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. 2026 Jul 17;96(1):e70288. doi: 10.1111/aji.70288

Ferroptosis‐Mediated Placental Dysfunction in the Pathophysiology of Gestational Diabetes Mellitus

S Monisha 1, K L Milan 1, M Anuradha 2, K M Ramkumar 1,✉
PMCID: PMC13377263  PMID: 42464732

ABSTRACT

Problem

Gestational diabetes mellitus (GDM) is a common metabolic complication of pregnancy associated with adverse maternal and fetal outcomes. Nevertheless, the molecular mechanism of placental dysfunction in GDM are still not clear, especially the role of ferroptosis and its interplay with oxidative stress, inflammation, and angiogenesis.

Method of Study

Placental tissues from GDM pregnancies were examined to assess oxidative stress, antioxidant defense, ferroptosis regulation, inflammatory signaling, and angiogenic pathways. Expression levels of key molecular markers were evaluated, and correlation analyses were performed to identify mechanistic interrelationships.

Results

GDM placenta demonstrated elevated oxidative stress markers, including P22PHOX and TXNIP, accompanied by reduced antioxidant markers, such as HO‐1, NQO1, SOD2, and CAT, indicating impaired cellular defense. Ferroptosis regulation was disrupted, as anti‐ferroptotic markers GPX4, SLC7A11, and NRF2 were significantly downregulated, while pro‐ferroptotic markers TFR1 and ACSL4 were increased, suggesting enhanced iron accumulation and lipid peroxidation. This was accompanied by heightened inflammation, evidenced by increased IL‐6, IL‐1β, TNF‐α, and NF‐κB activation, alongside reduced IL‐10 expression. Furthermore, angiogenesis was impaired, reflected by decreased VEGFA, HIF‐1α, and SDF‐1α levels, highlighting poor vascular development in the placenta. Additionally, Correlation analyses demonstrated strong associations between ferroptosis markers and oxidative stress, inflammatory, and angiogenic pathways, suggesting the possible presence of an interconnected regulatory network.

Conclusions

These findings identify ferroptosis as a central regulator of GDM‐associated placental dysfunction, through a possible interconnected network of oxidative stress, inflammation, and impaired angiogenesis. Targeting ferroptosis may offer a possible therapeutic option to restore placental function and improve maternal‐fetal outcomes in GDM.

Keywords: ferroptosis, GDM, impaired angiogenesis, inflammation, oxidative stress, placental dysfunction

1. Introduction

Gestational diabetes mellitus (GDM) is a type of glucose intolerance initially identified during pregnancy and most commonly between 24 and 28 weeks of gestation, and affects approximately 1 in 6 pregnancies worldwide [1]. The development of GDM is influenced by many risk factors, including advanced maternal age, maternal obesity, family history of diabetes, and metabolic predisposition [2]. GDM is associated with both immediate and long‐term complications for the mother and the fetus, such as gestational hypertension, fetal macrosomia, intrauterine growth restriction, and lifetime predisposition to type 2 diabetes [3]. Iron metabolism during pregnancy is tightly regulated to support the expansion of maternal blood volume, placental development, and fetal growth [4]. However, disruption of this tightly controlled mechanism can predispose the placenta to iron overload, particularly under metabolic stress such as GDM. The growing clinical evidence indicates that GDM is strongly associated with dysregulated iron homeostasis, highlighting iron metabolism as a key contributor to placental dysfunction in GDM [5].

The placenta is a critical organ that transports oxygen and nutrients, produces hormones, and regulates immunological function during pregnancy. Placental iron overload promotes the generation of reactive oxygen species (ROS) through the Fenton reaction, leading to excessive oxidative stress and impairment of placental function [6]. In line with this, several studies have reported markedly elevated oxidative stress markers in placental tissues from GDM pregnancies, represented by increased ROS, along with reduced antioxidant enzyme activity [7]. These oxidative changes are closely associated with lipid peroxide accumulation, rendering placental cells more susceptible to ferroptosis, an iron‐dependent form of regulated cell death. Placental trophoblasts are particularly vulnerable to iron‐induced oxidative stress under hyperglycemic conditions. The resulting burden contributes to a significant reduction in glutathione peroxidase 4 (GPX4) expression, a key antioxidant enzyme responsible for detoxifying lipid peroxides and a central regulator of ferroptosis [8]. Accordingly, both clinical and experimental studies have demonstrated evidence of ferroptosis in GDM placentas, including elevated malondialdehyde (MDA), increased iron deposition, and reduced glutathione (GSH) and GPX4 levels [9].

In addition to oxidative stress, GDM is linked to an increased inflammatory condition. The coexistence of oxidative and inflammatory microenvironments within maternal circulation and placental tissues increases the risk of ferroptosis in trophoblasts, thereby contributing to placental dysfunction [10]. Moreover, key antioxidant signaling pathways, including the Nuclear Factor Erythroid 2‐Related Factor 2/Heme Oxygenase‐1 (NRF2/HO‐1) axis and the AMP‐activated protein kinase/acetyl‐CoA carboxylase (AMPK/ACC) pathway, have been demonstrated to be dysregulated, which can contribute to oxidative damage and ferroptotic susceptibility of GDM placentas, which can be mitigated by therapeutic interventions such as metformin and ferroptosis inhibitors [9, 11].

Beyond injury to the trophoblast, emerging evidence shows that ferroptosis impairs placental angiogenesis, a critical determinant of maternal‐fetal exchange. Placental angiogenesis is tightly regulated by the redox balance, growth factor signaling, and coordinated interactions between trophoblast and endothelial cells. Studies have demonstrated a reduction in Vascular Endothelial Growth Factor A (VEGFA) and its receptor, Vascular Endothelial Growth Factor Receptor 2 (VEGFR2), and an increase in anti‐angiogenic soluble fms‐like tyrosine kinase‐1 (sFlt‐1) in GDM [12, 13]. Moreover, endothelial progenitor cells derived from GDM pregnancies have a lower migration, growth, and tube‐forming capacity, showing the characteristic features of impaired angiogenesis [14].

In line with this mechanistic connection, recent evidence indicates that increased p53 activity inhibits the Solute Carrier Family 7 Member 11 (SLC7A11)/GPX4 pathway, thereby enhancing ferroptosis in placental trophoblasts. This process is characterized by elevating the levels of MDA and iron accumulation, decreasing VEGFA and Placental Growth Factor (PLGF) expression, and increasing Vascular Endothelial Growth Factor Receptor 1 (VEGFR1), which, in turn, impairs placental angiogenesis under GDM conditions [15].

Collectively, these findings suggest ferroptosis as a key pathological mechanism leading to impaired angiogenesis in GDM through the induction of oxidative imbalance and facilitation of inflammation, resulting in the worsening of the function of the trophoblast. Although comprehensive evidence exists, the crosstalk between ferroptosis, oxidative stress, inflammation, antioxidant defenses, and angiogenic regulation in the GDM placenta is not well understood. Therefore, the present study aims to evaluate ferroptotic markers, oxidative stress markers, inflammatory mediators, antioxidant enzymes, and angiogenic markers in placental tissues from GDM and normal pregnancies to understand the mechanistic role of ferroptosis in placental angiogenic failure associated with diabetic pregnancy.

2. Materials and Methodology

2.1. Enrollment of Study Participants and Placental Sample Collection

A total of 60 pregnant women delivering in SRM Medical College Hospital and Research Centre, Chennai, were recruited in this prospective longitudinal study. The study participants were divided into normal glucose‐tolerant pregnant women (NGDM) (n = 30) and women diagnosed with GDM (n = 30). GDM was diagnosed based on fasting plasma glucose, postprandial glucose levels, and oral glucose tolerance test (OGTT) values, according to standard clinical diagnostic protocols followed at the study center. The OGTT values reported represent post‐load plasma glucose levels. Women with pregnancy‐related complications, including maternal hypertension, pre‐gestational diabetes, fetal anomalies, multifetal pregnancies, and other chronic medical conditions, were excluded from the study. Placental tissue samples were collected within 20 min of delivery under sterile conditions, and about 50 mg of maternal placental tissue was excised in the area 5–10 cm distant from the umbilical cord. Following collection, tissues were rinsed with ice‐cold sterile PBS and immediately preserved at –80°C until further analysis. The clinical and biochemical indicators of all study participants were recorded at the time of delivery and documented using the standard clinical protocols of SRM Medical College Hospital and Research Centre. All procedures were carried out after receiving approval from the Institutional Ethical Committee (SRMIEC‐01‐25‐8989).

2.2. Total RNA Extraction From Placental Tissues

The mRNA was isolated from placental tissues by homogenization using TRIzol reagent. An equal amount of chloroform was added to the homogenate, which was then subjected to centrifugation at 12 000 rpm for 20 min at 4°C, resulting in the formation of three distinct layers. The top aqueous layer was carefully separated into a fresh tube and mixed with an equal amount of isopropanol and incubated overnight at –20°C to precipitate the RNA. Subsequently, the samples were centrifuged at 12 000 rpm for 20 min, resulting in the RNA pellet. The pellet was washed with 80% ethanol by centrifugation at 12 000 rpm, after which the supernatant was discarded. Finally, the pellet was then air‐dried for 10–15 min to remove the residual ethanol, resuspended in 20–30 µL of nuclease‐free water and stored at –80°C until further analysis.

2.3. cDNA Synthesis and Quantitative Real‐Time PCR (qPCR)

The purity and concentration of isolated RNA were evaluated using a Nanoquant spectrophotometer (Tecan Switzerland) by measuring absorbance ratios at 260/280 nm. In accordance with the manufacturer's instructions, 1 µg of extracted RNA was reverse‐transcribed into cDNA using the iScript cDNA Synthesis Kit. The synthesized cDNA was used as a template for qPCR, and the qPCR primers were designed with the online software Primer3Plus, as shown in Table  1 . The qPCR was carried out with SYBR Green Master Mix on a Bio‐Rad CFX‐Connect Real‐Time PCR System. Glyceraldehyde 3‐phosphate dehydrogenase (GAPDH) is used as an internal reference gene to normalize the expression of target genes.

TABLE 1.

List of primers and their sequences used in the study.

S. No Target gene Forward sequence Reverse sequence
1. GPx4 CTCATCATTTGGAGCCC GAGGGTTGGGAGAGGAA
2. SLC7A11 CATCCACATTCCAATCAT CACCATCTGGCATTGTGA
3. TFR1 GAGGAGCCAGGAGAGGACTT ACGCCAGACTTTGCTGAGTT
4. ACSL4 AGTTTGGGAAGAAGGACAGC CACTCTGCGATTCACTTCAAGATAG
5. FPN AGATGGATGGGTCTCCTA CGAACCAAACCACATTTT
6. FTH AGAGGGAACATGCTGAGA TGGGGGTCATTTTTGTCA
7. Nrf2 TGTAGATGACAATGAGGTTTC ACTGAGCCTGATTAGTAGCAA
8. HO‐1 GGGAATTCTCTTGGCTGGCT AACTGAGGATGCTGAAGGGC
9. NQO‐1 AGGATGGAAGAAACGCCTGG TCAGTTGGGATGGACTTGCC
10. SOD‐2 GGCATCATCAATTTCGAG CCGTAGTAGTTAAAGCTC
11. CAT ATCCGTGTAACCCGCTCATC ACCTTCATTTTCCCCTGGGG
12. P22PHOX TTGTGTGCCTGCTGGAGTAC CAGTAGGTAGATGCCGCTCG
13. TXN1P AGCCAGCCAACTCAAGAGAC CCGCCCATCAGGAATGAACA
14. IL‐6 G TCCAGTTG CCTTCT CCCTG AGTGCCTCTTTGCTGCTTTC
15. IL‐1β GAAATGATGGCTTATTACAGTGG TGGTGGTCGGAGATTCGT
16. IL‐10 GCCAATCTCTCACTCACCTT CTTGCTCTTGTTTTCACAGG
17. TNF‐α TCTGGGCAGGTCTACTTTGG GGTTGAGGGTGTCTGAAGGA
18. VEGFA GCACCCATGGCAGAAGG CTCGATTGGATGGCAGTAGC
19. HIF‐1α GGCAACCTTGGATTGGATGG TCTCCGTCCCTCAACCTCTC
20. SDF‐1α CGCACTTTCACTCTCCGTCA AGCACGACCACGACCTTG
21. GAPDH AAGAAGGTGGTGAAGCAGGC GTCAAAGGTGGAGGAGTGGG
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2.4. Western Blotting

Placental tissues were homogenized in 200 µL of RIPA buffer (Sigma) and supplemented with a protease inhibitor cocktail (Abcam) to extract total protein. The homogenates were centrifuged at 12 000 rpm for 15–20 min at 4°C, and the resultant supernatant were collected, and the concentration was measured using Bradford protein assay.

Approximately 30 µg of each sample of proteins were resolved by SDS‐PAGE electrophoresis and subsequently transferred onto a nitrocellulose membrane using a wet transfer apparatus (Bio‐Rad). The membranes were blocked with 3% Bovine Serum Albumin (BSA) and incubated at 4°C with primary antibodies GPX4 (1:1000, ab125066, Abcam), SLC7A11 (1:1000, A2413, Abclonal), Transferrin Receptor 1 (TfR1) (1:1000, ab214039, Abcam), Acyl‐CoA Synthetase Long‐Chain Family Member 4 (ACSL4) (1:1000, A20414, Abclonal), NRF2 (1:1000, A0674, Abclonal), HO‐1 (1:1000, #A19062), NAD(P)H: quinone oxidoreductase 1 (NQO1) (1:500, A1518, Abclonal), Superoxide Dismutase 2 (SOD2) (1:200, sc‐30080, Santa Cruz), Nuclear Factor kappa‐light‐chain‐enhancer of activated B cells (NF‐κB‐p65 RelA) (1:500, #A19653, Abclonal), VEGF (1:500, #sc7269, Santa Cruz), and Hypoxia‐Inducible Factor 1 Alpha Subunit (HIF‐1α) (1:1000, #ab228649, Abcam).

Following overnight incubation with primary antibodies, the membranes were washed with 1X TBST and subsequently incubated with a horseradish peroxidase (HRP)‐conjugated secondary antibody for 1 h at room temperature. Protein bands were analyzed using an enhanced chemiluminescence reagent (Bio‐Rad) and imaged with a Fusion SL imaging system (Vilber Lourmat). After imaging, membranes were subjected to gentle stripping with mild stripping buffer (#ab282569, Abcam) and reprobed overnight with β‐actin (dilution 1:500; #sc‐47778, Santa Cruz) as a loading control, followed by incubation with respective HRP‐ conjugated secondary antibodies and imaging as previously described. The resulting image was subsequently quantitatively measured through densitometric analysis utilizing ImageJ software (version 1.53).

2.5. Statistical Analysis

A G* power (version 3.1) analysis was conducted to determine the required sample size for the study. All values are represented as mean ± SD. Statistical analysis was performed using Student's t‐test, followed by Bonferroni correction to account for multiple comparisons. Pearson's correlation analyses were performed in SPSS software (Version 20.0; IBM SPSS, Chicago, IL). A p value of <0.05 after correction was considered statistically significant. Graphs were constructed using GraphPad Prism (v. 8.0) software. Densitometric analyses of Western blot findings were performed using ImageJ software (version 1.53).

3. Results

3.1. Clinical and Biochemical Parameters of Study Participants

The study population comprised 60 pregnant women, including 30 women with GDM and 30 NGDM. The mean maternal age did not vary considerably between the two groups. Biochemical parameters showed significant changes in the GDM group, with significantly increased fasting glucose, OGTT, and HbA1c compared to NGDM. No significant differences were observed in hemoglobin levels or diastolic blood pressure between the groups. Additional clinical characteristics, such as maternal age and Body Mass Index (BMI), are summarized in Table 2.

TABLE 2.

Clinical and biochemical parameters of study subjects.

Clinical variables NGDM (n = 30) GDM (n = 30)
Age 27.71 ± 4.93 28.66 ± 2.69ns
BMI 29.42 ± 6.04 34.23 ± 3.22**
Fasting glucose (mg/dL) 81.8 ± 7.98 92.57 ± 11.88*
Postprandial glucose (mg/dL) 109.33 ± 16.69 152.71 ± 28.77**
Hemoglobin (g/dL) 10.97 ± 0.97 10.61 ± 0.53ns
OGTT 103.72 ± 14.74 135 ± 23.47**
HbA1C (%) 4.91 ± 0.33 5.45 ± 0.67*
Systolic blood pressure (mmHg) 123.07 ± 8.54 122.14 ± 10.57ns
Diastolic blood pressure (mmHg) 66.61 ± 8.21 70.28 ± 9.86ns
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3.2. Expression of Ferroptosis Markers in the GDM Placenta

The mRNA expression analysis revealed a significant downregulation of the anti‐ferroptotic regulators GPX4 and SLC7A11 (Figures 1a,b) in placental tissues from women with GDM, indicating impaired antioxidant defense mechanisms. However, the pro‐ferroptotic markers TFR1 and ACSL4 (Figures 1c,d) were significantly upregulated, indicating increased iron uptake and enhanced susceptibility to lipid peroxidation. Additionally, Ferroportin (FPN) and Ferritin Heavy Chain (FTH) expressions were significantly reduced (Figures 1e,f), suggesting dysregulated iron homeostasis. In agreement with the mRNA expression, protein levels showed downregulation of GPX4 and SLC7A11 (Figures 1g,h) in the GDM placenta, while TFR1 and ACSL4 (Figure 1i,j) were significantly elevated compared with NGDM.

FIGURE 1.

FIGURE 1

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Relative mRNA expression of (a) GPX4, (b) SLC7A11, (c) TFR1, (d) ACSL4, (e) FPN, and (f) FTH in placental tissues from NGDM and GDM subjects. Protein expression with representative immunoblots for (g) GPX4, (h) SLC7A11, (i) TFR1, and (j) ACSL4 is shown for the same groups. Data are presented as mean ± SD (n = 30). Statistical significance compared with NGDM is indicated as *p < 0.05, **p < 0.01, ***p < 0.001.

3.3. Expression of NRF2 and Its Downstream Targets in the GDM Placenta

The mRNA expression of NRF2 (Figure 2a) was significantly reduced in placental tissues from the GDM group, showing impaired activation of the key regulator of antioxidant response. In accordance with this, downstream antioxidant genes controlled by NRF2, including HO‐1, NQO1, SOD2, and Catalase (CAT) (Figures 2b‐e), exhibited markedly reduced mRNA expression in the GDM group compared with NGDM. Protein studies complemented these transcriptional changes, as NRF2 (Figure 2f) protein expression was lower in the GDM placenta with parallel decreases in HO‐1, NQO1, and SOD2 expression, resulting in impaired antioxidant defense (Figures 2g‐i). These findings indicate an impaired antioxidant defense system in the GDM placenta.

FIGURE 2.

FIGURE 2

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Relative mRNA expression of (a) NRF2, and its downstream targets (b) HO‐1, (c) NQO1, (d) SOD2, and (e) CAT in placental tissues from NGDM and GDM subjects. Protein expression with representative immunoblots for (f) NRF2, (g) HO‐1, (h) NQO1, and (i) SOD2 is shown for the same groups. Data are presented as mean ± SD (n = 30). Statistical significance compared with NGDM is indicated as *p < 0.05, **p < 0.01, ***p < 0.001.

3.4. Expression of Oxidative Stress and Inflammatory Markers in the GDM Placenta

The mRNA expression of P22PHOX (Figure 3a) was significantly upregulated in GDM placental tissues, indicating increased ROS generation, while thioredoxin‐interacting protein (TXNIP) (Figure 3b) was significantly higher in the GDM placenta, reflecting heightened redox imbalance. Furthermore, the mRNA levels of significant pro‐inflammatory cytokines, including interleukin‐6 (IL‐6), interleukin‐1β (IL‐1β), and tumor necrosis factor‐alpha (TNF‐α) (Figures 3c‐e), were significantly elevated, whereas the anti‐inflammatory marker, Interleukin‐10 (IL‐10) (Figure 3f) was downregulated in the GDM placenta, indicating an enhanced inflammatory environment. Protein expression analysis of NF‐κB in GDM placental tissues revealed a significant increase compared to cNGDM (Figure 3g).

FIGURE 3.

FIGURE 3

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Relative mRNA expression of oxidative stress markers (a) P22PHOX, (b) TXNIP, pro and anti‐inflammatory markers (c) IL‐6, (d) IL‐1β, (e) TNF‐α, and (f) IL‐10 in placental tissues from NGDM and GDM subjects. Representative protein expression is shown with an immunoblot for (g) NF‐κB. Data are presented as mean ± SD (n = 30). Statistical significance compared with NGDM is indicated as *p < 0.05, **p < 0.01, ***p < 0.001.

3.5. Expression of Angiogenic Markers in the GDM Placenta

The mRNA expression of angiogenic regulators, such as VEGFA, HIF‐1α, and stromal cell‐derived factor 1 alpha (SDF‐1α), was significantly reduced in the GDM, indicating impaired angiogenesis (Figure 4a‐c). In line with these results, protein expression analysis indicates that there was a significant reduction in VEGFA and HIF‐1α levels of GDM placental tissues compared to NGDM (Figure 4d,e). The concomitant reduction in mRNA and protein expression of these angiogenic mediators indicates reduced endothelial growth, impaired vascular remodeling, and poor placental vascularization in GDM.

FIGURE 4.

FIGURE 4

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Relative mRNA expression of angiogenic markers (a) VEGFA, (b) HIF‐1α, and (c) SDF‐1α, in placental tissues from NGDM and GDM subjects. Representative protein expression is shown with immunoblots for (d) VEGFA and (e) HIF‐1α for the same groups. Data are presented as mean ± SD (n = 30). Statistical significance compared with NGDM is indicated as *p < 0.05, **p < 0.01, ***p < 0.001.

3.6. Correlation Analysis of Ferroptosis Markers With Oxidative Stress, Inflammation, and Angiogenesis

Correlation analysis revealed significant associations between ferroptosis markers and key molecular pathways. GPX4 showed strong positive correlations with antioxidant markers including NRF2 (r = 0.672, p < 0.001), HO‐1 (r = 0.679, p < 0.001), NQO1 (r = 0.812, p < 0.001), SOD2 (r = 0.783, p < 0.001), and CAT (r = 0.782, p < 0.001). Similarly, GPX4 positively correlated with angiogenic markers VEGFA (r = 0.729, p < 0.001), HIF‐1α (r = 0.751, p < 0.001), and SDF‐1α (r = 0.771, p < 0.001). In contrast, GPX4 showed strong negative correlations with oxidative stress markers TXNIP (r = –0.809, p < 0.001) and P22PHOX (r = –0.706, p < 0.001), as well as pro‐inflammatory cytokines IL‐6 (r = –0.748, p < 0.001), TNF‐α (r = –0.802, p < 0.001), and IL‐1β (r = –0.607, p < 0.001). Similar trends were observed for SLC7A11. The pro‐ferroptotic marker ACSL4 showed positive correlations with inflammatory markers and oxidative stress markers such as TNF‐α (r = 0.381, p = 0.038) and TXNIP (r = 0.374, p = 0.041), and inverse trends with antioxidant and angiogenic markers however no significance was found (Table 3).

TABLE 3.

Pearson's correlation analysis of Ferroptosis markers with oxidative stress, inflammation, and angiogenesis in GDM placenta.

GPX4 SLC7A11 ACSL4
r p r p r p
NRF2 0.672 <0.001 0.575 <0.001 −0.311 0.094
HO‐1 0.679 <0.001 0.671 <0.001 −0.182 0.335
CAT 0.782 <0.001 0.521 0.003 −0.435 0.016
NQO1 0.812 <0.001 0.543 0.002 −0.383 0.037
SOD 0.783 <0.001 0.609 0.002 −0.330 0.075
P22PHOX −0.706 <0.001 −0.560 <0.001 0.290 0.120
TXNIP −0.809 <0.001 −0.534 0.002 0.374 0.041
TNFα −0.802 <0.001 −0.526 0.003 0.381 0.038
IL‐1β −0.607 <0.001 −0.552 0.002 0.216 0.251
IL‐6 −0.748 <0.001 −0.564 <0.001 0.325 0.080
IL‐10 0.748 <0.001 0.565 <0.001 −0.350 0.058
VEGF‐A 0.729 <0.001 0.526 0.003 −0.245 0.193
HIF‐1α 0.751 <0.001 0.560 <0.001 −0.311 0.095
SDF‐1α 0.771 <0.001 0.564 <0.001 −0.315 0.090
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Abbreviations: p, significance value; r, correlation coefficient.

4. Discussion

GDM is a common pregnancy‐related metabolic complication that is defined by glucose intolerance and progressive insulin resistance, resulting in the rise of maternal hyperglycemia and fetal complications. The biochemical results of the present study, such as elevated fasting blood glucose, OGTT and HbA1c, indicated the presence of a hyperglycemic condition in the women with GDM. Previous studies have observed excessive iron accumulation and disturbed iron transportation in the GDM placenta [16]. Consistent with this, our findings showed higher expression of TFR1, an iron importer, along with reduced levels of FPN and FTH, which together predispose placental cells to iron‐catalyzed oxidative damage. These results are in line with the recent evidence indicating iron‐mediated oxidative injury in the GDM placenta [17]. Such dysregulation of iron handling significantly increases the Labile iron pool (LIP), resulting in an intracellular accumulation of redox‐active Fe2+, thus predisposing the placenta to oxidative injury.

As a result of the increased availability of redox‐active iron, the GDM placenta exhibits significant elevation in oxidative stress, characterized by increased lipid peroxidation and decreased antioxidant defenses. Several studies have reported elevated indicators of oxidative damage, including MDA, 8‐isoprostane, and protein carbonyls and reduced antioxidant enzymes such as SOD and CAT in GDM pregnancies [18, 19]. These observations are consistent with our findings, which exhibited an increased level of TXNIP and P22PHOX, key mediators of ROS production and redox imbalance. Previous studies have shown that TXNIP promotes trophoblast dysfunction induced by oxidative stress in GDM [20, 21], while P22PHOX has been found to induce NADPH oxidase (NOX)‐mediated ROS production in placental tissue [22].

Interestingly, our results revealed a marked suppression of NRF2 and its downstream antioxidant enzymes, including HO‐1, NQO1, SOD2, and CAT, in the GDM placenta, indicating an impaired antioxidant defense system. This decreased NRF2 signaling aggravates oxidative damage and increases placental cells' susceptibility to ferroptosis. In line with this, fetal endothelial cells from GDM pregnancies have shown disrupted NRF2 activation due to reduced Parkinsonism‐associated deglycase (DJ‐1) and enhanced Glycogen Synthase Kinase‐3 Beta (GSK3β) activity, leading to decreased glutathione and antioxidant response [23]. Moreover, sulforaphane‐activated maternal NRF2 restored antioxidant gene expression in fetal tissues, such as NQO1, Glutamate‐Cysteine Ligase (GCL) and copper‐zinc superoxide dismutase (CuZnSOD), and also reduced cardiac oxidative stress indicators [24]. Also, dysregulation of Glutathione Peroxidase 3 (GPX3) has been identified as a ferroptosis‐related biomarker in the GDM placenta and is correlated with altered ferritin/sTFR ratios and systemic redox imbalance [25]. The combined effect of iron overload, ROS production, and suppressed NRF2 signaling facilitates the onset of ferroptosis, a regulated type of cell death that occurs as a result of iron‐dependent lipid peroxidation.

In accordance with this ferroptotic predisposition, our study shows a marked downregulation of GPX4 in the GDM placenta. This decline is mechanistically significant, as GPX4 serves as the critical barrier that maintains lipid redox homeostasis and prevents ferroptotic death [26]. Supporting our observations, Xu et al. demonstrated that the dysregulated NRF2‐GPX4 axis promotes lipid peroxidation and increases placental susceptibility to ferroptosis in pregnancy‐related metabolic disorders [27]. Moreover, Jiang et al. provided mechanistic evidence that high glucose initiates circHIPK3‐dependent hypermethylation and silencing of GPX4 through miR‐1278/DNA methyltransferase 1 (DNMT1) axis, which directly induces ferroptosis in placental trophoblasts [28].

Along with the suppression of GPX4, our study observed an altered expression of ferroptosis‐related genes, including the downregulation of SLC7A11 and upregulation of ACSL4, as evidence of ferroptosis in the GDM placenta. The decline of SLC7A11 limits cystine uptake and glutathione synthesis, thereby downregulating GPX4 activity and lipid peroxide detoxification [29]. Simultaneously, elevated ACSL4 enhances the incorporation of polyunsaturated fatty acids into membrane phospholipids, making them more susceptible to peroxidation [30]. These findings are in accordance with findings by Zhang et al., who showed that lipid remodeling of Glucose Transporter Type 1 (GLUT1)‐AMPK/ACC‐ACSL4 causes ferroptosis in GDM and that inhibition enhances adverse fetal outcome in GDM mouse models [11]. In addition, recent evidence indicates that ferroptotic activation of M1 macrophages through ACSL4 was reported to enhance local inflammatory responses in the GDM placenta, which supports the relationship between ferroptosis and inflammatory response in the placenta [10].

As ferroptosis and oxidative stress increase in the GDM placenta, they act as potent triggers for the activation of pro‐inflammatory signaling pathways. In the current study, IL‐6, IL‐1β, TNF‐α, and NF‐κB were significantly elevated in the GDM placental tissues. These are consistent with earlier findings of increased inflammatory activity in GDM pregnancies [31, 32]. Additionally, Metastasis‐Associated Lung Adenocarcinoma Transcript 1 (lncRNA MALAT1) has been revealed to enhance this reaction by enhancing the IL‐6 and TNF‐α expressions via Transforming Growth Factor beta (TGF‐β)/NF‐κB signaling [33], and TXNIP triggers the NOD‐like Receptor Protein 3 (NLRP3) mediated inflammasome and proliferation of IL‐18 in GDM placenta [34].

Last, our study demonstrated a significant reduction in key angiogenic regulators, VEGFA, HIF‐1α, and SDF‐1α, indicating impaired placental angiogenesis in GDM. These findings are in agreement with our earlier findings, indicating the reduced expression of angiogenic signaling in GDM placental tissues [35]. Other studies demonstrated the downregulation of VEGFA/VEGFR‐2 expression in GDM placentas [13]. Moreover, GDM has also been linked to reduced functional characteristics, including lower vasorelaxation response and endothelial gene expression in embryonic placental arteries [36].

Interestingly, accumulating evidence suggests that iron overload and ferroptosis‐mediated lipid peroxidation may directly impair endothelial cell survival and inhibit VEGFA, HIF‐1α signaling thereby linking ferroptosis stress to dysfunctional placental angiogenesis [37, 38]. Further, treatment of GDM rat models with metformin restored the expression of VEGF, VEGFR and CD31, which indicates that angiogenic defects can partially be reversed [39].

To further elucidate the interplay between ferroptosis and other pathological processes in GDM, correlation analyses were performed between key ferroptosis regulators and markers of oxidative stress, inflammation, antioxidant defense, and angiogenesis. Notably, anti‐ferroptotic markers GPX4 and SLC7A11 exhibited strong positive correlations with antioxidant regulators, including NRF2, HO‐1, NQO1, SOD2, and CAT, indicating that ferroptosis may decrease the cellular antioxidant defense system. These findings are consistent with the established role of the NRF2‐GPX4 axis in maintaining redox homeostasis and preventing lipid peroxidation. In contrast, GPX4 and SLC7A11 demonstrated significant negative correlations with oxidative stress markers such as TXNIP and P22PHOX, as well as pro‐inflammatory cytokines including IL‐6, TNF‐α, and IL‐1β. This inverse relationship suggests that ferroptosis may promote increased oxidative stress and inflammation in the GDM placenta. Additionally, both GPX4 and SLC7A11 showed strong positive correlations with key angiogenic factors such as VEGFA, HIF‐1α, and SDF‐1α, indicating that ferroptosis may promote impaired angiogenesis. Conversely, the pro‐ferroptotic marker ACSL4 showed positive correlations with inflammatory mediators and a negative trend with antioxidant and angiogenic markers, further supporting its role in promoting lipid peroxidation and inflammatory responses.

The present study provides a comprehensive integrative analysis of ferroptosis, oxidative stress, inflammation, and angiogenesis within the same clinical cohort, offering new insights into their interconnected roles in GDM placental dysfunction. However, there are several limitations that should be considered while interpreting the findings. First, although consistent alterations in ferroptosis‐related markers were observed at both mRNA and protein levels, direct biochemical measurements of ferroptosis, such as lipid peroxidation (e.g., malondialdehyde), glutathione depletion, and iron accumulation, were not assessed. Therefore, the current findings provide indirect evidence of ferroptosis and should be interpreted as associative rather than establishing a causal relationship. Further, although multiple molecular pathways were assessed, functional validation experiments, such as ferroptosis inhibition or rescue assays, were not performed. Such studies would be necessary to establish a direct mechanistic role of ferroptosis in placental dysfunction. Additionally, there was a significant difference in maternal BMI between the control and GDM groups. Maternal obesity is a well‐established risk factor for GDM and is independently associated with increased oxidative stress, inflammation, and altered placental function. Therefore, the observed molecular changes may reflect the combined effects of hyperglycemia and increased maternal adiposity. Information regarding treatment modalities in GDM participants (e.g., diet, insulin, or oral hypoglycemic agents) was not systematically recorded, and treatment‐related effects on placental biomarkers cannot be excluded. Finally, although correlation analyses revealed strong associations between ferroptosis markers and oxidative stress, inflammatory, and angiogenic pathways, these relationships do not establish causality and should be interpreted as indicative of potential crosstalk. Further mechanistic and longitudinal studies are required to confirm these interactions.

Collectively, these findings indicate that iron overload, oxidative stress, and ferroptosis interact to facilitate inflammation and angiogenic dysfunction in the GDM placenta, and ferroptosis is a central axis of placental dysfunction in GDM. Though the current evidence demonstrates a close relationship between ferroptosis and dysfunctional placental angiogenesis in GDM, further research with functional angiogenic assays and ferroptosis‐targeted interventions will be essential to establish causal relationships and therapeutic implications.

5. Conclusion

In conclusion, hyperglycemia in GDM disturbs placental iron homeostasis, resulting in increased iron overload and inhibition of NRF2 activation, which creates a pro‐ferroptotic environment in the placenta with reduced levels of GPX4 and SLC7A11, as well as increased levels of ACSL4. This results in elevated placental inflammation by NF‐κB activation and enhanced pro‐inflammatory cytokines. This is accompanied by the impairment of angiogenic markers, evidenced by decreased VEGFA, HIF‐1α, and SDF‐1α in the GDM placenta. Importantly, correlation analyses revealed strong associations between ferroptosis markers and oxidative stress, inflammatory, and angiogenic pathways, suggesting the presence of a coordinated and interconnected regulatory network in the GDM placenta. Together, our findings identify ferroptosis as a central mechanism linking oxidative stress, inflammation and angiogenic dysfunction in GDM, highlighting it as a potential therapeutic target to improve placental and fetal outcomes.

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgments

The authors gratefully acknowledge the financial support from the Indian Council of Medical Research (ICMR), India (Grant No: 59/02/2022‐TRM/BMS [2021‐11487] and  SRM‐DBT Partnership Platform for Contemporary Research Services and Skill Development in Advanced Life Sciences Technologies. This research work was, in part, supported by SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India.

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