Diagnostic significance and predictive efficiency of metabolic risk score for fertility-sparing treatment in patients with atypical endometrial hyperplasia and early endometrial carcinoma

Abstract

Objective

This study aims to assess the impact of the metabolic risk score (MRS) on time to achieve complete remission (CR) of fertility-sparing treatments for atypical endometrial hyperplasia (AEH) and early endometrial cancer (EC) patients.

Methods

Univariate and multivariate cox analyses were employed to identify independent risk factors affecting the time to CR with patients at our center. These factors were subsequently incorporated into receiver operator characteristic curve analysis and decision curve analysis to assess the predictive accuracy of time to CR. Additionally, Kaplan–Meier analysis was utilized to determine the cumulative CR rate for patients.

Results

The 173 patients who achieved CR following fertility preservation treatment (FPT) were categorized into three subgroups based on their time to CR (<6, 6–9, >9 months). Body mass index (hazard ratio [HR]=0.20; 95% confidence interval [CI]=0.03, 0.38; p=0.026), MRS (HR=0.31; 95% CI=0.09, 0.52; p=0.005), insulin resistance (HR=1.83; 95% CI=0.05, 3.60; p=0.045), menstruation regularity (HR=3.77; 95% CI=1.91, 5.64; p=0.001), polycystic ovary syndrome (HR=−2.16; 95% CI=−4.03, −0.28; p=0.025), and histological type (HR=0.36; 95% CI=0.10, 0.62; p=0.005) were identified as risk factors for time to CR, with MRS being the independent risk factor (HR=0.29; 95% CI=0.02, 0.56; p=0.021). The inclusion of MRS significantly enhanced the predictive accuracy of time to CR (area under the curve [AUC]=0.789 for Model 1, AUC=0.862 for Model 2, p=0.032). Kaplan–Meier survival curves revealed significant differences in the cumulative CR rate among different risk groups.

Conclusion

MRS emerges as a novel evaluation system that substantially enhances the predictive accuracy for the time to achieve CR in AEH and early EC patients seeking fertility preservation.

Synopsis

The results of our study indicated that MRS is an independent risk factor for AEH and early EC patients, which could also improve the accuracy of the prediction for time to CR. We want to support the future investigations with these findings and provide some evidence to the therapy of fertility-sparing treatment for these patients.

Graphical Abstract

INTRODUCTION

Endometrial cancer (EC) stands as the most prevalent gynecological malignancy in developed nations [1]. Typically, it affects women in their postmenopausal years. However, approximately 5% of cases emerge in women under the age of 40. In such instances, EC often manifests as well-differentiated endometrioid adenocarcinoma with minimal myometrial invasion or lymph node metastasis [2]. The conventional treatment approach for EC patients involves radical surgery. Unfortunately, these surgical interventions frequently lead to the complete loss of fertility. Consequently, a significant number of women diagnosed with atypical endometrial hyperplasia (AEH) or early-stage EC decline standard treatment options [3]. Thus, the exploration of fertility-preserving treatment options through hormonal therapy is gaining prominence, offering a promising prospect for both patients and medical practitioners. Common conservative treatment modalities for AEH or early EC patients include progestin therapies, such as medroxyprogesterone acetate (MPA), megestrol acetate (MA), and the levonorgestrel intrauterine system [4]. These conservative approaches are associated with complete remission (CR) rates ranging from 70% to 80% [5], rendering them a viable option for AEH and early EC. Nonetheless, it is crucial to acknowledge that approximately 30% of patients experience only partial remission following conservative treatment. As such, an in-depth investigation into the risk factors influencing the time to achieve CR in AEH and early EC patients undergoing drug therapy is of paramount importance.

Metabolic syndrome (MetS) constitutes a cluster of metabolic disorders encompassing obesity, hypertension, diabetes, hyperlipidemia, and related conditions. Numerous studies have demonstrated that metabolic aberrations, including diabetes and hyperlipidemia, pose significant risks for the initiation and progression of EC, as well as other malignancies such as breast cancer, gastric cancer, and various others [6]. Additionally, reports have indicated that a body mass index (BMI) ≥25 kg/m² and advanced age at diagnosis are associated with reduced CR rates [7,8]. While the link between MetS and EC is well-documented, it remains unclear whether MetS exerts any influence on the efficacy of fertility-sparing treatments for AEH and early-stage well-differentiated EC. Recent attention has been directed towards a metabolic risk score (MRS) established upon a panel of markers, including BMI, pulse pressure (PP), fasting blood glucose (FBG), triglycerides (TG), and high-density lipoprotein (HDL). This score is emerging as a superior indicator of metabolic status, as evidenced by its robust predictive capacity compared to traditional clinicopathological characteristics across a spectrum of malignancies, including esophageal cancer [9].

In this investigation, we assessed the MRS in patients with AEH or early EC undergoing fertility-preserving treatment at our institution. We unveiled the correlation between MRS and the time required to achieve CR following the initiation of conservative therapy. Importantly, MRS emerged as an independent risk factor. To substantiate the impact of MRS on CR, we conducted categorical analyses and generated receiver operator characteristic (ROC) curves.

MATERIALS AND METHODS

1. Study design and participants

We conducted a retrospective investigation of eligible patients treated at Peking University People’s Hospital (PKUPH) between January 2012 and December 2021. Our study focused on women diagnosed with AEH or early-stage EC who underwent fertility-sparing treatments. We meticulously reviewed clinical and histopathological data, as well as follow-up information. Inclusion criteria encompassed patients who: 1) were aged 45 or younger, 2) expressed a strong desire for and consented to fertility-sparing treatment, 3) had no contraindications for progestin therapy, and 4) were not pregnant. Exclusion criteria comprised patients with: 1) severe medical conditions such as kidney impairment, dialysis, or compromised liver/cardiac function, and 2) missing medical records.

2. Patient evaluation

We retrieved demographic and clinical data from medical records, including age at diagnosis, BMI, gestation, parity, history of hypertension, diabetes mellitus (DM), menstrual regularity, polycystic ovary syndrome (PCOS), thyroid disease, family history, FBG, TG, HDL, systolic blood pressure, diastolic blood pressure, PP, homeostasis model assessment-insulin resistance (HOMA-IR), low-density lipoprotein (LDL), cancer antigen 125 (CA125), serum insulin resistance (IR), histological types, and time to CR. PCOS was diagnosed using the Rotterdam Consensus Criteria from 2003. Body weight and BMI were assessed at the time of diagnosis and on the last day of follow-up, with BMI categorized as normal (BMI <25 kg/m²) or overweight (BMI ≥25 kg/m²). Missing data were obtained from primary care clinicians. The duration from baseline histology to the last follow-up was recorded for all patients. We employed the HOMA-IR index to assess IR status, with the HOMA-IR value calculated as: FBG (mmol/L) × Fasting Insulin (μU/mL)/22.5. Based on the distribution of HOMA-IR values in non-diabetic patients from our prior study [10], we utilized a cutoff value of 2.95, corresponding to the lower limit of the top quarter [11]. MRS were evaluated following established criteria (Table S1) [12].

3. Conservative treatment and evaluation of response to progestin treatment

Commonly reported regimens included MPA at doses of 250–600 mg/day or MA at doses of 160–480 mg/day. In our center, patients typically received an oral dose of MPA at 250 mg or 500 mg daily for a period of 12 weeks [13,14,15]. Following treatment, endometrial biopsies were conducted via dilation and curettage (D&C) to assess the efficacy of MPA therapy. The histological response to progestin treatment was evaluated based on specimens obtained during each hysteroscopic examination. CR was defined as the absence of hyperplasia or carcinoma. Patients underwent follow-up assessments every 3 to 6 months, which included ultrasound examinations and endometrial biopsies using a Pipelle device. The primary outcome measures included remission status and the time required to achieve CR.

4. Statistical analysis

Continuous variables are presented as mean ± standard deviation (SD), while categorical variables are expressed as numbers and percentages (%). We employed Student’s t-test or the Mann-Whitney U test to compare values between two groups, and the χ² test to assess frequency distributions. Univariate and multivariate cox analyses were conducted to examine the relationship between various covariates and the achievement of CR following treatment. All statistical analyses were performed using the R version 3.4.3 (http://www.R-project.org; The R Foundation, Vienna, Austria) and EmpowerStats (http://www.empowerstats.com; X&Y Solutions, Inc., Boston, MA, USA) software packages. A 2-sided significance level of p<0.05 was deemed statistically significant.

5. Ethics statement

All participants provided informed consent for the utilization of their personal data for health research purposes. Ethical approval for this retrospective study was obtained from the Ethics Committees of PKUPH (Approval No. 2022PHB397-001).

RESULTS

1. Patient characteristics

The flowchart depicting the study design is presented in Fig. 1. A total of 173 patients with AEH or early-stage EC who underwent fertility-sparing treatment were included in this analysis. Demographic information, histological distribution at the time of diagnosis, and comorbidities are summarized in Table 1. Patients were categorized into 3 subgroups based on their time to achieve CR: <6, 6–9, and >9 months. The median age at diagnosis was 32.07, 33.21, and 34.03 years, respectively. MRS scores for the different CR time subgroups were 1.05, 1.79, and 2.42, respectively. In the CR <6 months group, 58 out of 121 (47.93%) patients were classified as insulin resistant, with proportions of 57.14% and 65.79% in the other two groups. Among the 173 patients, 24 (19.83%) were diagnosed with DM in the CR <6 months group, 7 (18.42%) in the CR >9 months group, and only 1 DM patient in the CR time between 3–9 months. There was no significant difference in the distribution of DM patients among the 3 groups. The distribution of FBG and menstrual regularity significantly differed among groups with different CR times. However, no significant differences were observed in the remaining variables, including MRS, age, BMI, TG, HDL, PP, CA125, serum insulin, HOMA-IR, cholesterol, LDL, gestation, parity, PCOS, hypertension, family history, metformin use, and histological type (p>0.05).

Fig. 1. Flowchart of the study design.

AEH, atypical endometrial hyperplasia; CR, complete remission; DCA, decision curve analysis; EC, endometrial cancer; PKUPH, Peking University People’s Hospital; ROC, receiver operator characteristic.

Table 1. Demographic information for the enrolled patients.

Variables		<6 mo (n=121)	6–9 mo (n=14)	>9 mo (n=38)	p-value
MRS		1.05±3.61	1.79±5.26	2.42±5.00	0.501
Age (yr)		32.07±5.11	33.21±3.87	34.03±4.91	0.148
BMI (kg/m2)		26.50±4.86	26.99±3.65	28.01±5.62	0.455
FBG (mmol/L)		4.91±0.75	5.32±1.21	7.00±6.19	0.011
TG (mmol/L)		1.58±0.97	1.38±0.56	1.59±1.02	0.919
HDL (mmol/L)		1.15±0.34	1.16±0.39	1.21±0.27	0.303
SBP (mmHg)		122.65±14.46	124.50±13.52	123.76±13.82	0.829
DBP (mmHg)		78.20±8.89	81.07±7.75	82.00±11.17	0.064
PP (mmHg)		44.45±10.40	43.43±10.20	41.76±8.21	0.611
CA125 (mmol/L)		20.81±19.42	17.59±8.52	16.34±8.88	0.491
Serum insulin (mmol/L)		16.51±10.92	18.45±15.28	18.05±11.08	0.611
HOMO-IR		3.74±2.91	4.31±3.66	5.36±4.81	0.100
Cholesterol (mmol/L)		4.59±0.86	4.69±1.10	4.69±0.72	0.762
LDL (mmol/L)		2.89±0.74	3.02±0.91	2.96±0.66	0.819
Insulin resistance					0.148
	No	63 (52.07)	6 (42.86)	13 (34.21)
	Yes	58 (47.93)	8 (57.14)	25 (65.79)
Hypertension					0.726
	No	113 (93.39)	13 (92.86)	34 (89.47)
	Yes	8 (6.61)	1 (7.14)	4 (10.53)
Diabetes					0.511
	No	97 (80.17)	13 (92.86)	31 (81.58)
	Yes	24 (19.83)	1 (7.14)	7 (18.42)
Menstrual regularity					<0.001
	No	71 (58.68)	13 (92.86)	36 (94.74)
	Yes	50 (41.32)	1 (7.14)	2 (5.26)
Gestation					0.370
	No	78 (64.46)	9 (64.29)	20 (52.63)
	Yes	43 (35.54)	5 (35.71)	18 (47.37)
Parity					0.858
	No	101 (83.47)	11 (78.57)	31 (81.58)
	Yes	20 (16.53)	3 (21.43)	7 (18.42)
PCOS					0.078
	No	74 (61.16)	11 (78.57)	30 (78.95)
	Yes	47 (38.84)	3 (21.43)	8 (21.05)
Thyroid disease					0.063
	No	111 (91.74)	10 (71.43)	33 (86.84)
	Yes	10 (8.26)	4 (28.57)	5 (13.16)
Family history					0.325
	No	108 (89.26)	14 (100.00)	31 (81.58)
	Yes	13 (10.74)	0 (0.00)	7 (18.42)
Metformin					0.699
	No	73 (60.33)	8 (57.14)	20 (52.63)
	Yes	48 (39.67)	6 (42.86)	18 (47.37)
Histological type					0.311
	AEH	71 (58.68)	7 (50.00)	18 (47.37)
	Early EC	50 (41.32)	7 (50.00)	20 (52.63)

Values are presented as mean ± standard deviation or number (%).

AEH, apical endometrial hyperplasia; BMI, body mass index; CA125, cancer antigen 125; DBP, diastolic blood pressure; EC, endometrial cancer; FBG, fasting blood glucose; HDL, high-density lipoprotein; HOMO-IR, homeostasis model assessment-insulin resistance, LDL, low-density lipoprotein; PCOS, polycystic ovary syndrome; PP, pulse pressure; SBP, systolic blood pressure; TG, triglycerides.

2. Risk factors for time to CR in fertility preservation patients

To identify risk factors influencing the time to CR, we conducted univariate cox regression analysis. As presented in Table 2, BMI (hazard ratio [HR]=0.20; 95% confidence interval [CI]=0.03, 0.38; p=0.026), MRS (HR=0.31; 95% CI=0.09, 0.52; p=0.005), IR (HR=1.83; 95% CI=0.05, 3.60; p=0.045), menstrual regularity (HR=3.77; 95% CI=1.91, 5.64; p=0.001), PCOS (HR=−2.16; 95% CI=−4.03, −0.28; p=0.025), and histological type (HR=0.36; 95% CI=0.10, 0.62; p=0.005) were identified as 6 risk factors affecting the time to CR. Subsequently, multivariate analysis was performed to ascertain whether MRS was an independent risk factor for time to CR. The multivariate analysis revealed that MRS (HR=0.29; 95% CI=0.02, 0.56; p=0.021), IR (HR=0.43; 95% CI=0.05, 0.80; p=0.027), menstrual regularity (HR=3.66; 95% CI=1.73, 5.60; p=0.001), PCOS (HR=0.30; 95% CI=0.04, 0.57; p=0.031), and histological type (HR=0.33; 95% CI=0.09, 0.58; p=0.008) were independent risk factors influencing time to CR. Subsequently, we conducted stratified analysis among the 173 patients who achieved CR through fertility treatment to assess the impact of MRS on different pathological characteristics. Notably, the effect of MRS on time to CR was significantly pronounced in patients under the age of 35, with a BMI higher than 25 kg/m², IR, absence of hypertension or diabetes, menstrual irregularity, absence of PCOS or thyroid disease, absence of metformin use, and those with AEH histological type. Meanwhile, the influence of MRS on time to CR was also significantly evident in other patient groups (Fig. 2). These findings underscored the substantial influence of MRS on the time to CR in fertility preservation patients with AEH or early EC, particularly in specific subpopulations.

Table 2. Univariate and multivariate analyses of risk factors for CR in patients with AEH and early EC.

Variables		Univariate analysis HR (95% CI)	p-value	Multivariate analysis HR (95% CI)	p-value
Age (yr)		0.15 (−0.03, 0.32)	0.110
BMI (kg/m2)		0.20 (0.03, 0.38)	0.026	0.47 (−1.44, 2.38)	0.630
CA125 (mmol/L)		−0.03 (−0.09, 0.02)	0.229
Serum insulin (mmol/L)		0.04 (−0.04, 0.12)	0.339
MRS		0.31 (0.09, 0.52)	0.005	0.29 (0.02, 0.56)	0.021
Insulin resistance			0.045		0.027
	Without	0
	With	1.83 (0.05, 3.60)		0.43 (0.05, 0.80)
Hypertension			0.284
	No	0
	Yes	1.86 (−1.53, 5.26)
Diabetes			0.472
	No	0
	Yes	0.85 (−1.46, 3.16)
Menstrual regularity			<0.001		0.001
	No	0
	Yes	3.77 (1.91, 5.64)		3.66 (1.73, 5.60)
Gestation			0.645
	No	0
	Yes	0.49 (−1.59, 2.56)
Parity			0.824
	No	0
	Yes	−0.27 (−2.69, 2.14)
PCOS			0.026		0.031
	No	0
	Yes	−2.16 (−4.03, -0.28)		0.30 (0.04, 0.57)
Thyroid disease			0.526
	No	0
	Yes	0.93 (−1.94, 3.80)
Family history			0.707
	No	0
	Yes	0.55 (−2.33, 3.43)
Metformin			0.282
	No	0
	Yes	1.00 (−0.82, 2.82)
Histological type			0.006		0.008
	AEH	0
	Early EC	0.36 (0.10, 0.62)		0.33 (0.09, 0.58)

AEH, apical endometrial hyperplasia; BMI, body mass index; CA125, cancer antigen 125; CI, confidence interval; CR, complete regression; EC, endometrial cancer; HR, hazard ratio; MRS, metabolic risk score; PCOS, polycystic ovary syndrome.

Fig. 2. Stratified analysis for the influence of MRS on time to CR in different subgroups of AEH and early EC patients.

AEH, atypical endometrial hyperplasia; BMI, body mass index; CR, complete remission; EC, endometrial cancer; MRS, metabolic risk score; PCOS, polycystic ovary syndrome.

3. Evaluation of predictive accuracy for MRS on time to CR

To ascertain whether MRS enhanced the predictive accuracy for time to CR in AEH or early EC patients undergoing fertility-sparing treatment, we conducted ROC curve analysis and decision curve analysis (DCA). We utilized the five risk factors mentioned earlier to construct ROC and DCA curves for the clinical model (Model 1), and subsequently, we incorporated MRS into the clinical model (Model 2). As depicted in Fig. 3A, the ROC curve revealed that the area under the curve (AUC) for the clinical model was 0.789, and this value increased to 0.862 upon the inclusion of MRS, indicating a high diagnostic accuracy for predicting time to CR. To further validate the predictive utility of MRS, we employed DCA (Fig. 3B). The predicted probability thresholds ranged from 0% to nearly 80%, demonstrating a positive net benefit for fertility-sparing treatment patients when MRS was considered in addition to clinical factors. These results highlighted the significant enhancement in predictive accuracy for time to CR achieved through the incorporation of MRS. Furthermore, we employed a smooth curve fitting approach to examine whether MRS could be divided into intervals. Segmented regression analysis, which uses separate line segments for different intervals, revealed that when MRS was equal to or below 5, the time required to achieve CR was the shortest. Importantly, 87.8% (152/173) of patients had an MRS score of 5 or lower, indicating that MRS was particularly effective in predicting time to CR and treatment efficacy for patients with AEH or early EC (Fig. 3C).

Fig. 3. Evaluation of predictive accuracy with independent risk factors. (A) ROC curve of Model 1 and Model 2. (B) DCA curve for models with or without MRS. (C) Smooth curve for examining whether the MRS is partitioned into intervals.

CR, complete remission; DCA, decision curve analysis; MRS, metabolic risk score; ROC, receiver operator characteristic.

4. Cumulative curve of time to CR in different patients

To further elucidate the impact of the 6 risk factors identified in the univariate analysis on CR, we generated cumulative CR curves using stratified log-rank tests. The results revealed significant differences in cumulative CR probabilities among patients with different MRS scores (Fig. 4A, p<0.001), BMI levels (Fig. 4B, p=0.026), IR status (Fig. 4C, p=0.045), menstrual regularity (Fig. 4D, p<0.001), PCOS status (Fig. 4E, p=0.025), and histological types (Fig. 4F, p=0.005). These findings indicated that patients with higher BMI, IR, irregular menstruation, PCOS, early EC, and higher MRS scores took longer time to achieve CR compared to those without these factors.

Fig. 4. Cumulative probability of CR for 173 patients who underwent fertility-sparing treatment in different subgroups. (A) Low or high MRS. (B) BMI <25 kg/m² or ≥25 kg/m². (C) With or without insulin resistance. (D) Different menstrual regularity. (E) With or without PCOS. (F) AEH or early EC.

AEH, atypical endometrial hyperplasia; BMI, body mass index; CR, complete remission; EC, endometrial cancer; MensRegularity, menstruation regularity; MRS, metabolic risk score; PCOS, polycystic ovary syndrome.

DISCUSSION

Given the increasing incidence of EC in women of reproductive age and the promising treatment outcomes in this demographic, the provision of effective fertility-sparing treatment options is of paramount importance. Conservative approaches for the management of AEH and early-stage EC in young women have gained acceptance as alternatives to definitive surgical interventions, offering both disease treatment and fertility preservation. To date, progestin-based conservative treatments, particularly oral progestin therapy, have yielded promising results [16]. Consequently, there is a pressing need to investigate the risk factors affecting CR in patients undergoing fertility-sparing treatment.

In this study, we presented our institutional retrospective analysis of AEH and early EC patients eligible for conservative treatment. Our findings underscore that MRS emerged as an independent risk factor for achieving CR, with higher MRS scores correlating with longer CR times. Additionally, IR was found to influence CR. When assessing the predictive accuracy of CR, the inclusion of MRS significantly improved the AUC. Cumulative CR rate analysis further revealed that the 6 risk factors identified in our study substantially impacted the cumulative CR rate.

MetS, encompassing obesity, dyslipidemia, hypertension, and hyperglycemia, represents a cluster of risk factors for various common cancers, including liver cancer, colorectal cancer, bladder cancer, and esophageal cancer [17]. Epidemiological evidence has established MetS as a significant risk factor for EC development [18]. However, the association between MetS and the prognosis of fertility preservation treatments, especially in EC patients, has received limited attention. In a prior study, we observed a strong relationship between MetS and proliferative endometrium disorders, endometrial hyperplasia, and EC [19]. BMI, a crucial measure of obesity and a component of MetS, has been linked to an increased risk of EC later in life [20]. In a randomized controlled trial, weight loss interventions in obese women improved outcomes in various cancers, including EC [21]. Although the underlying mechanisms connecting obesity, MetS, and CR remain subject to debate, MetS and IR appear to play pivotal roles. Our study indicated that fertility preservation patients with higher BMI, IR, irregular menstruation, PCOS, early EC, and elevated MRS exhibited lower treatment efficacy and CR rates.

Despite the fact that early-stage EC falls within the cancer spectrum, radical surgery remains the standard treatment modality. However, social and cultural factors sometimes necessitate the adoption of fertility-sparing treatment strategies to preserve reproductive function. Therefore, predicting the likelihood of CR in these patients carries significant clinical relevance. The MRS scoring system we employed significantly enhances the accuracy of CR prediction. In clinical practice, MRS scores not only enable the precise prediction of complete remission rates but also facilitate comprehensive management of the factors encompassed by the MRS. Consequently, weight control, blood glucose regulation, lipid and blood pressure control, and weight loss assume critical roles in the lifelong management of fertility-sparing treatment. Nevertheless, further research is warranted to thoroughly assess the specific impact of MRS and its related risk factors during treatment on both oncologic and reproductive outcomes. Future studies should focus on elucidating the relationship between MRS and post-CR recurrence.

IR has been implicated in the development of EC [22,23]. However, the role of IR in AEH and early EC patients undergoing fertility-sparing treatment remains unclear. A previous study indicated that IR negatively influenced the duration of progesterone-based conservative treatment in AEH patients, with IR patients requiring a longer time to achieve CR compared to those without IR [24]. Our prior research demonstrated that IR represents a risk factor for recurrence and CR, suggesting a potential synergistic role between IR and MetS in counteracting the effects of progestin therapy during treatment [15]. Building upon our previous findings, we not only confirmed IR as an independent risk factor for CR but also demonstrated its impact on the time to CR. Strategies to mitigate IR, such as weight loss, blood glucose control, regular diabetes management, and daily exercise, not only exert a favorable effect on disease progression but also expedite the achievement of CR.

A previous study has reported that in early EC fertility-sparing treatment, patients with PCOS exhibited a lower cumulative CR rate at 16 weeks compared to those without PCOS. Furthermore, PCOS patients experienced a prolonged time to CR and shorter relapse times after CR [25]. In our current study, we also observed a prolonged time to CR in patients with PCOS compared to the control group. Additionally, the impact of MRS on time to CR appeared to be attenuated in PCOS patients. This observation could potentially be linked to progesterone resistance induced by low-grade chronic inflammation, IR, and hyperandrogenemia frequently observed in women with PCOS [26].

Metformin, known as an insulin sensitizer, has been widely employed as an adjunctive treatment for various malignant diseases. It has shown effectiveness in fertility-sparing treatments, reducing the relapse rate after MPA therapy, particularly in obese patients. This improved CR rate associated with metformin may be attributed to enhanced progestin efficacy and a direct or indirect anti-cancer effect of metformin [27]. Indeed, studies have confirmed that combining MPA with metformin achieves high CR rates and low disease recurrence rates in fertility-sparing treatment for AEH and EC patients, and it significantly prolongs uterine preservation [28]. In our study, we observed that the effect of MRS on CR was attenuated in the metformin group, suggesting that taking metformin during fertility-sparing treatment is conducive to achieving CR in AEH and EC patients. However, the underlying mechanisms behind this phenomenon remain unclear. Some studies have suggested that metformin’s therapeutic potential lies in its ability to act through multiple pathways, including the down-regulation of insulin levels [29], suppression of GloI expression [30], and elevation of progesterone receptor expression [31]. Our study demonstrated a highly predictive capacity for CR infertility preservation for EC patients, with an AUC of 0.862. Furthermore, the addition of MRS to the model significantly improved prediction accuracy (from 0.788 to 0.862). Another study combining patient age and HE4 level achieved an AUC of 0.786 for predicting CR [32]. DCA also highlighted the clinical utility of a comprehensive model incorporating MRS over single clinical parameters alone.

Nonetheless, our study has certain limitations. Firstly, it was a single-center retrospective study, and the choice of specific regimens was primarily based on physician preference. Thus, prospective multi-center clinical trials are needed to validate the efficacy of MRS. Secondly, due to the limited number of patients and some still undergoing treatment, which could potentially influence the research outcomes, our study assumes that future research with larger sample sizes should be conducted to further evaluate the observed effects. Finally, the underlying mechanism for the influence of MRS on CR time remains elusive and requires further investigation.

In conclusion, our study's findings suggest that BMI, IR, irregular menstruation, PCOS, early EC, and MRS are significant risk factors affecting the time to achieve CR in fertility preservation for AEH and early EC patients. Moreover, MRS stands out as an independent risk factor capable of enhancing the accuracy of CR prediction. As this represents an initial study, we aim to support future investigations with these findings and contribute evidence to the therapy of fertility-sparing treatment for AEH and early EC patients.

SUPPLEMENTARY MATERIAL

Table S1

Determination of scores associated with each of the quintile ranges of metabolic risk factors in derivation group