Iron metabolism study: The characteristics of iron metabolism in patients with ovarian endometriosis complicated with infertility

mian li huang: Conceptualization; investigation; funding acquisition; writing – original draft; methodology; validation; visualization; writing – review and editing; software; formal analysis; project administration; data curation; supervision; resources. Weiling Qin: Methodology; investigation; software; project administration. Tongye Su: Conceptualization; investigation; validation; data curation. Haiyan Pang: Investigation; conceptualization; software; data curation. Delong Xie: Software; formal analysis; validation. Jiejie Liao: Methodology; visualization; data curation. Xiao Qin: Writing – review and editing; conceptualization; investigation; methodology; software; resources.

The comparison of age, body mass index, and duration of infertility between the study group and the control group demonstrated no statistically significant differences in these parameters ( p  > 0.05), as shown in Table  1 . General comparison of patients in study group and control group (M [P25, P75]). Abbreviation: BMI, body mass index. The study group had significantly higher SI levels than the control group ( p  < 0.01). TRF, UIBC, and TIBC were significantly lower in the study group ( p   0.05). See Table  2 and Figure  1 . Comparison of serum iron metabolism index levels between study group and control group ([ x ¯ ± S ], M [P25, P75]). Abbreviations: SI, serum iron; sTFR, soluble transferrin receptor; TIBC, total iron binding capacity; TRF, transferrin; TS, transferrin saturation; UIBC, unsaturated iron binding capacity. p  < 0.01; p  < 0.001. Comparison of serum iron metabolism index levels between study and control groups. (a) Serum iron (SI); (b) transferrin (TRF); (c) unsaturated iron binding capacity (UIBC); (d) total iron binding capacity (TIBC); (e) soluble transferrin receptor (sTFR); (f) transferrin saturation (TS). ** p  < 0.01, *** p  < 0.001. Patients in the study group were classified into two stages (I–II and III–IV) for the comparison of iron metabolism parameters. Statistical analysis demonstrated no significant differences in SI, TRF, UIBC, TIBC, sTfR, or TS between the two groups ( p  > 0.05), as presented in Table  3 . Comparison of serum iron metabolism index levels among patients of different stages within the study group [( x ¯ ± S ), M (P25, P75)]. Abbreviations: SI, serum iron; sTFR, soluble transferrin receptor; TIBC, total iron binding capacity; TRF, transferrin; TS, transferrin saturation; UIBC, unsaturated iron binding capacity. ROC curve analysis demonstrated that the area under the ROC curve (AUC) for SI in distinguishing EMs‐associated infertility from non‐EMs infertility was 0.707, with a sensitivity of 91.7%, specificity of 62.5%, Youden index of 54.2%, and cutoff value of 3.40. TRF had an AUC of 0.969, 83.3% sensitivity, 100% specificity, a Youden index of 83.3%, and a cutoff of 2.00. UIBC demonstrated an AUC of 0.837, 86.1% sensitivity, 70.0% specificity, a Youden index of 56.1%, and a cutoff of 36.05. TIBC resulted in an AUC of 0.925, 80.6% sensitivity, 5.0% specificity, a Youden index of 95.0%, and a cutoff of 43.65. Combined detection yielded an AUC of 0.983, 5.0% sensitivity, 91.9% specificity, and a Youden index of 91.9%. All indices (SI, TRF, UIBC, TIBC, and combined detection) were statistically significant ( p  < 0.01), as shown in Table  4 and Figure  2 . Application of iron metabolism index analysis in the differentiation between study and control groups. Abbreviations: SI, serum iron; TIBC, total iron binding capacity; TRF, transferrin; UIBC, unsaturated iron binding capacity. p  < 0.001. ROC curves of iron metabolism indicators to identify study and control groups. ROC, receiver operating characteristic.

EMs is a prevalent disease that significantly affects female fertility. Its underlying pathological mechanisms are complex and have yet to be fully elucidated. 5 Laparoscopy remains the gold standard for diagnosing EMs. In this study, patients undergoing surgery were selected to ensure the accuracy of pathological diagnosis and to minimize the impact of potential imaging false negatives. In recent years, iron metabolism disorders have increasingly been recognized as a critical factor in both the pathogenesis and progression of EMs. 6 However, current research on iron metabolism in EMs patients remains limited, particularly in the systematic evaluation of iron metabolism features among infertile EMs patients. This study systematically analyzed iron metabolism‐related indicators in patients with EMs and the controls, with particular emphasis on the dynamic alterations in SI, TRF, UIBC, and TIBC during the pathological process of EMs. Moreover, it innovatively demonstrated the auxiliary diagnostic value of combining multiple indicators for EMs. The results not only deepen the theoretical understanding of the association between EMs and iron metabolism disorders but also provide valuable insights for the optimization of clinical non‐invasive diagnostic approaches. (1) The correlation between iron metabolism imbalance and pathological features of EMs. The results of this study demonstrated that SI levels were significantly elevated in patients with EMs ( p  < 0.01), whereas TRF, UIBC, and TIBC were significantly reduced ( p  < 0.001). These findings suggest an iron overload state in EMs patients, consistent with the observations reported by Woo JH et al. 7 The SI levels in patients at stages III/IV are inclined to increase compared to those at stages I/II, suggesting that iron metabolism disorders are closely associated with tumor burden and may contribute to the exacerbation of infertility through the mediation of inflammatory responses. 8 Meanwhile, the expression levels of TRF, UIBC, and TIBC in stage III–IV patients tended to be higher than those in stage I–II patients; however, these differences did not reach statistical significance. This observation deviated from the overall trend and may reflect a phenomenon termed “phased contradiction.” The reasons for this trend are analyzed as follows: (1) The relatively small sample size introduced a pronounced statistical bias. Following staging, the number of cases in each group decreased substantially (e.g., only nine cases in stage I–II), leading to high variability and enabling individual extreme values to disproportionately affect the mean, thereby contributing to sample bias. (2) Disease progression may be associated with changes in iron homeostasis regulation mechanisms. In advanced EMs (stages III–IV), persistent inflammation could potentially lead to increased TRF levels to facilitate the transport of excess iron ions, resulting in a “stress‐induced increase in TRF.” This phenomenon represents a compensatory mechanism and is consistent with the iron redistribution characteristics observed in chronic diseases. 9 (3) A discrepancy exists between local and peripheral iron metabolism. EMs patients may exhibit localized iron deposition in the abdominal cavity; however, this phenomenon might not be adequately reflected in peripheral blood. Specifically, in advanced‐stage patients with tissue inflammation and fibrosis, iron may become sequestered within cystic structures, leading to aberrant alterations in serum markers. 10 Therefore, despite the observed fluctuations in TRF, UIBC, and TIBC, the overall conclusion of increased iron load remained valid. In EMs, the retrograde flow of menstrual blood and subsequent inflammatory responses within the local tissue microenvironment may contribute to abnormal iron accumulation. This iron overload has been shown to exacerbate the pathological progression of EMs by inducing oxidative stress, promoting apoptosis, and causing DNA damage, 11 which in turn may impair ovarian function, disrupt embryo implantation, and compromise reproductive capacity. 12 Specifically, iron metabolic disorders encompass the following aspects: (1) abnormal levels of ferritin and SI: Ng SW et al. 12 demonstrated that patients with EMs may exhibit altered iron storage, which subsequently modulates the local immune microenvironment. (2) oxidative stress induced by iron overload: excessive iron accumulation can lead to the overproduction of reactive oxygen species (ROS) through the “Fenton reaction” thereby exacerbating inflammatory responses and contributing to tissue damage. (3) Dysregulation of iron metabolism‐related molecules, such as hepcidin, ferroportin, and TRF receptor, may contribute to the progression of EMs lesions and the onset of infertility. In addition, infertile patients often present with systemic iron metabolism disorders, which may be closely linked to the endometrial microenvironment, ovarian reserve function, and impaired embryo implantation. 13 Periodic bleeding of ectopic endometrial lesions leads to local iron deposition. Excessive iron accumulation induces the production of ROS, thereby causing cellular and tissue injury. This process further triggers the inflammatory cascade and promotes angiogenesis, which aligns closely with the “iron‐mediated oxidative stress theory” previously described in the literature. 14 This study demonstrated that the reduction in TRF was attributable to the suppression of hepatic TRF synthesis caused by excessive iron accumulation. Concurrently, as a key mediator of iron transport, the decreased levels of TRF may be linked to the impaired iron redistribution resulting from the upregulation of hepatic hepcidin expression, which plays a critical role in regulating iron homeostasis. This relationship is modulated by factors such as inflammation and iron status, which may consequently affect the development of treatment strategies for iron overload and other related diseases. Simultaneously, the pronounced reduction in TIBC further intensifies the tissue toxicity of free iron, ultimately forming a vicious cycle. 15 It is noteworthy that no statistically significant differences were observed in sTfR and TS between the two groups ( p  > 0.05). This may indicate that the cellular iron uptake mechanism in patients with EMs does not experience marked alterations, and the imbalance in iron metabolism is more likely to be attributed to an impaired iron storage and transport system rather than a dysfunction in iron utilization. 16 This finding establishes a theoretical foundation for the development of targeted intervention strategies to regulate iron homeostasis. 17 In mouse models, curcumin alone or in combination with deferoxamine was found to promote cell proliferation in a rat model of EMs. Iron chelators may similarly facilitate cellular responses in women with EMs. 18 (2) The clinical diagnostic value of integrating multiple indicator measurements. ROC curve analysis demonstrated that the combined detection of SI, TRF, UIBC, and TIBC yielded statistically significant results for the differential diagnosis of EMs ( p  < 0.01). Meanwhile, by determining the cutoff values of iron metabolism‐related indicators, the diagnostic specificity for infertility associated with EMs was significantly enhanced, thereby providing a quantitative and objective tool for non‐invasive screening. This study, for the first time, established the diagnostic threshold of iron metabolism in patients with infertility caused by EMs, offering a novel and effective approach to reducing laparoscopic trauma. Compared with traditional single biomarkers (e.g., CA125), the combined detection model proposed in this study is capable of more comprehensively reflecting the dynamic imbalance of iron metabolism through the complementation of multi‐dimensional parameters, thereby effectively addressing the limitation of insufficient sensitivity inherent in single indices. For instance, the synergistic alteration pattern of elevated SI and decreased TRF may serve as a specific indicator of iron accumulation characteristics within the microenvironment of EMs lesions. The interplay between heightened iron levels and diminished TRF is modulated by factors such as iron overload, inflammation, and cytokine activity. These interactions play a pivotal role in elucidating the body's responses to various diseases and conditions, thereby emphasizing the dual significance of TRF as both a marker and mediator in iron metabolism and inflammatory processes. 9 In addition, detection based on iron metabolism indices not only provides the advantages of low cost and operational simplicity but also, when integrated with imaging examinations, is expected to reduce dependence on invasive diagnostic procedures such as laparoscopy. Consequently, it holds substantial potential for facilitating early screening of EMs in primary healthcare settings. (3) Research innovations and limitations. In conclusion, iron metabolism plays a pivotal role in the pathogenesis of EMs. Oxidative stress and inflammation induced by iron overload may contribute to the survival and progression of ectopic lesions. Furthermore, genes and proteins implicated in iron metabolism could serve as promising targets for the diagnosis and treatment of EMs. The innovation of this study is reflected in the following three aspects: (1) uncovering the distinct characteristics of iron metabolism indices in EMs patients, thereby providing a novel dimension for disease classification and prognosis evaluation; (2) developing a more clinically applicable diagnostic model through multi‐parameter joint analysis, addressing the limitations of prior studies that predominantly relied on single indicators such as CA125; (3) providing an experimental basis for elucidating the regulatory mechanisms of the inflammatory microenvironment in EMs from the perspective of iron metabolism. However, this study still has certain limitations that should be acknowledged: (1) the sample size and geographical distribution of the population need to be further expanded to ensure the robust validation of the findings' generalizability; (2) the correlation between iron metabolism indices and the pain severity of EMs remains to be fully explored; (3) longitudinal follow‐up studies assessing the intervention treatment of iron overload are currently insufficient. In the future, we aim to conduct multi‐center cohort studies and animal model experiments in conjunction with iron chelators and other therapeutic strategies to investigate the potential role of iron metabolism regulation in the targeted treatment of EMs.

The authors declare that there are no potential conflicts of interest concerning the research, authorship, and publication of this article.

According to the World Health Organization, infertility is defined as the failure of a couple of reproductive age to achieve pregnancy after 12 months or more of regular, unprotected sexual intercourse. All patients included in this study met this criterion, with male factors excluded as causes of infertility. The patients in the study group were diagnosed with infertility accompanied by ovarian cysts of undetermined etiology. Imaging findings suggested a likely diagnosis of ovarian endometriotic cysts, prompting the need for laparoscopic exploration. During surgery, the severity and distribution of lesions were recorded in accordance with the r‐AFS staging system. From December 2023 to December 2024, a total of 36 female patients aged 20 to 45 years were recruited from the gynecological department of Baise People's Hospital and underwent laparoscopic surgery for infertility treatment. These patients were assigned to the EMs with infertility group (hereafter referred to as the study group). According to the 1985 revised Endometriosis Staging System of the American Fertility Society (r‐AFS), 9 cases were classified as stage I–II (mild), and 27 cases were classified as stage III–IV (moderate‐to‐severe). Meanwhile, 40 infertile patients without EMs who presented to the hospital during the same period were enrolled as the control group. Inclusion criteria: (1) women in a non‐pregnant state; (2) no history of iron or hormonal supplementation in the past month; (3) no history of hepatitis or other infectious diseases; (4) no comorbidities of severe systemic medical or surgical conditions, such as cardiovascular, hepatic, cerebral, renal, endocrine, or autoimmune diseases, systemic lesions, or malignant tumors, etc. Exclusion criteria: (1) patients with recent severe acute infections, active inflammation, or ongoing bleeding; (2) patients who received blood transfusions within the past month; (3) patients with anemia or iron overload disorders, such as hemochromatosis or Wilson's disease; (4) patients with incomplete clinical records. The study was formally approved by the Ethics Committee of Baise People's Hospital and conducted in strict accordance with the principles of the Declaration of Helsinki. All patients in both groups provided 3 mL of fasting blood samples before surgery. The samples were centrifuged at 3000 r/min for 10 min, and the resultant supernatant was collected and stored at −80°C in a freezer for subsequent analysis. The specimens were transported to Kingmed Clinical Laboratories (Guangxi) Co., Ltd. in China for analysis. The analyses involved the determination of serum iron (SI), unsaturated iron binding capacity (UIBC), total iron binding capacity (TIBC), and transferrin saturation (TS) by colorimetry, as well as the quantification of transferrin (TRF) and soluble transferrin receptor (sTfR) by immunoturbidimetry. Reagents for measuring SI, UIBC, TIBC, TS, and TRF were supplied by Shenzhen Mindray Bio‐Medical Electronics Co., Ltd. (China). The reagent for detecting sTfR was supplied by Ningbo Purebio Biotechnology Co., Ltd. (China). The tests were carried out strictly following the instructions of each reagent kit to assess the expression levels of iron metabolism indicators in each group of specimens. SPSS 25.0 statistical software was used for data processing and statistical analysis, whereas GraphPad Prism 9.0 was utilized for figure creation. In this study, the normality of the data distribution was initially assessed based on the data type and sample size. Data conforming to a normal distribution were presented as mean ± standard deviation ( x ¯ ± S ) and compared using an independent‐samples t ‐test. Non‐normal data were presented as median (interquartile range) [M (P25, P75)] and compared using a non‐parametric test. The receiver operating characteristic (ROC) curve was utilized to evaluate the diagnostic performance of iron metabolism‐related parameters in terms of sensitivity and specificity for disease identification. Statistical significance was defined as a p ‐value <0.05.