Do GST-M, GST-T, and TP53 Gene Polymorphisms Have a Role in Childhood Obesity?

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Onur Dirican, Derya Bulus, Abbas Husseini, Yücel Hanilçe, Serpil Oğuztüzün This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4502132/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: This study investigates genotypic variations in GST-M, GST-T, and TP53;rs1042522 among children with obesity. Methods: Blood samples from 60 patients with childhood obesity were analyzed. Deletions in GST-M and GST-T were identified using quantitative PCR with melting curve analysis, while TP53;rs1042522 was genotyped via sequence analysis. Deviation from Hardy-Weinberg proportion was examined, and associations with clinical and demographic variables were assessed. Results: We observed deviations in the genotypes of GST-M and GST-T, while TP53;rs1042522 remained aligned. Higher cholesterol, LDL, and GGT levels were found in individuals with null GST-M genotypes. Notably, individuals with Wt/null GST-T genotypes had remarkably higher waist circumference and levels of albumin. The wild-type GST-T genotype correlated with reduced BMI and creatinine levels. Individuals with TP53;rs1042522 mutations showed decreased LDL and cholesterol levels but increased ALT levels. Conclusion: The research highlighted the notable influence of genetic variations in GST-M and GST-T on obesity in children, while the TP53 polymorphism, rs1042522, did not show a significant impact. Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity Biological sciences/Genetics Childhood obesity Genetic variations GST-M GST-T TP53 rs1042522 Figures Figure 1 Impact Statement Deletions in GST-M and GST-T genes, which encode key enzymes in cellular detoxification, may related to obesity in children. Clinical features associated with GST-M and GST-T genotypes suggest that these enzymes may contribute to obesity in children through impaired lipid metabolism, hormonal and metabolic imbalances, and renal and liver dysfunction. 1 INTRODUCTION Childhood obesity, with its metabolic complications, poses a significant public health challenge, contributing to the global health burden. It is strongly linked to a wide range of subsequent chronic diseases 1 . Childhood obesity significantly increases the risk of developing cardiovascular diseases, certain types of cancer, musculoskeletal disorders, type 2 diabetes, prediabetes, dyslipidemia, hypertension, non-alcoholic fatty liver disease, obstructive sleep apnea, polycystic ovary syndrome, weight stigma, depression, and reduced quality of life 2–4 . Recent estimates suggest that on a worldwide scale in 2016, approximately 40 million children below the age of 5 and over 330 million children aged 5–19 were grappling with issues of being overweight 5 . The prevalence exceeded 30% in numerous Pacific Island nations, and surpassed 20% in various regions such as North Africa, Micronesia, Polynesia, the Caribbean, and the USA 6 . Childhood obesity in Turkey is a significant concern, and its prevalence varies across regions and age groups. Studies have revealed that approximately 20–25% of children aged 6–19 years in Turkey are facing challenges related to being overweight 7 . The prevalence among 10-year-olds in the capital city of Ankara stands out as exceptionally high, surpassing the figures recorded in European childhood obesity surveillance by more than 1.5 times 7 . Furthermore, the prevalence of childhood obesity in Turkey witnessed a substantial increase from 0.6–7.3% between 1990–1995 and 2011–2015, highlighting a concerning upward trend over the years 8 . It is well-established that a bio-socioecological framework, along with environmental, behavioral, early-life, and medical conditions, may collectively contribute to the development of obesity in children 6,9–11 . A significant contributor to the risk of obesity is genetic predisposition 12,13 . Presently, more than 1100 independent genetic loci linked to obesity traits have been identified, prompting considerable interest in unraveling their biological functions and understanding the interplay between genes and the environment 14 . The genomic diversity within specific loci may play a role in the intricate cellular and molecular mechanisms influencing the complex metabolic imbalance associated with obesity. These mechanisms include the regulation of appetite and energy balance, the maintenance of glucose, lipid, and adipose tissue homeostasis, as well as their interconnections 14 . For instance, activation of mitogen-activated protein kinases can impact insulin sensitivity, adipocyte function, and energy expenditure 15 . Dysregulation of the phosphatidylinositol 3-kinase/protein kinase B pathway is associated with insulin resistance and obesity-related inflammation 16 . Hypothalamic IKKβ/NF-κB and endoplasmic reticulum stress stress are linked to overnutrition, energy imbalance, and obesity. Inflammatory mechanisms involving the resistin/ toll-like receptor 4 pathways contribute to obesity-induced insulin resistance 16 . Pro-inflammatory cytokines like tumor necrosis factor α can regulate obesity-induced insulin resistance through adipose tissue inflammation 17 . Detoxification pathways may contribute to childhood obesity through various mechanisms. Children with obesity exhibit increased oxidative stress, characterized by an imbalance between oxidants and antioxidants, leading to impaired redox signaling and metabolic complications 18 . Additionally, metabolomic studies have shown that metabolic alterations in children with obesity may be associated with changes in detoxification mechanisms 19 . The latest studies highlight the potential role of detoxification pathways in the development of childhood obesity and the need for further research to understand the underlying molecular mechanisms 19 . Glutathione S-transferase (GST) enzymes potentially play a significant role in childhood obesity by modulating oxidative stress 20 . GSTs are a family of phase II detoxification enzymes that catalyze the conjugation of glutathione to various substrates, including reactive oxygen species and electrophilic compounds. Despite this, the genomic variation of GST enzymes in childhood obesity has not yet been addressed. Therefore, the current study aims to investigate the polymorphic states of glutathione enzyme subgroups, glutathione S-transferase mu (GST-M), and theta (GST-T), in individuals diagnosed with childhood obesity due to deletions in the corresponding gene regions. Additionally, we adress single nucleotide polymorphism (SNP) within the genetic segment that encodes the tumor suppressor protein 53 (TP53), focusing on exon 4 codon 72, referred to as rs1042522. These genetic analyses will help determine the congenital predisposition to obesity in study population. Furthermore, our study evaluates the associations between these genetic factors and certain clinical and demographic data obtained from the clinic. 2 METHODS 2.1 Study design and sampling In 2023, clinical data from patients receiving treatment for childhood obesity at a pediatric clinic were retrospectively evaluated. The inclusion criteria involved children and adolescents aged 5 to 18 years, who had a body mass index (BMI) exceeding the 95th percentile for their age and gender based on established growth charts. Participants meeting these criteria needed to be free of any known chronic diseases or significant medical conditions. Exclusion criteria were implemented to enhance the study's validity by excluding individuals with pre-existing chronic conditions, including diabetes, cardiovascular diseases, and metabolic disorders. Additionally, participants with known genetic disorders that could potentially interfere with the analysis of GST-M, GST-T, and TP53 gene polymorphisms were excluded. The criteria also excluded individuals or their legal guardians who were unwilling to provide informed consent for study participation. Patients with incomplete clinical or demographic data were also excluded from the analysis. The research involved a group of 60 participants. An exhaustive checklist was used to collect demographic and clinical information, including parameters such as age, gender, waist circumference, hip circumference, body fat percentage, glucose levels, urea levels, creatinine levels, total protein levels, albumin levels, cholesterol levels, triglyceride levels, high-density lipoprotein (HDL) levels, low-density lipoprotein (LDL) levels, alanine aminotransferase (ALT) levels, aspartate aminotransferase (AST) levels, gamma-glutamyl transferase (GGT) levels, Thyroxine levels, thyroid-stimulating hormone (TSH) levels, cortisol levels, insulin levels, vitamin D levels, adrenocorticotropic hormone (ACTH) levels, white blood cell count, and hemoglobin levels. Blood samples collected in EDTA tubes were processed for the extraction of genomic DNA using the Invitrogen PureLink Genomic DNA isolation mini kit, enabling subsequent genotypic analysis. Genomic DNA was successfully extracted from all participants, allowing for genotyping of the GST-M and GST-T gene regions. This genotyping was carried out using a melting curve analysis-based quantitative polymerase chain reaction (qPCR) method to detect gene deletions. It's worth noting that this method adheres to established standards in the field, which typically do not require control groups for such analyses. 21 . The consistency and reliability of this approach are further supported by its alignment with sequence analysis techniques, along with validation through assessments of gene region proliferation, bioinformatics analyses, and data obtained from publicly available databases such as NCBI. In our investigation, discrepancies observed in normalized qPCR values among various samples serve as markers for gene loss, collectively termed "Gene Dosage." These deviations are classified according to predetermined threshold criteria, which determine the presence or absence of deletions. According to Girault et al. (2005), normalized mean values offer valuable insights into the presence and implications of gene deletions for GST-M and GST-T. For GST-M, values ranging between 1.00 and 0.80, or surpassing 1.00, indicate the absence of deletion (wild genotype (-/-)), whereas values from 0.79 to 0.42 suggest some degree of gene loss due to deletion (wt/null (+/-)). Notably, a significant impact is evident as values decline from 0.41 to 0.0, indicative of a null genotype (+/+). Similarly, for GST-T, values within the range of 1.00-0.80 or exceeding 1.00 signify no deletion (wild genotype (-/-)), while values from 0.79 to 0.36 suggest gene loss attributable to deletion (wt/null (+/-)). Noteworthy effects are observed as values drop from 0.35 to 0, representing a null genotype (+/+), indicating gene deletion within this locus 21 . Furthermore, genomic DNA from patients was used for sequence analysis aimed at determining the SNP genotype of the TP53 rs1042522 gene region (Fig. 1 ). This region is characterized by the change from arginine (Arg) to proline (Pro) resulting from a guanine to cytosine base conversion, impacting the phenotypic expression of the tumor suppressor p53 gene due to a point mutation within this gene region 22,23 . 2.2 Melting Curve Analysis In our investigation, we utilized a qPCR technique incorporating melting-curve analysis to assess the presence of deletions within the GST-M and GST-T gene regions. The Roche Lightcycler 480 qPCR system was employed for this purpose, in conjunction with the Bio-Rad SSO Advanced Universal SYBR Green Supermix. The primer sequences used in the study were as follows: for GST-T1; Forward: 5'-CAAGTCCCAGAGCACCTCACCTC-3' (NM_000853), Reverse: 5'-GTGTGCATCATTCTCATTGTGGCTT-3' (NM_000853); and for GST-M1; Forward: 5'-TGCATTCGTTCATGTGACAGTATTCT-3' (NM_000561), Reverse: 5'-GAGAGGAGACCGGGCACTCA-3' (NM_000561) 21 . Additionally, these primer sequences were synthesized in the laboratory using the oligosynthesis method and purified using a C18 column and the reverse phase high-performance liquid chromatography (Agilent) system. The controls underwent confirmation through the electrophoresis gel method, ensuring both the visualization of graphs within the chromatographic system and the determination of base sizes 24 . The mixture ratios established during the pre-PCR stage prior to utilization in the qPCR system were as follows: 5µL of SYBR Green PCR Master Mix (Power), 0.4µL of forward primer, 0.4µL of reverse primer, 1µL of DNA (100ng/µL), and 3.2µL of DNAse-free water, with a total volume of 10µL. Furthermore, the melting curve analysis program employed in the qPCR system comprised the following steps: an initial denaturation phase of 3 minutes at 98°C, with a single loop; each cycle involved denaturation at 95°C for 10 seconds followed by annealing at 60°C for 15 seconds, encompassing 40 cycles of amplification, and concluding with a ramp rate of 0.3°C/second from 65°C to 95°C. 2.3 Fragment Sequencing Analysis To determine if the TP53 rs1042522 SNP exists within the patient group under investigation, we analyzed the amplified gene segment obtained through polymerase chain reaction using the Applied Biosystems-3130XL platform. We used primers specific to the TP53 gene region (NM000546.6) for this analysis 22,23,25 . 2.4 Statistical Analysis All statistical analyses were conducted using the R project for statistical computing version 4.3.2 (R Foundation, Vienna, Austria). Continuous data were summarized as the mean ± standard deviation, while categorical data were presented as frequencies (n) accompanied by relative frequencies (%). The assessment of normality assumptions for parametric analyses involved implementing the kolmogorov-smirnov test. In instances where variables exhibited a departure from normal distribution, nonparametric analyses were employed as a suitable alternative. The correlation between clinical variables and gene doses of GST-M and GST-T was examined utilizing the pearson correlation coefficient for parametric variables and spearman's rank correlation coefficient respectively. The congruence of allele frequencies with the expectations of the Hardy-Weinberg proportions was evaluated employing the chi-square test. Subsequently, the disparity of clinical parameters among genotypic groups was achieved through ANOVA for normally distributed values, while the kruskal-wallis test was chosen where the normality assumption was not met. For data exhibiting statistically significant F-ratio scores in ANOVA, post hoc pairwise comparisons were conducted utilizing the Tukey's honestly significant difference procedure. Moreover, the T-test was utilized to evaluate the significant distinctions between clinical characteristics of wild and mutant genotypes. To investigate the interrelation between genotypic variations and categorical variables, the pearson chi-square test was employed. All analyses were conducted at a 95% confidence level, and statistical significance was determined at a p-value below 0.05. 3 RESULTS This study included a cohort of 60 pediatric patients diagnosed with obesity, for whom data on demographic characteristics, clinical parameters, and genotypic information for GST-M, GST-T, and TP53;rs1042522 were collected. The study participants had a mean age of 13.5 ± 2.2 years, ranging from 10 to 17 years old, with a male-to-female ratio of 29:31. The normality assumption for demographic and clinical variables was tested and summarized in Table 1 . The normality assumption was fulfilled for all demographic and clinical variables except for insulin, ALT, ACTH, and GST-T gene dose (P ≤ 0.05). Table 1 The distribution summary of demographic and clinical parameters of participants Parameters Mean value Standart deviation Median Range Reference range P-value Age 13.5 2.23 13.6 10–17 - 0.43 Waist Circumference 97.6 11.1 96 81–138 - 0.14 Hip Circumference 112.1 10.2 111 92–141 - 0.46 Fat percentage 37.8 6.3 38.5 27–57.1 - 0.58 Body mass index 31 3.9 30.7 19.1–46.1 * 0.12 BMI SDS 2.5 0.5 2.5 1.1–3.9 * 0.7 Glucose 93.1 7.3 93.5 77–112 60–100 0.87 Urea 20.7 5.9 20.5 9–35 10–40 0.72 Creatine 0.7 0.1 0.7 0.53–0.94 0.5–1.1 0.70 Total protein 7.3 0.4 7.3 6.3–8 5.7-8 0.80 Albumin 4.3 0.2 4.3 3.8–5.2 3.8–5.4 0.95 Cholesterol 165.9 27.6 167 78–226 ˂150 0.86 triglyceride 144.3 83.6 118 39–590 ˂100 0.04 HDL 43.2 6.9 44 30–59 ≥ 45 0.61 LDL 96.4 22.4 95.5 36–152 ˂100 0.70 *ALT 25 15 20.5 7–75 0–35 0.005 AST 22.6 7.4 21.5 12–50 0–35 0.31 GGT 19.7 7.8 18 9–43 0–30 0.04 Thyroxine 0.9 0.1 0.93 0.29–1.16 0.8–2.3 0.19 TSH 2.9 1.6 2.6 0.87–7.78 0.5–4.8 0.41 Cortisol 9.6 4.3 8.8 1.9–20.61 3–21 0.49 *Insulin 22.1 17.3 17.4 5.1–102.25 ˂2–17 0.003 Vitamin D 15.7 7.6 15.8 4.4–34.9 ≥ 30 0.58 *ACTH 22.5 16.2 19.5 5–102 10–60 0.0008 WBC 7851.7 1691.3 7750 4800–13100 4500–13500 0.92 HGB 14.1 1.2 14 10.8–16.8 12.1–15.1/16.6 0.81 GST-M Gene dose 0.53093 0.313294 0.56376 0.11860–1.17080 ** 0.14 *GST-T Gene dose 0.49867 0.369196 0.324169 0.10481–1.23272 ** 0.01664 The p-value indicates whether data differ significantly from that which is normally distributed. (*;P ≤ 0.05) (** The GST-M and GST-T gene dose reference interval was based on Hardy-Weinberg proportion determined by Girault et al 21 . Among the clinical variables, GGT (P = 0.04) and glucose level (P = 0.03) demonstrate a significant correlation with GST-M and GST-T gene dose, respectively (Table 2 ). However, it appears that there is no association between gene dose value and other parameters (Tables 2 and 3 ). Table 2 Correlation between clinical parameters value and GST gene doses. Parameters GST-M Gene dose GST-T Gene dose r P-value r s P-value Age 0.1201 0.360698 0.00106 0.99361 Waist Circumference -0.081 0.538398 -0.09768 0.45778 Hip Circumference -0.0276 0.83419 0.0197 0.88126 Fat percentage -0.0036 0.978221 0.02726 0.83619 Glucose 0.0459 0.727657 0.27046 0.03662 Urea -0.2067 0.113063 0.04282 0.74527 Creatine 0.0141 0.914848 -0.1079 0.41189 Total protein 0.0387 0.769084 -0.10889 0.40756 Albumin -0.0508 0.699892 0.00044 0.99731 Cholesterol -0.1073 0.4144 -0.07789 0.55415 triglyceride -0.0457 0.728798 -0.07251 0.58192 HDL 0.1017 0.439403 -0.1077 0.41273 LDL -0.2074 0.111821 -0.03311 0.80173 ALT 0.00459 0.97222 0.07502 0.56889 AST -0.0139 0.916051 0.02066 0.87551 GGT -0.2583 0.046563 0.00936 0.9434 Thyroxine -0.0709 0.59036 0.1788 0.17166 TSH -0.2241 0.085192 0.09287 0.48035 Cortisol 0.1607 0.21998 0.0304 0.81763 Insulin -0.24353 0.06079 0.13368 0.30854 Vitamin D 0.0047 0.971569 -0.08097 0.53853 ACTH -0.24353 0.06079 0.17682 0.17653 WBC 0.0853 0.516976 -0.0342 0.79529 HGB -0.0452 0.732796 0.02553 0.84649 Table 3 GST gene doses disparity among categorical groups. Parameters Subgroups N (%) GST-M gene dose mean P value GST-T gene dose median P value Gender Male 29 0.48 0.192469 0.275155 0.61708 Female 31 0.58 0.365225 Hepatobiliyer USG 0 27 0.58 0.169499 0.434258 0.4654 1 21 0.45 0.280164 2 12 0.53 0.204705 The general view of the melting curve and sequence analysis was illustrated in Fig. 1 , and the genotypic information related to GST-M, GST-T, and TP53;rs1042522 SNP was described in Table 4 . The genotype frequencies were 31.6%, 31.6%, and 36.6% for GSTM -/- (Wt/Wt), GSTM +/- (Wt/null), and GSTM +/+ (null/null), respectively. However, the observed genotype frequencies were 25%, 23.3%, and 51.6% for GST-T -/- (Wt/Wt), GSTT1 +/- (Wt/null), and GSTT1 +/+ (null/null), respectively (Table 4 ). On the other hand, the most frequent genotype of TP53 codon 72 polymorphism was Arg/Pro (Wt/Mt) (45%), followed by Arg/Arg (Wt/Wt) (36.6%). There was a deviation from the expected Hardy-Weinberg proportion in the observed GST-M (χ2 = 7.99, P = 0.01) and GST-T (χ2 = 14.75, P = 0.0006) genotype distribution. On the other hand, observed genotypic variation for TP53;rs1042522 was in alignment with Hardy-Weinberg proportion (χ2 = 1.05, P = 0.58). Table 4 Concordance Between Observed GST-M, GST-T, and TP53 codon 72 Allele Frequencies and Expected Proportions Genotyping n (%) p-value GST-M wild genotypes (-/-) 19 (31.6%) P = 0.01834 χ2 = 7.997 GST-M wt/null genotypes (+/-) 19 (31.6%) GST-M null genotypes (+/+) 22 (36.6%) Total 60 (100%) GST-T wild genotypes (-/-) 15 (25%) P = 0.00063 χ2 = 14.754 GST-T wt/null genotypes (+/-) 14 (23.3%) GST-T null genotypes (+/+) 31(51.6%) Total 60 (100%) TP53;rs1042522 Arg/Arg ( wild) 22(36.6%) P = 0.588951 χ2 = 1.0588 TP53;rs1042522 Arg/Pro ( wt/mt ) 27 (45%) TP53;rs1042522 Pro/Pro ( mutant ) 11(18.3%) Total 60 (100%) Moreover, the study uncovered significant differences in genotypes for GST-M, GST-T, and TP53; rs1042522 across various demographic and clinical characteristics. Specifically, analyses indicated significant variations in cholesterol (F = 3.26, p = 0.045), LDL (F = 4.66, p = 0.01), and GGT (F = 7.6, p = 0.001) values within the context of GST-M genotypic variation. Further analysis reveals that the levels of cholesterol, LDL, and GGT were remarkably higher among individuals with null genotypes (p ≤ 0.01). Furthermore, the mean value of ACTH exhibited a significant decrease in GST-M null ( 20 ) when compared to the wild type (27.8) (P = 0.04). Similarly, within the GST-T genotypic variation, there were notable variations in waist circumference (F = 3.8, p = 0.02) and albumin (F = 5, p = 0.001) values. Notably, the waist circumference and level of albumin were remarkably higher among individuals with Wt/null genotypes (p ≤ 0.03). The BMI mean value displayed a significant reduction in individuals possessing the wild GST-T genotype (29.3) as opposed to those with deletions (31.7) (P = 0.02). In a similar vein, creatinine levels were lower (0.68) in individuals with the wild genotype in contrast to those harboring the mutagenic allele (0.74) (P = 0.02). Contrarily, the albumin mean value demonstrated a significant increase in the wild GST-T genotype (99.6) in comparison to genotypes with evidence of deletion, regardless of whether such deletion occurred in one or both alleles (91.6) (P = 0.007). Furthermore, significant differences in LDL, cholesterol, and ALT values were observed within the p53; rs1042522 genotypic variation (P ≤ 0.05). Further analysis indicates a significant decrease in LDL and cholesterol levels among mutant individuals (P = 0.001). Similarly, ALT values displayed significant differences within the p53; rs1042522 genotypic variation (F = 4.2, P = 0.01). Evidence highlights a notable increase in ALT level among mutant individuals (P = 0.003). Remarkably, no significant associations were discerned between genotypic variations of GST-M, GST-T, and p53 codon 72 (p ≥ 0.05). Additionally, analyses of other demographic and clinical variables' mean values did not unveil significant differences across various genotypic disparities (p ≥ 0.05). 4 DISCUSSION This study is the first attempt to investigate the distribution and correlation of genotypic variations of GST-M, GST-T, and TP53 rs1042522 specifically in children with obesity within the Turkish population. Previous research in adults suggests that the relative frequency of GST-M (ranging from 14–57%) and GST-T (ranging from 8–52%) null genotypes in healthy individuals may vary across different populations 26–31 . It is important to note that there is a lack of information regarding these genotypes among children with obesity. However, studies in adults suggest that the null genotype of GST-M and GST-T may be associated with a higher risk of obesity 26,32,33 . Results of the current study revealed a significant deviation from Hardy-Weinberg proportion in GST-M and GST-T genotype distributions among children who suffer from obesity, with relatively higher null genotypes. This deviation suggests a potential genetic influence on obesity susceptibility related to these gene polymorphisms and aligns with the study hypothesis. The potential health risks associated with GST-M and GST-T genotype distributions in individuals with obesity are primarily related to increased oxidative stress and impaired antioxidant defense mechanisms 33 . Deletions in GST-M and GST-T genes, which lead to the absence of corresponding enzyme activities, can result in elevated oxidative stress levels. This imbalance in oxidant/antioxidant status can contribute to the development of obesity and increase susceptibility to obesity-related complications such as dyslipidemia and hypertension. The absence of these enzymes hampers proper detoxification processes, reduces defense against oxidative stress, and may lead to cellular damage, thereby influencing the pathophysiology of obesity 33 . In the current study, the presence of deletion in GST-M has been linked to elevated levels of cholesterol, LDL, and GGT, suggesting disruptions in lipid metabolism and liver function. Furthermore, the significant reduction in ACTH levels observed in individuals with null genotypes suggests that GST-M may play a role in hormonal or metabolic imbalances. Deletion in GST-T, on the other hand, may be linked to metabolic pathways related to adiposity and fat distribution due to the association of GST-T wt/null genotype with increasing waist circumference. In chilhood obesity, the wild genotype of GST-T appears to confer a protective effect, as it is associated with lower BMI, increasing Albumin levels, and improved renal function. The association of glucose and GGT levels with GST gene doses also highlights the potential contribution of deletion in these genes to impaired glucose metabolism and liver dysfunction. Intrinsic elements of glucose metabolism can influence not just the accumulation of adipose tissue but also the distribution of the acquired fat mass 34 . Liver dysfunction is often associated with glucose homeostasis 35 . The common rs1042522 SNP genotypes in the TP53 gene among children with obesity in our sample were the Arg/Pro variant (45%), followed by the wild-type Arg/Arg (36.6%), and the mutant Pro/Pro genotype. The prevalence of these variants in healthy Turkish-origin individuals was previously reported as 48.9%, 38.3%, and 12.8%, respectively 36 . However, no deviation from Hardy-Weinberg proportion was observed in TP53 variants in the current cohort, suggesting no association between TP53; rs1042522 SNP genotypes and obesity in our sample. The TP53 gene plays a prominent role in regulating various metabolic activities such as glycolysis, lipolysis, and glycogen synthesis 37 . Inconsistent with study results, several studies in the literature have highlighted the potential role of TP53 variants as genetic modifiers for obesity development 38–40 . This inconsistency may be attributed to the role of TP53 variants in obesity development being influenced by gene-environment interactions that were not accounted for in the current study. Factors such as diet, lifestyle, and other genetic variations could modulate the effects of TP53 variants 37,38,41 . Research indicates that the TP53 variant has been associated with influencing cholesterol, LDL, and insulin levels in individuals struggling with obesity 37 . Moreover, factors such as hormonal imbalances, adipokine secretion, hyperinsulinemia, and the PI3K/Akt/mTOR signaling pathway can interact with TP53 variants to modulate the risk of obesity development 42,43 . These findings highlight the complex interplay between genetic variations in TP53, metabolic factors, and hormonal influences in shaping the risk of obesity. The significant alterations in LDL, cholesterol levels, and ALT values due to the conversion of arginine to proline amino acid in TP53 in individuals with mutant genotype displayed the influence of the mutation on cholesterol metabolism and liver function. This genetic variation in rs1042522 impacts lipid profiles by influencing cholesterol synthesis, storage, and export, leading to alterations in LDL and total cholesterol levels 37,44 . Moreover, the rs1042522 polymorphism has been linked to metabolic dysfunction, including obesity, insulin resistance, and cardiovascular risk factors 37,44 . Cholesterol metabolism and liver function are closely linked to obesity, especially in children, due to various factors. In children with obesity, dysregulation of cholesterol metabolism can lead to alterations in lipid profiles, including increased LDL levels and decreased HDL functionality 45,46 . Additionally, obesity can impact liver function, leading to conditions like non-alcoholic fatty liver disease and elevated ALT levels, indicating liver inflammation or damage 46 . While there is a significant deviation from Hardy-Weinberg proportion in the genotype distributions of GST-M and GST-T among individuals with obesity, no significant associations were discerned between genotypic variations of GST-M, GST-T, and TP53;rs1042522. This discrepancy suggests that these gene polymorphisms may not directly correlate with obesity-related traits despite their impact on oxidative stress levels due to the absence of corresponding enzyme activities. Although the findings of this research study provide valuable insights into the congenital genetic predisposition of childhood obesity within the Turkish population, the study is hampered by two limitations related to sampling and data collection that should be taken into account. These limitations cast a shadow over the findings and warrant cautious consideration. The first limitation is related to the sample size, which, while sufficient for initial analysis, might not adequately represent the diversity of genetic backgrounds and environmental factors influencing pediatric obesity. Secondly, the study primarily focuses on genotypic variation in GST-M, GST-T, and TP53; rs1042522 SNP and does not encompass the other GST classes in the detoxification mechanism. This may limit the comprehensive understanding of the contribution of variation in the detoxification pathway in pediatric obesity pathogenesis in the study population. 5 CONCLUSION The study supports the significant role of glutathione S-transferase mu and theta genotypic variations in childhood obesity. Deletions in glutathione S-transferase enzymes may contribute significantly to the network of factors that increase the risk of obesity in children. This contributes valuable data to the literature and supports an epidemiological perspective on the disease. Although the significance of rs1042522 point mutations in the tumor protein 53 gene region was not confirmed in our study, supportive results have been reported in the literature. Therefore, we believe that further studies with a larger sample size, both in the tumor protein 53 gene region and in all classes of glutathione S-transferase, may provide clearer insights into understanding the disease from an epidemiological and hereditary standpoint. Declarations Ethics approval and consent to participate: Ethics committee approval for this study was obtained from the IRB Health Sciences University Ankara Atatürk Sanatorium Training and Research Hospital (No: 2012-KAEK-15/2491). Participants were provided with comprehensive explanations about the study, and written informed consent was obtained from each participant. The study adhered to relevant ethical regulations and was conducted in accordance with the principles outlined in the Declaration of Helsinki and its subsequent amendments. Consent for publication: Not applicable. Availability of data and material: The authors affirm that all data supporting the findings of the study are included within the article, and the raw data supporting the findings were generated and are available from the corresponding author upon request. Competing interests: The authors have no conflict of interest to declare. Funding: This study has been supported by University of Kırıkkale, scientific research projects coordination unit grant No 2022/052. Authors' contributions: OD, SO, YH, ADB, AHH: Conceptualization, Methodology, Formal analysis. AHH: Data management. OD, AAH: Writing, Reviewing and Editing. All authors have read and approved the manuscript. Acknowledgements: Not applicable. References Ameer B, Weintraub MA. Pediatric Obesity: Influence on Drug Dosing and Therapeutics. The Journal of Clinical Pharmacology 2018; 58. doi: 10.1002/jcph.1092 . Farpour-Lambert NJ, Baker JL, Hassapidou M, Holm JC, Nowicka P, O’Malley G et al. Childhood Obesity Is a Chronic Disease Demanding Specific Health Care–a Position Statement from the Childhood Obesity Task Force (COTF) of the European Association for the Study of Obesity (EASO). Obes Facts 2015; 8: 342–9. Vajravelu ME, Tas E, Arslanian S. Pediatric Obesity: Complications and Current Day Management. Life 2023; 13: 1591. WHO. Noncommunicable diseases: Childhood overweight and obesity. https://www.who.int/news-room . 2020. Di Cesare M, Sorić M, Bovet P, Miranda JJ, Bhutta Z, Stevens GA et al. The epidemiological burden of obesity in childhood: a worldwide epidemic requiring urgent action. BMC Med 2019; 17: 212. Jebeile H, Kelly AS, O’Malley G, Baur LA. Obesity in children and adolescents: epidemiology, causes, assessment, and management. Lancet Diabetes Endocrinol 2022; 10: 351–365. Yardim MS, Özcebe LH, Araz OM, Uner S, Li S, Unlu H et al. Prevalence of childhood obesity and related parental factors across socioeconomic strata in Ankara, Turkey. Eastern Mediterranean Health Journal 2019; 25: 374–384. PULAT DEMİR H. Prevalence of Obesity Between 6–15 Years Children in Istanbul. İstanbul Gelişim Üniversitesi Sağlık Bilimleri Dergisi 2022;: 497–512. Sserwanja Q, Mutisya LM, Olal E, Musaba MW, Mukunya D. Factors associated with childhood overweight and obesity in Uganda: a national survey. BMC Public Health 2021; 21: 1494. Williams AS, Ge B, Petroski G, Kruse RL, McElroy JA, Koopman RJ. Socioeconomic Status and Other Factors Associated with Childhood Obesity. J Am Board Fam Med 2018; 31: 514–521. Oktaviani S, Mizutani M, Nishide R, Tanimura S. Factors associated with overweight/obesity of children aged 6–12 years in Indonesia. BMC Pediatr 2023; 23: 484. Littleton SH, Berkowitz RI, Grant SFA. Genetic Determinants of Childhood Obesity. Mol Diagn Ther 2020; 24: 653–663. Chesi A, Grant SFA. The Genetics of Pediatric Obesity. Trends Endocrinol Metab 2015; 26: 711–721. Vourdoumpa A, Paltoglou G, Charmandari E. The Genetic Basis of Childhood Obesity: A Systematic Review. Nutrients 2023; 15: 1416. Wen X, Zhang B, Wu B, Xiao H, Li Z, Li R et al. Signaling pathways in obesity: mechanisms and therapeutic interventions. Signal Transduct Target Ther 2022; 7: 298. Benomar Y, Taouis M. Molecular Mechanisms Underlying Obesity-Induced Hypothalamic Inflammation and Insulin Resistance: Pivotal Role of Resistin/TLR4 Pathways. Front Endocrinol (Lausanne) 2019; 10. doi: 10.3389/fendo.2019.00140 . Lee B-C, Lee J. Cellular and molecular players in adipose tissue inflammation in the development of obesity-induced insulin resistance. Biochim Biophys Acta 2014; 1842: 446–62. D’Alessandro A, Di Felice G, Manco M, Pastore A, Pezzi S, Mariani M et al. Study of the Association between Thiols and Oxidative Stress Markers in Children with Obesity. Nutrients 2022; 14. doi: 10.3390/nu14173637 . De Spiegeleer M, De Paepe E, Van Meulebroek L, Gies I, De Schepper J, Vanhaecke L. Paediatric obesity: a systematic review and pathway mapping of metabolic alterations underlying early disease processes. Mol Med 2021; 27: 145. DAVUDOV M, BULUŞ H, DİRİCAN O, KAYGIN P, GÜLER ŞİMŞEK G, YILMAZ SARIALTIN S et al. Immunohistochemical approach to obesity disease in terms of expression levels of glutathione s-transferase (sigma, zeta, theta) isozymes. The European Research Journal 2023; 9: 543–554. Girault I, Lidereau R, Bièche I. Trimodal GSTT1 and GSTM1 genotyping assay by real-time PCR. Int J Biol Markers 2005; 20: 81–6. Piña-Sánchez P, Hernández-Hernández DM, Taja-Chayeb L, Cerda-Flores RM, González-Herrera AL, Rodea-Avila C et al. Polymorphism in exon 4 of TP53 gene associated to HPV 16 and 18 in Mexican women with cervical cancer. Medical Oncology 2011; 28: 1507–1513. Huszno J, Grzybowska E. TP53 mutations and SNPs as prognostic and predictive factors in patients with breast cancer (Review). Oncol Lett 2018. doi: 10.3892/ol.2018.8627 . Song L-F, Deng Z-H, Gong Z-Y, Li L-L, Li B-Z. Large-Scale de novo Oligonucleotide Synthesis for Whole-Genome Synthesis and Data Storage: Challenges and Opportunities. Front Bioeng Biotechnol 2021; 9. doi: 10.3389/fbioe.2021.689797 . Słomiński B, Skrzypkowska M, Ryba-Stanisławowska M, Myśliwiec M, Trzonkowski P. Associations of TP53 codon 72 polymorphism with complications and comorbidities in patients with type 1 diabetes. J Mol Med 2021; 99: 675–683. Tcheandjieu C, Cordina-Duverger E, Mulot C, Baron-Dubourdieu D, Guizard A-V, Schvartz C et al. Role of GSTM1 and GSTT1 genotypes in differentiated thyroid cancer and interaction with lifestyle factors: Results from case-control studies in France and New Caledonia. PLoS One 2020; 15: e0228187. Martin NJ, Collier AC, Bowen LD, Pritsos KL, Goodrich GG, Arger K et al. Polymorphisms in the NQO1, GSTT and GSTM genes are associated with coronary heart disease and biomarkers of oxidative stress. Mutation Research/Genetic Toxicology and Environmental Mutagenesis 2009; 674: 93–100. Nasr A, Sami R, Ibrahim N, Darwish D. Glutathione S transferase (GSTP 1, GSTM 1, and GSTT 1) gene polymorphisms in Egyptian patients with acute myeloid leukemia. Indian J Cancer 2015; 52: 490. Nomani H, Hagh-Nazari L, Aidy A, Vaisi-Raygani A, Kiani A, Rahimi Z et al. Association between GSTM1, GSTT1, and GSTP1 variants and the risk of end stage renal disease. Ren Fail 2016; 38: 1455–1461. Zheng YX, Chan P, Pan ZF, Shi NN, Wang ZX, Pan J et al. Polymorphism of metabolic genes and susceptibility to occupational chronic manganism. Biomarkers 2002; 7: 337–346. Zakiullah Z, Ahmadullah A, Khisroon M, Saeed M, Khan A, Khuda F et al. Genetic Susceptibility to Oral Cancer due to Combined Effects of GSTT1, GSTM1 and CYP1A1 Gene Variants in Tobacco Addicted Patients of Pashtun Ethnicity of Khyber Pakhtunkhwa Province of Pakistan. Asian Pacific Journal of Cancer Prevention 2015; 16: 1145–1150. Almoshabek HA, Mustafa M, Al-Asmari MM, Alajmi TK, Al-Asmari AK. Association of glutathione S-transferase GSTM1 and GSTT1 deletion polymorphisms with obesity and their relationship with body mass index, lipoprotein and hypertension among young age Saudis. JRSM Cardiovasc Dis 2016; 5: 2048004016669645. Ünsal A, Buluş H, Dirican O, Oğuztüzün serpil, Öztürk D, Cihan M et al. Investigation of GSTM1 and GSTT1 Polymorphisms in Obesity Patients Under Bariatric Surgery. 2021. Gower BA, Hunter GR, Chandler-Laney PC, Alvarez JA, Bush NC. Glucose Metabolism and Diet Predict Changes in Adiposity and Fat Distribution in Weight‐reduced Women. Obesity 2010; 18: 1532–1537. Cotrozzi G, Casini Raggi V, Relli P, Buzzelli G. [Role of the liver in the regulation of glucose metabolism in diabetes and chronic liver disease]. Ann Ital Med Int 1997; 12: 84–91. Dirican O, Kaygın P, Oğuztüzün S, Husseini AA, Sarıaltın SY, Yılmaz C et al. Unveiling the etiological impact of GST-M1, GST-T1, and P53 genotypic variations on brain carcinogenesis. Mol Biol Rep 2024; 51: 45. Sabir JSM, El Omri A, Shaik NA, Banaganapalli B, Hajrah NH, Zrelli H et al. The genetic association study of TP53 polymorphisms in Saudi obese patients. Saudi J Biol Sci 2019; 26: 1338–1343. Molchadsky A, Ezra O, Amendola PG, Krantz D, Kogan-Sakin I, Buganim Y et al. p53 is required for brown adipogenic differentiation and has a protective role against diet-induced obesity. Cell Death Differ 2013; 20: 774–783. Gloria-Bottini F, Banci M, Saccucci P, Magrini A, Bottini E. Is there a role of p53 codon 72 polymorphism in the susceptibility to type 2 diabetes in overweight subjects? A study in patients with cardiovascular diseases. Diabetes Res Clin Pract 2011; 91: e64–e67. Reiling E, Lyssenko V, Boer JMA, Imholz S, Verschuren WMM, Isomaa B et al. Codon 72 polymorphism (rs1042522) of TP53 is associated with changes in diastolic blood pressure over time. Eur J Hum Genet 2012; 20: 696–700. Li Y, Chang S-C, Niu R, Liu L, Crabtree-Ide CR, Zhao B et al. TP53 genetic polymorphisms, interactions with lifestyle factors and lung cancer risk: a case control study in a Chinese population. BMC Cancer 2013; 13: 607. Harris BHL, Macaulay VM, Harris DA, Klenerman P, Karpe F, Lord SR et al. Obesity: a perfect storm for carcinogenesis. Cancer and Metastasis Reviews 2022; 41: 491–515. Ajabnoor GMA. The Molecular and Genetic Interactions between Obesity and Breast Cancer Risk. Medicina (B Aires) 2023; 59: 1338. Abraham J, Mahapatra D, Agrawal P, James MJ. Association of p53 codon 72 polymorphism with weight and metabolic diseases in a Central Indian population. Egyptian Journal of Medical Human Genetics 2024; 25: 6. Mascarenhas P, Furtado JM, Almeida SM, Ferraz ME, Ferraz FP, Oliveira P. Pediatric Overweight, Fatness and Risk for Dyslipidemia Are Related to Diet: A Cross-Sectional Study in 9-year-old Children. Nutrients 2023; 15. doi: 10.3390/nu15020329 . Martin M, Gaete L, Tetzlaff W, Ferraro F, Lozano Chiappe E, Botta EE et al. Vascular inflammation and impaired reverse cholesterol transport and lipid metabolism in obese children and adolescents. Nutrition, Metabolism and Cardiovascular Diseases 2022; 32: 258–268. Additional Declarations There is NO conflict of interest to disclose Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4502132","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":311715517,"identity":"089cc085-993d-4e2b-9611-9a4d9e8bb84d","order_by":0,"name":"Onur Dirican","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYFCCxAaGBCDFz8DAeCCBQQIkZECcFskGBgYkLQn4tEAlDQ4AtUCF8GvhZ09u+/Cgxi7P+PziAwce/LGIZmBv3ibB+OMeTi2SPQ+bZyQcSy42u/Es4UBim0RuA8+xMgmGhGKcWgxuJDYDvcOcuO3GGYMDiQ1ALRI5ZkAtuF0G1VKfuHnG+Q8HEv4Atci/IUrL4cQN/D3AEGMD2cKDXwvILwwJx44nzrjBZgD2SxtPWrFFQhpuLfzs6Y8Zf9RUJ/b3H3748Mefutx+9sMbb3ywwa0FASSgithABDEagPYdIErZKBgFo2AUjEAAAMJhW0Po+RxUAAAAAElFTkSuQmCC","orcid":"","institution":"İstanbul Gelisim University","correspondingAuthor":true,"prefix":"","firstName":"Onur","middleName":"","lastName":"Dirican","suffix":""},{"id":311715518,"identity":"bffad90a-e73a-4dab-a72b-45c69401811a","order_by":1,"name":"Derya Bulus","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Derya","middleName":"","lastName":"Bulus","suffix":""},{"id":311715519,"identity":"d8022f3d-112c-485d-ab61-d40654f97c70","order_by":2,"name":"Abbas Husseini","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Abbas","middleName":"","lastName":"Husseini","suffix":""},{"id":311715520,"identity":"3e54b2a4-4093-491c-8277-e89c6ffeca16","order_by":3,"name":"Yücel Hanilçe","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yücel","middleName":"","lastName":"Hanilçe","suffix":""},{"id":311715521,"identity":"cb68cfe7-8eda-45d3-b279-f8f462274807","order_by":4,"name":"Serpil Oğuztüzün","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Serpil","middleName":"","lastName":"Oğuztüzün","suffix":""}],"badges":[],"createdAt":"2024-05-30 10:00:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4502132/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4502132/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59138319,"identity":"52f4f323-f7c0-4e12-932f-78f2489c96b3","added_by":"auto","created_at":"2024-06-26 19:08:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1083639,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003e Overview of Melting Curve plots after qPCR analysis for GST-M1 in children diagnosed with obesity. \u003cstrong\u003eB:\u003c/strong\u003e Overview of Melting Curve plots after qPCR analysis for GST-T1 in children diagnosed with obesity.. \u003cstrong\u003eC:\u003c/strong\u003e General view of the polymorphic state resulting from a nucleotide change in the TP53 exon 4 codon 72 gene region. G/G (Arg/Arg-WT); \u003cstrong\u003eD:\u003c/strong\u003eGeneral view of the polymorphic state resulting from a nucleotide change in the TP53 exon 4 codon 72 gene region G/C (Arg/Pro-HT); \u003cstrong\u003eE:\u003c/strong\u003e General view of the polymorphic state resulting from a nucleotide change in the TP53 exon 4 codon 72 gene region C/C (Pro/Pro-MT). WT: Wild type (C), WT/MT (D), MT: Mutant type (E).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4502132/v1/158daf6202819aa749ca0480.png"},{"id":76200586,"identity":"541f8523-b7dd-471f-a44c-99fe701ad348","added_by":"auto","created_at":"2025-02-13 11:23:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1820069,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4502132/v1/6a024a5e-7765-4f57-84d9-c6046f59da08.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Do GST-M, GST-T, and TP53 Gene Polymorphisms Have a Role in Childhood Obesity?","fulltext":[{"header":"Impact Statement","content":"\u003cp\u003eDeletions in GST-M and GST-T genes, which encode key enzymes in cellular detoxification, may related to obesity in children. Clinical features associated with GST-M and GST-T genotypes suggest that these enzymes may contribute to obesity in children through impaired lipid metabolism, hormonal and metabolic imbalances, and renal and liver dysfunction.\u003c/p\u003e"},{"header":"1 INTRODUCTION","content":"\u003cp\u003eChildhood obesity, with its metabolic complications, poses a significant public health challenge, contributing to the global health burden. It is strongly linked to a wide range of subsequent chronic diseases \u003csup\u003e1\u003c/sup\u003e. Childhood obesity significantly increases the risk of developing cardiovascular diseases, certain types of cancer, musculoskeletal disorders, type 2 diabetes, prediabetes, dyslipidemia, hypertension, non-alcoholic fatty liver disease, obstructive sleep apnea, polycystic ovary syndrome, weight stigma, depression, and reduced quality of life \u003csup\u003e2\u0026ndash;4\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent estimates suggest that on a worldwide scale in 2016, approximately 40\u0026nbsp;million children below the age of 5 and over 330\u0026nbsp;million children aged 5\u0026ndash;19 were grappling with issues of being overweight \u003csup\u003e5\u003c/sup\u003e. The prevalence exceeded 30% in numerous Pacific Island nations, and surpassed 20% in various regions such as North Africa, Micronesia, Polynesia, the Caribbean, and the USA \u003csup\u003e6\u003c/sup\u003e. Childhood obesity in Turkey is a significant concern, and its prevalence varies across regions and age groups. Studies have revealed that approximately 20\u0026ndash;25% of children aged 6\u0026ndash;19 years in Turkey are facing challenges related to being overweight\u003csup\u003e7\u003c/sup\u003e. The prevalence among 10-year-olds in the capital city of Ankara stands out as exceptionally high, surpassing the figures recorded in European childhood obesity surveillance by more than 1.5 times \u003csup\u003e7\u003c/sup\u003e. Furthermore, the prevalence of childhood obesity in Turkey witnessed a substantial increase from 0.6\u0026ndash;7.3% between 1990\u0026ndash;1995 and 2011\u0026ndash;2015, highlighting a concerning upward trend over the years \u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIt is well-established that a bio-socioecological framework, along with environmental, behavioral, early-life, and medical conditions, may collectively contribute to the development of obesity in children \u003csup\u003e6,9\u0026ndash;11\u003c/sup\u003e. A significant contributor to the risk of obesity is genetic predisposition \u003csup\u003e12,13\u003c/sup\u003e. Presently, more than 1100 independent genetic loci linked to obesity traits have been identified, prompting considerable interest in unraveling their biological functions and understanding the interplay between genes and the environment \u003csup\u003e14\u003c/sup\u003e. The genomic diversity within specific loci may play a role in the intricate cellular and molecular mechanisms influencing the complex metabolic imbalance associated with obesity. These mechanisms include the regulation of appetite and energy balance, the maintenance of glucose, lipid, and adipose tissue homeostasis, as well as their interconnections \u003csup\u003e14\u003c/sup\u003e. For instance, activation of mitogen-activated protein kinases can impact insulin sensitivity, adipocyte function, and energy expenditure \u003csup\u003e15\u003c/sup\u003e. Dysregulation of the phosphatidylinositol 3-kinase/protein kinase B pathway is associated with insulin resistance and obesity-related inflammation \u003csup\u003e16\u003c/sup\u003e. Hypothalamic IKKβ/NF-κB and endoplasmic reticulum stress stress are linked to overnutrition, energy imbalance, and obesity. Inflammatory mechanisms involving the resistin/ toll-like receptor 4 pathways contribute to obesity-induced insulin resistance \u003csup\u003e16\u003c/sup\u003e. Pro-inflammatory cytokines like tumor necrosis factor α can regulate obesity-induced insulin resistance through adipose tissue inflammation \u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDetoxification pathways may contribute to childhood obesity through various mechanisms. Children with obesity exhibit increased oxidative stress, characterized by an imbalance between oxidants and antioxidants, leading to impaired redox signaling and metabolic complications \u003csup\u003e18\u003c/sup\u003e. Additionally, metabolomic studies have shown that metabolic alterations in children with obesity may be associated with changes in detoxification mechanisms \u003csup\u003e19\u003c/sup\u003e. The latest studies highlight the potential role of detoxification pathways in the development of childhood obesity and the need for further research to understand the underlying molecular mechanisms \u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGlutathione S-transferase (GST) enzymes potentially play a significant role in childhood obesity by modulating oxidative stress \u003csup\u003e20\u003c/sup\u003e. GSTs are a family of phase II detoxification enzymes that catalyze the conjugation of glutathione to various substrates, including reactive oxygen species and electrophilic compounds. Despite this, the genomic variation of GST enzymes in childhood obesity has not yet been addressed. Therefore, the current study aims to investigate the polymorphic states of glutathione enzyme subgroups, glutathione S-transferase mu (GST-M), and theta (GST-T), in individuals diagnosed with childhood obesity due to deletions in the corresponding gene regions. Additionally, we adress single nucleotide polymorphism (SNP) within the genetic segment that encodes the tumor suppressor protein 53 (TP53), focusing on exon 4 codon 72, referred to as rs1042522. These genetic analyses will help determine the congenital predisposition to obesity in study population. Furthermore, our study evaluates the associations between these genetic factors and certain clinical and demographic data obtained from the clinic.\u003c/p\u003e"},{"header":"2 METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and sampling\u003c/h2\u003e \u003cp\u003eIn 2023, clinical data from patients receiving treatment for childhood obesity at a pediatric clinic were retrospectively evaluated. The inclusion criteria involved children and adolescents aged 5 to 18 years, who had a body mass index (BMI) exceeding the 95th percentile for their age and gender based on established growth charts. Participants meeting these criteria needed to be free of any known chronic diseases or significant medical conditions. Exclusion criteria were implemented to enhance the study's validity by excluding individuals with pre-existing chronic conditions, including diabetes, cardiovascular diseases, and metabolic disorders. Additionally, participants with known genetic disorders that could potentially interfere with the analysis of GST-M, GST-T, and TP53 gene polymorphisms were excluded. The criteria also excluded individuals or their legal guardians who were unwilling to provide informed consent for study participation. Patients with incomplete clinical or demographic data were also excluded from the analysis.\u003c/p\u003e \u003cp\u003eThe research involved a group of 60 participants. An exhaustive checklist was used to collect demographic and clinical information, including parameters such as age, gender, waist circumference, hip circumference, body fat percentage, glucose levels, urea levels, creatinine levels, total protein levels, albumin levels, cholesterol levels, triglyceride levels, high-density lipoprotein (HDL) levels, low-density lipoprotein (LDL) levels, alanine aminotransferase (ALT) levels, aspartate aminotransferase (AST) levels, gamma-glutamyl transferase (GGT) levels, Thyroxine levels, thyroid-stimulating hormone (TSH) levels, cortisol levels, insulin levels, vitamin D levels, adrenocorticotropic hormone (ACTH) levels, white blood cell count, and hemoglobin levels.\u003c/p\u003e \u003cp\u003eBlood samples collected in EDTA tubes were processed for the extraction of genomic DNA using the Invitrogen PureLink Genomic DNA isolation mini kit, enabling subsequent genotypic analysis. Genomic DNA was successfully extracted from all participants, allowing for genotyping of the GST-M and GST-T gene regions. This genotyping was carried out using a melting curve analysis-based quantitative polymerase chain reaction (qPCR) method to detect gene deletions. It's worth noting that this method adheres to established standards in the field, which typically do not require control groups for such analyses. \u003csup\u003e21\u003c/sup\u003e. The consistency and reliability of this approach are further supported by its alignment with sequence analysis techniques, along with validation through assessments of gene region proliferation, bioinformatics analyses, and data obtained from publicly available databases such as NCBI.\u003c/p\u003e \u003cp\u003eIn our investigation, discrepancies observed in normalized qPCR values among various samples serve as markers for gene loss, collectively termed \"Gene Dosage.\" These deviations are classified according to predetermined threshold criteria, which determine the presence or absence of deletions.\u003c/p\u003e \u003cp\u003eAccording to Girault et al. (2005), normalized mean values offer valuable insights into the presence and implications of gene deletions for GST-M and GST-T. For GST-M, values ranging between 1.00 and 0.80, or surpassing 1.00, indicate the absence of deletion (wild genotype (-/-)), whereas values from 0.79 to 0.42 suggest some degree of gene loss due to deletion (wt/null (+/-)). Notably, a significant impact is evident as values decline from 0.41 to 0.0, indicative of a null genotype (+/+). Similarly, for GST-T, values within the range of 1.00-0.80 or exceeding 1.00 signify no deletion (wild genotype (-/-)), while values from 0.79 to 0.36 suggest gene loss attributable to deletion (wt/null (+/-)). Noteworthy effects are observed as values drop from 0.35 to 0, representing a null genotype (+/+), indicating gene deletion within this locus \u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, genomic DNA from patients was used for sequence analysis aimed at determining the SNP genotype of the TP53 rs1042522 gene region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This region is characterized by the change from arginine (Arg) to proline (Pro) resulting from a guanine to cytosine base conversion, impacting the phenotypic expression of the tumor suppressor p53 gene due to a point mutation within this gene region \u003csup\u003e22,23\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Melting Curve Analysis\u003c/h2\u003e \u003cp\u003eIn our investigation, we utilized a qPCR technique incorporating melting-curve analysis to assess the presence of deletions within the GST-M and GST-T gene regions. The Roche Lightcycler 480 qPCR system was employed for this purpose, in conjunction with the Bio-Rad SSO Advanced Universal SYBR Green Supermix. The primer sequences used in the study were as follows: for GST-T1; Forward: 5'-CAAGTCCCAGAGCACCTCACCTC-3' (NM_000853), Reverse: 5'-GTGTGCATCATTCTCATTGTGGCTT-3' (NM_000853); and for GST-M1; Forward: 5'-TGCATTCGTTCATGTGACAGTATTCT-3' (NM_000561), Reverse: 5'-GAGAGGAGACCGGGCACTCA-3' (NM_000561) \u003csup\u003e21\u003c/sup\u003e. Additionally, these primer sequences were synthesized in the laboratory using the oligosynthesis method and purified using a C18 column and the reverse phase high-performance liquid chromatography (Agilent) system.\u003c/p\u003e \u003cp\u003eThe controls underwent confirmation through the electrophoresis gel method, ensuring both the visualization of graphs within the chromatographic system and the determination of base sizes \u003csup\u003e24\u003c/sup\u003e. The mixture ratios established during the pre-PCR stage prior to utilization in the qPCR system were as follows: 5\u0026micro;L of SYBR Green PCR Master Mix (Power), 0.4\u0026micro;L of forward primer, 0.4\u0026micro;L of reverse primer, 1\u0026micro;L of DNA (100ng/\u0026micro;L), and 3.2\u0026micro;L of DNAse-free water, with a total volume of 10\u0026micro;L.\u003c/p\u003e \u003cp\u003eFurthermore, the melting curve analysis program employed in the qPCR system comprised the following steps: an initial denaturation phase of 3 minutes at 98\u0026deg;C, with a single loop; each cycle involved denaturation at 95\u0026deg;C for 10 seconds followed by annealing at 60\u0026deg;C for 15 seconds, encompassing 40 cycles of amplification, and concluding with a ramp rate of 0.3\u0026deg;C/second from 65\u0026deg;C to 95\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Fragment Sequencing Analysis\u003c/h2\u003e \u003cp\u003eTo determine if the TP53 rs1042522 SNP exists within the patient group under investigation, we analyzed the amplified gene segment obtained through polymerase chain reaction using the Applied Biosystems-3130XL platform. We used primers specific to the TP53 gene region (NM000546.6) for this analysis\u003csup\u003e22,23,25\u003c/sup\u003e .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using the R project for statistical computing version 4.3.2 (R Foundation, Vienna, Austria). Continuous data were summarized as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while categorical data were presented as frequencies (n) accompanied by relative frequencies (%).\u003c/p\u003e \u003cp\u003eThe assessment of normality assumptions for parametric analyses involved implementing the kolmogorov-smirnov test. In instances where variables exhibited a departure from normal distribution, nonparametric analyses were employed as a suitable alternative. The correlation between clinical variables and gene doses of GST-M and GST-T was examined utilizing the pearson correlation coefficient for parametric variables and spearman's rank correlation coefficient respectively.\u003c/p\u003e \u003cp\u003eThe congruence of allele frequencies with the expectations of the Hardy-Weinberg proportions was evaluated employing the chi-square test. Subsequently, the disparity of clinical parameters among genotypic groups was achieved through ANOVA for normally distributed values, while the kruskal-wallis test was chosen where the normality assumption was not met.\u003c/p\u003e \u003cp\u003eFor data exhibiting statistically significant F-ratio scores in ANOVA, post hoc pairwise comparisons were conducted utilizing the Tukey's honestly significant difference procedure. Moreover, the T-test was utilized to evaluate the significant distinctions between clinical characteristics of wild and mutant genotypes. To investigate the interrelation between genotypic variations and categorical variables, the pearson chi-square test was employed. All analyses were conducted at a 95% confidence level, and statistical significance was determined at a p-value below 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 RESULTS","content":"\u003cp\u003eThis study included a cohort of 60 pediatric patients diagnosed with obesity, for whom data on demographic characteristics, clinical parameters, and genotypic information for GST-M, GST-T, and TP53;rs1042522 were collected. The study participants had a mean age of 13.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2 years, ranging from 10 to 17 years old, with a male-to-female ratio of 29:31. The normality assumption for demographic and clinical variables was tested and summarized in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The normality assumption was fulfilled for all demographic and clinical variables except for insulin, ALT, ACTH, and GST-T gene dose (P\u0026thinsp;\u0026le;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe distribution summary of demographic and clinical parameters of participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandart deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u0026ndash;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist Circumference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81\u0026ndash;138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip Circumference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92\u0026ndash;141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u0026ndash;57.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.1\u0026ndash;46.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI SDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1\u0026ndash;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77\u0026ndash;112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53\u0026ndash;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u0026ndash;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.3\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.7-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.8\u0026ndash;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.8\u0026ndash;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78\u0026ndash;226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˂150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etriglyceride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39\u0026ndash;590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˂100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36\u0026ndash;152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˂100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*ALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u0026ndash;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u0026ndash;43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroxine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.29\u0026ndash;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8\u0026ndash;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87\u0026ndash;7.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u0026ndash;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCortisol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9\u0026ndash;20.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u0026ndash;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*Insulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.1\u0026ndash;102.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e˂2\u0026ndash;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.4\u0026ndash;34.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*ACTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u0026ndash;102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7851.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1691.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4800\u0026ndash;13100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4500\u0026ndash;13500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.8\u0026ndash;16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.1\u0026ndash;15.1/16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGST-M Gene dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.53093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.313294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11860\u0026ndash;1.17080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e*GST-T Gene dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.369196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.324169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10481\u0026ndash;1.23272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eThe p-value indicates whether data differ significantly from that which is normally distributed. (*;P\u0026thinsp;\u0026le;\u0026thinsp;0.05) (** The GST-M and GST-T gene dose reference interval was based on Hardy-Weinberg proportion determined by Girault et al \u003csup\u003e21\u003c/sup\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the clinical variables, GGT (P\u0026thinsp;=\u0026thinsp;0.04) and glucose level (P\u0026thinsp;=\u0026thinsp;0.03) demonstrate a significant correlation with GST-M and GST-T gene dose, respectively (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, it appears that there is no association between gene dose value and other parameters (Tables \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between clinical parameters value and GST gene doses.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGST-M Gene dose\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eGST-T Gene dose\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003csub\u003es\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.360698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist Circumference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.538398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.09768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip Circumference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.88126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.978221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.83619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.727657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.2067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.113063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74527\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.914848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.1079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.769084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.10889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.40756\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.699892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.1073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.07789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.55415\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etriglyceride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.728798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.07251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.58192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.439403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.1077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.2074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.111821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.03311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.80173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56889\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.916051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.87551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.2583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.046563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroxine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.17166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.2241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.085192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCortisol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.81763\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.24353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.30854\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.971569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.08097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.53853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.24353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.17653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.516976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.79529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.732796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.84649\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGST gene doses disparity among categorical groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubgroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGST-M gene dose mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGST-T gene dose median\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.192469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.275155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.61708\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.365225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHepatobiliyer USG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.169499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.434258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.4654\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.280164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.204705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe general view of the melting curve and sequence analysis was illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and the genotypic information related to GST-M, GST-T, and TP53;rs1042522 SNP was described in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The genotype frequencies were 31.6%, 31.6%, and 36.6% for GSTM -/- (Wt/Wt), GSTM +/- (Wt/null), and GSTM +/+ (null/null), respectively. However, the observed genotype frequencies were 25%, 23.3%, and 51.6% for GST-T -/- (Wt/Wt), GSTT1 +/- (Wt/null), and GSTT1 +/+ (null/null), respectively (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). On the other hand, the most frequent genotype of TP53 codon 72 polymorphism was Arg/Pro (Wt/Mt) (45%), followed by Arg/Arg (Wt/Wt) (36.6%). There was a deviation from the expected Hardy-Weinberg proportion in the observed GST-M (χ2\u0026thinsp;=\u0026thinsp;7.99, P\u0026thinsp;=\u0026thinsp;0.01) and GST-T (χ2\u0026thinsp;=\u0026thinsp;14.75, P\u0026thinsp;=\u0026thinsp;0.0006) genotype distribution. On the other hand, observed genotypic variation for TP53;rs1042522 was in alignment with Hardy-Weinberg proportion (χ2\u0026thinsp;=\u0026thinsp;1.05, P\u0026thinsp;=\u0026thinsp;0.58).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConcordance Between Observed GST-M, GST-T, and TP53 codon 72 Allele Frequencies and Expected Proportions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotyping\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGST-M wild genotypes (-/-)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.01834\u003c/p\u003e \u003cp\u003eχ2\u0026thinsp;=\u0026thinsp;7.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGST-M wt/null genotypes (+/-)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGST-M null genotypes (+/+)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (36.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGST-T wild genotypes (-/-)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.00063\u003c/p\u003e \u003cp\u003eχ2\u0026thinsp;=\u0026thinsp;14.754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGST-T wt/null genotypes (+/-)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGST-T null genotypes (+/+)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31(51.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP53;rs1042522 Arg/Arg (\u003cem\u003ewild)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(36.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.588951\u003c/p\u003e \u003cp\u003eχ2\u0026thinsp;=\u0026thinsp;1.0588\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP53;rs1042522 Arg/Pro (\u003cem\u003ewt/mt\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (45%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP53;rs1042522 Pro/Pro (\u003cem\u003emutant\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(18.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMoreover, the study uncovered significant differences in genotypes for GST-M, GST-T, and TP53; rs1042522 across various demographic and clinical characteristics. Specifically, analyses indicated significant variations in cholesterol (F\u0026thinsp;=\u0026thinsp;3.26, p\u0026thinsp;=\u0026thinsp;0.045), LDL (F\u0026thinsp;=\u0026thinsp;4.66, p\u0026thinsp;=\u0026thinsp;0.01), and GGT (F\u0026thinsp;=\u0026thinsp;7.6, p\u0026thinsp;=\u0026thinsp;0.001) values within the context of GST-M genotypic variation. Further analysis reveals that the levels of cholesterol, LDL, and GGT were remarkably higher among individuals with null genotypes (p\u0026thinsp;\u0026le;\u0026thinsp;0.01). Furthermore, the mean value of ACTH exhibited a significant decrease in GST-M null (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) when compared to the wild type (27.8) (P\u0026thinsp;=\u0026thinsp;0.04).\u003c/p\u003e \u003cp\u003eSimilarly, within the GST-T genotypic variation, there were notable variations in waist circumference (F\u0026thinsp;=\u0026thinsp;3.8, p\u0026thinsp;=\u0026thinsp;0.02) and albumin (F\u0026thinsp;=\u0026thinsp;5, p\u0026thinsp;=\u0026thinsp;0.001) values. Notably, the waist circumference and level of albumin were remarkably higher among individuals with Wt/null genotypes (p\u0026thinsp;\u0026le;\u0026thinsp;0.03).\u003c/p\u003e \u003cp\u003eThe BMI mean value displayed a significant reduction in individuals possessing the wild GST-T genotype (29.3) as opposed to those with deletions (31.7) (P\u0026thinsp;=\u0026thinsp;0.02). In a similar vein, creatinine levels were lower (0.68) in individuals with the wild genotype in contrast to those harboring the mutagenic allele (0.74) (P\u0026thinsp;=\u0026thinsp;0.02). Contrarily, the albumin mean value demonstrated a significant increase in the wild GST-T genotype (99.6) in comparison to genotypes with evidence of deletion, regardless of whether such deletion occurred in one or both alleles (91.6) (P\u0026thinsp;=\u0026thinsp;0.007).\u003c/p\u003e \u003cp\u003eFurthermore, significant differences in LDL, cholesterol, and ALT values were observed within the p53; rs1042522 genotypic variation (P\u0026thinsp;\u0026le;\u0026thinsp;0.05). Further analysis indicates a significant decrease in LDL and cholesterol levels among mutant individuals (P\u0026thinsp;=\u0026thinsp;0.001). Similarly, ALT values displayed significant differences within the p53; rs1042522 genotypic variation (F\u0026thinsp;=\u0026thinsp;4.2, P\u0026thinsp;=\u0026thinsp;0.01). Evidence highlights a notable increase in ALT level among mutant individuals (P\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e \u003cp\u003eRemarkably, no significant associations were discerned between genotypic variations of GST-M, GST-T, and p53 codon 72 (p\u0026thinsp;\u0026ge;\u0026thinsp;0.05). Additionally, analyses of other demographic and clinical variables' mean values did not unveil significant differences across various genotypic disparities (p\u0026thinsp;\u0026ge;\u0026thinsp;0.05).\u003c/p\u003e"},{"header":"4 DISCUSSION","content":"\u003cp\u003eThis study is the first attempt to investigate the distribution and correlation of genotypic variations of GST-M, GST-T, and TP53 rs1042522 specifically in children with obesity within the Turkish population. Previous research in adults suggests that the relative frequency of GST-M (ranging from 14\u0026ndash;57%) and GST-T (ranging from 8\u0026ndash;52%) null genotypes in healthy individuals may vary across different populations \u003csup\u003e26\u0026ndash;31\u003c/sup\u003e. It is important to note that there is a lack of information regarding these genotypes among children with obesity. However, studies in adults suggest that the null genotype of GST-M and GST-T may be associated with a higher risk of obesity \u003csup\u003e26,32,33\u003c/sup\u003e. Results of the current study revealed a significant deviation from Hardy-Weinberg proportion in GST-M and GST-T genotype distributions among children who suffer from obesity, with relatively higher null genotypes. This deviation suggests a potential genetic influence on obesity susceptibility related to these gene polymorphisms and aligns with the study hypothesis.\u003c/p\u003e \u003cp\u003eThe potential health risks associated with GST-M and GST-T genotype distributions in individuals with obesity are primarily related to increased oxidative stress and impaired antioxidant defense mechanisms \u003csup\u003e33\u003c/sup\u003e. Deletions in GST-M and GST-T genes, which lead to the absence of corresponding enzyme activities, can result in elevated oxidative stress levels. This imbalance in oxidant/antioxidant status can contribute to the development of obesity and increase susceptibility to obesity-related complications such as dyslipidemia and hypertension. The absence of these enzymes hampers proper detoxification processes, reduces defense against oxidative stress, and may lead to cellular damage, thereby influencing the pathophysiology of obesity \u003csup\u003e33\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the current study, the presence of deletion in GST-M has been linked to elevated levels of cholesterol, LDL, and GGT, suggesting disruptions in lipid metabolism and liver function. Furthermore, the significant reduction in ACTH levels observed in individuals with null genotypes suggests that GST-M may play a role in hormonal or metabolic imbalances.\u003c/p\u003e \u003cp\u003eDeletion in GST-T, on the other hand, may be linked to metabolic pathways related to adiposity and fat distribution due to the association of GST-T wt/null genotype with increasing waist circumference. In chilhood obesity, the wild genotype of GST-T appears to confer a protective effect, as it is associated with lower BMI, increasing Albumin levels, and improved renal function.\u003c/p\u003e \u003cp\u003eThe association of glucose and GGT levels with GST gene doses also highlights the potential contribution of deletion in these genes to impaired glucose metabolism and liver dysfunction. Intrinsic elements of glucose metabolism can influence not just the accumulation of adipose tissue but also the distribution of the acquired fat mass \u003csup\u003e34\u003c/sup\u003e. Liver dysfunction is often associated with glucose homeostasis \u003csup\u003e35\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e The common rs1042522 SNP genotypes in the TP53 gene among children with obesity in our sample were the Arg/Pro variant (45%), followed by the wild-type Arg/Arg (36.6%), and the mutant Pro/Pro genotype. The prevalence of these variants in healthy Turkish-origin individuals was previously reported as 48.9%, 38.3%, and 12.8%, respectively \u003csup\u003e36\u003c/sup\u003e. However, no deviation from Hardy-Weinberg proportion was observed in TP53 variants in the current cohort, suggesting no association between TP53; rs1042522 SNP genotypes and obesity in our sample. The TP53 gene plays a prominent role in regulating various metabolic activities such as glycolysis, lipolysis, and glycogen synthesis \u003csup\u003e37\u003c/sup\u003e. Inconsistent with study results, several studies in the literature have highlighted the potential role of TP53 variants as genetic modifiers for obesity development \u003csup\u003e38\u0026ndash;40\u003c/sup\u003e. This inconsistency may be attributed to the role of TP53 variants in obesity development being influenced by gene-environment interactions that were not accounted for in the current study. Factors such as diet, lifestyle, and other genetic variations could modulate the effects of TP53 variants \u003csup\u003e37,38,41\u003c/sup\u003e. Research indicates that the TP53 variant has been associated with influencing cholesterol, LDL, and insulin levels in individuals struggling with obesity \u003csup\u003e37\u003c/sup\u003e. Moreover, factors such as hormonal imbalances, adipokine secretion, hyperinsulinemia, and the PI3K/Akt/mTOR signaling pathway can interact with TP53 variants to modulate the risk of obesity development \u003csup\u003e42,43\u003c/sup\u003e. These findings highlight the complex interplay between genetic variations in TP53, metabolic factors, and hormonal influences in shaping the risk of obesity.\u003c/p\u003e \u003cp\u003eThe significant alterations in LDL, cholesterol levels, and ALT values due to the conversion of arginine to proline amino acid in TP53 in individuals with mutant genotype displayed the influence of the mutation on cholesterol metabolism and liver function. This genetic variation in rs1042522 impacts lipid profiles by influencing cholesterol synthesis, storage, and export, leading to alterations in LDL and total cholesterol levels \u003csup\u003e37,44\u003c/sup\u003e. Moreover, the rs1042522 polymorphism has been linked to metabolic dysfunction, including obesity, insulin resistance, and cardiovascular risk factors \u003csup\u003e37,44\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCholesterol metabolism and liver function are closely linked to obesity, especially in children, due to various factors. In children with obesity, dysregulation of cholesterol metabolism can lead to alterations in lipid profiles, including increased LDL levels and decreased HDL functionality \u003csup\u003e45,46\u003c/sup\u003e. Additionally, obesity can impact liver function, leading to conditions like non-alcoholic fatty liver disease and elevated ALT levels, indicating liver inflammation or damage \u003csup\u003e46\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile there is a significant deviation from Hardy-Weinberg proportion in the genotype distributions of GST-M and GST-T among individuals with obesity, no significant associations were discerned between genotypic variations of GST-M, GST-T, and TP53;rs1042522. This discrepancy suggests that these gene polymorphisms may not directly correlate with obesity-related traits despite their impact on oxidative stress levels due to the absence of corresponding enzyme activities.\u003c/p\u003e \u003cp\u003eAlthough the findings of this research study provide valuable insights into the congenital genetic predisposition of childhood obesity within the Turkish population, the study is hampered by two limitations related to sampling and data collection that should be taken into account. These limitations cast a shadow over the findings and warrant cautious consideration. The first limitation is related to the sample size, which, while sufficient for initial analysis, might not adequately represent the diversity of genetic backgrounds and environmental factors influencing pediatric obesity. Secondly, the study primarily focuses on genotypic variation in GST-M, GST-T, and TP53; rs1042522 SNP and does not encompass the other GST classes in the detoxification mechanism. This may limit the comprehensive understanding of the contribution of variation in the detoxification pathway in pediatric obesity pathogenesis in the study population.\u003c/p\u003e"},{"header":"5 CONCLUSION","content":"\u003cp\u003eThe study supports the significant role of glutathione S-transferase mu and theta genotypic variations in childhood obesity. Deletions in glutathione S-transferase enzymes may contribute significantly to the network of factors that increase the risk of obesity in children. This contributes valuable data to the literature and supports an epidemiological perspective on the disease. Although the significance of rs1042522 point mutations in the tumor protein 53 gene region was not confirmed in our study, supportive results have been reported in the literature. Therefore, we believe that further studies with a larger sample size, both in the tumor protein 53 gene region and in all classes of glutathione S-transferase, may provide clearer insights into understanding the disease from an epidemiological and hereditary standpoint.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics committee approval for this study was obtained from the IRB Health Sciences University Ankara Atat\u0026uuml;rk Sanatorium Training and Research Hospital (No: 2012-KAEK-15/2491). Participants were provided with comprehensive explanations about the study, and written informed consent was obtained from each participant. The study adhered to relevant ethical regulations and was conducted in accordance with the principles outlined in the Declaration of Helsinki and its subsequent amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u003c/strong\u003e The authors affirm that all data supporting the findings of the study are included within the article, and the raw data supporting the findings were generated and are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors have no conflict of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study has been supported by\u0026nbsp;University of Kırıkkale, scientific research projects coordination unit grant No 2022/052.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOD, SO, YH, ADB, AHH: Conceptualization, Methodology, Formal analysis. AHH: Data management. OD, AAH: Writing, Reviewing and Editing. All authors have read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmeer B, Weintraub MA. Pediatric Obesity: Influence on Drug Dosing and Therapeutics. The Journal of Clinical Pharmacology 2018; 58. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jcph.1092\u003c/span\u003e\u003cspan address=\"10.1002/jcph.1092\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarpour-Lambert NJ, Baker JL, Hassapidou M, Holm JC, Nowicka P, O\u0026rsquo;Malley G \u003cem\u003eet al.\u003c/em\u003e Childhood Obesity Is a Chronic Disease Demanding Specific Health Care\u0026ndash;a Position Statement from the Childhood Obesity Task Force (COTF) of the European Association for the Study of Obesity (EASO). Obes Facts 2015; 8: 342\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVajravelu ME, Tas E, Arslanian S. Pediatric Obesity: Complications and Current Day Management. Life 2023; 13: 1591.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. Noncommunicable diseases: Childhood overweight and obesity. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Cesare M, Sorić M, Bovet P, Miranda JJ, Bhutta Z, Stevens GA \u003cem\u003eet al.\u003c/em\u003e The epidemiological burden of obesity in childhood: a worldwide epidemic requiring urgent action. BMC Med 2019; 17: 212.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJebeile H, Kelly AS, O\u0026rsquo;Malley G, Baur LA. Obesity in children and adolescents: epidemiology, causes, assessment, and management. Lancet Diabetes Endocrinol 2022; 10: 351\u0026ndash;365.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYardim MS, \u0026Ouml;zcebe LH, Araz OM, Uner S, Li S, Unlu H \u003cem\u003eet al.\u003c/em\u003e Prevalence of childhood obesity and related parental factors across socioeconomic strata in Ankara, Turkey. Eastern Mediterranean Health Journal 2019; 25: 374\u0026ndash;384.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePULAT DEMİR H. Prevalence of Obesity Between 6\u0026ndash;15 Years Children in Istanbul. İstanbul Gelişim \u0026Uuml;niversitesi Sağlık Bilimleri Dergisi 2022;: 497\u0026ndash;512.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSserwanja Q, Mutisya LM, Olal E, Musaba MW, Mukunya D. Factors associated with childhood overweight and obesity in Uganda: a national survey. BMC Public Health 2021; 21: 1494.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams AS, Ge B, Petroski G, Kruse RL, McElroy JA, Koopman RJ. Socioeconomic Status and Other Factors Associated with Childhood Obesity. J Am Board Fam Med 2018; 31: 514\u0026ndash;521.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOktaviani S, Mizutani M, Nishide R, Tanimura S. Factors associated with overweight/obesity of children aged 6\u0026ndash;12 years in Indonesia. BMC Pediatr 2023; 23: 484.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLittleton SH, Berkowitz RI, Grant SFA. Genetic Determinants of Childhood Obesity. Mol Diagn Ther 2020; 24: 653\u0026ndash;663.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChesi A, Grant SFA. The Genetics of Pediatric Obesity. Trends Endocrinol Metab 2015; 26: 711\u0026ndash;721.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVourdoumpa A, Paltoglou G, Charmandari E. The Genetic Basis of Childhood Obesity: A Systematic Review. Nutrients 2023; 15: 1416.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen X, Zhang B, Wu B, Xiao H, Li Z, Li R \u003cem\u003eet al.\u003c/em\u003e Signaling pathways in obesity: mechanisms and therapeutic interventions. Signal Transduct Target Ther 2022; 7: 298.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenomar Y, Taouis M. Molecular Mechanisms Underlying Obesity-Induced Hypothalamic Inflammation and Insulin Resistance: Pivotal Role of Resistin/TLR4 Pathways. Front Endocrinol (Lausanne) 2019; 10. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2019.00140\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2019.00140\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee B-C, Lee J. Cellular and molecular players in adipose tissue inflammation in the development of obesity-induced insulin resistance. Biochim Biophys Acta 2014; 1842: 446\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026rsquo;Alessandro A, Di Felice G, Manco M, Pastore A, Pezzi S, Mariani M \u003cem\u003eet al.\u003c/em\u003e Study of the Association between Thiols and Oxidative Stress Markers in Children with Obesity. Nutrients 2022; 14. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu14173637\u003c/span\u003e\u003cspan address=\"10.3390/nu14173637\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Spiegeleer M, De Paepe E, Van Meulebroek L, Gies I, De Schepper J, Vanhaecke L. Paediatric obesity: a systematic review and pathway mapping of metabolic alterations underlying early disease processes. Mol Med 2021; 27: 145.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDAVUDOV M, BULUŞ H, DİRİCAN O, KAYGIN P, G\u0026Uuml;LER ŞİMŞEK G, YILMAZ SARIALTIN S \u003cem\u003eet al.\u003c/em\u003e Immunohistochemical approach to obesity disease in terms of expression levels of glutathione s-transferase (sigma, zeta, theta) isozymes. The European Research Journal 2023; 9: 543\u0026ndash;554.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGirault I, Lidereau R, Bi\u0026egrave;che I. Trimodal GSTT1 and GSTM1 genotyping assay by real-time PCR. Int J Biol Markers 2005; 20: 81\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePi\u0026ntilde;a-S\u0026aacute;nchez P, Hern\u0026aacute;ndez-Hern\u0026aacute;ndez DM, Taja-Chayeb L, Cerda-Flores RM, Gonz\u0026aacute;lez-Herrera AL, Rodea-Avila C \u003cem\u003eet al.\u003c/em\u003e Polymorphism in exon 4 of TP53 gene associated to HPV 16 and 18 in Mexican women with cervical cancer. Medical Oncology 2011; 28: 1507\u0026ndash;1513.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuszno J, Grzybowska E. TP53 mutations and SNPs as prognostic and predictive factors in patients with breast cancer (Review). Oncol Lett 2018. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3892/ol.2018.8627\u003c/span\u003e\u003cspan address=\"10.3892/ol.2018.8627\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong L-F, Deng Z-H, Gong Z-Y, Li L-L, Li B-Z. Large-Scale de novo Oligonucleotide Synthesis for Whole-Genome Synthesis and Data Storage: Challenges and Opportunities. Front Bioeng Biotechnol 2021; 9. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fbioe.2021.689797\u003c/span\u003e\u003cspan address=\"10.3389/fbioe.2021.689797\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSłomiński B, Skrzypkowska M, Ryba-Stanisławowska M, Myśliwiec M, Trzonkowski P. Associations of TP53 codon 72 polymorphism with complications and comorbidities in patients with type 1 diabetes. J Mol Med 2021; 99: 675\u0026ndash;683.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTcheandjieu C, Cordina-Duverger E, Mulot C, Baron-Dubourdieu D, Guizard A-V, Schvartz C \u003cem\u003eet al.\u003c/em\u003e Role of GSTM1 and GSTT1 genotypes in differentiated thyroid cancer and interaction with lifestyle factors: Results from case-control studies in France and New Caledonia. PLoS One 2020; 15: e0228187.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin NJ, Collier AC, Bowen LD, Pritsos KL, Goodrich GG, Arger K \u003cem\u003eet al.\u003c/em\u003e Polymorphisms in the NQO1, GSTT and GSTM genes are associated with coronary heart disease and biomarkers of oxidative stress. Mutation Research/Genetic Toxicology and Environmental Mutagenesis 2009; 674: 93\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNasr A, Sami R, Ibrahim N, Darwish D. Glutathione S transferase (GSTP 1, GSTM 1, and GSTT 1) gene polymorphisms in Egyptian patients with acute myeloid leukemia. Indian J Cancer 2015; 52: 490.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNomani H, Hagh-Nazari L, Aidy A, Vaisi-Raygani A, Kiani A, Rahimi Z \u003cem\u003eet al.\u003c/em\u003e Association between GSTM1, GSTT1, and GSTP1 variants and the risk of end stage renal disease. Ren Fail 2016; 38: 1455\u0026ndash;1461.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng YX, Chan P, Pan ZF, Shi NN, Wang ZX, Pan J \u003cem\u003eet al.\u003c/em\u003e Polymorphism of metabolic genes and susceptibility to occupational chronic manganism. Biomarkers 2002; 7: 337\u0026ndash;346.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZakiullah Z, Ahmadullah A, Khisroon M, Saeed M, Khan A, Khuda F \u003cem\u003eet al.\u003c/em\u003e Genetic Susceptibility to Oral Cancer due to Combined Effects of GSTT1, GSTM1 and CYP1A1 Gene Variants in Tobacco Addicted Patients of Pashtun Ethnicity of Khyber Pakhtunkhwa Province of Pakistan. Asian Pacific Journal of Cancer Prevention 2015; 16: 1145\u0026ndash;1150.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmoshabek HA, Mustafa M, Al-Asmari MM, Alajmi TK, Al-Asmari AK. Association of glutathione S-transferase GSTM1 and GSTT1 deletion polymorphisms with obesity and their relationship with body mass index, lipoprotein and hypertension among young age Saudis. JRSM Cardiovasc Dis 2016; 5: 2048004016669645.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Uuml;nsal A, Buluş H, Dirican O, Oğuzt\u0026uuml;z\u0026uuml;n serpil, \u0026Ouml;zt\u0026uuml;rk D, Cihan M et al. Investigation of GSTM1 and GSTT1 Polymorphisms in Obesity Patients Under Bariatric Surgery. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGower BA, Hunter GR, Chandler-Laney PC, Alvarez JA, Bush NC. Glucose Metabolism and Diet Predict Changes in Adiposity and Fat Distribution in Weight‐reduced Women. Obesity 2010; 18: 1532\u0026ndash;1537.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCotrozzi G, Casini Raggi V, Relli P, Buzzelli G. [Role of the liver in the regulation of glucose metabolism in diabetes and chronic liver disease]. Ann Ital Med Int 1997; 12: 84\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDirican O, Kaygın P, Oğuzt\u0026uuml;z\u0026uuml;n S, Husseini AA, Sarıaltın SY, Yılmaz C \u003cem\u003eet al.\u003c/em\u003e Unveiling the etiological impact of GST-M1, GST-T1, and P53 genotypic variations on brain carcinogenesis. Mol Biol Rep 2024; 51: 45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSabir JSM, El Omri A, Shaik NA, Banaganapalli B, Hajrah NH, Zrelli H \u003cem\u003eet al.\u003c/em\u003e The genetic association study of TP53 polymorphisms in Saudi obese patients. Saudi J Biol Sci 2019; 26: 1338\u0026ndash;1343.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMolchadsky A, Ezra O, Amendola PG, Krantz D, Kogan-Sakin I, Buganim Y \u003cem\u003eet al.\u003c/em\u003e p53 is required for brown adipogenic differentiation and has a protective role against diet-induced obesity. Cell Death Differ 2013; 20: 774\u0026ndash;783.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGloria-Bottini F, Banci M, Saccucci P, Magrini A, Bottini E. Is there a role of p53 codon 72 polymorphism in the susceptibility to type 2 diabetes in overweight subjects? A study in patients with cardiovascular diseases. Diabetes Res Clin Pract 2011; 91: e64\u0026ndash;e67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReiling E, Lyssenko V, Boer JMA, Imholz S, Verschuren WMM, Isomaa B \u003cem\u003eet al.\u003c/em\u003e Codon 72 polymorphism (rs1042522) of TP53 is associated with changes in diastolic blood pressure over time. Eur J Hum Genet 2012; 20: 696\u0026ndash;700.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Chang S-C, Niu R, Liu L, Crabtree-Ide CR, Zhao B \u003cem\u003eet al.\u003c/em\u003e TP53 genetic polymorphisms, interactions with lifestyle factors and lung cancer risk: a case control study in a Chinese population. BMC Cancer 2013; 13: 607.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarris BHL, Macaulay VM, Harris DA, Klenerman P, Karpe F, Lord SR \u003cem\u003eet al.\u003c/em\u003e Obesity: a perfect storm for carcinogenesis. Cancer and Metastasis Reviews 2022; 41: 491\u0026ndash;515.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAjabnoor GMA. The Molecular and Genetic Interactions between Obesity and Breast Cancer Risk. Medicina (B Aires) 2023; 59: 1338.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbraham J, Mahapatra D, Agrawal P, James MJ. Association of p53 codon 72 polymorphism with weight and metabolic diseases in a Central Indian population. Egyptian Journal of Medical Human Genetics 2024; 25: 6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMascarenhas P, Furtado JM, Almeida SM, Ferraz ME, Ferraz FP, Oliveira P. Pediatric Overweight, Fatness and Risk for Dyslipidemia Are Related to Diet: A Cross-Sectional Study in 9-year-old Children. Nutrients 2023; 15. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu15020329\u003c/span\u003e\u003cspan address=\"10.3390/nu15020329\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin M, Gaete L, Tetzlaff W, Ferraro F, Lozano Chiappe E, Botta EE \u003cem\u003eet al.\u003c/em\u003e Vascular inflammation and impaired reverse cholesterol transport and lipid metabolism in obese children and adolescents. Nutrition, Metabolism and Cardiovascular Diseases 2022; 32: 258\u0026ndash;268.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Childhood obesity, Genetic variations, GST-M, GST-T, TP53;rs1042522","lastPublishedDoi":"10.21203/rs.3.rs-4502132/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4502132/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective:\u003c/h2\u003e \u003cp\u003eThis study investigates genotypic variations in GST-M, GST-T, and TP53;rs1042522 among children with obesity.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eBlood samples from 60 patients with childhood obesity were analyzed. Deletions in GST-M and GST-T were identified using quantitative PCR with melting curve analysis, while TP53;rs1042522 was genotyped via sequence analysis. Deviation from Hardy-Weinberg proportion was examined, and associations with clinical and demographic variables were assessed.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eWe observed deviations in the genotypes of GST-M and GST-T, while TP53;rs1042522 remained aligned. Higher cholesterol, LDL, and GGT levels were found in individuals with null GST-M genotypes. Notably, individuals with Wt/null GST-T genotypes had remarkably higher waist circumference and levels of albumin. The wild-type GST-T genotype correlated with reduced BMI and creatinine levels. Individuals with TP53;rs1042522 mutations showed decreased LDL and cholesterol levels but increased ALT levels.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eThe research highlighted the notable influence of genetic variations in GST-M and GST-T on obesity in children, while the TP53 polymorphism, rs1042522, did not show a significant impact.\u003c/p\u003e","manuscriptTitle":"Do GST-M, GST-T, and TP53 Gene Polymorphisms Have a Role in Childhood Obesity?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-26 19:07:55","doi":"10.21203/rs.3.rs-4502132/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a17776e6-1c84-4bf1-8227-8f1fe7c5953b","owner":[],"postedDate":"June 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32946515,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity"},{"id":32946516,"name":"Biological sciences/Genetics"}],"tags":[],"updatedAt":"2025-02-13T11:15:44+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-26 19:07:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4502132","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4502132","identity":"rs-4502132","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}