Dietary Behavior of Spanish Schoolchildren in Relation to the Polygenic Risk of Obesity
by
, , ,
Andrea Calderón García
1,2,3
,
Roberto Pedrero Tomé
2,4,
Ana Alaminos-Torres
2,4,
Consuelo Prado Martínez
2,5,
Jesús Román Martínez Álvarez
1,2,
Noemí López Ejeda
1,2,4,
María Dolores Cabañas Armesilla
2 and
María Dolores Marrodán Serrano
1,2,4,* 1
Sociedad Española de Dietética y Ciencias de la Alimentación (SEDCA), 28080 Madrid, Spain
2
Research Group EPINUT (Nutritional Epidemiology), Faculty of Medicine, Complutense University of Madrid, 28040 Madrid, Spain
3
Department of Nursing and Nutrition, Faculty of Biomedical Sciences, European University of Madrid, UEM, Villaviciosa de Odón, 28670 Madrid, Spain
4
Department of Biodiversity, Ecology and Evolution, Faculty of Biology, Complutense University of Madrid, 28040 Madrid, Spain
5
Department of Biology, Faculty of Sciences, Universidad Autónoma de Madrid, 28049 Madrid, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(20), 11169; https://doi.org/10.3390/app132011169
Submission received: 13 September 2023
/
Revised: 1 October 2023
/
Accepted: 7 October 2023
/
Published: 11 October 2023
(This article belongs to the Special Issue Uses and Applications of Body Composition in Different Population Groups’ Health, Disease and Physical Exercise)
Abstract
:Several precedents support an association between single nucleotide genetic polymorphisms (SNPs), the obese phenotype, and eating behavior in the infant-juvenile population. This study aims to study this aspect in depth, analyzing the eating behavior of a sample of schoolchildren from Madrid in regard to their genetic predisposition to obesity. A total of 258 schoolchildren, aged 6 to 16 years, were evaluated through the Children’s Eating Behaviour Questionnaire (CEBQ) and the genotyping of 32 SNPs. Associations were observed between the total genetic risk score and eating behaviors related to emotional eating and food responsiveness. Individually, different SNPs were associated with eating behaviors, primarily those related to pro-eating behaviors or increased risk of developing obesity. However, diverse results are obtained, depending on the SNP. These results highlighted the strongest associations between the rs1801725 SNP risk allele (CASR) and increased enjoyment of food by 1846-fold. Likewise, the satiety response was associated with SNP rs11676272 (ADCY3) 2.39 and SNP rs7566605 (INSIG2), increasing this response by 2.39 and 1.63 times, respectively. Emotional anti-ingesting behaviors were inversely associated with SNP rs1421085 (FTO) and SNP rs987237 (TFAP2B). In contrast, SNP rs55915917 (CRHR1) increased the risk of these behaviors. SNPs rs4788099 (SH2B1), rs6857 (NECTIN2), and rs180172 (ADCY3) were associated with slow feeding. In conclusion, associations were found between most of the analyzed SNPs and the CEBQ items. This suggests that feeding behavior exists as a mediator between genotype and obesity phenotype, beginning in childhood.
1. Introduction
Obesity in children and adolescents is a public health problem with a multifactorial origin in which eating behavior plays a role [1]. This behavior, although influenced by the environment, can also present as genetic conditioning [2], which can be analyzed through the detection of single nucleotide polymorphisms (SNPs) that are associated with the obese phenotype, mediated by eating behavior [3].
Numerous studies support the association between SNPs and the risk of developing overweight or obesity. This is explained by the fact that specific genotypes seem to condition attitudes towards food, such as appetite or satiety, speed of intake, frequency of food consumption, or the tendency to experience emotional hunger. With this approach, research groups such as Jääskelainen et al. [4], Llewellyn et al. [5], Steinbeck et al. [6], and Monnereau et al. [7] have associated eating behavior with polygenic risk, quantified in a range between 8 and 32 SNPs.
Other investigations have related obesity with lower responsiveness to internal satiety signals and greater receptivity to olfactory or gustatory stimuli, which would lead to a higher caloric intake [8]. It has also been shown that an infant’s feeding self-regulation arises from genetic–environmental interactions responsible for behaviors such as rapid eating and delayed satiety [8,9]. Hardle et al. [10] investigated the role of eating behavior in genetic susceptibility to obesity, and their thorough literature analysis over the decade of 2010–2020 concluded that emotional and uncontrolled eating behaviors lead to being overweight.
A pioneering study from the United Kingdom by Wardle et al. [11] investigated the association of the SNP rs9939609 genotype of the FTO gene with adiposity and satiety control indicators in children. It proved the usefulness of psychometric scales (Satiety et al. of Food and the Child Eating Behavior Questionnaire) as objective measures of eating behavior. This is also emphasized by Hardle’s literature review [10], in which the authors highlight the value of psychometric tests that objectively measure attitudes toward food.
Undoubtedly, the development of eating habits is a process with a complex etiology, including psychological aspects, particularly the influence of family, social, and affective factors. Some research has highlighted a particular circumstance, reporting that in overweight subjects, there is a positive relationship between an emotional eating pattern and body mass index [12]. Hence, food is eaten to satisfy physiological hunger, to fill an inner emptiness, or to comfort the mood.
Determining the interactions between genotype and eating behavior in greater depth is particularly interesting in childhood and adolescence because it is at these stages when most future habits are shaped. This is a window of opportunity for prevention, and eventually, for an individualized nutritional approach. Therefore, this study aims to analyze the eating behavior of a sample of Madrid schoolchildren concerning their genetic predisposition to obesity through the conduction of the Children’s Eating Behavior Questionnaire (CEBQ), in combination with a genotyping assessment from a panel of 32 SNPs.
2. Methodology
2.1. Participants
Data were collected between 2019 and 2021 during a cross-sectional descriptive study in which 258 schoolchildren, aged 6–16 (67.83% male; 32.17% female), from different communities of Madrid, Spain, participated. The data collected were anonymized and disaggregated to remove information that could identify the subject. In order to be included, informed consent was obtained from the parents or guardians of each subject, respecting the bioethical principles of the Declaration of Helsinki in its most updated version [13]. The Ethics Committee of the Universidad Autónoma de Madrid approved the study.
2.2. Instruments
Each participant was assessed anthropometrically, and their polygenic risk of obesity was calculated by genotyping 32 SNPs. Parents or guardians completed the Child Eating Behaviour Questionnaire (CEBQ) to assess eating the behavior of the subjects [14].
2.2.1. CEBQ Questionnaire
The CEBQ [14] is a validated questionnaire that indicates the participant’s responses to satiety, taste for food, speed of intake, and emotional food consumption, among other parameters. It consists of 35 items that evaluate eight subscales of eating behavior and whose questions are answered using a Likert-type scale, with an option to score each item from 1 to 5, according to the intensity of the behavior: never (1), seldom (2), sometimes (3), often (4), always (5).
The items are classified into eight subscales: food responsiveness (FR; 5 items), enjoyment of food (EF; 4 items), emotional overeating (EOE; 4 items), desire to drink (DD; 3 items), slowness in eating (SE; 4 items), satiety responsiveness (SR; 5 items), food fussiness (FF; 6 items), and emotional undereating (EUE; 4 items). The first four subscales have a positive focus or are related to increased food intake (pro-eating dimension). In contrast, the last four subscales are related to food avoidance or negative food-related responses (anti-eating dimension). The present study used a Spanish-language translated and adapted version of the CEBQ that was previously validated among Spanish and Chilean populations [15].
2.2.2. Genetic Analysis
A total of 32 SNPs were analyzed (Table A1).
The extraction protocol employed was that of BioTools B&M S.A. Laboratories for saliva [16], and the Speedtools Tissue DNA Extraction kit was used. Subsequently, genotyping was carried out in the laboratories of the University of Santiago de Compostela Node of the National Genotyping Center (CeGen), which is part of the Network Platform of Biomolecular and Bioinformatics Resources of the Carlos III Institute of Health. The iPLEX® Gold technology for the MassArray platform of Agena Bioscience Inc. [17] was used. Once genotyping was completed, all samples were burned for the data protection of the participants.
A total of 32 SNPs were analyzed: all risk alleles of the selected SNPs have been associated with the childhood and adolescent obese phenotype or eating behaviors in previous research [16,17,18]. The SNPs selected are reported in Table A1 (Supplementary file: Table A1).
The extraction protocol employed was that of BioTools B&M S.A. Laboratories for saliva [19], and the Speedtools Tissue DNA Extraction kit was used. Subsequently, genotyping was carried out in the laboratories of the University of Santiago de Compostela Node of the National Genotyping Center (CeGen), which is part of the Network Platform of Bio-molecular and Bioinformatics Resources of the Carlos III Institute of Health. The iPLEX® Gold technology for the MassArray platform of Agena Bioscience Inc. (Hamburg, Germany) [20] was used. Once genotyping was completed, all samples were burned for the data protection of the participants.
Calculation of the Genetic Risk for Obesity
The genotype of each SNP was ranked by awarding 2 points if the individual was homozygous for the risk allele, 1 point if heterozygous, and 0 points if homozygous for the alternate allele. The total genetic risk score (GRS) was obtained from the sum of the scores of the 32 SNPs. The simplest possible model uses unweighted scores, which is appropriate in this type of study, especially when the sample is insignificant [16]. The total score can be obtained from 0 to 64 points (0: not a single risk allele; 64: homozygous for risk at all 32 SNPs). Quartiles were calculated for GRS by establishing four categories, from lowest to highest risk (GRS ≤ Q1; GRS > Q1–≤Q2; GRS > Q2–≤Q3; PRG > Q3).
2.2.3. Anthropometric Study
To evaluate nutritional status, height (cm), weight (kg), waist circumference (cm), and bicipital, tricipital, subscapular, and suprailiac adipose skinfolds (mm) were measured. For the prevalence analysis, the sample was stratified by sex, and nutritional categories were established based on the body mass index [BMI= weight (kg)/height (m2)] using the cutoff points of Cole et al. [17] and the waist-to-hip ratio [WHR = waist circumference (cm)/height (cm)], using the cutoff points established by Marrodán et al. [18]. The percentage of body fat (%BF) was calculated using the formulas of Siri 1961 [17] after calculating the density (Brook, 1971; Durnin and Rahaman, 1967) [21,22]. The degree of adiposity and its distribution were classified following the patterns of Marrodán et al. [23].
2.3. Statistical Procedures
Cronbach’s alpha was applied to evaluate the internal consistency of the CEBQ subscales, which was found to be adequate (greater than 0.7). Kruskal–Wallis tests were performed to determine whether GRS and individual SNPs were associated with these subscales, followed by linear logistic regression tests. A CEBQ score ≥ 50th percentile was the dependent variable, and at least one reference allele at each SNP was used as a reference point to measure genetic risk. All models were adjusted for sex and age. Statistical processing was performed using the IBM SPSS 24 statistical package.
3. Results
3.1. Anthropometric and Genetic Description of the Sample
The anthropometric description of the sample is shown in Table 1. The genotypic frequencies and risk allele frequency of each selected SNP are presented in Table A2, and it can be observed that no allele showed a frequency lower than 0.05, which corresponds with the minimal data quality criterion for the association study (Hardy–Weinberg equilibrium).
The Genetic Risk Score and Prevalence of Overweight and Adiposity
The resulting genetic risk categories were Q1: ≤19 points; Q2: 20–22 points; Q3: 23–25 points; and Q4: >25 points.
As shown in Table 2, the proportion of overweight and obesity categorized by BMI is significantly higher among subjects with a higher genetic predisposition to obesity (p = 0.040). The WHR and %BF trend also reflects a higher prevalence of overweight or adiposity in genetically predisposed individuals, without significance.
3.2. Genetic Risk Score for Obesity and Eating Behavior
Based on the sample participant’s anthropometric and genotypic description, the resulting genetic risk score (GRS) categories were Q1: ≤19 points; Q2: 20–22 points; Q3: 23–25 points; and Q4: >25 points. Table 3 presents the mean scores of the eight subscales of the CEBQ, stratifying the results according to GRS quartile categories. The trend is increasing for the pro-eating scales and decreasing for the anti-eating scales.
The pro-eating subscales of food responsiveness (FR) and emotional overeating (EOE), as well as the anti-eating subscale of emotional undereating (EUE), were the eating behavioral traits significantly associated with GRS. As genetic predisposition to obesity increases, items related to food cravings and emotional hunger score higher.
The multinomial logistic regression model highlighted associations between all pro-intake subscales and selected SNPs, analyzed independently (Figure 1).
The SNPs rs1801725 (CASR) and rs1137101 (LEPR) were independently associated with enjoyment of food (EF), showing, in the first case, an increase in the score of this scale with an OR of 1.846 (95% CI: 1.023–3.329; p = 0.024). In the second case, the same scale (EF) was reduced, with an OR of 0.586 (95% CI: 0.329–0.980; p = 0.024).
Food responsiveness (FR) was increased by 1.827-fold (95% IC: 1.090–3.062; p = 0.041) for each risk allele in SNP rs10887741 (PAPSS2) and by 2.224-fold in SNP rs1421085 (95% IC: 1.117–4.427 p = 0.042). In contrast, for each risk allele in SNP rs693839 (SPRY2), FR was reduced, with an OR of 0.519 (95% IC: 0.309–0.872; p = 0.019).
Emotional overeating (EOE) subscale scores were increased by 1.628-fold for each risk allele of SNP rs10887741 (PAPSS2) (95% IC: 1.016–2.698; p = 0.049). Finally, the desire to drink (DD) subscale score was reduced for each risk allele of SNP rs3761445 (PLA2G6), with an OR of 0.501 (95% IC: 0.291–0.864; p = 0.021).
To investigate the degree to which the risk allele of each SNP increases the possible obesogenic behavior, the inverse OR values were plotted, corresponding to the anti-intake subscales, when constructing Figure 2. Thus, the obesogenic behaviors are positioned on the right and the non-obesogenic behaviors on the left of Figure 2.
Associations were found with several SNPs except for the food fussiness (FF) subscale. Each risk allele of SNP rs11676272 (ADCY3) reduced satiety responsiveness (SR) by 2.390-fold (95% CI: 1.367–4.184; p < 0.001). Similarly, each risk allele of SNP rs7566605 (INSIG2) reduced it by 1.627-fold (95% CI: 1.060–2.775; p = 0.033).
The SNPs rs4788099 (SH2B1), rs6857 (NECTIN2), and rs180172 (ADCY3) showed a decrease in slowness in eating (SE), or in other words, an increase in eating speed of 1.65 (95% CI: 1.258–2.945; p = 0.010), 2.06 (95% CI: 1.179–3.709; p = 0.008), and 2.37 times (95% CI: 1. 124–5.000; p = 0.025) respectively. In contrast, for each copy of the risk allele of rs987237 (TFAP2B), intake speed was reduced, with an OR of 0.492 (95% CI: 0.263–0.921 p = 0.042).
Emotional undereating (EUE) was nominally associated with 4 SNPs. The risk alleles of SNPs rs55915917 (CRHR1) and rs7132908 (FAI2M) reduced the probability of emotional undereating (EUE) by 1.779 (95% CI: 1.044–3.030; p = 0.014) and 1.751 (95% CI: 1.007–3.719; p = 0.040) times, respectively. In contrast, the risk alleles corresponding to SNPs rs1421085 (FTO) and rs987237 (TFAP2B), indicated on the left side of the graph, were associated with emotional undereating anti-intake behavior, with an OR of 0.385 (95% CI: 0.189–0.781; p = 0.009) and 0.391 (95% CI: 0.214–0.713; p = 0.011), respectively. Finally, for each risk allele of SNP rs6857 (INSIG2), the lower food fussiness (FF) is increased by 2.303 times (1.045–5.142; p = 0.023).
4. Discussion
The scientific literature supports that childhood eating behaviors may be modulated by genetic factors [25]. The behavioral susceptibility theory hypothesizes that genetics influences the obesity phenotype by regulating appetite, expressed in eating behavior. The review by Herle et al. [10] is illuminating, as it highlights the value of studies associating the genetic risk scores and the psychometric measures of eating behaviors. The same authors highlight the need for further research, particularly longitudinal studies in children.
In the present study, an association was found between GRS and obesity and the food responsiveness (FR) subscale, in addition to the subscales related to emotional eating, both by excess (EOE) and deficiency (EUE). The results using other subscales are generally in line with the expectations that the average scores increase with higher genetic risk scores in the pro-intake scales and decrease in the anti-intake scales, following the trend of previous studies, which highlight the more significant evidence concerning the ability to satiate and the demand to food [10].
Some studies have observed that schoolchildren genetically predisposed to obesity show a more significant response to or enjoyment of food, greater appetite, and less responsiveness [24,26]. In the present study, despite not finding such an association with the total risk score, the trend of the averages is along the same lines.
In turn, in the literature, a more significant response to food, understood as a greater desire to consume food, is associated with a greater risk of being overweight. The literature concludes that children who enjoy or express a greater desire for food consume more daily energy on average [27]. These schoolchildren also eat more processed products of low nutritional quality, which are high in calories, refined and saturated fats, and free or added sugars, a dietary pattern associated with an increased risk of obesity [28].
Genetic predisposition to emotional eating is controversial. Further evidence is still required in school populations, and previous studies reached contradictory results. Some authors suggest that the genetic component predisposes a greater or lesser tendency to emotional eating [29], an observation in line with the findings of the present study. However, other studies find that the heritability of the emotional eating behavior is low and, in any case, is less deterministic than learned behavior [30,31]. Certain studies report associations between a lower genetic risk for obesity and slower eating, leading to earlier satiety and lower food intake, possibly because of less responsiveness to internal satiety cues and greater appetite [32]. Our study finds both trends, although without significant association with GRS.
Children who are pickier and who reject new foods might have a less varied and obesogenic diet [10]. However, the present study found no relationship between food pickiness and genetic predisposition to obesity, as previous studies have described [33].
On the other hand, 14 of 32 SNPs analyzed could be independently associated with eating behaviors. Thus, five SNPs from different loci were associated with the anti-eating subscale of emotional undereating (EUE) and two other SNPs with the pro-intake subscale of increased risk of emotional overeating (EOE). In particular, the SNPs of the FTO gene, rs1421085 (significant in the present study) and rs9939609, have been associated in the literature with uncontrolled eating (loss of control eating), which is usually accompanied by a preference for more palatable foods, usually higher in calories and rich in fat or sugar [34].
In the anti-intake subscale of slowness in eating (SE), lower scores have also been found in children with the risk genotype in three SNPs, implying a possible higher eating speed. Scientific evidence shows that eating slowly reduces food intake, a protective factor against weight gain. Eating slowly promotes earlier satiety, while eating fast means higher total intake [6]. A recent study by Jackson et al. [35] concludes that children with lower scores on the slowness to eat subscale of the CEBQ have a higher daily energy intake, around 169 kcal on average, compared to those with higher scores.
In our study, schoolchildren with the risk allele in the SNP rs11676272 (ADCY3) exhibited lower average scores in the anti-eating subscale of satiety responsiveness (SR). The SNP rs11676272 (ADCY3) has been previously associated with lower satiety capacity, possibly due to alterations in the regulation of intake and energy expenditure [36]. The study by Jackson et al., mentioned previously [37], concludes that children with lower scores in this subscale of the CEBQ have a higher daily energy intake of 83 kcal on average. In the literature, other SNPs associated with lower satiety capacity have not reached significant association in our study, highlighting those in or near the FTO, MC4R, GNPDA2, LMX1B, or SH2B1 genes [27,38].
The limitations of this study include a relatively small sample size. It would be interesting to replicate the study in a larger population because this may reveal more associations between other eating behaviors and SNPs that have not obtained significance in this study. Similarly, due to the young age of the participants, there may sometimes be limited control over their intake and eating behaviors. However, the continuity of such behaviors in early life has been demonstrated. Likewise, genetic predisposition may explain some variations in eating behaviors during childhood and adolescence. However, environmental influences undoubtedly play a fundamental role, especially in adolescence, when environmental factors are of significant importance [39].
5. Conclusions
No strong associations were found between GRS and obesity and most eating behaviors. However, the present study highlights the role in subscales related to emotional eating, either by over or under eating. In addition, a potential relationship was found between the emotional eating score and different SNPs in regards to eating behaviors, again highlighting its influence on emotional underfeeding, the speed of intake and enjoyment, and response to food. The relationship between genetic phenotypes and eating behavior could be consolidated as an approach of interest in the future for the comprehensive approach to evaluating obesity from an early age.
Author Contributions
Conceptualization, A.C.G., N.L.E. and M.D.M.S.; data curation, A.C.G., A.A.-T., R.P.T. and C.P.M.; formal analysis, C.P.M.; funding acquisition, J.R.M.Á.; investigation, A.C.G. and M.D.C.A.; methodology, M.D.C.A., R.P.T., C.P.M. and M.D.M.S.; project administration, J.R.M.Á.; software, R.P.T.; supervision, J.R.M.Á., N.L.E. and M.D.M.S.; writing—original draft, A.C.G. and R.P.T.; writing—review and editing, M.D.M.S. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by Project PR41/17_21008 BANCO DE SANTANDER.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Autonomous University of Madrid (CEI-91-1699-2018).
Informed Consent Statement
Informed consent was obtained from the parents of guardians of all subjects involved in the study.
Data Availability Statement
The original data are not published in any repository.
Acknowledgments
We thank the participating children, their families, and their teachers for their contributions to this study.
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A
Table A1.
Description of the genotypic and risk allele frequencies of the 32 SNPs selected for inclusion in the study.
Table A1.
Description of the genotypic and risk allele frequencies of the 32 SNPs selected for inclusion in the study.
| Chromosome | Associated Gene | SNP | Position | Alelles * |
|---|---|---|---|---|
| 1 | LEPR | rs1137101 | 65.592.830 | A/G |
| 1 | SEC16B | rs543874 | 177.920.345 | A/G |
| 1 | GPR61 | rs7550711 | 110.082.886 | C/T |
| 2 | TMEM18 | rs6548238 | 634.905 | C/T |
| 2 | TMEM18 | rs4854349 | 647.861 | T/C |
| 2 | INSIG2 | rs7566605 | 118.836.025 | C/G |
| 2 | ADCY3 | rs11676272 | 24.918.669 | A/G |
| 2 | COBLL1 | rs6738627 | 164.687.940 | G/A |
| 3 | CASR | rs1801725 | 122.284.910 | G/T |
| 4 | GNPDA2 | rs10938397 | 45.182.530 | A/G |
| 4 | CLOCK | rs1801260 | 56.301.369 | A/G |
| 6 | TFAP2B | rs987237 | 50.835.337 | A/G |
| 7 | EXOC4 | rs7804463 | 133.762.898 | T/C |
| 8 | ELP3 | rs13253111 | 28.204.457 | A/G |
| 9 | FAM120AOS | rs944990 | 96.191.004 | C/T |
| 9 | LMX1B | rs3829849 | 126.628.521 | C/T |
| 10 | PAPSS2 | rs10887741 | 87.683.553 | T/C |
| 12 | FAI2M | rs7132908 | 49.869.365 | G/A |
| 12 | FAIM2 | rs7138803 | 50.242.468 | A/G |
| 13 | OLFM4 | rs12429545 | 54.102.206 | A/G |
| 13 | SPRY2z | rs693839 | 80.384.153 | T/C |
| 16 | FTO | rs1558902 | 53.803.547 | A/T |
| 16 | FTO | rs17817449 | 53.813.367 | G/T |
| 16 | FTO | rs9939609 | 53.820.527 | A/T |
| 16 | FTO | rs1421085 | 53.767.042 | T/C |
| 16 | IRX3 | rs3751723 | 54.286.285 | G/T |
| 16 | SH2B1 | rs4788099 | 28.844.406 | A/G |
| 18 | MC4R | rs6567160 | 60.161.902 | T/C |
| 18 | RAB27B | rs8092503 | 54.812.256 | A/G |
| 19 | NECTIN2 | rs6857 | 44.888.997 | T/C |
| 19 | CRTC1 | rs757318 | 18.709.498 | C/A |
| 22 | PLA2G6 | rs3761445 | 38.199.404 | G/A |
* The risk allele is highlighted in bold.
Table A2.
Description of the genotypic and risk allele frequencies of the 32 obesity-associated polymorphisms included in the study sample.
Table A2.
Description of the genotypic and risk allele frequencies of the 32 obesity-associated polymorphisms included in the study sample.
| SNP | Genotype Frequency (%) | FAR | European FAR * | H-W Equilibrium | ||
|---|---|---|---|---|---|---|
| rs1137101 (LEPR) Risk allele: G | AA: 33.20 | AG: 48.00 | GG: 18.80 | 0.43 | 0.45 | p = 0.844 |
| rs543874 (SEC16B) Risk allele: G | AA: 69.50 | AG: 27.70 | GG: 2.80 | 0.17 | 0.18 | p = 0.984 |
| rs7550711 (GPR61) Risk allele: T | CC: 94.90 | CT: 0.00 | TT: 5.10 | 0.07 | 0.04 | p = 0.070 |
| rs6548238(TMEM 18) Risk allele: T | CC: 70.80 | CT: 27.40 | TT: 1.80 | 0.16 | 0.15 | p = 0.646 |
| rs4854349 (TMEM18) Risk allele: C | TT: 3.10 | TC: 33.20 | CC: 63.70 | 0.82 | 0.83 | p = 0.621 |
| rs7566605 (INSIG2) Risk allele: C | GG: 43.80 | GC: 43.60 | CC: 12.60 | 0.34 | 0.32 | p = 0.734 |
| rs11676272 (ADCY3) Risk allele: G | AA: 27.70 | AG: 53.50 | GG: 18.80 | 0.46 | 0.48 | p = 0.432 |
| rs6738627 (COBLL1) Risk allele: A | GG: 39.80 | GA: 50.40 | AA: 9.80 | 0.35 | 0.36 | p = 0.281 |
| rs1801725 (CASR) Risk allele: T | GG: 73.40 | GT: 25.00 | TT: 1.60 | 0.14 | 0.15 | p = 0.748 |
| rs10938397(GNPDA2) Risk allele: G | AA: 32.20 | AG: 50.00 | GG: 17.80 | 0.43 | 0.37 | p = 0.838 |
| rs1801260 (CLOCK) Risk allele: G | AA: 53.40 | AG: 37.00 | GG: 9.60 | 0.28 | 0.29 | p = 0.399 |
| rs987237 (TFAP2B) Risk allele: G | AA: 70.30 | AG: 27.30 | GG: 2.30 | 0.16 | 0.19 | p = 0.854 |
| rs7804463 (EXOC4) Risk allele: C | TT: 25.80 | TC: 53.10 | CC: 21.10 | 0.48 | 0.46 | p = 0.519 |
| rs13253111 (ELP3) Risk allele: G | AA: 23.80 | AG: 50.00 | GG: 26.20 | 0.51 | 0.44 | p = 0.995 |
| rs944990 (FAM120AOS) Risk allele: T | CC: 50.50 | CT: 42.70 | TT: 6.80 | 0.28 | 0.28 | p = 0.578 |
| rs3829849 (LMX1B) Risk allele: T | CC: 39.50 | CT: 48.40 | TT: 12.10 | 0.36 | 0.36 | p = 0.217 |
| rs10887741 (PAPSS2) Risk allele: C | TT: 46.10 | TC: 40.20 | CC: 13.70 | 0.34 | 0.39 | p = 0.984 |
| rs7132908 (FAI2M) Risk allele: A | GG: 35.90 | GA: 49.60 | AA: 14.50 | 0.34 | 0.39 | p = 0.309 |
| rs7138803 (FAIM2) Risk allele: A | GG: 42.20 | GA: 42.90 | AA: 14.08 | 0.36 | 0.38 | p = 0.555 |
| rs12429545 (OLFM4) Risk allele: A | GG: 74.00 | GA: 23.50 | AA: 2.50 | 0.14 | 0.07 | p = 0.701 |
| rs693839 (SPRY2z) Risk allele: C | TT: 45.70 | TC: 42.60 | CC: 11.70 | 0.33 | 0.31 | p = 0.714 |
| rs1558902 (FTO) Risk allele: A | TT: 34.50 | TA: 44.70 | AA: 20.80 | 0.43 | 0.42 | p = 0.374 |
| rs17817449 (FTO) Risk allele: G | TT: 36.10 | GT: 44.30 | GG: 19.60 | 0.42 | 0.41 | p = 0.374 |
| rs9939609 (FTO) Risk allele: A | TT: 36.10 | TA: 44.10 | AA: 19.90 | 0.42 | 0.41 | p = 0.984 |
| SNP | Genotype frequency (%) | FAR | European FAR * | Balance H-W | ||
| rs1421085 (FTO) Risk allele: C | TT: 35.20 | TC: 46.10 | CC: 18.80 | 0.42 | 0.42 | p = 0.372 |
| rs3751723 (IRX3) Risk allele: T | GG: 41.40 | GT: 45.70 | TT: 12.90 | 0.36 | 0.39 | p = 0.959 |
| rs4788099 (SH2B1) Risk allele: G | AA: 43.00 | AG: 43.70 | GG: 13.30 | 0.35 | 0.38 | p = 0.678 |
| rs6567160 (MC4R) Risk allele: C | TT: 59.80 | TC: 35.90 | CC: 4.30 | 0.22 | 0.22 | p = 0.706 |
| rs8092503 (RAB27B) Risk allele: G | AA: 66.80 | AG: 28.90 | GG: 4.30 | 0.19 | 0.23 | p = 0.607 |
| rs6857 (NECTIN2) Risk allele: T | CC: 85.20 | CT: 14.10 | TT: 0.80 | 0.08 | 0.16 | p = 0.798 |
| rs757318 (CRTC1) Risk allele: A | CC: 27.30 | CA: 52.00 | AA: 20.70 | 0.47 | 0.48 | p = 0.656 |
| rs3761445 (PLA2G6) Risk allele: G | AA: 34.00 | AG: 52.70 | GG: 13.30 | 0.40 | 0.42 | p = 0.311 |
SNP: single nucleotide polymorphism; FAR: frequency of risk allele; * FAR Spain: risk allele frequency according to ALFA Project (NCBI). Iberian population reference in Spain [40].
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Figure 1.
Associations between some independent SNPs and the pro-ingestion subscales of the CEBQ (N= 258). CEBQ-EF: food enjoyment subscale; CEBQ-FR: food response subscale; CEBQ-EOE: emotional overeating subscale; CEBQ-DD: desire to drink subscale. Binary logistic regression analysis was applied. Values are OR (95% CI) for sex- and age-adjusted models. Significant association * p < 0.05. Independent variable: containing or not at least one risk allele of each SNP. Reference group = category having no risk allele of each SNP. Dependent variable: CEBQ score classified from p50.
Figure 1.
Associations between some independent SNPs and the pro-ingestion subscales of the CEBQ (N= 258). CEBQ-EF: food enjoyment subscale; CEBQ-FR: food response subscale; CEBQ-EOE: emotional overeating subscale; CEBQ-DD: desire to drink subscale. Binary logistic regression analysis was applied. Values are OR (95% CI) for sex- and age-adjusted models. Significant association * p < 0.05. Independent variable: containing or not at least one risk allele of each SNP. Reference group = category having no risk allele of each SNP. Dependent variable: CEBQ score classified from p50.
Figure 2.
Associations between some independent SNPs and the anti-ingestion subscales of the CEBQ (N= 258). CEBQ-SR: response to satiety subscale; CEBQ-SE: slowness to eat subscale; CEBQ-EUE: emotional undereating subscale; CEBQ-FF: food demand subscale. A binary logistic regression analysis was applied. Values are OR (95% CI) for sex- and age-adjusted models. Significant association * p < 0.05, ** p < 0.01, *** p < 0.0001. Independent variable: containing or not at least one risk allele of each SNP. Reference group = category having no risk allele of each SNP. Dependent variable: CEBQ score classified from p50.
Figure 2.
Associations between some independent SNPs and the anti-ingestion subscales of the CEBQ (N= 258). CEBQ-SR: response to satiety subscale; CEBQ-SE: slowness to eat subscale; CEBQ-EUE: emotional undereating subscale; CEBQ-FF: food demand subscale. A binary logistic regression analysis was applied. Values are OR (95% CI) for sex- and age-adjusted models. Significant association * p < 0.05, ** p < 0.01, *** p < 0.0001. Independent variable: containing or not at least one risk allele of each SNP. Reference group = category having no risk allele of each SNP. Dependent variable: CEBQ score classified from p50.
Table 1.
Direct anthropometric measurements of the sample as a function of sexual dimorphism by age group.
Table 1.
Direct anthropometric measurements of the sample as a function of sexual dimorphism by age group.
| 6–10 Years (Mean ± SD) | 11–16 Years (Mean ± SD) | |||||
|---|---|---|---|---|---|---|
| Male | Female | p | Male | Female | p | |
| Weight (kg) | 33.54 ± 10.50 | 34.46 ± 8.18 | 0.440 | 51.28 ± 13.30 | 50.53 ± 12.52 | 0.939 |
| Height (cm) | 134.42 ± 10.15 | 136.23 ± 9.42 | 0.161 | 155.48 ± 11.46 | 155.92 ± 8.30 | 0.179 |
| Waist circumference (cm) | 65.10 ± 10.54 | 63.91 ± 8.12 | 0.323 | 75.45 ± 11.31 | 70.61 ± 8.70 | <0.001 * |
| Tricipital skinfold (mm) | 12.64 ± 6.17 | 13.82 ± 4.94 | 0.100 | 14.70 ± 9.60 | 15.76 ± 6.12 | 0.179 |
| Bicipital skinfold (mm) | 8.06 ± 4.83 | 8.76 ± 3.75 | 0.202 | 9.66 ± 5.34 | 9.61 ± 4.25 | 0.838 |
| Subescapular skinfold (mm) | 9.14 ± 6.37 | 10.00 ± 4.87 | 0.235 | 12.01 ± 7.50 | 12.52 ± 6.50 | 0.548 |
| Suprailiac skinfold (mm) | 10.57 ± 7.84 | 11.24 ± 6.57 | 0.477 | 15.16 ± 8.77 | 14.30 ± 6.92 | 0.372 |
* Significant differences between groups.
Table 2.
Prevalence of overweight and adiposity compared between established obesity genetic risk groups based on median genetic risk score (p50 = 22 points).
Table 2.
Prevalence of overweight and adiposity compared between established obesity genetic risk groups based on median genetic risk score (p50 = 22 points).
| GRS ≤ Q2 (≤22 Points) | GRS ≥ Q3 (≥23 Points) | Comparison | ||
|---|---|---|---|---|
| BMI 1 | Normal weight | 69.60% | 74.30% | X2 = 6.245 p = 0.040 * |
| Overweight | 24.10% | 14.60% | ||
| Obesity | 6.30% | 11.10% | ||
| WHR 2 | Normal weight | 58.90% | 52.80% | X2 = 1.235 p = 0.556 |
| Abdominal overweight | 14.60% | 16.70% | ||
| Abdominal obesity | 26.60% | 30.60% | ||
| %BF 3 | Medium (p < 90) | 54.90% | 52.10% | X2 = 2.679 p = 0.220 |
| High (p90–p97) | 18.20% | 20.80% | ||
| Very High (>p97) | 26.90% | 27.10% | ||
Q: quartile; BMI: body mass index; WHI: waist-height index; NS: not significant, according to a Chi-square test (all variables show non-normal distribution): (*) significant differences. 1 Applying the cutoff points for BMI proposed by Cole et al. [21]. 2 Applying the cutoff points for WHR proposed by Marrodán et al. [22]. 3 Applying the cutoff points for fat percentage proposed by Marrodán et al. [24].
Table 3.
Comparison of mean scores of CEBQ subscales according to categories of polygenic risk of obesity (GRS), calculated with 32 SNPs.
Table 3.
Comparison of mean scores of CEBQ subscales according to categories of polygenic risk of obesity (GRS), calculated with 32 SNPs.
| Dimension | Subscale | GRS (Q1: ≤19 Points) | GRS (Q2: 20–22 Points) | GRS (Q3: 23–25 Points) | GRS (Q4: >25 Points) | Comparison |
|---|---|---|---|---|---|---|
| Pro-intake | 1. Enjoyment of food (EF) (X ± SD) | 3.84 ± 0.68 | 3.92 ± 0.67 | 3.90 ± 0.64 | 3.92 ± 0.63 | F = 0.513 p = 0.916 |
| 2. Food responsiveness (FR) (X ± SD) | 2.29 ± 0.91 | 2.13 ± 0.92 | 2.53 ± 0.90 | 2.51 ± 1.21 | F = 7.118 p = 0.048 * | |
| 3. Emotional overeating (EOE) (X ± SD) | 2.19 ± 0.79 | 1.91 ± 0.92 | 2.30 ± 0.95 | 2.18 ± 0.92 | F = 7.334 p = 0.042 * | |
| 4. Desire to drink (DD) (X ± SD) | 2.44 ± 0.93 | 2.18 ± 0.87 | 2.45 ± 0.92 | 2.40 ± 0.92 | F = 4.372 p = 0.224 | |
| Anti-intake | 5. Satiety responsiveness (SR) (X ± SD) | 2.46 ± 0.65 | 2.46 ± 0.48 | 2.45 ± 0.71 | 2.38 ± 0.70 | F = 0.736 p = 0.865 |
| 6. Slowness in eating (SE) (X ± SD) | 2.53 ± 1.05 | 2.49 ± 0.83 | 2.55 ± 0.90 | 2.28 ± 1.02 | F = 2.810 p = 0.122 | |
| 7. Emotional undereating (EUE) (X ± SD) | 2.50 ± 0.83 | 2.65 ± 0.97 | 2.19 ± 0.86 | 2.41 ± 0.90 | F = 8.561 p = 0.036 * | |
| 8. Food fussiness (FF) (X ± SD) | 2.63 ± 0.86 | 2.76 ± 0.98 | 2.87 ± 0.97 | 2.55 ± 0.79 | F = 3.452 p = 0.327 |
Q: quartile; X: mean; SD: standard deviation. Analysis by Kruskal–Wallis test; (*) significant differences.
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Calderón García, A.; Pedrero Tomé, R.; Alaminos-Torres, A.; Prado Martínez, C.; Martínez Álvarez, J.R.; López Ejeda, N.; Cabañas Armesilla, M.D.; Marrodán Serrano, M.D. Dietary Behavior of Spanish Schoolchildren in Relation to the Polygenic Risk of Obesity. Appl. Sci. 2023, 13, 11169. https://doi.org/10.3390/app132011169
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Calderón García A, Pedrero Tomé R, Alaminos-Torres A, Prado Martínez C, Martínez Álvarez JR, López Ejeda N, Cabañas Armesilla MD, Marrodán Serrano MD. Dietary Behavior of Spanish Schoolchildren in Relation to the Polygenic Risk of Obesity. Applied Sciences. 2023; 13(20):11169. https://doi.org/10.3390/app132011169
Chicago/Turabian StyleCalderón García, Andrea, Roberto Pedrero Tomé, Ana Alaminos-Torres, Consuelo Prado Martínez, Jesús Román Martínez Álvarez, Noemí López Ejeda, María Dolores Cabañas Armesilla, and María Dolores Marrodán Serrano. 2023. "Dietary Behavior of Spanish Schoolchildren in Relation to the Polygenic Risk of Obesity" Applied Sciences 13, no. 20: 11169. https://doi.org/10.3390/app132011169
APA StyleCalderón García, A., Pedrero Tomé, R., Alaminos-Torres, A., Prado Martínez, C., Martínez Álvarez, J. R., López Ejeda, N., Cabañas Armesilla, M. D., & Marrodán Serrano, M. D. (2023). Dietary Behavior of Spanish Schoolchildren in Relation to the Polygenic Risk of Obesity. Applied Sciences, 13(20), 11169. https://doi.org/10.3390/app132011169
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