Risk Factors and a Predictive Model for the Co-Occurrence of Endometrial Polyps in Patients with Endometriosis: A Retrospective Study

Background: The incidence of endometrial polyps (EPs) is higher in patients with endometriosis (EM) compared to the general pop- ulation. This study aims to analyze the various indices in EM patients with and without EPs and to establish an effective combined prediction model to predict the presence of EPs in EM patients. Method: This retrospective study included 1250 EM patients. Logis- tic regression analysis was employed to develop a combined diagnostic model. Results: Compared to EM patients without EPs, those with EPs had significantly higher age, gravidity, parity, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), luteinizing hormone (LH), estradiol (E 2), platelet (PLT), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting plasma glucose (FPG), and significantly lower hemoglobin (HGB) and white blood cells (WBCs) ( p < 0.05). After adjusting for potential confounding factors, a prediction model for the presence of EPs in EM patients was developed based on BMI, DBP , gravidity, parity, LH, WBCs, HGB, TC, and FPG. The receiver operating characteristic (ROC) area under the curve (AUC) for the combined diagnostic model was 0.78 (95% confidence interval (95% CI): 0.75–0.82, p < 0.001). The sensitivity, specificity, cut-off value, and Y ouden index of the model were 77.6%, 66.1%, 0.159, and 0.437, respectively. Conclusions: Metabolic alterations were found to be associated with the presence of EPs in EM patients. The diagnostic model based on these potential risk factors may offer a novel approach for the early diagnosis and targeted treatment of EPs in EM patients.

endometrial polyps; endometriosis; prediction model 1. Introduction Endometriosis (EM) is a gynecological condition af- fecting over 170 million women worldwide [ 1], and has a prevalence ranging from 6–10% in the general population [2]. It is characterized by the abnormal growth of endome- trial tissue (glands and stroma) outside the uterus, typically in areas such as the ovaries, fallopian tubes, and pelvic cav- ity. EM is known to be estrogen-dependent. Symptoms often include dysmenorrhea, abnormal menstruation, dys- pareunia, and infertility. It is estimated that between 25% to 50% of patients undergoing fertility treatments have a history of EM, as it often correlates with a reduction in ovar- ian reserve [ 3]. Patients with EM often endure both phys- ical and mental challenges, including depression and anxi- ety, which significantly impact their quality of life. These struggles also contribute to a substantial economic burden on both individuals and communities. Endometrial polyps (EPs) are characterized by the overgrowth of endometrial glands and stroma within the uterine cavity [ 4]. These polyps can vary in size, ranging from tiny fractions of a millimeter to several centimeters in diameter, and are commonly observed in women aged 40 to 49 years. They are detected in approximately 10 percent of women during autopsy [ 5]. Patients with EPs may be asymptomatic, but the most common symptom is abnormal uterine bleeding, which does not correlate with the size or growth rate of EPs [ 6]. Other rare symptoms that may be associated with EPs include abdominal pain, pelvic pain, and infertility. In asymptomatic patients, EPs may regress spontaneously with menstruation. Hysteroscopic excision is recommended as a safe and efficient treatment for symp- tomatic women, as well as for those in the perimenopause and postmenopausal stages [ 7]. In clinical practice, we observed a higher incidence of EPs in patients with EM. Considering the common ab- normalities in the biological behavior of endometrial cells, we hypothesize a relationship between the development of these two diseases. Previous study has reported a higher oc- currence of EPs in women with EM who also experience in- Fig. 1. Flowchart of the retrospective study design. EM, endometriosis; EPs, endometrial polyps. fertility [8]. EPs were detected by hysteroscopy in 47.83% of the EM group and 29.82% of the control group among infertile patients [9]. Both conditions involve excessive en- dometrial growth, and various mechanisms contribute to the association between EM and EPs. At present, the pathogen- esis of EM and EPs remains unclear. Furthermore, there is currently no noninvasive test that can fully confirm the presence of polyps in patients with EM. Our study conducted a retrospective analysis of the clinical characteristics of EM patients, comparing those with and without EPs. Additionally, we developed a predic- tive model to identify the presence of EPs in EM patients. This model provides insights into the mechanisms underly- ing the co-occurrence of polyps in patients with EM. 2. Materials and Methods This retrospective case-control study was conducted at the Women’s Hospital, Zhejiang University School of Medicine, China, and was approved by the Institutional Ethics Committee. Subjects were identified from the elec- tronic medical record system of Women’s Hospital, Zhe- jiang University School of Medicine, covering the period from August 1, 2020 to July 31, 2021. A cohort of 1250 EM patients who underwent surgical interventions at this hos- pital was included in the study, and their clinical data were included in the final statistical analysis (Fig. 1). All pa- tients underwent laparoscopic resection of ectopic lesions. If B-mode ultrasound indicated the presence of EPs before surgery, simultaneous hysteroscopy was performed, and the diagnoses of EM and EPs were pathologically confirmed. Patients scheduled for surgery were hospitalized within 3– 7 days of the onset of their menstrual cycle, without con- sidering hormone replacement therapy. Age, body weight, height, fertility status, blood pressure, and laboratory in- dices were extracted from medical records. Reproductive history was self-reported, and baseline clinical characteris- tics were assessed at hospital admission. This encompassed various factors such as body weight, height, and blood pres- sure. Body mass index (BMI) was determined by divid- ing weight in kilograms by the square of height in meters. According to Chinese adult criteria, BMI categories were classified as follows: underweight ( <18.5 kg/m 2), normal weight (18.5–23.9 kg/m 2), overweight (24.0–27.9 kg/m 2), and obesity (≥28 kg/m2) [10]. Upon hospital admission, a routine blood examina- tion, glucose and lipid metabolism parameters, and repro- ductive endocrine hormone measurements were conducted on peripheral blood collected from all subjects. Partici- pants fasted for at least 8 h before blood sampling. The evaluated indices included serum levels of hemoglobin (HGB), white blood cells (WBCs), platelet (PLT), fast- ing plasma glucose (FPG), high-density lipoprotein choles- terol (HDL-C), low-density lipoprotein cholesterol (LDL- C), total cholesterol (TC), triglycerides (TG), estradiol (E2), follicle-stimulating hormone (FSH), luteinizing hor- mone (LH), prolactin (PRL), and anti-Müllerian hormone (AMH). All measurements were conducted utilizing a Roche Modular Analytics E170 fully automated analyzer (Roche Diagnostics, Mannheim, Germany). All patients underwent ultrasound examinations after admission, and those with ultrasound findings suggestive of EPs were con- firmed to exhibit EPs during hysteroscopy. Postoperative pathology confirmed the diagnosis of EPs in all 248 EM patients, while 1002 patients were diagnosed without EPs. Statistical analyses were conducted using SPSS for Windows (V ersion 24.0., IBM Corp., Armonk, NY , USA), with statistical significance set at a two-sided p < 0.05. Continuous variables were expressed as mean ± standard 2 Table 1. The baseline clinical characteristics of the EM group and EM combined with EPs group a. EM group (n = 1002) EM combined with EPs group (n = 248) p-value Age (years) 32.3 ± 5.0 35.4 ± 7.5 <0.001 Height (cm) 160.5 ± 7.0 161.3 ± 4.7 0.088 Weight (kg) 54.7 ± 7.5 57.4 ± 7.5 <0.001 BMI (kg/m2) 21.2 ± 2.8 22.4 ± 2.9 <0.001 <18.5 (n = 154) 137 (13.7%) 17 (6.9%) 0.003 ≥18.5 to <24 (n = 881) 719 (71.8%) 162 (65.3%) 0.047 ≥24 to <28 (n = 188) 132 (13.2%) 56 (22.6%) <0.001 ≥28 (n = 27) 14 (1.4%) 13 (5.2%) <0.001 SBP (mmHg) 115.0 ± 10.8 117.8 ± 11.7 0.001 DBP (mmHg) 71.2 ± 8.4 74.5 ± 9.0 <0.001 Parity 0 (0, 0) 0 (0, 1) <0.001 Abortion 0 (0, 1) 0 (0, 1) 0.931 Gravidity 0 (0, 1) 1 (0, 2) <0.001 a Continuous variables are presented as mean ± standard deviation (SD), and skewed variables are presented as the median (interquartile range). Student’s t-test for independent samples was used for normally distributed continuous variables, and the Chi-squared ( χ2) test for categorical variables. BMI, body mass index; EPs, endometrial polyps; EM, endometriosis; SBP , systolic blood pressure; DBP , diastolic blood pressure. deviation (SD), while skewed variables are presented as median (interquartile range) and use non-parametric test (Mann-Whitney U test). Student’s t-test for independent samples was employed for normally distributed continuous variables, and the Chi-squared (χ2) test was applied for cat- egorical variables. Logistic regression analysis, utilizing the Enter method, was employed to investigate the risk factors asso- ciated with the concurrent presence of EPs in EM patients. A prediction model was also constructed. Receiver oper- ating characteristic (ROC) curve analysis was used to as- sess the performance of this predictive model. The Y ouden index, representing the maximum sum of sensitivity and specificity across all feasible cut-off points, was mathemat- ically defined as J = sensitivity + specificity – 1 [ 11]. 3. Results Our study included 248 EM patients with EPs and 1002 EM patients without EPs. Table 1 presents the base- line clinical characteristics of the participants. Participants in the EM combined with EPs group exhibited older age (35.4 years vs. 32.3 years, p < 0.001), higher gravidity (1 vs. 0, p < 0.001), parity (0 vs. 0, p < 0.001), higher mean BMI (22.4 kg/m 2 vs. 21.2 kg/m 2, p < 0.001), higher sys- tolic blood pressure (SBP) (117.8 mmHg vs. 115.0 mmHg, p = 0.001), and higher diastolic blood pressure (DBP) (74.5 mmHg vs. 71.2 mmHg, p < 0.001). There was no statisti- cally significant difference observed in height and abortion between the two groups ( p = 0.088 and p = 0.931, respec- tively). As shown in Table 2, the PLT count (245.6 × 109/L vs. 256.6 × 109/L, p = 0.016), TC (4.4 mmol/L vs. 4.6 mmol/L, p = 0.004), LDL-C (2.6 mmol/L vs. 2.8 mmol/L, p < 0.001), LH (5.1 IU/L vs. 7.8 IU/L, p < 0.001), E2 (132.8 pmol/L vs. 198.9 pmol/L, p < 0.001) and FPG (5.1 mmol/L vs. 5.4 mmol/L, p < 0.001) were significantly higher in the EM combined with EPs group compared to the EM group. In contrast, HGB (121.9 g/L vs. 126.3 g/L, p < 0.001) and WBCs (5.9 × 109/L vs. 6.4 × 109/L, p = 0.002) were sig- nificantly higher in the EM group. There were no statis- tically significant differences in PRL, FSH, TG, HDL-C, and AMH between the two groups ( p = 0.790, p = 0.938, p = 0.096, p = 0.398 and p = 0.816, respectively). After adjusting for potential confounding factors (age, SBP , E2, LDL-C), logistic regression analysis employing the enter method, revealed significant correlations between the incidence of EPs in patients with EM and several fac- tors, including BMI, DBP , parity, gravidity, WBCs, HGB, LH, FPG, and TC ( p < 0.001 for BMI, DBP , parity, gra- vidity, LH; p = 0.024 for WBCs; p = 0.011 for HGB; p = 0.010 for FPG; and p = 0.008 for TC). A combined pre- diction model based on BMI, DBP , gravidity, parity, LH, WBCs, HGB, TC, and FPG was established (Table 3). Us- ing the EM group as a reference, we plotted the ROC curve to analyze the diagnostic efficacy of the combined model. The ROC curve demonstrated that the combined diagnostic model had an area under the curve (AUC) of 0.78 (0.75– 0.82, p < 0.001) (Fig. 2). The optimal cut-off point was de- termined as the point on the ROC curve closest to the (0, 1) point. The optimal cut-off value, determined as 0.159 with a Y ouden index of 0.437, provided the best balance between sensitivity (77.6%) and specificity (66.1%), as shown in Ta- ble 4. 3 Table 2. Laboratory indeces of the EM group and EM combined with EPs group. Indeces EM group (n = 1002) EM combined with EPs group (n = 248) p-value PRL (ng/mL) 14.6 (0.0, 21.5) 16.6 (12.3, 23.4) 0.790 LH (IU/L) 5.1 (3.7, 7.0) 7.8 (5.4, 11.1) <0.001 FSH (IU/L) 7.0 (5.7,8.7) 6.2 (5.1, 8.1) 0.938 E2 (pmol/L) 132.8 (94.8, 182.8) 198.9 (113.0, 389.0) <0.001 WBCs (109/L) 6.4 ± 2.3 5.9 ± 1.8 0.002 PLT (109/L) 245.6 ± 63.0 256.6 ± 69.0 0.016 HGB (g/L) 126.3 ± 12.5 121.9 ± 14.5 <0.001 TG (mmol/L) 0.9 (0.7, 2.0) 1.0 (0.7, 1.3) 0.096 TC (mmol/L) 4.4 ± 0.8 4.6 ± 0.8 0.004 LDL-C (mmol/L) 2.6 ± 0.7 2.8 ± 0.6 <0.001 HDL-C (mmol/L) 1.4 ± 0.3 1.4 ± 0.4 0.398 FPG (mmol/L) 5.1 ± 0.7 5.4 ± 1.1 <0.001 AMH (ng/mL) 2.1 (1.1–3.7) 2.2 (1.1–3.7) 0.816 Steroid hormone levels measured in 3–7 days after the onset of menstrual cycle, without considering hormone replacement therapy. EPs, endometrial polyps; EM, endometriosis; HGB, hemoglobin; WBCs, white blood cells; PLT, platelet; FPG, fasting plasma glucose; HDL-C, high-density lipoprotein cholesterol; LDL-C, low- density lipoprotein cholesterol; TC, total cholesterol; TG, triglycerides; E 2, estradiol; FSH, follicle- stimulating hormone; PRL, prolactin; LH, luteinizing hormone; AMH, anti-Müllerian hormone. Fig. 2. ROC curve for the prediction model based on BMI, DBP, gravidity, parity, WBCs, HGB, TC, FPG and LH. AUC, area under the curve; ROC, receiver operating characteristic; BMI, body mass index; DBP , diastolic blood pressure; WBCs, white blood cells; HGB, hemoglobin; TC, total cholesterol; FPG, fasting plasma glucose; LH, luteinizing hormone; CI, confidence interval. 4. Discussion In this study, we conducted a retrospective analysis to compare the differences in laboratory indices and baseline clinical features between EM patients with and without EPs. After adjusting for potential confounding factors, we devel- oped a prediction model incorporating BMI, DBP , gravid- ity, parity, WBCs, HGB, TC, FPG, and LH to predict the concurrent presence of EPs in patients with EM. The ROC curve for this combined diagnostic model yielded an AUC of 0.78, with sensitivity of 77.6%, specificity of 66.1%, cut- off value of 0.159, and Y ouden index of 0.437. 4.1 Evidence for the Co-Occurrence of EPs in EM Patients A 2015 meta-analysis involving 2896 women sug- gested a higher risk of EPs in women with EM compared to those without, with a pooled relative risk (RR) 2.81 and 95% confidence interval (95% CI): 2.48–3.18. Previous studies have recommended hysteroscopy for infertile pa- 4 Table 3. Logistic regression to construct the prediction model. Parameters β coefficient p-value OR 95% CI BMI 0.108 <0.001 1.114 1.051–1.180 DBP 0.036 <0.001 1.036 1.016–1.057 Parity 1.690 <0.001 5.422 3.452–8.514 Gravidity –0.541 <0.001 0.582 0.461–0.734 LH 0.038 <0.001 1.039 1.021–1.057 WBCs –0.088 0.024 0.915 0.848–0.988 HGB –0.015 0.011 0.985 0.974–0.997 TC 0.255 0.008 1.290 1.068–1.558 FPG 0.243 0.010 1.275 1.059–1.534 Constant –6.860 OR, odds ratio; CI, confidence interval; BMI, body mass index; DBP , diastolic blood pressure; WBCs, white blood cells; LH, luteinizing hormone; HGB, hemoglobin; TC, total cholesterol; FPG, fasting plasma glucose. Table 4. Y ouden index of the prediction model. Cut-off point Sensitivity Specificity Y ouden index* p-value 0.159 77.6% 66.1% 0.437 *Y ouden index formula is defined as J = sensitivity + specificity – 1. tients with EM [12,13]. In our clinical experience, while we cannot provide exact data in this study, many EM patients admitted for surgery concurrently present with EPs, as iden- tified by B-mode ultrasonography during routine preopera- tive exams. This discovery impacts surgical plans, neces- sitating both laparoscopic and hysteroscopic approaches, thereby increasing the complexity and duration of the pro- cedure. 4.2 Metabolic Alterations in EPs and EM Metabolic alterations, including changes in various energy-related, ketogenic, and glucogenic metabolites, have been reported to at different stages of EM [ 14]. Glu- cose metabolism is generally believed to be increased in EM patients [ 15], accompanied by increased levels of TG, TC, and LDL-C, rendering them more prone to hyperc- holesterolemia and hypertension [ 16,17]. Additionally, a higher BMI has been associated with a reduced risk of EM [18]. In contrast, risk factors for EPs include advanced age, tamoxifen use, inflammation, obesity, hypertension, dia- betes, endocrine dysfunction, and altered estrogen secretion [4,6]. Metabolic parameters such as BMI, insulin levels, waist circumference (WC), and the homeostatic model as- sessment of insulin resistance (HOMA-IR) have also been correlated with the presence of EPs [ 19]. In summary, both EM and EPs, which are prevalent in women of child- bearing age, exhibit abnormal biological behavior of en- dometrial cells [20,21] and metabolism disorders [ 22]. EPs are primarily associated with dysregulated lipid metabolism and increased BMI, while EM is linked to impaired glu- cose metabolism and decreased BMI. Based on the above- described clinical observation and similarities analogy in the pathogenesis in EM and EPs, we propose the following hypothesis: metabolic dysregulation may play a significant role in the development of both conditions, with potential differences in metabolic profiles between EM patients with and without EPs. However, few studies have examined the differences in metabolic markers between these two groups. Identifying these metabolic disparities could facilitate early diagnosis and development of tailored treatment strategies for EPs in individuals with EM. Our findings show that EM patients with EPs had significantly higher BMI, SBP , DBP , TC, LDL-C, FPG, and PLT compared to those without EPs. However, our metabolic indicators may not fully capture the alterations in metabolic profile. Further research is war- ranted to elucidate the precise mechanisms underlying these metabolic differences and their roles in the development of EPs among individuals with EM. 4.3 Steroid Hormone Dysregulation in EPs and EM It was well known that elevated levels of E 2 is one of the contributing factors to EM. Exposure to diethyl- stilbestrol and an early age at menarche have been re- ported to correlate with an increased risk of EM [ 23]. An early age at menarche indicates an earlier onset of ovula- tion and prolonged exposure to estrogen and progesterone. EM is dependent on estrogen for its continued growth. The onset of EPs may involve both estrogen-related and non-estrogen-related pathways, with potential overlap be- tween these mechanisms. On the other hand, rearrange- ments within the family of high mobility group (HMG) tran- scription factors, potentially resulting from a mechanism in- volving aromatase-dependent focal hyperestrogenism, have been identified in EPs. Immunohistochemistry has revealed increased estrogen receptor expression in EPs [ 24], further elucidating hormonal imbalances present in these lesions. While EM may result from prolonged or heightened estro- gen exposure, EPs primarily arise from focal hyperestro- genism. However, research on the distinct hormonal patho- genesis of EM and EPs is currently lacking. The prevalence of EM and EPs in adolescence is very low; however, a cor- relation exists with steroid hormone levels. Diagnosing EM in adolescents poses a clinical challenge due to prolonged delays in diagnosis. However, early imaging interventions can help in mitigate this delay, particularly in young pa- tients exhibiting suggestive symptoms, such as severe men- strual cramps or abnormal uterine bleeding [ 25,26]. Our study found that patients with EM combined with EPs had higher levels of LH and E 2 compared to EM-only patients, warranting further investigation into the underly- ing pathogenesis. Additionally, AMH levels were similar in both groups, indicating no significant difference in ovarian reserve function and suggesting that variations in ovarian reserve contribute minimally to the pathogenesis of EP in patients with EM. 5 4.4 Inflammation Indices in EPs and EM There may be a dependent relationship between chronic endometritis (CE) and EPs in premenopausal women [ 27]. The inflammatory response in patients with EM can impact glucose and lipid metabolism [16], although studies on systemic inflammatory markers are limited. A study published in 2016 found that in both EM patients and the control group exhibited largely similar profiles of three categories of molecules associated with systemic in- flammation: oxylipins, immunomodulatory proteins, and C-reactive protein (CRP) [ 28]. Interestingly, we observed higher PLT levels, lower HGB, and lower WBCs in EM pa- tients with EPs compared to those without EPs for the first time. Lower WBCs counts reflect abnormal immune func- tion and a different inflammatory state in EM patients with EPs. On the other hand, lower HGB may contribute to a higher risk of anemia, indicating dysregulated inflamma- tion and immune response in EM patients with EPs. Col- lectively, these findings suggest a potential role of systemic inflammation in the development of EPs in individuals with EM. As demonstrated above, we have identified numerous differences between EM patients with and without EPs. The convergence of these various factors strongly suggests the potential role of steroid hormones and inflammation in the development of EM [ 29]. In order to develop a more pre- cise treatment strategy for EM patients, we established a model based on these different indices to predict the pres- ence of EPs in patients with EM. Although this prediction model has not been fully validated in infertile patients, it still provides valuable insights. Patients with EM preparing for surgery can undergo hysteroscopy if they meet the crite- ria of this prediction model, thus avoiding a second anesthe- sia and surgery. Hysteroscopy is clinically recommended for infertile patients with EM who are planning to undergo laparoscopic surgery. However, as an invasive procedure, hysteroscopic surgery carries risks, including water intoxi- cation and uterine perforation. Furthermore, postoperative risks, including pelvic infection, intrauterine adhesion, cer- vical adhesion and cervical incompetence, may adversely affect subsequent pregnancy process. Our prediction model offers a strategy for early diagnosis and targeted treatment of patients with EM, potentially reducing unnecessary hys- teroscopies, safeguarding fertility, and improving repro- ductive outcomes. Further studies using in vivo and in vitro models may help elucidate the pathogenesis of EPs in EM patients and identify potential therapeutic targets. 4.5 Strengths and Limitations The strengths of this study lie in its thorough analysis of the differences between EM patients with and without EPs, as well as in the establishment of an efficient com- bined prediction model. This model predicts the presence of EPs in patients with EM for the first time, providing valu- able insights into diagnostic and treatment strategies. The primary limitation is the lack of accurate incidence rates of EPs in patients with EM. The absence of precise data may affect the generalizability of the findings and the reliability of the prediction model. The large number of included pa- tients facilitates the identification of statistically significant differences, which may not always translate to clinical sig- nificance. Additionally, we did not consider the severity or stage of EM and the size of EPs due to the limited number of subjects. Further investigations with larger sample sizes are warranted to explore whether the severity of EM corre- lates with the incidence of EPs. This could provide deeper insights into the relationship between these two conditions and assist in refining diagnostic and management strategies. 5. Conclusions Certain metabolic alterations were associated with the presence of EPs in patients with EM. The development of a diagnostic model incorporating these potential risk factors could offer a novel approach for the early detection and tar- geted treatment of EPs in individuals with EM. Abbreviations EPs, endometrial polyps; EMs, endometriosis; BMI, body mass index; SBP , systolic blood pressure; DBP , dias- tolic blood pressure; LH, luteinizing hormone; E 2, estra- diol; PLT, platelet; TC, total cholesterol; LDL-C, low- density lipoprotein cholesterol; FPG, fasting plasma glu- cose; HGB, hemoglobin; WBCs, white blood cells; ROC, receiver operating characteristic; AUC, area under the curve; HOMA-IR, homeostatic model assessment of in- sulin resistance; HMG, high mobility group; AMH, anti- Müllerian hormone; CE, chronic endometritis; CRP , C- reactive protein. Availability of Data and Materials The datasets used and analyzed during the current study are available from the corresponding author on rea- sonable request. Author Contributions JYL and JHZ designed the study. XJW, JL and JPC supervised the laboratory exams and data collection. ZMS and TZ analyzed and interpreted the data. ZMS and JYL wrote the first draft of the paper. JYL edited the paper. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript. All au- thors have participated sufficiently in the work and agreed to be accountable for all aspects of the work. Ethics Approval and Consent to Participate This retrospective study was approved by the ethics committee of the Women’s Hospital, Zhejiang University School of Medicine (IRB-20220207-R). This article does not contain any studies with animals performed by any of the authors. Furthermore, the consent of the study partici- pants was deemed unnecessary as the study only involves 6 the retrospective review of the medical database. The need of informed consent was waived by the ethics committee (Medical Ethics Committee of the Women’s Hospital, Zhe- jiang University School of Medicine) for this retrospective study. We confirm that all methods were performed in ac- cordance with the 1964 Declaration of Helsinki and its later amendments. Acknowledgment We thank all the patients and their families for their cooperation and contribution. Funding This study was funded by the National Natural Sci- ence Foundation of China (No. 82001537), the Funda- mental Research Funds for the Central Universities (No. 2021FZZX003-02-18) and Zhejiang University Education Foundation Global Partnership Fund. Conflict of Interest The authors declare no conflict of interest.

[1] Della Corte L, Di Filippo C, Gabrielli O, Reppuccia S, La Rosa VL, Ragusa R, et al. The Burden of Endometriosis on Women’s Lifespan: A Narrative Overview on Quality of Life and Psy- chosocial Wellbeing. International Journal of Environmental Re- search and Public Health. 2020; 17: 4683. [2] Greene AD, Lang SA, Kendziorski JA, Sroga-Rios JM, Herzog TJ, Burns KA. Endometriosis: where are we and where are we going? Reproduction. 2016; 152: R63–R78. [3] Calagna G, Della Corte L, Giampaolino P , Maranto M, Perino A. Endometriosis and strategies of fertility preservation: a sys- tematic review of the literature. European Journal of Obstetrics, Gynecology, and Reproductive Biology. 2020; 254: 218–225. [4] Li F, Wei S, Y ang S, Liu Z, Nan F. Post hysteroscopic proges- terone hormone therapy in the treatment of endometrial polyps. Pakistan Journal of Medical Sciences. 2018; 34: 1267–1271. [5] Mansour T, Chowdhury YS. Endometrial Polyp. StatPearls Pub- lishing: Treasure Island (FL). 2023. [6] Wong M, Crnobrnja B, Liberale V , Dharmarajah K, Wid- schwendter M, Jurkovic D. The natural history of endometrial polyps. Human Reproduction. 2017; 32: 340–345. [7] Nijkang NP , Anderson L, Markham R, Manconi F. Endome- trial polyps: Pathogenesis, sequelae and treatment. SAGE Open Medicine. 2019; 7: 2050312119848247. [8] Lin S, Xie X, Guo Y , Zhang H, Liu C, Yi J, et al . Clinical characteristics and pregnancy outcomes of infertile patients with endometriosis and endometrial polyps: A retrospective cohort study. Taiwanese Journal of Obstetrics & Gynecology. 2020; 59: 916–921. [9] Zhang YN, Zhang YS, Y u Q, Guo ZZ, Ma JL, Y an L. Higher Prevalence of Endometrial Polyps in Infertile Patients with En- dometriosis. Gynecologic and Obstetric Investigation. 2018; 83: 558–563. [10] Zhou B, Coorperative Meta-Analysis Group Of China Obesity Task Force. Predictive values of body mass index and waist cir- cumference to risk factors of related diseases in Chinese adult population. Zhonghua Liu Xing Bing Xue Za Zhi. 2002; 23: 5– 10. (In Chinese) [11] Zhou H, Qin G. New nonparametric confidence intervals for the Y ouden index. Journal of Biopharmaceutical Statistics. 2012; 22: 1244–1257. [12] Kim MR, Kim Y A, Jo MY , Hwang KJ, Ryu HS. High fre- quency of endometrial polyps in endometriosis. The Journal of the American Association of Gynecologic Laparoscopists. 2003; 10: 46–48. [13] Shen L, Wang Q, Huang W, Wang Q, Y uan Q, Huang Y , et al. High prevalence of endometrial polyps in endometriosis- associated infertility. Fertility and Sterility. 2011; 95: 2722– 4.e1. [14] Dutta M, Singh B, Joshi M, Das D, Subramani E, Maan M, et al. Metabolomics reveals perturbations in endometrium and serum of minimal and mild endometriosis. Scientific Reports. 2018; 8: 6466. [15] Santonastaso M, Pucciarelli A, Costantini S, Caprio F, Sorice A, Capone F, et al. Correction: Metabolomic profiling and bio- chemical evaluation of the follicular fluid of endometriosis pa- tients. Molecular BioSystems. 2017; 13: 1246. [16] Melo AS, Rosa-e-Silva JC, Rosa-e-Silva ACJDS, Poli-Neto OB, Ferriani RA, Vieira CS. Unfavorable lipid profile in women with endometriosis. Fertility and Sterility. 2010; 93: 2433–2436. [17] Mu F, Rich-Edwards J, Rimm EB, Spiegelman D, Forman JP , Missmer SA. Association Between Endometriosis and Hyper- cholesterolemia or Hypertension. Hypertension. 2017; 70: 59– 65. [18] Liu Y , Zhang W. Association between body mass index and en- dometriosis risk: a meta-analysis. Oncotarget. 2017; 8: 46928– 46936. [19] Özkan NT, Tokmak A, Güzel Aİ, Özkan S, çİçek MN. The as- sociation between endometrial polyps and metabolic syndrome: a case-control study. The Australian & New Zealand Journal of Obstetrics & Gynaecology. 2015; 55: 274–278. [20] Y ang S, Wang H, Li D, Li M. Role of Endometrial Autophagy in Physiological and Pathophysiological Processes. Journal of Cancer. 2019; 10: 3459–3471. [21] Munro MG. Uterine polyps, adenomyosis, leiomyomas, and en- dometrial receptivity. Fertility and Sterility. 2019; 111: 629– 640. [22] Taylor HS, Kotlyar AM, Flores V A. Endometriosis is a chronic systemic disease: clinical challenges and novel innovations. Lancet. 2021; 397: 839–852. [23] Zondervan KT, Becker CM, Missmer SA. Endometriosis. The New England Journal of Medicine. 2020; 382: 1244–1256. [24] Kossaï M, Penault-Llorca F. Role of Hormones in Common Be- nign Uterine Lesions: Endometrial Polyps, Leiomyomas, and Adenomyosis. Advances in Experimental Medicine and Biol- ogy. 2020; 1242: 37–58. [25] Millischer AE, Santulli P , Da Costa S, Bordonne C, Cazaubon E, Marcellin L, et al. Adolescent endometriosis: prevalence in- creases with age on magnetic resonance imaging scan. Fertility and Sterility. 2023; 119: 626–633. [26] Martire FG, Piccione E, Exacoustos C, Zupi E. Endometrio- sis and Adolescence: The Impact of Dysmenorrhea. Journal of Clinical Medicine. 2023; 12: 5624. [27] Vitagliano A, Cialdella M, Cicinelli R, Santarsiero CM, Greco P , Buzzaccarini G, et al. Association between Endometrial Polyps and Chronic Endometritis: Is It Time for a Paradigm Shift in the Pathophysiology of Endometrial Polyps in Pre-Menopausal Women? Results of a Systematic Review and Meta-Analysis. Diagnostics. 2021; 11: 2182. [28] Lee YH, Cui L, Fang J, Chern BSM, Tan HH, Chan JKY . Limited value of pro-inflammatory oxylipins and cytokines as circulating biomarkers in endometriosis - a targeted ’omics study. Scientific Reports. 2016; 6: 26117. [29] Parazzini F, Esposito G, Tozzi L, Noli S, Bianchi S. Epidemiol- ogy of endometriosis and its comorbidities. European Journal of Obstetrics, Gynecology, and Reproductive Biology. 2017; 209: 3–7. 7