Abstract
This study aimed to explore the correlations of nuclear factor erythroid 2-related factor-2 (NRF2) gene polymorphisms, prepregnancy body mass index (BMI) and the interaction between them with the risk of preeclampsia (PE). A case–control study was conducted in which pregnant women with PE (n = 198) and normotensive pregnant women (n = 396) were recruited as the case group and control group, respectively, from two tertiary hospitals in Hunan Province. Data collection was achieved through face-to-face interviews utilizing a standardized questionnaire, along with perinatal health care records. Blood samples were also collected, and genotyping of nine single-nucleotide polymorphisms (SNPs) in the NRF2 gene was subsequently performed using the MassArray platform. Both univariate and multivariate logistic regression analyses were employed to assess the associations of NRF2 gene polymorphisms with prepregnancy BMI and their interactions with the risk of PE. Multivariate logistic regression analyses revealed a significant association between prepregnancy BMI and PE susceptibility. Specifically, prepregnancy overweight/obesity (BMI ≥ 24.0 kg/m2) was associated with an elevated risk of PE (adjusted OR = 4.59, 95% CI: 2.82–7.45), whereas underweight status (BMI < 18.5 kg/m2) was correlated with a reduced PE risk (adjusted OR = 0.38, 95% CI: 0.18–0.78). The NRF2 polymorphism rs13005431 exhibited a protective effect against PE under the additive genetic model (adjusted OR = 0.59, 95% CI: 0.37–0.93). Furthermore, logistic regression analyses revealed a significant effect of the multiplicative interaction between prepregnancy overweight/obesity and polymorphisms rs35652124 (adjusted OR = 0.24, 95% CI: 0.06–0.89) and rs2627765 (adjusted OR = 3.62, 95% CI: 1.07–12.23) on susceptibility to PE. These findings collectively underscore the critical and independent roles of prepregnancy BMI, NRF2 polymorphisms, and their interactions in modulating PE susceptibility, suggesting that the combined effects of metabolic profiles and genetic determinants may act synergistically to shape PE risk.
Keywords: preeclampsia, NRF2, gene polymorphism, prepregnancy BMI, gene-environment interaction
1. Introduction
Preeclampsia (PE) is a multisystem disorder that poses a severe threat to maternal and fetal health. It is characterized by new-onset hypertension after 20 weeks of gestation, accompanied by proteinuria and/or maternal end-organ dysfunction and/or utero-placental insufficiency [1,2]. Globally, PE is estimated to contribute to more than 500,000 preterm births and more than 60,000 maternal deaths annually [3]. In addition, studies have shown that women with PE have a 3- to 5-fold increased risk of severe pregnancy complications (e.g., disseminated intravascular coagulation, pulmonary edema) during pregnancy and a 6-fold elevated risk of hypertension within two years postpartum compared with non-PE pregnancies [4,5]. Notably, despite its profound morbidity, no targeted therapies exist for PE in clinical practice beyond pregnancy termination and symptom management [5]. Thus, prevention and early risk stratification are critical for reducing perinatal morbidity and mortality; elucidating the pathogenesis of PE remains pivotal for achieving this goal [6]. Although multiple hypotheses have been proposed, no consensus has been reached about its etiological mechanisms.
Existing evidence, including the marked ethnic/population disparities in PE incidence and its familial aggregation, suggests that genetic polymorphisms play a central role in PE pathogenesis [7,8]. Nevertheless, the current understanding of PE-associated polymorphisms remains limited, as the currently identified susceptibility loci account for only a minor fraction of its heritable risk. The discovery of novel genetic variants through ongoing research may provide critical insights for PE prevention strategies.
The NRF2 gene, located on chromosome 2q31.2 and comprising 5 exons, encodes the NRF2 protein—a master transcriptional regulator of the antioxidant stress response [9]. Given that increased oxidative stress and antioxidant imbalance during pregnancy are established core pathophysiological drivers of PE [10,11,12], NRF2 is hypothesized to be a key mediator of PE development. This finding is further supported by the significant differences in placental NRF2 levels observed between PE patients and normotensive pregnant patients [13,14]. Several of the SNPs examined in this study have well-documented biological and clinical effects in other oxidative-stress-related conditions. Among promoter variants, rs6721961 disrupts an ARE-like these loci are linked to earlier onset of Parkinson’s disease and altered risk of sequence, reduces NRF2 activity, and is associated with acute lung injury [15], peripheral arterial stiffness [16], and most relevantly, early-onset preeclampsia in an Iranian cohort [17]. rs35652124 reduces NRF2 activity, increases oxidative stress burden, and is linked to alcoholic liver cirrhosis [18], diabetic foot ulcers and type 2 diabetes [19], ischemic stroke [20], and Parkinson’s disease [21]. Among intronic variants, rs13005431 modulates NRF2 activity and is associated with tuberculosis susceptibility [22]; rs2364723 is linked to tuberculosis risk and reduced FEV1 [23]; rs1806649 is associated with postmenopausal breast cancer and cardiovascular/COPD mortality [24]; and rs7557529, rs6721961, and haplotypes spanning amyotrophic lateral sclerosis [21]. Collectively, these findings indicate that common NRF2 variants modulate disease risk, predominantly in conditions dominated by oxidative stress and chronic inflammation, both of which are also central to preeclampsia, motivating a systematic evaluation of these variants in PE. However, no study has comprehensively characterized NRF2 variants in preeclampsia.
In addition to genetic factors, environmental exposures and gene–environment interactions are also implicated in PE etiology. Previous studies have demonstrated that an elevated prepregnancy body mass index (BMI) is associated with increased PE risk, with obesity potentially promoting PE through mechanisms such as insulin resistance, placental hypoxia, and increased oxidative stress [25,26]. Given that both prepregnancy overweight/obesity and NRF2 dysregulation may converge on oxidative stress pathways within the placenta, we hypothesized that interactions between prepregnancy BMI and NRF2 polymorphisms could synergistically modulate PE risk. However, this gene–environment interplay remains unexplored.
To address these knowledge gaps, this study aimed to investigate the independent and joint effects of NRF2 polymorphisms and prepregnancy BMI on PE risk. These findings may advance early risk prediction models and inform personalized interventions targeting oxidative stress pathways in high-risk populations.
2. Results
2.1. Baseline Characteristics of the Participants
A total of 198 PE patients and 396 normotensive controls were enrolled in this study. The baseline characteristics of the PE patients and controls are shown in Table 1. Significant between-group differences were observed in maternal age, with a greater proportion of participants aged ≥ 35 years in the PE group (χ2 = 18.417, p < 0.001). Education level also differed significantly, as fewer PE patients had received college level or above education (p < 0.001). Prepregnancy BMI distributions also showed notable disparities, with higher rates of overweight (27.8% compared with 8.0%) and obesity (12.0% compared with 0.8%) in the PE group (p < 0.001). PE patients presented elevated frequencies of hyperlipidemia (p = 0.002) and a history of pregnancy complications (p = 0.045). Behavioral factors, including passive smoking during the periconceptional period (p = 0.003), alcohol consumption during the periconceptional period (p = 0.047), and tea intake during the periconceptional period (p = 0.004), also showed significant group differences.
Table 1.
Baseline characteristics of PE cases and controls.
| Baseline Characteristic | Control Group (n = 396) |
Case Group (n = 198) |
χ 2 | p |
|---|---|---|---|---|
| Maternal age | 18.417 | <0.001 | ||
| <35 | 346 (87.4%) | 145 (73.2%) | ||
| ≥35 | 50 (12.6%) | 53 (26.8%) | ||
| Education level | 18.824 | <0.001 | ||
| Less than junior high school | 8 (2.0%) | 16 (8.1%) | ||
| High school or Technical secondary school | 44 (11.1%) | 35 (17.7%) | ||
| College or above | 344 (86.9%) | 147 (74.2%) | ||
| Annual household income (RMB) | 1.204 | 0.558 | ||
| <60,000 | 75 (18.9%) | 44 (22.3%) | ||
| 60,000–100,000 | 135 (34.1%) | 69 (34.8%) | ||
| >100,000 | 186 (47.0%) | 85 (42.9%) | ||
| Pre-pregnancy BMI a (kg/m2) | 96.400 | <0.001 | ||
| <18.5 | 76 (19.2%) | 10 (5.1%) | ||
| 18.5–23.9 | 285 (72.0%) | 109 (55.1%) | ||
| 24.0–27.9 | 32 (8.0%) | 55 (27.8%) | ||
| ≥28.0 | 3 (0.8%) | 24 (12.0%) | ||
| Hyperlipemia | 9.353 | 0.002 | ||
| No | 391 (98.7%) | 187 (94.4%) | ||
| Yes | 5 (1.3%) | 11 (5.6%) | ||
| History of adverse pregnancies | 2.655 | 0.103 | ||
| No | 234 (59.1%) | 104 (52.5%) | ||
| Yes | 162 (40.9%) | 94 (47.5%) | ||
| History of pregnancy complications | ||||
| No | 358 (90.4%) | 168 (84.8%) | 4.022 | 0.045 |
| Yes | 38 (9.6%) | 30 (15.2%) | ||
| Colds during the periconceptional period b | 10.284 | 0.001 | ||
| No | 280 (70.7%) | 164 (82.8%) | ||
| Yes | 116 (29.3%) | 34 (17.2%) | ||
| Active smoking during the periconceptional period b | 0.737 | 0.389 | ||
| No | 390 (98.5%) | 193 (97.5%) | ||
| Yes | 6 (1.5%) | 5 (2.5%) | ||
| Passive smoking during the periconceptional period b | 9.134 | 0.003 | ||
| No | 221 (55.8%) | 136 (68.7%) | ||
| Yes | 175 (44.2%) | 62 (31.3%) | ||
| Alcohol consumption during the periconceptional period b | 3.953 | 0.047 | ||
| No | 371 (93.7%) | 193 (97.5%) | ||
| Yes | 25 (6.3%) | 5 (2.5%) | ||
| Tea intake during the periconceptional period b | 8.309 | 0.004 | ||
| No | 357 (90.2%) | 162 (81.8%) | ||
| Yes | 39 (9.8%) | 36 (18.2%) | ||
| Medication history during the periconceptional period b | 3.469 | 0.063 | ||
| No | 239 (60.4%) | 135 (68.2%) | ||
| Yes | 157 (39.6%) | 63 (31.8%) | ||
| Folic acid supplementation during the periconceptional period b | 0.514 | 0.476 | ||
| No | 7 (1.8%) | 2 (1.0%) | ||
| Yes | 389 (98.2%) | 196 (99.0%) |
a: Body Mass Index (classification according to Chinese standard for obesity BMI). b: from 3 months before pregnancy to early pregnancy.
2.2. Prepregnancy BMI and the Risk of PE
The associations between prepregnancy BMI and PE risk are detailed in Table 2. Multivariable logistic regression analyses were performed to assess the association between prepregnancy BMI and PE risk, with adjustment for potential confounders including maternal age, education level, history of pregnancy complications, hyperlipidemia, colds during the periconceptional period, passive smoking, alcohol consumption, and tea intake during the periconceptional period. Compared with women with a normal prepregnancy BMI, underweight women exhibited a reduced risk of PE development, with an adjusted OR (aOR) of 0.38 (95% CI: 0.18–0.78). Conversely, overweight/obese women had a significantly elevated PE risk (aOR = 4.59, 95% CI: 2.82–7.45). After adjustment for potential confounders, both underweight (aOR = 0.38, 95% CI: 0.18–0.78) and overweight/obese status (aOR = 5.90, 95% CI: 3.74–9.30) remained significantly associated with altered PE risk.
Table 2.
The association of pre-pregnancy BMI with the risk of PE.
| Pre-Pregnancy BMI | Case Group (n = 198) |
Control Group (n = 396) |
OR (95% CI) |
aOR (95% CI) a |
|---|---|---|---|---|
| 18.5–23.9 | 109 (55.1%) | 285 (72.0%) | 1.00 | 1.00 |
| <18.5 | 10 (5.0%) | 76 (19.2%) | 0.34 (0.17, 0.69) | 0.38 (0.18, 0.78) |
| ≥24.0 | 79 (39.9%) | 35 (8.8%) | 5.90 (3.74, 9.30) | 4.59 (2.82, 7.45) |
a: Adjusted for maternal age, education level, history of pregnancy complications, hyperlipemia, colds during the periconceptional period, passive smoking during the periconceptional period, alcohol exposure during the periconceptional period, and tea intake during the periconceptional period.
2.3. NRF2 Gene Polymorphisms and the Risk of PE
The associations between NRF2 gene polymorphisms and the risk of PE are summarized in Table 3. The genotype distributions of rs13005431 differed between the case and control groups (χ2 = 6.64, p = 0.036). Univariate analysis based on genetic models revealed that polymorphisms in rs13005431 were significantly associated with the risk of PE, and this association remained statistically significant after adjusting for potential confounders. Under the primary additive genetic model, the rs13005431 polymorphism demonstrated a significant association with a reduced risk of PE. Each additional C allele was associated with an approximately 41% lower risk (adjusted OR = 0.59, 95% CI: 0.37–0.93).
Table 3.
The association of NRF2 gene polymorphisms with the risk of PE.
| SNP | Case Group (n = 198) |
Control Group (n = 396) |
χ 2 | p | Multivariate Analysis | |
|---|---|---|---|---|---|---|
| aOR (95% CI) a | p | |||||
| rs35652124 | ||||||
| Genotype | 0.31 | 0.856 | ||||
| CC | 67 (33.8) | 137 (34.6) | 1.00 | - | ||
| CT | 84 (42.5) | 173 (43.7) | 1.18 (0.73, 1.92) | 0.576 | ||
| TT | 47 (23.7) | 86 (21.7) | 1.26 (0.72, 2.23) | 0.737 | ||
| Additive model | - | - | 0.93 (0.72, 1.19) | 0.683 | ||
| rs13005431 | ||||||
| Genotype | 6.64 | 0.036 | ||||
| TT | 165 (83.3) | 300 (75.8) | 1.00 | - | ||
| TC | 32 (16.2) | 85 (21.4) | 0.81 (0.47, 1.38) | 0.178 | ||
| CC | 1 (0.5) | 11 (2.8) | 0.11 (0.09, 0.66) | 0.240 | ||
| Additive model | - | - | 0.59 (0.37, 0.93) | 0.019 | ||
| rs2364723 | ||||||
| Genotype | 0.73 | 0.695 | ||||
| CC | 51 (25.8) | 115 (29.1) | 1.00 | - | ||
| GC | 103 (52) | 195 (49.2) | 1.18 (0.72, 1.93) | 0.333 | ||
| GG | 44 (22.2) | 86 (21.7) | 1.18 (0.65, 2.15) | 0.876 | ||
| Additive model | - | - | 0.90 (0.69, 1.17) | 0.578 | ||
| rs7557529 | ||||||
| Genotype | 0.12 | 0.941 | ||||
| TT | 74 (37.4) | 144 (36.4) | 1.00 | - | ||
| TC | 94 (47.5) | 188 (47.5) | 1.04 (0.66, 1.65) | 0.705 | ||
| CC | 30 (15.1) | 64 (16.1) | 0.98 (0.52, 1.87) | 0.903 | ||
| Additive model | - | - | 1.01 (0.77, 1.32) | 0.754 | ||
| rs1806649 | ||||||
| Genotype | 3.27 | 0.195 | ||||
| CC | 172 (86.9) | 333 (84.1) | 1.00 | - | ||
| CT | 26 (13.1) | 57 (14.4) | 0.81 (0.44, 1.48) | - | ||
| TT | 0 (0.0) | 6 (1.5) | 0.00 (0.00, NA) b | - | ||
| Additive model | - | - | 0.74 (0.45, 1.21) | 0.210 | ||
| rs6721961 | ||||||
| Genotype | 1.29 | 0.524 | ||||
| GG | 99 (50.0) | 211 (53.3) | 1.00 | - | ||
| GT | 79 (39.9) | 155 (39.1) | 1.10 (0.71, 1.71) | 0.352 | ||
| TT | 20 (10.1) | 30 (7.6) | 1.41 (0.66, 3.06) | 0.708 | ||
| Additive model | - | - | 0.84 (0.63, 1.11) | 0.300 | ||
| rs2627765 | ||||||
| Genotype | 2.60 | 0.272 | ||||
| GG | 141 (71.2) | 282 (71.2) | 1.00 | - | ||
| GT | 56 (28.3) | 105 (26.5) | 0.88 (0.55, 1.41) | 0.193 | ||
| TT | 1 (0.5) | 9 (2.3) | 0.21 (0.02, 1.92) | 0.177 | ||
| Additive model | - | - | 0.96 (0.66, 1.40) | 0.682 | ||
| rs3827760 | ||||||
| Genotype | 1.18 | 0.554 | ||||
| AA | 178 (89.9) | 346 (87.4) | 1.00 | - | ||
| AG | 20 (10.1) | 49 (12.3) | 0.63 (0.32, 1.25) | 0.046 | ||
| GG | 0 (0.0) | 1 (0.3) | 0.00 (0.00, NA) b | - | ||
| Additive model | - | - | 1.68 (0.86, 3.27) | 0.333 | ||
| rs3775319 | ||||||
| Genotype | 2.87 | 0.239 | ||||
| AA | 90 (45.5) | 205 (51.8) | 1.00 | - | ||
| AC | 85 (42.9) | 158 (39.9) | 1.05 (0.68, 1.64) | 0.452 | ||
| CC | 23 (11.6) | 33 (8.3) | 1.52 (0.73, 3.16) | 0.380 | ||
| Additive model | - | - | 1.23 (0.93, 1.63) | 0.093 | ||
a: Adjusted for maternal age, education level, history of pregnancy complications, hyperlipemia, colds during the periconceptional period, passive smoking during the periconceptional period, alcohol exposure during the periconceptional period, tea intake during the periconceptional period. b: OR and 95% CI could not be estimated due to zero cell count.
Haplotype analysis of the NRF2 gene identified two distinct blocks (Block 1 and Block 2), as illustrated in Figure 1. BLOCK1 (SNP-marker combination rs35652124 and rs13005431) comprised three haplotypes (T-T, C-T, and C-C), whereas BLOCK2 (SNP-marker combination rs7557529, rs1806649, and rs6721961) contained four haplotypes: C-C-T, T-C-G, T-T-T, and T-C-T. As detailed in Table 4, within BLOCK1, the frequency distributions of the C-T (38.4% compared with. 31.7%, p = 0.017, OR = 1.20) and C-C (7.8% compared with 12.2%, p = 0.035, OR = 0.68) haplotypes differed significantly between PE patients and controls, suggesting potential associations with PE risk. In contrast, no haplotype in BLOCK2 exhibited significant differences in frequency between groups (all p > 0.05).
Figure 1.
Haplotype analysis of NRF2 gene.
Table 4.
Haplotypes of NRF2 gene associated with risk of PE.
| Block | Haplotype | Frequency | Case Group | Control Group | p | OR (95% CI) |
|---|---|---|---|---|---|---|
| Block1 a | T-T | 55.5% | 53.8% | 56.1% | 0.560 | 0.97 (0.76, 1.24) |
| C-T | 33.6% | 38.4% | 31.7% | 0.017 | 1.20 (1.15, 1.55) | |
| C-C | 10.9% | 7.8% | 12.2% | 0.035 | 0.68 (0.45, 0.95) | |
| Block2 b | C-C-T | 59.8% | 59.7% | 59.8% | 0.980 | 1.04 (0.81, 1.33) |
| T-C-G | 27.6% | 30.6% | 26.2% | 0.105 | 1.15 (0.88, 1.50) | |
| T-T-T | 7.8% | 6.5% | 8.5% | 0.209 | 0.75 (0.46, 1.21) | |
| T-C-T | 4.8% | 3.2% | 5.5% | 0.085 | 0.63 (0.34, 1.20) |
a SNP-marker combination rs35652124-rs13005431. b SNP-marker combination rs7557529-rs1806649-rs6721961.
The color gradient follows the standard Haploview scheme: squares with D′ ≥ 0.7 (values ≥ 70, multiplied by 100 and shown within each square) are colored in red/pink, with darker red indicating stronger LD (higher D′).
Haplotype blocks (Block 1 and Block 2) are defined by solid black triangles.
2.4. Functional Annotation of Key SNPs via eQTL Analysis
To functionally annotate the identified PE-associated variants and investigate their potential regulatory mechanisms, an in silico expression quantitative trait loci (eQTL) analysis was performed. The in silico eQTL analysis revealed that the PE-protective allele (C) of rs13005431 was significantly associated with higher NRF2 expression (β = 0.084, p = 1.68 × 10−11; Supplementary Table S4). In contrast, the *rs35652124-C* allele did not show a significant eQTL association (β = −0.022, p = 0.086).
2.5. Effect of NRF2 Gene Polymorphisms and Prepregnancy Overweight/Obesity Interaction on the Risk of PE
This study comprehensively evaluated the effects of multiplicative interactions between NRF2 gene polymorphisms and prepregnancy overweight/obesity on PE risk using logistic regression models. The model incorporated NRF2 SNPs, prepregnancy overweight/obesity, and their interaction terms under a dominant genetic model while adjusting for covariates to control for confounding factors. Analyses revealed significant multiplicative interactions between prepregnancy overweight/obesity and two SNPs: rs35652124 (aOR = 0.24, 95% CI: 0.06–0.89) and rs2627765 (aOR = 3.62, 95% CI: 1.07–12.23) (Table 5). These findings indicate that the combined effects of genetic susceptibility and metabolic status synergistically modulate PE risk.
Table 5.
Interactions of NRF2 gene polymorphisms and pre-pregnancy overweight/obesity on PE based on multivariate logistic regression.
|
a: Adjusted for maternal age, education level, history of pregnancy complications, hyperlipemia, colds during the periconceptional period, passive smoking during the periconceptional period, alcohol exposure during the periconceptional period, tea intake during the periconceptional period.
To further delineate the nature of this interaction, we conducted analyses stratified by prepregnancy BMI status. Stratified analyses by prepregnancy BMI category revealed distinct genetic associations across weight groups (Supplementary Table S5). The protective effect of rs13005431 was only significant in women with normal prepregnancy BMI (aOR = 0.52, p = 0.017). In underweight women (BMI < 18.5), two SNPs—rs35652124 (aOR = 0.25, p = 0.016) and rs2364723 (aOR = 0.25, p = 0.015)—showed strong protective associations not observed in other BMI strata. These findings indicate that prepregnancy BMI modifies the relationship between NRF2 polymorphisms and PE susceptibility in a stratum-specific manner.
3. Discussion
Despite extensive genetic investigations into numerous genes and their SNPs to elucidate their roles in PE pathogenesis, our knowledge of SNPs-PE association remains constrained by limitations in genomic coverage, methodological quality, and statistical power of existing studies. This study is the first to explore the roles of NRF2 gene polymorphisms, prepregnancy BMI, and their interactions in PE susceptibility. Key findings revealed that specific NRF2 variants—particularly rs13005431 and the C-C haplotype (rs35652124-rs13005431)—were significantly associated with reduced PE risk, whereas prepregnancy overweight/obesity markedly elevated this risk. Furthermore, multiplicative interactions between pre-pregnancy overweight/obesity and NRF2 rs35652124 and rs2627765 further modified PE susceptibility, highlighting the interplay between genetic and environmental factors. These results emphasize the multifactorial nature of PE, integrate genetic predispositions and metabolic determinants, and propose NRF2 variants and BMI as potential biomarkers for early risk stratification and personalized preventive strategies in high-risk pregnancies.
As a master transcriptional regulator of cellular antioxidant responses, NRF2 is hypothesized to play a key role in the pathogenesis of preeclampsia (PE) by coordinating comprehensive cytoprotective programs—through the induction of genes involved in antioxidant defense, glutathione synthesis, phase II detoxification, and anti-inflammatory processes—to maintain redox homeostasis in the placental microenvironment [5,27,28,29]. However, existing research on NRF2 and PE has largely focused on its downstream targets or tissue-specific dysregulation. Our study provides novel evidence by linking NRF2 polymorphisms to PE susceptibility. Specifically, we found that the NRF2 rs13005431 polymorphism was significantly associated with a reduced risk of PE under an additive model, and in silico eQTL analysis indicated that its protective C allele correlates with higher NRF2 expression. This enhanced expression likely strengthens placental reactive oxygen species (ROS) scavenging capacity, mitigating oxidative damage and preventing the endothelial dysfunction characteristic of PE, whereas the risk allele (rs13005431 T) may attenuate this protective response, increasing placental vulnerability to oxidative injury [13,30,31]. Furthermore, haplotype analysis in our study revealed that the C(rs35652124)-C(rs13005431) haplotype was associated with a significantly lower PE risk, whereas the C(rs35652124)-T(rs13005431) haplotype was linked to an increased risk. These findings extend the genetic architecture of PE beyond previously established loci (e.g., near FLT1 and PGF), though validation in more diverse populations beyond the Central Chinese cohort studied here remains essential to confirm these associations and elucidate their underlying mechanistic pathways.
Extending these genetic insights, we also examined the interplay between genetic polymorphisms and metabolic status. Consistent with existing evidence, this study confirmed that pre-pregnancy overweight/obesity significantly increases PE risk [32,33,34,35,36,37], which may mediate by its induction of chronic low-grade inflammation and heightened oxidative stress driven by excessive lipid peroxidation and pro-inflammatory adipokines [38,39,40]. Furthermore, we identified significant multiplicative interactions between pre-pregnancy overweight/obesity and specific NRF2 SNPs (rs35652124 and rs2627765) in modulating PE risk. Specifically, the interaction with rs35652124 exerted a protective effect (adjusted OR = 0.24), whereas the interaction with rs2627765 increased PE risk (adjusted OR = 3.62), indicating that metabolic status can synergistically or antagonistically modulate the effects of NRF2 genetic variation. Stratified analysis further clarified this context-dependency: the protective effect of rs13005431 was only significant in women with normal pre-pregnancy BMI (adjusted OR = 0.52, p = 0.017), while two other SNPs (rs35652124 and rs2364723) showed strong protective associations exclusively in underweight women. We speculate that obesity-induced oxidative stress may overwhelm the NRF2 pathway (leading to “NRF2 exhaustion”) or alter its functional state, thereby masking or amplifying the effects of specific genetic variants. Conversely, underweight status might create a distinct biochemical milieu that unveils the protective effects of certain NRF2 SNPs not observed in normal or overweight/obese groups. These findings underscore the importance of integrating genetic predisposition and metabolic status in PE risk assessment. Therefore, we propose that combining pre-pregnancy weight management with NRF2 variant screening to optimize PE risk prediction. Future studies should validate these interactions across diverse populations and elucidate their molecular mechanisms to inform targeted preventive strategies.
Several limitations should be acknowledged. First, the case–control design, while useful for exploring associations, is inherently limited by its observational nature. To mitigate potential confounding factors, we conducted a literature review to identify relevant factors, designed a comprehensive questionnaire, and applied multivariable models to adjust for these variables. Second, the data were primarily collected from two tertiary hospitals, excluding lower-level health care facilities, which may limit the sample’s diversity and representativeness, potentially introducing selection bias. Consequently, future studies should validate these findings in broader and more diverse populations. Furthermore, the exclusive recruitment of Han Chinese participants, while methodologically advantageous for minimizing genetic confounding, inherently limits the generalizability of our findings. The allele frequencies of NRF2 SNPs and their linkage disequilibrium patterns can vary substantially across different ethnic groups and ancestral backgrounds. Therefore, replication studies in other Asian populations, as well as in cohorts of European, African, and Hispanic descent, are essential to determine whether the observed associations and gene-environment interactions are specific to the Han Chinese population or represent universal pathophysiological mechanisms in preeclampsia. Additionally, while this study identified significant genetic associations, the specific biological mechanisms underlying these variants remain unexplored at the cellular and animal model levels. Future research is needed to elucidate these mechanisms and confirm the functional roles of the identified genetic variants.
Despite these limitations, this study provides novel evidence for the synergistic effects of prepregnancy BMI and NRF2 polymorphisms on PE risk. Clinically, integrating prepregnancy BMI screening and genetic risk assessment (e.g., NRF2 rs13005431) into prenatal care could enhance risk stratification and support personalized prevention strategies, particularly through intensified oxidative stress monitoring and lifestyle modifications for overweight/obese individuals. Public health initiatives should prioritize prepregnancy weight management education and community-based interventions to mitigate metabolic-related pregnancy risks. Furthermore, future research should validate these findings in multicenter cohorts to strengthen their generalizability.
4. Materials and Methods
4.1. Study Design and Population
This hospital-based case–control study enrolled participants from Hunan Provincial Maternal and Child Health Hospital and the Third Xiangya Hospital of Central South University in Changsha, Hunan Province, China. Between October 2021 and October 2022, eligible patients with PE were recruited as cases from the obstetrics inpatient population, whereas normotensive pregnant women who received pregnancy care within the same hospitals were recruited as controls. Written informed consent was obtained from both groups, ensuring voluntary participation and full comprehension of the research’s purpose and procedures. Clinical data, questionnaire information, and biological samples were collected, and diagnoses were reviewed through the hospital system.
The study protocol adhered to the ethical principles of the 1964 Declaration of Helsinki and received prior approval from the Ethics Committee of Xiangya School of Public Health, Central South University (No. XYGW-2019-020).
4.2. Inclusion and Exclusion Criteria
In this study, PE was diagnosed by obstetricians following the diagnostic criteria outlined in the Guidelines for the Management of Hypertensive Disorders in Pregnancy (2020) [41]. According to these guidelines, PE is defined as new-onset hypertension (systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg on at least two occasions ≥ 4 h apart) occurring after 20 weeks of gestation in a previously normotensive woman, accompanied by one or more of the following: proteinuria (≥0.3 g/24 h or protein/creatinine ratio ≥ 0.3 mg/mg), maternal organ dysfunction (renal, hepatic, neurological, or hematological), and uteroplacental insufficiency. To minimize confounding from genetic and cultural heterogeneity, enrollment was restricted to individuals of Han Chinese ethnicity—the predominant ethnic group in Hunan Province, comprising approximately 90% of the local population. This strategy is essential for reducing the risk of spurious associations arising from underlying differences in allele frequencies among distinct ancestral populations. Accordingly, individuals of other ethnic backgrounds, including the Tujia, Miao, Dong, and Yao minority groups native to Hunan, as well as any non-Asian ethnicities, were excluded from the study. Additional inclusion criteria for both cases and controls included: (1) voluntary participation, (2) completion of a standardized questionnaire after providing written informed consent, and (3) provision of adequate biological samples (e.g., venous blood). Exclusion criteria were rigorously applied: (1) multifetal pregnancies, (2) conception via assisted reproductive technologies, (3) chronic medical conditions, including pre-existing hypertension, cardiovascular diseases, or disorders predisposing to secondary hypertension (e.g., hyperadrenalism).
4.3. Sample Size Estimation
Sample size estimation was performed for an unmatched case–control study design with a case-to-control ratio of 1:2. Based on the minor allele frequency (MAF) of the studied NRF2 single-nucleotide polymorphisms (SNPs) retrieved from the dbSNP database, the lowest MAF (0.119 for rs1806649) was used as the expected exposure frequency in the control group (*p*0). Assuming a relative risk (RR) of 1.80 derived from previous genetic association studies of PE in Han Chinese populations and setting α = 0.05 (two-tailed) and β = 0.20 (power = 80%) [42], the minimum required sample size was calculated to be 190 cases and 380 controls. The detailed calculation procedure is provided in Supplementary File S1. The present study ultimately enrolled 198 cases and 396 controls, exceeding the estimated minimum sample size.
4.4. Sample and Information Collection
The exposures of interest in this study were pre-pregnancy BMI and NRF2 gene polymorphisms. BMI was calculated as weight in kilograms divided by height in meters squared (kg/m2), with both parameters obtained from the perinatal health care handbook provided by local maternal and child health hospitals. BMI categories were defined according to the Chinese Guidelines for Prevention and Control of Overweight and Obesity in Adults [43]: underweight (BMI < 18.5 kg/m2), normal weight (18.5 ≤ BMI < 24 kg/m2), overweight (24 ≤ BMI < 28 kg/m2), and obese (BMI ≥ 28 kg/m2).
Background information was systematically collected through standardized face-to-face interviews conducted by trained investigators. Data domains included maternal age, education level, annual household income hyperlipemia, history of adverse pregnancies, history of pregnancy complications, as well as active smoking, passive smoking, alcohol exposure, tea exposure, medication history, folate use and colds during the perinatal period. Here, the periconceptional period refers to the time period encompassing three months before pregnancy and the early pregnancy stage. Self-reported disease and medication-related data were cross-validated through a systematic review of perinatal health care records to ensure data accuracy.
For NRF2 genotyping, peripheral venous blood (3–5 mL) was collected by nurses using EDTA-coated (ethylenediaminetetraacetic acid) anticoagulant tubes within 24 h of hospital admission. Following collection, blood samples were immediately stored at 4 °C and transported to the laboratory under temperature-controlled conditions within 12 h. Samples were centrifuged at 3500 rpm for 15 min to separate plasma and cellular components into two equal aliquots. These aliquots were labeled with anonymized study IDs and stored in −80 °C ultra-low temperature freezers until further analysis.
4.5. SNP Selection and Genotyping
Prior to genotyping, single nucleotide polymorphisms (SNPs) in the NRF2 gene were identified through a systematic search of relevant domestic and international databases, guided by prior studies and cross-referenced with the NCBI database. SNPs with minor allele frequencies (MAFs) below 5% were excluded from subsequent analysis. The MAF distribution of NRF2 SNPs is detailed in Table S1. Twelve candidate genetic loci were included in this study.
Genomic DNA was extracted using the QIAamp DNA Blood Mini Kit (Qiagen, Valencia, CA, USA) in accordance with the manufacturer’s protocol. Genotyping was performed using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry on the Mass ARRAY system(Agena Bioscience, San Diego, CA, USA). The workflow included: primer design for targeting candidate SNPs (primer sequences are presented in Supplementary Table S3); primer synthesis and dilution; DNA extraction; multiplex PCR amplification; shrimp alkaline phosphatase treatment to dephosphorylate residual nucleotides; single-base extension reactions; resin purification to remove salts; spectrometric data acquisition and analysis. To minimize bias, laboratory personnel conducting genotyping were blinded to case–control status.
4.6. Statistical Analysis
A double-entry database was constructed using EpiData 3.1 (EpiData Association, Odense, Denmark), with independent data verification by two researchers to ensure accuracy. Hardy–Weinberg equilibrium (HWE) testing for the 10 NRF2 SNPs was performed via Haploview v4.2 (Broad Institute, Cambridge, MA, USA), with compliance defined as p > 0.05. The genotype distribution of rs13001694 in controls deviated from HWE (p < 0.05) (Table S1) and was consequently excluded from downstream analyses.
Categorical variables are presented as counts (percentages). Differences in baseline characteristics were assessed using the chi-square or Fisher’s exact test. Associations of pre-pregnancy BMI and NRF2 polymorphisms with PE risk were evaluated using logistic regression, yielding odds ratios (ORs) and 95% confidence intervals (CIs). We constructed a multivariate model adjusted for confounders that differed significantly between cases and controls, including maternal age, education level, history of pregnancy complications, hyperlipidemia, and colds, passive smoking, alcohol exposure and tea intake during the periconceptional period. In the genetic analyses, genotypes for each SNP were denoted as homozygous for the major allele (e.g., CC), heterozygous (e.g., CT), and homozygous for the minor allele (e.g., TT). Association testing was performed under the additive model (effect per copy of the minor allele).
Linkage disequilibrium (LD) and haplotype blocks were analyzed using Haploview v4.2 (results in Table S2). Strong LD (r2 > 0.8) was observed between rs2364723 and rs2886161, and between rs7557529 and rs4893819; one SNP from each pair (rs2886161 and rs4893819) was excluded from subsequent interaction analyses. Haplotype distributions were compared between cases and controls.
To explore potential functional mechanisms of the identified PE-associated NRF2 variants, we performed an in silico eQTL analysis. Summary-level data for the association of SNPs rs13005431 and rs35652124 with NRF2 (ENSG00000116044) expression were retrieved from the IEU Open GWAS project (https://gwas.mrcieu.ac.uk/ (accessed on 18 December 2025)).
To examine gene-environment interactions, we used multivariate logistic regression to test for multiplicative interaction between NRF2 polymorphisms and pre-pregnancy overweight/obesity on PE risk. Given that underweight status may act as a protective factor with limited relevance to interaction effects, only overweight/obesity was included in this analysis. The model included PE status as the dependent variable, with the SNP, overweight/obesity status, relevant confounders, and their interaction term as independent variables. Statistical significance was set at p < 0.05. Based on the modifying effect of prepregnancy body weight suggested by these interaction analyses, we further performed stratified analyses based on prepregnancy BMI category (underweight: <18.5 kg/m2; normal weight: 18.5–23.9 kg/m2; overweight/obesity: ≥24.0 kg/m2). Within each BMI stratum, separate multivariable logistic regression models were applied to assess the associations between NRF2 SNPs and PE risk, adjusting for the same set of covariates as in the primary analysis.
Statistical analysis was performed using R software, version 3.5.0 (R Foundation for Statistical Computing), and SPSS 26.0 software (SPSS Inc., Chicago, IL, USA), with a two-tailed p-value < 0.05 considered statistically significant.
5. Conclusions
In summary, this study revealed significant associations of prepregnancy BMI and NRF2 gene polymorphisms with PE risk. The statistically significant multiplicative interactions observed between prepregnancy overweight/obesity and specific NRF2 SNPs collectively underscore the critical role of gene–environment interplay in PE pathogenesis. These findings highlight the imperative to integrate genetic predispositions and metabolic determinants for refined risk stratification and support the development of personalized prevention strategies, such as combining preconception weight management with genetic screening. This work provides a foundation for elucidating the underlying molecular mechanisms and developing targeted interventions to mitigate PE risk in high-risk populations.
Acknowledgments
The Hunan Provincial Maternal and Child Health Care Hospital and The Third Xiangya Hospital of Central South University fully supported this research. The staff in the department helped us collect blood samples from cases and provided support for our epidemiological investigation.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms27135705/s1.
Author Contributions
L.C., T.W. and Z.L. contributed to the conceptualization, methodology, and supervision. Z.L. and S.G. contributed to the methodology, visualization, and writing—original draft. X.Z., J.M. and F.X. contributed to data curation and formal analysis. All authors made significant contributions to the article and approved the final version of the manuscript. All authors have read and agreed to the published version of the manuscript.
Institutional Review Board Statement
The study protocol adhered to the ethical principles of the 1964 Declaration of Helsinki and received prior approval from the Ethics Committee of Xiangya School of Public Health, Central South University (approval number: No. XYGW-2019-020, 4 March 2019).
Informed Consent Statement
Written informed consent has been obtained from all the participants.
Data Availability Statement
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Additional supporting data, including minor allele frequencies, primer sequences, and genotypic distribution are provided in the Supplementary Materials (Tables S1–S3).
Conflicts of Interest
The authors have no competing interests to declare that are relevant to the content of this article.
Funding Statement
This research was funded by the National Natural Science Foundation of China (grant number 82404362 to Tingting Wang, and grant number 82173608 to Lizhang Chen) and the Excellent Youth Project of Hunan Province Office of Education (grant number 23B0001 to Tingting Wang).
Footnotes
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References
- 1.Kinshella M.L.W., Omar S., Scherbinsky K., Vidler M., Magee L.A., von Dadelszen P., Moore S.E., Elango R., The PRECISE Conceptual Framework Working Group Maternal nutritional risk factors for pre-eclampsia incidence: Findings from a narrative scoping review. Reprod. Health. 2022;19:188. doi: 10.1186/s12978-022-01485-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Magee L.A., Brown M.A., Hall D.R., Gupte S., Hennessy A., Karumanchi S.A., Kenny L.C., McCarthy F., Myers J., Poon L.C., et al. The 2021 International Society for the Study of Hypertension in Pregnancy classification, diagnosis & management recommendations for international practice. Pregnancy Hypertens. 2022;27:148–169. doi: 10.1016/j.preghy.2021.09.008. [DOI] [PubMed] [Google Scholar]
- 3.Ma’Ayeh M., Costantine M.M. Prevention of preeclampsia. Semin. Fetal Neonatal Med. 2020;25:101123. doi: 10.1016/j.siny.2020.101123. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Giorgione V., Ridder A., Kalafat E., Khalil A., Thilaganathan B. Incidence of postpartum hypertension within 2 years of a pregnancy complicated by pre-eclampsia: A systematic review and meta-analysis. BJOG. 2021;128:495–503. doi: 10.1111/1471-0528.16545. [DOI] [PubMed] [Google Scholar]
- 5.Tossetta G., Fantone S., Piani F., Crescimanno C., Ciavattini A., Giannubilo S.R., Marzioni D. Modulation of NRF2/KEAP1 Signaling in Preeclampsia. Cells. 2023;12:1545. doi: 10.3390/cells12111545. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Thangaratinam S., Langenveld J., Mol B.W., Khan K.S. Prediction and primary prevention of pre-eclampsia. Best Pract. Res. Clin. Obstet. Gynaecol. 2011;25:419–433. doi: 10.1016/j.bpobgyn.2011.02.008. [DOI] [PubMed] [Google Scholar]
- 7.Cincotta R.B., Brennecke S.P. Family history of pre-eclampsia as a predictor for pre-eclampsia in primigravidas. Int. J. Gynaecol. Obstet. 1998;60:23–27. doi: 10.1016/s0020-7292(97)00241-5. [DOI] [PubMed] [Google Scholar]
- 8.Thakoordeen S., Moodley J., Naicker T. Candidate Gene, Genome-Wide Association and Bioinformatic Studies in Pre-eclampsia: A Review. Curr. Hypertens. Rep. 2018;20:91. doi: 10.1007/s11906-018-0891-x. [DOI] [PubMed] [Google Scholar]
- 9.Li J., Ichikawa T., Janicki J.S., Cui T. Targeting the Nrf2 pathway against cardiovascular disease. Expert Opin. Ther. Targets. 2009;13:785–794. doi: 10.1517/14728220903025762. [DOI] [PubMed] [Google Scholar]
- 10.Kalyanaraman B. Teaching the basics of redox biology to medical and graduate students: Oxidants, antioxidants and disease mechanisms. Redox. Biol. 2013;1:244–257. doi: 10.1016/j.redox.2013.01.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Lappas M., Mitton A., Permezel M. In response to oxidative stress, the expression of inflammatory cytokines and antioxidant enzymes are impaired in placenta, but not adipose tissue, of women with gestational diabetes. J. Endocrinol. 2010;204:75–84. doi: 10.1677/joe-09-0321e. [DOI] [PubMed] [Google Scholar]
- 12.Agarwal A., Gupta S., Sharma R.K. Role of oxidative stress in female reproduction. Reprod. Biol. Endocrinol. 2005;3:28. doi: 10.1186/1477-7827-3-28. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Kweider N., Huppertz B., Wruck C.J., Beckmann R., Rath W., Pufe T., Kadyrov M. A role for Nrf2 in redox signalling of the invasive extravillous trophoblast in severe early onset IUGR associated with preeclampsia. PLoS ONE. 2012;7:e47055. doi: 10.1371/journal.pone.0047055. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Kweider N., Fragoulis A., Rosen C., Pecks U., Rath W., Pufe T., Wruck C.J. Interplay between vascular endothelial growth factor (VEGF) and nuclear factor erythroid 2-related factor-2 (Nrf2): Implications for preeclampsia. J. Biol. Chem. 2011;286:42863–42872. doi: 10.1074/jbc.m111.286880. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Marzec J.M., Christie J.D., Reddy S.P., Jedlicka A.E., Vuong H., Lanken P.N., Aplenc R., Yamamoto T., Yamamoto M., Cho H.-Y., et al. Functional polymorphisms in the transcription factor NRF2 in humans increase the risk of acute lung injury. FASEB J. 2007;21:2237–2246. doi: 10.1096/fj.06-7759com. [DOI] [PubMed] [Google Scholar]
- 16.Shimizu S., Mimura J., Hasegawa T., Shimizu E., Imoto S., Tsushima M., Kasai S., Yamazaki H., Ushida Y., Suganuma H., et al. Association of single nucleotide polymorphisms in the NRF2 promoter with vascular stiffness with aging. PLoS ONE. 2020;15:e236834. doi: 10.1371/journal.pone.0236834. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Khadir F., Rahimi Z., Ghanbarpour A., Vaisi-Raygani A. Nrf2 rs6721961 and Oxidative Stress in Preeclampsia: Association with the Risk of Preeclampsia and Early-Onset Preeclampsia. Int. J. Mol. Cell. Med. 2022;11:127–136. doi: 10.52547/rbmb.11.3.493. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Dos Santos K.N., Florentino R.M., França A., Filho A.C.M.L., dos Santos M.L., Missiaggia D., Fonseca M.d.C., Costa I.B., Vidigal P.V.T., Nathanson M.H., et al. Polymorphism in the Promoter Region of NFE2L2 Gene Is a Genetic Marker of Susceptibility to Cirrhosis Associated with Alcohol Abuse. Int. J. Mol. Sci. 2019;20:3589. doi: 10.3390/ijms20143589. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Teena R., Dhamodharan U., Ali D., Rajesh K., Ramkumar K.M. Genetic Polymorphism of the Nrf2 Promoter Region (rs35652124) Is Associated with the Risk of Diabetic Foot Ulcers. Oxidative Med. Cell. Longev. 2020;2020:9825028. doi: 10.1155/2020/9825028. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Wang P., Lu J., Wang M., Wang G., Chen H. NRF2 polymorphism and susceptibility to ischemic stroke in a Chinese population. PeerJ. 2025;13:e19742. doi: 10.7717/peerj.19742. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Cho H., Marzec J., Kleeberger S.R. Functional polymorphisms in Nrf2: Implications for human disease. Free Radic. Biol. Med. 2015;88:362–372. doi: 10.1016/j.freeradbiomed.2015.06.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Ji G., Zhang M., Liu Q., Wu S., Wang Y., Chen G., Sandford A.J., He J.-Q. Functional Polymorphism in the NFE2L2 Gene Associated with Tuberculosis Susceptibility. Front. Immunol. 2021;12:660384. doi: 10.3389/fimmu.2021.660384. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Sasaki H., Suzuki A., Shitara M., Hikosaka Y., Okuda K., Moriyama S., Yano M., Fujii Y. Polymorphisms of NRF2 gene correlated with decreased FEV1 in lung cancers of smokers. Biomed. Rep. 2013;1:484–488. doi: 10.3892/br.2013.83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Figarska S.M., Vonk J.M., Boezen H.M. NFE2L2 polymorphisms, mortality, and metabolism in the general population. Physiol. Genom. 2014;46:411–417. doi: 10.1152/physiolgenomics.00178.2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Lopez-Jaramillo P., Barajas J., Rueda-Quijano S.M., Lopez-Lopez C., Felix C. Obesity and Preeclampsia: Common Pathophysiological Mechanisms. Front. Physiol. 2018;9:1838. doi: 10.3389/fphys.2018.01838. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Vats H., Saxena R., Sachdeva M.P., Walia G.K., Gupta V. Impact of maternal pre-pregnancy body mass index on maternal, fetal and neonatal adverse outcomes in the worldwide populations: A systematic review and meta-analysis. Obes. Res. Clin. Pr. 2021;15:536–545. doi: 10.1016/j.orcp.2021.10.005. [DOI] [PubMed] [Google Scholar]
- 27.Romero R., Chaiworapongsa T. Preeclampsia: A link between trophoblast dysregulation and an antiangiogenic state. J. Clin. Investig. 2013;123:2775–2777. doi: 10.1172/jci70431. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Ngo V., Duennwald M.L. Nrf2 and Oxidative Stress: A General Overview of Mechanisms and Implications in Human Disease. Antioxidants. 2022;11:2345. doi: 10.3390/antiox11122345. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Llurba E., Gratacós E., Martín-Gallán P., Cabero L., Dominguez C. A comprehensive study of oxidative stress and antioxidant status in preeclampsia and normal pregnancy. Free Radic. Biol. Med. 2004;37:557–570. doi: 10.1016/j.freeradbiomed.2004.04.035. [DOI] [PubMed] [Google Scholar]
- 30.George E.M., Granger J.P. Recent insights into the pathophysiology of preeclampsia. Expert Rev. Obstet. Gynecol. 2010;5:557–566. doi: 10.1586/eog.10.45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Granitzer S., Widhalm R., Ellinger I., Zeisler H., Forsthuber M., Foessleitner P., Geschrey E., Saleh L., Knöfler M., Desoye G., et al. LAT1-NRF2 axis controls sFlt-1/PlGF imbalance and oxidative stress in preeclampsia. Nat. Commun. 2025;16:9112. doi: 10.1038/s41467-025-64160-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Mbah A.K., Kornosky J.L., Kristensen S., August E.M., Alio A.P., Marty P.J., Belogolovkin V., Bruder K., Salihu H.M. Super-obesity and risk for early and late pre-eclampsia. BJOG. 2010;117:997–1004. doi: 10.1111/j.1471-0528.2010.02593.x. [DOI] [PubMed] [Google Scholar]
- 33.Lisonkova S., Joseph K.S. Incidence of preeclampsia: Risk factors and outcomes associated with early- versus late-onset disease. Am. J. Obstet. Gynecol. 2013;209:541–544. doi: 10.1016/j.ajog.2013.08.019. [DOI] [PubMed] [Google Scholar]
- 34.Rasmussen S., Irgens L.M., Espinoza J. Maternal obesity and excess of fetal growth in pre-eclampsia. BJOG. 2014;121:1351–1357. doi: 10.1111/1471-0528.12677. [DOI] [PubMed] [Google Scholar]
- 35.Sohlberg S., Stephansson O., Cnattingius S., Wikström A.-K. Maternal body mass index, height, and risks of preeclampsia. Am. J. Hypertens. 2012;25:120–125. doi: 10.1038/ajh.2011.175. [DOI] [PubMed] [Google Scholar]
- 36.Mrema D., Lie R.T., Østbye T., Mahande M.J., Daltveit A.K. The association between pre pregnancy body mass index and risk of preeclampsia: A registry based study from Tanzania. BMC Pregnancy Childbirth. 2018;18:56. doi: 10.1186/s12884-018-1687-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Shao Y., Qiu J., Huang H., Mao B., Dai W., He X., Cui H., Lin X., Lv L., Wang D., et al. Pre-pregnancy BMI, gestational weight gain and risk of preeclampsia: A birth cohort study in Lanzhou, China. BMC Pregnancy Childbirth. 2017;17:400. doi: 10.1186/s12884-017-1567-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Yang L., Zhang G., Ren X., Hao H., Zhang X., Xu M., Zhang N., Zhang R., Yang H. Astragalus ameliorates oxidative stress in rat models of preeclampsia through the Nrf2/Keap1/NQO1 pathway. Placenta. 2025;168:232–239. doi: 10.1016/j.placenta.2025.07.004. [DOI] [PubMed] [Google Scholar]
- 39.Geng J., Zhang C. Liensinine attenuates inflammatory response and oxidative stress by activation of Nrf2/HO-1 signaling in L-NAME-induced gestational hypertension. Naunyn-Schmiedebergs Arch. Pharmacol. 2025;398:14089–14098. doi: 10.1007/s00210-025-04105-y. [DOI] [PubMed] [Google Scholar]
- 40.Zakeri S., Rahimi Z., Rezvani N., Vaisi-Raygani A., Alibakhshi R., Zakeri S., Yari K. The influence of Nrf2 gene promoter methylation on gene expression and oxidative stress parameters in preeclampsia. BMC Med. Genom. 2024;17:64. doi: 10.1186/s12920-023-01791-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Hypertensive Disorders in Pregnancy Subgroup; Chinese Society of Obstetrics and Gynecology; Chinese Medical Association. Diagnosis and treatment of hypertension and pre-eclampsia in pregnancy: A clinical practice guideline in China (2020) Zhonghua Fu Chan Ke Za Zhi. 2020;55:227–238. doi: 10.3760/cma.j.cn112141-20200114-00039. [DOI] [PubMed] [Google Scholar]
- 42.Yanan F., Rui L., Xiaoying L., Shuang Z., Feng Z., Yingnan W., Tianshuang J., Xuan Y., Xiaolei Y., Litao S. Association between ACVR2A gene polymorphisms and risk of hypertensive disorders of pregnancy in the northern Chinese population. Placenta. 2020;90:1–8. doi: 10.1016/j.placenta.2019.11.004. [DOI] [PubMed] [Google Scholar]
- 43.Chen C., Lu F.C., Department of Disease Control Ministry of Health, PR China The guidelines for prevention and control of overweight and obesity in Chinese adults. Biomed. Environ. Sci. 2004;17:1–36. [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Additional supporting data, including minor allele frequencies, primer sequences, and genotypic distribution are provided in the Supplementary Materials (Tables S1–S3).