Abstract
Background
Obesity is the largest global public health epidemic, increasingly affecting children and adolescents. Studies suggest that genetic markers such as single nucleotide polymorphisms (SNPs) may be associated with the development of obesity. Obesity susceptibility genes identified include alpha-ketoglutarate-dependent dioxygenase (FTO), endothelial nitric oxide (NOS3) and apolipoprotein B (APOB). Furthermore genetic predisposition can interact with other environmental factors, such as clinical risk factors for obesity. In this context, the potential interaction between these SNPs and clinical risk factors such as non-exclusive breastfeeding, high birth weight, and a family history of chronic diseases warrants investigation. There is a clear need for more research on the FTO, NOS3 and APOB genes in Brazilian children. The purpose of this study was to evaluate the associations between SNPs in the FTO (rs1121980), NOS3 (rs1799983) and APOB (rs693) genes and obesity as well as to investigate the combined influence of significant SNPs in children and adolescents in Ouro Preto, Minas Gerais, Brazil.
Methods
A cross-sectional population-based study was conducted with elementary school students aged 6–17 years in Ouro Preto, Minas Gerais, between April and December 2021. The study evaluated sociodemographic, clinical, and biochemical variables and the SNPs rs1121980, rs1799983 and rs693 in the FTO, NOS3 and APOB genes, respectively, for associations with obesity.
Results
The study revealed that the prevalence of obesity was notably high, reaching 8.5% in the study population. Homozygotes for the risk alleles of the FTO and NOS3 genes (genotypes AA and TT, respectively) remained significant, with both showing a more than twofold increased likelihood of being obese [OR: 2.07 (CI: 1.02–4.20) and 2.49 (CI: 1.08–5.73), respectively]. The same combination of alleles associated with clinical risk factors (nonexclusive breastfeeding, high birth weight, family history of diabetes, obesity and dyslipidemia) was associated with a significantly greater chance of being obese at a young age.
Conclusions
Our results support the idea that the SNP rs1121980 in the FTO gene and rs1799983 in the NOS3 gene can affect the occurrence of obesity in Brazilian children and adolescents living in urban areas.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12887-025-05570-3.
Keywords: Obesity, Adolescent health, Native American child health, Genetics, Epidemiology
Background
Obesity, a multifactorial and polygenic condition, is the largest global public health epidemic and increasingly affects children and adolescents [1]. Body adiposity is hereditary, making the identification of genes that contribute to the most common forms of obesity critically important. The alpha-ketoglutarate-dependent dioxygenase (FTO) gene was identified as the first obesity susceptibility gene [2, 3]. The presence of single nucleotide polymorphisms (SNPs) clustered in the first intron of the FTO gene has been associated with an increased risk of obesity in adults [3]. One SNP located in this region is rs1121980, where a guanine (G) is replaced by an adenine (A). The frequency of the A allele is around 53% in obese people compared to 40.7% in eutrophic people [3]. In addition, the HapMap project shows that the global distribution of the A allele is 41% and that of the G allele is 59%. For Americans, this distribution is approximately 36% and 64%, respectively [4]. Studies indicate that this SNP affects the transcriptional activity of the gene and is associated with increased body mass index (BMI), waist circumference (WC), and extreme obesity in individuals carrying the variant allele A [2, 5, 6]. The endothelial nitric oxide (NOS3) gene is also under investigation for its association with obesity [7]. The SNP rs1799983 is located in exon 7 of the NOS3 gene, where a G is substituted by a thymine (T), resulting in the substitution of a glutamate for an aspartate at position 298 of NOS3 (Glu298Asp), which compromises its functionality [8]. According to the HapMap project, the worldwide distribution of the T allele is 17% and that of the G allele is 83%, while in Americans this distribution increases to 34% and 66%, respectively [9]. Studies suggest that the SNP rs1799983 is associated with the development of obesity, systemic hypertension, coronary artery disease, alterations in low-density lipoprotein (LDL-c) and high-density lipoprotein (HDL-c) fractions, and diabetes mellitus type 2 (DM2) [10–14].
The SNP rs693 or XbaI is located in exon 26 of the APOB gene, where there is a G to A substitution. The presence of the A allele has been extensively studied for its involvement in generalised or regional obesity and dyslipidemia [15–18]. Similarly, studies in Brazilian populations show the same results [19, 20]. The global frequency of the A risk allele is 23%. In the American population it is about 29% [21].
Furthermore, genetic predispositions can interact with other environmental factors, such as clinical risk factors for obesity. In this context, the potential interaction between the FTO, NOS3 and APOB SNPs and clinical risk factors such as nonexclusive breastfeeding, high birth weight, and a family history of chronic diseases warrants investigation. These clinical factors are well-established contributors to obesity [22], and their combination with genetic risk may synergistically increase the likelihood of obesity development in children and adolescents. Studies conducted in Ouro Preto have examined both adult and child populations, revealing a high prevalence of overweight and obesity [23–25]. There is a clear need for more research on the FTO and NOS3 genes in Brazilian children. The purpose of this study was to evaluate the associations between SNPs in the FTO (rs1121980), NOS3 (rs1799983) and APOB (rs693) genes and obesity as well as to investigate the combined influence of significant SNPs in children and adolescents in Ouro Preto, Minas Gerais, Brazil.
Methods
Study design and population
A population-based cross-sectional study was performed with elementary school students aged 6–17 years in Ouro Preto, Minas Gerais, between April and December 2021.
Sampling plan
A stratified sample size for the population survey was calculated on the basis of four parameters: (1) 14.9% of the population in the studied age group with overweight and obesity; (2) 4,864 students enrolled in public and private elementary schools in Ouro Preto; (3) a 3% margin of error; and (4) a 95% confidence level. This resulted in a minimum sample size of 876, which was increased by 20% to account for potential losses, leading to the invited of 1,168 individuals. An 18.8% loss rate due to nonattendance and low blood volume resulted in a final sample of 921 individuals.
Inclusion and exclusion criteria
The inclusion criteria were as follows: the student had to be enrolled in primary school in Ouro Preto, and the person responsible for the student to be interviewed had to be over eighteen years old and have lived in the same household as the student. The exclusion criteria were pregnant adolescents, students in youth and adult education, and the Association of Parents and Friends of the Exceptional.
Data collection
Data collection consisted of the following steps: (1) a face-to-face interview with the student’s legal guardians, who answered a questionnaire about the student’s sociodemographic, behavioral, and clinical data; (2) a physical examination consisting of weight and height measurements; and (3) blood tests for biochemical and molecular evaluation of SNPs.
Questionnaire
Sociodemographic
Sociodemographic data included data such as gender, age, skin color (self-reported), school (private or public), family income (minimum wage) and the schooling of the chief of the family. In 2021, the minimum wage was approximately 207 dollars.
Clinical
Neonatal history (gestational age and birth weight), breastfeeding and family history of obesity, DM2 or dyslipidemia were recorded.
Anthropometric parameters
Weight
Weight was measured on a Tanita Ironman InnerScan® digital anthropometric scale in an orthostatic position wearing light clothing.
Height
Height was measured with a Sanny® field stadiometer, graduated in millimeters (mm), with the student standing with his back to the marker, with bare feet together, in an orthostatic position, and looking straight ahead.
BMI
BMI was calculated by weight (in kilogram) divided by height squared (in meters), being classified as eutrophic (if BMI/age ≤ + 1 in the Z score) or excess weight (if BMI/age > + 1 in the Z score) [26].
Biochemical analysis
For the biochemical analysis, the participant was in a stable metabolic state with the usual diet state, and as recommended for the child population, fasting was not needed [27, 28].
Glycated hemoglobin
Following the manufacturer’s protocol, the glycated hemoglobin (HbA1c) level was measured via immunoturbidimetry on a Cobas Integra 400 plus® automatic analyzer (Roche, Germany). The HbA1c level was classified as normal (if < 5.7%) or high (if ≥ 5.7%) [29].
Lipid profile
The serum concentrations of triglycerides (TG), total cholesterol (TC), and HDL-c were measured via the enzymatic‒colorimetric method (Triglicérides Monoreagent®, Cholesterol Monoreagent®, HDL Direto®, Bioclin/Quibasa, Brazil) and evaluated via Chemwell R6® Automated Analyzer, Awareness Technology. The Friedewald formula was used to calculate the LDL-c fraction: LDL-c (mg/dL) = (TC-HDL-c) - (TG/5), applicable when the concentration of TG is ≤ 400 mg/dL [30]. The Brazilian Society of Cardiology [31] criteria were used to classify dyslipidemia. According to this criteria, a normal value is TC < 170 mg/dL, LDL-c < 110 mg/dL, HDL-c >45 mg/dL, and TG < 85 mg/dL (for ages 0 to 9) and < 100 mg/dL (for ages 10 to 19).
Molecular analysis
Genomic DNA extraction was performed via the Wizard® Genomic DNA Purification Kit (Promega®) according to the manufacturer’s protocol. The SNPs rs1121980, rs1799983 and rs693 were chosen on the basis of the following criteria: (1) positive association with obesity in previous studies, including GWAS, in similar ethnic groups as the study population [2, 3, 17, 32, 33]; and (2) absence of rare alleles in studies with African and European populations. The allele and genotype frequencies were evaluated and tested for Hardy‒Weinberg equilibrium (HWE). The allelic discrimination technique was performed via real-time PCR and a set of primers and probes specific for each SNP (TaqMan® Minor Groove Binder-MGB, TaqMan® System; 7500 fast real-time PCR Systems, Applied Biosystems). The next steps involved initial denaturation at 95 °C for 10 min, 40 cycles of annealing at 95 °C for 15 s, and a final extension at 60 °C for 1 min. Two negative controls were included for quality control and to detect potential contamination. The results were read, and samples underwent supplementary amplification if there was uncertainty in interpretation.
Exposure, outcome, and confounders
Sociodemographic, clinical, anthropometric, and biochemical variables and the rs1121980, rs693 and rs1799983 SNPs were evaluated as exposures. Obesity was considered the outcome.
Genetic score
Considering the SNPs that were statistically significant in the bivariate analysis, a genetic score was constructed to assess the combined influence of rs1121980 and rs1799983 on the association with obesity. Although rs693 was initially analyzed, it was not included in the genetic score as it did not show statistical significance in the univariate analysis. This score, which is based on the frequency of alleles considered “risk” or “protective,” was defined according to the literature. The criteria were as follows: (1) rs1121980, characterized by the exchange of G for A, where allele G (wild type) is considered protective and allele A (altered) is a risk allele. (2) rs1799983 is characterized by the exchange of G for T, where allele G (wild type) is considered protective and allele T (altered) is a risk allele. (3) The total score for everyone was obtained by summing the genotypes of the SNPs as follows: Σ = FTO genotype + NOS3 genotype. The scoring system allocated 0 points for the protective allele, 1 point for one risk allele, and 2 points for two risk alleles. Thus, the total genetic risk score, derived from the points of both SNPs, could range from 0 to 4. A score of 0 indicated that the individual carried protective alleles for both SNPs, whereas a score of 4 indicated the presence of two risk alleles in both SNPs.
Statistical analysis
The database was built and analyzed via Stata software version 15.0. The participants were characterized through bivariate analysis. Pearson’s chi-square test or Fisher’s exact test was used for differences in these groups. The allele frequencies were obtained via gene counting and tested for HWE.
Logistic regression was conducted to assess the link between SNPs and obesity. A theoretical causality model using a directed acyclic graph (DAG) was created to guide the analysis (Dagitty® version 3.2). The DAG outlined the exposure variable (SNPs), outcome (obesity), and covariates (Fig. 1). To avoid unnecessary adjustments, spurious associations, and estimation errors, the backdoor criterion was employed to identify a minimal set of confounding variables [34]. Two models were proposed on the basis of the DAG: (1) adjusting for skin color alone to eliminate backdoor biases and (2) adjusting for age, gestational weight, skin color, family income, glycemic and lipid profile. Both models were analyzed to compare their differences. The variance inflation factor was used to assess collinearity among covariates via the “subsetByVIF” package with a maximum cutoff of 10 (VIF < 10) [35].
Fig. 1.
DAGs on the influence of SNPs in the FTO and NOS3 genes on obesity. Description: Causal connections represented by arrows were established between variables. The variable in green with the symbol “►” is the exposure variable; the variable in blue with the letter “I” is the outcome variable; the blue variables are antecedents of the outcome variable; the pink variables are antecedents of the outcome and exposure variables; and the white variable is a collider and indicates a closed or blocked path
In addition to the primary analyses conducted on the total sample, we performed a stratified analysis by age, categorizing participants into two groups: children (6–9 years) and adolescents (10–17 years). This approach aimed to assess potential differences in the association between the studied polymorphisms and obesity across age groups. The results of these stratified analyses are available in the Supplementary Material.
An additive interaction analysis was performed to assess whether the impact of the number of risk alleles on the likelihood of obesity was influenced by other clinical factors. In the models, the number of risk alleles was treated as a continuous variable, whereas the other factors were included as categorical predictors. To explore potential modification effects, we included a first-degree (additive) interaction term between the number of risk alleles and each categorical variable in the models via the interaction operator #. This allowed us to evaluate whether the impact of risk alleles on obesity varied with different levels of the explanatory variables. By incorporating this interaction term, the model estimated the differential effect of risk alleles on the likelihood of obesity for each category of clinical risk factor, helping us identify and quantify how the combination of genetic risk factors and clinical conditions influences obesity development.
Results
The study involved 921 students, 43.0% of whom were children (6–9 years) and 57.0% of whom were adolescents (10–17 years). For children and adolescents, the median ages were 7 (IQR: 6–8) and 12 (IQR: 11–14) years, respectively. The prevalence of obesity is 8.5%. Table 1 presents the sociodemographic and clinical characteristics of the individuals.
Table 1.
Sociodemographic, behavioral, and clinical characteristics related to obesity in children and adolescents
| Characteristics | Total, N (%) | Non obese, N (%) | Obese, N (%) | Odds ratio, (CI95%) | p-value a |
|---|---|---|---|---|---|
| Sociodemographic variables | |||||
| Sex | |||||
| Female | 452 (49.1) | 420 (49.7) | 32 (42.1) | 1.00 | |
| Male | 469 (50.9) | 425 (50.3) | 44 (57.9) | 1.36 (0.84–2.18) | 0.206 |
| Age (years) | |||||
| 6–9 | 393 (42.7) | 356 (42.1) | 37 (48.7) | 1.00 | |
| 10–17 | 528 (57.3) | 489 (57.9) | 39 (51.3) | 0.77 (0.48–1.23) | 0.270 |
| Skin color | |||||
| White | 288 (31.3) | 270 (31.9) | 18 (23.7) | 1.00 | |
| Brown | 426 (46.2) | 387 (45.8) | 39 (51.3) | 1.51 (0.85–2.70) | 0.162 |
| Black | 207 (22.5) | 188 (22.3) | 19 (25.0) | 1.52 (0.77–2.97) | 0.224 |
| School | |||||
| Private | 124 (13.5) | 118 (14.0) | 6 (7.9) | 1.00 | |
| Public | 797 (86.5) | 727 (86.0) | 70 (92.1) | 1.89 (0.80–4.46) | 0.144 |
| Family income | |||||
| > 10 MW | 40 (4.3) | 39 (4.6) | 1 (1.2) | 1.00 | |
| 4–10 MW | 146 (15.7) | 136 (16.0) | 10 (12.3) | 2.87 (0.36–23.10) | 0.322 |
| 2–4 MW | 250 (26.9) | 222 (26.2) | 28 (34.6) | 4.92 (0.65–37.21) | 0.123 |
| ≤ 2 MW | 493 (53.1) | 451 (53.2) | 42 (51.9) | 3.63 (0.49–27.11) | 0.209 |
| Schooling of the chief of family | |||||
| High | 194 (21.1) | 181 (21.4) | 13 (17.1) | 1.00 | |
| Medium | 557 (60.5) | 512 (60.6) | 45 (59.2) | 1.22 (0.54–2.32) | 0.536 |
| Low | 170 (18.4) | 152 (17.8) | 18 (23.7) | 1.67 (0.79–3.52) | 0.177 |
| Clinical variables | |||||
| Gestational age (in weeks) | |||||
| ≥ 37 | 832 (91.6) | 762 (91.5) | 70 (93.3) | 1.00 | |
| < 37 | 76 (8.4) | 71 (8.5) | 5 (6.7) | 0.77 (0.30–1.96) | 0.579 |
| Birth weight (in grams) | |||||
| 2.500–4.000 | 776 (87.5) | 712 (87.8) | 64 (86.5) | 1.00 | |
| < 2.500 | 78 (8.8) | 74 (9.1) | 4 (5.4) | 0.60 (0.21–1.70) | 0.337 |
| ≥ 4.000 | 31 (3.5) | 25 (3.1) | 6 (8.1) | 2.67 (1.06–6.75) | 0.038 |
| Breast-feeding | |||||
| Exclusive | 617 (67.0) | 569 (67.3) | 48 (63.2) | 1.00 | |
| Complemented | 220 (23.9) | 199 (23.6) | 21 (27.6) | 1.25 (0.73–2.14) | 0.414 |
| Formula milk | 84 (9.1) | 77 (9.1) | 7 (9.2) | 1.08 (0.47–2.47) | 0.859 |
| Family history of diseases | |||||
| Dyslipidemia | 148 (16.1) | 135 (15.6) | 13 (17.1) | 1.08 (0.58–2.03) | 0.797 |
| Obesity | 364 (39.5) | 311 (36.8) | 53 (69.7) | 3.96 (2.38–6.58) | < 0.001 |
| Diabetes | 88 (9.6) | 73 (8.6) | 15 (19.7) | 2.60 (1.41–4.80) | 0.002 |
Legend: MW: minimum wage; CI: Confidence Interval
ap-value corresponding to the Pearson’s chi-square test
Significant differences in biochemical variables (HbA1c, HDL-c, and TG) and the AA genotype at rs1121980 and the TT genotype at rs1799983 were detected between students with and without obesity (Table 2). rs1121980 and rs1799983 were consistent with HWE (p = 0.466 and p = 0.899, respectively). rs693 showed no difference between groups. It was therefore removed from the adjusted analyses.
Table 2.
Biochemical and genetics characteristics related to obesity in children and adolescents
| Characteristics | Total, N (%) | Non obese, N (%) | Obese, N (%) | Odds ratio, (CI 95%) | p-value a |
|---|---|---|---|---|---|
| Biochemical variables | |||||
| Glycated hemoglobin | |||||
| < 5.7 | 875 (95.5) | 807 (96.1) | 68 (89.5) | 1.00 | |
| ≥ 5.7 | 41 (4.5) | 33 (3.9) | 8 (10.5) | 2.87 (1.27–6.47) | 0.008 |
| Total Cholesterol (mg/dL) | |||||
| < 170 | 284 (31.0) | 261 (31.1) | 23 (30.7) | 1.00 | |
| ≥ 170 | 631 (69.0) | 579 (68.9) | 52 (69.3) | 0.77 (0.48–1.23) | 0.942 |
| LDL-c (mg/dL) | |||||
| < 110 | 553 (60.4) | 507 (60.4) | 46 (61.3) | 1.00 | |
| ≥ 110 | 362 (39.6) | 333 (39.6) | 29 (38.7) | 0.96 (0.59–1.55) | 0.868 |
| HDL-c (mg/dL) | |||||
| > 45 | 852 (93.1) | 787 (93.7) | 65 (86.7) | 1.00 | |
| ≤ 45 | 63 (6.9) | 53 (6.3) | 10 (13.3) | 2.28 (1.11–4.70) | 0.021 |
| Triglycerides (mg/dL) b | |||||
| Normal | 402 (43.9) | 383 (45.6) | 19 (25.3) | 1.00 | |
| Elevated | 513 (56.1) | 136 (16.0) | 56 (74.7) | 2.47 (1.44–4.22) | 0.001 |
| Genetic variables | |||||
| FTO (rs1121980) | |||||
| Genotype | |||||
| G/G | 289 (31.4) | 272 (32.2) | 17 (22.4) | 1.00 | |
| A/G | 483 (52.4) | 441 (52.2) | 42 (55.3) | 1.52 (0.85–2.79) | 0.154 |
| A/A | 149 (16.2) | 132 (15.6) | 17 (22.4) | 2.06 (1.01–4.20) | 0.040 |
| Allele | |||||
| G | 1061 (57.6) | 985 (58.3) | 76 (50.0) | 1.00 | |
| A | 781 (42.4) | 705 (41.7) | 76 (50.0) | 1.39 (1.00-1.94) | 0.047 |
| APOB (rs693) | |||||
| Genotype | |||||
| G/G | 411 (44.6) | 372 (44.0) | 39 (51.3) | 1.00 | |
| A/G | 403 (43.8) | 373 (44.1) | 30 (39.5) | 0.76 (0.46–1.26) | 0.296 |
| A/A | 107 (11.6) | 100 (11.8) | 7 (9.2) | 0.66 (1.26–1.48) | 0.339 |
| Allele | |||||
| G | 1225 (66.5) | 1117 (66.1) | 108 (71.1) | 1.00 | |
| A | 617 (33.5) | 573 (33.9) | 44 (28.9) | 0.79 (0.55–1.14) | 0.215 |
| NOS3 (rs1799983) | |||||
| Genotype | |||||
| G/G | 528 (57.3) | 491 (58.1) | 37 (48.7) | 1.00 | |
| T/G | 342 (37.1) | 311 (36.8) | 31 (40.8) | 1.32 (0.80–2.17) | 0.270 |
| T/T | 51 (5.5) | 43 (5.1) | 8 (10.5) | 2.46 (1.08–5.63) | 0.027 |
| Allele | |||||
| G | 1398 (75.9) | 1293 (76.5) | 105 (69.1) | 1.00 | |
| T | 444 (24.1) | 397 (23.5) | 47 (30.9) | 1.45 (1.01–2.09) | 0.040 |
| Genetic score c | |||||
| 0 | 165 (17.9) | 157 (18.6) | 8 (10.5) | 1.00 | |
| 1 | 389 (42.2) | 358 (42.4) | 31 (40.8) | 1.70 (0.76–3.78) | 0.194 |
| 2 | 271 (29.4) | 250 (29.6) | 21 (27.6) | 1.65 (0.71–3.81) | 0.243 |
| 3 | 90 (9.8) | 76 (9.0) | 14 (18.4) | 3.61 (1.45–8.98) | 0.006 |
| 4 | 6 (0.7) | 4 (0.5) | 2 (2.6) | 9.81 (1.56–16.78) | 0.015 |
a p-value corresponding to the Pearson’s chi-square test
bThe cut-off point for triglycerides varies according to age, and is considered normal when the value is < 85 mg/dL for children (up to 9 years of age) and < 100 mg/dL for adolescents (between 10 and 17 years of age) (Brazilian Society of Cardiology, 2017). CI: Confidence Interval; HDL-c: High Density Lipoprotein Cholesterol;; LDL-c: Low Density Lipoprotein Cholesterol
cRisk allele score was created to evaluate the combined influence of polymorphisms in the FTO (rs1121980) and NOS3 (rs1799983) genes on obesity. This score ranges from 0 (no risk alleles) to 4 (maximum risk alleles), based on the presence of protective or risk alleles. For FTO, allele G is protective and allele A is a risk allele. For NOS3, allele G is protective and allele T is a risk allele. Although the APOB (rs693) gene was analyzed, it was not included in the genetic risk score as it did not remain significant in the univariate analysis
Table 3 presents the associations between the SNPs rs1121980 and rs1799983 and obesity based on two theoretical models suggested by the DAG (Fig. 1). After the necessary adjustments, homozygotes for the risk alleles of rs1121980 and rs1799983 (genotypes AA and TT, respectively) remained significant, with both showing a more than a twofold increased likelihood of being obese.
Table 3.
Association between polymorphisms FTO and NOS3 with obesity in childrens and adolescents
| SNPs | Genotype | Total, N (%) | Unadjusted OR (CI95%) | p-valueb | Model adjusted 1a OR (CI95%) | p-valueb | Model adjusted 2a OR (CI95%) | p-valueb |
|---|---|---|---|---|---|---|---|---|
| FTO (rs1121980) | GG | 289 (31.4) | 1.00 | 1.00 | 1.00 | |||
| AG | 483 (52.4) | 1.52 (0.85–2.73) | 0.157 | 1.56 (0.87–2.81) | 0.149 | 1.53 (0.85–2.75) | 0.156 | |
| AA | 149 (16.2) | 2.06 (1.02–4.16) | 0.044 | 2.12 (1.05–4.32) | 0.041 | 2.07 (1.02–4.20) | 0.044 | |
| NOS3 (rs1799983) | GG | 528 (57.3) | 1.00 | 1.00 | 1.00 | |||
| TG | 342 (37.1) | 1.31 (0.80–2.16) | 0.287 | 1.29 (0.78–2.13) | 0.313 | 1.34 (0.81–2.22) | 0.252 | |
| TT | 51 (5.6) | 2.51 (1.10–5.73) | 0.029 | 2.61 (1.13–5.98) | 0.024 | 2.49 (1.08–5.73) | 0.032 | |
| Risk allele score | (0–4) | 1.33 (1.27–1.39)* | 1.45 (1.13–1.89) | 0.004 | 1.46 (1.13–1.88) | 0.004 | 1.46 (1.13–1.89) | 0.004 |
Legend: OR: Odds ratio; CI: Confidence interval
aThe directed acyclic graph (DAG) was used to support the theoretical model for the adjusted analysis between FTO and NOS3 polymorphism genotypes (exposure) and obesity (outcome). Analysis adjusted for the following minimum and sufficient variables, according to two possible suggested models. Model 1: adjusted for skin color. Model 2: adjusted for age, birth weight, breastfeeding, family income, glycemic and lipid profile
Collinearity among variables in the adjusted model 2 evaluated by variance inflance factor (VIF) with the maximum remaining VIF = 1.2179
bp-value corresponding to the Wald test
*Data are presented as a mean and 95% confidence interval
Risk allele score was created to evaluate the combined influence of polymorphisms in the FTO (rs1121980) and NOS3 (rs1799983) genes on obesity. This score ranges from 0 (no risk alleles) to 4 (maximum risk alleles), based on the presence of protective or risk alleles. For FTO, allele G is protective and allele A is a risk allele. For NOS3, allele G is protective and allele T is a risk allele. Although the APOB (rs693) gene was analyzed, it was not included in the genetic risk score as it did not remain significant in the univariate analysis
In addition, the distribution of the genetic risk score showed that 17.9% of students had zero risk alleles, 42.2% had one risk allele, 29.4% had two risk alleles, 9.8% had three risk alleles, and only 0.7% carried four risk alleles (Table 2 ). When considering the sum of risk alleles from both SNPs as a continuous variable, for both models used, each additional risk allele increased the odds of the youth being obese by 1.4 times (Table 3 ). Moreover, individuals carrying 3 or 4 risk alleles had a markedly higher obesity prevalence compared to those with fewer risk alleles, reinforcing a potential cumulative genetic effect (Table 2 ).
The stratified analysis by age revealed that the associations between SNPs and obesity were stronger among adolescents (10–17 years) than among children (6–9 years). In the younger age group, the reduced sample size resulted in wider confidence intervals, indicating lower estimate precision. Despite these differences, the overall association patterns remained consistent in both groups. Full results of the stratified analyses are provided in the Supplementary Material (Table S2 and S3).
Figure 2 illustrates the modification of obesity risk on the basis of the interaction between risk alleles and clinical factors. Each additional risk allele significantly increased the likelihood of obesity in the presence of specific clinical conditions. Specifically, for children and adolescents who were not exclusively breastfed, the addition of one risk allele increased the likelihood of obesity by 62% [OR = 1.62 (CI: 1.20–2.20)]. For those with a high birth weight, each additional risk allele increased the likelihood of obesity by 93% [OR = 1.93 (CI: 1.08–3.45)]. Similarly, each additional risk allele was associated with a 64% increase in the likelihood of obesity in individuals with a family history of obesity [OR = 1.64 (CI: 1.13–2.37)], a 79% increase in those with a family history of DM2 [OR = 1.79 (CI: 1.38–2.32)], and a 142% increase in those with a family history of dyslipidemia [OR = 2.42 (CI: 1.63–3.61)].
Fig. 2.
Modification of obesity risk based on the interaction between risk alleles and clinical factors.
Discussion
The present study was one of the first to evaluate the combination of SNPs in the FTO and NOS3 genes in association with obesity in children and adolescents from Ouro Preto, Brazil. Data from the last few decades indicate that pediatric obesity is associated with numerous complications and comorbidities, and as its prevalence has increased, diseases traditionally seen in adulthood have become more common early in life [36–38]. In Brazil, obesity has also significantly increased among children and adolescents [39]. On the basis of a previous study conducted 15 years ago within the same target population [24], this trend among youth has persisted, increasing from 6.7 to 8.5%.
In Brazil, studies involving children and adolescents that have investigated the SNPs rs1121980 and rs1799983 and their associations with obesity are scarce. The present study revealed that for each additional risk allele of these SNPs, the chance of obesity increased by 1.46 times (p = 0.004). The frequencies of the risk allele A at rs1121980 and the risk allele T at rs1799983 were 42.4% and 24.1%, respectively. Considering this frequency within the group of obese children and adolescents, allele A was significantly different from that in the eutrophic group (50.0% versus 41.7%, p = 0.047). Similarly, the allele T also exhibited a significant difference between the obese and eutrophic groups, with frequencies of 30.9% and 23.5%, respectively (p = 0.040). These findings align with those of several studies in the literature [2, 5, 6, 12].
The presence of the A allele of the SNP rs1121980 may affect the transcriptional activity of the FTO gene, resulting in increased BMI, WC, and extreme obesity [2, 5, 6]. Its presence was associated with Grade III obesity in adults (BMI > 40) [2], and with higher BMI (increase of 0.31 units per allele; p < 0.001) and larger WC (increase of 0.77 cm per allele; p < 0.001) [6]. Therefore, the data from the present study reinforce these findings.
Studies indicate that decreased bioavailability of nitric oxide is also involved in the pathophysiology of metabolic diseases, such as obesity and DM2 [40, 41]. Therefore, the contribution of NOS3 gene SNPs, particularly the T allele at rs1799983, to susceptibility to these conditions has been investigated. Consistent with our data, Podolski et al. (2007) reported that among African and Euro-American youth, carriers of the T allele of the same SNP presented increased BMI and WC [12]. Another study reported that, in addition to the T allele (OR = 1.72; P = 0.001), the TT genotype in North Africans was associated with a nearly threefold increased risk of developing obesity (OR = 2.93; P = 0.03) [40]. These findings reinforce the suggestion that this SNP may influence genetic susceptibility to obesity. What remains to be investigated is whether this relationship is plausible in children and adolescents, as few studies, particularly in Brazil, have evaluated this SNP and obesity in young populations.
Our study focused on clinical factors that may also interact with the aforementioned SNPs in the development of obesity. Regarding neonatal history, those born with excess weight (≥ 4,000 g) were found to have 2.67 (CI 1.06–6.75, p = 0.038) times higher odds of obesity compared to those born with an appropriate weight (between 2,500 and 4,000 g), which aligns with several other studies [22, 42–44]. In interaction with the risk alleles assessed in this study, the odds of obesity were 1.93 times greater for individuals with this profile.
Zhu et al. (2023) reported that for children over 8 years of age, inadequate free play time was a key factor in childhood obesity, whereas for those under 8 years of age, birth weight and parental BMI were significant contributors [44]. The current study also revealed that students with a family history of obesity were more likely to be obese [OR 3.96, p < 0.0001] than were students without a family history of obesity. Additionally, other studies have reported this relationship [45–47], with a significantly greater risk if both parents are obese [OR 6.83, p < 0.05] than when only one parent is obese [OR 2.49, p < 0.05] [47]. Furthermore, if the parents had severe obesity (BMI ≥ 35), the risk increased to 22.34 (p < 0.01), regardless of age, sex, socioeconomic status, and ethnicity [45]. Few studies have focused on rs1121980 and a family history of obesity. In Brazil, Todendi et al. (2020) reported that parental weight also contributed to obesity and additionally interacted with the FTO gene, which supports our findings [48].
The relationships between a family history of diabetes and dyslipidemia and obesity development are complex and involve various factors. In addition to genetics, families with these conditions tend to have poorer eating behaviors, creating an obesogenic environment that promotes overweight and obesity in children [49].
Finally, we observed that children and adolescents carrying the risk alleles of both SNPs who were not exclusively breastfed had 1.62 times greater odds of obesity (p = 0.002). The protective effect of breastfeeding is attributed to slower growth rates than those of formula feeding [50, 51], with significant differences in bioactive nutrient composition influencing endocrine modulation [52]. Further studies are recommended to explore this relationship.
This study highlights the importance of assessing genetic predisposition to obesity in a representative sample of children and adolescents. This is one of the first studies to find a significant association between childhood obesity and the SNPs rs1121980 and rs1799983 in the Brazilian population, both individually and together. Given the scarcity of genetic studies in pediatric populations, these findings may enhance the understanding of pathophysiological mechanisms and aid in the development of new prognostic and therapeutic tools for obesity.
While our findings offer valuable insights, there are limitations to the study. The cross-sectional design prevents causal inferences between exposure factors and obesity. Additionally, we did not assess key factors such as physical activity levels, dietary patterns, or pubertal stage. It is also important to point out that the blood collection without fasting may have influenced the high triglyceride levels, as it was not possible to obtain information about the previous meal, so it was not possible to assess the fat load ingested. Although stratification by age provided useful insights into potential differences between children and adolescents, it reduced the sample size in each group, leading to lower statistical power and wider confidence intervals, particularly in the 6–9-year-old group. Finally, the potential impact of the pandemic on BMI and biochemical parameters should be considered, although it remains unclear whether these effects are temporary or permanent.
In conclusion, our results suggest that rs1121980 and rs1799983 may influence obesity development in Brazilian children and adolescents in urban settings. Given the heterogeneity of the literature and the complex interplay between genes and the environment, we recommend further studies to explore the physiological effects of allelic variations in the FTO and NOS3 genes, particularly rs1121980 and rs1799983, in relation to environmental factors.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
To all members of the Coraçõezinhos de Ouro Preto group. Laboratory of Clinical Analysis (LAPAC), Federal University of Ouro Preto/MG, for technical support in blood biochemistry analysis. We also thank the local health team and participants and their parents for their collaboration in the field work. We are also grateful for the support and encouragement of the Federal University of Ouro Preto (UFOP), the National Council for Scientific and Technological Development (CNPq, process No. 306467/2018-6) and the Foundation for Research Support of the State of Minas Gerais (FAPEMIG-APQ-01933-21). To the Coordination for the Improvement of Higher Education Personnel (CAPES) for the postgraduate and postdoctoral scholarships.
Abbreviations
- SNPs
Single nucleotide polymorphisms
- FTO
Alpha-ketoglutarate-dependent dioxygenase
- G
Guanine
- A
Adenine
- BMI
Body mass index
- WC
Waist circumference
- NOS3
Endothelial nitric oxide
- DM2
Diabetes mellitus type 2
- TC
Total cholesterol
- TG
Triglyceride
- LDL
Low-density lipoprotein
- HbA1c
Glycated hemoglobin
- HDL
High-density lipoprotein
- DAG
Directed acyclic graph
- APOB
Apolipoprotein B
- HWE
Hardy‒Weinberg equilibrium
Author contributions
T.S.V. and G.L.L.M.C. conceptualized and designed the study, coordinated and supervised data collection, and critically reviewed the manuscript for important intellectual content.A.P.B. carried out the biochemical and molecular analyses, drafted the initial manuscript and critically reviewed the manuscript for important intellectual content.L.A.A.M.J. carried out the statistical analyses, drafted the figures and tables and critically reviewed the manuscript for important intellectual content.L.G.L., W.W.O., C.B.C., A.C.M.C., G.T.A., D.F.A.R., I.M.C.R., A.V.C.R., J.P.M.M. field data collection and manuscript review for important intellectual content.All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.
Funding
FAPEMIG (APQ-01933-21) and CNPq (306467/2018-6) supported this work.
Data availability
The datasets generated and/or analyzed as part of the current study are not publicly available due to confidentiality agreements with subjects. However, they can be made available solely for the purpose of review and not for the purpose of publication from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
This study was performed in accordance with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Federal University of Ouro Preto (CAAE 28680020.0.0000.5150).
Consent for publication
Informed consent was obtained from all individual participants included in the study, and written informed consent was obtained from the parents.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.World Health Organization. Obesity. http://who.int/health-topics/obesity#tab=tab_1. Accessed 21 July 2024.
- 2.Dina C, Meyre D, Gallina S, Durand E, Körner A, Jacobson P, et al. Variation in FTO contributes to childhood obesity and severe adult obesity. Nat Genet. 2007;39(6):724–6. 10.1038/ng2048. [DOI] [PubMed] [Google Scholar]
- 3.Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007;316(5826):889–94. 10.1126/science.1141634. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.National library of medicine. National center for biotecnology, Information R. 2025. https://www.ncbi.nlm.nih.gov/snp/rs1121980. Accessed 30 Jan 2025.
- 5.Hinney A, Nguyen TT, Scherag A, Friedel S, Brönner G, Müller TD, et al. Genome wide association (GWA) study for early onset extreme obesity supports the role of fat mass and obesity associated gene (FTO) variants. PLoS ONE. 2007;2(12):e1361. 10.1371/journal.pone.0001361. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Vimaleswaran KS, Li S, Zhao JH, Luan J, Bingham SA, Khaw KT, et al. Physical activity attenuates the body mass index-increasing influence of genetic variation in the FTO gene. Am J Clin Nutr. 2009;90(2):425–8. 10.3945/ajcn.2009.27652. [DOI] [PubMed] [Google Scholar]
- 7.Francis SH, Busch JL, Corbin JD, Sibley D. cGMP-dependent protein kinases and cGMP phosphodiesterases in nitric oxide and cGMP action. Pharmacol Rev. 2010;62(3):525–63. 10.1124/pr.110.002907. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Joshi MS, Mineo C, Shaul PW, Bauer JA. Biochemical consequences of the NOS3 Glu298Asp variation in human endothelium: altered caveolar localization and impaired response to shear. FASEB J. 2007;21(11):2655–63. 10.1096/fj.06-7088com. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.National Library of Medicine. National Center for Biotecnology Information, Rockville. 2025. https://www.ncbi.nlm.nih.gov/snp/rs1799983. Accessed 30 Jan 2025.
- 10.Monti LD, Barlassina C, Citterio L, Galluccio E, Berzuini C, Setola E, et al. Endothelial nitric oxide synthase polymorphisms are associated with type 2 diabetes and the insulin resistance syndrome. Diabetes. 2003;52(5):1270–5. 10.2337/diabetes.52.5.1270. [DOI] [PubMed] [Google Scholar]
- 11.Pereira AC, Sposito AC, Mota GF, Cunha RS, Herkenhoff FL, Mill JG, et al. Endothelial nitric oxide synthase gene variant modulates the relationship between serum cholesterol levels and blood pressure in the general population: new evidence for a direct effect of lipids in arterial blood pressure. Atherosclerosis. 2006;184(1):193–200. 10.1016/j.atherosclerosis.2005.03.035. [DOI] [PubMed] [Google Scholar]
- 12.Podolsky RH, Barbeau P, Kang HS, Zhu H, Treiber FA, Snieder H. Candidate genes and growth curves for adiposity in African- and European-American youth. Int J Obes (Lond). 2007;31(10):1491–9. 10.1038/sj.ijo.0803673. [DOI] [PubMed] [Google Scholar]
- 13.Chang MH, Yesupriya A, Ned RM, Mueller PW, Dowling NF. Genetic variants associated with fasting blood lipids in the U.S. Population: third National health and nutrition examination survey. BMC Med Genet. 2010;11:62. 10.1186/1471-2350-11-62. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Kingah PL, Luu HN, Volcik KA, Morrison AC, Nettleton JA, Boerwinkle E. Association of NOS3 Glu298Asp SNP with hypertension and possible effect modification of dietary fat intake in the ARIC study. Hypertens Res. 2010;33(2):165–9. 10.1038/hr.2009.198. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Kathiresan S, Melander O, Anevski D, Guiducci C, Burtt NP, Roos C, Hirschhorn JN, Berglund G, Hedblad B, Groop L, Altshuler DM, Newton-Cheh C, Orho-Melander M. Polymorphisms associated with cholesterol and risk of cardiovascular events. N Engl J Med. 2008;358(12):1240–9. 10.1056/NEJMoa0706728. [DOI] [PubMed] [Google Scholar]
- 16.Deo RC, Reich D, Tandon A, Akylbekova E, Patterson N, Waliszewska A, Kathiresan S, Sarpong D, Taylor HA Jr, Wilson JG. Genetic differences between the determinants of lipid profile phenotypes in African and European Americans: the Jackson heart study. PLoS Genet. 2009;5(1):e1000342. 10.1371/journal.pgen.1000342. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Niu C, Luo Z, Yu L, Yang Y, Chen Y, Luo X, Lai F, Song Y. Associations of the APOB rs693 and rs17240441 polymorphisms with plasma APOB and lipid levels: a meta-analysis. Lipids Health Dis. 2017;16(1):166. 10.1186/s12944-017-0558-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Colima Fausto AG, Topete J, González García JR, Hernández Flores TJ, Rodríguez Preciado SY, Magaña Torres MT. Effect of APOB gene polymorphisms on body mass index, blood pressure, and total cholesterol levels: A cross-sectional study in Mexican population. Medicine. 2022;101(38):e30457. 10.1097/MD.0000000000030457. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Rodrigues AC, Sobrino B, Genvigir FD, Willrich MA, Arazi SS, Dorea EL, Bernik MM, Bertolami M, Faludi AA, Brion MJ, Carracedo A, Hirata MH, Hirata RD. Genetic variants in genes related to lipid metabolism and atherosclerosis, dyslipidemia and Atorvastatin response. Clin Chim Acta. 2013;417:8–11. 10.1016/j.cca.2012.11.028. [DOI] [PubMed] [Google Scholar]
- 20.de Souza TV, Batista AP, de Menezes-Júnior LAA, Costa ACM, Louzada CF, Lourenção LG, de Oliveira WW, Lima MC, Araujo MAM, Baptista IVD, de Souza Soares AC, Guedes RL, Machado-Coelho GLL. XbaI polymorphism in the APOB gene and its association with increased cholesterol in children and adolescents: Ouro Preto study. Sci Rep. 2024;14(1):31452. 10.1038/s41598-024-83099-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.National Library of Medicine. National Center for Biotecnology, Information R. 2025. https://www.ncbi.nlm.nih.gov/snp/rs693. Accessed 30 Jan 2025.
- 22.Ní Chonaill D, Brady B, Ball E, Keaver L. The influence of birthweight and breastfeeding status on BMI outcome at first year of primary school: a retrospective study of an Irish child cohort. Eur J Pediatr. 2023;182(4):1879–85. 10.1007/s00431-023-04874-9. [DOI] [PubMed] [Google Scholar]
- 23.Cândido AP, Ferreira S, Lima AA, de Carvalho Nicolato RL, de Freitas SN, et al. Lipoprotein(a) as a risk factor associated with ischemic heart disease: Ouro Preto study. Atherosclerosis. 2007;191(2):454–9. 10.1016/j.atherosclerosis.2006.04.031. [DOI] [PubMed] [Google Scholar]
- 24.Cândido AP, Benedetto R, Castro AP, Carmo JS, Nicolato RL, Nascimento-Neto RM, et al. Cardiovascular risk factors in children and adolescents living in an urban area of Southeast of Brazil: Ouro Preto study. Eur J Pediatr. 2009;168(11):1373–82. 10.1007/s00431-009-0940-1. [DOI] [PubMed] [Google Scholar]
- 25.Batista AP, Barbosa KF, Masioli CZ, Queiroz EM, Marinho CC, Cândido APC, et al. High levels of chemerin associated with variants in the NOS3 and APOB genes in rural populations of Ouro Preto, Minas Gerais, Brazil. Braz J Med Biol Res. 2020;53(6):e9113. 10.1590/1414-431x20209113. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.World Health Organization. The WHO Child Growth Standards tools and toolkits Child growth standards. https://www.who.int/toolkits/child-growth-standards/standards. Accessed 21 July 2024.
- 27.Steiner MJ, Skinner AC, Perrin EM. Fasting might not be necessary before lipid screening: a nationally representative cross-sectional study. Pediatrics. 2011;128(3):463–70. 10.1542/peds.2011-0844. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Nordestgaard BG, Langsted A, Mora S, Kolovou G, Baum H, Bruckert E, et al. Fasting is not routinely required for determination of a lipid profile: clinical and laboratory implications including flagging at desirable concentration Cutpoints-A joint consensus statement from the European atherosclerosis society and European federation of clinical chemistry and laboratory medicine. Clin Chem. 2016;62(7):930–46. 10.1373/clinchem.2016.258897. [DOI] [PubMed] [Google Scholar]
- 29.American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2020. Diabetes Care. 2020;43(Suppl 1):S14-S31. 10.2337/dc20-S002 [DOI] [PubMed]
- 30.Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18(6):499–502. [PubMed] [Google Scholar]
- 31.Faludi AA, Izar MCO, Saraiva JFK, Chacra APM, Bianco HT, Afiune A, Neto et al. Atualização da Diretriz Brasileira de Dislipidemias e Prevenção da Aterosclerose– 2017. Erratum in: Arq Bras Cardiol. [published correction appears in Arq Bras Cardiol. 2017;109(5):499. 10.5935/abc.20170159]. Arq Bras Cardiol. 2017;109(2 Supl 1):1–76. Portuguese. 10.5935/abc.20170121
- 32.Oliveira-Paula GH, Lacchini R, Tanus-Santos JE. Clinical and Pharmacogenetic impact of endothelial nitric oxide synthase polymorphisms on cardiovascular diseases. Nitric Oxide. 2017;63:39–51. 10.1016/j.niox.2016.08.004. [DOI] [PubMed] [Google Scholar]
- 33.Kathiresan S, Srivastava D. Genetics of human cardiovascular disease. Cell. 2012;148(6):1242–57. 10.1016/j.cell.2012.03.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Hair JF. Multivariate data analysis: An overview. In: Lovric, M, editors international encyclopedia of statistical science. Springer, Berlin, Heidelberg. 2011. 10.1007/978-3-642-04898-2_395. Accessed 30 July 2024.
- 35.Plummer WR Jr., DuPont WD. eds. SUBSETBYVIF: Stata module to select a subset of covariates constrained by VIF. Washington, DC: Boston College Department of Economics. 2019. https://ideas.repec.org/s/boc/bocode.html. Accessed 30 July 2024.
- 36.Weihrauch-Blüher S, Schwarz P, Klusmann JH. Childhood obesity: increased risk for cardiometabolic disease and cancer in adulthood. Metabolism. 2019;92:147–52. 10.1016/j.metabol.2018.12.001. [DOI] [PubMed] [Google Scholar]
- 37.Horesh A, Tsur AM, Bardugo A, Twig G. Adolescent and childhood obesity and excess morbidity and mortality in young Adulthood-a systematic review. Curr Obes Rep. 2021;10(3):301–10. 10.1007/s13679-021-00439-9. [DOI] [PubMed] [Google Scholar]
- 38.NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet. 2017;390(10113):2627–42. 10.1016/S0140-6736(17)32129-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Brazil. Ministry of health. Secretariat of primary health.Care. Department of health.Promotion. PROTEJA: National strategy for the prevention and care.of childhood obesity. Brasília, DF: Ministry of Health, Department of Health Promotion; 2022.
- 40.Nasr HB, Dimassi S, M’hadhbi R, Debbabi H, Kortas M, Tabka Z, et al. Functional G894T (rs1799983) polymorphism and intron-4 VNTR variant of nitric oxide synthase (NOS3) gene are susceptibility biomarkers of obesity among Tunisians. Obes Res Clin Pract. 2016 Jul-Aug;10(4):465–75. 10.1016/j.orcp.2015.04.008. [DOI] [PubMed]
- 41.Bressler J, Pankow JS, Coresh J, Boerwinkle E. Interaction between the NOS3 gene and obesity as a determinant of risk of type 2 diabetes: the atherosclerosis risk in communities study. PLoS ONE. 2013;8(11):e79466. 10.1371/journal.pone.0079466. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Fan H, Guan T, Zhang X. Association of birthweight with overweight, obesity, and blood pressure among adolescents. Child (Basel). 2023;10(4):617. 10.3390/children10040617. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Kumar D, Sharma S, Raina SK. Risk of childhood obesity in children with high birth weight in a rural cohort of Northern India. Indian Pediatr. 2023;60(1):103–7. [PubMed] [Google Scholar]
- 44.Zhu W, Marchant R, Morris RW, Baur LA, Simpson SJ, Cripps S. Bayesian network modeling to identify on-ramps to childhood obesity. BMC Med. 2023;21(1):105. 10.1186/s12916-023-02789-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Whitaker KL, Jarvis MJ, Beeken RJ, Boniface D, Wardle J. Comparing maternal and paternal intergenerational transmission of obesity risk in a large population-based sample. Am J Clin Nutr. 2010;91(6):1560–7. 10.3945/ajcn.2009.28838. [DOI] [PubMed] [Google Scholar]
- 46.Liu Y, Sun X, Zhang E, Li H, Ge X, Hu F, et al. Association between types of screen time and weight status during the COVID-19 pandemic: A longitudinal study in children and adolescents. Nutrients. 2023;15(9):2055. 10.3390/nu15092055. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Moschonis G, Siopis G, Anastasiou C, Iotova V, Stefanova T, Dimova R, et al. Prevalence of childhood obesity by country, family Socio-Demographics, and parental obesity in Europe: the Feel4Diabetes study. Nutrients. 2022;14(9):1830. 10.3390/nu14091830. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Todendi PF, Martínez JA, Reuter CP, Klinger EI, Fiegenbaum M, Rosane de Moura Valim A. Influence of FTO (Fat mass and obesity) gene and parental obesity on Brazilian children and adolescents adiposity. J Pediatr Endocrinol Metab. 2020 Jul;22. 10.1515/jpem-2019-0594. http://jpem.ahead-of-print/jpem-2019-0594/jpem-2019-0594.xml. [DOI] [PubMed]
- 49.Haslam D. Obesity and diabetes: the links and common approaches. Prim Care Diabetes. 2010;4(2):105–12. 10.1016/j.pcd.2010.04.002. [DOI] [PubMed] [Google Scholar]
- 50.Koletzko B, von Kries R, Closa R, Escribano J, Scaglioni S, Giovannini M et al. Can infant feeding choices modulate later obesity risk? Am J Clin Nutr. 2009;89(5):1502S-1508S. 10.3945/ajcn.2009.27113D. Erratum in: Am J Clin Nutr. 2009;90(1):248. Monasterolo, Ricardo Closa [corrected to Closa, Ricardo]; Subias, Joaquín Escribano [corrected to Escribano, Joaquín]. [DOI] [PubMed]
- 51.Singhal A, Kennedy K, Lanigan J, Fewtrell M, Cole TJ, Stephenson T, et al. Nutrition in infancy and long-term risk of obesity: evidence from 2 randomized controlled trials. Am J Clin Nutr. 2010;92(5):1133–44. 10.3945/ajcn.2010.29302. [DOI] [PubMed] [Google Scholar]
- 52.Larnkjær A, Mølgaard C, Michaelsen KF. Early nutrition impact on the insulin-like growth factor axis and later health consequences. Curr Opin Clin Nutr Metab Care. 2012;15(3):285–92. 10.1097/MCO.0b013e328351c472. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analyzed as part of the current study are not publicly available due to confidentiality agreements with subjects. However, they can be made available solely for the purpose of review and not for the purpose of publication from the corresponding author upon reasonable request.