ghosh S Marwaha RK Tandon N . Common variants of FTO are associated with childhood obesity in a cross-sectional study of 3,126 urban Indian children. PLoS ONE. (2012) 7:e47772. 10.1371/journal.pone.004777223091647 Mei H Chen W Jiang F He J Srinivasan S Smith EN . Longitudinal replication studies of GWAS risk SNPs influencing body mass index over the course of childhood and adulthood. PLoS ONE. (2012) 7:e31470. 10.1371/journal.pone.003147022355368 Yang Y Liu B Xia W Yan J Liu H-Y Hu L Liu S-M. FTO Genotype and Type 2 diabetes mellitus: spatial analysis and meta-analysis of 62 case-control studies from different regions. Genes. (2017) 8:70. 10.3390/genes802007028208657 Hunt SC Stone S Xin Y Scherer CA Magness CL Iadonato SP . Association of the FTO gene with BMI. Obesity. (2008) 16:902–4. 10.1038/oby.2007.12618239580 Quan L-L Wang H Tian Y Mu X Zhang Y Tao K. Association of fat-mass and obesity-associated gene FTO rs9939609 polymorphism with the risk of obesity among children and adolescents: a meta-analysis. Eur Rev Med Pharmacol Sci. (2015) 19:614–23.25753879 Liu C Mou S Cai Y. FTO gene variant and risk of overweight and obesity among children and adolescents: a systematic review and meta-analysis. PLoS ONE. (2013) 8:e82133. 10.1371/journal.pone.008213324278475 Xi B Takeuchi F Meirhaeghe A Kato N Chambers JC Morris AP . Associations of genetic variants in/near body mass index-associated genes with type 2 diabetes: a systematic meta-analysis. Clin Endocrinol. (2014) 81:702–10. 10.1111/cen.1242824528214 Willer CJ Speliotes EK Loos RJF Li S Lindgren CM Heid IM Berndt SI . Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet. (2009) 41:25–34. 10.1038/ng.28719079261 Lee H-J Jang HB Kim H-J Ahn Y Hong K-W Cho SB . The dietary monounsaturated to saturated fatty acid ratio modulates the genetic effects of GCKR on serum lipid levels in children. Clin Chim Acta. (2015) 450:155–61. 10.1016/j.cca.2015.08.01226291577 Horvatovich K Bokor S Polgar N Kisfali P Hadarits F Jaromi L . Functional glucokinase regulator gene variants have inverse effects on triglyceride and glucose levels, and decrease the risk of obesity in children. Diabetes Metab. (2011) 37:432–9. 10.1016/j.diabet.2011.02.00321511510 Santoro N Zhang CK Zhao H Pakstis AJ Kim G Kursawe R . Variant in the glucokinase regulatory protein (GCKR) gene is associated with fatty liver in obese children and adolescents. Hepatology. (2012) 55:781–9. 10.1002/hep.2480622105854 Alfred T Ben-Shlomo Y Cooper R Hardy R Deary IJ Elliott J . Associations between a polymorphism in the pleiotropic GCKR and Age-related phenotypes: the HALCyon programme. PLoS ONE. (2013) 8:e70045. 10.1371/journal.pone.007004523894584 Wang Y Cai L Wu Y Wilson RF Weston C Fawole O . What childhood obesity prevention programmes work? A systematic review and meta-analysis. Obes Rev. (2015) 16:547–65. 10.1111/obr.1227725893796 Chang H-W Lin F-H Li P-F Huang C-L Chu N-F Su S-C . Association between a glucokinase regulator genetic variant and metabolic syndrome in taiwanese adolescents. Genet Test Mol Biomark. (2016) 20:137–42. 10.1089/gtmb.2015.024126799416 Zain SM Mohamed Z Mohamed R. Common variant in the glucokinase regulatory gene rs780094 and risk of nonalcoholic fatty liver disease: a meta-analysis. J Gastroenterol Hepatol. (2015) 30:21–7. 10.1111/jgh.1271425167786 Fesinmeyer MD Meigs JB North KE Schumacher FR BuŽková P Franceschini N . Genetic variants associated with fasting glucose and insulin concentrations in an ethnically diverse population: results from the Population Architecture using Genomics and Epidemiology (PAGE) study. BMC Med Genet. (2013) 14:98. 10.1186/1471-2350-14-9824063630 Li H Xu R Peng X Wang Y Wang T. Association of glucokinase regulatory protein polymorphism with type 2 diabetes and fasting plasma glucose: a meta-analysis. Mol Biol Rep. (2013) 40:3935–42. 10.1007/s11033-012-2470-623307301 Xi B Cheng H Shen Y Chandak GR Zhao X Hou D . Study of 11 BMI-associated loci identified in GWAS for associations with central obesity in the Chinese children. PLoS ONE. (2013) 8:e56472. 10.1371/journal.pone.005647223424664 Beyerlein A von Kries R Ness AR Ong KK. Genetic markers of obesity risk: stronger associations with body composition in overweight compared to normal-weight children. PLoS ONE. (2011) 6:e19057. 10.1371/journal.pone.001905721526213 Li S Zhao JH Luan J Ekelund U Luben RN Khaw K-T . Physical activity attenuates the genetic predisposition to obesity in 20,000 men and women from EPIC-Norfolk prospective population study. PLoS Med. (2010) 7:e1000332. 10.1371/journal.pmed.100033220824172 Queiroz EM Cândido APC Castro IM Bastos AQA Machado-Coelho GLL Freitas RN. IGF2, LEPR, POMC, PPARG, and PPARGC1 gene variants are associated with obesity-related risk phenotypes in Brazilian children and adolescents. Braz J Med Biol Res. (2015) 48:595–602. 10.1590/1414-431x2015415525923461 Zandoná MR Rodrigues RO Albiero G Campagnolo PDB Vitolo MR Almeida S . Polymorphisms in LEPR, PPARG and APM1 genes: associations with energy intake and metabolic traits in young children. Arq Bras Endocrinol Metabol. (2013) 57:603–11. 10.1590/S0004-2730201300080000424343628 Yang MM Wang J Fan JJ Ng TK Sun DJ Guo X . Variations in the obesity gene “LEPR” contribute to risk of type 2 diabetes mellitus: evidence from a meta-analysis. J Diabetes Res. (2016) 2016:5412084. 10.1155/2016/541208427195302 Mahmoudi T Farahani H Nobakht H Dabiri R Zali MR. Genetic variations inLeptin andLeptin receptor and susceptibility to colorectal cancer and obesity. Iran J Cancer Prev. (2016) 9:e7013. 10.17795/ijcp-701327703650 Domínguez-Reyes T Astudillo-López CC Salgado-Goytia L Muñoz-Valle JF Salgado-Bernabé AB Guzmán-Guzmán IP . Interaction of dietary fat intake with APOA2, APOA5 and LEPR polymorphisms and its relationship with obesity and dyslipidemia in young subjects. Lipids Health Dis. (2015) 14:106. 10.1186/s12944-015-0112-426365669 Askari G Heidari-Beni M Mansourian M Esmaeil-Motlagh M Kelishadi R. Interaction of lipoprotein lipase polymorphisms with body mass index and birth weight to modulate lipid profiles in children and adolescents: the CASPIAN-III Study. São Paulo Med J. (2016) 134:121–9. 10.1590/1516-3180.2015.0079260826786614 Emamian M Avan A Pasdar A Mirhafez SR Sadeghzadeh M Moghadam MS . The lipoprotein lipase S447X and cholesteryl ester transfer protein rs5882 polymorphisms and their relationship with lipid profile in human serum of obese individuals. Gene. (2015) 558:195–9. 10.1016/j.gene.2014.12.07025579610 White MJ Eren F Agirbaşli D Chen J Hu T Moore JH . Systems genetics approach to dyslipidemia in children and adolescents. OMICS. (2015) 19:248–59. 10.1089/omi.2014.014025671407 Legry V Bokor S Beghin L Galfo M Gonzalez-Gross M Molnar D . Associations between common genetic polymorphisms in the liver X receptor alpha and its target genes with the serum HDL-cholesterol concentration in adolescents of the HELENA Study. Atherosclerosis. (2011) 216:166–9. 10.1016/j.atherosclerosis.2011.01.03121316679 Sagoo GS Tatt I Salanti G Butterworth AS Sarwar N van Maarle M . Seven lipoprotein lipase gene polymorphisms, lipid fractions, and coronary disease: a HuGE association review and meta-analysis. Am J Epidemiol. (2008) 168:1233–46. 10.1093/aje/kwn23518922999 Xi B Takeuchi F Chandak GR Kato N Pan HW AGEN-T2D Consortium . Common polymorphism near the MC4R gene is associated with type 2 diabetes: data from a meta-analysis of 123,373 individuals. Diabetologia. (2012) 55:2660–6. 10.1007/s00125-012-2655-522869321 den Hoed M Ekelund U Brage S Grontved A Zhao JH Sharp SJ . Genetic susceptibility to obesity and related traits in childhood and adolescence: influence of loci identified by genome-wide association studies. Diabetes. (2010) 59:2980–8. 10.2337/db10-037020724581 Xi B Chandak GR Shen Y Wang Q Zhou D. Association between common polymorphism near the MC4R gene and obesity risk: a systematic review and meta-analysis. PLoS ONE. (2012) 7:e45731. 10.1371/journal.pone.004573123049848 Loos RJF Lindgren CM Li S Wheeler E Zhao JH Prokopenko I Inouye M . Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat Genet. (2008) 40:768–75. 10.1038/ng.14018454148 Qi L Kraft P Hunter DJ Hu FB. The common obesity variant near MC4R gene is associated with higher intakes of total energy and dietary fat, weight change and diabetes risk in women. Hum Mol Genet. (2008) 17:3502–8. 10.1093/hmg/ddn24218697794 Lauria F Siani A Picó C Ahrens W Bammann K De Henauw S . A Common variant and the transcript levels of MC4R gene are associated with adiposity in children: the IDEFICS Study. J Clin Endocrinol Metab. (2016) 101:4229–36. 10.1210/jc.2016-199227583473 Hester JM Wing MR Li J Palmer ND Xu J Hicks PJ Roh BH . Implication of European-derived adiposity loci in African Americans. Int J Obes. (2012) 36:465–73. 10.1038/ijo.2011.13121750520 Wheeler E Huang N Bochukova EG Keogh JM Lindsay S Garg S . Genome-wide SNP and CNV analysis identifies common and low-frequency variants associated with severe early-onset obesity. Nat Genet. (2013) 45:513–7. 10.1038/ng.260723563609 Cheung CYY Tso AWK Cheung BMY Xu A Ong KL Fong CHY . Obesity susceptibility genetic variants identified from recent genome-wide association studies: implications in a chinese population. J Clin Endocrinol Metab. (2010) 95:1395–403. 10.1210/jc.2009-146520061430 Zain SM Mohamed Z Jalaludin MY Fauzi F Hamidi A Zaharan NL. Comprehensive evaluation of the neuropeptide-Y gene variants in the risk of obesity: a case-control study and meta-analysis. Pharmacogenet Genomics. (2015) 25:501–10. 10.1097/FPC.000000000000016426240981 Olza J Gil-Campos M Leis R Rupérez AI Tojo R Cañete R . Influence of variants in the NPY gene on obesity and metabolic syndrome features in Spanish children. Peptides. (2013) 45:22–7. 10.1016/j.peptides.2013.04.00723624317 Hohmann S Buchmann AF Witt SH Rietschel M Jennen-Steinmetz C Schmidt MH . Increasing association between a neuropeptide Y promoter polymorphism and body mass index during the course of development. Pediatr Obes. (2012) 7:453–60. 10.1111/j.2047-6310.2012.00069.x22941950 Muñoz-Yáñez C Pérez-Morales R Moreno-Macías H Calleros-Rincón E Ballesteros G González RA . Polymorphisms FTO rs9939609, PPARG rs1801282 and ADIPOQ rs4632532 and rs182052 but not lifestyle are associated with obesity related-traits in Mexican children. Genet Mol Biol. (2016) 39:547–53. 10.1590/1678-4685-gmb-2015-026727560839 Stryjecki C Peralta-Romero J Alyass A Karam-Araujo R Suarez F Gomez-Zamudio J . Association between PPAR-γ2 Pro12Ala genotype and insulin resistance is modified by circulating lipids in Mexican children. Sci Rep. (2016) 6:24472. 10.1038/srep2447227075119 Dedoussis GV Vidra N Butler J Papoutsakis C Yannakoulia M Hirschhorn JN . Peroxisome proliferator-activated receptor-gamma (PPARgamma) Pro12Ala polymorphism and risk for pediatric obesity. Clin Chem Lab Med. (2009) 47:1047–50. 10.1515/CCLM.2009.24219634985 Li Y Zhu J Ding JQ. Association of the PPARγ2 Pro12Ala polymorphism with increased risk of cardiovascular diseases. Genet Mol Res. (2015) 14:18662–74. 10.4238/2015.December.28.1526782516 Gouda HN Sagoo GS Harding A-H Yates J Sandhu MS Higgins JPT. The association between the peroxisome proliferator-activated receptor-gamma2 (PPARG2) Pro12Ala gene variant and type 2 diabetes mellitus: a HuGE review and meta-analysis. Am J Epidemiol. (2010) 171:645–55. 10.1093/aje/kwp45020179158 Galbete C Toledo E Martínez-González MA Martínez JA Guillén-Grima F Marti A. Pro12Ala variant of the PPARG2 gene increases body mass index: an updated meta-analysis encompassing 49,092 subjects. Obesity. (2013) 21:1486–95. 10.1002/oby.2015023666678 Bordoni L Marchegiani F Piangerelli M Napolioni V Gabbianelli R. Obesity-related genetic polymorphisms and adiposity indices in a young Italian population. IUBMB Life. (2017) 69:98–105. 10.1002/iub.159628090739 Hotta K Nakamura M Nakamura T Matsuo T Nakata Y Kamohara S . Association between obesity and polymorphisms in SEC16B, TMEM18, GNPDA2, BDNF, FAIM2 and MC4R in a Japanese population. J Hum Genet. (2009) 54:727–31. 10.1038/jhg.2009.10619851340 Xi B Shen Y Reilly KH Zhao X Cheng H Hou D . Sex-dependent associations of genetic variants identified by GWAS with indices of adiposity and obesity risk in a Chinese children population. Clin Endocrinol. (2013) 79:523–8. 10.1111/cen.1209123121087 Cropano C Santoro N Groop L Dalla Man C Cobelli C Galderisi A . The rs7903146 variant in theTCF7L2Gene increases the risk of prediabetes/type 2 diabetes in obese adolescents by impairing β-cell function and hepatic insulin sensitivity. Diabetes Care. (2017) 40:1082–9. 10.2337/dc17-029028611053 Locke AE Kahali B Berndt SI Justice AE Pers TH Day FR . Genetic studies of body mass index yield new insights for obesity biology. Nature. (2015) 518:197–206. 10.1038/nature1417725673413 Reinehr T Friedel S Mueller TD Toschke AM Hebebrand J Hinney A. Evidence for an influence of TCF7L2 polymorphism rs7903146 on insulin resistance and sensitivity indices in overweight children and adolescents during a lifestyle intervention. Int J Obes. (2008) 32:1521–4. 10.1038/ijo.2008.14618762805 Körner A Berndt J Stumvoll M Kiess W Kovacs P. TCF7L2 gene polymorphisms confer an increased risk for early impairment of glucose metabolism and increased height in obese children. J Clin Endocrinol Metab. (2007) 92:1956–60. 10.1210/jc.2006-251417311858 Verma SS Lucas AM Lavage DR Leader JB Metpally R Krishnamurthy S . Identifying genetic associations with variability in metabolic health and blood count laboratory values: diving into the quantitative traits by leveraging longitudinal data from an EHR. Pac Symp Biocomput. (2017) 22:533–44. 10.1142/9789813207813_004927897004 Povel CM Boer JMA Reiling E Feskens EJM. Genetic variants and the metabolic syndrome: a systematic review. Obes Rev. (2011) 12:952–67. 10.1111/j.1467-789X.2011.00907.x26621002 Guan Y Yan LH Liu XY Zhu XY Wang SZ Chen LM. Correlation of the TCF7L2 (rs7903146) polymorphism with an enhanced risk of type 2 diabetes mellitus: a meta-analysis. Genet Mol Res. (2016) 15:gmr.15037969. 10.4238/gmr.1503796927706609 Wang S Song K Srivastava R Fathzadeh M Li N Mani A. The protective effect of transcription factor 7-like 2 risk allele rs7903146 against elevated fasting plasma triglyceride in type 2 diabetes: a meta-analysis. J Diabetes Res. (2015) 2015:468627. 10.1155/2015/46862726576435 Chang S Wang Z Wu L Lu X Shangguan S Xin Y . Association between TCF7L2 polymorphisms and gestational diabetes mellitus: a meta-analysis. J Diabetes Investig. (2017) 8:560–70. 10.1111/jdi.1261228002648 Speliotes EK Willer CJ Berndt SI Monda KL Thorleifsson G Jackson AU . Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. (2010) 42:937–48. 10.1038/ng.68620935630 Graff M Scott RA Justice AE Young KL Feitosa MF Barata L . Correction: Genome-wide physical activity interactions in adiposity - A meta-analysis of 200,452 adults. PLoS Genet. (2017) 13:e1006972. 10.1371/journal.pgen.100697228832619 Prats-Puig A Grau-Cabrera P Riera-Pérez E Cortés-Marina R Fortea E Soriano-Rodríguez P . Variations in the obesity genes FTO, TMEM18 and NRXN3 influence the vulnerability of children to weight gain induced by short sleep duration. Int J Obes. (2013) 37:182–7. 10.1038/ijo.2012.2722391885 Rask-Andersen M Jacobsson JA Moschonis G Chavan RA Sikder MAN Allzén E . Association of TMEM18 variants with BMI and waist circumference in children and correlation of mRNA expression in the PFC with body weight in rats. Eur J Hum Genet. (2012) 20:192–7. 10.1038/ejhg.2011.17621952719 Takeuchi F Yamamoto K Katsuya T Nabika T Sugiyama T Fujioka A . Association of genetic variants for susceptibility to obesity with type 2 diabetes in Japanese individuals. Diabetologia. (2011) 54:1350–9. 10.1007/s00125-011-2086-821369819 Lee H Ash GI Angelopoulos TJ Gordon PM Moyna NM Visich PS . Obesity-related genetic variants and their associations with physical activity. Sports Med Open. (2015) 1:34. 10.1186/s40798-015-0036-626495240 Almén MS Jacobsson JA Shaik JHA Olszewski PK Cedernaes J Alsiö J . The obesity gene, TMEM18, is of ancient origin, found in majority of neuronal cells in all major brain regions and associated with obesity in severely obese children. BMC Med Genet. (2010) 11:58. 10.1186/1471-2350-11-5820380707 Abadi A Peralta-Romero J Suarez F Gomez-Zamudio J Burguete-Garcia AI Cruz M . Assessing the effects of 35 European-derived BMI-associated SNPs in Mexican children. Obesity. (2016) 24:1989–95. 10.1002/oby.2159027486100 Thorleifsson G Walters GB Gudbjartsson DF Steinthorsdottir V Sulem P Helgadottir A . Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet. (2009) 41:18–24. 10.1038/ng.27419079260 Ochoa MC Santos JL Azcona C Moreno-Aliaga MJ Martínez-González MA Martínez JA . Association between obesity and insulin resistance with UCP2-UCP3 gene variants in Spanish children and adolescents. Mol Genet Metab. (2007) 92:351–8. 10.1016/j.ymgme.2007.07.01117870627 Zhang M Wang M Zhao Z-T. Uncoupling protein 2 gene polymorphisms in association with overweight and obesity susceptibility: a meta-analysis. Meta Gene. (2014) 2:143–59. 10.1016/j.mgene.2013.10.00925606398 Brondani LA Assmann TS de Souza BM Bouças AP Canani LH Crispim D. Meta-analysis reveals the association of common variants in the uncoupling protein (UCP) 1-3 genes with body mass index variability. PLoS ONE. (2014) 9:e96411. 10.1371/journal.pone.009641124804925 Dhamrait SS Williams AG Day SH Skipworth J Payne JR World M . Variation in the uncoupling protein 2 and 3 genes and human performance. J Appl Physiol. (2012) 112:1122–7. 10.1152/japplphysiol.00766.201122241057 de Luis DA Aller R Izaola O Romero E. Effect of−55CT Polymorphism of UCP3 on insulin resistance and cardiovascular risk factors after a high protein/low carbohydrate versus a standard hypocaloric diet. Ann Nutr Metab. (2016) 68:157–63. 10.1159/00044415026848765 Gable DR Stephens JW Cooper JA Miller GJ Humphries SE. Variation in the UCP2-UCP3 gene cluster predicts the development of type 2 diabetes in healthy middle-aged men. Diabetes. (2006) 55:1504–11. 10.2337/db05-164516644712 Otabe S Clement K Dina C Pelloux V Guy-Grand B Froguel P . genetic variation in the 5' flanking region of the UCP3 gene is associated with body mass index in humans in interaction with physical activity. Diabetologia. (2000) 43:245–9. 10.1007/s00125005003710753049 Salopuro T Pulkkinen L Lindström J Kolehmainen M Tolppanen A-M Eriksson JG . Variation in the UCP2 and UCP3 genes associates with abdominal obesity and serum lipids: the Finnish Diabetes Prevention Study. BMC Med Genet. (2009) 10:94. 10.1186/1471-2350-10-9419769793 HGNC Database of Human Gene Names. HUGO Gene Nomenclature Committee. Available online at: https://www.genenames.org/ (accessed February 26, 2018). Ortíz H Cuadrado J León K Esteban M Galán I Bravo J . Diseño del estudio ELOIN y prevalencia de sobrepeso y obesidad en la población infantil de 4 años de la Comunidad de Madrid. Madrid: Informe de la Dirección General de Atención Primaria. (2014). Available online at: https://www.comunidad.madrid/sites/default/files/doc/sanidad/epid/diseno_eloin.pdf European Food Safety Authority. General principles for the collection of national food consumption data in the view of a pan-European dietary survey. EFSA J. (2009) 7:51. 10.2903/j.efsa.2009.1435 Ortega R López-Sobaler A Andrés P Requejo A Aparicio A Molinero L. DIAL Software for Assessing Diets and Food Calculations (for Windows, version 3.3.6.0). Madrid: Departamento de Nutrición y Ciencia de los Alimentos (UCM) y Alceingeniería, S.A. (2013). Serra-Majem L Ribas L Ngo J Ortega RM García A Pérez-Rodrigo C . Food, youth and the Mediterranean diet in Spain. Development of KIDMED, Mediterranean Diet Quality Index in children and adolescents. Public Health Nutr. (2004) 7:931–5. 10.1079/PHN200455615482620 Ortega R Requejo A López-Sobaler A. “Modelos de cuestionario de actividad.,” Nutriguía. Manual de nutrición clínica en atención primaria. Madrid: Complutense (2006). 468 p. WHO. Physical Status: The Use and Interpretation of Anthropometry. Available online at: http://www.who.int/childgrowth/publications/physical_status/en/ (accessed December 22, 2019). Lupton JR Brooks J Butte NF Caballero B Flatt JP Fried SK. Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids. Washington, DC: National Academy Press (2002).12449285 OMROM Healthcare. Available online at: https://www.omron-healthcare.es/on/demandware.static/-/Sites-master-catalog/default/dw50f02349/pdfs/ES/IM-HBF-511B-E-ES-10-08-2017.pdf (accessed December 22, 2019). Fernández C Lorenzo H Vrotsou K Aresti U Rica I Sánchez E. Estudio de Crecimiento de Bilbao. Curvas y tablas de crecimiento. Estudio Transversal. Bilbao: Fundación Faustino Orbegozo (2011). Stewart A Marfell-Jones M. International Society for Advancement of Kinanthropometry. International Standards for Anthropometric Assessment. Lower Hutt: International Society for the Advancement of Kinanthropometry (2011). Frisancho AR. New norms of upper limb fat and muscle areas for assessment of nutritional status. Am J Clin Nutr. (1981) 34:2540–5. 10.1093/ajcn/34.11.25406975564 Cole TJ Bellizzi MC Flegal KM Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ. (2000) 320:1240–3. 10.1136/bmj.320.7244.124010797032 WHO. Growth Reference Data for 5-19 Years. Available online at: http://www.who.int/growthref/en/ (accessed December 22, 2019). Mataix Verdú J López Jurado M. “Valoración del estado nutricional,” Nutrición y Alimentación Humana. Barcelona: Ergón (2015). p. 751–800. López-Sobaler A Quintas Herrero M. “Estudio antopométrico” NutriguíaR Manual de Nutrición Clínica. Madrid: Médica Panamericana (2015). p. 153–63.26976238 Panjikkaran ST Kumari K. Augmenting BMI and waist-height ratio for establishing more efficient obesity percentiles among school-going children. Indian J Commun Med. (2009) 34:135–9. 10.4103/0970-0218.5123319714259 Ashwell M. Obesity risk: importance of the waist-to-height ratio. Nurs Stand. (2009) 23:49–54; quiz 55. 10.7748/ns2009.06.23.41.49.c705019591427 de la Cerda F Herrero C. Hipertensión Arterial en Niños y Adolescentes. Asociación Española de Pediatría y Asociación Española de Nefrología Pediátrica. Protoc Diagn Pediatr. (2014) 1:171–89. https://www.aeped.es/sites/default/files/documentos/12_hta.pdf Bahreynian M Qorbani M Khaniabadi BM Motlagh ME Safari O Asayesh H . Association between obesity and parental weight status in children and adolescents. J Clin Res Pediatr Endocrinol. (2017) 9:111–7. 10.4274/jcrpe.379028008863 Bel-Serrat S Heinen MM Mehegan J O'Brien S Eldin N Murrin CM . School sociodemographic characteristics and obesity in schoolchildren: does the obesity definition matter? BMC Public Health. (2018) 18:337. 10.1186/s12889-018-5246-729523113 Wadolowska L Ulewicz N Sobas K Wuenstel JW Slowinska MA Niedzwiedzka E . Dairy-related dietary patterns, dietary calcium, body weight and composition: a study of obesity in polish mothers and daughters, the MODAF project. Nutrients. (2018) 10:90. 10.3390/nu1001009029337863 Koca T Akcam M Serdaroglu F Dereci S. Breakfast habits, dairy product consumption, physical activity, and their associations with body mass index in children aged 6–18. Eur J Pediatr. (2017) 176:1251–7. 10.1007/s00431-017-2976-y28799014 Estudio de la situación nutricional de la población infantil en la ciudad de Madrid - Ayuntamiento de Madrid. Available online at: https://www.madrid.es/portales/munimadrid/es/Inicio/Servicios-sociales-y-salud/Salud/Publicaciones/Estudio-de-la-situacion-nutricional-de-la-poblacion-infantil-en-la-ciudad-de-Madrid/?vgnextfmt=default&vgnextoid=72cde4ceef1ee510VgnVCM1000001d4a900aRCRD&vgnextchannel=e6898fb9458fe410VgnVCM1000000b205a0aRCRD (accessed January 26, 2021). Mǎrginean CO Mǎrginean C Melit LE. New insights regarding genetic aspects of childhood obesity: a minireview. Front Pediatr. (2018) 6:271. 10.3389/fped.2018.0027130338250 Gilvary C Madhukar N Elkhader J Elemento O. The missing pieces of artificial intelligence in medicine. Trends Pharmacol Sci. (2019) 40:555–64. 10.1016/j.tips.2019.06.00131277839 Dugan TM Mukhopadhyay S Carroll A Downs S. Machine learning techniques for prediction of early childhood obesity. Appl Clin Inform. (2015) 6:506–20. 10.4338/ACI-2015-03-RA-003626448795 Nishtar S Gluckman P Armstrong T. Ending childhood obesity: a time for action. Lancet. (2016) 387:825–7. 10.1016/S0140-6736(16)00140-926823046 Visiedo A de Baranda PS Crone D Aznar S Perez-Llamas F. Programs to prevent obesity in school children 5 to 10 years old: a review. Nutr Hosp. (2016) 33:814–24. 10.20960/nh.37527571653 Wolfenden L Nathan NK Sutherland R Yoong SL Hodder RK Wyse RJ . Strategies for enhancing the implementation of school-based policies or practices targeting risk factors for chronic disease. Cochr Database Syst Rev. (2017) 11:CD011677. 10.1002/14651858.CD011677.pub229185627 Belsky DW Moffitt TE Sugden K Williams B Houts R McCarthy J . Development and evaluation of a genetic risk score for obesity. Biodemogr Soc Biol. (2013) 59:85–100. 10.1080/19485565.2013.77462823701538 Goldstein BA Yang L Salfati E Assimes TL. Contemporary considerations for constructing a genetic risk score: an empirical approach. Genet Epidemiol. (2015) 39:439–45. 10.1002/gepi.2191226198599 de Toro-Martín J Arsenault BJ Després J-P Vohl M-C. Precision nutrition: a review of personalized nutritional approaches for the prevention and management of metabolic syndrome. Nutrients. (2017) 9:913. 10.3390/nu908091328829397 Abbreviations ACM

Autonomous Community of Madrid

 SNPs

Single Nucleotide Polymorphisms

 IPAC

Individual Physical Activity Coefficient

 PAC

Physical Activity Coefficient

 EW

Excess Weight.