Association of PPAR γ and TRHR Gene Variations, with Metabolic Factors in Diabetic and Obesity Individuals in the Population; An Investigation Study

doi:10.21203/rs.3.rs-4992600/v1

Obesity, a global pandemic with multiple causes, significantly impacts physical health. Factors such as genetics, ethnicity, physical inactivity, excessive calorie intake, stress, and socioeconomic status contribute to the development of metabolic disorders like diabetes, cardiovascular disease, and metabolic syndrome. This study was designed to examine the genotype differences of PPAR γ (rs1801282) and TRHR (rs16892496) polymorphisms known to be associated with obesity in different populations and to determine the role of these variants in the etiology of obesity in the Turkish population. Genotyping of rs1801282 and rs16892496 polymorphisms were conducted by using the Real Time-PCR in study group which consists of 160 patients with diabetes and obesity and 79 healty controls. Upon analyzing the relationship between genotypes and metabolic parameters, individuals with GG genotype showed significantly different BMI values compared to individuals with CC and CG genotypes in the non-diabetic obese group, indicating the possible effect of GG genotype on obesity severity (p = 0,008). In the control group individuals carrying C allele exhibited levels of HDL cholesterol compared to G allele carriers with statistical significance observed (p = 0.034). Analyzing the relationship between genotypes and metabolic parameters we observed that BMI values of A allele carriers in the control group were notably lower than those of C allele carriers with a difference (p = 0.026). These results underscore the significance of influences in obesity and diabetes pathogenesis indicating roles for PPAR γ and TRHR genes in these conditions development. Nonetheless further research, with more diverse samples is necessary to comprehend these connections.

Polymorphism

PPAR-γ

TRHR

Diabetes

Obesity

Obesity and diabetes are chronic diseases imposing a significant burden on global healthcare systems. These conditions negatively impact individual quality of life and incur substantial economic costs. According to the World Health Organization (WHO), in 2016, 1.9 billion adults worldwide were overweight, with 650 million classified as obese. Alarmingly, obesity rates have nearly tripled since 1975 [1]. The situation is particularly alarming concerning diabetes. As per the 2019 International Diabetes Federation (IDF) report there are currently 463 million adults living with diabetes worldwide a figure projected to reach 700 million by 2045 [2]. These statistics highlight the global significance of obesity and diabetes. Turkey mirrors this trend, with the Turkish Diabetes, Hypertension, Obesity and Endocrinological Diseases Prevalence Study II (TURDEP II) study reporting a 32% obesity prevalence and 13.7% diabetes prevalence. Compared to TURDEP I in 1998, these figures represent a 44% increase in obesity and a 90% increase in diabetes, emphasizing the urgent need for effective interventions [3]. It is widely recognized that genetic factors contribute substantially to the development of obesity and diabetes. Recent genome wide association studies (GWAS) have identified gene variants linked to these conditions. Notably genes like PPAR γ and TRHR have garnered interest [4].

PPARγ, a critical transcription factor located on chromosome 3p25, plays a pivotal role in regulating adipocyte differentiation, insulin sensitivity, and lipid metabolism [5]. Its abundance in adipose tissue makes it a key regulator of adipogenesis and metabolic homeostasis, promoting insulin sensitivity and reducing inflammatory responses [6]. The Pro12Ala polymorphism (rs1801282) in PPARγ has been linked to an increased susceptibility to obesity and type 2 diabetes. This genetic variation, replacing proline with alanine at position 12, may potentially influence cell development and insulin responsiveness by reducing PPARγ gene transcription activation [7]. The association between this polymorphism and obesity risk remains inconclusive, with conflicting findings across studies. While some research suggests that the Ala allele increases the likelihood of obesity, other studies have failed to establish a significant association between this allele and obesity risk [8].

The TRHR gene, which codes for a receptor, to Thyrotropin Releasing Hormone and is situated on chromosome 8q23.1 controls thyroid hormone metabolism [9]. TRHR plays a role in governing the hypothalamic pituitary thyroid axis. Thyroid hormones are vital for regulating metabolism, energy levels and body weight [10]. Thus genetic differences in TRHR are believed to be linked to obesity and metabolic issues. Research has revealed connections between TRHR variations and body composition well as metabolic indicators [11]. For example the rs16892496 polymorphism has been associated with muscle mass and strength. Nonetheless there is literature exploring the relationship between TRHR gene variants and conditions, like obesity and diabetes with conflicting results available [12].

This research investigates the association between PPARγ (rs1801282) and TRHR (rs16892496) gene polymorphisms and the prevalence of diabetes and obesity in a diverse Turkish population. The study’s novelty lies in its simultaneous analysis of both polymorphisms, aiming to understand their potential interactive effects on metabolic outcomes. By examining a comprehensive set of metabolic markers, this research seeks to elucidate the intricate relationship between these gene variants and metabolic health. Findings could contribute to identifying individuals at risk for diabetes and obesity, enabling tailored preventive strategies. Furthermore, insights into the functional implications of these polymorphisms may lead to the identification of novel therapeutic targets and more effective treatment approaches.

In summary this study marks a milestone in unraveling the underpinnings of complex conditions like obesity and diabetes. The insights gained will not benefit the population but also contribute to our understanding of genetic diversity and differences, among ethnic groups. The main objective of this research was to explore the correlation, between PPAR γ (rs1801282) and TRHR (rs16892496) gene variations and their link to diabetes and obesity within the population. Additionally we sought to analyze how these genetic variations may influence metabolic parameters. By unraveling this connection we hope to gain insights into the underpinnings of obesity and diabetes potentially paving the way for novel treatment approaches.

Study Population

This observational study, conducted at the Department of Endocrinology and Metabolic Diseases, Istanbul University Faculty of Medicine Hospital, enrolled 239 participants. The study included 80 individuals with both diabetes and obesity, 80 individuals with obesity but without diabetes, and 79 healthy controls. Participants were selected based on BMI ≥ 30 kg/m² for obesity, and diabetes diagnosis according to the 2021 ADA (American Diabetes Association) guidelines. Exclusion criteria included insulin use, family involvement, and presence of endocrine disorders or chronic inflammation.

In our research to assess obesity status, we utilized Body Mass Index (BMI) as a primary measure, categorized according to World Health Organization standards: Underweight (< 18.5 kg/m²), Normal Weight (18.5–24.9 kg/m²), Overweight (25.0-29.9 kg/m²), Obese (30.0-39.9 kg/m²), and Morbidly Obese (≥ 40.0 kg/m²).

Analysis of Biochemical Parameters

The samples were centrifuged at 4500 rpm for 15 minutes at + 4°C to separate the serum. The analysis of glucose, HbA1c, total cholesterol, HDL, LDL, triglycerides, urea, creatinine, AST, and ALT from the serum samples were performed by using AU5800 Series Clinical Chemistry Analyzers (Beckman Coulter Inc., Brea, CA, USA).

Genetic Analysis

The extraction of DNA, from samples of blood was carried out using the RINA™ M14 automated extraction system (Rina Biotechnology, Istanbul, Turkey). To assess the concentration and purity of the obtained DNA, a NanoDrop 2000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA) was utilized [13].

Genotyping for PPARγ (rs1801282) and TRHR (rs16892496) polymorphisms was performed using real-time PCR on a Bio-Rad CFX96 Touch system. Each 20 µL reaction contained 2X SYBR Green PCR Master Mix, 0.5 µL of each primer (10 pmol), 2 µL of genomic DNA (50 ng), and nuclease-free water. Amplification was conducted using a thermal profile of 95°C for 10 minutes, followed by 40 cycles of 95°C for 15 seconds and 60°C for 1 minute.

The primers PPAR-ɣ rs1801282; Forward 1: 5’-TCAGTGAAGGAATCGCTTTCTGG − 3’, Forward 2: 5’-TCAGTGAAGGAATCGCTTTCTGC − 3’ and Revers: 5’-TCAAGCCCAGTCCTTTCTGTGTT − 3’, TRHR rs16892496; Forward 1: 5’-GTTGAAGAGCAAGCCCCCA − 3’, Forward 2: 5’-GTTGAAGAGCAAGCCCCCC − 3’ and Revers: 5’-TGGAAACTCCAAGGGTGAAGAA-3’ were designed by using the Primer3 Program.

Statistical Analysis

Statistical analyses were performed using SPSS version 22.0 with a significance level of p < 0.05. Continuous variables were summarized using means and standard deviations, or medians with minimum and maximum values, while categorical variables were presented as frequencies and percentages. Data distribution was assessed using the Kolmogorov-Smirnov test. Group comparisons employed one-way ANOVA for normally distributed variables and Kruskal-Wallis test for non-normally distributed variables. Chi-square test with Bonferroni correction for post-hoc analyses was used for categorical variables. Genotype and allele frequencies were calculated, and Hardy-Weinberg equilibrium was confirmed using chi-square testing. Genotype distributions between groups were compared using chi-square tests. A power analysis using G*Power 3.1 software was conducted prior to the study to determine the required sample size (f = 0.25, 80% power, 5% alpha error). This comprehensive statistical approach aims to ensure the reliability and validity of the research findings.

Demographic and Clinical Characteristics

The demographic and clinical profiles of 239 participants (80 with diabetes and obesity 80 with obesity but no diabetes and 79 healthy individuals) included in our study are elaborated in Table 1. We noted distinctions among the groups concerning age, BMI and various biochemical factors.

Post-hoc analyses revealed significant differences in biochemical parameters between the patient groups and the control group. The diabetic obese group showed significant differences compared to the control group in BMI, HDL, HbA1c, urea, triglycerides, glucose, and creatinine (p<0.001; p=0.001, respectively). The non-diabetic obese group also displayed a significant difference in BMI compared to the control group (p<0.001). When comparing the diabetic obese and non-diabetic obese groups, significant differences were observed in glucose, HDL, creatinine, HbA1c, triglycerides, and urea (p<0.001; p=0.001; p=0.003, respectively) (Table 1).

Determination of Gene Polymorphism of PPAR-γ, TRHR Genes

The statistical significance of PPAR-γ and TRHR gene polymorphisms was assessed using gene counting and chi-square (χ²) tests to compare genotype distributions across study groups. The association between allele distribution and demographic data was evaluated using ANOVA and Kruskal-Wallis tests.

A significantly higher frequency of the GG genotype was observed in the diabetic obese group (62.5%) compared to both the non-diabetic obese group (33.75%) and the control group (35.45%; p<0.001). Conversely, the CG genotype was significantly more prevalent in the control group (59.5%) compared to the diabetic obese group (35%) and the non-diabetic obese group (47.5%; p<0.001). Notably, the CC genotype showed a higher prevalence in the non-diabetic obese group (18.75%) compared to the diabetic obese group (2.5%) and the control group (5.06%; p<0.001).

C allele distribution was higher in the non-diabetic obese group (42.5%) compared to the control group (35%), but this difference was not statistically significant (p>0.05). A significantly higher frequency of C allele was observed in the control group (35%) compared to the diabetic obese group (20%) (p=0.001). The non-diabetic obese group (42.5%) showed a significantly higher frequency of C alleles compared to the diabetic obese group (20%) (p <0.001). The control group exhibited a significantly higher G allele frequency (65%) compared to the non-diabetic obese group (57.5%) (p = 0.008). Although the diabetic obese group demonstrated a higher G allele frequency (80%) compared to the control group (65%), this difference was not statistical significance (p> 0.05). The frequency of the G allel was slightly lower, in the group of non diabetic obese (57.5%) compared to the diabetic obese group (80%) but this variation showed statistical significance (p=0.001). Analysis of the PPAR γ (rs1801282) polymorphism revealed information, on genotype and allele distributions as outlined in Table 2.

Analysis of the PPAR-γ rs1801282 gene variant revealed significant differences (p<0.05) in creatinine, HDL, and urea levels across study groups when comparing the distribution of C and G alleles. Within the diabetic obese group, although the C allele (0.70 mg/dl) was less prevalent than the G allele (0.80 mg/dl) in terms of creatinine levels, the difference was statistically significant (p=0.017). In the control group, individuals carrying the C allele (55.0 mg/dl) exhibited significantly higher levels of HDL cholesterol compared to G allele (53.0 mg/dl) carriers (p=0.034). Analysis of urea levels within the non-diabetic obese group revealed a statistically significant difference between the distributions of the G (23.5 mg/dl) and C (25.0 mg/dl) alleles, despite a lower prevalence of the G allele (p=0.042) (Table 3).

Analysis of TRHR rs16892496 polymorphism revealed distributions of genotypes and alleles as presented in Table 3 encompassing three genotypes; AA, AC and CC. The AA genotype was more common, in the groups (40% for obese and 47.5% for non diabetic obese) compared to the control group (51.9%) but this variation did not show statistical significance (p=0.314). The frequency of AC genotype was significantly higher in the diabetic obese group (37.5%) compared to the other groups (non-diabetic obese; 20%, control; 35.45%; p=0.012). The frequency of the CC genotype was significantly higher in the non-diabetic obese group (32.5%) compared to the diabetic obese (22.5%) and control groups (12.7%) (p=0.011).

The frequency of the A allele was notably higher, in the group with control standing at 70% which was significantly more than in the group (non diabetic obese; 57.5%, p=0.003). The frequency of allele A did not show a significant difference in comparisons with groups other than the control and non-diabetic obese groups (p>0,05). When we looked at the allele frequencies, we found that the C allele was more common in the non-diabetic obese group (42.5%) than in the other groups (41.25% for diabetic obese and 30% for control), but this difference did not reach statistical significance in group comparisons (p>0.05) (Table 4).

No statistically significant differences were observed in biochemical parameters, excluding BMI, weight, and height, when comparing the allelic distribution of TRHR (rs16892496) A and C alleles. Analysis of the control group revealed a significant difference in the distribution of the A allele compared to the C allele for BMI (p=0.026) and weight (p=0.048), with the A allele exhibiting a lower frequency in these parameters. A statistically significant difference (p=0.044) was observed in the distribution of the C allele compared to the A allele for height in the diabetic obese group, with the C allele exhibiting a higher frequency (Table 5).

In our discussion we present the study on how PPAR γ (rs1801282) and TRHR (rs16892496) gene variations may relate to obesity and diabetes in the Turkish population. Our results suggest that these genetic differences could play roles in the evelopment of obesity and diabetes potentially serving as targets for therapeutic interventions. PPARγ plays a crucial role in regulating fat cell differentiation and enhancing insulin sensitivity. The Pro12Ala polymorphism diminishes PPARγ activity, leading to increased inflammation in adipose tissue. Reduced PPARγ activity triggers NF-κB activation, which increases the number of resident macrophages that secrete pro-inflammatory cytokines like TNF-α, IL-6, and IL-1β, contributing to insulin resistance and obesity [14–15].

Study participants underwent evaluation for anthropometric and biochemical parameters including BMI, glucose, HDL, creatinine, LDH, LDL, total cholesterol, total protein, triglycerides, age, HbA1c, and urea. A statistically significant difference (p < 0.001) was observed in these parameters between the study groups. Consistent with previous research, biochemical parameters such as BMI, glucose, urea, creatinine, triglycerides, and HbA1c levels exhibited statistically significant differences and are known to increase susceptibility to obesity and serve as risk factors for diabetes development [16–18].

The average age was markedly higher, in the diabetic obese group (55.00 ± 10.23 years) compared to the other groups (non diabetic obese; 40.00 ± 12.56 years, controls; 42.00 ± 11.78 years, p < 0.001). This observation underscores the onset of diabetes at ages. As expected, Body Mass Index (BMI) values were significantly higher in the groups compared to the control group (diabetic obese; 34.53 ± 3.87 kg/m², non-diabetic obese; 35.66 ± 6.45 kg/m², control; 25.35 ± 2.89 kg/m²; p < 0.001). Interestingly, non-diabetic obese subjects had higher mean BMI than diabetic obese subjects, but this difference did not reach statistical significance (p > 0.05).

Metabolic parameters, including fasting blood glucose and HbA1c levels, were significantly higher in the diabetic obese group (glucose; 172.0 ± 68.5 mg/dL, HbA1c; 8.1 ± 1.9%, p < 0.001) compared to the control group (p < 0.05). Examination of the lipid profile revealed markedly higher triglyceride levels in the diabetic obese group (164.0 ± 11.23 mg/dL) compared to other groups (p < 0.001), while HDL cholesterol levels were significantly lower (44 ± 9.8mg/dL, p < 0.001), suggesting dyslipidemia associated with obesity. Additionally, urea and creatinine levels were elevated in the diabetic obese group (urea; 31.5 ± 15.2 mg/dL, creatinine; 0.8 ± 0.4 mg/dL, p < 0.001), indicating potential kidney dysfunction and emphasizing the need for close monitoring of renal function in diabetic patients. Furthermore, Feng R et al. found that several biochemical markers, including glucose, HbA1c, BMI, age, triglyceride, HDL, LDL, creatinine, and urine levels, exhibited statistically significant differences and were associated with an increased susceptibility to obesity. These markers were also identified as risk factors for developing diabetes [19–21].

Our study found that non-diabetic obese individuals with the GG genotype had significantly higher BMI compared to those with other genotypes (p = 0.008), suggesting a potential association between this genotype and increased susceptibility to obesity. This finding aligns with a meta-analysis of 25 studies, which showed significant associations between the PPARγ Pro12Ala polymorphism and obesity risk across diverse populations, including Caucasians, Asians, and mixed populations [22]. Our study revealed a higher prevalence of the GG genotype for PPARγ rs1801282 in individuals with obesity (p < 0.001), consistent with existing research.

A meta-analysis by Li et al. showed that carrying the G allele increased susceptibility to obesity and hypercholesterolemia. Further research linked the G allele of PPARγ rs1801282 polymorphism with increased obesity and hypercholesterolemia, suggesting a protective effect against dyslipidemia and explaining its association with cardiovascular disease [23–24]. Our study confirmed this finding, observing higher weight values in obese diabetic individuals carrying the G allele. These statistically significant findings are consistent with previous studies and suggest a link between the PPARγ (rs1801282) variant and both obesity and diabetes.

We observed an increase in HDL cholesterol levels among individuals carrying the C allele in the control group (p = 0.034), aligning with the known influence of PPARγ on lipid processing. Activation of PPARγ has been linked to HDL levels, suggesting that the C allele (Ala) may unexpectedly raise HDL levels by potentially reducing PPARγ activity and influencing lipase expression in tissue [25–26].

Our findings regarding TRHR rs16892496 polymorphism somewhat match research. We found that the AA genotype was more prevalent in groups although this difference wasn't statistically significant (p > 0.05). This suggests that TRHR polymorphisms impact on obesity and diabetes might be intricate. A genome-wide association study (GWAS) found a significant genetic association between the TRHR (rs16892496) variant and both BMI and waist-hip ratio in Northern Han Chinese [27]. Similarly, Urrutia et al. found a significant association between this variant and BMI and obesity risk (p < 0.05) [28]. Our study observed that individuals carrying the A allele in the control group had significantly higher BMI values compared to those carrying the C allele (p = 0.026), aligning with previous research and suggesting a potential role of the TRHR gene in influencing body composition.

Considering TRHR’s role in thyroid hormone metabolism, this genetic variation could potentially impact energy metabolism and body weight regulation. TRHR influences the hypothalamus-pituitary-thyroid axis, controlling thyroid hormone production and release, which in turn impact metabolic rate and energy expenditure [29–30]. Polymorphisms in TRHR are thus thought to affect metabolic rate and potentially contribute to obesity risk. Further research is needed to understand these processes, particularly exploring the influence of TRHR variations on thyroid hormone levels and basal metabolic rate [31].

In addition to TRHR, which indirectly influences adipose tissue through a complex pathway involving Hypothalamus–Pituitary–Thyroid Axis and TSH, PPAR-γ is known to directly affect adipose tissue. While these two pathways operate independently, PPAR-γ and TRHR have been shown to be associated with obesity and diabetes in our study and in the literature, suggesting a potential for indirect interaction between them.

These interactions highlight the complex nature of obesity and diabetes, where interplay between genetic predisposition and environmental influences can significantly impact disease susceptibility. Wherrett et al. has shown that physical activity can alter the impact of PPARγ genetic variations on obesity, emphasizing the importance of lifestyle modifications for individuals with a predisposition [32].

Our research investigates genetic variations associated with obesity and diabetes in a Turkish population. Its strengths include a diverse sample, the inclusion of diabetic participants, and a comprehensive assessment of metabolic parameters. This is the first study to explore PPAR-γ and TRHR gene polymorphisms together in this population. However, limitations include a relatively small sample size, the methodologies used, and limited case numbers within each group. The cross-sectional design prevents causal inference, and the lack of in-depth dietary and physical activity data limits the analysis. Future research should address these limitations by employing a longitudinal design and investigating the impact of environmental factors, including diet and lifestyle, on the gene-environment interactions associated with obesity and diabetes.

This study marks the investigation into the link between PPAR γ (rs1801282) and TRHR (rs16892496) gene variations with obesity and diabetes within the Turkish population. Our results indicate that these genetic variations may have implications in the development of obesity and diabetes potentially serving as targets for therapy.

The PPARγ rs1801282 variation showed a higher prevalence of the GG genotype in diabetic obese individuals, suggesting an increased susceptibility to obesity and diabetes for carriers of this genotype. Among diabetic obese individuals, those carrying the GG genotype exhibited significantly higher BMI values compared to other genotypes, hinting at a potential link between this genotype and obesity risk through its impact on adipose tissue metabolism. These findings align with the established role of PPARγ in adipocyte differentiation and insulin sensitivity. Conversely, elevated HDL levels observed in C allele carriers in the control group support a connection between PPARγ polymorphism and lipid metabolism, implying potential benefits of the C allele in reducing cardiovascular risk.

Our study highlights the need for further investigation into the relationship between PPARγ rs1801282 and obesity/diabetes risk. Findings on the TRHR rs16892496 variation suggest a complex interplay with these conditions. While the AA genotype’s increased prevalence in both groups hints at a possible link, the lack of statistical significance emphasizes the intricacy of its impact. Notably, A allele carriers in the control group exhibited lower BMI values, suggesting a potential role in body weight regulation. This aligns with TRHR’s involvement in thyroid hormone metabolism, highlighting its potential influence on energy regulation.

The results imply that a significant correlation between BMI levels and lifestyle individuals carrying the PPARγ GG genotype and the TRHR A allele suggests that lifestyle choices can modify the effects of genetic predisposition. This also suggests that genetic variations in the PPARγ and TRHR genes could serve as biomarkers for risk of obesity and diabetes. This highlights the potential for personalized medicine and underscores the need for further research into drug efficacy based on genetic makeup. This study contributes to the existing literature by highlighting the link between PPARγ and TRHR gene variations and obesity and diabetes risk. To delve deeper into the functional implications of these gene variants, further research is needed, focusing on studies in different ethnic groups. Further research is also needed to elucidate how these gene variants interact with metabolic pathways.

Ethics Approval

This research, conducted in accordance with the Helsinki Declaration and relevant ethical standards, was approved by the Istanbul University Clinical Research Ethics Committee (Approval Number: 2020/342).

Conflict of Interest

The authors declare no competing interests.

Consent to Participate

Informed consent was obtained from all participants, including written consent for data publication.

Funding

This research was funded by Istanbul University Scientific Research Projects Unit (Project Number: TDK-2019-36926).

Author Contribution

All authors contributed to the conception and design of the study; data acquisition: V.A., S.D., R.C. and S.B.A.S.; data analysis and interpretation: V.A., Ü.Z., S.D. and F.Ç.; manuscript drafting: V.A., S.D.; manuscript critical revision: Ü.Z., A.O.G., H.A.E. and M.K.O.; statistical analysis: M.D.; study supervision: Ü.Z. and V.A.

Acknowledgement

The authors are grateful to the Istanbul University Scientific Research Projects Unit for supporting the research.

Data Availability

Data is provided within the manuscript or supplementary information files.

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Table 1. Difference of significance according to biochemical parameters in study groups.

	Sample 1-Sample 2	Test Statistic	Std. Eror	Std. Test Statistic	Sig.	p
	Control-Diabetic Obese	-94,100	9,088	-10,354	<0,001	<0,001
BMI (kg/m²)	Control-Nondiabetic Obese	-102,659	10,958	-9,369	<0,001	<0,001
	Diabetic Obese-Nondiabetic Obese	8,559	10,784	0,794	0,427	1,000
	Nondiabetic Obese -Control	5,790	12,269	0,472	0,637	1,000
Glucose (mg/dl)	Nondiabetic Obese- Diabetic Obese	-85,546	12,124	-7,056	<0,001	<0,001
	Control- Diabetic Obese	-79,756	8,181	-9,749	<0,001	<0,001
	Diabetic Obese- Nondiabetic Obese	39,558	11,998	3,297	0,001	0,003
HDL (mg/dl)	Diabetic Obese -Control	40,793	8,139	5,012	<0,001	<0,001
	Nondiabetic Obese -Control	1,235	12,144	0,102	0,919	1,000
	Nondiabetic Obese -Control	21,620	12,170	1,777	0,076	0,227
Creatinine (mg/dl)	Nondiabetic Obese- Diabetic Obese	-53,513	12,079	-4,430	<0,001	<0,001
	Control- Diabetic Obese	-31,893	8,148	-3,914	<0,001	<0,001
	Nondiabetic Obese -Control	7,946	12,150	0,654	0,513	1,000
Triglyceride (mg/dl)	Nondiabetic Obese- Diabetic Obese	-51,050	12,004	-4,253	<0,001	<0,001
	Control- Diabetic Obese	-43,104	8,143	-5,293	<0,001	<0,001
	Control- Nondiabetic Obese	-81,186	12,099	-0,015	0,988	1,000
HbA1c (%)	Control- Diabetic Obese	-81,741	8,215	-9,951	<0,001	<0,001
	Nondiabetic Obese - Diabetic Obese	-81,556	11,989	-6,803	<0,001	<0,001
	Nondiabetic Obese -Control	14,172	11,982	1,183	0,237	0,711
Urea (mg/dl)	Nondiabetic Obese - Diabetic Obese	-42,444	11,908	-3,564	<0,001	0,001
	Control- Diabetic Obese	-28,273	8,089	-3,495	<0,001	0,001

Post-hoc test was used for statistical analysis of the parameters used in the study between groups.

Bold font indicates significance p-values.

Table 2. Distribution of PPAR-γ (rs1801282) genotypes and alleles according to study groups.

PPAR-γ (rs1801282)	Control n (%)	Non-diabetic Obese n (%)	Diabetic Obese n (%)	χ²	p
GG	28 (%35,45)	27 (%33,75)	50 (%62,5)	16,876	<0,001
CG	47 (%59,5)	38 (%47,50)	28 (%35,0)	28,390	<0,001
CC	4 (%5,06)	15 (%18,75)	2 (%2,5)	15,220	<0,001
C Allele	55 (%35)	68 (%42,5)	32 (%20)	0,050* 11,644 13,244*	0,822* 0,001 <0,001*
G Allele	103 (%65)	92 (%57,5)	128 (%80)	0,077* 0,719 11,123*	0,008* 0,396 0,001*

n: number of individuals, %: percentage. Gene calculation method and Chi-Square (χ 2 ) test were used for

statistical analysis of genotypes and alleles of the genes in the study between groups. The genotype and allele

distribution of the groups were calculated in a four-well table.

*Control&Non-diabetic Obese (for C allele χ2=0,050, p=0,822; For G allele χ2=0,077, p=0,008)

**Control&Diabetic Obese (for C allele χ2=11,644, p=0,001; For G allele χ2=0,719, p=0,396)

***Non-diabetic Obese&Diabetic Obese (For C allele χ2=13,244 p˂0,001; for G allele χ2=11,123, p=0,001)

Table 3. Distribution of demographic data in study groups according to PPAR-γ C and G alleles

Biochemical Parameters

Diabetic Obese

Non diabetic Obese

Control

C Allele

G Allele

C Allele

G Allele

C Allele

G Allele

Median (min/max)

p

Median (min-max)

p

Median (min-max)

p

Median (min-max)

p

Median (min-max)

p

Median (min-max)

p

BMI (kg/m²)

35,21

(30-45)

0,181

34,83

(30-45)

0,380

32,87

(30-52)

0,004

34,73

(30-65)

0,852

25,82

(18-30)

0,105

25,33

(18-30)

0,783

Glucose (mg/dl)

182,0

(97-385)

0,279

176,0

(48-422)

0,183

90,0

(75-105)

0,594

90,0

(75-105)

0,141

92,5

(80-111)

0,935

92,0

(72-111)

0,177

HDL (mg/dl)

44,0

(26-60)

0,622

44,0

(23-66)

1,000

55,0

(34-111)

0,594

51,5

(20-73)

0,722

55,0

(33-75)

0,034

53,0

(32-80)

0,237

Weight (kg)

96,5

(68-125)

0,409

95,0

(68-140)

0,035

87,9

(73-160)

0,080

93,65

(73-160)

0,712

71,0

(53-96)

0,496

70,0

(50-98)

0,664

Creatinine (mg/dl)

0,70

(0,4-1,6)

0,017

0,80

(0,4-2,66)

0,337

0,58

(0,37-0,9)

0,967

0,57

(0,34-0,9)

0,227

0,66

(0,41-1,1)

0,602

0,68

(0,39-1,1)

0,743

Trygliceride

(mg/dl)

168,0

(59-815)

0,371

165,0

(50-815)

0,136

84,5

(29-936)

0,712

99,5

(36-936)

0,273

100,5

(31-278)

0,924

100,5

(31-364)

0,695

Hba1c (%)

8,20

(5,5-13,7)

0,579

8,10

(5,1-13,7)

0,795

5,40

(4,9-6)

0,174

5,40

(4,5-6)

0,166

5,30

(4,8-5,9)

0,859

5,35

(4,7-6)

0,824

Urea (mg/dl)

29,0

(16-64)

0,041

31,5

(16-105)

0,611

25,0

(13-37)

0,652

25,0

(15-35)

0,042

26,5

(16-47)

0,627

26,0

(12-47)

0,916

Non-normally distributed parameters are shown as Median (min-max). ANOVA test and Kruskal-Wallis test were used for statistical analysis of the parameters used in the study between groups. Bold font indicates significance p-values.

Table 4. Distribution of TRHR (rs16892496) genotypes and alleles according to study groups.

	TRHR (rs16892496)
Genotypes and Alleles	Control n (%)	Non-diabetic Obese n (%)	Diabetic Obese n (%)	χ²	p
AA	41 (%51,9)	38 (%47,5)	32 (%40)	2,316	0,314
AC	28 (%35,45)	16 (%20,00)	30 (%37,50)	12.889	*0,012*
CC	10 (%12,7)	26 (%32,50)	18 (%22,50)	8,948	*0,011*
A Allele	110 (%70)	92 (%57,5)	94 (%58,75)	8,934* 2,654 2,006*	0,003* 0,103 0,157*
C Allele	48 (%30)	68 (% 42,5)	66 (%41,25)	0,308* 2,266 0,914*	0,579* 0,132 0,339*

n: number of individuals, %: percentage. Gene calculation method and Chi-Square(^χ2) test were used for statistical analysis of genotypes and alleles of the genes in the study between groups. Bold font indicates significance p-values. The genotype and allele distribution of the groups were calculated in a four-well table.

*Control&Non-diabetic Obese (For A allele χ2=8,934, p=0,003; For C allele χ2=0,308, p=0,579)

**Control&Diabetic Obese (For A allele χ2=2,654, p=0,103; For C allele χ2=2,266, p=0,132)

*** Non-diabetic Obese&Diabetic Obese (For A allele χ2=2,006, p=0,157; For C allele χ2=0,914, p=0,339)

Table 5. Distribution of demographic data in the study groups according to TRHR A and C alleles.

Biochemical Parameters

Diabetic Obese

Non diabetic Obese

Control

A Allele

C Allele

A Allele

C Allele

A Allele

C Allele

Median (min/max)

p

Median (min-max)

p

Median (min-max)

p

Median (min-max)

p

Median (min-max)

p

Median (min-max)

p

BMI (kg/m²)

34,71

(30-45)

0,592

33,33

(30-45)

0,582

36,61

(30-65)

0,359

37,49

(30-52)

0,638

25,22

(18-30)

0,026

25,60

(19-30)

0,947

Height (cm)

165,0

(148-184)

0,221

167,0

(149-184)

0,044

160,0

(120-192)

0,889

162,0

(153-186)

0,512

169,0

(150-186)

0,624

166,0

(155-181)

0,934

Glucose (mg/dl)

172,0

(48-422)

0,917

169,5

(48-390)

0,720

90,0

(75-105)

0,218

92,0

(82-105)

0,447

92,0

(72-111)

0,615

91,0

(80-111)

0,700

HDL (mg/dl)

44,0

(23-66)

0,574

44,0

(23-57)

0,220

54,0

(20-69)

0,249

55,0

(40-111)

0,581

53,0

(32-80)

0,974

53,5

(32-75)

0,760

Weight (kg)

95,5

(68-140)

0,205

95,0

(68-140)

0,366

95,5

(74-147)

0,269

98,7

(73-160)

0,695

69,0

(50-98)

0,048

72,0

(51-96)

0,778

Creatinine (mg/dl)

0,78

(0,4-2,6)

0,915

0,80

(0,5-2,6)

0,086

0,53

(0,34-0,82)

0,058

0,60

(0,4-0,9)

0,267

0,66

(0,39-1,07)

0,395

0,66

(0,39-0,92)

0,573

Trygliceride

(mg/dl)

165,0

(59-815)

0,264

169,0

(50-815)

0,199

96,0

(46-936)

0,762

111,0

(29-166)

0,945

100,0

(31-364)

0,228

102,5

(43-266)

0,765

Hba1c (%)

8,2

(5,1-13,7)

0,301

8,05

(5,1-13,7)

0,626

5,2

(4,5-6)

0,150

5,40

(5,2-5,6)

0,278

5,4

(4,8-6)

0,146

5,30

(4,7-5,9)

0,308

Urea (mg/dl)

32,0

(16-105)

0,925

32,0

(16,6-92)

0,860

22,0

(13-37)

0,249

29,0

(13-36)

0,680

25,0

(12-47)

0,289

25,5

(12-40)

0,676

Non-normally distributed parameters are shown as Median (min-max). ANOVA test and Kruskal-Wallis test were used for statistical analysis of the parameters used in the study between groups. Bold font indicates significance p-values.

No competing interests reported.

Association of PPAR γ and TRHR Gene Variations, with Metabolic Factors in Diabetic and Obesity Individuals in the Population; An Investigation Study

Status:

Version 1

Abstract

Introduction

Material and Method

Study Population

Analysis of Biochemical Parameters

Genetic Analysis

Statistical Analysis

Results

Discussion

Conclusion

Declarations

Funding

Author Contribution

Acknowledgement

Data Availability

References

Tables

Additional Declarations

Status:

Version 1