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CYP2E1 C-1054T and 96-bp I/D genetic variations and risk of gestational diabetes mellitus in chinese women: a case-control study

BMC Pregnancy and Childbirth volume 23, Article number: 403 (2023) Cite this article

Abstract

Background

Cytochrome P450 2E1 (CYP2E1) plays a key role in the metabolism of xenobiotic and endogenous low-molecular-weight compounds. This study aimed to determine if the genetic variations of 96-bp insertion/deletion (I/D) and C-1054T (rs2031920) in CYP2E1 were associated with the risk of gestational diabetes mellitus (GDM).

Methods

CYP2E1 polymorphisms were genotyped in a case-control study of 1,134 women with uncomplicated pregnancies and 723 women with GDM. The effects of genotype on the clinical, metabolic, and oxidative stress indices were assessed.

Results

The CYP2E1 C-1054T variant was associated with an increased risk of GDM based on the genotype, recessive, dominant, and allele genetic models (P < 0.05). The TT + CT genotype remained a significant predictive factor for GDM risk after correcting for maternal age and pre-pregnancy body mass index (OR = 1.277, 95% CI: 1.042–1.563, P = 0.018). Moreover, fasting insulin concentrations and homeostatic model assessment of insulin resistance were significantly higher in GDM patients carrying the T allele than in those with the CC genotype (P < 0.05). Furthermore, the combined genotype II + ID/TT + CT of the 96-bp I/D and C-1054T polymorphisms further increased the risk of GDM when the combined genotype DD/CC was set as the reference category (OR = 1.676, 95% CI: 1.182–2.376, P = 0.004).

Conclusions

The T allele of the C-1054T polymorphism and its combination with the I allele of the 96-bp I/D variation in CYP2E1 are associated with an increased risk of GDM in the Chinese population. The − 1054T allele may be associated with more serious insulin resistance in patients.

Peer Review reports

Background

Gestational diabetes mellitus (GDM) is one of the most common gestational complications. It is characterized by carbohydrate intolerance leading to hyperglycemia with an onset or first identification during pregnancy [1, 2]. It is a growing health concern in pregnancy because it impairs the health of several million women worldwide [1, 3]. The incidence of GDM varies from 5 to 25.5% globally depending on the diagnostic criteria, ethnic group, age, and body mass index (BMI) [2, 4, 5]. Its prevalence in China is 14.8% [4]. GDM may result in unfavorable pregnancy outcomes in both the mother and infant, including macrosomia, neonatal hypoglycemia, higher cesarean rate, and preeclampsia [2, 6, 7]. It is associated with increased long-term health risks, including type 2 diabetes mellitus and cardiovascular diseases in mothers, and metabolic syndrome, overweight, and obesity in both the mother and offspring [3, 7,8,9,10]. The etiology of GDM is unknown and may be related to genetic variants [11,12,13], increased oxidative stress [11, 14,15,16], dyslipidemia [17], chronic inflammation [18], abnormal expression of placental hormones and cytokines [19,20,21], and assisted reproduction technology [22].

Cytochrome P450 2E1 (CYP2E1) belongs to the cytochrome P450 family [23]. It is an abundant enzyme that accounts for approximately 21% of all CYP proteins in the human liver [24]. It can metabolize various low-molecular-weight xenobiotics, including medications and environmental toxins, and endogenous compounds to their highly active intermediate metabolites during phase I metabolic reactions [23]. These high-reactivity intermediates are then combined with hydrophilic molecules or chemical groups during phase II metabolic reactions and converted into water-soluble and non-toxic metabolites [23]. Nevertheless, some active intermediates, including reactive oxygen species and carcinogenic or hepatotoxic metabolites, can covalently conjugate with biological macromolecules, influence the function and molecular framework of these biomolecules, and play key roles in the development of some cancers, alcohol or drug-induced liver impairment, and non-alcoholic fatty liver disease [23, 25, 26]. Moreover, CYP2E1 has been reported to participate in the metabolism of some fatty acids such as arachidonic acid, which may affect signal transduction and cellular homeostasis [23].

CYP2E1 is a 493-amino acid protein encoded by CYP2E1 [27]. Genetic polymorphisms, such as the 96-bp insertion/deletion (I/D) and C-1054T (RsaI, rs2031920) in the 5′-flanking regulatory region of CYP2E1 may affect the transcriptional activity of CYP2E1 [28,29,30]. Usually, the CYP2E1*5A or RsaI wild-type (c1) allele refers to the C allele of the single nucleotide polymorphism (SNP) C-1054T, whereas the CYP2E1*5B or RsaI variant c2 allele represents the T allele [23, 31]. There is almost a complete link disequilibrium between the 96-bp I/D and C-1054T variations (D′ = 0.94) [32]. Notably, these two polymorphisms are associated with the occurrence of some cancers [31,32,33], adverse birth outcomes [34], polycystic ovary syndrome (PCOS) [27], and drug-induced liver injury [35].

CYP2E1 catalyzes the production of reactive intermediates from xenobiotics and endogenous substances. These intermediates may damage the structure and function of biomacromolecules, resulting in increased oxidative stress, epigenetic changes, cell dysfunction, and apoptosis of cells [23, 26, 36]. Thus, CYP2E1 may be involved in the pathogenesis of GDM. However, limited data are available on the relationship between CYP2E1 and GDM, and it remains unknown whether the C-1054T and 96-bp I/D genetic variations in CYP2E1 are associated with GDM. The present study explored the association between these two genetic polymorphisms and the risk of GDM, and assessed the effect of genotype on oxidative stress and clinical and metabolic parameters in the Chinese population.

Methods

Study subjects

This case-control study included 723 patients with GDM and 1,134 controls. All participants were recruited from the Department of Obstetrics and Gynecology of the West China Second University Hospital between 2013 and 2021. This study was conducted in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from all the study subjects. The study was approved by the Institutional Review Board of West China Second University Hospital, Sichuan University (approval numbers: 2020-036 to Ping Fan and 2017- 033 to Xinghui Liu).

At 24–28 gestational weeks, each pregnant woman underwent a routine 75 g oral glucose tolerance test. GDM was diagnosed based on the guidelines of the International Association of Diabetes Pregnancy Study Groups by a woman having one or more of the following findings: fasting glucose ≥ 5.1 mmol/L; 1 h glucose ≥ 10.0 mmol/L; or 2 h glucose ≥ 8.5 mmol/L [37]. Control participants with uncomplicated pregnancies were enrolled at the same hospital during the same period. The inclusion criterion for participants was singleton pregnancy.

The exclusion criteria were chronic hypertension; diabetes mellitus before pregnancy; twin/multiple pregnancies; preeclampsia; intrahepatic cholestasis of pregnancy; and autoimmune, renal, cardiac, hepatic, and other endocrine disorders. Women who had premature deliveries or underwent in vitro fertilization were excluded from the control group.

Clinical and anthropometric variables of the participants, including systolic blood pressure (SBP), diastolic blood pressure (DBP), BMI (kg/m2), gestational age, and birth height and weight of infants were measured or assessed.

Blood samples were obtained after at least 8 h of fasting during the third trimester of pregnancy or before delivery, kept on ice, and centrifuged at 1500 × g for 15 min at 4 °C within 2 h. Plasma and serum aliquots were stored at -80 °C for later analysis. Blood cells in EDTA anticoagulant tubes were stored at 4 °C before deoxyribonucleic acid (DNA) extraction.

Analysis of metabolic and oxidative stress parameters

Serum total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), apolipoprotein (apo)A1, apoB, plasma insulin and glucose concentrations, malondialdehyde (MDA), total oxidant status (TOS), total antioxidant capacity (TAC), oxidative stress index (OSI; i.e., TOS/TAC ratio), and homeostatic model assessment of insulin resistance (HOMA-IR) were measured or evaluated as previously described [14, 38, 39]. The intra- and inter-assay coefficients of variation for all measurements did not exceed 5% and 10%, respectively.

DNA extraction and genotyping

Genomic DNA was extracted from the leukocytes of participants using a routine method. CYP2E1 genetic polymorphisms were genotyped using polymerase chain reaction (PCR) and/or restriction fragment length polymorphism methods as previously described [27]. To guarantee genotyping quality, another operator randomly re-genotyped approximately 30% of the DNA samples and the results of the two genotypes were identical.

Statistical analyses

All statistical analyses were conducted using Statistical Program for Social Sciences (SPSS) version 21.0 (IBM SPSS Statistics, IBM Corporation, Armonk, New York, USA). Data are expressed as the mean ± standard deviation. Hardy-Weinberg equilibrium was tested in cases and controls using chi-square (χ2) analysis. Allelic and genotypic frequencies in different genetic models were compared between the cases and controls using the χ2 test. The differences in variables between GDM and control were estimated using an independent-sample Student’s t-test or a non-parametric test (for variables with an asymmetric distribution). Analysis of covariance was used to assess differences in biochemical parameters between the groups after correcting for differences in age and pre-pregnancy BMI. Odds ratios (OR) and 95% confidence intervals (CI) were used to evaluate the risk of GDM associated with CYP2E1 genetic variants using a logistic regression method or the χ2 test. The effect of genotype, GDM status and their interaction was evaluated by a two-way univariate general linear model. Statistical significance was set at P-value < 0.05.

The power value due to the minor allele frequency of CYP2E1 C-1054T SNP and sample size was determined according to a previously described method [27]. The analysis of linkage disequilibrium between the 96-bp I/D and C-1054T variants was conducted by the SHEsis online software at http://analysis.bio-x.cn/myAnalysis.php.

Results

Clinical and biochemical properties of the participants

As shown in Table 1, the pre-pregnancy BMI was higher in the GDM group than in the control group. Among the 723 patients, 81 required insulin therapy, whereas the remaining patients only underwent lifestyle modifications. After correcting for differences in age and pre-pregnancy BMI, fasting Glu and Ins concentrations, HOMA-IR, TG, TG/HDL-C ratio, apoB/apoA1 ratio, MDA, TOS, and OSI were significantly higher, whereas LDL-C and apoA1 concentrations, weight gain during pregnancy, gestational age (days), and neonatal birth weight and height were significantly lower in the GDM group than in the control group.

Table 1 Clinical, metabolic, and oxidative stress parameters in patients with GDM and control women
Full size table

CYP2E1 96-bp I/D and C-1054T genotypic and allelic frequencies

Genotypic frequencies of the 96-bp I/D and C-1054T variants were in accordance with Hardy–Weinberg equilibrium in both the GDM and control groups (all P > 0.05). There is a reasonably high linkage disequilibrium between the C-1054T and 96-bp I/D variants (D′ = 0.943). As shown in Table 2, the frequencies of the TT genotype (5.7 vs. 3.5%), CT genotype (32.8 vs. 29.3%), and T allele (22.1 vs. 18.2%) of the C-1054T SNP were significantly higher in patients with GDM than in the control group (OR = 1.644, 95% CI:1.053–2.568, P = 0.027 for the recessive model; OR = 1.280, 95% CI:1.054–1.554, P = 0.013 for the dominant model; OR = 1.275, 95% CI:1.082–1.502, P = 0.004 for the allele model). The TT + CT genotype had a significant predictive role for GDM risk after correcting for differences in age and pre-pregnancy BMI (OR = 1.277, 95% CI: 1.042–1.563, P = 0.018). The statistical power to discern an inheritance correlation was 0.939 for C-1054T variation (prevalence = 0.15; significance level = 0.05). No significant differences were identified between case and control subjects based on the different genetic models for the 96-bp I/D variation (P > 0.05, Table 2).

Table 2 Association of CYP2E1 C-1054T and 96-bp I/D polymorphisms with GDM using different genetic models
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The association between the combined genotypes of the C-1054T and 96-bp I/D polymorphisms and the risk of GDM was also estimated. Owing to the relatively small sample size of the 96-bp II and − 1054 TT homozygotes, we integrated these homozygotes into the heterozygous subgroups. The frequency of the II + ID/TT + CT combined genotype was higher in the GDM group than that in the control group (12.0 vs. 7.8%; P = 0.013, Table 3). The II + ID/TT + CT combined genotype was a risk factor for GDM when the wild-type combined genotype DD/CC was used as a reference in a multinomial logistic regression model, including age and pre-pregnancy BMI as covariates (OR = 1.676, 95% CI: 1.182–2.376, P = 0.004).

Table 3 Combined genotypes of CYP2E1 96-bp I/D and C-1054T variants in GDM and control women
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Effects of CYP2E1 C-1054T and 96-bp I/D variation on clinical, metabolic, and oxidative stress indices

As shown in Table 4, GDM patients carrying the TT + CT genotype had higher fasting Ins levels, HOMA-IR, and gestational age (P < 0.05), but lower TG and TG/HDL-C ratio (P < 0.05) than those with the CC genotype. No significant differences in oxidative stress indices were observed between the TT + CT and CC genotype subgroups in patients with GDM and controls (P > 0.05). In all subjects, the TT + CT genotype subgroup had higher fasting Ins levels and HOMA-IR (P < 0.05), but lower SBP (P = 0.025) than the CC genotype subgroup; GDM status was associated with most of the parameters (P < 0.05) and an obvious interaction between the C-1054T variant and GDM status was observed in these parameters (P < 0.05) except for SBP, DBP, TC, HDL-C, apoB, and TAC (P > 0.05).

Table 4 Clinical and biochemical parameters according to CYP2E1 C-1054T genotypes in GDM and control women
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Regarding the 96-bp I/D polymorphisms (Table 5), participants in the control group with genotype II + ID had higher DBP and parity (P < 0.05) than those with the DD genotype. There were no significant differences in oxidative stress and metabolic indices between the II + ID and DD genotype subgroups in the GDM and control groups and all subjects (P > 0.05). However, similar to the C-1054T variant, GDM status and its interaction with the 96-bp I/D polymorphism were significantly associated with most of the parameters (P < 0.05) except for SBP, DBP, TC, HDL-C, apoB, and TAC (P > 0.05).

Table 5 Clinical and biochemical parameters according to CYP2E1 96-bp I/D genotypes in GDM and control women
Full size table

Effects of the combined genotypes of CYP2E1 96-bp I/D and C-1054T polymorphisms on clinical and biochemical indices were shown in Table 6. In patients with GDM, compared with the DD/CC genotype subgroup, the DD/TT + CT genotype subgroup had higher fasting Glu levels (P = 0.046), while the II + ID/TT + CT genotype subgroup had higher fasting Ins, HOMA-IR, and gestational age (P < 0.05). Patients with the II + ID/CC genotype had higher TG and TG/HDL-C ratio than those with the DD/TT + CT or the II + ID/TT + CT genotype (P < 0.05), and higher DBP than those with the II + ID/TT + CT genotype (P = 0.042). In all subjects, the II + ID/TT + CT genotype subgroup had higher fasting Ins levels and HOMA-IR than the DD/CC genotype subgroup (P < 0.05), and higher BMI at delivery, gestational age, and HOMA-IR, but lower TG levels than the II + ID/CC genotype subgroup (P < 0.05); the DD/TT + CT genotype subgroup also had higher BMI at delivery but lower SBP and DBP (P < 0.05) than the II + ID/CC genotype subgroup (P < 0.05); there was an obvious interaction between the combined genotype variants and GDM status (P < 0.05) for weight gain during pregnancy, gestational age, parity, fasting Glu and Ins, HOMA-IR, TG, LDL-C, TG/HDL-C ratio, TOS, OSI, and MDA (P < 0.05).

Table 6 Clinical and biochemical parameters according to the combined genotypes of CYP2E1 C-1054T and 96-bp I/D variants in GDM women and all subjects
Full size table

Discussion

To the best of our knowledge, this study is the first to demonstrate that the C-1054T variant, but not the 96-bp I/D variant in CYP2E1 is associated with GDM risk according to genotype, recessive, dominant, and allele models in the Chinese population. We also showed that the combined genotype II + ID/TT + CT further increased the risk of GDM when the combined genotype DD/CC was used as the reference. Moreover, we found that GDM patients carrying the T allele of C-1054T variation had lower TG levels and TG/HDL-C ratio but higher fasting insulin and HOMA-IR than those carrying the CC genotype; GDM patients carrying the DD/TT + CT or II + ID/TT + CT combined genotype had higher fasting Glu or Ins levels and HOMA-IR values, and those with the II + ID/CC genotype had higher TG and TG/HDL-C ratio, implying that the C-1054T and 96-bp I/D variants in CYP2E1 may be linked to lipid metabolism, hyperinsulinemia, and insulin resistance in the patients.

The protein levels and activities of CYP2E1 are influenced by genetic variations, environmental factors, and disease status [23, 36]. Moreover, the distribution of CYP2E1 genetic variations shows clear ethnic differences [23]. Therefore, investigating CYP2E1 genetic variations in patients with GDM may help identify genetic predispositions and elucidate the etiopathogenesis of GDM.

Reports regarding the effect of C-1054T variation on the function and expression of CYP2E1 are inconsistent. The T allele (RsaI c2) was reported to increase transcription of the CYP2E1 gene [30] but was associated with low enzyme activity [40] and low inducible activity after ethanol induction [41] or did not influence enzyme activity [42]. The study found that the T allele frequency of the C-1054T variant ranges from 17.7 to 25.0% in the East Asian population [23, 32], and is higher than that in Caucasians (4.0%) and Iranians (1.5%) [23, 43]. The T allele is a genetic risk factor for colorectal cancer in the Brazilian population [31], hepatitis B-related hepatocellular carcinoma [44], and PCOS in Chinese women [27]. However, it is a protective factor for bladder cancer in Asian populations [45] and patients with lung cancer or drug-related liver damage [25, 35]. A study reported that the T allele is related to lower birth weight of newborns whose maternal disinfection by-products are exposed during gestation [34]. In this study, we demonstrated that the T allele of the C-1054T SNP is a genetic risk factor for GDM in the Chinese population. Moreover, we found that compared with GDM patients carrying the CC genotype, those carrying the T allele had lower TG levels and TG/HDL-C ratio but higher fasting insulin and HOMA-IR. This implies that the C-1054→T genetic variation may affect lipid metabolism and aggravate insulin resistance in patients. Nevertheless, the underlying mechanisms, including whether the T allele increases the risk of GDM by influencing xenobiotic degradation, should be further explored.

The I allele of the 96-bp I/D variation in CYP2E1 enhances the transcriptional activity of CYP2E1 [28]. Studies have found a relatively high frequency of the 96-bp I allele in Asians (15−23.7%) [29, 32], but it is relatively low in African-Americans (10%) and Caucasians (2%) [29]. Genotype II or allele I carriers are associated with a higher risk of drug-induced liver injury [35] and colorectal cancer [32]. In this study, the I allele frequency was 21.2% in all participants. No significant differences were observed between the GDM and control groups according to the different genetic models for the 96-bp I/D variation. However, we found that the II + ID/TT + CT combined genotype of the 96-bp I/D and C-1054T polymorphisms further increased the risk of GDM when the reference genotype DD/CC was used. We also demonstrated that GDM patients carrying the DD/TT + CT or II + ID/TT + CT combined genotype had higher fasting Glu, Ins, and HOMA-IR, and those with the II + ID/CC combined genotype had higher TG levels and TG/HDL-C ratio, suggesting that these two genetic variants may be involved in insulin resistance and dyslipidemia in the patients. Further research is required to elucidate this issue and its underlying mechanisms.

Placental dysfunction plays an important role in the pathogenesis of GDM [19,20,21, 46]. An increase in maternal pre-pregnancy BMI, glucose levels, and weight gain during pregnancy are associated with the abnormal expression of placental hormones and cytokines [19,20,21]. Upregulation of placental inflammatory cytokines, oxidative stress-related genetic variants (myeloperoxidase G-463 A, CYBA C242T, CYP2E1 C-1054T, etc.), glycation and oxidation of proteins caused by hyperglycemia were associated with unfavourable metabolic profiles, insulin resistance, increased oxidative stress, and state of chronic inflammation in patients with GDM, which might increase the risk of adverse perinatal outcomes [6, 8, 11, 20, 21, 47]. In contrast with most of published data in literature [2, 6, 21], in the present study, we found that the gestational weight gain and the birth height and weight of neonates were lower, whereas the incidence of macrosomia were similar in the GDM group than in the control group. One explanation might be that the patients with GDM recruited in our study were subjected to standardized and good pregnancy health care, the blood glucose of most patients with GDM were controlled to an ideal level only by diet control and exercise, except for approximately 10% of patients who required insulin therapy. Our results support the findings that decreased gestational weight gain and continuous glucose monitoring use in pregnancy may help to prevent the occurrence of GDM and improve the treatment and outcomes of GDM [1, 7, 48].

This study has some limitations. First, because of the comparatively low frequencies of minor allelic homozygosity (96-bp II and − 1054 TT), we could not analyze them in the subgroup analysis. A larger sample size is required to evaluate the dose-dependent genotype characteristics. Second, we did not determine the levels or activities of CYP2E1. It may be helpful to further analyze enzyme function to reveal the association between genetic variation and GDM risk. Third, based on the function of CYP2E1, further analysis of the state of xenobiotics in the GDM and control groups may help determine the potential mechanism underlying CYP2E1 genotypic variations and risk of GDM. Fourth, we did not measure metabolic or oxidative parameters in some subjects due to inadequate sample volume or samples with bilirubin or hemolysis, which might influence the power of these parameters or result in the absence of statistical significance.

Conclusions

This study demonstrated that the CYP2E1 genetic polymorphism C-1054T, but not 96-bp I/D, is associated with an increased risk of GDM in the Chinese population. We also showed that the combined genotype II + ID/TT + CT of these two polymorphisms was associated with a higher risk of GDM. Furthermore, we found that GDM patients with the T allele of the C-1054T variant had more serious insulin resistance. Our findings provide new evidence that genetic variants of xenobiotic metabolism-related enzymes may contribute to the pathogenesis of GDM.

Data Availability

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

GDM:

gestational diabetes mellitus

CYP2E1:

cytochrome P450 2E1

BMI:

body mass index

SBP:

systolic blood pressure

DBP:

diastolic blood pressure

Glu:

glucose

Ins:

insulin

HOMA-IR:

homeostatic model assessment of insulin resistance

TG:

triglycerides

TC:

total cholesterol

HDL-C:

high-density lipoprotein cholesterol

LDL-C:

low-density lipoprotein cholesterol

apo:

apolipoprotein

TOS:

total oxidant status

TAC:

total antioxidant capacity

MDA:

malondialdehyde

OSI:

oxidative stress index

OR:

Odds ratio

CI:

confidence interval

References

  1. Saravanan P, Diabetes in Pregnancy Working G, Maternal Medicine Clinical Study G, Royal College of O, Gynaecologists UK. Gestational diabetes: opportunities for improving maternal and child health. Lancet Diabetes Endocrinol. 2020;8:793–800.

    Article  PubMed  Google Scholar 

  2. Committee on Practice Bulletins–Obstetrics. ACOG Practice Bulletin No. 190: gestational diabetes Mellitus. Obstet Gynecol. 2018;131:e49–e64.

    Article  Google Scholar 

  3. Choudhury AA, Devi Rajeswari V. Gestational diabetes mellitus - A metabolic and reproductive disorder. Biomed Pharmacother. 2021;143:112183.

    Article  CAS  PubMed  Google Scholar 

  4. Gao C, Sun X, Lu L, Liu F, Yuan J. Prevalence of gestational diabetes mellitus in mainland China: a systematic review and meta-analysis. J Diabetes Investig. 2019;10:154–62.

    Article  CAS  PubMed  Google Scholar 

  5. Sacks DA, Hadden DR, Maresh M, Deerochanawong C, Dyer AR, Metzger BE, et al. Frequency of gestational diabetes mellitus at collaborating centers based on IADPSG consensus panel-recommended criteria: the hyperglycemia and adverse pregnancy outcome (HAPO) study. Diabetes Care. 2012;35:526–8.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Ye W, Luo C, Huang J, Li C, Liu Z, Liu F. Gestational diabetes mellitus and adverse pregnancy outcomes: systematic review and meta-analysis. BMJ. 2022;377:e067946.

    Article  PubMed  PubMed Central  Google Scholar 

  7. McIntyre HD, Catalano P, Zhang C, Desoye G, Mathiesen ER, Damm P. Gestational diabetes mellitus. Nat Rev Dis Primers. 2019;5:47.

    Article  PubMed  Google Scholar 

  8. Pathirana MM, Andraweera PH, Aldridge E, Leemaqz SY, Harrison M, Harrison J, et al. Gestational diabetes mellitus and cardio-metabolic risk factors in women and children at 3 years postpartum. Acta Diabetol. 2022;59:1237–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Li Z, Cheng Y, Wang D, Chen H, Chen H, Ming WK, et al. Incidence rate of type 2 diabetes Mellitus after Gestational Diabetes Mellitus: a systematic review and Meta-analysis of 170,139 women. J Diabetes Res. 2020;2020:3076463.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Kramer CK, Campbell S, Retnakaran R. Gestational diabetes and the risk of cardiovascular disease in women: a systematic review and meta-analysis. Diabetologia. 2019;62:905–14.

    Article  PubMed  Google Scholar 

  11. Jiang C, Zhou M, Bai H, Chen M, Yang C, Hu K, et al. Myeloperoxidase G-463A and CYBA C242T genetic variants in gestational diabetes mellitus. Endocr Connect. 2023;12:e220369.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Popova PV, Klyushina AA, Vasilyeva LB, Tkachuk AS, Vasukova EA, Anopova AD, et al. Association of Common Genetic Risk Variants with Gestational Diabetes Mellitus and their role in GDM Prediction. Front Endocrinol (Lausanne). 2021;12:628582.

    Article  PubMed  Google Scholar 

  13. Zhang C, Bao W, Rong Y, Yang H, Bowers K, Yeung E, et al. Genetic variants and the risk of gestational diabetes mellitus: a systematic review. Hum Reprod Update. 2013;19:376–90.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Zhou M, Liu XH, Liu QQ, Chen M, Bai H, Guan LB, et al. Lactonase activity, Status, and genetic variations of paraoxonase 1 in women with gestational diabetes Mellitus. J Diabetes Res. 2020;2020:3483427.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Lopez-Tinoco C, Roca M, Garcia-Valero A, Murri M, Tinahones FJ, Segundo C, et al. Oxidative stress and antioxidant status in patients with late-onset gestational diabetes mellitus. Acta Diabetol. 2013;50:201–8.

    Article  CAS  PubMed  Google Scholar 

  16. Lappas M, Hiden U, Desoye G, Froehlich J, Hauguel-de Mouzon S, Jawerbaum A. The role of oxidative stress in the pathophysiology of gestational diabetes mellitus. Antioxid Redox Signal. 2011;15:3061–100.

    Article  CAS  PubMed  Google Scholar 

  17. Ryckman KK, Spracklen CN, Smith CJ, Robinson JG, Saftlas AF. Maternal lipid levels during pregnancy and gestational diabetes: a systematic review and meta-analysis. BJOG. 2015;122:643–51.

    Article  CAS  PubMed  Google Scholar 

  18. Mrizak I, Arfa A, Fekih M, Debbabi H, Bouslema A, Boumaiza I, et al. Inflammation and impaired endothelium-dependant vasodilatation in non obese women with gestational diabetes mellitus: preliminary results. Lipids Health Dis. 2013;12:93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Sirico A, Rossi ED, Degennaro VA, Arena V, Rizzi A, Tartaglione L, et al. Placental diabesity: placental VEGF and CD31 expression according to pregestational BMI and gestational weight gain in women with gestational diabetes. Arch Gynecol Obstet. 2023;307:1823–31.

    Article  CAS  PubMed  Google Scholar 

  20. Sirico A, Dell’Aquila M, Tartaglione L, Moresi S, Fari G, Pitocco D et al. PTH-rP and PTH-R1 expression in Placentas from Pregnancies complicated by gestational diabetes: New Insights into the pathophysiology of hyperglycemia in pregnancy. Diagnostics (Basel). 2021;11.

  21. Magee TR, Ross MG, Wedekind L, Desai M, Kjos S, Belkacemi L. Gestational diabetes mellitus alters apoptotic and inflammatory gene expression of trophobasts from human term placenta. J Diabetes Complications. 2014;28:448–59.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Bosdou JK, Anagnostis P, Goulis DG, Lainas GT, Tarlatzis BC, Grimbizis GF, et al. Risk of gestational diabetes mellitus in women achieving singleton pregnancy spontaneously or after ART: a systematic review and meta-analysis. Hum Reprod Update. 2020;26:514–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Chen J, Jiang S, Wang J, Renukuntla J, Sirimulla S, Chen J. A comprehensive review of cytochrome P450 2E1 for xenobiotic metabolism. Drug Metab Rev. 2019;51:178–95.

    Article  CAS  PubMed  Google Scholar 

  24. Couto N, Al-Majdoub ZM, Achour B, Wright PC, Rostami-Hodjegan A, Barber J. Quantification of proteins involved in Drug Metabolism and Disposition in the Human Liver using label-free global proteomics. Mol Pharm. 2019;16:632–47.

    Article  CAS  PubMed  Google Scholar 

  25. Wang Y, Yang H, Li L, Wang H, Zhang C, Yin G, et al. Association between CYP2E1 genetic polymorphisms and lung cancer risk: a meta-analysis. Eur J Cancer. 2010;46:758–64.

    Article  CAS  PubMed  Google Scholar 

  26. Kathirvel E, Chen P, Morgan K, French SW, Morgan TR. Oxidative stress and regulation of anti-oxidant enzymes in cytochrome P4502E1 transgenic mouse model of non-alcoholic fatty liver. J Gastroenterol Hepatol. 2010;25:1136–43.

    Article  CAS  PubMed  Google Scholar 

  27. Pu Y, Liu Q, Liu H, Bai H, Huang W, Xi M, et al. Association between CYP2E1 C-1054T and 96-bp I/D genetic variations and the risk of polycystic ovary syndrome in chinese women. J Endocrinol Invest. 2023;46:67–78.

    Article  CAS  PubMed  Google Scholar 

  28. Nomura F, Itoga S, Uchimoto T, Tomonaga T, Nezu M, Shimada H, et al. Transcriptional activity of the tandem repeat polymorphism in the 5’-flanking region of the human CYP2E1 gene. Alcohol Clin Exp Res. 2003;27:42S–6S.

    Article  CAS  PubMed  Google Scholar 

  29. Fritsche E, Pittman GS, Bell DA. Localization, sequence analysis, and ethnic distribution of a 96-bp insertion in the promoter of the human CYP2E1 gene. Mutat Res. 2000;432:1–5.

    CAS  PubMed  Google Scholar 

  30. Watanabe J, Hayashi S, Kawajiri K. Different regulation and expression of the human CYP2E1 gene due to the RsaI polymorphism in the 5’-flanking region. J Biochem. 1994;116:321–6.

    Article  CAS  PubMed  Google Scholar 

  31. Silva TD, Felipe AV, Pimenta CA, Barao K, Forones NM. CYP2E1 RsaI and 96-bp insertion genetic polymorphisms associated with risk for colorectal cancer. Genet Mol Res. 2012;11:3138–45.

    Article  CAS  PubMed  Google Scholar 

  32. Morita M, Le Marchand L, Kono S, Yin G, Toyomura K, Nagano J, et al. Genetic polymorphisms of CYP2E1 and risk of colorectal cancer: the Fukuoka Colorectal Cancer Study. Cancer Epidemiol Biomarkers Prev. 2009;18:235–41.

    Article  CAS  PubMed  Google Scholar 

  33. Zhang H, Li H, Yu H. Analysis of the role of rs2031920 and rs3813867 polymorphisms within the cytochrome P450 2E1 gene in the risk of squamous cell carcinoma. Cancer Cell Int. 2018;18:67.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Zhou B, Yang P, Gong YJ, Zeng Q, Lu WQ, Miao XP. Effect modification of CPY2E1 and GSTZ1 genetic polymorphisms on associations between prenatal disinfection by-products exposure and birth outcomes. Environ Pollut. 2018;243:1126–33.

    Article  CAS  PubMed  Google Scholar 

  35. Richardson M, Kirkham J, Dwan K, Sloan DJ, Davies G, Jorgensen AL. CYP genetic variants and toxicity related to anti-tubercular agents: a systematic review and meta-analysis. Syst Rev. 2018;7:204.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Zhang W, Lu D, Dong W, Zhang L, Zhang X, Quan X, et al. Expression of CYP2E1 increases oxidative stress and induces apoptosis of cardiomyocytes in transgenic mice. FEBS J. 2011;278:1484–92.

    Article  CAS  PubMed  Google Scholar 

  37. International Association of D, Pregnancy Study Groups, Consensus P, Metzger BE, Gabbe SG, Persson B, Buchanan TA, et al. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010;33:676–82.

    Article  Google Scholar 

  38. Zhang R, Liu H, Bai H, Zhang Y, Liu Q, Guan L, et al. Oxidative stress status in chinese women with different clinical phenotypes of polycystic ovary syndrome. Clin Endocrinol (Oxf). 2017;86:88–96.

    Article  PubMed  Google Scholar 

  39. Zhang J, Fan P, Liu H, Bai H, Wang Y, Zhang F. Apolipoprotein A-I and B levels, dyslipidemia and metabolic syndrome in south-west chinese women with PCOS. Hum Reprod. 2012;27:2484–93.

    Article  CAS  PubMed  Google Scholar 

  40. Marchand LL, Wilkinson GR, Wilkens LR. Genetic and dietary predictors of CYP2E1 activity: a phenotyping study in Hawaii Japanese using chlorzoxazone. Cancer Epidemiol Biomarkers Prev. 1999;8:495–500.

    CAS  PubMed  Google Scholar 

  41. Lucas D, Menez C, Girre C, Berthou F, Bodenez P, Joannet I, et al. Cytochrome P450 2E1 genotype and chlorzoxazone metabolism in healthy and alcoholic caucasian subjects. Pharmacogenetics. 1995;5:298–304.

    Article  CAS  PubMed  Google Scholar 

  42. Powell H, Kitteringham NR, Pirmohamed M, Smith DA, Park BK. Expression of cytochrome P4502E1 in human liver: assessment by mRNA, genotype and phenotype. Pharmacogenetics. 1998;8:411–21.

    Article  CAS  PubMed  Google Scholar 

  43. Le Marchand L, Donlon T, Seifried A, Wilkens LR. Red meat intake, CYP2E1 genetic polymorphisms, and colorectal cancer risk. Cancer Epidemiol Biomarkers Prev. 2002;11:1019–24.

    PubMed  Google Scholar 

  44. Bose S, Tripathi DM, Sukriti, Sakhuja P, Kazim SN, Sarin SK. Genetic polymorphisms of CYP2E1 and DNA repair genes HOGG1 and XRCC1: association with hepatitis B related advanced liver disease and cancer. Gene. 2013;519:231–7.

    Article  CAS  PubMed  Google Scholar 

  45. Yin X, Xiong W, Wang Y, Tang W, Xi W, Qian S, et al. Association of CYP2E1 gene polymorphisms with bladder cancer risk: a systematic review and meta-analysis. Med (Baltim). 2018;97:e11910.

    Article  CAS  Google Scholar 

  46. Meng Q, Shao L, Luo X, Mu Y, Xu W, Gao L, et al. Expressions of VEGF-A and VEGFR-2 in placentae from GDM pregnancies. Reprod Biol Endocrinol. 2016;14:61.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Sirico A, Raffone A, Maruotti GM, Travaglino A, Paciullo C, Diterlizzi A, et al. Third trimester myocardial performance index in fetuses from women with hyperglycemia in pregnancy: a systematic review and Meta-analysis. Ultraschall Med. 2023;44:e99–e107.

    Article  PubMed  Google Scholar 

  48. Tartaglione L, di Stasio E, Sirico A, Di Leo M, Caputo S, Rizzi A, et al. Continuous glucose monitoring in women with normal OGTT in pregnancy. J Diabetes Res. 2021;2021:9987646.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We would like to thank the women with or without GDM who donated blood samples for this study. We also thank Qian Gao, Guolin He, Fangyuan Luo, Zeyun Li, and Xiaoli Yan for their support in this study.

Funding

This work was funded by the Key Research and Development Project of Sichuan Province (grant no. 2019YFS0401), the National Key Research and Development Program of China (grant no. 2016YFC1000400), and the Program for Changjiang Scholars and Innovative Research Team in University, Ministry of Education (grant no. IRT0935).

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Authors and Affiliations

  1. Laboratory of Genetic Disease and Perinatal Medicine, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of China

    Yifu Pu, Qingqing Liu, Kaifeng Hu, Huai Bai & Ping Fan

  2. Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of China

    Xinghui Liu, Yujie Wu & Mi Zhou

Authors
  1. Yifu Pu
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  2. Qingqing Liu
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  5. Huai Bai
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  8. Ping Fan
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Contributions

PF conceived and designed the study, analyzed the data, and revised the manuscript. YP performed experiments and wrote the manuscript. QL and KH participated in the experiments and verification. YW and MZ participated in the sample and data collection. XL recruited the patients and participated in the acquisition or interpretation of the data. HB assisted with experiments and revised the manuscript. All authors commented on the previous versions of the manuscript. All the authors have read and approved the final manuscript.

Corresponding author

Correspondence to Ping Fan.

Ethics declarations

Ethics approval and consent to participate

All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the Institutional Review Board of West China Second University Hospital, Sichuan University (approval numbers: 2020-036 to Ping Fan and 2017-033 to Xinghui Liu). Informed consent was obtained from all the participants included in the study.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Pu, Y., Liu, Q., Hu, K. et al. CYP2E1 C-1054T and 96-bp I/D genetic variations and risk of gestational diabetes mellitus in chinese women: a case-control study. BMC Pregnancy Childbirth 23, 403 (2023). https://doi.org/10.1186/s12884-023-05742-y

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Keywords

BMC Pregnancy and Childbirth

ISSN: 1471-2393

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