Altered placental iron transport and putative ferroptosis pathways in pregnancies with excessive gestational weight gain: A prospective cohort study

Abstract

Excessive gestational weight gain (GWG) is associated with various adverse pregnancy outcomes, including disruption of placental function and fetal development. Iron transport through the placenta is crucial for fetal growth, and transferrin receptor 2 (TfR2) plays a key role in iron homeostasis. However, the effect of excessive GWG on placental TfR2 expression and neonatal iron parameters remains unclear. This study aimed to investigate the effect of excessive GWG on placental TfR2 expression and its association with neonatal iron levels, including cord blood serum iron levels and total iron-binding capacity. A prospective study was conducted with 90 pregnant women divided into 2 groups: 45 with normal weight gain and 45 with excessive GWG. Placental TfR2 expression was assessed via immunohistochemistry, whereas neonatal iron parameters were analyzed in umbilical cord blood using biochemical assays. Additionally, in silico analyses were performed to explore the molecular pathways linking TfR2 expression and iron homeostasis. Placental TfR2 expression was significantly increased in the excessive GWG group compared to controls, with high immunoreactivity observed in the trophoblastic layer, capillaries, and villous connective tissue. Neonates from mothers with excessive GWG had significantly higher cord blood serum iron levels (P = .025) and lower total iron-binding capacity levels (P = .017). Bioinformatics analysis revealed that TfR2 is involved in iron homeostasis regulation, and ferroptosis emerged as a potentially relevant pathway. Excessive GWG may be associated with altered placental iron transport and increased TfR2 expression, which could contribute to iron overload and involvement of ferroptosis-related pathways. However, the lack of direct ferroptosis markers such as GPX4, ACSL4, reactive oxygen species levels, or cell-death assays limits mechanistic confirmation. Further studies are required to validate the role of ferroptosis in this context.

1. Introduction

Excessive gestational weight gain (GWG), defined as maternal weight gain exceeding the Institute of Medicine guidelines relative to prepregnancy body mass index (BMI), is a growing global health concern. Recent data indicate that over 50% of women in high-income countries exceed recommended GWG ranges, and similar trends are emerging in developing countries.^[1] Excessive GWG has been associated with an increased risk of gestational hypertension, cesarean section, macrosomia, preterm birth, and long-term metabolic dysfunction in offspring.^[1–3] These adverse outcomes are increasingly attributed to alterations in placental structure and function, which can impair nutrient transfer and fetal development.^[4–6]

The placenta is the central organ for maternal-fetal iron exchange, and proper regulation of iron transport is critical for fetal growth, erythropoiesis, and neurodevelopment.^[3] Disruption of iron homeostasis in pregnancy may result in either fetal iron deficiency or overload, both of which are linked to oxidative stress, developmental delays, and immune dysfunction.^[7,8] Transferrin receptor 2 (TfR2), a transmembrane protein primarily studied in hepatic and erythroid tissues, facilitates the uptake of transferrin-bound iron and regulates systemic iron balance through the hepcidin signaling pathway.^[9,10] Although TfR2 has been implicated in iron sensing and transport, its specific role in placental iron regulation, especially in the setting of excessive GWG, remains poorly understood.^[11]

Histopathological investigations have shown that placentas from women with excessive GWG may exhibit structural alterations such as villous immaturity, trophoblastic hyperplasia, inflammation, and vascular malperfusion, all of which can compromise placental function.^[4,6] These morphological changes may be accompanied by molecular dysregulation, including abnormal expression of iron transport proteins. Excessive iron accumulation in trophoblasts may trigger ferroptosis, a non-apoptotic form of regulated cell death driven by iron-dependent lipid peroxidation and reactive oxygen species (ROS) generation.^[12–14] Ferroptosis has been increasingly linked to pregnancy complications such as preeclampsia, intrauterine growth restriction (IUGR), and gestational diabetes mellitus.^[15–17] However, its mechanistic connection to TfR2 upregulation in the placental tissue of women with excessive GWG has not been thoroughly investigated.

Despite the growing recognition of oxidative stress and iron metabolism in obstetric complications, a significant gap remains in our understanding of how maternal overnutrition affects placental iron transport and ferroptosis pathways. In particular, it is unclear whether excessive GWG alters placental TfR2 expression and contributes to iron overload and oxidative damage in fetal tissues.

This study aimed to investigate the expression of TfR2 in placental tissues of women with excessive GWG and to evaluate its relationship with oxidative stress (malondialdehyde [MDA] levels) and iron parameters (serum iron and total iron-binding capacity [TIBC]) in neonatal cord blood. Furthermore, in silico analyses were conducted to explore the molecular pathways linking TfR2 to ferroptosis and iron homeostasis. By integrating histological, biochemical, and computational approaches, this study provides novel insights into the pathophysiological mechanisms underlying placental dysfunction in pregnancies complicated by excessive maternal weight gain.

2. Materials and methods

2.1. Study design and ethics

This prospective cohort study was conducted at the Department of Gynecology and Obstetrics, Dicle University, between June 2024 and December 2024. Participant recruitment occurred during routine prenatal visits and continued through delivery. The study was approved by the Dicle University Faculty of Medicine Non-Interventional Clinical Research Ethics Committee (approval number: 2024/24; date: November 20, 2024) and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology guidelines. Sample size was determined via G*Power (version 3.1, developed by Faul F, University of Kiel, Kiel, Germany) using an effect size of 0.2, alpha = 0.05, and power = 0.80.

2.2. Participant selection and grouping

A total of 90 pregnant women (45 normal GWG and 45 excessive GWG) were enrolled based on the following criteria: singleton pregnancies with no known chronic illness, medication use, alcohol/tobacco/drug use, or fetal anomalies. The exposure of interest was excessive GWG, defined according to the Institute of Medicine guidelines relative to prepregnancy BMI. Women who experienced fetal growth restriction, fetal anomalies, preterm birth, or multiple gestation were excluded. Maternal age, parity, and BMI were considered as potential confounding variables, and their distribution between groups was documented and statistically assessed. The control group included 45 healthy pregnant women who were of normal weight according to the BMI at the beginning and end of pregnancy. BMI was determined according to World Health Organization criteria.^[18] Fetal sex was recorded based on clinical assignment at birth. Sex/gender reporting was conducted in line with the Sex and Gender Equity in Research guidelines.^[19]

2.3. Measurement of lipid peroxidation (MDA assay)

Maternal peripheral venous blood samples were collected at the time of delivery into sterile, heparinized tubes and immediately placed on ice. Plasma was separated by centrifugation at 4000g for 15 minutes at 4°C. Lipid peroxidation was evaluated by quantifying MDA levels using a colorimetric thiobarbituric acid reactive substances assay. The assay was conducted with a commercial kit (ZellBio GmbH, Ulm, Germany; catalog no. ZB-MDA-96A), following the manufacturer’s protocol. Absorbance was measured at 532 nm using a microplate reader (Thermo Multiskan FC; Thermo Fisher Scientific, Waltham), and MDA concentrations were calculated from a standard curve and expressed in µmol/L. All samples were measured in duplicate to ensure reproducibility.^[20]

2.4. Measurement parameters of neonatal blood cord

Immediately after delivery, umbilical cord blood was collected in sterile heparinized collection tubes. To minimize preanalytical variability, all samples were processed within 30 minutes of collection. Samples were centrifuged at 4000g for 15 minutes at 4°C to separate plasma from cellular components. All plasma samples were aliquoted into multiple sterile polypropylene tubes immediately after centrifugation to prevent repeated freeze-thaw cycles. Each iron and TIBC assay were conducted using a single thawed aliquot, and all measurements were performed in duplicate to ensure reproducibility. Throughout sample handling, low-light conditions were maintained to prevent oxidation artifacts, and all laboratory equipment and reagents were maintained in accordance with the manufacturer’s guidelines. Prior to measurement, the samples were thawed once on ice and mixed gently to ensure uniformity without introducing air bubbles. Standardized protocols for sample collection, processing, and storage were used to preserve the integrity and stability of markers, thereby ensuring reliable and reproducible analytical results. Serum samples were analyzed for iron (Fe) and TIBC using a Cobas 6000 (Roche Diagnostics, Mannheim, Germany). All measurements were performed using commercial immunoassay kits (Elecsys, Roche, Germany; coefficient of correlation ≥0.95). Venous umbilical blood represents placental function, as it flows from the placenta to the fetus. This blood can be used to evaluate markers associated with maternal-fetal transfer, placental function, and nutrient delivery.^[21] Sample collection, processing, and reporting adhered to the Biospecimen Reporting for Improved Study Quality guidelines to ensure transparency and reproducibility in biospecimen research.^[22]

2.5. Immunostaining of TfR2 antibody

The samples were deparaffinized, dehydrated using a graded series of alcohol, and rinsed with distilled water. A hydrogen peroxide solution (catalog no: TA-015-HP, Thermo Fisher Scientific) was applied to the sections and incubated for 20 minutes. Next, the sections were treated with Ultra V Block (catalog no: TA-015-UB, Thermo Fisher Scientific) for 7 minutes. Afterward, they were incubated overnight at +4°C with the primary antibody TfR2 (catalog no: MA5-34739, Thermo Fisher, Fremont) at a dilution of 1:100. The sections were then exposed to a biotinylated secondary antibody (catalog no: TP-015-BN; Thermo Fisher) for 14 minutes, followed by a 15-minute incubation with streptavidin-peroxidase (catalog no: TS-015-HR; Thermo Fisher). Diaminobenzidine (catalog no: TA-001-HCX; Thermo Fisher) was used as a chromogen to visualize protein expression. After washing with phosphate-buffered saline, the sections were counterstained with Harris hematoxylin. The slides were mounted and examined under a Zeiss Imager A2 light microscope.^[23] The immunohistochemistry methodology, including antibody source, dilution, incubation conditions, and chromogen use, was reported following the Minimum Information for Publication of Experimental Pathology Data guidelines to promote methodological transparency and reproducibility.^[24] Negative controls were processed in parallel by omitting the primary antibody to confirm staining specificity. No nonspecific background staining was observed. For each placenta, 3 representative areas from non-infarcted, central villous regions were selected for imaging. The most consistent and reproducible staining zones were chosen for photomicrograph documentation.

Immunostained placental sections were digitally photographed under identical light and exposure conditions. Semiquantitative analysis of TfR2 expression was performed using ImageJ (version 1.54, developed by National Institutes of Health, Bethesda) software (NIH, USA). Staining intensity was measured in 5 randomly selected high-power fields (400× magnification) per sample, and results were expressed as mean optical density. Background correction was applied using control regions. Image analysis was conducted by 2 independent investigators blinded to group allocation. Immunostaining was performed on placental sections from all participants in both groups (n = 45 per group). Each sample was sectioned and stained in duplicate to ensure staining consistency.

2.6. In silico analysis of TfR2 and its association with iron homeostasis

To elucidate the mechanistic relationship between the increased TfR2 expression and elevated iron levels observed in excessive GWG, in silico analyses were performed. An interaction network for TfR2 (UniProt number: Q9UP52) was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins database within the Cytoscape software (version 3.10.2, Seattle). A protein interaction network associated with iron ion homeostasis (GO:0055072) was retrieved from the same database. For the iron ion homeostasis network, the “first shell” setting was applied with 100 additional interactors, while for TfR2, 100 additional interactions were selected directly in Cytoscape (v.3.10.3). A medium confidence score threshold of 0.400 was applied for both networks.^[25,26] Subsequently, common protein targets between the 2 networks were identified and analyzed using Cytoscape. Shared protein targets between the 2 networks were identified using the “Merge” module in Cytoscape by selecting the “intersection” option. To identify functionally related groups among these shared proteins, the Molecular Complex Detection (MCODE) plugin was applied using default parameters: degree cutoff = 2, node score cutoff = 0.2, and K-core = 2. Functional annotation of these proteins was conducted using ShinyGO (version 0.82, South Dakota State University, Brookings) based on the Kyoto Encyclopedia of Genes and Genomes pathway database.^[27] The top 10 significantly enriched pathways were ranked in ascending order according to their false discovery rate values, with false discovery rate <0.05 considered statistically significant.

2.7. Statistical analysis

All statistical analyses were performed using IBM SPSS for Windows version 29.0 (IBM Corp., Armonk). The Kolmogorov–Smirnov and Shapiro–Wilk tests were used to assess the normality assumption. Continuous variables are presented as mean ± standard deviation or median and interquartile range. Categorical variables are summarized as counts and percentages. Comparisons between groups were carried out using the independent Student t test (parametric) and Mann–Whitney U test (nonparametric) to observe statistical significance between 2 parameters. Multivariable linear regression analyses were performed to assess the independent associations between maternal and obstetric variables and cord blood iron, TIBC, and maternal MDA levels. Statistical significance was set at P < .05.

3. Results

3.1. Comparison of maternal anthropometric and obstetric characteristics between control and excessive GWG groups

Maternal baseline characteristics, including age, parity, BMI, weight changes, delivery mode, and obstetric history, were compared between the control group and the excessive GWG group in Table 1. The control and excessive GWG groups were comparable in terms of maternal age, gravida, and parity, indicating similar baseline obstetric profiles. As expected, the GWG group had significantly higher prepregnancy and end-of-pregnancy BMI and weight values, validating the classification based on weight gain. The rate of cesarean section was higher in the GWG group, suggesting a possible association between excessive weight gain and delivery complications. Additionally, the GWG group had a higher frequency of previous abortions and curettage procedures, potentially reflecting a more complex reproductive history. Other variables such as maternal blood type, consanguinity, and labor indications were similarly distributed between groups, minimizing their potential as confounders.

Table 1.

Baseline maternal demographic and clinical characteristics in control and excessive GWG groups.

Parameters	Control (n = 45)	GWG(n = 45)
Maternal age, mean ± SD (yr)	26.93 ± 4.90	28.40 ± 6.27
Gravida, median (IQR)	1.77 (1.19–3.00)	1.67 (1.04–2.58)
Parity, median (IQR)	1.54 (1.05–2.25)	1.62 (1.04–2.63)
Birth week, mean ± SD	38.59 ± 0.95	39.12 ± 0.72
Maternal height, mean ± SD (cm)	164.60 ± 4.85	162.87 ± 5.52
Weight at the onset of pregnancy, median (IQR) (kg)	57.33 (50.75–59.83)	73.33 (68.83–74.88)
Weight at the end of pregnancy, median (IQR) (kg)	69.50 (62.17–76.75)	84.00 (79.50–86.63)
BMI at the onset of pregnancy, median (IQR) (kg/m2)	22.10 (20.85–22.46)	25.70 (25.25–29.45)
BMI at the end of pregnancy, median (IQR) (kg/m2)	27.06 (25.8–27.82)	31.30 (29.85–34.02)
Maternal blood type
A (+)	12	16
O (+)	18	14
B (+)	6	5
AB (+)	6	7
A (−)	3	1
B (−)	0	2
Consanguineous marriage
No	40	38
Yes	5	7
Delivery method
NSVD	15	9
C/S	30	36
Abortus
Yes	9	18
No	36	27
Curettage
Yes	6	12
No	39	33
Labor indication
Recurrent	21	15
Prolonged	12	12
Others	12	18

BMI = body mass index, C/S = cesarean section, GWG = gestational weight gain, IQR = interquartile range, NSVD = normal spontaneous vaginal delivery, SD = standard deviation.

3.2. Comparison of neonatal anthropometric and clinical outcomes between control and excessive GWG groups

Table 2 compares neonatal characteristics between the control and excessive GWG groups, showing no significant differences in fetal weight, length, head circumference, or appearance, pulse, Grimace, activity, respiration scores at 1 and 5 minutes, indicating comparable immediate postnatal status. However, the proportion of large for gestational age infants was higher in the GWG group, suggesting that excessive maternal weight gain may influence fetal growth patterns. Fetal sex distribution was relatively balanced across groups. Overall, while general neonatal parameters were similar, the increased large for gestational age rate in the GWG group reflects the impact of excessive GWG on fetal growth trajectory.

Table 2.

Neonatal anthropometric and clinical characteristics in control and excessive GWG groups.

Clinical features	Control(n = 45)	GWG (n = 45)
Fetal height, mean ± SD (cm)	50.60 ± 1.12	49.60 ± 1.40
Fetal weight, mean ± SD (kg)	3341.33 ± 440.97	3245.33 ± 371.34
Fetal head circumference, mean ± SD (cm)	34.93 ± 0.70	34.83 ± 0.72
APGAR score at 1st minute, median (IQR)	8.67 (8.17–8.89	8.53 (8.03–9.04)
APGAR score at 5th minute, median (IQR)	9.73 (9.23–9.96)	9.53 (9.03–9.75)
Fetal gestational age
AGA	38	30
LGA	5	12
SGA	2	3
Fetal sex (Assigned at birth)
Female	18	24
Male	27	21

AGA = average for gestational age, APGAR = appearance, pulse, grimace, activity, respiration, GWG = gestational weight gain, IQR = interquartile range, LGA = large for gestational age, SD = standard deviation, SGA = small for gestational age.

3.3. Comparison of oxidative stress in maternal blood between control and excessive GWG groups

Table 3 presents maternal oxidative stress data. MDA levels, a marker of lipid peroxidation, were significantly higher in the excessive GWG group compared to controls (P < .05), indicating elevated oxidative damage in the maternal circulation associated with excessive weight gain.

Table 3.

Oxidative stress marker (MDA) in maternal blood of control and excessive GWG groups.

Parameters	Control (n = 45)	GWG (n = 45)	P
MDA, mean ± SD	2.5 ± 0.4 nmol/mL	3.2 ± 0.5 nmol/mL	<.001

Student t test.

GWG = gestational weight gain, MDA = malondialdehyde, SD = standard deviation.

3.4. Comparison of iron metabolism markers in cord blood between control and excessive GWG groups

Table 4 presents a comparison of iron metabolism markers in neonatal umbilical cord blood between the control and excessive GWG groups. Serum iron levels were significantly elevated and TIBC levels were significantly reduced in the excessive GWG group (P < .05 for both). These findings suggest enhanced placental iron transfer and possible neonatal iron overload, consistent with increased TfR2 expression in placental tissue.

Table 4.

Iron parameters (serum iron and TIBC) in umbilical cord blood of neonates from control and excessive GWG groups.

Parameters	Control (n = 45)	GWG (n = 45)	P
Iron, mean ± SD	136.73 ± 34.72	144.27 ± 34.24	.025*
TIBC, median (IQR)	100.53 ± 58.97	81.80 ± 52.25	.017†

GWG = gestational weight gain, IQR = interquartile range, SD = standard deviation, TIBC = total iron-binding capacity.

^*Student t test.

^†Mann–Whitney U test.

3.5. Multivariable regression analysis showed that none of the maternal or obstetric variables independently predicted cord blood iron, TIBC, or MDA levels

Multivariable linear regression analysis (Table 5) revealed that none of the individual maternal or obstetric variables were statistically significant predictors of neonatal cord serum iron, TIBC, or maternal MDA levels after adjustment (all P > .05). The R² values were low to moderate across models (iron R² = 0.098; TIBC R² = 0.153; MDA R² = 0.361), suggesting limited explanatory power. While gravida showed a near-significant trend in the TIBC model (P = .101), no covariates emerged as independently associated with iron metabolism or oxidative stress markers. These findings suggest that although univariate differences were observed between normal and excessive GWG groups, the observed alterations in iron metabolism and oxidative stress are not independently explained by traditional maternal variables such as BMI, age, parity, or gestational age. This supports the hypothesis that excessive GWG itself may contribute to changes in placental iron handling and oxidative stress, independent of baseline maternal characteristics.

Table 5.

Multivariable linear regression analysis of neonatal iron, TIBC, and maternal MDA levels in relation to maternal and obstetric variables from control and excessive GWG groups.

Dependent variable	Predictor	B (Unstd.)	Std. error	95% CI	P-value
Iron	(Constant)	341.82	343.77	–	.331
	BMI (onset)	1.44	4.10	–	.729
	BMI (end)	–1.13	4.46	–	.802
	Gestational age	–6.12	8.80	–	.494
	Maternal age	–0.55	1.70	–	.748
	Gravida	–6.01	12.92	–	.646
	Parity	13.74	15.90	–	.397
	Birth weight	0.011	0.018	–	.531
TIBC	(Constant)	1.80	542.67	–	.997
	BMI (onset)	–1.87	6.48	–	.776
	BMI (end)	2.63	7.05	–	.712
	Gestational age	0.81	13.89	–	.954
	Maternal age	–1.30	2.68	–	.632
	Gravida	34.93	20.40	–	.101
	Parity	–36.41	25.10	–	.161
	Birth weight	0.017	0.028	–	.554
MDA	(Constant)	–0.75	3.90	–	.849
	BMI (onset)	0.053	0.046	–	.268
	BMI (end)	0.019	0.051	–	.708
	Gestational age	0.038	0.100	–	.705
	Maternal age	–0.010	0.019	–	.594
	Gravida	–0.112	0.146	–	.453
	Parity	0.134	0.180	–	.464
	Birth weight	0.000	0.000	–	.518

BMI = body mass index, CI = confidence interval, GWG = gestational weight gain, MDA = malondialdehyde, Std. error = standard error, TIBC = total iron-binding capacity.

3.6. Moderate TfR2 expression in placental tissue of control pregnancies

Figure 1 illustrates the immunohistochemical staining of placental tissue from the control group, showing moderate expression of TfR2 primarily localized in the trophoblastic layer and the villous connective tissue. The staining is evenly distributed and not excessively intense, indicating a physiologically regulated expression of TfR2 under normal GWG conditions (Fig. 1A and B). This baseline expression pattern serves as a reference for evaluating the alterations seen in the excessive GWG group. The defined localization also confirms the cellular compartments involved in iron transfer within the placenta under non-stressed conditions. Figure 1C and D demonstrates a significant upregulation of TfR2 expression in placental tissues from women with excessive GWG, compared to the control group. The immunoreactivity is notably stronger and more widespread, encompassing the trophoblastic layer, capillary endothelium, and villous stroma. This heightened expression pattern suggests enhanced iron uptake activity within the placenta, which may lead to iron overload and contribute to ferroptosis-related oxidative damage. The findings visually support the biochemical and ImageJ data indicating dysregulated placental iron metabolism in the GWG group. Figure 1E indicated negative control of TfR2 antibody in placenta. Figure 1F shows ImageJ-based semiquantitative analysis of TfR2 immunostaining demonstrated a significantly higher mean optical density in the excessive GWG group (0.382 ± 0.045) compared to the control group (0.295 ± 0.038; P < .001), supporting increased protein expression consistent with the immunohistochemical observations.

Figure 1.

TfR2 expression in placentas from control (A and B) and excessive GWG group (C and D). (A) Moderate TfR2 expression is visible in the trophoblastic layer and villous connective tissue. (B) Higher magnification of the same field highlights the localization more clearly. (C) Strong immunoreactivity is observed in the trophoblastic layer and villous connective components. (D) Higher magnification of the same region confirms intense staining in both trophoblasts and stromal components. (E) Negative staining control shows no TfR2 immune stained placental components. (F) Immunoscores of TfR2 in groups shows expression increased in excessive GWG group (Mann–Whitney U test). Arrowhead: trophoblast layer; asterisk: villous connective tissue. GWG = gestational weight gain, TfR2 = transferrin receptor 2.

3.7. Pathway enrichment analysis reveals ferroptosis as the top pathway associated with TfR2 and iron homeostasis

Given the observed upregulation of placental TfR2 expression and corresponding alterations in neonatal iron parameters, we sought to further investigate the potential molecular mechanisms underlying these findings. Specifically, we employed in silico bioinformatics analyses to explore how TfR2 might be functionally connected to iron regulation pathways and to assess whether ferroptosis-related processes could be involved in pregnancies with excessive GWG. To investigate the potential mechanisms linking increased TfR2 expression with elevated iron levels in excessive GWG, network and functional annotation analyses were performed. A total of 43 proteins were identified as common interactors between TfR2 and iron homeostasis. Clustering analysis of the 43 shared proteins using the MCODE algorithm revealed 3 distinct functional modules consisting of 19 (Cluster 1), 10 (Cluster 2), and 6 nodes (Cluster 3), respectively, suggesting the presence of tightly interconnected subnetworks involved in iron metabolism and ferroptosis-related processes. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of all intersected proteins revealed significant associations with multiple biological pathways, including ferroptosis (2.9E−15), mineral absorption (6.9E−12), TGF-β signaling pathway (1.8E−10), porphyrin metabolism (8.8E−04), antifolate resistance (1.3E−02), hematopoietic cell lineage (5.9E−04), HIF-1 signaling pathway (7.2E−04), signaling pathways regulating pluripotency of stem cells (1.5E−03), cytokine-cytokine receptor interaction (1.2E−06), and Hippo signaling pathway (2.0E−03) (Fig. 2). These findings suggest a potential role of TfR2 in modulating iron levels through diverse cellular processes, including ferroptosis, cytokine signaling, and hematopoiesis.

Figure 2.

Network and functional annotation of TfR2 and iron homeostasis-related proteins. Protein-protein interaction networks for TfR2 and iron ion homeostasis were generated using the STRING database and merged to identify 43 common interacting proteins. MCODE clustering revealed 3 functionally coherent protein clusters within the network. KEGG pathway enrichment analysis of these proteins revealed the top 10 significantly associated pathways, ranked by ascending false discovery rate (FDR). Ferroptosis was identified as the most enriched pathway, followed by mineral absorption, TGF-β signaling, and others. The bar plot displays these pathways along with their −log10(FDR) values, highlighting their statistical significance. KEGG = Kyoto Encyclopedia of Genes and Genomes, MCODE = Molecular Complex Detection, STRING = Search Tool for the Retrieval of Interacting Genes/Proteins, TfR2 = transferrin receptor 2.

4. Discussion

Histopathological changes in the placentas of pregnant women with high GWG can negatively affect pregnancy outcomes and pose risks to fetal development. These changes include placental hypertrophy, altered villous structures, vascular abnormalities, inflammatory processes, and placental hypoxia.^[4–6] The transferrin receptor is a protein found on the cell surface that plays an important role in the transport of iron to cells. Iron is transported in the bloodstream by a protein called transferrin and is taken into the cell via the transferrin receptor.^[28–30] Transferrin receptor in the placenta is critical for meeting the iron needs of the fetus.^[3] Changes in transferrin receptor expression may affect iron metabolism and alter pregnancy outcome. In particular, differences in placental transferrin receptor levels in pregnant women with high GWG values may affect fetal iron uptake.^[31,32]

Investigating these molecular changes, such as transferrin receptor expression in pregnant women with high GWG values, may contribute to a better understanding of placental function. The findings of this study provide valuable insights into the interplay among excessive GWG, placental iron transport, and neonatal iron status. In our study, the upregulation of TfR2 in the placenta may have led to excessive iron uptake, resulting in iron overload in trophoblast cells. Excessive iron accumulation promotes lipid peroxidation, a key driver of ferroptosis, which can trigger oxidative damage, inflammation, and placental dysfunction. Ferroptosis has been implicated in pregnancy-related complications such as preeclampsia, IUGR, and gestational diabetes. The observed alterations in TfR2 expression and neonatal iron parameters in our study suggest that excessive GWG may contribute to placental ferroptosis, thereby affecting fetal iron homeostasis and overall neonatal health. This is consistent with previous studies suggesting that placental histopathological changes associated with excessive maternal weight gain can compromise nutrient delivery and fetal growth.^[33,34] Placental dysfunction owing to ferroptosis may impair nutrient and oxygen transport, leading to adverse fetal outcomes.^[17,35] Given the critical role of iron homeostasis during pregnancy, our findings highlight the importance of monitoring maternal weight gain and iron metabolism. The dysregulation of TfR2 and the potential activation of ferroptosis pathways in excessive GWG warrant further investigation to explore targeted interventions that could mitigate these effects.

Our in silico analyses suggest that increased TfR2 expression in excessive GWG is intricately linked to dysregulation of iron homeostasis, with ferroptosis emerging as the most enriched pathway. Ferroptosis, an iron-dependent cell death mechanism, is driven by lipid peroxidation and oxidative stress, which are 2 processes implicated in placental dysfunction and adverse pregnancy outcomes.^[15,36] Elevated iron levels may amplify ferroptotic activity, potentially leading to oxidative damage in the placenta and disruption of fetal development. This aligns with previous studies showing that iron overload in pregnancy contributes to oxidative stress, inflammation, and complications such as preeclampsia and gestational diabetes.^[16,37] The enrichment of pathways related to cytokine signaling and HIF-1-mediated hypoxic adaptation further suggests that TfR2 may regulate the placental immune environment under iron-rich conditions, potentially influencing trophoblast survival and invasion.^[38] Additionally, the involvement of hematopoietic pathways indicates that altered TfR2-mediated iron metabolism could affect fetal erythropoiesis, which is essential for proper oxygen transport and developmental homeostasis.^[39] Given that ferroptosis has been linked to pregnancy-related disorders, such as IUGR and preeclampsia, it is plausible that TfR2 overexpression exacerbates ferroptotic processes, creating a pro-oxidative state detrimental to both maternal and fetal health. These findings suggest that TfR2 is a key modulator of iron homeostasis in pregnancy, potentially influencing ferroptosis-driven placental dysfunction and adverse maternal-fetal outcomes, warranting further experimental validation.

The observed increase in MDA levels in cord blood samples from the GWG group provides biochemical evidence of lipid peroxidation, which is a central feature of ferroptosis. This supports our in silico findings and immunohistochemical results, indicating that excessive GWG may induce oxidative damage in the placenta through TfR2-mediated iron overload. While additional markers such as GPX4 or ACSL4 could offer further confirmation, the elevated MDA levels strengthen the hypothesis that ferroptosis may contribute to placental dysfunction in this context.

Our study demonstrated that placental TfR2 expression was significantly increased in the GWG group, which, in turn, enhanced placental iron uptake. This mechanism results in elevated cord blood serum iron levels and decreased TIBC levels. Normally, there is an inverse relationship between iron and TIBC, as increased iron levels reduce the available transferrin-binding capacity, thereby lowering TIBC.^[40] In the present study, excessive GWG appeared to accelerate this process by upregulating TfR2 expression. Increased placental iron uptake may lead to iron overload in trophoblasts, triggering ferroptosis through elevated ROS and lipid peroxidation. Ferroptosis induces oxidative stress and placental dysfunction, which may contribute to pregnancy complications such as preeclampsia and IUGR. Our in silico analyses further revealed a direct association between TfR2 and the ferroptosis pathway. Excessive GWG appears to disrupt placental iron transport mechanisms, adversely affecting both placental and fetal health. Therefore, careful monitoring of GWG and maintenance of placental iron homeostasis are crucial for optimizing maternal and neonatal outcomes.

Dysregulated iron metabolism in neonates may lead to iron overload or deficiency, both of which are associated with adverse neurodevelopmental outcomes, impaired immune function, and altered growth trajectories.^[7] Iron excess in early life has also been linked to increased oxidative stress and potential long-term metabolic dysregulation.^[8] Moreover, while ferroptosis is increasingly recognized in various disease contexts, current therapeutic efforts have primarily focused on cancer treatment, with agents such as ferrostatins and liproxstatins being explored to inhibit lipid peroxidation.^[41,42] Understanding ferroptosis in pregnancy-related pathologies could open avenues for novel prenatal interventions, although such therapies remain experimental and are not yet translated into obstetric care.

This study has several limitations. First, the relatively small sample size from a single tertiary center in southeastern Turkey may limit the generalizability of the findings to broader populations with different ethnic, nutritional, or socioeconomic profiles. Selection bias may have occurred due to nonrandom recruitment during routine prenatal visits. Although strict inclusion criteria minimized confounding, residual bias from unmeasured factors such as dietary iron intake, inflammation, or oxidative stress cannot be excluded. Second, while we observed increased TfR2 expression, elevated cord blood iron, and higher MDA levels suggestive of ferroptosis-related activity, our study did not include direct ferroptosis markers such as GPX4 or ACSL4, nor did we quantify ROS or conduct cell-death assays in placental tissue. Consequently, ferroptosis remains a plausible, but unconfirmed, mechanistic pathway. Third, the immunohistochemical analysis, while robust, was limited by chromogenic staining, which does not allow for precise cell-type-specific localization. Future studies employing immunofluorescence co-staining (e.g., with cytokeratin-7 or CD31), longitudinal neonatal follow-up, and more comprehensive molecular assays are warranted to validate and expand upon our findings.

5. Conclusion

In conclusion, our study revealed the potential consequences of excessive GWG on placental iron transport and neonatal iron status. Addressing excessive maternal weight gain during pregnancy may play a pivotal role in optimizing placental function and improving neonatal health outcomes. In silico analyses suggest that elevated TfR2 expression is closely associated with dysregulated iron levels and may contribute to activation of ferroptosis-related pathways, a key iron-dependent mechanism of cell death. However, due to the absence of direct ferroptosis markers in our methodology, these findings should be interpreted as hypothesis-generating. Further mechanistic studies incorporating oxidative stress markers, ferroptosis-specific proteins, and cell-death assays are warranted to elucidate the causal role of ferroptosis in placental dysfunction associated with excessive GWG.

Author contributions

Conceptualization: Hasan Akduman, Firat Aşir.

Data curation: Ayşegül Aşir.

Formal analysis: Ayşegül Aşir, Tuğcan Korak.

Investigation: Serhat Ege, Hasan Akduman, Ayşegül Aşir, Tuğcan Korak.

Methodology: Serhat Ege, Hasan Akduman, Ayşegül Aşir, Tuğcan Korak, Firat Aşir.

Project administration: Serhat Ege, Hasan Akduman.

Resources: Ayşegül Aşir, Tuğcan Korak.

Software: Ayşegül Aşir, Tuğcan Korak, Firat Aşir.

Supervision: Hasan Akduman.

Validation: Ayşegül Aşir, Firat Aşir.

Visualization: Tuğcan Korak, Firat Aşir.

Writing – original draft: Serhat Ege, Hasan Akduman, Tuğcan Korak, Firat Aşir.

Writing – review & editing: Serhat Ege, Hasan Akduman, Tuğcan Korak, Firat Aşir.