Endometriosis-Associated Infertility in China: Epidemiological Trends, Future Projection, and Real-World Clinical Insights

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.

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. 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 Abbreviations : ASPR, Age-standardized prevalence rate; AAPC, Average annual percent change. 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 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. Six multi-line graphs comparing China, Japan and Korea trends in endometriosis-associated infertility measures. Image A shows a line graph. X-axis label: Year (1990 to 2023). Y-axis label: ASPR (per 100,000), range 20 to 35. Three marker lines represent China, Republic of Korea and Japan. China declines from about 31 in 1990 to around 29 by the late 1990s, decreases more rapidly from the mid-2000s to about 22 around 2010, remains near 21 to 23 through 2022, and rises to about 26 in 2023. Republic of Korea remains around 24 to 25 during the 1990s, gradually declines from about 24 in the late 2000s to about 20 in 2023. Japan remains around 33 to 34 through the 1990s, declines markedly between 2001 and 2005 to about 27, remains between approximately 27 and 29 from 2005 to 2022, and rises to about 31 in 2023. Image B shows a line graph. X-axis label: Year (1990 to 2023). Y-axis label: ASPR (per 100,000), range 1.5 to 8.5. Republic of Korea declines from about 7 in 1990 to about 6 around 2000, stays near 6 through about 2011, then gradually declines to about 5 by 2022 and remains near 5 in 2023. Japan declines from about 6 in 1990 to about 5 by the early 2000s, drops to about 4.5 around 2005, stays near 4.5 through about 2022, then rises to between 5 and 6 in 2023. China remains relatively stable at around 2 to 3 from 1990 to 2022, with a slight decline after the mid-2000s, and rises slightly in 2023. Image C shows a line graph. X-axis label: Year (1990 to 2023). Y-axis label: ASPR (per 100,000), range 15 to 30. China declines from about 29 in 1990 to about 26 by the mid-1990s, stays near 26 through the mid-2000s, then drops to about 20 around 2010, remains near 20 through 2022, and rises to about 24 in 2023. Japan remains near 27 through the 1990s, declines to about 22 by the mid-2000s, stays near 22 to 24 through 2022, and rises to about 26 in 2023. Republic of Korea remains around 17 to 18 through the 1990s and 2000s, then gradually declines to about 15 by 2023. Image D shows a line graph. X-axis label: Year (1990 to 2023). Y-axis label: ASR-YLDs (per 100,000), range 0.10 to 0.20. Japan remains near 0.19 through the 1990s, declines to about 0.15 by the mid-2000s, remains relatively stable thereafter, and rises to about 0.18 in 2023. Republic of Korea remains around 0.14 to 0.15 through the 1990s and 2000s, then gradually declines to about 0.11 by 2023. China gradually declines from about 0.17 in 1990 to about 0.15 by the mid-2000s, drops to about 0.12 around 2010, remains near 0.12 through 2022, and rises to about 0.14 in 2023. Image E shows a line graph. X-axis label: Year (1990 to 2023). Y-axis label: ASR-YLDs (per 100,000), range 0.01 to 0.07. Republic of Korea declines from about 0.05 in 1990 to about 0.04 around 2000, remains relatively stable through the early 2010s, then gradually declines and remains near 0.04 through 2023. Japan remains near 0.05 through the 1990s, declines to about 0.03 by the mid-2000s, remains relatively stable through 2022, and rises slightly in 2023. China remains relatively stable at around 0.02 throughout the period, with a slight decline after the mid-2000s and a slight rise in 2023. Image F shows a line graph. X-axis label: Year (1990 to 2023). Y-axis label: ASR-YLDs (per 100,000), range 0.07 to 0.17. China declines from about 0.15 in 1990 to about 0.13 by the mid-1990s, remains relatively stable through the mid-2000s, then drops to about 0.10 around 2010, remains near 0.10 to 0.11 through 2022, and rises to about 0.12 in 2023. Japan remains near 0.14 through the 1990s, declines to about 0.12 by the mid-2000s, remains relatively stable thereafter, and rises to about 0.13 in 2023. Republic of Korea remains relatively stable at around 0.08 to 0.09 through the 1990s and 2000s, then gradually declines and remains near 0.08 through 2023. Abbreviations : ASPR, Age-Standardized Prevalence Rate; ASR-YLDs, Age-Standardized Rate of Years Lived with Disability. 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. 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. Six graphs on endometriosis-related infertility in China by age/year, using bar and line charts. Image A: Bar and line graph showing prevalence number and rate of endometriosis-associated infertility by age group in China. X-axis: age groups 15-19 to 45-49. Left Y-axis: prevalence number up to 45,000. Right Y-axis: prevalence rate per 100,000 up to 80. Peaks at ages 30-34. Image B: Similar graph with lower prevalence numbers and rates, peaking at ages 20-24. Image C: Similar to A, peaks at ages 35-39. Image D: Multi-line graph showing prevalence rate per 100,000 from 1990 to 2023. X-axis: years. Y-axis: prevalence rate per 100,000. Lines for age groups 15-19 to 45-49, with a drop around 2005-2007 and rise in 2023. Image E: Similar to D, with lower rates, 20-24 group highest. Image F: Similar to D, with a rise in 2023. Lines distinguished by markers for each age group. Panels A-C show different age peaks, while D-F show trends over time. 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. A set of 27 line graphs showing age curves, period rate ratios and cohort rate ratios for infertility. The image A showing nine line graphs in three rows and three columns. Row labels: Rate, Infertility; Rate, Primary; Rate, Secondary. Column titles: Longitudinal Age Curve; Period RR; Cohort RR. Longitudinal Age Curve x axis label: Age, unit not shown, range 15 to 50. Rate, Infertility y axis label: Rate, Infertility, unit not shown, range 0 to 70. Plotted points rise from about 0 at age 15 to about 40 at age 25, about 60 at age 30, peak about 70 at age 35, then about 68 at age 40, about 66 at age 45, about 62 at age 50. Rate, Primary y axis label: Rate, Primary, unit not shown, range 0 to 12. Points peak near age 25 at about 12, then decline to about 9 at age 30, about 7 at age 35, about 5 at age 40, about 3 at age 45, about 2 at age 50. Rate, Secondary y axis label: Rate, Secondary, unit not shown, range 0 to 70. Points rise from about 0 at age 15 to about 30 at age 25, about 55 at age 30, peak about 65 at age 35, then about 63 at age 40, about 62 at age 45, about 58 at age 50. Period RR x axis label: Period, unit not shown, ticks 1995, 2000, 2005, 2010, 2015, 2020. Y axis label: Rate Ratio, unit not shown. For all three rows, the line declines from about 1.05 at 1995 to about 1.00 at 2005, drops to about 0.82 at 2010, then stays near 0.84 to 0.86 through 2020. Cohort RR x axis label: Cohort, unit not shown, ticks 1950, 1960, 1970, 1980, 1990, 2000. Y axis label: Rate Ratio, unit not shown. For all three rows, the line declines from about 1.35 to 1.40 at cohort 1950 to about 0.70 to 0.80 by cohort 2000, with a shaded band around the line. The image B showing nine line graphs with the same row labels and column titles. Longitudinal Age Curve x axis label: Age, unit not shown, range 15 to 50. Rate, Infertility y axis label: Rate, Infertility, unit not shown, range 0 to 100. Points rise from about 0 at age 15 to about 50 at age 25, about 70 at age 30, about 70 at age 35, about 80 at age 40, peak about 100 at age 45, then drop to about 50 at age 50. Rate, Primary y axis label: Rate, Primary, unit not shown, range 0 to 25. Points rise from about 0 at age 15 to about 20 at age 25, peak about 25 at age 30, then decline to about 15 at age 35, about 8 at age 40, about 5 at age 45, about 3 at age 50. Rate, Secondary y axis label: Rate, Secondary, unit not shown, range 0 to 100. Points rise from about 0 at age 15 to about 30 at age 25, about 50 at age 30, about 55 at age 35, about 70 at age 40, peak about 95 at age 45, then drop to about 50 at age 50. Period RR x axis label: Period, unit not shown, ticks 1995, 2000, 2005, 2010, 2015, 2020. Y axis label: Rate Ratio, unit not shown. Rate, Infertility line declines from about 1.30 at 1995 to about 1.10 at 2005, reaches about 1.00 at 2010, then rises to about 1.15 by 2020. Rate, Primary line declines from about 1.35 at 1995 to about 1.10 at 2005, reaches about 1.00 at 2010, then rises to about 1.10 by 2020. Rate, Secondary line declines from about 1.25 at 1995 to about 1.05 at 2005, reaches about 1.00 at 2010, then rises to about 1.15 by 2020. Cohort RR x axis label: Cohort, unit not shown, ticks 1950, 1960, 1970, 1980, 1990, 2000. Y axis label: Rate Ratio, unit not shown. Rate, Infertility line stays near 1.00 to 1.10 from 1950 to 1970, then declines to about 0.85 by 2000, with a wide shaded band near cohort 2000. Rate, Primary line declines from about 1.10 at 1950 to about 0.80 by 2000, with a shaded band. Rate, Secondary line stays near 1.00 to 1.10 from 1950 to 1970, then declines to about 0.85 by 2000, with a wide shaded band near cohort 2000. The image C showing nine line graphs with the same row labels and column titles. Longitudinal Age Curve x axis label: Age, unit not shown, range 15 to 50. Rate, Infertility y axis label: Rate, Infertility, unit not shown, range 0 to 100. Points rise from about 0 at age 15 to about 50 at age 25, about 55 at age 30, about 50 at age 35, about 70 at age 40, peak about 90 at age 45, then drop to about 30 at age 50. Rate, Primary y axis label: Rate, Primary, unit not shown, range 0 to 30. Points rise from about 0 at age 15 to about 25 at age 25, peak about 28 at age 30, then decline to about 15 at age 35, about 10 at age 40, about 7 at age 45, about 3 at age 50. Rate, Secondary y axis label: Rate, Secondary, unit not shown, range 0 to 80. Points rise from about 0 at age 15 to about 20 at age 25, about 25 at age 30, about 30 at age 35, about 50 at age 40, peak about 80 at age 45, then drop to about 30 at age 50. Period RR x axis label: Period, unit not shown, ticks 1995, 2000, 2005, 2010, 2015, 2020. Y axis label: Rate Ratio, unit not shown. Rate, Infertility line declines from about 1.10 at 1995 to about 1.00 at 2000, stays near 1.00 to 1.02 through 2010, then declines to about 0.90 by 2020. Rate, Primary line declines from about 1.10 at 1995 to about 1.00 at 2000, stays near 1.00 through 2010, then declines to about 0.80 by 2020. Rate, Secondary line declines from about 1.10 at 1995 to about 1.00 at 2000, stays near 1.00 through 2010, then declines to about 0.90 by 2020. Cohort RR x axis label: Cohort, unit not shown, ticks 1950, 1960, 1970, 1980, 1990, 2000. Y axis label: Rate Ratio, unit not shown. Rate, Infertility line is about 1.05 at 1950, about 1.10 at 1960, then declines to about 0.75 by 2000, with a wide shaded band near cohort 2000. Rate, Primary line declines from about 1.20 at 1950 to about 0.70 by 2000, with a wide shaded band near cohort 2000. Rate, Secondary line is about 1.05 at 1950, about 1.10 at 1960, then declines to about 0.75 by 2000, with a wide shaded band near cohort 2000. Across A, B and C, the three columns pair age based rates with period rate ratios and cohort rate ratios for the same three outcomes and the period and cohort plots include a line with circular markers and a shaded uncertainty band. 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. 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. 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 ). 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. A multi line graph and bar chart set showing infertility prevalence and cases by age group and year. Image A displays line graphs by age groups (15-49) for infertility prevalence, with rows labeled Infertility, Primary and Secondary prevalence. The x-axis spans 1990-2030, with a dashed line near 2023. Graphs show low, flat lines until 2020, then rise sharply, especially for ages 35-39. Image B features stacked bar and line graphs for Infertility, Primary and Secondary infertility from 1990-2030. The left y-axis shows cases (0 to 600,000) and the right y-axis shows ASR per 100,000 (0 to 150). A legend differentiates observed (solid) and predicted (dotted) lines. Infertility bars dip from 200,000 in the 1990s to 160,000 in 2010, then rise to 600,000 by 2030; ASR follows a similar pattern. Primary infertility bars remain between 20,000 and 30,000, with a slight post-2023 rise; ASR stays flat. Secondary infertility bars drop from 180,000-200,000 in the 1990s to 150,000 in 2010, then rise to 550,000 by 2030; ASR increases to 150 by 2030. Abbreviation : ASPR, Age-Standardized Prevalence Rate. Projection of endometriosis-associated infertility in China by 2030. ( A ) Prevalence rate by age group. ( B ) Prevalence cases and ASPR. 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/m 2 ), with no statistically significant difference observed between groups. In the secondary infertility cohort, overweight individuals (BMI 24–28 kg/m 2 ) 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. Infographics on endometriosis-associated infertility: types, age, BMI and ASRM stage distribution. Image A: Pie chart shows 11.9% primary and 88.1% secondary infertility. Image B: Bar chart displays age groups (15-49 years) with counts for both infertility types. Image C: Scatter plot of age for infertility types, significant difference marked. Image D: Bar chart of BMI categories (<18.5, 18.5-24, 24-28, greater than or equal to 28) with counts for infertility types. Image E: Scatter plot of BMI for infertility types, marked as not significant. Image F: Bar charts of BMI distribution by proportion and count for infertility types. Image G: Bar chart of ASRM stage (I-IV) distribution with counts for infertility types. Image H: Scatter plot of ASRM scores for infertility types, significant difference marked. Image I: Bar charts of ASRM stage distribution by proportion and count for infertility types. Notes : * p < 0.05, ****p < 0.0001. Abbreviation : ns, not significant. 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.

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/m 2 (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. 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: \documentclass[12pt]{minimal} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \DeclareFontFamily{T1}{linotext}{} \DeclareFontShape{T1}{linotext}{m}{n} {linotext }{} \DeclareSymbolFont{linotext}{T1}{linotext}{m}{n} \DeclareSymbolFontAlphabet{\mathLINOTEXT}{linotext} \begin{document}$${\mathrm{ASR}} = {{\mathop \sum \nolimits_{{\mathrm{i}} = 1}^{\mathrm{A}} {{\mathrm{a}}_{\mathrm{i}}}{{\mathrm{w}}_{\mathrm{i}}}} \over {\mathop \sum \nolimits_{{\mathrm{i}} = 1}^{\mathrm{A}} {{\mathrm{w}}_{\mathrm{i}}}}} \times 100,000$$\end{document} Where a i and w i 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: \documentclass[12pt]{minimal} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \DeclareFontFamily{T1}{linotext}{} \DeclareFontShape{T1}{linotext}{m}{n} {linotext }{} \DeclareSymbolFont{linotext}{T1}{linotext}{m}{n} \DeclareSymbolFontAlphabet{\mathLINOTEXT}{linotext} \begin{document}$$Y = \log (M) = \mu + \alpha ag{e_i} + \beta perio{d_j} + \gamma cohor{t_k} + \varepsilon$$\end{document} 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. 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.

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.

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.