FTO rs17817449 Variant Increases the Risk of Severe Obesity in a Brazilian Cohort: A Case-Control Study

Abstract

Purpose: Obesity is a complex disease caused by a combination of genetic, environmental, and epigenetic factors, and is associated with an increased risk of chronic diseases. The leptin-melanocortin pathway integrates peripheral signals about the body’s energy stores with a central neuronal circuit in the hypothalamus. This pathway has been extensively studied over the years, as genetic variations in genes related to it may play a crucial role in determining an individual’s susceptibility to obesity. Therefore, we analyzed the association between obesity and specific polymorphisms in leptin-melanocortin-related genes such as LEPR rs1137101, POMC rs1042571, LEP rs7799039, BDNF rs6265, FTO rs17817449, CART rs121909065, and NPY rs16147/rs5574.

Patients and Methods: The study enrolled 501 participants from Rio de Janeiro, Brazil, with obesity class II or greater (BMI ≥ 35 kg/m2) and normal weight controls (18.5≤ BMI ≤ 24.9 kg/m2). We collected demographic, body composition, biochemical, and genotyping data by real-time PCR, and performed logistic and linear regression analyses to investigate the association of polymorphisms with severe obesity status and obesity-related quantitative parameters.

Results: Individuals with severe obesity had significantly higher anthropometric measures, blood pressure, and biochemical levels. The FTO rs17817449 TT genotype was associated with a significantly higher risk of developing severe obesity, and distinct cytokine expression was observed across the FTO rs17817449 genotypes. The BDNF rs6265 dominant-model and NPY rs16147 CC genotypes were associated with triglyceride levels and childhood obesity, respectively. Finally, individuals with obesity were more likely to carry a greater number of risk alleles than those without obesity.

Conclusion: Our study observed an important association between FTO rs17817449 polymorphism with obesity and obesity-related traits. Additionally, BDNF rs6265 dominant-model was associated with triglyceride serum levels, and NPY rs16147 may have a role in obesity onset.

Introduction

Obesity is a major global health epidemic. Currently, obesity affects 13% of adults and 124 million children worldwide.¹ According to The World Obesity Atlas 2023,² by 2035, this number is projected to increase to over 4 billion people, representing a prevalence of 24% worldwide. The most dramatic scenario is expected among children and adolescents, with a rise from 10% to 20% in boys and from 8% to 18% in girls. In Brazil, the prevalence of obesity is also on the rise, with projections indicating that 41% of adults will be obese by 2035.²

It’s well known that obesity is a complex and multifactorial disease influenced by a combination of genetic, environmental, and epigenetic factors.¹ It is associated with an increased risk of cardiovascular disease, type 2 diabetes, cancer, hypertension³ and increased oxidative stress.⁴ Individuals with a BMI of 40 kg/m² or higher experience a significant reduction in life expectancy, with up to 13.9 years of life lost for those with a BMI of 55–59.9 kg/m², compared to individuals with a normal BMI range of 18.5–24.9 kg/m².^1,5 Additionally, severe obesity has been linked to an increased risk of severe outcomes and hospitalizations due to COVID-19.⁶

The high prevalence of obesity may be attributed partially to environmental factors, such as urban progress which promotes facilitation mechanisms to a sedentary lifestyle.^3,7 Besides the factors related to the decrease in energy expenditure, there is a concomitant increase in calorie consumption by the intake of foods with very high percentages of sugars and fat.⁸ However, individuals exposed to the same obesogenic environmental factors may express different phenotypes, with or without obesity. Individual genetic background has been strongly shown to play a crucial role in susceptibility to external stimuli.⁹ Over time, hypotheses have been raised to explain the different susceptibility to obesity among ethnicities, such as the “predation release”, “drifty gene”, and “thrifty epigenotype” hypotheses.¹⁰ Interestingly, several loci have been described and associated with multifactorial and polygenic obesity, resulting from the interaction between the cumulative effect of genetic polymorphism with the environment.^11–16

Most of the studied genes associated with obesity act directly or indirectly in a neuroendocrine system related to modulating the body’s energy homeostasis by the control of appetite and energy expenditure.^17–19 This system called the leptin-melanocortin pathway integrates peripheral signals about the body’s energy stores with a central neuronal circuit in the hypothalamus.^20,21 This circuit consists of two distinct groups of neurons in the arcuate nucleus: orexigenic neurons, which promote hunger and decrease energy expenditure, and anorexigenic neurons, which promote satiety and increase energy expenditure.²² During fasting or calorie restriction, the stomach secretes the hormone ghrelin, which activates the orexigenic pathway. Ghrelin crosses the blood-brain barrier and binds to receptors on orexigenic neurons, thereby stimulating the production of neuropeptide Y (NPY) and Agouti-related protein (AgRP). NPY primarily stimulates food intake by activating Y1 and Y5 receptors on orexigenic neurons. Additionally, NPY binds to Y1 receptors on anorexigenic neurons in the hypothalamus, inhibiting these neurons and reducing their ability to suppress feeding.^23–25 Conversely, AgRP is a potent antagonist of the melanocortin-4 receptor (MC4R), which is a key regulator of food intake and energy expenditure.²⁶

On the other hand, the anorexigenic pathway is activated in response to excess adiposity or recent feeding. Leptin, a hormone secreted by white adipose tissue, binds to receptors (LEPR) on both orexigenic and anorexigenic neurons after crossing the blood-brain barrier. While leptin inhibits the production of NPY and AgRP in orexigenic neurons, it stimulates the production of pro-opiomelanocortin (POMC) in anorexigenic neurons. POMC is cleaved into the neuropeptide α-melanocyte-stimulating hormone (α-MSH), which binds to MC4R. This binding triggers a cascade of events that leads to decreased hunger and increased basal energy expenditure.^27–29

Moreover, there is a gene called Fat mass and obesity-associated (FTO) that has been previously associated with both childhood and adult.^19,30,31 The FTO gene is expressed in neurons of the arcuate nucleus, a region of the brain known to play a crucial role in energy homeostasis and responsive to leptin signaling.³² Although the exact role of the FTO gene is not yet fully understood, it has been described as having multiple functions. For instance, it has been found to regulate gene expression through DNA demethylation,^33,34 which can influence the activity of other genes and may play a role in various biological processes, including energy balance. Additionally, FTO is involved in DNA repair and fatty acid metabolism.^35,36 Further research is needed to fully understand the role of the FTO gene in the regulation of energy balance and contributing to obesity. However, the FTO gene may interact with the anorexigenic signaling pathway, along with other factors, to contribute to the development of obesity.^37,38

This study aimed to examine the association of genetic polymorphisms, including LEPR rs1137101, POMC rs1042571, LEP rs7799039, BDNF rs6265, FTO rs17817449, CART rs121909065, NPY rs16147 and NPY rs5574 (Table 1) with obesity class II or greater and related obesity-traits in a Brazilian cohort, characterized by its unique genetic diversity from indigenous, European, and African ancestries.³⁹ We also have investigated the effect of combined risk allele variants in obesity susceptibility.

Table 1 Genetic variants included in this study

Materials and Methods

Study Population

This cross-sectional case-control study enrolled 501 participants (73.5% women and 26.5% men) aged 18 to 70 years from Rio de Janeiro, southeast of Brazil. The case group consisted of 305 participants with severe obesity (BMI ≥ 35 kg/m2) who were stratified into three groups based on the self-reported period of obesity onset. All participants were recruited from a non-governmental organization called Rescue Group to Self-Esteem and Citizenship of the Obese (in Portuguese, Grupo de Resgate a Autoestima e Cidadania do Obeso). All participants were candidates for bariatric surgery in our country. The control group consisted of 196 participants with normal weight (18.5≤ BMI ≤24.9 kg/m2) who were volunteers from public hospitals in the same city. The exclusion criteria were pregnancy, lactation, and the use of medication to lose or gain weight directly.

This study was performed according to the Declaration of Helsinki (1964) and approved by the Ethics Committee of the Oswaldo Cruz Foundation (CAAE: 09225113.0.0000/ Protocol N°: 346.634). All individuals accepted to participate and provided written informed consent before enrollment.

Demographic Variables

Race/skin color: Self-reported and categorized according to the criteria of the Demographic Census conducted by the Brazilian Institute of Geography and Statistics (IBGE). Marital status: Stratified as single, married/cohabiting, separated/divorced, and widowed. Physical activity: Classified as “yes” if the participant has been practicing activities during the last month, or “no” otherwise. All information was self-reported and collected using standardized questionnaires by trained interviewers.

Body Composition and Biochemical Analyses

All participants were examined after an overnight fast, as previously described.^19,40 Body weight, height, waist, and hip circumferences were obtained for each participant. Waist-to-hip ratio (WHR) was calculated to estimate the pattern of fat distribution. BMI was calculated as weight (kg) divided by the square of height (m). Body adiposity index (BAI) estimates the percentage of total fat and was calculated by the formula: hip circumference/(height^1.5)-18.⁴¹ To record the participant’s blood pressure, we used a wrist blood pressure monitor while they were in a seated position.⁴²

Blood samples were collected to measure fasting plasma glucose, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) levels using the oxidase-peroxidase method (BioSystems). Low-density lipoprotein cholesterol (LDL) was calculated using the Friedewald formula (LDL = TC - HDL - TG/5). C-reactive protein (CRP) levels were measured using the latex agglutination method. Serum glycated hemoglobin values were assessed turbidimetric inhibition immunoassay (TINIA). Individuals who were taking medication for any of these biochemical parameters or blood pressure were excluded from the statistical analyses.

Obesity and Inflammatory Biomarkers

Leptin, resistin, adiponectin, monocyte chemoattractant protein-1/CCL2 (MCP1), and plasminogen activator inhibitor-1 (PAI-1) levels were quantified using the Human Adipocyte Magnetic Bead (Millipore-Merck [cat# HADCYMAG-61k]) on the Bio-Plex 200 Multiplexing Analyzer System, following the manufacturer’s protocol. IL-6, IL-1β, TNF, and IFN-α levels were quantified by ELISA, following the R&D Systems’ instructions.

Genotyping

Genomic DNA was isolated from peripheral blood leukocytes using the QIAamp Blood Kit (Qiagen, Valencia, CA, USA), according to the manufacturer’s protocol.

Genotypes for the LEPR rs1137101(A>G), POMC rs1042571 (G>A), LEP rs7799039 (G>A), BDNF rs6265 (C>T), FTO rs17817449 (T>G), CART rs121909065 (C>G), NPY rs16147 (T>C) and NPY rs5574 (T>C) polymorphisms were obtained by real-time PCR allelic discrimination using TaqMan^® assays (ThermoFisher, Foster City, CA, USA). Amplification was carried out in a StepOne^® Plus Real-Time PCR System (ThermoFisher Scientific, Waltham, MA, USA). Data analyses were done using StepOne software v2.3.

Statistical Analysis

To ascertain the necessary sample size, we employed an iterative technique focusing on the smallest participant number essential for effectively contrasting qualitative variables between case and control groups. In our analysis of diverse polymorphisms, we opted for a combination of conservative estimation and convenience sampling, aiming for 80% statistical power.⁴³

The normality of continuous variables was analyzed using the Kolmogorov–Smirnov and Shapiro–Wilk tests. Continuous variables were not normally distributed and were presented as the median and interquartile range (IQR). Categorical variables were presented as numbers and percentages.

Differences between the case and control groups were evaluated using the Mann–Whitney test (quantitative parameters) or the chi-square test (qualitative parameters).

Genotypes and allele frequencies were estimated by gene counting. Hardy–Weinberg Equilibrium (HWE) was tested for all polymorphisms using the chi-square test. The association of each polymorphism with severe obesity status was performed by logistic regression analysis in additive, dominant, over-dominant, and recessive models. Odds ratios (ORs) and 95% confidence intervals (CIs) were also calculated. All analyses were adjusted for age, gender, race/skin color, marital status, and exercise habits.

The influence of each polymorphism on the obesity-related quantitative was tested using linear regression analysis after a logarithmic transformation of these parameters. Gender, age, race/skin color, marital status, and exercise habits were used as possible confounding variables for body weight and BMI. The confounders for all other continuous parameters studied included BMI and these variables.

The statistical analyses were conducted using SPSS software (IBM Corp., Armonk, NY, USA). To account for the increased likelihood of false positives when multiple statistical tests are performed simultaneously, Bonferroni correction for multiple comparisons was applied with a significance cutoff of p ≤ 0.007. This correction is a method to adjust for the family-wise error rate and maintain the overall significance level of the study.

To visually represent cytokine expression across different FTO rs17817449 genotypes in the case group, we used the function heatmap.2 from the gplots package in R to create a heatmap cluster with a z-score. We analyzed the median serum levels of IL-1β, TNF-α, IL-6, MCP-1, adiponectin, leptin, resistin, and PAI-1 across each FTO rs17817449 genotype group. The analysis included 26 participants in the TT genotype group, 38 participants in the GT genotype group, and 21 participants in the GG genotype group.

Results

A cohort of 501 participants with a median age of 36 (27; 45) was included in this study. The participants were divided into two groups: 196 individuals with normal weight (median BMI 22.8 kg/m²) and 305 people with severe obesity (median BMI 46.7 kg/m²) stratified into groups based on the period of obesity onset: the childhood-onset group (0–11 years) comprised 106 patients, the adolescence/youth-onset group (12–21 years) included 72 patients, and the adult-onset group (>21 years) consisted of 127 patients. The participants with severe obesity had significantly higher anthropometric measures (weight, height, waist circumference, and BMI) than the normal-weight subjects. They also had higher blood pressure and biochemical levels (total cholesterol, LDL cholesterol, triglycerides, and fasting glucose), except for HDL cholesterol (Table 2).

Table 2 Phenotypic characteristics of the study population

Association of Genetic Variants With Severe Obesity

We conducted a case-control analysis to investigate the association between genetic variants and severe obesity susceptibility. The results are presented in Table 3. Due to the low frequency of the POMC rs1042571 and BDNF rs6265 variant alleles, we only used the dominant and over-dominant models for these variants. Additionally, the CART rs121909065 variant allele was not observed in either the control or case groups, indicating that this variant is extremely rare in our cohort.

Table 3 Genotypic and allelic frequencies

Our analysis revealed that individuals with the FTO rs17817449 TT genotype had a significantly higher risk of developing severe obesity (OR = 3.30, 95% CI = 1.66–6.55, p = 0.001). We also observed an association in the recessive model (OR = 2.89, 95% CI = 1.55–5.38, p = 0.001). Allelic tests showed that individuals carrying the risk allele (T) were 1.68 times more likely to develop severe obesity than the control group (OR = 1.68, 95% CI = 1.22–2.31, p = 0.001). Our analysis did not find a significant association between severe obesity and the LEPR rs1137101, POMC rs1042571, LEP rs7799039, BDNF rs6265, CART rs121909065, NPY rs16147, and NPY rs5574 polymorphisms. However, we observed a significant difference in the distribution of NPY rs16147 genotypes when we stratified the analysis by obesity onset periods. The CC genotype was found to occur at a higher frequency in the childhood-onset group (p = 0.003). The remaining five variants did not exhibit any significant differences (p > 0.007) among the different obesity onset periods.

Association of LEPR, LEP, FTO, and NPY Polymorphisms With Anthropometric, Blood Pressure, and Biochemical Parameters

We conducted an assessment of the potential influence of LEPR, LEP, FTO, and NPY polymorphisms on anthropometric, blood pressure, and biochemical parameters, as presented in Table 4. Our results revealed that the FTO rs17817449 polymorphism was significantly associated with body weight (p = 0.003), BMI (p = 0.002), and body adiposity index (BAI) (p = 0.002). Specifically, individuals with the TT genotype had higher values than those with the GT or GG genotypes. These results remained significant even after correcting for multiple tests. Furthermore, we found that the LEPR rs1137101 variant was significantly associated with PAI-1 serum levels (p = 0.041). The LEP rs7799039 polymorphism was significantly associated with waist circumference (WC) (p = 0.029), hip circumference (HC) (p = 0.015), glycated hemoglobin (p = 0.047), and circulating leptin levels (p = 0.042). The NPY rs5574 polymorphism was significantly associated with TG (p = 0.048). However, these associations did not remain significant after correcting for multiple tests. We did not observe any significant association with the NPY rs16147 variant.

Table 4 Association of LEPR, LEP, FTO and NPY variants and anthropometric, biochemical and blood pressure variables

Association of POMC and BDNF Dominant and Over-Dominant Models With Anthropometric, Blood Pressure, and Biochemical Parameters

In this study, we observed a low frequency of variant alleles for the POMC and BDNF genes. To address this issue, we used the dominant and over-dominant models in our analyses, as presented in Table 5. Our findings revealed that the POMC rs1042571 polymorphism was associated with PAI-1 (p = 0.028) and resistin (p = 0.035) using the dominant model. However, when using the over-dominant model, only the association with PAI-1 remained (p = 0.016). It is important to note that these associations did not remain significant after the Bonferroni test correction. In the analysis of the BDNF rs6265 polymorphism using the dominant and over-dominant models, we found that the dominant model was associated with TG (p = 0.008). Additionally, the over-dominant model was associated with HDL (p = 0.028) and TG (p = 0.007). However, only the over-dominant model association with TG remained significant.

Table 5 Association of POMC rs1042571 and BDNF rs6265 dominant and over; dominant models with anthropometric, biochemical and blood pressure variables

Inflammatory and Adipokine Expression Across FTO rs17817449 Genotypes

Our heatmap analysis reveals distinct cytokine expression across the FTO rs17817449 polymorphism genotypes. The TT genotype group shows a significant cytokine expression increase, including PAI1, Resistin, Adiponectin, MCP1, TNF-α, and IL1-β. Interestingly, the GG mutant genotype exhibits an elevated leptin expression. Despite an expression increase in the heterozygous GT group, IL-6 expression remains consistent between the TT and GG groups (Figure 1).

Figure 1 Inflammatory and adipokine expression varies across FTO rs17817449 genotypes. The TT genotype group exhibits a significant increase in cytokine expression, while the GG mutant genotype shows elevated leptin expression. IL-6 expression remains consistent between the TT and GG genotypes.

Combined Effects of Risk Alleles on Obesity

Regarding the distribution of risk alleles between groups, the study found that the prevalence of individuals with 8 or more risk alleles was significantly higher in the cases group (24%) compared to the control group (11.8%), with a p-value of 0.001. Additionally, the mean number of risk alleles was significantly higher in the cases group (7.10 ± 1.92) compared to the control group (6.40 ± 1.84), with a p-value of 0.013. The frequency of individuals with eight or more risk alleles was higher in the cases group, highlighting the potential role of these risk alleles in the development of obesity. The graph (Figure 2) emphasizes the importance of considering the combined effect of multiple risk alleles on the development of obesity. It is important to note that the significance level was adjusted for multiple tests, and the corrected p-value was 0.007. These findings suggest that individuals with obesity are more likely to carry a greater number of risk alleles than those without obesity.

Figure 2 Combined Effect of the Risk Alleles of the Studied Polymorphisms. The graph illustrates the prevalence of individuals with eight or more risk alleles in the cases and control groups, with a significant difference (p= 0.001) observed between the two groups.

Discussion

Obesity is a complex disease. The prevalence of obesity has increased globally, and it is expected to continue increasing in the next years. The high prevalence of obesity is attributed to several factors, including environmental changes and genetic background. Genetic factors play an important role in obesity, in which some genes, such as those in the leptin-melanocortin pathway, have been identified as being associated with the development of this disease.

FTO is highly expressed in the hypothalamus, visceral fat, and liver, and plays a decisive role in regulating neuronal circuits related to feeding behavior, appetite, and energy homeostasis.⁴⁴ Previous animal studies showed that FTO deficiency decreases body weight and fat mass, mainly in white adipose tissue (WAT), and enhances the browning of adipose tissue.^45,46 Furthermore, individuals who carry the obesity risk allele of FTO show fat cell lipolysis, which may elucidate the primary role of the FTO gene in lipidic metabolism.⁴⁷ Several FTO variants have been linked to obesity and/or related traits, such as BMI, WC, fat mass, and insulin resistance, such as the rs17817449. This variant has been associated with higher BMI and fast insulin among obese children from Egypt.⁴⁸ However, the FTO-obesity association may differ across populations and ethnicities, and depend on other factors, such as age, gender, lifestyle, and diet.^49–51

In our study, we observed a statistically significant association between the FTO rs17817449 T allele with weight, BMI, and BAI. Furthermore, a recent preliminary study by our group evaluated the contribution of two FTO polymorphisms (rs9939609 and rs17817449) for extreme obesity in a Brazilian cohort. Interestingly, individuals with extreme obesity had higher rs17817449 TT allele frequency. Homozygous carriers had a 3.3-fold risk of extreme obesity and heterozygous showed a 1.24 greater risk. Moreover, this previous study also observed an association with body weight, BMI, and triglyceride levels.⁵² In addition to these findings, a strong association with extreme obesity was observed in a non-Hispanic Caucasian sample consisting of 583 cases, 544 controls, and a subset of discordant sister pairs, with 99 obese sisters and 99 thin sisters,⁵³ which further supports our results. Furthermore, the FTO variant studied by GONZÁLEZ et al (2012)⁵⁴ was associated with obesity in a homogeneous Spanish population with a family history.

A study conducted on the western Saudi population indicated that the FTO rs17817449 variant is significantly associated with susceptibility to Polycystic Ovary Syndrome (PCOS), which is characterized by infertility, obesity, and insulin resistance in women. However, the researchers observed a significant association of BMI only with the FTO rs8050136 variant.⁵⁵ Additionally, the FTO rs17817449 variant was considered a risk allele for obesity in an American PCOS family-based and case-control study.⁵⁶ Therefore, it may suggest that the results are nationality-related, and other specificities of these populations must be analyzed and counterpointed.

Additionally, we investigated the relationship between FTO rs17817449 genotypes and cytokine expression in the case group. The results of our analysis revealed distinct cytokine expression across the FTO rs17817449 polymorphism genotypes. The TT genotype group showed a significant cytokine expression increase, including PAI1, Resistin, Adiponectin, MCP1, TNF-α, and IL1-β. In contrast, a study conducted on a Romanian children’s cohort found that individuals with the TT genotype had lower adiponectin levels.⁵⁷ We also observed an increase in leptin expression in the GG mutant genotype, which is consistent with the results of the Quebec Family study, prior to BMI adjustment.⁵⁸ However, the leptin levels were the same in Saudi individuals with obesity, regardless of the FTO rs17817449 genotypes and gender.⁵⁹ Furthermore, the G allele was linked to reduced leptin levels in a Swedish cohort, even after adjusting for WC and BMI.⁶⁰ Altogether, these findings suggest that the relationship between FTO rs17817449 genotypes and cytokine expression is complex and highlight the need for further research to fully understand the relationship between FTO rs17817449 genotypes and cytokine expression, as well as the potential impact on obesity and related health outcomes. It is worth noting that our analysis has some limitations. The sample size was relatively small, which may limit the generalizability of our findings. Furthermore, the ethnic plurality of the Brazilian population, resulting from the miscegenation of native Amerindians, Europeans, Africans, and more recent migratory groups, could also be a potential limitation. Additionally, the results were not independent of body adiposity.

Leptin is a 16 kD hormone encoded by the LEP gene.⁶¹ This hormone is secreted predominantly by white adipose tissue and its expression is proportional to the body’s fat mass storage.^62,63 By diffusion transport, leptin crosses the blood-brain barrier and binds to the LEPR located in the hypothalamus. It activates a signaling cascade that promotes a decrease in food intake and an increase in energy expenditure. In addition, leptin acts on immune and endocrine modulation.^17,64 Some studies have shown that variations in the LEP gene can affect leptin expression and circulation, for example, the LEP rs7799039 variant.^61,63,65,66

Our study found no significant variation in allele and genotype frequencies between cases and controls. Similarly, although there was a statistical association with WC, HC, glycated hemoglobin, and circulating leptin levels, the significance was lost due to the correction for multiple tests. Interestingly, a case-control study in a Southern Chilean reported that the LEP rs7799039 allele A was associated with lower total and LDL cholesterol levels, but the difference was not statistically significant after adjusting for multiple comparisons.⁶⁷

The leptin receptor is a transmembrane protein that belongs to the cytokine receptor family.⁶⁸ Variations in the LEPR gene can lead to improper protein function.⁶⁹ Several variants were studied in this gene, including a missense LEPR rs1137101 polymorphism. This variant has been linked to leptin resistance and obesity. Previous studies have found that the LEPR rs1137101 polymorphism is associated with obesity in European-Caucasian descent Brazilians. Mattevi et al⁷⁰ observed a significant effect of this variant on BMI, and De Oliveira et al⁷¹ found that the presence of the G allele was associated with increased levels of leptin and WC. The GG genotype was associated with an increased risk of obesity. However, our study did not find any positive correlation between the LEPR rs1137101 polymorphism and obesity. This may be explained by the genetic admixture that is characteristic of the state of Rio de Janeiro, where the sample was recruited. Our findings are in line with a study with Mexican young adults, which also did not find any correlation between the LEPR rs1137101 polymorphism and obesity. This suggests that the association between the LEPR rs1137101 and obesity could be specific to European-Caucasian descent populations.⁷²

POMC is a precursor protein that is encoded by the POMC gene. It is processed by the prohormone convertase enzyme to form melanocyte-stimulating hormones (MSHs), which interact with MC4R and MC3R modulating energy expenditure and food intake.⁹ Some studies have reported that variations in the sequence of the POMC gene can lead to improper processing of proopiomelanocortin.^73–75 One such mutation is the SNP rs1042571.

A study in northern India found that individuals who carried the mutant allele (T) of POMC rs1042571 had a BMI of ≥30 kg/m² or normal.⁷⁶ However, a family-based study in Thailand found that the rs1042571 mutant allele (T) did not show any correlation with obesity, anthropometric, or biochemical parameters.⁷⁷ Our study also found no significant correlation between the POMC rs1042571 polymorphism and obesity in our Brazilian sample. These findings are consistent with the findings of the Thai study, and they suggest that the role of POMC rs1042571 may be population-specific.

NPY gene encodes a protein, which is a key regulator of appetite, energy balance, and body weight. Few studies have investigated the association between the polymorphism NPY rs5574 and obesity traits.^78,79 TIWARI et al (2013)⁸⁰ observed a significant association between this SNP and body mass increase in European ancestry patients treated with clozapine or olanzapine. Likewise, a longitudinal birth cohort study linked NPY rs5574 TT carriers with increased abdominal obesity.⁷⁸ Conversely, a case-control study with Malaysian children reported that allele T was associated with a reduced risk of obesity.⁸¹

The NPY rs16147 is another variant related to obesity. In Spanish children, this variant was associated with higher BMI and WC.⁸² A case-control study and meta-analysis observed that the NPY rs16147 variant was significantly associated with obesity risk in Asians, but not in Caucasians.⁸¹ Lin et al (2015) investigated the association between central obesity and abdominal fat distribution. They found a significant association between central obesity and abdominal fat distribution in Caucasians, but not in African Americans or Hispanics.⁷⁹ In our study, we identified an association between obesity and the NPY rs16147 dominant model (OR = 1.60, 95% CI = 1.00–2.55, p = 0.047). However, this association did not remain significant after the Bonferroni test. In the stratified analysis by the period of obesity onset, we observed a significant association between childhood-onset obesity and the rs16147 CC genotype (p = 0.003) of the NPY gene. Nevertheless, we did not detect any significant associations between the NPY rs5574 variant and obesity-related traits. These findings suggest that the NPY gene may have a role in the development of obesity across different populations, particularly in childhood-onset obesity. However, further studies are necessary to fully comprehend the mechanisms underlying this association.

Brain-derived neurotrophic factor (BDNF) is a member of the neurotrophin family of proteins that play important roles in neuronal survival, proliferation, differentiation, apoptosis, excitability, and synaptic plasticity.^83,84 BDNF has been proposed to be involved in the regulation of appetite and metabolism.^85,86 The BDNF rs6265 is the most commonly studied variant. This SNP has been linked to impaired functioning of several processes involved in the regulation of extracellular BDNF levels.⁸⁷ In addition to being broadly associated with neuronal processes, the aforementioned SNP has been recently linked to eating disorders, metabolic imbalance, and obesity risk increase in several populations.⁸⁸ However, due to the Bonferroni correction for multiple comparisons, only the association between BDNF rs6265 over-dominant model and TG (p = 0.007) remained statistically significant. Therefore, the significance of the association between BDNF rs6265 and TG is strengthened by the application of this correction.

Our study has some limitations. 1- This study’s cross-sectional design limits our ability to establish causality, as the anthropometric and biochemical status may change over time and influence the observed associations. 2- While we controlled for various confounding factors, unmeasured variables may impact the results. 3- Additionally, the lack of functional validation limits our understanding of the biological mechanisms underlying the polymorphisms’ effects on severe obesity.

Conclusion

In our study, we investigated the association of genetic polymorphisms with severe obesity in a Brazilian cohort. Our findings indicate that individuals with the FTO rs17817449 TT genotype are at higher risk of severe obesity as they tend to have greater body weight and BMI. We also observed a significant association between the over-dominant model of BDNF rs6265 and TG. Moreover, we stratified the analysis by obesity onset periods, we found a significant difference in the distribution of NPY rs16147 genotypes. Specifically, the CC genotype was associated with childhood-onset obesity. Our study also revealed that the prevalence of individuals with eight or more risk alleles was significantly higher in the cases group than in the control group. The cases group also had a significantly higher mean number of risk alleles than the control group. Finally, in our heatmap analysis of cytokine expression across FTO rs17817449 genotypes, we found that subjects with the TT genotype had increased cytokine expression, while those with the GG genotype exhibited elevated leptin expression. Although our study has a few limitations, it still provides valuable insights.

Ethics Approval and Informed Consent

The Ethics Committee of the Oswaldo Cruz Foundation approved the studies involving human participants (CAAE: 09225113.0.0000/ Protocol N°: 346.634). The patients/participants gave written informed consent before enrollment.

Acknowledgments

The authors would like to thank the “Programa de Desenvolvimento Tecnologico em Insumos para Saude” (PDTIS) platform for multiplex analysis and to all patients and all individuals in the control group who generously agreed to participate in this research.; Fabiana Barzotto Kohlrausch and Ana Carolina Proença da Fonseca share last authorship.

Funding

TThis work was supported by the Oswaldo Cruz Foundation (Fiocruz, Brazil) and Carlos Chagas Filho Foundation for Research Support in the State of Rio de Janeiro (FAPERJ) [GRANT: E-26/210.663/2021 and E-26/202.291/2019].

Disclosure

The authors report no conflicts of interest in this work.

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