Abstract
Background
Endometriosis-associated infertility (EAI) has emerged as an increasingly pressing reproductive health problem. Nevertheless, existing epidemiological research on EAI remains inadequate.
Methods
This study integrated the Global Burden of Disease (GBD) 2023 data and single-center clinical data. We analyzed time trends in prevalence and years lived with disability (YLDs) of EAI in China, Japan, and the Republic of Korea. For China, we conducted age-period-cohort analysis and adopted the Bayesian age-period-cohort model to forecast disease burden trends up to 2030. Clinical data from a single center were used to compare age, BMI, and surgical stage among EAI subgroups.
Results
In 2023, the number of prevalent cases of EAI in China was 181587 (95% UI: 114233, 278960), with the age-standardized prevalence rate (ASPR) of 26.38 (95% UI: 16.45, 39.91) per 100,000 population. Although the overall ASPR of EAI in China showed a downward trend from 1990 to 2023, with an average annual percent change of −0.57% (95% CI: −0.80, −0.43), an increase was observed during the recent period from 2021 to 2023, with an annual percent change of 9.75% (95% CI: 4.06, 13.51). Trends in YLDs were consistent with prevalence patterns. ASPR and prevalent cases are projected to rise through 2030. Both GBD and real-world data identified a peak in secondary infertility at ages 35–39; for primary infertility, the peak age was 20–24 years based on GBD data and 25–29 years according to real-world data. Secondary infertility is the predominant type, and most patients were classified as the American Society for Reproductive Medicine (ASRM) stage III.
Conclusion
The recent rebound in EAI burden reveals a growing hidden threat to female reproductive health. Its upward trend is expected to continue to 2030. Early screening for primary infertility in women aged 20–29 years and targeted management of secondary infertility in those aged 35–39 years are warranted.
Keywords: endometriosis, primary infertility, secondary infertility, prevalence, years lived with disability
Introduction
Endometriosis-associated infertility (EAI) denotes a reduction in female fertility or the inability to conceive as a result of endometriosis.1,2 Clinically, EAI can be further categorized into endometriosis-associated primary infertility and endometriosis-associated secondary infertility, based on the patient’s prior pregnancy history.3
The risk of infertility among individuals with endometriosis is approximately 2–4 times greater than that of the general population, and the disease burden associated with endometriosis in East Asia is escalating.4,5 China experiences a particularly significant endometriosis burden, as evidenced by its disability-adjusted life years (DALYs), which rank second globally.6 This high burden is likely attributable to multiple factors, including China’s large population and increasing recognition of endometriosis. Previous research has primarily concentrated on endometriosis, while investigations into EAI remain notably sparse.7–9 One study utilizing Global Burden of Disease (GBD) 2021 mapped the global distribution of EAI prevalence,10 while another employed the age-period-cohort model to analyze temporal trends in EAI prevalence in China.11 Nonetheless, these studies present limitations. First, they focus exclusively on prevalence and overlook other vital burden metrics, such as years lived with disability (YLDs). Second, they lack current data that reflects the post-pandemic context. Third, they rely solely on macro-level databases, failing to integrate real-world clinical data that could complement epidemiological trends and provide richer clinical context.
To address the identified research gap, this study combined macro-level database analysis with real-world clinical data. We utilized the GBD 2023 data to evaluate the disease burden of EAI in three East Asian countries, with a primary focus on China, from 1990 to 2023. Concurrently, we collected data from 168 clinical patients to delineate their clinical characteristics, thereby establishing an evidence-based foundation for the development of targeted clinical and public health intervention strategies.
Materials and Methods
Data Collection
For the GBD database, we utilized the Global Health Data Exchange query tool (http://ghdx.healthdata.org/gbd-results-tool)12 to obtain prevalence, YLDs, and corresponding age-standardized prevalence rate (ASPR), age-standardized YLDs rate (ASR-YLDs) of EAI and its subgroups among women aged 15–49 years in China, Japan and the Republic of Korea from 1990 to 2023. The International Classification of Diseases-10 code of endometriosis is under N80 to N80.9, while infertility is classified under N97 to N97.9. GBD2023 provides estimates of 375 diseases and injuries and 88 risk factors across 204 countries and territories from 1990 to 2023, stratified by location, year, age, and sex.13 The quality of GBD data is widely recognized on a global scale.14,15
For real-world clinical data, we retrospectively enrolled 168 patients with laparoscopically confirmed EAI from the Department of Obstetrics and Gynecology at the Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, from 2021 to 2025. Patients were eligible for inclusion if they met the following criteria: (1) aged 15–49 years; (2) diagnosed with EAI based on laparoscopic confirmation; and (3) complete clinical information available, including age, infertility type, body mass index (BMI), and American Society for Reproductive Medicine (ASRM) staging. Patients were excluded if they had incomplete clinical records, other causes of infertility, or concurrent malignancies. EAI was defined as infertility occurring in women with laparoscopically confirmed endometriosis. Infertility type was classified according to reproductive history: primary infertility was defined as failure to achieve a clinical pregnancy without prior pregnancy, whereas secondary infertility was defined as failure to conceive after at least one previous clinical pregnancy.16 BMI classification followed the Chinese Adult BMI Standard:17 BMI<18.5 kg/m2 (underweight); 18.5≤BMI<24.0 (normal weight); 24.0≤BMI<28.0 (overweight); and BMI≥28.0 (obesity). ASRM staging,18,19 based on laparoscopic assessment of lesion extent, adhesion severity, and other anatomical criteria, comprises four stages: I (1–5 points), II (6–15 points), III (16–40 points), and IV (≥40 points). Clinical data were used to describe age, BMI, and ASRM distributions, not to independently validate temporal trends.
Statistical Analysis
The age-standardized rate (ASR), standardized to the GBD world population age structure, was calculated and reported per 100,000 population, allowing comparisons across populations and over time independent of age composition.20 The 95% uncertainty intervals (UI) for each of the above indicators were directly obtained from the database. The formula calculation ASR is as follows:
 |
Where ai and wi denote the age-specific rate and the number of persons (or weight) in the same age subgroup of the selected reference population, respectively.
We employed the Joinpoint Regression Program version 5.4.0 (National Cancer Institute, Bethesda, MD, USA) to analyze the trend of EAI from 1990 to 2023. The Joinpoint analysis was performed using the Grid Search method with a maximum of 6 joinpoints and a minimum segment length of 2 observations. The annual percent change (APC) and its 95% confidence interval (CI) served as the primary measures for assessing internal trends across different intervals of the piecewise function. The average change trend over the entire study period is determined by the average annual percent change (AAPC) and its 95% CI.21
This study employed the web-based Age-Period-Cohort Analysis Tool (https://analysistools.cancer.gov/apc/) to estimate the independent effects of age, period, and cohort on prevalence. The age groups are categorized within the 15–49 age range, with five-year intervals. The prevalence data are organized into six groups based on five consecutive years. Additionally, twelve consecutive birth cohorts are established. Longitudinal age curves were employed to visualize age-specific disease burden patterns, while rate ratios (RR) for period and cohort effects offered insights into the temporal and generational variations in infertility prevalence.22 The tool is based on the intrinsic estimator (IE) algorithm, which solved the age-period-cohort model from the point view of statistical methods, choosing a solution with the smallest sum of squares of parameters. The model based on IE was expressed as:
 |
Where M is defined as the prevalence rates; α, β, and γ refers to the age, period, and cohort effects, respectively; γ is the cohort effect, the risk of prevalence for all people in the same birth cohort. μ is the intercept; and ε is defined as the random error. Standard errors and RR were calculated accordingly.
The prevalence of EAI by 2030 was estimated using the Bayesian Age-Period-Cohort (BAPC) analysis model,23,24 with projections standardized according to the GBD standard population figures. This model was implemented using the BAPC and INLA packages in R version 4.5.2 (R core team).
Hospital data were analyzed and compared to evaluate the distribution of primary and secondary infertility, highlighting the differences between the groups. The Shapiro–Wilk test was used to assess normality. When the data conformed to a normal distribution, analysis of variance was conducted; otherwise, the Mann–Whitney U-test was applied. Results with P < 0.05 were considered statistically significant. Given the exploratory nature, no adjustment for multiple comparisons was applied; findings should be interpreted cautiously.
Ethics Approval and Consent to Participate
The GBD data used in this study are publicly available, de-identified, and aggregated at the population level, and thus did not require separate ethical approval. The clinical component of this study was approved by the Institutional Ethics Committee of the Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine (No.2023-LWKY-018). The requirement for informed consent was waived by the Ethics Committee because this was a retrospective study using clinical data collected during routine clinical care without intervention. All patient data were anonymized before analysis, and no identifiable personal information was disclosed. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki.
Results
Trend Analysis of China, Japan, and the Republic of Korea
In China, the prevalent cases of EAI were 181587 (95% UI: 114233, 278960) in 2023. The ASPR was 26.38 (95% UI: 16.45, 39.91) per 100,000 population in 2023, which was lower than the value of 31.52 (95% UI: 19.66, 48.02) per 100,000 population determined for the year 1990, with an AAPC of −0.57% (95% CI: −0.80, −0.43). Primary infertility accounts for 8%, and secondary infertility accounts for 92% (Table 1). For secondary infertility, the ASPR showed a significant increase from 2021 to 2023, with an APC of 9.26% (95% CI: 3.17, 13.19). Similarly, the ASPR of primary infertility experienced a significant rise during the same period, with an APC of 7.68% (95% CI: 3.57, 10.22) (Table 2). In Japan and the Republic of Korea, the ASPR presented an overall downward trend.
Table 1.
Prevalence and Average Annual Percent Change of Endometriosis-Associated Infertility and Its Subgroups in China, Japan, and the Republic of Korea in 1990 and 2023
| 1990 | 2023 | 1990–2023 | |||
|---|---|---|---|---|---|
| Prevalence Number No. (95% UI) |
ASPR per 100,000 No. (95% UI) |
Prevalence Number No. (95% UI) |
ASPR per 100,000 No. (95% UI) |
AAPC No. (95% CI) |
|
| Endometriosis-associated infertility | |||||
| China | 192845 (118383, 295785) | 31.52 (19.66, 48.02) | 181587 (114233, 278960) | 26.38 (16.45, 39.91) | −0.57 (−0.80, −0.43) |
| Japan | 21672 (13001, 35555) | 33.06 (19.54, 53.92) | 15999 (9652, 26015) | 31.52 (18.47, 51.48) | −0.23 (−0.36, −0.15) |
| Republic of Korea | 6041 (3008, 10826) | 24.20 (12.37, 41.82) | 5066 (2685, 8615) | 20.13 (10.49, 34.55) | −0.55 (−0.58, −0.52) |
| Endometriosis-associated primary infertility | |||||
| China | 16995 (6179, 38908) | 2.44 (0.94, 5.56) | 14076 (5480, 30823) | 2.44 (0.95, 5.68) | −0.11 (−0.29, 0.04) |
| Japan | 3573 (1301, 8015) | 6.27 (2.25, 14.06) | 2278 (760, 4970) | 5.36 (1.83, 11.85) | −0.53 (−0.66, −0.44) |
| Republic of Korea | 1980 (686, 4444) | 7.22 (2.55, 15.97) | 1051 (364, 2471) | 4.89 (1.60, 11.67) | −1.19 (−1.22, −1.16) |
| Endometriosis-associated secondary infertility | |||||
| China | 175851 (107815, 270746) | 29.09 (17.87, 44.69) | 167511 (104338, 263064) | 23.94 (14.84, 37.23) | −0.63 (−0.88, −0.47) |
| Japan | 18099 (10312, 29254) | 26.79 (14.91, 43.31) | 13721 (7644, 21967) | 26.16 (14.56, 42.61) | −0.17 (−0.31, −0.08) |
| Republic of Korea | 4061 (1868, 6974) | 16.97 (7.49, 29.54) | 4015 (1884, 6934) | 15.25 (7.02, 26.60) | −0.34 (−0.37, −0.30) |
Abbreviations: ASPR, Age-standardized prevalence rate; AAPC, Average annual percent change.
Table 2.
Joinpoint Regression Analysis of the Annual Percent Change in Age-Standardized Prevalence Rate for Endometriosis-Associated Infertility in China, Japan, and the Republic of Korea Between 1990 and 2023
| Cohort | Infertility | Primary Infertility | Secondary Infertility | |||
|---|---|---|---|---|---|---|
| Range | APC No. (95% CI) |
Range | APC No. (95% CI) |
Range | APC No. (95% CI) |
|
| China | ||||||
| 1990–2006 | −0.65 (−0.98, −0.30) | 1990–1994 | 3.85 (2.50, 5.88) | 1990–2006 | −0.73 (−1.13, −0.31) | |
| 2006–2010 | −6.22 (−8.87, −0.82) | 1994–2000 | −1.41 (−3.40, −0.66) | 2006–2010 | −6.21 (−8.88, −0.76) | |
| 2010–2016 | 1.27 (−4.99, 5.10) | 2000–2005 | 0.96 (−0.20, 2.82) | 2010–2016 | 1.29 (−5.21, 5.06) | |
| 2016–2021 | −1.77 (−5.35, −0.29) | 2005–2010 | −5.42 (−7.40, −4.27) | 2016–2021 | −1.76 (−5.31, −0.17) | |
| 2021–2023 | 9.75 (4.06, 13.51) | 2010–2015 | 1.21 (0.24, 3.87) | 2021–2023 | 9.26 (3.17, 13.19) | |
| – | – | 2015–2021 | −1.32 (−4.01, −0.71) | – | – | |
| – | – | 2021–2023 | 7.68 (3.57, 10.22) | – | – | |
| Japan | ||||||
| 1990–2000 | 0.10 (−0.21, 0.43) | 1990–2000 | −0.47 (−0.78, −0.14) | 1990–2000 | 0.24 (−0.10, 0.57) | |
| 2000–2005 | −4.64 (−6.12, −3.78) | 2000–2005 | −6.04 (−7.51, −5.23) | 2000–2005 | −4.35 (−5.96, −3.37) | |
| 2005–2021 | 0.36 (0.14, 0.52) | 2005–2021 | 0.19 (0.02, 0.33) | 2005–2021 | 0.39 (0.04, 0.55) | |
| 2021–2023 | 4.84 (1.67, 6.07) | 2021–2023 | 7.98 (5.12, 9.42) | 2021–2023 | 4.18 (0.87, 5.43) | |
| Republic of Korea | ||||||
| 1990–1992 | 1.45 (0.72, 2.05) | 1990–1995 | −0.79 (−0.97, −0.60) | 1990–1994 | 1.79 (1.30, 2.18) | |
| 1992–1995 | 0.38 (−1.80, 0.59) | 1995–2000 | −3.07 (−3.23, −2.91) | 1994–2001 | −0.87 (−1.21, −0.68) | |
| 1995–2000 | −1.61 (−1.89, 0.25) | 2000–2005 | 0.64 (0.45, 0.90) | 2001–2011 | 0.22 (0.11, 0.38) | |
| 2000–2011 | 0.20 (0.09, 0.31) | 2005–2011 | −0.18 (−0.39, −0.02) | 2011–2015 | −2.77 (−3.36, −2.30) | |
| 2011–2014 | −3.36 (−3.58, −3.05) | 2011–2014 | −3.92 (−4.19, −3.56) | 2015–2023 | −0.41 (−0.55, −0.21) | |
| 2014–2019 | −1.14 (−1.49, −0.86) | 2014–2019 | −2.00 (−2.27, −1.68) | – | – | |
| 2019–2023 | −0.08 (−0.36, 0.55) | 2019–2023 | 0.00 (−0.31, 0.44) | – | – | |
Abbreviations: APC, Annual percent change; ASPR, Age-standardized prevalence rate.
The trends in YLDs and prevalence across the three countries exhibit notable similarities (Figure 1). The ASR-YLDs for EAI in China in 2023 was 0.14 (95% UI: 0.05, 0.35) per 100,000 population, lower than that of Japan but higher than that of the Republic of Korea (Table S1). In China, the ASR-YLDs for secondary infertility increased significantly during 2021–2023, with an APC of 9.97% (95% CI: 4.72, 13.88), consistent with the increasing trend in the ASPR, which showed an APC of 9.26% (95% CI: 3.17, 13.19). Both China and Japan experienced a significant increase in ASR-YLDs for EAI from 2021 to 2023. In contrast, the Republic of Korea showed no significant change in ASR-YLDs for primary infertility during 2019–2023, with APC of −0.17% (95% CI: −0.69, 0.82) (Table S2).
Figure 1.
Trends in endometriosis-associated infertility in China, Japan, and the Republic of Korea, 1990–2023. (A) ASPR for infertility. (B) ASPR for primary infertility. (C) ASPR for secondary infertility. (D) ASR-YLDs for infertility. (E) ASR-YLDs for primary infertility. (F) ASR-YLDs for secondary infertility.
Abbreviations: ASPR, Age-Standardized Prevalence Rate; ASR-YLDs, Age-Standardized Rate of Years Lived with Disability.
The Age Pattern in China
In China, both the prevalent cases and the age-specific prevalence rate of primary infertility due to EAI peak in the 20–24 age group. The prevalent cases for secondary infertility is highest in the 35–39 age group, reaching 39893.87 (95% UI: 21674.93, 68744.23). Conversely, the prevalence rate of secondary infertility is greatest in the 40–44 age group, at 72.01 (95% UI: 39.78, 124.02). From 1990 to 2023, trends across all age groups demonstrated similar patterns, with a significant increase noted from 2022 to 2023 (Figure 2). The age distribution patterns in Japan and the Republic of Korea are comparable (Figure 3).
Figure 2.
Prevalence number and prevalence rate of endometriosis-associated infertility by age in China. (A) Infertility, 2023. (B) Primary infertility, 2023. (C) Secondary infertility, 2023. (D) Infertility by age group, 1990–2023. (E) Primary infertility by age group, 1990–2023. (F) Secondary infertility by age group, 1990–2023.
Figure 3.
Age-period-cohort effects for age-standardized prevalence rate of endometriosis-associated infertility, endometriosis-associated primary infertility, and endometriosis-associated secondary infertility in China, Japan, and the Republic of Korea. (A) China. (B) Japan. (C) Republic of Korea.
The Age-Period-Cohort Effect in China, Japan, and the Republic of Korea
Age effects indicate that the EAI risk in China peaked within the 30–34 age group, whereas in Japan and the Republic of Korea, the highest risk was observed in the 40–44 age group. Period effects show that the estimated NetDrift for EAI in China from 1994 to 2023 was −1.24 (95% CI: −1.40, −1.12). In comparison to 2006 (RR=1), the risk for both primary and secondary infertility in China had decreased by 2021, with RRs of 0.81 (95% CI: 0.78, 0.84) and 0.84 (95% CI: 0.81, 0.86), respectively. The period effect curve for Japan exhibited a V-shaped pattern (Figure 3). Regarding cohort effects, the curve for China showed a declining trend, with the most recent birth cohort having the lowest risk (Figures 3 and S1). The cohort effect curves for Japan and the Republic of Korea demonstrated an initial increase followed by a decline (Figure 3).
Prediction of the Prevalence of EAI Through 2030
The age group of 35–39 years is anticipated to be the fastest-growing segment within the secondary infertility population, with an expected ASPR of 233.32 (95% CI: 96.71, 501.17) per 100,000 population in 2030. By 2030, the ASPR of EAI is projected to exhibit a continuous upward trend, with the prevalent cases expected to reach 484097.27 (95% CI: 95605.02, 872589.52), although the wide 95% confidence intervals indicate considerable uncertainty in the projections (Figure 4 and Tables S3–S5).
Figure 4.
Projection of endometriosis-associated infertility in China by 2030. (A) Prevalence rate by age group. (B) Prevalence cases and ASPR.
Abbreviation: ASPR, Age-Standardized Prevalence Rate.
Real-World Data
Among the 168 patients diagnosed with EAI, 20 (11.9%) presented with primary infertility and 148 (88.1%) with secondary infertility. Patients with primary infertility were predominantly aged 25–29 years, whereas those with secondary infertility were most frequently aged 35–39 years, demonstrating a distinct age distribution between the two groups. Regarding BMI, the majority of patients in both cohorts fell within the normal range (18.5–24 kg/m2), with no statistically significant difference observed between groups. In the secondary infertility cohort, overweight individuals (BMI 24–28 kg/m2) constituted the second largest subgroup. According to the ASRM staging system, stage III was the most common in both primary and secondary infertility patients. Moreover, a significant difference in ASRM scores was noted between the groups, with median scores of 24 in the primary infertility group and 31 in the secondary infertility group (Figure 5).
Figure 5.
Real-world data in an endometriosis-associated infertility cohort. (A) Infertility types. (B) Distribution by age group in an EAI cohort. (C) Distribution of individual patient ages. (D) BMI in an EAI patient cohort. (E) Distribution of BMI values in an EAI patient cohort. (F) Distribution of patients by BMI: proportion and count. (G) ASRM stage in an EAI patient cohort. (H) Distribution of ASRM scores in the patient cohort. (I) Distribution of patients by ASRM stage: proportion and count.
Notes: *p < 0.05, ****p < 0.0001.
Abbreviation: ns, not significant.
Discussion
EAI imposes a substantial and growing reproductive health burden on women of childbearing age. Utilizing data from the GBD 2023, this study examined the macroepidemiological characteristics of EAI, focusing primarily on China while also including Japan and the Republic of Korea. Additionally, these epidemiological findings were complemented by single-center real-world clinical data from China, providing clinically relevant insights into the age distribution, infertility characteristics, and disease patterns of EAI.
This study revealed a marked increase in China’s ASPR of EAI from 2021 to 2023, with an APC of 9.27. This trend contrasts sharply with the sustained decline reported in earlier GBD estimates,11 suggesting a significant shift in the disease burden in recent years. Several factors may have contributed to this observed increase. The delayed impact of the COVID-19 pandemic, including delayed healthcare access, pandemic-related stress, and subsequent recovery of healthcare services, may have contributed to the observed increase.25 Nevertheless, whether the pandemic has a direct effect on EAI remains unclear based on the available data. In addition, the enhancement of diagnostic awareness, the refinement of the registration system,26 and differences between GBD 2021 and GBD 2023 estimation methods may partially explain the revised trend. Moreover, the higher APC of ASR-YLDs than ASPR for secondary infertility in China during 2021–2023 may suggest that the increasing burden of EAI was not solely attributable to rising case numbers. This pattern may reflect an increasing health loss associated with the disease. This interpretation is further supported by recent evidence indicating that chronic pain and mental health conditions contribute substantially to the overall disability burden of endometriosis.27
A similar trend was observed in Japan, suggesting that post-pandemic factors may have contributed to the recent increase in EAI burden in some East Asian countries. Notably, the Republic of Korea did not show a similar post-2021 increase in YLDs, suggesting heterogeneity in post-pandemic recovery, diagnostic practices, or healthcare system factors across East Asia.
We have observed that secondary infertility is overwhelmingly prevalent, which is strongly linked to an altered pelvic immune microenvironment. Many individuals have their first child and subsequently delay the decision to have a second or third child for several years. The decline in ovarian reserve function caused by aging is a physiological basis that cannot be ignored.28,29 In addition, the first delivery, induced abortion, and potential pelvic infections may lead to organic lesions, such as tubal obstruction, intrauterine adhesions, or endometrial damage, establishing a potential foundation for secondary infertility.30,31
Different types of infertility exhibit distinct age patterns. There is a five-year lag in clinical data and GBD data for primary infertility. This lag may reflect differences between the population-level estimates from the GBD database and the hospital-based clinical cohort, where referral patterns may influence the age distribution of patients. To begin with, the single-center data for this research originates from Jiangsu Province in China. In developed regions, factors such as increased occupational demands and changing marriage and childbearing patterns may be associated with delayed childbearing, which could partly contribute to differences in the age distribution of infertility patients.32–34 Additionally, variations in reproductive intentions and fertility-related healthcare-seeking behaviors across age groups may partly influence the timing of infertility evaluation.33 Ultimately, variations in genetic backgrounds across different races and regions may also play a role.35–37
In contrast, secondary infertility demonstrated a consistent age pattern across both datasets, with the burden peaking among women aged 35–39 years. Aging is a major contributor, as advancing age is associated with deterioration of oocyte quality and age-related remodeling of the ovarian microenvironment, including increased ovarian stromal stiffness, which may progressively impair ovarian function.38,39 Age-related extracellular matrix remodeling may alter the endometrial microenvironment, impair endometrial receptivity, and reduce embryo implantation potential.40 Emerging evidence further suggests that age-related immune alterations may contribute to ovarian functional decline.41 Pregnancy-related events, including childbirth, postpartum complications, induced abortion, or associated pelvic infections, may contribute to cumulative pelvic inflammatory damage and adhesion formation, thereby further compromising reproductive function.42
This study also utilized the age-period-cohort model. Concerning the age effect, Japan and the Republic of Korea exhibit similarities that may stem from their comparable levels of economic and social development, as well as shared social and cultural factors and patterns of delayed marriage. Importantly, the period effect reveals a declining trend in the prevalence risk of EAI in China from 1994 to 2023, characterized by a notably rapid decrease between 2006 and 2011. This trend is closely associated with the widespread adoption of laparoscopic diagnostic techniques throughout the country following 2005. This observation aligns with Lao’s findings.11 Notably, the cohort effect results suggest that newer birth cohorts demonstrate a lower disease risk, a trend likely attributable to the improved overall social health and medical environment experienced by the new generation.
It is projected that the risk of EAI will continue to rise by 2030. Focus should be placed on identifying primary infertility in young women and managing secondary infertility in women aged 35–39. Moreover, it is recommended that endometriosis screening be incorporated into routine physical examinations for women of reproductive age, especially high-risk groups such as those with dysmenorrhea or advanced maternal age, using imaging modalities such as ultrasound. Addressing the rising burden of EAI will require a shift from hormone-centered management toward integrated immunomodulatory approaches, including immune-based biomarkers for early diagnosis and targeted immunotherapies for high-risk populations.
Chronic exposure to an inflammatory peritoneal environment can lead to cumulative damage to ovarian reserve and endometrial function over time. Laparoscopic surgery is critical in diagnosing and treating endometriosis.43,44 As the diagnostic gold standard, it provides direct visual assessment and detailed staging of pelvic disease.45 In our study, the median score was significantly higher in those with secondary infertility than in those with primary infertility. Prior pregnancy-related events, such as childbirth, postpartum complications, induced abortions, or associated surgical procedures, can induce further inflammation and adhesion formation, thereby aggravating pre-existing endometriosis.31,42,46 Ectopic lesion growth is associated with immune-inflammatory disturbances, characterized by altered macrophage polarization, disrupted cytokine networks, B cell-related autoimmune responses, and imbalanced T-cell subsets.47 Among the dysregulated T-cell populations, increasing evidence highlights an imbalance between regulatory T cells (Tregs) and T helper 17 (Th17) cells as an important feature of immune dysfunction in endometriosis. Specifically, patients with endometriosis exhibit reduced tolerogenic Treg populations, increased inflammatory Th17 cells, and a decreased Treg/Th17 ratio, reflecting a persistent pro-inflammatory immune milieu that may facilitate lesion progression.48 Recent evidence further suggests that the functional integrity of activated Tregs, rather than their absolute numbers alone, represents a central immunological determinant of disease progression in endometriosis.49
Collectively, these immune-inflammatory alterations may contribute to more extensive pelvic adhesions and anatomical distortion in patients with secondary infertility. Clinicians managing these patients should anticipate more extensive pelvic adhesions and complex pelvic anatomy, thus preparing for potentially more challenging surgery. Moreover, the higher ASRM scores observed in these patients may indicate a reduced likelihood of natural conception and a correspondingly greater need for assisted reproductive technology.50 Accordingly, integrating infertility type into the preoperative evaluation and counseling process for endometriosis patients could help tailor individual management plans and refine prognostic predictions.
This study has several limitations. First, the clinical cohort was derived from a single tertiary center and included a relatively small number of patients, particularly in the primary infertility subgroup, which may have limited the statistical power of subgroup comparisons and the generalizability of the findings. Second, the GBD database lacks provincial-level data. Third, historical data-based predictions risk being skewed by future medical advances and policy shifts. In addition, because this was an ecological study without patient-level infection data, the potential impact of the COVID-19 pandemic on EAI should be interpreted with caution.
Future studies should further investigate regional heterogeneity in EAI burden trends using interaction models across countries. Moreover, multicenter clinical cohorts with larger sample sizes are needed to improve the generalizability of clinical findings. Meanwhile, the potential impact of the COVID-19 pandemic on EAI warrants further investigation. Additionally, future studies should identify and validate immune biomarkers for the diagnosis, disease stratification, and prognostic evaluation of EAI.
Conclusion
EAI imposes a considerable overall disease burden, with a notable surge from 2021 to 2023 and a potential continued increase in burden through 2030 in China, although with substantial uncertainty. Disease trends varied across East Asia, with the Republic of Korea not showing a similar post-2021 increase to that observed in China and Japan. Real-world clinical data complemented the epidemiological findings by providing additional insights into EAI characteristics. Secondary infertility is the predominant subtype, and most cases are classified as ASRM stage III. Distinguishing between primary and secondary infertility may facilitate age-specific risk assessment and tailored management strategies. In particular, our findings highlight the need for early screening among women aged 20–29 years with primary infertility and targeted management among those aged 35–39 years with secondary infertility.
Acknowledgments
The authors gratefully acknowledge the Institute for Health Metrics and Evaluation (IHME) and the Global Burden of Disease (GBD) Collaborative Network for providing the open-access data resources that enabled this study.
Funding Statement
This study was supported by the National Natural Science Foundation of China (grant number 82274190 and 82574678).
Abbreviations
EAI, Endometriosis-associated infertility; DALYs, Disability-adjusted life years; GBD, Global Burden of Disease; YLDs, Years lived with disability; ASPR, age-standardized prevalence rate; ASR-YLDs, age-standardized YLDs rate; BMI, Body mass index; ASRM, American Society for Reproductive Medicine; ASR, Age-standardized rate; UI, Uncertainty intervals; APC, Annual percent change; CI, Confidence interval; AAPC, Average annual percent change; IE, intrinsic estimator; RR, Rrate ratios; BAPC, Bayesian Age-Period-Cohort.
Data Sharing Statement
The data for this study are available from Global Burden of Disease Study 2023 at http://ghdx.healthdata.org/gbd-results-tool.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Disclosure
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
References
- 1.Li Y, Zhou W, Ding J, et al. Integrative single-cell analysis reveals iron overload-induced senescence and metabolic reprogramming in ovarian endometriosis-associated infertility. Adv Sci. 2025;12(29):e17528. doi: 10.1002/advs.202417528 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Lin X, Gu W, Tong X, et al. EETs reduction contributes to granulosa cell senescence and endometriosis-associated infertility via the PI3K/AKT/mTOR signaling pathway. Adv Sci. 2025;12(42):e05656. doi: 10.1002/advs.202505656 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Melesse YD, Amha H, Getnet A, et al. Epidemiology of infertility and coping mechanisms in Ethiopia, 2025: a systematic review and meta-analysis. Reprod Health. 2025;22(1):217. doi: 10.1186/s12978-025-02158-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Velarde MC, Bucu MEM, Habana MAE. Endometriosis as a highly relevant yet neglected gynecologic condition in Asian women. Endocr Connect. 2023;12(11). doi: 10.1530/ec-23-0169 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Tang WZ, Cai QY, Huang KJ, et al. The global burden of polycystic ovary syndrome, endometriosis, uterine fibroids, cervical cancer, uterine cancer, and ovarian cancer from 1990 to 2021. BMC Public Health. 2025;25(1):1774. doi: 10.1186/s12889-025-22881-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Xu S, Zhang Y, Ye P, et al. Global, regional, and national burden of endometriosis among women of childbearing age from 1990 to 2021: a cross-sectional analysis from the 2021 global burden of disease study. Int J Surg. 2025;111(9):5927–14. doi: 10.1097/js9.0000000000002647 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Chen Y, Li Q, Liu M, Niu Y, Liu H, Zhang L. Burden analysis of uterine fibroids and endometriosis in reproductive-age women globally, in China, India, and the United States, 1990–2021. Front Nutr. 2025;12:1683847. doi: 10.3389/fnut.2025.1683847 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Li R, Zhang L, Liu Y. Global and regional trends in the burden of surgically confirmed endometriosis from 1990 to 2021. Reprod Biol Endocrinol. 2025;23(1):88. doi: 10.1186/s12958-025-01421-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Tang Z, Ma C, Liu J, Liu C. Endometriosis burden and trends among women of childbearing age from 1990 to 2021. Front Endocrinol. 2025;16:1561673. doi: 10.3389/fendo.2025.1561673 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Chen Y, Liu C, Wang X, Liu Y, Liu H. Global, regional and national burden of infertility due to endometriosis: results from the global burden of disease study 2021 and forecast to 2044. Bjog. 2025;132(7):944–960. doi: 10.1111/1471-0528.18108 [DOI] [PubMed] [Google Scholar]
- 11.Lao J, Shen D, Hu P, Song Y, Yang H. Endometriosis-related infertility in China: analysis of the global burden of disease study 2021. Front Public Health. 2025;13:1651254. doi: 10.3389/fpubh.2025.1651254 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Liang Y, Huang J, Zhao Q, et al. Global, regional, and national prevalence and trends of infertility among individuals of reproductive age (15–49 years) from 1990 to 2021, with projections to 2040. Hum Reprod. 2025;40(3):529–544. doi: 10.1093/humrep/deae292 [DOI] [PubMed] [Google Scholar]
- 13.Long Z, Yu LL, Yan FS, et al. National and subnational burden and causes of anemia in China from 1990 to 2023: findings from the Global Burden of Disease Study 2023. Mil Med Res. 2025;12(1):91. doi: 10.1186/s40779-025-00681-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Stafford LK, Gage A, Xu YY, et al. Global, regional, and national cascades of diabetes care, 2000–23: a systematic review and modelling analysis using findings from the Global Burden of Disease Study. Lancet Diabetes Endocrinol. 2025;13(11):924–934. doi: 10.1016/s2213-8587(25)00217-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Li J, Zhong Y, Wang X, et al. Global prevalence, incidence and years lived with disability of polycystic ovary syndrome highlights disease burden among adolescents. Reprod Biomed Online. 2025;51(5):105051. doi: 10.1016/j.rbmo.2025.105051 [DOI] [PubMed] [Google Scholar]
- 16.Garolla A, Pizzol D, Carosso AR, et al. Practical clinical and diagnostic pathway for the investigation of the infertile couple. Front Endocrinol. 2020;11:591837. doi: 10.3389/fendo.2020.591837 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Sun S, Zhou S, Huang Q, Sun J. The unique relationship between body mass index and metabolic syndrome in AIDS patients. Sci Rep. 2025;15(1):13915. doi: 10.1038/s41598-025-97033-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Metzemaekers J, Haazebroek P, Smeets M, et al. EQUSUM: Endometriosis QUality and grading instrument for SUrgical performance: proof of concept study for automatic digital registration and classification scoring for r-ASRM, EFI and Enzian. Hum Reprod Open. 2020;2020(4):hoaa053. doi: 10.1093/hropen/hoaa053 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Valenti M, Schliep KC, Paulsen M, et al. Incident endometriosis diagnosis and anti-müllerian hormone (AMH): how surgical staging and typology relate to serum AMH levels. Am J Obstet Gynecol. 2026;234:1450–1458. doi: 10.1016/j.ajog.2025.12.070 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Lin Y, Fang K, Zheng Y, Wang HL, Wu J. Global burden and trends of neglected tropical diseases from 1990 to 2019. J Travel Med. 2022;29(3). doi: 10.1093/jtm/taac031 [DOI] [PubMed] [Google Scholar]
- 21.Tuo Y, Li Y, Li Y, et al. Global, regional, and national burden of thalassemia, 1990–2021: a systematic analysis for the global burden of disease study 2021. EClinicalMedicine. 2024;72:102619. doi: 10.1016/j.eclinm.2024.102619 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Ananth CV, Fields JC, Brandt JS, Graham HL, Keyes KM, Zeitlin J. Evolving stillbirth rates among Black and White women in the United States, 1980–2020: a population-based study. Lancet Reg Health Am. 2022;16:100380. doi: 10.1016/j.lana.2022.100380 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Jin W, Huang K, Ding Z, et al. Global, regional, and national burden of esophageal cancer: a systematic analysis of the Global Burden of Disease Study 2021. Biomark Res. 2025;13(1):3. doi: 10.1186/s40364-024-00718-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Li M, Xu S, Yang Q, et al. Epidemiological trends of gynecologic cancer burden in east Asian countries from 1990 to 2021 and projections to 2031. Int J Gynaecol Obstet. 2026;172(1):255–262. doi: 10.1002/ijgo.70351 [DOI] [PubMed] [Google Scholar]
- 25.Hebert PL, Kumbier KE, Smith VA, et al. Changes in outpatient health care use after COVID-19 infection among veterans. JAMA Netw Open. 2024;7(2):e2355387. doi: 10.1001/jamanetworkopen.2023.55387 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Wang Y, Kong F, Fu Y, Qiao J. How can China tackle its declining fertility rate? BMJ. 2024;386:e078635. doi: 10.1136/bmj-2023-078635 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Kordowitzki P, Kelley LP, Mechsner S. Endometriosis: from genes to global burden. Int J Mol Sci. 2025;27(1):151. doi: 10.3390/ijms27010151 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Li CJ, Lin LT, Tsai HW, Wen ZH, Tsui KH. Phosphoglycerate mutase family member 5 maintains oocyte quality via mitochondrial dynamic rearrangement during aging. Aging Cell. 2022;21(2):e13546. doi: 10.1111/acel.13546 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Olsen KW, Castillo-Fernandez J, Chan AC, et al. Identification of a unique epigenetic profile in women with diminished ovarian reserve. Fertil Steril. 2021;115(3):732–741. doi: 10.1016/j.fertnstert.2020.09.009 [DOI] [PubMed] [Google Scholar]
- 30.Bujold E, Romero R. Uterine closure after cesarean delivery: surgical technique, biological rationale, and clinical implications. Am J Obstet Gynecol. 2026;233(6s):S82–s102. doi: 10.1016/j.ajog.2025.10.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Zhai YJ, Feng Y, Ma X, Ma F. Defensins: defenders of human reproductive health. Hum Reprod Update. 2023;29(1):126–154. doi: 10.1093/humupd/dmac032 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Yu B, Song Z, Shen Z. Job quality and employee happiness: evidence from China. Front Psychol. 2025;16:1550200. doi: 10.3389/fpsyg.2025.1550200 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Lan Z, Wu X, Xing X, Qian Y. Factors influencing the fertility intentions among women of childbearing age in China. Acta Psychol. 2025;260:105640. doi: 10.1016/j.actpsy.2025.105640 [DOI] [PubMed] [Google Scholar]
- 34.Yang X, Shen Y, Bai Y. Family orientation, working years and childbearing age: evidence from the China Family Panel Study 2014. J Biosoc Sci. 2023;55(5):921–930. doi: 10.1017/s002193202200044x [DOI] [PubMed] [Google Scholar]
- 35.Meyer R, Maxey C, Hamilton KM, et al. Associations between race and ethnicity and perioperative outcomes among women undergoing hysterectomy for adenomyosis. Fertil Steril. 2024;121(6):1053–1062. doi: 10.1016/j.fertnstert.2024.02.003 [DOI] [PubMed] [Google Scholar]
- 36.Van Der Kelen A, Okutman Ö, Javey E, et al. A systematic review and evidence assessment of monogenic gene-disease relationships in human female infertility and differences in sex development. Hum Reprod Update. 2023;29(2):218–232. doi: 10.1093/humupd/dmac044 [DOI] [PubMed] [Google Scholar]
- 37.Zaritsky EF, Tucker LY, Hen E, Childs AJ, Ritterman Weintraub ML, Wicks C. Racial Disparities in Endometriosis and Pelvic Pain Treatment Within an Integrated Health Care Delivery System. Obstet Gynecol. 2025;146(6):888–897. doi: 10.1097/aog.0000000000006045 [DOI] [PubMed] [Google Scholar]
- 38.Li L, Xi Y, Li J, et al. Bushen Tiaojing decoction ameliorates meiotic impairment to improve oocyte quality during aging through blocking the iron overload-induced p53/CDK1/ESPL1 pathway. Free Radic Biol Med. 2026;253:785–802. doi: 10.1016/j.freeradbiomed.2026.05.333 [DOI] [PubMed] [Google Scholar]
- 39.Wu M, Zhu Q, Xiong J, et al. Modulating IL-11-dependent matrix stiffness to delay ovarian aging. Nat Aging. 2026. doi: 10.1038/s43587-026-01159-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Lu N, Wang J, Li YH, et al. Extracellular matrix: new insights into its role in female reproductive aging and potential therapeutic strategies. NPJ Aging. 2026;12(1). doi: 10.1038/s41514-026-00371-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Du Y, Zhou C, Tian Y, Li Z, Huang X. Single‑cell atlas reveals a Wilms’ tumor 1‑mediated axis driving granulosa cell senescence in human ovarian aging. Int J Mol Med. 2026;58(3):1–17. doi: 10.3892/ijmm.2026.5916 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Habib N, Upane A, Ivanova L, et al. Isthmocele: from Detection to Treatment: a Narrative Review. Adv Ther. 2025. doi: 10.1007/s12325-025-03412-3 [DOI] [PubMed] [Google Scholar]
- 43.Donnez O, Donnez J. Deep endometriosis: the place of laparoscopic shaving. Best Pract Res Clin Obstet Gynaecol. 2021;71:100–113. doi: 10.1016/j.bpobgyn.2020.05.006 [DOI] [PubMed] [Google Scholar]
- 44.Tomassetti C, Bafort C, Vanhie A, et al. Estimation of the endometriosis fertility index prior to operative laparoscopy. Hum Reprod. 2021;36(3):636–646. doi: 10.1093/humrep/deaa346 [DOI] [PubMed] [Google Scholar]
- 45.Arakawa T, Ishihara S. Omental Endometriosis. N Engl J Med. 2021;384(7):e21. doi: 10.1056/NEJMicm2027936 [DOI] [PubMed] [Google Scholar]
- 46.Biagioli V, Matera M, Ramenghi LA, Falsaperla R, Striano P. Microbiome and pregnancy dysbiosis: a narrative review on offspring health. Nutrients. 2025;17(6):1033. doi: 10.3390/nu17061033 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Zou X, Wang X, Xiong B, et al. Deciphering immune-inflammatory dysregulation in the endometriotic microenvironment: insights from single-cell omics and artificial intelligence. Front Immunol. 2026;17:1848643. doi: 10.3389/fimmu.2026.1848643 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Le NXH, Loret de Mola JR, Bremer P, et al. Alteration of systemic and uterine endometrial immune populations in patients with endometriosis. Am J Reprod Immunol. 2021;85(3):e13362. doi: 10.1111/aji.13362 [DOI] [PubMed] [Google Scholar]
- 49.Tanaka Y, Maeda E, Okimura H, et al. Functional impairment of activated regulatory T cells as a central immunological defect in endometriosis. Reprod Med Biol. 2026;25(1):e70051. doi: 10.1002/rmb2.70051 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Maignien C, Santulli P, Marcellin L, et al. Infertility in women with bowel endometriosis: first-line assisted reproductive technology results in satisfactory cumulative live-birth rates. Fertil Steril. 2021;115(3):692–701. doi: 10.1016/j.fertnstert.2020.09.032 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data for this study are available from Global Burden of Disease Study 2023 at http://ghdx.healthdata.org/gbd-results-tool.