Links between gene variations and obesity traits and metabolism in school-aged children

This descriptive cross-sectional study was part of a larger project (Nutritional, Biochemical, and Genetic Study of an Overweight and Obese Child Population in the Southern Region) approved by the Directorate General of Health, the Ministry of Science and Education of Portugal and by the Ethics Committee of the Hospital Garcia de Orta, according to the principles of Helsinki Declaration. The project was conductd from January 2009 to June 2013 in a population of prepubertal children (based on Tanner stage) recruited from 87 public schools in Lisbon and the Tagus Valley metropolitan region. Initially, 5989 subjects were initially recruited based on the assessment of anthropometric measurements, bioelectrical impedance, biochemical and genetic analysis.

Clinical assessments were performed in the schools under the supervision of two pediatric consultants. All anthropometric measurements (weight, height, BMI, BMI z-score (zBMI), waist circumference (WC), hip circumference (HC), waist-hip ratio (WHR), waist-circumference-to-height ratio (WHtR), mid-upper arm circumference (MUAC), calf circumference (CC), percent body fat (%BF), percent skeletal muscle (%SM), and resting metabolic rate (RMR)) were obtained from barefoot participants dressed with lightweight clothing using methods described previously [30]. zBMI was determined using the least mean squares method [30]. Children were categorized as normal weight (control group) or overweight/obese (case group) per the World Obesity/Policy and Prevention standards [formerly International Obesity Task Force (IOTF)] [31].

Participants were instructed to fast for 12 hours before venipuncture for blood sampling in the morning at school. Blood samples were refrigerated at approximately 5 °C until transferred to our center immediately processed for serum separation, frozen at −80 °C on the same day, and stored until further analysis. The following serum biochemical parameters were assessed: total cholesterol (TC), high-density lipoprotein (HDL-c), low-density lipoprotein (LDL-c), triglycerides (TG), apolipoproteins A1 (Apo 1) and B (Apo B), glucose, creatinine, total proteins, ferritin, serum insulin and leptin. The homeostasis model assessment of insulin resistance (Homa-IR) was calculated from glucose (mg/dl) and insulin (µU/ml) using the following formula: Homa-IR = (insulin (µU/ml) × glucose (mg/dl))/405. All assessments were made using methods previously described [Furtado JM, Almeida SM, Mascarenhas, P, et al. Anthropometric features as predictors of atherogenic dyslipidemia and cardiovascular risk in a large population of school-aged children. (Under review)].

Genomic DNA was extracted from a whole peripheral blood sample using the MagNA Pure Compact Nucleic Acid Isolation Kit (Roche Diagnosis, 03730964001) and the MagNA Pure Compact Instrument (Roche Diagnostics GmbH, Germany) per the manufacturer’s instructions. DNA samples were stored at −20 °C until use. Real-time polymerase chain reaction (PCR)was performed in 96-well plates on an automated LightCycler 480 Real-Time PCR System (Roche Diagnostics, Vienna, Austria) using LightCycler FastStart DNA Master Hybprobe (Roche Diagnostics, Berlin, Germany) and LightSNPs (rs1137101 LEPR, rs9939609 FTO, rs2229616 and rs17782313 MC4R, and rs1801282 PPARG-2; TIB Molbiol Synthese labor, Berlin, Germany). The initial step for the allelic discrimination genotyping assay protocol included preincubation at 95 °C for 4 min, followed by 45 cycles of denaturation at 95 °C for 15 s, and annealing, extension, and detection for 40 s at 60 °C. To assess genotyping reproducibility, 10% of the sample was double-genotyped for all SNPs. Concordance rates >99% were obtained for the five tested SNPs. For negative control, Sterile PCR-grade H₂O was used for the negative control.

SPSS Statistics (IBM, version 24, Armonk, NY, USA) was used for all statistical test procedures. Unless otherwise indicated, variables in tables are means ± standard deviations. Multiple comparisons were made by pairwise t-tests with Dunn-Bonferroni adjustment. Pearson’s chi-square statistic was utilized to examine allele/genotype distribution differences across categories and to test for Hardy-Weinberg equilibrium. Phenotype mean differences for each SNP genotype were obtained against the homozygous wild type genotype. Genotype and allele effects on BMI (kg/m²) and zBMI for each SNP were calculated according to their formulae as described in Falconer [32]. Major allele dominance type was evaluated using the relationship between dominance and additive effects. Effect size for polymorphic allele on zBMI was graded according to the following cutoffs: very weak effect (|x|<0.05), weak effect (0.05<|x|<0.2), medium effect (0.8>|x|>0.2), and strong effect (|x|>0.8). A two-sided P < 0.05 determined the significance level for statistical analysis.

The LEPR rs11371101 variant is one of the most frequent LEPR gene polymorphisms and the most likely to have functional consequences [35]. Previous studies reported conflicting results with either positive [21–23] or no association [36] with obesity traits and metabolic parameters. For example, Pyrzaket et al. analyzed a cohort of 101 obese children (12−18 years old) and found that the LEPR gene variant was not associated with obesity, leptin, insulin resistance, or other metabolic abnormalities [36]. Similarly, Endo et al. verified that the LEPR Gln223Arg (rs11371101) polymorphism was not associated with obesity in 553 Japanese school children aged 9−15 years [37]. A meta-analysis of case-control studies and a systematic review also reported that there was no association between the LEPR gene polymorphism and obesity [35, 38]. By contrast, Shabana and Hasnain reported that the LEPR polymorphism was associated with weight, BMI, plasma glucose levels, TC, TG, HDL-c, and LDL-c, whereas it was not associated with WC, HC, and WHR in 475 Pakistani subjects (10–78 years) [39]. Our results show no statistical association between the LEPR polymorphism, anthropometric, and metabolic parameters in normal and overweight/obese children, potentially due to a recessive weak effect on zBMI despite his high frequency (47%) in the studied population.

FTO functions have not yet been fully established. In an in vitro study, Wu et al. reported that FTO is a co-activator of the CCAAT enhancer-binding protein (C/EBP) family of transcriptional regulators, required in combination with PPARG for adipocyte differentiation, suggesting a role for FTO in the epigenetic regulation of adipose tissue development and maintenance [40]. Several studies reported a positive association between this polymorphism and BMI or other obesity traits in Caucasian populations [8, 10–12], including a study of a few anthropometric parameters by Albuquerque et al. in a cohort of 730 Portuguese children (6−12 years old) [9]. The frequency of the polymorphic allele described by Albuquerque et al. is within the range of our reported values, although the effect of the minor allele on BMI was higher (0.6 kg/m²) in the previous study. Studies in Oceanic [41], African [42], and Asian [43] populations found no association between the FTO variant and BMI. By contrast Wu et al. studied Han Chinese adolescents and reported that this FTO variant was positively associated with BMI and metabolic traits such as fasting glucose, insulin, TG, and TC [44]. Conversely, Li et al. found no association between this FTO variant and BMI, WC, %BF, fasting levels of plasma glucose, hemoglobin A1C, insulin, or β-cell function (estimated by homeostasis model assessment) in an adult Han Chinese population [43]. Mangee et al. also found no associations between this FTO variant and the previous biochemical parameters, HDL-c, oxidized LDL, insulin, Homa-IR, and leptin in Austrian (Styrian) adolescents [45]. Consistent with these results, the results of the current study suggest that this polymorphism has no effect, or eventually a recessive very weak increasing one, on zBMI, despite being present at a high frequency (46%) in the studied population.

The MC4R variant is expressed in the central nervous system, and is part of the melanocortin pathway that controls food intake and energy homeostasis [21]. The most common coding MC4R polymorphism is MC4R rs2229616 (V103I missense variant) and it was the first described as showing no association with BMI, plasma insulin, and glucose levels in white British males [46]. Another study reported similar heterozygous frequencies in lean and obese individuals (4.2 vs. 4.5%, respectively), and found no homozygous frequencies for the polymorphic allele [26]. These results are consistent with our findings and indicate that the MC4R rs2229616 polymorphic allele is rare. By contrast, Geller et al. performed a meta-analysis of 7000 individuals and reported that the MC4R polymorphic allele was negatively associated with obesity [47], consistent with the results of other studies [26, 27]. A possible mechanism underlying the protective effect of the MC4R V103I polymorphism could be an increase in energy expenditure [47]. Consistent with others, our results indicate that the MC4R rs17782313 variant is positively associated with obesity traits in overweight/obese children [22–25]. This gene variant may facilitate obesity by increasing the intake of high-energy or fatty foods or promote overeating in response to emotional eating [48]. We did not identify any differences in biochemical parameters associated with different genotypes of this polymorphism.

García-Solis et al. studied 580 children (8−13 years old) and found that heterozygous subjects for this polymorphism risk allele were significantly associated with obesity but not with TC, HDL-c, or insulin levels [22]. Furthermore, Loos et al. confirmed that BMI in children (7−11 years old) was positively associated with each additional copy of the polymorphic allele, with a BMI increase of 0.10 kg/m² and 0.13 z-score units (P < 7.3 × 10^-6), twice of that observed in adults (P = 0.001) [23] and in agreement with the results of the present study (0.26 zBMI units increase for changing to minor allele). In our population, this recessive increasing effect polymorphism for zBMI yielded a weak effect size. The results from populations of African-American children remain controversial [49].

PPARG isoforms 1 and 2 are transcription factors that activate adipocyte differentiation and mediate the expression of specific fat cell genes [28]. However, PPARG-2 rs1801282 may not be associated with obesity and type 2 diabetes mellitus [50]. Although we did not detect significant associations, the results showed a trend toward reduced mean BMI in heterozygous overweight/obese children than in homozygous wild type. Furthermore, our results also suggest the presence of a recessive medium decreasing effect on zBMI, although there was a low frequency (9%) of this minor allele in the studied population, which is a limitation to this finding. This is consistent with a study of 194 premenopausal Caucasian Portuguese females, which also found no significant differences in BMI between the control and case groups for this polymorphism [51]. However, a meta-analysis concluded that PPARG variants contributed to human adiposity variation and predisposition for obesity [29]. These inconsistencies may suggest that PPARG polymorphisms may be labeled differently in different ethnic populations, or that there are dissimilar gene-environment interactions.

Supplementary Table S1 Supplementary Table S2A Supplementary Table S2B