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. 2023 Feb 8;12(3):e220369. doi: 10.1530/EC-22-0369

Myeloperoxidase G-463A and CYBA C242T genetic variants in gestational diabetes mellitus

Chenyu Jiang 1, Mi Zhou 1, Huai Bai 2, Meng Chen 1, Chunyi Yang 1, Kaifeng Hu 2, Yujie Wu 1, Qingqing Liu 2, Yangyu Zhao 3, Xinghui Liu 1,✉, Ping Fan 2,✉
PMCID: PMC9986406  PMID: 36607164

Abstract

Oxidative stress plays an important role in the pathophysiology of gestational diabetes mellitus (GDM). We investigated the relationship between NADPH oxidase p22phox subunit (CYBA) C242T (rs4673) and myeloperoxidase (MPO) G-463A (rs2333227) genetic variants and GDM in 719 patients with GDM and 1205 control women. Clinical, metabolic, and oxidative stress parameters were analyzed. We found that frequencies of the A allele (15.6% vs 12.3%) and GA + AA genotype (28.5% vs 23.2%) of the MPO G-463A variation were significantly higher in patients with GDM than in the control women (OR = 1.318, 95% CI: 1.068–1.625, P = 0.010 for the dominant model; OR = 1.999, 95% CI: 1.040–3.843, P = 0.034 for the recessive model; OR = 1.320, 95% CI: 1.095–1.591, P = 0.004 for the allele model). Genotype GA + AA remained a significant predictor of GDM in a logistic regression model including age and BMI at delivery (OR = 1.282, 95% CI: 1.037‒1.583, P = 0.021). Furthermore, the ‒463A allele was associated with higher TG and the 242T allele was related to higher pre-pregnancy BMI and oxidative stress index in all subjects (P < 0.05). The 242T allele was also associated with higher homeostatic model assessment of insulin resistance but lower serum total antioxidant capacity in patients with GDM (P < 0.05). We conclude that the MPO G-463A, but not the CYBA C242T, genetic variation is associated with an increased risk of GDM in Chinese women. These two genetic polymorphisms may be linked to obesity, dyslipidemia, insulin resistance, and oxidative stress.

Keywords: gestational diabetes mellitus, CYBA, myeloperoxidase, genetic polymorphism, oxidative stress

Introduction

Gestational diabetes mellitus (GDM) is one of the most common metabolic diseases in obstetrics, characterized by carbohydrate intolerance that develops during pregnancy (1). Along with the increasing prevalence of obesity, sedentary lifestyles, and advanced maternal age, the incidence of GDM is increasing throughout the world (1). The prevalence of GDM varies in different populations and ethnicities, which is 14.7% in China (1, 2). GDM has serious adverse effects on both the mother and the infant, such as preeclampsia, higher cesarean rate, macrosomia, neonatal hypoglycemia, and long-term effects, including a higher risk of type 2 diabetes, cardiovascular events of mothers, and overweight and metabolic syndrome of offspring (1, 3). The pathogenesis of GDM is still unclear, but there is growing evidence that genetic variants, advanced maternal age, pre-pregnancy BMI, oxidative stress, chronic inflammation, dyslipidemia, unbalanced hormone secretion, and/or β-cell injury might be associated with the onset of GDM (4, 5, 6, 7, 8). Given that family history is an important risk factor of GDM (8), genetic variants have raised more and more attention globally. Although there are controversial opinions about values in genetic tests in identification of GDM, the study on genetic variation and its interaction with environmental factors or combination of risk factors may be helpful to clarify the etiology and pathogenesis of GDM (4, 7, 8).

Myeloperoxidase (MPO) is a heme-containing peroxidase abundantly expressed in neutrophils and to a lesser extent in monocytes (9). It catalyzes the formation of reactive oxygen intermediates, including hypochlorous acid, and plays an important role in the immune system for host defense against invading microorganisms (10). Excessive generation of oxidants by MPO and other peroxidases has been linked to tissue damage in many inflammatory diseases (9, 10, 11). The G-463A (rs2333227), a functional single-nucleotide polymorphism (SNP), which lies within a specific Alu-receptor response element in the 5’ upstream promoter region of MPO gene, has been reported to be related to a variety of cancers (12, 13), coronary artery disease (14), type 2 diabetes (15), and polycystic ovary syndrome (PCOS) (16).

NADPH oxidases (NOXs), found in virtually every cell type of the organism, can catalyze the production of superoxide anion, hydrogen peroxide, and downstream reactive oxygen species and serve important roles in physiological and pathological signaling (17). The membrane-bound p22phox subunit, encoded by the CYBA gene located on the long arm of chromosome 16 at position 24, is an important ingredient of the NOX complexes (17, 18). The SNP C242T (rs4673) is situated in exon 4 of the CYBA gene and causes a substitution of His72Tyr in p22phox protein (18). The T allele (72Tyr) has been shown to impair the function of the heme-binding region of the p22phox subunit, leading to a decline in the activity and stability of NOXs (17, 18). The SNP CYBA C242T has been found to be associated with coronary artery disease (19), type 2 diabetes (20), metabolic syndrome (21), preeclampsia (22), and complications of prematurity (18).

Oxidative stress plays an important part in the pathophysiology of GDM (5, 23, 24, 25, 26, 27). Nevertheless, it remains unclear whether the SNPs CYBA C242T and MPO G-463A are associated with GDM. This study examined the association between the two genetic polymorphisms and the risk of GDM and assessed the influences of the genetic variants on clinical and biochemical characteristics in Chinese women.

Materials and methods

Study subjects

This is a case–control study including 719 patients with GDM and 1205 healthy pregnant women. The study participants were recruited from the Department of Obstetrics and Gynecology of West China Second University Hospital between 2013 and 2020. Written informed consent was obtained from the participants. The study was executed in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of West China Second University Hospital, Sichuan University (no. 2017-033 to Xinghui Liu and no. 2020-036 to Ping Fan).

Each pregnant woman was subjected to a 75 g oral glucose tolerance test (OGTT) between 24 and 28 weeks of gestation. GDM was diagnosed according to 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 (28). The inclusion criteria of the subjects were singleton pregnancy. Healthy control pregnant subjects were enrolled during the same period from the same hospital.

The exclusion criteria were diabetes mellitus before pregnancy, chronic hypertension, and other pregnancy complications including but not limited to preeclampsia, intrahepatic cholestasis of pregnancy, as well as cardiac, renal, hepatic, autoimmune, and other endocrine diseases.

Anthropometric and clinical variables, including maternal age, gestational age, systolic blood pressure, diastolic blood pressure, and BMI (kg/m2), as well as birth height and weight of infants of these mothers were measured. Macrosomia was defined by neonate birth weight ≥ 4000 g. Large for gestational age (LGA) and small for gestational age (SGA) infants were defined as birth weight higher than the 90th percentile and lower than the 10th percentile, respectively, for gestational age based on the Hadlock curve data of the B-ultrasound.

Blood samples were obtained after at least 8 h of fasting during the third trimester of pregnancy or before delivery. The samples were transported on ice and centrifuged at 1500 × g for 15 min at 4°C within 2 h. Serum and plasma aliquots were stored at −80 °C for later analysis.

Analysis of genotypes

Genomic DNA was isolated and purified from the blood cells of the study subjects. The SNPs CYBA C242T and MPO G-463A were determined using PCR-restriction fragment length polymorphism method, as previously described (16). Approximately 30% of DNA samples were re-genotyped by another experimenter, and the re-genotypes were consistent with the original results.

Analysis of oxidative stress and metabolic parameters

Plasma glucose (Glu) was determined by the glucose oxidase method (Roche Diagnostics GmbH) and insulin (Ins) was measured by chemiluminescence assay (Diagnostic Products Corporation, Los Angeles, CA, USA). Serum triglyceride (TG), total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-C), and low-density lipoprotein-cholesterol (LDL-C) were measured by enzymatic assay (Siemens Healthcare Diagnostics Inc., Tarrytown, NY, USA). Apolipoprotein (apo)A1 and apoB were determined by the polyethylene glycol-enhanced immunoturbidimetric assay (Siemens Healthcare Diagnostics Inc.). Serum malondialdehyde (MDA) was determined colorimetrically by the thibabituric acid reactive substance method (1,3,3,3-tetraethoxypropane as standard) using micro-MDA detection kits (NanJing Jiancheng Bioengineering Institute Co. Ltd., Nanjing, China). Serum total oxidant status (TOS) and total antioxidant capacity (TAC) were measured using the semiautomatic microplate colorimetric methods. Briefly, TAC was determined based on the reaction of 2,2-azino-bis-3-ethylbenzothiazoline-6-sulphonic acid radical cation (ABTS˙+) with antioxidants present in serum using Trolox (6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid) as calibration and the results are expressed in terms of millimolar Trolox equivalent per liter (mmol Trolox Equiv./L) (29). TOS was measured based on the oxidation of Fe2+ to Fe3+ in the presence of the oxidants contained in sample using hydrogen peroxide (H2O2) as standard, and the results are expressed in micromole H2O2 equivalents per liter serum (μmol H2O2 Equiv./L) (30). Oxidative stress index (OSI, arbitrary unit) is expressed as the ratio of TOS to TAC (5, 23). The homeostatic model assessment of insulin resistance (HOMA-IR) was assessed as previously described (31). For all determinations, the intra-assay variations were less than 5% and the inter-assay variations were less than 10%.

Statistical analyses

The data for continuous variables are shown as mean ± s.d. Comparisons between control and GDM subjects were performed parametrically using an independent samples t-test. Differences in variables for symmetric variables between the two groups were estimated using Mann–Whitney U test. The chi-square (χ2) test was used to examine the deviation of genotype distribution from Hardy–Weinberg equilibrium and to estimate the differences in frequencies between the GDM and control groups. Odds ratios (ORs) and 95% confidence intervals (CIs) were computed to assess the relative risk for GDM associated with the genetic variations by logistic regression analysis or χ2 analysis. The biochemical indexes after correction for differences in age and delivery BMI between the two groups were estimated using covariance analysis. Statistical significance was set as P < 0.05. Statistical analyses were conducted using SPSS 21.0 (IBM).

A power calculation based on sample size and the minor allele frequency of the SNP MPO G-463A (significance level = 0.05, prevalence = 0.15) was conducted by the Genetic Association Study (GAS) Power Calculator (http://csg.sph.umich.edu/abecasis/gas_power_calculator/index.html).

Results

Clinical and biochemical features of the subjects

As shown in Table 1, the pre-pregnancy BMI and plasma fasting Glu, 1-h Glu, and 2-h Glu concentrations during OGTT between 24 and 28 weeks of gestation were higher in the GDM group than in the control group. Among the 719 patients with GDM, 75 patients required insulin treatment, while 644 patients received diet and exercise guidance only. No statistical differences in the frequencies of macrosomia, LGA, and SGA were found between the GDM and control groups. After correcting for differences in age and BMI at delivery, the fasting Glu and Ins, HOMA-IR, TG, apoB/apoA1 ratio, MDA, TOS, and OSI were significantly higher, and the weight gain during pregnancy, gestational age, apoA1 and LDL-C levels, and neonatal birth height and weight were significantly lower in patients with GDM in comparison with the control women.

Table 1.

Clinical, metabolic, and oxidative stress indices in patients with GDM and women with uncomplicated pregnancies.

Controls (n = 1205) GDM (n = 719) P Pa
Clinical characteristics
Age (years) 35.69 ± 4.41 35.55 ± 4.03 0.495
Pre-pregnancy BMI (kg/m2) 21.22 ± 2.74 22.30 ± 3.12 <0.001
Delivery BMI (kg/m2) 26.70 ± 2.70 26.87 ± 3.36 0.243
Weight gain during pregnancy (kg) 13.92 ± 4.23 11.46 ± 4.20 <0.001 <0.001
SBP (mmHg) 115.23 ± 10.11 115.90 ± 10.99 0.173 0.291
DBP (mmHg) 72.19 ± 7.98 72.84 ± 8.61 0.091 0.121
Gestational age (weeks) 39.18 ± 1.07 38.94 ± 1.09 <0.001 <0.001
Parity 1.63 ± 0.55 1.62 ± 0.53 0.804 0.924
OGTT-fasting Glu (mmol/L)b 4.42 ± 0.30 4.89 ± 0.54 <0.001 <0.001
OGTT-1-h Glu (mmol/L)b 7.43 ± 1.30 9.86 ± 1.39 <0.001 <0.001
OGTT-2-h Glu (mmol/L)b 6.52 ± 1.02 8.71 ± 1.31 <0.001 <0.001
Neonatal birth height (cm) 49.86 ± 1.91 49.61 ± 1.83 0.006 0.005
Neonatal birth weight (g) 3373.56 ± 382.69 3338.24 ± 442.79 0.077 0.029
Macrosomia % (n) 4.3 (52) 5.4 (39) 0.268
LGA % (n) 15.2 (183) 17.0 (122) 0.301
SGA % (n) 0.2 (2) 0.1 (1) 1.00
Insulin treatment (n) 0 75
Metabolic profilec
Fasting Glu (mmol/L) 4.35 ± 0.43 4.46 ± 0.82 <0.001 <0.001
Fasting Ins (pmol/L) 72.15 ± 35.34 102.46 ± 125.39 <0.001 <0.001
HOMA-IR 2.04 ± 1.08 3.34 ± 5.56 <0.001 <0.001
TG (mmol/L) 3.64 ± 1.42 3.92 ± 1.71 0.001 <0.001
TC (mmol/L) 6.07 ± 1.08 5.97 ± 1.07 0.072 0.084
HDL-C (mmol/L) 1.99 ± 0.41 1.98 ± 0.43 0.642 0.777
LDL-C (mmol/L) 3.16 ± 0.94 2.94 ± 0.86 <0.001 <0.001
ApoA1 (g/L) 2.37 ± 0.43 2.29 ± 0.38 0.001 <0.001
ApoB (g/L) 1.15 ± 0.26 1.15 ± 0.25 0.752 0.803
ApoB/apoA1 0.50 ± 0.15 0.51 ± 0.13 0.030 0.039
Oxidative stress indicesd
TOS (μmol H2O2 Equiv./L) 21.09 ± 6.93 26.40 ± 10.69 <0.001 <0.001
TAC (mmol Trolox Equiv./L) 1.10 ± 0.19 1.12 ± 0.21 0.085 0.093
OSI 19.44 ± 7.22 23.53 ± 9.93 <0.001 <0.001
MDA (nmol/mL) 5.37 ± 1.21 5.90 ± 1.42 <0.001 <0.001
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Values are presented as mean ± s.d. unless otherwise noted.

aAll comparisons of parameters were corrected for differences in age and BMI at delivery; bblood glucose values during OGTT between 24 and 28 weeks of gestation; ccontrols: n = 1059; GDM: n = 640; dcontrols: n = 818; GDM: n = 524.

Apo, apolipoprotein; DBP, diastolic blood pressure; GDM, gestational diabetes mellitus; Glu, glucose; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostatic model assessment of insulin resistance; Ins, insulin; LDL-C, low-density lipoprotein cholesterol; LGA, large for gestational age; MDA, malondialdehyde; OGTT, oral glucose tolerance test; OSI, oxidative stress index; SBP, systolic blood pressure; SGA, small for gestational age; TAC, total antioxidant capacity; TC, total cholesterol; TG, triglyceride; TOS, total oxidant status.

Genotype and allele distribution of the SNPs CYBA C242T and MPO G-463A

The different genetic models of the MPO G-463A and CYBA C242T SNPs are shown in Table 2. The genotype distributions of the SNPs CYBA C242T and MPO G-463A in both GDM and control women were in Hardy–Weinberg equilibrium (P > 0.05). The frequencies of the A allele (15.6% vs 12.3%) and GA + AA genotype (28.5% vs 23.2%) in the SNP MPO G-463A were significantly higher in patients with GDM than in the control women (OR = 1.318, 95% CI: 1.068–1.625, P = 0.010 for the dominant model; OR = 1.999, 95% CI: 1.040–3.843, P = 0.034 for the recessive model; OR = 1.320, 95% CI: 1.095–1.591, P = 0.004 for the allele model). Logistic regression analysis revealed that the risk of GDM was higher in subjects carrying the GA + AA genotypes than in those carrying the GG genotype after correcting for differences in age and BMI at delivery (OR = 1.282, 95% CI: 1.037 ‒ 1.583, P = 0.021). A statistical power to determine the genetic correlation was 0.878 for the MPO G-463A SNP. No statistical differences in the allele and genotype frequencies of the SNP CYBA C242T were observed between the GDM and control groups.

Table 2.

Association of MPO G-463A and CYBA C242T polymorphisms with the risk of GDM using different genetic models.

Controls (n = 1205) GDM (n = 719) χ2 P
MPO G-463A
Genotype GG 925 (76.8%) 514 (71.5%)
GA 263 (21.8%) 185 (25.7%)
AA 17 (1.4%) 20 (2.8%) 9.024 0.011
Recessive AA 17 (1.4%) 20 (2.8%)
GG + GA 1188 (98.6%) 699 (97.2%) 4.487 0.034a
Dominant GG 925 (76.8%) 514 (71.5%)
GA + AA 280 (23.2%) 205 (28.5%) 6.647 0.010b
Allele G 2113 (87.7%) 1213 (84.4%)
A 297 (12.3%) 225 (15.6%) 8.482 0.004c
CYBA C242T
Genotype CC 1025 (85.1%) 615 (85.5%)
CT 175 (14.5%) 98 (13.6%)
TT 5 (0.4%) 6 (0.8%) 1.651 0.438
Recessive TT 5 (0.4%) 6 (0.8%)
CC + CT 1200 (99.6%) 713 (99.2%) 1.394 0.238
Dominant CC 1025 (85.1%) 615 (85.5%)
CT + TT 180 (14.9%) 104 (14.5%) 0.080 0.777
Allele C 2225 (92.3%) 1328 (92.4%)
T 185 (7.7%) 110 (7.6%) 0.001 0.976
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Data are presented as number (%).

aOdds ratio (OR) = 1.999, 95% confidence interval (CI): 1.040–3.843; bOR = 1.318, 95% CI: 1.068–1.625; cOR = 1.320, 95% CI: 1.095–1.591.

The correlation between CYBA C242T and MPO G-463A genotype combination and GDM was also assessed. Because of the small sample size of the CYBA 242TT and MPO –463AA homozygotes, we integrated them into heterozygote subgroups. The frequency of the AA + GA/CC combined genotype was relatively higher in the GDM group than in the control group (24.2% vs 20.1%, P = 0.065). We also conducted a multinomial logistic regression analysis and found that when using the genotype combination GG/CC as a reference, the GA + AA/CC combined genotype was a risk factor for GDM (OR = 1.277, 95% CI: 1.017–1.602, P = 0.035, Table 3).

Table 3.

Frequencies of combined genotypes of MPO G-463A and CYBA C242T in patients with GDM and women with uncomplicated pregnancies.

Genotype combinations Controls (n = 1205) GDM (n = 719) OR 95% CI P
GG/CC 783 (65.0%) 441 (61.3%) 1.00 – –
GA+AA/CC 242 (20.1%) 174 (24.2%) 1.277 1.017–1.602 0.035
GG/CT+TT 142 (11.8%) 73 (10.2%) 0.913 0.673–1.239 0.558
GA+AA/CT+TT 38 (3.2%) 31 (4.3%) 1.448 0.889–2.361 0.137
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Data on genotype combinations are presented as numbers (%). Chi-squared test: χ2 = 7.227, P = 0.065. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using a multinomial logistic regression model, with the GG/CC combined genotypes (wild-type) as the reference category.

Influences of the SNPs CYBA C242T and MPO G-463A on clinical and biochemical markers

As shown in Table 4, the control women with the GA + AA genotypes of the SNP MPO G-463A had statistically higher serum TG levels (P = 0.014) and were inclined to have higher serum TC concentrations (P = 0.056) than those with the GG genotype. There was no statistical difference in clinical, oxidative stress, and metabolic indexes between the two genotype subgroups in patients with GDM. In the whole study subjects, the women carrying the A allele had statistically higher TG levels (P = 0.022) and tended to have lower neonatal birth weight (P = 0.059) than those carrying the GG genotype.

Table 4.

Clinical, metabolic, and oxidative stress parameters according to MPO G-463A genotypes in patients with GDM and women with uncomplicated pregnancies.

Controls GDM All subjects
GG (n = 925) GA+AA (n = 263 + 17) GG (n = 514) GA+AA (n = 185 + 20) GG (n = 1439) GA+AA (n = 448 + 37)
Clinical characteristics
Age (years) 35.56 ± 4.77 35.42 ± 3.69 35.32 ± 4.00 35.23 ± 4.07 35.67 ± 4.42 35.54 ± 3.80
Pre-pregnancy BMI (kg/m2) 21.19 ± 2.77 21.26 ± 2.72 22.22 ± 3.14 22.49 ± 3.01 21.58 ± 2.93 21.80 ± 2.97
Delivery BMI (kg/m2) 26.73 ± 2.64 26.75 ± 2.90 26.84 ± 3.51 26.86 ± 2.98 26.73 ± 2.96 26.84 ± 2.97
Weight gain during pregnancy (kg) 13.99 ± 4.51 14.19 ± 3.54 11.50 ± 4.28 11.33 ± 4.20 12.97 ± 4.48 12.99 ± 4.10
SBP (mmHg) 114.92 ± 10.17 115.31 ± 9.83 115.65 ± 10.11 115.63 ± 13.33 115.43 ± 10.20 115.65 ± 11.16
DBP (mmHg) 72.17 ± 8.10 72.00 ± 7.65 72.72 ± 8.45 73.02 ± 9.13 72.36 ± 8.26 72.64 ± 8.14
Gestational age (weeks) 39.24 ± 1.02 39.12 ± 0.99 39.01 ± 0.89 38.98 ± 1.10 39.11 ± 1.06 39.04 ± 1.13
Parity 1.68 ± 1.54 1.69 ± 0.54 1.67 ± 0.54 1.60 ± 0.52 1.63 ± 0.54 1.62 ± 0.54
Neonatal birth height (cm) 49.86 ± 1.94 49.84 ± 1.81 49.61 ± 1.90 49.60 ± 1.63 49.77 ± 1.93 49.74 ± 1.74
Neonatal birth weight (g) 3386.50 ± 385.82 3331.50 ± 369.80 3339.75 ± 457.83 3332.34 ± 400.55 3369.75 ± 413.85 3331.86 ± 382.70
Metabolic parametersa
Fasting Glu (mmol/L) 4.36 ± 0.43 4.33 ± 0.42 4.63 ± 0.87 4.59 ± 0.69 4.46 ± 0.65 4.44 ± 0.57
Fasting Ins (pmol/L) 72.02 ± 36.59 72.60 ± 31.16 105.25 ± 135.84 95.09 ± 92.14 85.08 ± 90.91 82.40 ± 66.00
HOMA-IR 2.04 ± 1.12 2.03 ± 0.96 3.45 ± 6.05 3.03 ± 3.97 2.59 ± 3.94 2.46 ± 2.75
TG (mmol/L) 3.59 ± 1.31 3.82 ± 1.70c 3.90 ± 1.72 3.99 ± 1.68 3.70 ± 1.48 3.89 ± 1.69d
TC (mmol/L) 6.04 ± 1.07 6.17 ± 1.10 5.96 ± 1.07 5.98 ± 1.09 6.01 ± 1.07 6.10 ± 1.10
HDL-C (mmol/L) 1.98 ± 0.42 2.01 ± 0.39 1.98 ± 0.42 1.98 ± 0.47 1.98 ± 0.42 2.00 ± 0.42
LDL-C (mmol/L) 3.14 ± 0.94 3.21 ± 0.96 2.94 ± 0.87 2.92 ± 0.81 3.07 ± 0.92 3.09 ± 0.91
ApoA1 (g/L) 2.37 ± 0.45 2.39 ± 0.38 2.29 ± 0.36 2.30 ± 0.42 2.34 ± 0.42 2.35 ± 0.40
ApoB (g/L) 1.15 ± 0.26 1.17 ± 0.26 1.16 ± 0.25 1.15 ± 0.26 1.15 ± 0.26 1.16 ± 0.26
ApoB/apoA1 0.50 ± 0.15 0.50 ± 0.14 0.52 ± 0.13 0.51 ± 0.14 0.51 ± 0.14 0.51 ± 0.14
Oxidative stress parametersb
TOS (μmol H2O2 equivalent/L) 20.97 ± 6.85 21.49 ± 7.22 26.20 ± 10.52 26.96 ± 11.17 23.17 ± 8.96 24.05 ± 9.63
TAC (mmol Trolox Equiv./L) 1.10 ± 0.19 1.11 ± 0.20 1.12 ± 0.20 1.13 ± 0.21 1.11 ± 0.20 1.11 ± 0.21
OSI 19.32 ± 7.17 19.84 ± 7.41 23.52 ± 9.90 23.54 ± 10.04 21.09 ± 8.68 21.54 ± 8.89
MDA (nmol/mL) 5.40 ± 1.24 5.30 ± 1.19 5.90 ± 1.43 5.91 ± 1.41 5.60 ± 1.34 5.56 ± 1.32
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Values are presented as mean ± s.d. All comparisons of metabolic and oxidative stress parameters were corrected for differences in age and BMI between the two groups.

aControls (GG = 809, GA + AA = 236 + 14); GDM (GG = 464, GA + AA = 157 + 19); all subjects (GG = 1273, GA + AA = 393 + 33); bcontrols (GG = 633, GA + AA = 175+10); GDM (GG = 382, GA + AA = 126 + 16); all subjects (GG = 1015, GA + AA = 301 + 26); cP < 0.05 compared with the GG genotype subgroup in the control group; dP < 0.05 compared with the GG genotype subgroup in the all subjects.

Apo, apolipoprotein; DBP, diastolic blood pressure; Glu, glucose; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostatic model assessment of insulin resistance; Ins, insulin; LDL-C, low-density lipoprotein cholesterol; MDA, malondialdehyde; OSI, oxidative stress index; SBP, systolic blood pressure; TAC, total antioxidant capacity; TC, total cholesterol; TG, triglyceride; TOS, total oxidant status.

GDM patients carrying the 242T allele of the CYBA gene had significantly higher pre-pregnancy BMI, fasting insulin levels, and HOMA-IR (P < 0.05) and tended to have elevated OSI (P = 0.086) and delivery BMI (P = 0.073) but had significantly lower serum TAC levels (P = 0.014) than those carrying the CC genotype. No statistical differences in clinical, oxidative stress, and metabolic indices were observed between the two genotype subgroups in the control women. In all subjects, the CT + TT genotype subgroup had higher pre-pregnancy BMI (P = 0.043) and OSI (P = 0.027) and tended to have increased fasting insulin levels (P = 0.063) and HOMA-IR (P = 0.064) than the CC genotype subgroup (Table 5).

Table 5.

Clinical, metabolic, and oxidative stress parameters according to CYBA C242T genotypes in patients with GDM and women with uncomplicated pregnancies.

Controls GDM All subjects
CC (n = 1025) CT+TT (n = 175 + 5) CC (n = 615) CT+TT (n = 98 + 6) CC (n = 1640) CT+TT (n = 273 + 11)
Clinical characteristics
Age (years) 35.54 ± 4.69 35.43 ± 3.64 35.25 ± 4.01 35.52 ± 4.12 35.63 ± 4.35 35.67 ± 3.80
Pre-pregnancy BMI (kg/m2) 21.15 ± 2.80 21.51 ± 2.50 22.16 ± 3.13 23.05 ± 2.87a 21.58 ± 2.96 21.96 ± 2.78b
Delivery BMI (kg/m2) 26.71 ± 2.74 26.86 ± 2.46 26.75 ± 3.41 27.42 ± 3.09 26.73 ± 3.00 26.95 ± 2.74
Weight gain during pregnancy (kg) 14.11 ± 4.47 13.66 ± 3.21 11.52 ± 4.14 11.10 ± 4.79 13.02 ± 4.44 12.73 ± 4.04
SBP (mmHg) 115.18 ± 10.02 114.07 ± 10.43 115.36 ± 10.99 117.26 ± 11.53 115.57 ± 10.38 114.98 ± 10.82
DBP (mmHg) 72.12 ± 8.15 72.19 ± 7.13 72.57 ± 8.57 74.13 ± 8.98 72.39 ± 8.28 72.69 ± 7.89
Gestational age (weeks) 39.23 ± 1.03 39.27 ± 0.91 39.01 ± 0.96 38.94 ± 0.87 39.09 ± 1.09 39.09 ± 1.02
Parity 1.68 ± 0.55 1.66 ± 0.49 1.65 ± 0.54 1.67 ± 0.49 1.62 ± 0.55 1.66 ± 0.50
Neonatal birth height (cm) 49.82 ± 1.89 50.04 ± 2.06 49.63 ± 1.82 49.45 ± 1.89 49.75 ± 1.86 49.82 ± 2.01
Neonatal birth weight (g) 3374.17 ± 382.52 3371.92 ± 385.03 3342.48 ± 439.10 3309.03 ± 461.51 3362.27 ± 405.18 3349.03 ± 414.80
Metabolic parametersc
Fasting Glu (mmol/L) 4.36 ± 0.44 4.33 ± 0.37 4.06 ± 0.84 4.72 ± 0.74 4.45 ± 0.64 4.49 ± 0.58
Fasting Ins (pmol/L) 72.44 ± 36.13 70.66 ± 29.39 97.31 ± 105.19 131.63 ± 203.84a 82.50 ± 73.23 95.10 ± 134.17
HOMA-IR 2.05 ± 1.11 1.97 ± 0.88 3.11 ± 4.52 4.61 ± 9.48a 2.48 ± 3.02 3.03 ± 6.14
TG (mmol/L) 3.65 ± 1.44 3.57 ± 1.30 3.94 ± 1.68 3.89 ± 1.89 3.76 ± 1.53 3.69 ± 1.55
TC (mmol/L) 6.07 ± 1.07 6.04 ± 1.12 6.00 ± 1.07 5.82 ± 1.07 6.04 ± 1.07 5.95 ± 1.10
HDL-C (mmol/L) 1.99 ± 0.42 1.98 ± 0.38 1.99 ± 0.43 1.93 ± 0.43 1.99 ± 0.42 1.96 ± 0.40
LDL-C (mmol/L) 3.17 ± 0.93 3.13 ± 0.96 2.96 ± 0.85 2.82 ± 0.89 3.09 ± 0.91 3.02 ± 0.95
ApoA1 (g/L) 2.38 ± 0.44 2.36 ± 0.40 2.29 ± 0.37 2.34 ± 0.40 2.34 ± 0.42 2.35 ± 0.40
ApoB (g/L) 1.15 ± 0.26 1.14 ± 0.27 1.16 ± 0.25 1.12 ± 0.27 1.16 ± 0.27 1.13 ± 0.27
ApoB/apoA1 0.50 ± 0.14 0.50 ± 0.15 0.52 ± 0.13 0.49 ± 0.14 0.51 ± 0.14 0.50 ± 0.15
Oxidative stress parametersd
TOS (μmol H2O2 Equiv./L) 20.90 ± 6.84 22.23 ± 7.44 26.26 ± 10.55 27.38 ± 11.61 23.22 ± 9.03 24.44 ± 9.76
TAC (mmol Trolox Equiv./L) 1.10 ± 0.19 1.12 ± 0.23 1.13 ± 0.20 1.07 ± 0.24a 1.12 ± 0.19 1.10 ± 0.23
OSI 19.26 ± 7.23 20.23 ± 7.12 23.22 ± 9.52 25.49 ± 12.14 20.97 ± 8.52 22.63 ± 9.85b
MDA (nmol/mL) 5.39 ± 1.24 5.30 ± 1.17 5.92 ± 1.38 5.82 ± 1.66 5.60 ± 1.32 5.50 ± 1.41
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Values are presented as mean ± SD. All comparisons of metabolic and oxidative stress parameters were corrected for differences in age and BMI between the two groups.

a P < 0.05, compared with the CC genotype subgroup in GDM group; b P < 0.05, compared with the CC genotype subgroup in the all subjects; ccontrols (CC = 898, CT + TT = 156 + 5); GDM (CC = 544, CT + TT = 90 + 5); all subjects (CC = 1443, CT + TT = 246 + 10); dcontrols (CC = 700, CT + TT = 115 + 3); GDM (CC = 448, CT + TT = 73 + 3); all subjects (CC = 1148, CT + TT = 188 + 6).

Apo, apolipoprotein; DBP, diastolic blood pressure; Glu, glucose; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostatic model assessment of insulin resistance; Ins, insulin; LDL-C, low-density lipoprotein cholesterol; MDA, malondialdehyde; OSI, oxidative stress index; SBP, systolic blood pressure; TAC, total antioxidant capacity; TC, total cholesterol; TG, triglycerides; TOS, total oxidant status.

Discussion

Our study proved for the first time that the SNP MPO G-463A, but not the SNP CYBA C242T, is linked to the risk of GDM in Chinese women. We also showed that the risk of developing GDM is higher in individuals with both CYBA 242CC genotype and MPO −463A allele than in those carrying the wild-type CC/GG genotype. Furthermore, we found that the women with the −463A allele had higher TG levels than those carrying the GG genotype; the women with the 242T allele had higher pre-pregnancy BMI and OSI than those carrying the CC genotype; and the GDM patients carrying the 242T allele also had higher fasting insulin levels and HOMA-IR values but lower TAC levels than those carrying the CC genotype. Our findings suggest that these two variations may be linked to obesity, dyslipidemia, insulin resistance, and oxidative stress.

MPO catalyzes the formation of reactive oxygen intermediates and has been demonstrated to be a local mediator of tissue damage and inflammation and plays an important role in the pathogenesis of diseases (10, 12). The promoter region of MPO gene contains several response elements recognized by ligand-dependent transcription factors, including peroxisome proliferator-activated receptor (PPAR) γ/α, estrogen receptor α (ER-α), activator protein-2α (AP-2α), and SP1 (9, 12, 32, 33). The regulation of MPO expression and serum MPO levels is a complicated network that includes age, gender, and other factors (33, 34). The G-463A genetic variation was identified at the SP1-binding site (14, 16, 33). Generally, the G allele creates an SP1-binding site and has been associated with increased MPO mRNA and protein levels (9, 14), while the A allele disrupts the SP1-binding site and has been related to lower MPO expression (9, 14, 33). MPO expression is strongly regulated by PPARγ agonists. Opposite influences were found in macrophage colony-stimulating factor (MCSF)- versus granulocyte/macrophage colony-stimulating factor (GMCSF)-derived macrophages: the expression was markedly upregulated (26-fold) in MCSF macrophages and downregulated (34-fold) in GMCSF macrophages (32). The G-463→A variant creates an ER-α-binding site, which is located next to the receptor binding site of PPARγ, and ER-α binding to the promoter region of MPO in the -463A allele can hinder PPARγ binding to the promoter (32, 33). Consequently, in the presence of PPARγ ligands and estrogen, the MPO expression was significantly higher in the GMCSF macrophages with the A allele than in those with the G allele because MPO-463G allele expression was markedly downregulated by PPARγ ligands, whereas MPO-463A allele expression was maintained by estrogen (32). It has been reported that the MPO −463A allele is a genetic risk factor for PCOS, and patients carrying the AA genotype have a relatively higher activity of plasma MPO than in those carrying the GG genotype in Chinese women (16). The −463A allele increased Alzheimer’s disease risk in the Finnish cohort, and the −463 AA genotype was associated with selective mortality in man, but not in women, suggesting that the G-463A variant affected the MPO expression differently in males and females (33). Additionally, the transcription factor AP-2α has been found to have a higher binding capability to the -463 A allele than the G allele, thereby promoting MPO expression in the A allele carriers (12). The −463A allele has been shown to increase the risk of colorectal carcinoma and reduce the total survival rate by changing the binding characteristics of AP-2α and upregulating the MPO expression (12).

In this study, we found that the A allele of the MPO G-463A SNP was associated with an increased risk of GDM, and subjects with the A allele had higher TG levels but relatively lower neonatal birth weight than those with the GG genotype, suggesting that the −463A allele might be associated with unfavorable lipid metabolism and fetal development. Patients with GDM have been reported to have lower concentrations of sex hormone-binding globulin (SHBG) compared to healthy pregnant women (35). SHBG, a plasma transport protein of sex hormones, can bind to circulating sex steroids, with high affinity to androgens and low affinity to estrogen, and is an important determinant of bioactive sex hormone (36). Decreased SHBG levels have been found to be associated with hyperinsulinemia, hyperandrogenemia, obesity, oxidative stress, insulin resistance, metabolic syndrome, and type 2 diabetes (36, 37). Antioxidant steroids, such as estrogen or testosterone, could enhance MPO activity and improve bacterial killing activity of polymorphonuclear leukocytes (38, 39). MPO activity in neutrophils was higher in premenopausal women than in postmenopausal women (34). Both gonadal and adrenal androgens have been indicated to be potent direct activators of ER-α (40). The circulating testosterone levels were significantly higher, while SHBG levels were significantly lower in female patients with type 2 diabetes than in the control in a meta-analysis (41). An increase in total testosterone levels during the first trimester is an independent predictor of subsequent GDM development (42). However, further studies are warranted to clarify the role of MPO in GDM pathogenesis and its underlying mechanism.

The C242T variation (His72Tyr) of CYBA (p22phox) has a direct functional role in the dysfunction of NOXs (17, 18, 21, 22). The 242T allele has been indicated to be associated with decreased stability and activity of NOXs (17, 18) and be a genetic risk factor for type 2 diabetes (20) and the oxidative stress-associated complications of prematurity (18) but a protective factor of CHD in Asian population (19), metabolic syndrome in Iranian men (21), and preeclampsia in Chinese women (22). This study did not find statistical differences in the genotypic and allelic frequencies of the SNP CYBA C242T between the GDM and control groups. Nevertheless, pre-pregnancy BMI and OSI were higher in the women with the 242T allele than in those carrying the CC genotype; and fasting insulin concentrations and HOMA-IR were higher, while serum TAC levels were lower in GDM women carrying the T allele than in those carrying the CC genotype, suggesting that this genetic variant may be linked to obesity, insulin resistance, and oxidative stress. In addition, we also showed that the combination of the CYBA 242CC genotype and MPO −463A allele might be related to an increased risk for GDM.

Notably, this study had certain limitations. First, due to low genotype frequencies of the minor allele homozygotes, CYBA 242TT and MPO-463AA, it is difficult to divide them into subgroups. Secondly, we did not measure the plasma MPO and NOX activities. Further determination of these enzyme activities might be helpful to reveal the relationship between genetic variations and the etiopathogenesis of GDM. Thirdly, measurements of circulating SHBG and sex hormone levels may help in determining the potential mechanism underlying the MPO genotype and GDM occurrence.

In summary, we demonstrated that the SNP MPO G-463A, but not the SNP CYBA C242T, is related to an increased risk of GDM in Chinese women. The present study also proved that the MPO −463A allele may be associated with unfavorable lipid metabolism and fetal development and the CYBA 242T allele may be linked to obesity, insulin resistance, and oxidative stress. Our study implies that oxidative stress-related genetic variants may be involved in the pathogenesis of GDM.

Declaration of interest

The authors declare no competing interests.

Funding

This work was funded by the Key Research and Development Project of Sichuan Province (2019YFS0401), the National Key Research and Development Program of China (2016YFC1000400), and the Program for Changjiang Scholars and Innovative Research Team in University, Ministry of Education http://dx.doi.org/10.13039/100009950 (IRT0935).

Author contribution statement

P F conceived and designed the experiments, analyzed the data, and revised the manuscript. C J performed the experiments and wrote the manuscript. X L, M C, and Y Z participated in the acquisition or interpretation of data. M Z and Y W took part in sample collection. K H, C Y, and Q L performed the experiments. H B helped in revising the manuscript. All authors have read and approved the final manuscript.

Acknowledgements

The authors extend their thanks to the participants who donated blood samples and are also thankful to Qian Gao, Guolin He, Fangyuan Luo, Zeyun Li, and Xiaoli Yan for collecting the samples.

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