Journal of Genetics & Genomic Sciences Category: Genetics Type: Research Article

ACE, PPARG, SIRT1 Gene Polymorphisms but Not PPARGC1A Polymorphism are Risk Factors for Gestational Diabetes in the Russian Population

Dmitrenko OP1, Karpova NS1* and Nurbekov MK1
1 Federal State Budgetary Institution "Research Institute Of Pathology And Pathophysiology”, Moscow, Russian Federation

*Corresponding Author(s):
Karpova NS
Federal State Budgetary Institution "Research Institute Of Pathology And Pathophysiology”, Moscow, Russian Federation
Tel:+7 9256245603,
Email:nataliiakarpova.sp@gmail.com

Received Date: May 23, 2022
Accepted Date: Jun 02, 2022
Published Date: Jun 09, 2022

Abstract

Gestational Diabetes Mellitus (GDM) is the most frequently encountered extra genital pathology of gestation and represents a serious medical and social problem, increasing the incidence of unwanted pregnancy outcomes for both the mother and the fetus. The pathogenesis of GDM is complex and includes risk factors such as age, obesity, and a family history of diabetes. Studies have shown that genetic factors also play a role in the pathogenesis of GDM. The aim of our study was to assess the effect of Single Nucleotide Polymorphisms (SNPs) rs4646994 ACE gene, rs8192678 PPARGC1A gene, rs1801282 PPARG and rs7895833 SIRT1gene gene on the development of GDM in the Russian population. 

The study used the genomic DNA derived by phenol-chloroform extraction method from venous blood samples in 272 pregnant women, including DNA samples of 136 pregnant women with GDM and DNA samples of 136 pregnant women with normal glucose tolerance. Genotyping of the selected polymorphisms was performed by real-time PCR with detection by competing TaqMan probes.

A statistically significant association with GDM was found in homozygous carriers of the I allele of the rs4646994 and heterozygous carriers of the G rs7895833 allele in the general inheritance model, heterozygous carriers of the G rs1801282 allele in the general and dominant inheritance models. 

The data we obtained point to the need to further investigate the polymorphic loci we selected in a larger patient sample, which will enable using this genetic marker in the future as the assessment criterion in the individual outlook of preeclampsia development in GDM pregnant women to take efficient preventive measures to timely remedy and improve the pregnancy outcome.

Keywords

ACE gene; Gestational diabetes mellitus; PPARGC1A gene; PPARG gene; SIRT1 gene; SNP

Background

GDM is a diabetes that is first diagnosed in the second or third trimester of pregnancy that is not clearly either preexisting type 1 or type 2 diabetes [1]. Hyperglycemia in the mother, not diagnosed at the stage of pregnancy planning and in the first trimester, can have an adverse effect on both the pregnant woman's body and the fetus [2]. 

Over the past 20 years, the worldwide distribution of gestational diabetes mellitus has increased and prevalence rates vary depending on population characteristics, screening methods and diagnostic criteria from 1 to 28% [3,4]. This indicator is estimated at 4,5% in Russia [5]. It is expected that by 2045 the prevalence of GDM will reach 16% in all regions of the world [6]. 

GDM is characterized by the relative inability of pancreatic β-cells to adequately respond to the increased need for insulin during pregnancy, which contributes to the development of hyperglycemia of varying degrees [7]. Under conditions of hyperglycemia, glucose freely penetrates into the vascular endothelium and causes functional disorders of its cells [8]. In addition, lipid metabolism may change during pregnancy due to the effects of estrogen and insulin resistance. The development of dyslipidemia also leads to endothelial dysfunction [9]. 

To date, about 40 genetic loci associated with the synthesis and secretion of insulin, with the transmission of the insulin signal, and regulating carbohydrate and lipid metabolism have been identified [10]. Previous studies have shown that some of the genetic loci that predispose to the development of type 2 diabetes may also predispose to HDM [11,12]. 

Angiotensin-Converting Enzyme (ACE) plays not only a key role in the regulation of blood pressure, but also participates in the processes of the local renin-angiotensin system in pancreatic tissues, regulating local blood flow and insulin biosynthesis of islet β-cells [13,14]. The gene encoding the angiotensin converting enzyme is located at the 17q23.3 locus on the 17th chromosome on the plus strand of DNA. Of the more than 100 allelic variants of the ACE gene, the most important is the insertion-deletion I/D polymorphism in the 16th intron, which consists in the insertion (I) or loss (D) of the Alu repeat. Deletion of the Alu repeat leads to increased expression of the ACE gene [15]. I/D polymorphism of the ACE gene, according to various studies, increases the risk of such diseases as type 2 diabetes mellitus, diabetic nephropathy, diabetic neuropathy, and diabetic retinopathy [16]. 

SIRT1 belongs to the family of proteins with deacytylase activity and regulates energy balance, lipid homeostasis, and protects the structure and functions of microvascular endothelium [17,18]. SIRT1 is highly expressed in pancreatic β-cells and has the ability to enhance glucose sensitivity [19]. A decrease in SIRT1 expression is facilitated by increased insulin resistance and an inadequate response of β-cells, followed by an increase in glucose concentration, which may play a key role in the development of vascular dysfunction [20,21]. SIRT1 gene is located in the 10-th chromosome in the q21.3 locus and comprises 11 exons. Previous studies have shown that point mutations in promoters are more common than in coding regions of a gene and can affect its expression [22]. The rs7895833 polymorphism in the SIRT1 gene promoter region is associated with type 2 diabetes and obesity, according to few studies [23,24]. 

PGC-1α belongs to the family of transcription coactivators and is involved in the regulation of cellular energy metabolism, mitochondrial biogenesis in the vascular endothelium, carbohydrate and lipid metabolism [25-27]. Although the expression of PGC-1α in the pancreas is low compared with tissues such as the heart, skeletal muscle, liver, brown adipose tissue, and brain, it plays an important role in glucose transport in pancreatic β-cells [28]. The PGC-1α protein is encoded by the PPARGC1A gene, which is mapped to the 4p15 locus. 2 and consists of 24 exons. Among the numerous variations in PPARGC1A gene, of special interest is the substitution the nucleotide A for nucleotide G in position 1444 of the 8-th exon, which causes serine substitution for glycine in position 482 of PGC-1a protein (Gly482Ser polymorphism), which is accompanied by the reduction in PGC1a gene expression [29]. The single nucleotide polymorphism of PPARGC1A (rs8192678) is associated with type 2 diabetes and its complications, relative obesity risk and insulin resistance [30]. 

Deacetylation of PGC-1α through the action of SIRT1 increases the activity of PGC-1α, which leads to the coactivation of several transcription factors, including nuclear receptors such as PPARγ [31,32]. 

Peroxisome Proliferator-Activated Receptor-γ (PPARγ) is a ligand-dependent transcription factor and belongs to the nuclear hormone receptor superfamily. PPARγ plays an important role in lipid and glucose metabolism and controls endothelial function [33,34]. A moderate amount of PPARγ is expressed in pancreatic β-cells [35]. El Midaoui et al., (2006) showed that the infusion of insulin and glucose is a factor contributing to the decrease in the level of PPARγ protein [36]. The PPARG gene is located on the human chromosome locus 3p25 and consists of nine exons. The more common allele (C) of the single nucleotide polymorphism rs1801282 in exon B of the PPARG gene, also known as Pro12Ala, encodes the amino acid "Pro". The Pro to Ala substitution at codon 12 is associated with a decrease in PPARγ activity, which may be a risk factor for obesity, type 2 diabetes and GDM [37-40]. 

Identifying additional genetic risk markers for GDM may be useful in early diagnosis and would also allow for earlier prevention and treatment. In this regard, the purpose of this study was to study the relationship between GDM and the selected polymorphisms of the ACE, SIRT1, PPARG, and PPARGC1A genes, which affect the function of pancreatic beta cells and vascular endothelial cells, glucose sensitivity, carbohydrate and lipid metabolism.

Materials And Methods

Research objects 

The design of the study and the use of human material were approved by the ethics committee of the Research Institute of General Pathology and Pathophysiology. The study included patients who, in the period from April 2019 to December 2022, were observed and delivered in the Maternity Department of the State Clinical Hospital No. 29 (N.E. Bauman Hospital) of the Healthcare Department of Moscow. All respondents were native Russian speakers of indeterminate ethnicity (due to the ethical standards of the local medical register) and gave written consent to participate in the study. The diagnosis of GDM was established in accordance with the IADPSG recommendations and based on the criteria of the Russian National Consensus clinical guidelines "Gestational diabetes mellitus: diagnosis, treatment, postpartum care" [41,42]. The exclusion criteria were type 1 and type 2 diabetes mellitus, acute and chronic diseases in the acute stage, autoimmune, neuropsychiatric and oncological processes of any localization. The study did not include women with multiple pregnancies, other pregnancy complications, as well as disorders affecting glucose metabolism. QUANTO quantification software (Version 1.2.4, https://bio.tools/QUANTO), which takes into account the frequency of SNPs in the population and the prevalence of the disease [43]. In accordance with the above parameters, a sample size of 136 case-control pairs is required to identify the association between the selected polymorphisms and the risk of GDM. Blood samples were collected from pregnant women with GDM and pregnant women with normal glucose tolerance. All blood samples were obtained by venipuncture after an overnight fast and stored at -20 °C until analysis. 

Genomic DNA extraction and Polymerase Chain Reaction (PCR) 

DNA was extracted from venous blood with the standard phenol-chloroform extraction method of Maniatis et al., [44]. The blood cell element lysis was conducted with the Kunkel method [45]. The high-molecular DNA was desiccated at ambient temperature and dissolved in TE buffer, and the resulting DNA was stored at -20 °C. All DNA extractions were performed by a single investigator only. 

Genotyping polymorphisms rs7895833 of the SIRT1 gene in the promoter region, rs8192678 within the coding region of the PPARGC1A, rs1801282 of the PPARG gene andrs4646994 of the ACE gene was performed by real-time using the technology of competing TaqMan probes according to the method taken from the literature. All primers and Taq-man probes were synthetically produced by Evrogen LLC, Russia (Table 1). 

SNP

Oligonucleotide type and sequences

 rs4646994

Forward 1: GGAGACCACTCCCATCCTTTC

Forward 2: GCCTCAGCCTCCCAAG 

Revers: ATGTGGCCATCACATTCGTCAGAT

Taq-Man Probe for D allele:

FAM-GCTGCCTATACAGTCACTTTTATGTGGT-RTQ1

Taq-Man Probe for I allele:

R6G-CCCGCCACTACGCCCGGCTAA-BHQ2

 rs1801282

Forward: TCCATGCTGTTATGGGTGAAACT

Revers: CTTTACCTTGTGATATGTTTGCAGACA

Taq-Man Probe for reference allele:

FAM-TCTCCTATTGACCCAGAAAGCGATTCCTT-BHQ1

Taq-Man Probe for alternative allele:

HEX-TCTCCTATTGACGCAGAAAGCGATTCCTT-BHQ2 

rs8192678

Forward: CACTTCGGTCATCCCAGTCAA

Revers: TTATCACTTTCATCTTCGCTGTCATC

Taq-Man Probe for reference allele:

FAM-AGACAAGACCGGTGAA-BHQ1

Taq-Man Probe for alternative allele:

HEX-CAGACAAGACCAGTGAA-BHQ1

rs7895833

Forward: TTCTGAAGTAATGAGGTGG

Revers: AGGAGACTCTGCCAGAAAT

Taq-Man Probe for reference allele:

FAM-CCTACAGGAAATCAACGTAA-BHQ1

Taq-Man Probe for alternative allele:

HEX-CCTACAGGAAGTCAACGTAA-BHQ1

Table 1: Sequences of oligonucleotides for RT-PCR of the rs4646994, rs1801282, rs8192678 and rs7895833. 

The reaction mixture for rs7895833, rs8192678, rs1801282 RT-PCR for one 25 μl sample contained 20 ng DNA, 5 μl of 5x qPCRmix-HS, 200 μM forward primer, 200 μM reverse primer, 400 μM each of Taq-man probes. 

The reaction mixture for rs4646994 RT-PCR of 25 μl sample contained 20 ng DNA, 5 μl of 5x qPCRmix-HS, 100 μM each of the forward primers, 200 μM reverse primer, 400 μM each of Taq-man probes. 

Amplification was carried out in the CFX 96 programmable amplifier (Bio-Rad, USA) with the subsequent thermocycling parameters for rs7895833, rs8192678, rs1801282 and rs4646994: initial denaturation for 5 minutes at 95°C; then 40 cycles including denaturation at 95°C for 30 seconds, at 60°C for 30 seconds, at 72°C for 30 seconds with subsequent fluorescence pickup. The obtained data was examined using the CFX Manager TM software (Bio-Rad). 

To eliminate genotyping errors, 30% of randomly selected samples were re-genotyped and the results obtained were additionally evaluated. 

Statistical analysis 

Statistical analysis was performed using SPSS 17.0 (SPSS, Chicago, IL, USA). Continuous data were shown as mean ± standard deviation (±SD) if normally distributed. Differences in age between groups were analyzed using Student's t-test. The Hardy–Weinberg equilibrium test was performed using the chi-square test in cases and controls separately for each variant before association analysis. Differences in allele and genotype frequencies between groups were analyzed using Pearson's chi-square test. Logistic regression analysis was used to evaluate associations between SNP genotypes and alleles and GDM risk by calculating Odds Ratios (ORs) and their 95% confidence intervals (CIs). The anticipated risk factor was regarded as significant for pathology if OR adjusted by CI was greater than 1. The level of significance was considered significant at p < = 0.05.

Results

Clinical characteristics 

The study used DNA samples from 136 women with GDM (mean age 31.72±4.95 years) and 136 pregnant women with normal glucose tolerance (mean age 31.01±4.83 years). There were no significant differences in the average age indicators (p>0.05). 

Association analysis 

Analysis of the polymorphic loci rs4646994 of ACE gene, rs1801282 of the PPARG gene, rs8192678 of the PPARGC1A gene and rs7895833 of the SIRT1 gene made it possible to estimate the frequency of occurrence of alleles and genotypes of the polymorphic loci of the studied genes (Table 2). 

The distribution of frequencies of genotypes and alleles of polymorphic loci of the studied genes in the control group corresponded to that expected under the Hardy-Weinberg equilibrium. 

The distribution of the rs4646994, rs1801282, rs8192678 and rs7895833 genotypes in the GDM group differed from that expected for HWE. The probable reason for the deviation of the observed genotype frequencies for the three polymorphisms in this group is not the genotyping error, but the association of loci with the disease. 

Significant differences in the frequency of alleles and the distribution of genotypes between the studied groups were revealed. The prevalence of allele I and homozygous genotype II rs4646994 in the group of pregnant women with GDM were higher compared to the control group (79.0% vs. 51.8%, p=0,002; 54.4% vs. 27.2%, p=0.007). The frequency of occurrence of the G allele and heterozygous genotype CG rs1801282 in the group of pregnant women with GDM were higher in the group with GDM compared to the control group (22.4% vs. 12.5%, p=0.002; 36.0% vs. 12.5%, p=0.009). The frequency of occurrence of the G allele and the heterozygous genotype AG rs7895833 in the group of pregnant women with GDM were higher than in the control group (31.2% vs. 21.3%, p=0.009; 55.1% vs. 33.8%, p=0.002). Differences in genotype frequencies as well as rs8192678 allele frequency between GDM and healthy pregnancies were not statistically significant (p>0.05), so we excluded this variant from further analysis. 

Gene/SNP

Genotypes and alleles 

GDM, n=136

Control, n=136

ACE rs4646994

II

ID

DD

I

D

74 (54.4%)

67 (49.3%)

25 (3.7%)

215 (79.0%)

117 (21%)

37 (27.2%)

69 (50.7%)

30 (22.1%)

143 (51.8%)

129 (48.2%)

PPARG rs1801282

CC

CG

GG

C

G

81 (59.6%)

49 (36.0%)

6 (4.4%)

211 (77.6%)

61 (22.4%)

104 (76.5%)

30 (22.1%)

2 (2.4%)

238 (87.5%)

34 (12.5%)

PPARGC1A rs8192678

CC

CT

TT

C

T

 

69 (50.7%)

 

52 (38.2%)

 

15 (11.1%)

 

190 (69.9%)

 

82 (30.1%)

55 (40.4%)

64 (47.1%)

17 (12.5%)

174 (63.9%)

98 (36.1%)

SIRT1 rs7895833

AA

AG

GG

A

G

56 (41.2%)

75 (55.1%)

5 (3.7%)

187 (68.8%)

85 (31.2%)

84 (61.8%)

46 (33.8%)

6 (4.4%)

214 (78.7%)

58 (21.3%)

Table 2: Distribution of alleles and genotypes of polymorphisms rs4646994 of the ACE gene, rs1801282 of the PPARG gene, rs8192678 of the PPARGC1A gene and rs7895833 of the SIRT1 gene in pregnant women with GDM. 

The analysis of associations established the relationship of polymorphisms rs4646994 of the ACE gene, rs1801282 of the PPARG gene, and rs7895833 of the SIRT1 gene with GDM (Table 3). Thus, homozygous genotype II rs4646994 and heterozygous genotype AG rs7895833 in the general model of inheritance are genetic predisposition factors for this pregnancy complication, increasing the risk of its development by more than 2 times (p=0.009 and p=0.001 respectively). The heterozygous genotype CG rs1801282 in the common and dominant inheritance patterns increases the risk of developing GDM by 2.1 (p=0.007) and 2,2 (p=0.003) times, respectively. A significant GDM risk association was observed in carriers of the G rs1801282 allele in an additive inheritance model (2.024; p=0.002). The risk of developing GDM increased by 1.6 times in carriers of the I allele rs4646994 (p=0.002) and A allele of the rs7895833 (p=0.009). 

Gene/SNP

Model of inheritance

Genotypes

 GDM,

n=136

Control,

n=136

OR

(95% of CI)

chi2

P*

ACE rs4646994

Codominant

II

74

37

2.400
1.239-4.650

6.87

0.009

ID

67

69

0.486
0.289-0.815

7.55

0.006

DD

25

30

0.417
0.215-0.807

6.87

0.009

Dominant

II/ID + DD

74/92

37/99

0.465
0.286-0.755

9.71

0.002

Recessive

II + ID/DD

111/25

106/30

1.596
0.887-2.872

2.46

0.116

Additive

I

215

113

1.658
1.195-2.300

9.20

0.002

D

117

129

0.603
0.435-0.837

9.20

0.002

PARG

rs1801282

Codominant

CC

81

104

0.544
0.103-2.874

0.53

0.468

CG

49

30

2.097
1.223-3.596

7.37

0.007

GG

6

2

3.852
0.757-19.590

3.02

0.082

Dominant

CC/CG + GG

81/55

104/32

2.207
1.307-3.726

8.94

0.003

Recessive

CC + CG/GG

130/6

134/2

0.323
0.064-1.631

2.06

0.151

Additive

C

211

238

0.494
0.312-0.782

9.30

0.002

G

61

34

2.024
1.279-3.201

9.30

0.002

PPARGC1A rs8192678

Codominant

CC

69

55

0.877
0.404-1.904

0.11

0.739

CT

52

64

0.658
0.395-1.096

2.60

0.107

TT

15

17

0.750
0.348-1.619

0.54

0.463

Dominant

CC/ CT + TT

69/67

55/81

0.678
0.419-1.095

2.54

0.111

Recessive

CC + CT/ TT

121/15

119/17

1.090
0.526-2.257

0.05

0.817

Additive

C

190

174

0.788
0.552-1.127

1.70

0.192

T

82

98

1.268
0.887-1.813

1.70

0.192

SIRT1

rs7895833

Codominant

AA

56

84

1.957
0.565-6.776

1.15

0.283

AG

75

46

2.446
1.485-4.028

12.55

0.001

GG

5

6

1.250
0.364-4.294

0.13

0.723

Dominant

AA/AG + GG

56/80

84/52

2.308
1.419-3.752

11.54

0.001

Recessive

AA + AG/GG

131/5

130/6

1.209
0.360-4.061

0.09

0.758

Additive

A

187

214

0.596
0.405-0.878

6.92

0.009

G

85

58

1.677
1.139-2.470

6.92

0.009

Table 3: Association of genotypes of polymorphisms rs4646994 of the ACE gene, rs1801282 of the PPARG gene, rs8192678 of the PPARGC1A gene and rs7895833 of the SIRT1 gene with preeclampsia in pregnant women with GDM.

Descriptions for table 3: OR - odds ratio, CI - Confidence interval, chi2 - chi-square distribution, P - P Value Definition. 

* - P value is greater ≤ 0.05 

Discussion

A review of the world literature shows that Gestational Diabetes Mellitus (GDM) is the most frequently encountered extra genital pathology of gestation and represents a serious medical and social problem, increasing the incidence of unwanted pregnancy outcomes for both the mother and the fetus. The study of gene polymorphisms that affect the function of pancreatic beta-cells and vascular endothelial cells, insulin resistance, carbohydrate and lipid metabolism will allow not only early diagnosis, but also earlier preventive and therapeutic measures. 

The main goal of this study was to study the relationship between the polymorphic loci allele of the ACE gene, rs7895833 of the SIRT1 gene, rs1801282 of the PPARG gene, and rs8192678 of the PPARGC1A gene and the risk of developing gestational diabetes mellitus in the Russian population. The study involved 272 patients with GDM (136 women with GDM and 136 pregnant women with normal glucose tolerance). Our results showed that these ACE, PPARG, and SIRT1 gene polymorphisms were associated with the risk of developing GDM. PPARGC1A gene polymorphism was not associated with gestational diabetes mellitus, which is consistent with the results of studies by Leipold H et al., Shaat N et al., and Franzago M et al., [46-48]. 

According to experimental and clinical studies, I/D polymorphism of the ACE gene is associated with the risk of developing type 2 diabetes mellitus, diabetic nephropathy, and diabetic retinopathy [16]. The results of the association studies of I/D polymorphism with GDM are controversial. Therefore, Dostálová Z et al., Aggarwal P et al., Mirfeizi M et al., did not reveal a significant association in the distribution of genotypes or allele frequency between the control group and women with gestational diabetes mellitus [49-51]. Whereas Khan IA et al., (2014) showed in their study that the frequency of the I/D genotype significantly differs between individuals with and without GDM [52]. 

The Gly482Ser (rs8192678) polymorphism of the PPARGC1A gene is one of the most studied variants. Research results have shown that the minor allele of this polymorphism is associated with susceptibility to type 2 diabetes, relative risk of obesity, insulin resistance, and decreased beta-cell function [30, 53-55]. Even though the previous studies suggested that GDM and type 2 diabetes share genetic polymorphisms with the same effect size for the same risk alleles, the findings of the studies by Leipold H et al., Shaat N et al., and Franzago M et al., did not reveal any association between the rs8192678 of the PPARGC1A gene with GDM development risk [56-58]. 

The rs1801282 polymorphic variant, also known as Pro12Ala, is associated with obesity, type 2 diabetes, and GDM [37-40]. However, studies have shown conflicting results regarding the role of Pro12Ala in the development of GDM. Anghebem-Oliveira et al., found no relationship between the rs1801282 polymorphism and the risk of developing GDM in the Brazilian population [56]. The results of the study by Ustianowski et al., indicate that PPARG gene polymorphism (rs1801282) is not a significant risk factor for GDM in the Polish population [57]. Lin et al., after analyzing sixteen studies involving 3129 women with GDM and 7168 without it, found that the protective G allele of the rs1801282 polymorphism was associated with a reduced risk of GDM in Asians, especially Chinese, but not South Koreans [58]. Data from meta-analyses of studies of the genetic association of Pro12Ala polymorphism with the risk of developing GDM by Wu et al., and Wang et al., suggest a potential role for the Pro allele in the pathogenesis of GDM in Asian populations, but not in the Caucasian population [39,40]. 

According to previous studies, the rs7895833 polymorphism in the promoter region of the SIRT1 gene is associated with type 2 diabetes mellitus and obesity, but no research has focused on its relationship with GDM [23,24]. It should be noted that studies to identify the association of polymorphic variants of the SIRT1 gene with the risk of developing GDM have not been previously conducted. The genetic variant rs7895833 that we have chosen in association with GDM has also not been studied. 

In conclusion, the results of this study suggest the ACE (rs4646994), PPARG (rs1801282), SIRT1 (rs7895833) gene polymorphisms are significant risk factors for the development of GDM in the Russian population. The small sample size of the study groups is the key limitation of this study. Nonetheless, the data obtained point to the need to further investigate the polymorphic loci we selected in a larger patient sample, which will enable using this genetic markers in the future as the assessment criterion in the individual outlook of GDM pregnant women to take efficient preventive measures to timely remedy and improve the pregnancy outcome.

Data Availability

The data used to support the findings of this study are included within the article.

Acknowledgment

The authors would like to thank the patients who participated in this study, and our medical colleagues of the City Clinical Hospital No. NOT. Bauman Papysheva OV and Kotaish GA.

Conflicts of Interest

All authors declared no competing interests.

Source of Support

Departmental sources

References

  1. American Diabetes Association (2018) 2 Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2018. Diabetes Care 41: 13-27.
  2. Damm P, Oeregaard AH, Kelstrup L, Lauenborg J, Mathiesen ER, et al. (2016) Gestational diabetes mellitus and long-term consequences for mother and offspring: a view from Denmark. Diabetologia 59: 1396-1399.
  3. International Diabetes Federation (2019) IDF Diabetes Atlas Ninth Edition 2019. International Diabetes Federation, Belgium.
  4. Gupta RD, Gupta S, Das A, Biswas T, Haider MR, et al. (2018) Ethnic predisposition of diabetes mellitus in the patients with previous history of gestational diabetes mellitus: a review. Expert Rev Endocrinol Metab 13: 149-158.
  5. RU center (2022) Federal Register of Diabetes Mellitus of the Russian Federation. Ru Center, Russia.
  6. Yuen L, Saeedi P, Riaz M, Karuranga S, Divakar H, et al. (2019) Simmons Projections of the prevalence of hyperglycaemia in pregnancy in 2019 and beyond: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Research and Clinical Practice 157: 107841.
  7. Johns EC, Denison FC, Norman JE, Reynolds RM (2018) Gestational Diabetes Mellitus: Mechanisms, Treatment, and Complications. Trends Endocrinol Metab 29: 743-754.
  8. Guimarães MF, Brandão AH, Rezende CA, Cabral AC, Brum AP, et al. (2014) Assessment of endothelial function in pregnant women with preeclampsia and gestational diabetes mellitus by flow-mediated dilation of brachial artery. Arch Gynecol Obstet 290: 441-447. 
  9. Ormazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, et al. (2018) Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol 17: 122.
  10. Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, et al. (2019) The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res 47: 1005-1012. 
  11. Kawai VK, Levinson RT, Adefurin A, Kurnik D, Collier SP, et al. (2017) A genetic risk score that includes common type 2 diabetes risk variants is associated with gestational diabetes. Clin Endocrinol (Oxf) 87: 149-155. 
  12. Dias S, Pheiffer C, Abrahams Y, Rheeder P, Adam S (2018) Molecular Biomarkers for Gestational Diabetes Mellitus. Int J Mol Sci 19: 2926. 
  13. Wang L, Leung PS (2013) The role of renin-angiotensin system in cellular differentiation: implications in pancreatic islet cell development and islet transplantation. Mol Cell Endocrinol 381: 261-271.
  14. Rein J, Bader M (2017) Renin-Angiotensin System in Diabetes. Protein Pept Lett 24: 833-840.
  15. Rigat B, Hubert C, Corvol P, Soubrier F (1992) PCR detection of the insertion/deletion polymorphism of the human angiotensin converting enzyme gene (DCP1) (dipeptidyl carboxypeptidase 1). Nucleic Acids Res 20: 1433.
  16. Rahimi Z, Moradi M, Nasri H (2014) A systematic review of the role of renin angiotensin aldosterone system genes in diabetes mellitus, diabetic retinopathy and diabetic neuropathy. J Res Med Sci 19: 1090-1098.
  17. Guarente L (2011) Franklin H. Epstein Lecture: Sirtuins, aging, and medicine. N Engl J Med 364: 2235-2244.
  18. Knight JR, Milner J (2012) SIRT1, metabolism and cancer. Curr Opin Oncol 24: 68-75. 
  19. Luu L, Dai FF, Prentice KJ, Huang X, Hardy AB, et al. (2013) The loss of Sirt1 in mouse pancreatic beta cells impairs insulin secretion by disrupting glucose sensing. Diabetologia 56: 2010-2020.
  20. Mishra JS, Zhao H, Hattis S, Kumar S (2020) Elevated Glucose and Insulin Levels Decrease DHA Transfer across Human Trophoblasts via SIRT1-Dependent Mechanism. Nutrients 12: 1271.
  21. Alqudah A, Eastwood KA, Jerotic D, Todd N, Hoch D, et al. (2021) FKBPL and SIRT-1 Are Downregulated by Diabetes in Pregnancy Impacting on Angiogenesis and Endothelial Function. Front Endocrinol (Lausanne) 12: 650328. 
  22. Deihimi T, Niazi A, Ebrahimi M, Kajbaf K, Fanaee S (2012) Finding the undiscovered roles of genes: an approach using mutual ranking of coexpressed genes and promoter architecture-case study: dual roles of thaumatin like proteins in biotic and abiotic stresses. Springerplus 1: 30-35.
  23. Zillikens MC, Meurs JB, Rivadeneira F, Amin N, Hofman A, et al. (2009) SIRT1 genetic variation is related to BMI and risk of obesity. Diabetes 58: 2828-2834.
  24. Han J, Wei M, Wang Q, Li X, Zhu C, et al. (2015) Association of Genetic Variants of SIRT1 With Type 2 Diabetes Mellitus. Gene Expr 16: 177-185. 
  25. Górniak BG (2014) Peroxisome proliferator-activated receptors and their ligands: nutritional and clinical implications--a review. Nutr J 13: 17. 
  26. Puigserver P, Wu Z, Park CW, Graves R, Wright M, et al. (1998) A cold-inducible coactivator of nuclear receptors linked to adaptive thermogenesis. Cell 92: 829-839.
  27. Oropeza D, Jouvet N, Bouyakdan K, Perron G, Ringuette LJ, et al. (2015) PGC-1 coactivators in β-cells regulate lipid metabolism and are essential for insulin secretion coupled to fatty acids. Mol Metab 4: 811-822.
  28. Michael LF, Wu Z, Cheatham RB, Puigserver P, Adelmant G, et al. (2001) Restoration of insulin-sensitive glucose transporter (GLUT4) gene expression in muscle cells by the transcriptional coactivator PGC-1. Proc Natl Acad Sci USA 98: 3820-3825.
  29. Balampanis K, Chasapi A, Kourea E, Tanoglidi A, Hatziagelaki E, et al. (2018) Inter-Tissue Expression Patterns of the Key Metabolic Biomarker Pgc1a In Severely Obese Individuals: Implication in Obesity-Induced Disease. Hellenic J Cardiol 60: 282-293.
  30. Vandenbeek R, Khan NP, Estall JL (2018) Linking Metabolic Disease With the PGC-1α Gly482Ser Polymorphism. Endocrinology 159: 853-865.
  31. Nemoto S, Fergusson MM, Finkel T (2005) SIRT1 functionally interacts with the metabolic regulator and transcriptional coactivator PGC-1{alpha}. J Biol Chem 280: 16456-16460.
  32. Vega RB, Huss JM, Kelly DP (2000) The coactivator PGC-1 cooperates with peroxisome proliferator-activated receptor alpha in transcriptional control of nuclear genes encoding mitochondrial fatty acid oxidation enzymes. Mol Cell Biol 20: 1868-1876. 
  33. Magri CJ, Gatt N, Xuereb RG, Fava S (2011) Peroxisome proliferator-activated receptor-γ and the endothelium: implications in cardiovascular disease. Expert Rev Cardiovasc Ther 9: 1279-1294.
  34. Xu R, Luo X, Ye X, Li H, Liu H, et al. (2021) SIRT1/PGC-1α/PPAR-γ Correlate With Hypoxia-Induced Chemoresistance in Non-Small Cell Lung Cancer. Front Oncol 11: 682762.
  35. Xu P, Lou XL, Chen C, Yang ZW (2013) Effects of peroxisome proliferator-activated receptor-γ activation on apoptosis in rats with acute pancreatitis. Dig Dis Sci 58: 3516-3523. 
  36. Midaoui AE, Wu L, Wang R, Champlain J (2006) Modulation of cardiac and aortic peroxisome proliferator-activated receptor-gamma expression by oxidative stress in chronically glucose-fed rats. Am J Hypertens 19: 407-412.
  37. Sarhangi N, Sharifi F, Hashemian L, Doabsari MH, Heshmatzad K, et al. (2020) PPARG (Pro12Ala) genetic variant and risk of T2DM: a systematic review and meta-analysis. Sci Rep 10: 12764. 
  38. Hoffmann TJ, Choquet H, Yin J, Banda Y, Kvale MN, et al. (2018) A Large Multiethnic Genome-Wide Association Study of Adult Body Mass Index Identifies Novel Loci. Genetics 210: 499-515.
  39. Wu L, Cui L, Tam WH, Ma RC, Wang CC (2016) Genetic variants associated with gestational diabetes mellitus: a meta-analysis and subgroup analysis. Sci Rep 6: 30539. 
  40. Wang L, Xu W, Wang X (2016) Peroxisome proliferator-activated receptor Pro12Ala polymorphism and the risks of gestational diabetes mellitus: An updated meta-analysis of 12 studies. Medicine (Baltimore) 95: 5090.
  41. International Association of Diabetes and Pregnancy Study Groups Consensus Panel, Metzger BE, Gabbe SG, Persson B, Buchanan TA, et al. (2010) International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care 33: 676-682.
  42. Dedov II, Krasnopolskiy VI, Sukhikh GT (2012) Russian National Consensus Statement on gestational diabetes: Diagnostics, treatment and postnatal care. Diabetes Mellit 15: 4-10. 
  43. Gauderman WJ (2002) Sample size requirements for association studies of gene-gene interaction. Am J Epidemiol 155: 478-484.
  44. Maniatis T, Fritsch, Sambrook EFJ (1982) Molecular Cloning: A Laboratory Manual. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, USA.
  45. Kunkel LM, Smith KD, Boyer SH, Borgaonkar DS, Wachtel SS, et al. (1977) Analysis of human Y-chromosome-specific reiterated DNA in chromosome variants. Proc Natl Acad Sci USA 74: 1245-1249. 
  46. Leipold H, Knoefler M, Gruber C, Huber A, Haslinger P, et al. (2006) Peroxisome proliferator-activated receptor gamma coactivator-1alpha gene variations are not associated with gestational diabetes mellitus. J Soc Gynecol Investig 13: 104-107.
  47. Shaat N, Lernmark A, Karlsson E, Ivarsson S, Parikh H, et al. (2007) A variant in the transcription factor 7-like 2 (TCF7L2) gene is associated with an increased risk of gestational diabetes mellitus. Diabetologia 50: 972-979.
  48. Franzago M, Fraticelli F, Marchetti D, Celentano C, Liberati M, et al. (2018) Nutrigenetic variants and cardio-metabolic risk in women with or without gestational diabetes. Diabetes Res Clin Pract 137: 64-71. 
  49. Dostálová Z, Vasku AJB, Vasku A, Gerychová R, Unzeitig V (2006) Inzercne-delecní polymorfismus v genu pro angiotensin-konvertující enzym (I/D ACE) u tehotných s gestacním diabetem [Insertion-deletion polymorphism in the gene for angiotensin-converning enzyme (I/D ACE) in pregnant women with gestational diabetes. Ceska Gynekol 71: 369-373. 
  50. Aggarwal P, Agarwal N, Das N, Dalal K (2016) Association of polymorphisms in angiotensin-converting enzyme gene with gestational diabetes mellitus in Indian women. Int J Appl Basic Med Res 6: 31-37. 
  51. Mirfeizi M, Hasanzad M, Sattari M, Afshari M, Abbasi D, et al. (2020) Correction to: Association of eNOS and ACE gene polymorphisms as a genetic risk factor in gestational diabetes in Iranian women. J Diabetes Metab Disord 19: 2063.
  52. Khan IA, Jahan P, Hasan Q, Rao P (2014) Angiotensin-converting enzyme gene insertion/deletion polymorphism studies in Asian Indian pregnant women biochemically identifies gestational diabetes mellitus. J Renin Angiotensin Aldosterone Syst 15: 566-571.
  53. Xia W, Chen N, Peng W, Jia X, Yu Y, et al. (2019) Systematic Meta-analysis Revealed an Association of PGC-1α rs8192678 Polymorphism in Type 2 Diabetes Mellitus. Dis Markers 2019: 2970401.
  54. Fang P, He B, Yu M, Shi M, Zhu Y, et al. (2019) Treatment with celastrol protects against obesity through suppression of galanin-induced fat intake and activation of PGC-1α/GLUT4 axis-mediated glucose consumption. Biochim Biophys Acta Mol Basis Dis 1865: 1341-1350.
  55. Andrulionytè L, Zacharova J, Chiasson JL, Laakso M, STOP-NIDDM Study Group (2004) Common polymorphisms of the PPAR-gamma2 (Pro12Ala) and PGC-1alpha (Gly482Ser) genes are associated with the conversion from impaired glucose tolerance to type 2 diabetes in the STOP-NIDDM trial. Diabetologia 47: 2176-2184.
  56. Oliveira MIA, Martins BR, Alberton D, Ramos EAS, Picheth G, et al. (2017) Type 2 diabetes-associated genetic variants of FTO, LEPR, PPARg, and TCF7L2 in gestational diabetes in a Brazilian population. Arch Endocrinol Metab 61: 238-248.
  57. Ustianowski P, Malinowski D, Safranow K, Dziedziejko V, Tarnowski M, et al. (2022) PPARG, TMEM163, UBE2E2, and WFS1 Gene Polymorphisms Are Not Significant Risk Factors for Gestational Diabetes in the Polish Population. J Pers Med 12: 243.
  58. Lin PC, Chou PL, Wung SF (2018) Geographic diversity in genotype frequencies and meta-analysis of the association between rs1801282 polymorphisms and gestational diabetes mellitus. Diabetes Res Clin Pract 143: 15-23.

Citation: Dmitrenko OP, Karpova NS, Nurbekov MK (2022) ACE, PPARG, SIRT1 Gene Polymorphisms but Not PPARGC1A Polymorphism are Risk Factors for Gestational Diabetes in the Russian Population. J Genet Genomic Sci 7: 035.

Copyright: © 2022  Dmitrenko OP, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

© 2022, Copyrights Herald Scholarly Open Access. All Rights Reserved!