Global Trends and Burden of Infertility Attributable to Polycystic Ovary Syndrome, 1990–2021: A Comprehensive Analysis from the Global Burden of Disease Study

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Design : A population-based observational study. Subjects : Women aged 15 to 49 years diagnosed with infertility due to polycystic ovary syndrome in 204 countries and territories. Exposure (for observational studies) : The burden and prevalence of infertility associated with polycystic ovary syndrome, analyzed by age, time period, birth cohort, and sociodemographic index levels. Main Outcome Measures : Age-standardized prevalence rates of infertility related to polycystic ovary syndrome, stratified by primary and secondary infertility, age group, world region, and sociodemographic index. Results : Between 1990 and 2021, the global age-standardized prevalence of infertility due to polycystic ovary syndrome increased from 475.54 to 638.15 per 100,000 women. Secondary infertility increased at a faster rate than primary infertility. The highest burden was observed in high-income regions, but the most rapid increases occurred in low- and middle-income regions. The peak age-specific burden occurred in women aged 25 to 39 years. Time period and birth cohort effects both showed rising trends, particularly in younger generations in lower-income settings. Decomposition analysis attributed the rising burden to population growth and changing epidemiological patterns. Inequality analysis revealed widening absolute disparities and a shifting burden toward lower-income countries. Conclusion : Infertility related to polycystic ovary syndrome has increased steadily over the past three decades, with growing disparities between countries. Future policies should prioritize early diagnosis, targeted interventions, and expanded reproductive care, particularly in lower-resource settings, to mitigate this rising global health burden. Polycystic ovary syndrome infertility epidemiology global burden of disease health inequality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Infertility is defined as the failure to conceive after at least one year of regular, unprotected sexual intercourse. It is a globally recognized reproductive health concern, affecting approximately one in eight women of reproductive age[ 1 ]. Among the various causes of infertility, polycystic ovary syndrome (PCOS) stands out as one of the most common endocrine-metabolic disorders in women of reproductive age, with a global prevalence of 10–13%. PCOS is primarily characterized by three clinical features: polycystic ovarian morphology, menstrual irregularities, and hyperandrogenism[ 2 ]. Notably, PCOS is not only a reproductive disorder but also a multisystem metabolic syndrome frequently associated with significant insulin resistance (IR), obesity, and dyslipidemia. These pathological changes interact through a complex endocrine network, collectively exacerbating metabolic disturbances and reproductive dysfunction in affected individuals[ 3 ]. From a reproductive perspective, PCOS is the leading cause of anovulatory infertility, accounting for approximately 70–80% of infertility cases in women[ 4 ]. The underlying mechanisms of infertility in PCOS are multifactorial. First, ovulatory dysfunction primarily arises from dysregulation of the hypothalamic–pituitary–ovarian (HPO) axis, wherein abnormal luteinizing hormone (LH) pulsatility, hyperinsulinemia, and excessive local ovarian androgens impair follicular development and prevent the selection of a dominant follicle[ 5 ]. Second, elevated androgen and insulin levels negatively affect endometrial receptivity, thereby impairing embryo implantation[ 6 , 7 ]. Additionally, diminished oocyte quality is a critical contributing factor, with oxidative stress and mitochondrial dysfunction in granulosa cells potentially compromising oocyte maturation capacity[ 8 ]. Studies have shown that women with PCOS have a significantly higher risk of spontaneous miscarriage compared to non-PCOS populations, which may be linked to insulin resistance, fibrinolytic abnormalities, and impaired endometrial function[ 9 ]. Even when pregnancy is achieved, the risk of gestational complications such as gestational diabetes and preeclampsia remains substantially elevated[ 10 ]. Long-term management of PCOS typically involves lifestyle modifications (e.g., weight loss), insulin-sensitizing agents (e.g., metformin), and ovulation induction therapies[ 11 ]. For patients with refractory anovulation, assisted reproductive technologies (ART), such as in vitro fertilization (IVF), can improve pregnancy outcomes, although attention must be paid to the risk of ovarian hyperstimulation syndrome (OHSS)[ 12 ]. The health and socioeconomic burden of PCOS-related infertility has attracted increasing attention. From a healthcare economics perspective, patients often face considerable financial costs associated with ovulation induction drugs (e.g., clomiphene citrate, letrozole) and ART procedures such as IVF[ 13 ]. One study reported that approximately 46% of PCOS patients undergoing ovulation induction required further ART, with complications like OHSS further increasing medical expenditures[ 14 , 15 ]. Moreover, meta-analyses indicate that 40–60% of women with PCOS-related infertility experience symptoms of anxiety and depression, significantly higher than in the general population. These psychological burdens are closely associated with prolonged treatment durations, social expectations, and self-image concerns[ 16 ]. Despite the profound impact of PCOS, effective strategies for managing PCOS-related infertility remain limited. There are notable gaps in research and public health interventions, including: (1) a lack of systematic global and regional disease burden assessments; (2) insufficient analysis of temporal trends, particularly regarding age–period–cohort effects; (3) inadequate quantification and elucidation of the driving forces behind the increasing burden of PCOS-related infertility. Addressing these gaps requires enhanced research investment to better understand the epidemiological features of PCOS-related infertility, identify key sociodemographic risk factors, and improve healthcare services for affected populations. Therefore, the present study utilizes data from the 2021 Global Burden of Disease (GBD) database to comprehensively evaluate prevalence trends of PCOS-related infertility, providing critical evidence for epidemiological research. This study aims to identify current shortcomings in prevention, management, and treatment strategies, and to highlight deficiencies or areas in need of adjustment—particularly in high-prevalence age groups or regions. Ultimately, it offers evidence-based support for policy formulation and the optimization of healthcare strategies, aiming to improve reproductive health outcomes in women with PCOS and underscore the necessity of prioritizing PCOS in the women's health agenda. Methods Ethics Approval The data used in this study were obtained from the publicly available GBD database. The GBD project has obtained ethics approval from the University of Washington Institutional Review Board (IRB), and all data are anonymized and aggregated to ensure compliance with ethical standards. Our study is a secondary analysis of de-identified data and does not involve any direct interaction with human subjects. Therefore, separate IRB approval for this study was not required. Please note that the ethical standards of the GBD project and the use of de-identified data ensure the ethical integrity of our research. Data Sources This study utilized data from the Global Burden of Disease Study 2021 (GBD 2021), coordinated by the Institute for Health Metrics and Evaluation (IHME). The GBD provides standardized estimates of incidence, prevalence, mortality, and disability-adjusted life years (DALYs) for 371 diseases and injuries across 204 countries and territories from 1990 to 2021. Data were derived from over 100,000 sources, including systematic reviews, hospital and outpatient records, health surveys, insurance claims, and vital registration systems. Disease estimates were produced using the DisMod-MR 2.1 Bayesian meta-regression tool to correct for measurement bias, data sparsity, and inter-source heterogeneity, ensuring comparability across time and geography. Case Definition and Study Population PCOS and infertility were defined using ICD-9 and ICD-10 codes. Specifically, PCOS was identified using ICD-10 code E28.2, while female infertility was defined using codes N97–N98.9. Data were extracted for women of reproductive age (15–49 years), consistent with GBD-defined population groups. The study population was stratified by age (in 5-year groups), world regions (21 GBD regions), and country-level SDI. Infertility was categorized into primary infertility (no prior pregnancy despite 12 months of unprotected intercourse) and secondary infertility (inability to conceive following a previous pregnancy), as defined by GBD 2021. Age Standardization and Temporal Trend Analysis Prevalence data were age-standardized using the GBD global standard population to ensure comparability over time and between regions[ 17 ]. Subsequently, Joinpoint regression modeling was employed to analyze the temporal trends in age-standardized prevalence rates (ASPR)[ 18 ]. This model fits a segmented linear regression to the logarithmic values of ASPR and calculates the annual percent change (APC) and average annual percent change (AAPC) to characterize the trend over a specified time period. Using the Grid Search Method (GSM), the model automatically identifies significant turning points (joinpoints) that reflect changes in trend direction, aiming to minimize the sum of squared errors in the fit. The statistical significance of each candidate joinpoint is evaluated via Monte Carlo permutation tests, determining the optimal number of joinpoints (with a maximum of five). Within each segmented time interval, the model estimates the APC to quantify the rate of change in ASPR. The AAPC is then used to summarize the overall average trend over the study period. A positive AAPC indicates an increasing trend in ASPR, while a negative AAPC suggests a decreasing trend. This method provides a quantitatively robust and statistically supported analysis of epidemiological trends in PCOS-related infertility, offering valuable insights for future research and informing the development of public health policies. Age–Period–Cohort Model This study employed the Age–Period–Cohort (APC) model to systematically assess the epidemiological trends of infertility related to polycystic ovary syndrome (PCOS). The APC model decomposes changes in disease burden into three independent effects: the influence of biological aging (age effect), temporal changes in external social and environmental factors (period effect), and the characteristics specific to different birth cohorts (cohort effect). This framework facilitates a deeper understanding of the dynamic patterns underlying the epidemiology of PCOS-related infertility. The APC model enables the estimation of both local drift and net drift parameters. Local drift represents the age-specific annual percentage change in prevalence, whereas net drift reflects the overall temporal trend across all age groups. The model also generates longitudinal age curves to depict the age-specific distribution of PCOS-related infertility. Additionally, rate ratios (RR) for period and cohort effects are calculated to assess the relative risk of specific time periods or birth cohorts compared with a designated reference group[ 19 ]. An RR greater than 1 indicates a higher relative risk than the reference, while an RR less than 1 suggests a lower relative risk. BAPC Model Projection This study utilized the Bayesian Age–Period–Cohort (BAPC) model to project the long-term burden of infertility attributable to polycystic ovary syndrome (PCOS). The BAPC model simultaneously incorporates age, period, and cohort effects, thereby offering a comprehensive depiction of the temporal evolution and intergenerational dynamics of disease burden[ 20 ]. By applying smoothing techniques to historical data, the BAPC model effectively reduces the influence of random fluctuations and noise, enhancing the stability and accuracy of the forecasts. During model construction, prior distributions for parameters were defined based on data from previous Global Burden of Disease (GBD) studies. The Markov Chain Monte Carlo (MCMC) method was employed for iterative simulation to generate the posterior distributions of the parameters. Model outputs included projections of the prevalence and ASPR of PCOS-related infertility for future periods, accompanied by corresponding 95% credible intervals (CrI) to reflect the uncertainty in the forecasts. Based on the predictions derived from the BAPC model, this study provides a forward-looking assessment of trends in the burden of PCOS-related infertility up to the year 2045. These projections offer valuable data support and decision-making references for policy formulation and the strategic allocation of health resources. Decomposition Analysis We further conducted a decomposition analysis to assess the driving factors behind changes in the burden of infertility attributable to PCOS from 1990 to 2021. This method decomposes overall changes into contributions from demographic drivers (e.g., population growth, aging) and epidemiological changes, thereby clarifying the roles of population dynamics and risk transitions in shaping disease burden trends. By comparing actual observations with counterfactual baseline scenarios, we identified key contributors to global changes in PCOS-related infertility burden. This framework provides policymakers with quantitative, evidence-based insights to support targeted intervention design and optimized health system planning [ 21 ]. Cross-Country Inequality Analysis To assess how inequality influences the distribution of infertility burden due to PCOS across countries, this study conducted a cross-national health inequality analysis using two key metrics: the Slope Index of Inequality (SII) and the Concentration Index (CI). These indicators respectively quantify absolute and relative disparities in PCOS-related burden along the gradient of the Sociodemographic Index (SDI). The SII was estimated using a weighted linear regression model and reflects the absolute difference in the ASPR of PCOS-related infertility between countries with the lowest and highest SDI levels. This metric compares the burden of infertility across countries ranked by SDI. In contrast, the CI is used to evaluate relative inequality. Based on the Lorenz curve, the horizontal axis represents the cumulative percentage of the population ranked by ascending SDI, while the vertical axis represents the cumulative share of the infertility burden attributed to PCOS. The area between the concentration curve and the line of perfect equality (the 45-degree diagonal) reflects the degree of inequality. A negative CI indicates that the burden is concentrated in low-SDI countries, whereas a positive CI suggests that high-SDI countries bear a greater share of the burden. Result Global and Temporal Trends in PCOS-Related Infertility (1990–2021) From 1990 to 2021, the global burden of infertility attributable to polycystic ovary syndrome (PCOS) showed a persistent upward trend. The age-standardized prevalence rate (ASPR) increased from 475.54 to 638.15 per 100,000 women (Table 1, the end of this article), with AAPC of 0.96% (95% CI: 0.92–1.01%). Notably, secondary infertility rose faster than primary infertility, with AAPCs of 1.12% and 0.62%, respectively. These findings indicate that secondary infertility is becoming an increasingly prominent contributor to the overall burden of PCOS-related infertility. Trends by SDI Regions Disparities emerged across SDI levels (Table 1, Fig. 1). High-SDI regions exhibited the highest ASPRs, particularly in high-income Asia Pacific, Western Europe, and North America. However, these regions also demonstrated relatively modest growth. A unique U-shaped trend was observed in high-SDI countries—an initial decline during 2000–2010 followed by a marked rebound after 2010 (e.g., APC = 2.98% during 2016–2021). In contrast, middle and low-middle SDI regions exhibited sustained high-growth trajectories (AAPC = 1.72% in middle-SDI regions), potentially driven by increasing obesity, urbanization, and limited healthcare access. Age-Specific Burden and Infertility Subtype Patterns PCOS-related infertility displayed a unimodal age distribution, peaking at ages 25–39 across SDI strata (Figure S1 A). However, the age composition differed between subtypes: primary infertility cases clustered in younger age groups (20–29), whereas secondary infertility was more common in women aged 35–44 (Figure S1 B–C). The ratio of secondary to primary infertility increased with age and peaked at 35–39 years in most SDI groups, except in high-SDI regions where the ratio rose steadily across all ages (Figure S2 ). Spatial Distribution and correlation with SDI Substantial geographic variation was observed in both ASPR and AAPC across 204 countries in 2021 (Fig. 2; Figure S3; Table S3). High-burden countries were concentrated in high-income regions, including Italy (2345.40 per 100,000), Japan (1700.92), and New Zealand (1469.73). In contrast, lower ASPRs were reported in countries such as Albania (67.52), Bosnia and Herzegovina (72.54), and North Macedonia (74.19), mostly in South Asia, Central Africa, and Central Asia. Equatorial Guinea exhibited the fastest increase in ASPR (AAPC = 2.77%), followed by Peru and the Maldives. The smallest increases were seen in Malawi, Burundi, and Brazil. Correlation analysis confirmed a significant positive association between ASPR and national SDI levels for total, primary, and secondary infertility (R = 0.45, 0.48, and 0.38, respectively; all p < 0.001) (Fig. 3), suggesting both socioeconomic gradient and detection effects. Age–Period–Cohort (APC) Analysis APC modeling revealed that age, period, and cohort effects jointly influenced PCOS-related infertility trends (Fig. 4; Tables S4–S7). Local drift analysis showed increasing annual prevalence across all age groups, with the most pronounced rise in the 15–19 age group (1.19%, 95% CI: 1.06–1.32) and the smallest in the 45–49 group (0.65%, 95% CI: 0.46–0.83) (Fig. 4A). Period effects demonstrated a global increase in relative risk, rising from baseline in 1992–1996 to 1.28 in 2017–2021, with the most pronounced increases seen in middle-SDI countries (Fig. 4B). Cohort effects were also increasing, especially among individuals born between 1997–2006 (global RR = 1.35, 95% CI: 1.29–1.40), suggesting rising vulnerability in younger generations (Fig. 4C). The age effect peaked at 35–44 years, then declined sharply in the oldest age group (Fig. 4D). Future Projections Using Bayesian APC Model (2021–2045) Bayesian APC model projections (Fig. 5; Table S8) predict that total PCOS-related infertility cases will rise from 12.5 million in 2021 to nearly 20 million by 2045. ASPR is expected to increase to 876.38 per 100,000 (95% CI: 347.02–1405.74). Primary infertility cases are projected to reach 5.25 million (ASPR = 228.90), while secondary infertility is projected to account for over 15.9 million cases (ASPR = 683.23), with a more pronounced growth rate. These forecasts suggest that secondary infertility will become the dominant form of PCOS-related infertility and require targeted intervention planning. Determinants of Change: Decomposition Analysis Decomposition analysis (Fig. 6; Table S9) identified the key drivers of increased PCOS-related infertility burden from 1990 to 2021. Globally, population growth accounted for 55.3% of the increase, followed by epidemiological transitions (43.7%) and population aging (0.95%). In low-SDI regions, population growth was the predominant contributor (71.0%), while in high-middle SDI regions, epidemiological factors—such as changes in lifestyle, reproductive behavior, and environmental exposure—were dominant (up to 81.8%). Aging contributed minimally and showed subtype-specific effects: suppressive for primary infertility but promotive for secondary infertility. Health Inequality Trends Inequality analysis demonstrated widening absolute disparities(Tables S10–S11; Fig. 7). The Slope Index of Inequality (SII) increased from 257.95 (1990) to 324.13 (2021), indicating a growing gap in disease burden between countries of different SDI levels. Meanwhile, the Concentration Index (CI) declined from 0.25 to 0.19, suggesting a relative shift in burden toward lower-SDI countries. This redistribution may reflect a convergence in risk exposures, greater diagnostic reach, and the global diffusion of lifestyle-related risk factors. Discussion Polycystic ovary syndrome (PCOS) is a leading endocrine disorder contributing to female infertility, responsible for approximately 6–15% of cases among reproductive-aged women[ 22 , 23 ]. Its multifactorial pathophysiology—encompassing hypothalamic–pituitary–ovarian (HPO) axis dysfunction, insulin resistance, and chronic low-grade inflammation—leads to ovulatory disturbances and luteal phase insufficiency. The clinical manifestations of PCOS, such as hyperandrogenism and menstrual irregularities, significantly impair fertility. While treatment strategies include lifestyle modifications, ovulation induction agents (e.g., clomiphene citrate, letrozole), insulin sensitizers (e.g., metformin), and assisted reproductive technologies (ART) [ 24 ], their effectiveness varies, and some patients remain refractory to standard regimens. Moreover, long-term metabolic risks, including type 2 diabetes and cardiovascular disease, add further complexity to management[ 25 ]. Against this backdrop, assessing global epidemiological trends in PCOS-related infertility is essential to guide policy and clinical interventions. Our findings, based on the GBD 2021 data, indicate a steadily increasing global burden of infertility attributable to PCOS over the past three decades. The ASPR rose from 475.54 per 100,000 in 1990 to 638.15 per 100,000 in 2021, with AAPC of 0.96%. This upward trajectory aligns with prior studies, including that of Liu et al., which reported a rising ASPR between 1990 and 2019 [ 26 ]. The comparatively higher estimates in our study may be due to updated datasets and expanded methodologies. The burden has grown most rapidly in middle- and low-middle SDI regions, likely due to increasing obesity, unhealthy lifestyles, environmental exposures, and endocrine-disrupting chemicals[ 27 – 29 ]. Secondary infertility showed a faster rate of increase (AAPC = 1.12%) compared to primary infertility (AAPC = 0.62%), underscoring a pressing challenge in reproductive health management. The acceleration of secondary infertility may reflect the cumulative impact of metabolic deterioration post-pregnancy, progressive ovarian dysfunction, and insufficient postpartum care. This finding highlights a critical gap in managing recurrent infertility in PCOS patients, even amid ART advancements. Joinpoint and APC analyses further revealed regional disparities. Although high-SDI regions exhibited higher ASPR levels, their growth rates were lower, potentially due to superior healthcare infrastructure, early diagnosis, and broader ART access. However, the post-2010 rebound in ASPR in these regions may indicate emerging challenges, including delayed childbearing, cumulative metabolic burden, and increasing environmental risk exposures[ 30 ]. Regionally, high-income Asia Pacific, Western Europe, and North America reported the highest ASPRs, while Central and Eastern Europe and Central Asia reported the lowest. These differences likely stem from disparities in obesity prevalence, healthcare access, and diagnostic practice[ 31 , 32 ]. Age-specific analysis showed that PCOS-related infertility peaks between ages 25–39, aligning with peak reproductive years. APC modeling revealed increasing period and cohort effects, particularly in middle- and low-SDI regions. Factors such as adolescent overweight, sedentary behavior, and widespread exposure to endocrine disruptors likely contribute[ 33 , 34 ]. While the age effect peaked at 35–44 years and declined thereafter, this trend may reflect improved fertility management and evolving reproductive planning. Forecasts from the Bayesian Age–Period–Cohort (BAPC) model predict a sustained rise in PCOS-related infertility through 2045, with secondary infertility projected to increase more rapidly. These results emphasize the urgency of regionally tailored interventions. In low-SDI areas, cost-effective tools—such as simplified endocrine panels or portable ultrasound—could reduce diagnostic delays. Integrating PCOS into national chronic disease programs, following a tiered care model, may optimize outcomes. The application of digital health tools, including mobile apps and telehealth, can further streamline diagnosis and monitoring. Decomposition analysis revealed that population growth contributed 55.3% of the global increase in PCOS-related infertility cases—up to 71.0% in low-SDI regions. In contrast, epidemiological transitions (e.g., lifestyle, environment) drove the rise in high-middle-SDI areas, accounting for 81.8% of the burden. Environmental risks are becoming more prominent, particularly in middle-SDI countries. Exposure to endocrine-disrupting chemicals like bisphenol A has been implicated in PCOS pathogenesis[ 35 ], highlighting the need for interventions that address both demographic and environmental determinants of health. In low-SDI countries, the combination of rapid growth in the reproductive-age population and inadequate primary healthcare capacity creates a dual burden. Additionally, PCOS increases the risk of pregnancy complications such as gestational diabetes and hypertensive disorders, particularly in resource-limited settings[ 36 ]. Health inequality remains a critical concern. High-SDI countries currently bear a larger absolute burden, as reflected in the rising Slope Index of Inequality (SII). However, the decreasing Concentration Index (CI) indicates a diffusion of burden to lower-SDI regions, likely driven by urbanization, lifestyle convergence, and expanding healthcare access. Structural determinants—such as gender norms, stigma, and limited medical access—remain major barriers in many low-income countries[ 37 , 38 ]. While efforts to expand policy coverage have shown some success (e.g., reduced SII), systemic disparities in diagnostic access persist. Structural reforms—such as expanding public insurance and investing in primary care capacity—are essential to reduce these gaps. From a public health perspective, the findings underscore the need for enhanced awareness, early diagnosis, and effective PCOS management strategies. Lifestyle interventions, particularly those targeting weight management, are critical in high-SDI countries with high obesity prevalence. Integrating fertility counseling into routine care and providing psychosocial support, especially for women experiencing secondary infertility, can alleviate emotional stress and improve outcomes[ 39 , 40 ]. In summary, PCOS-related infertility is a growing global challenge characterized by persistent growth and widening inequalities. Without comprehensive, multi-tiered strategies adapted to each SDI context, this burden is likely to worsen, particularly in middle- and low-income regions. Coordinated efforts in early screening, reproductive education, and healthcare system strengthening are essential to mitigate this rising threat and improve reproductive health equity worldwide. Strengths and Limitations This study has several strengths. It integrates the most up-to-date and comprehensive global data from GBD 2021, covering 204 countries and territories over three decades. Compared to previous studies, we employed a broader range of advanced methods—including Bayesian Age–Period–Cohort (BAPC) modeling, decomposition analysis, and cross-national inequality assessment—to capture both temporal trends and structural disparities. Stratification by SDI and inclusion of inequality metrics allowed us to explore the complex interplay between socioeconomic development and PCOS-related infertility. The analysis also distinguishes between primary and secondary infertility, highlighting their divergent trajectories and policy implications. The prediction of a continuing rise in secondary infertility provides valuable foresight for targeted interventions. Nonetheless, the study has limitations. Estimates rely on modeled data, and input data remain sparse for many low- and middle-income countries, which may lead to underestimation of the actual burden[ 41 ]. Diagnostic criteria for PCOS vary by region and over time (e.g., NIH vs. Rotterdam definitions), potentially affecting prevalence comparability[ 2 ]. Additionally, we did not differentiate PCOS phenotypes, limiting insights into disease heterogeneity. Social and environmental determinants—such as diet, stress, pollution, and healthcare access—were also not included, restricting the scope of causal inference. Future studies should incorporate phenotype-specific data and multidimensional contextual variables to improve precision in prevention and intervention strategies. Conclusion This study provides a comprehensive assessment of the global burden and trends of infertility attributable to PCOS. Despite improvements in diagnostic capacity, the absolute burden has continued to rise over the past three decades—particularly among older reproductive-age women and in low- and middle-income countries. PCOS-related infertility is emerging as a major global public health concern, with distinct challenges across different SDI levels. Low-SDI regions require strengthened primary healthcare and health education. Middle-SDI regions should prioritize integration of reproductive and chronic disease management, while high-SDI regions must address the growing pressure of delayed childbearing. Our predictive modeling offers a scientific basis for policymaking and resource planning but highlights the need for dynamic updates using real-time data. Effective burden control will depend on promoting early screening and intervention, improving public awareness, and enhancing PCOS diagnostic capacity at the primary care level. Future research should evaluate the impact of national policy responses, encourage data sharing, and foster global collaboration to advance sustainable PCOS management and improve reproductive health outcomes worldwide. Declarations Data availability The datasets supporting the conclusions of this article are included within the manuscript and Methods. Data of the GBD study are publicly available at https://vizhub.healthdata.org/gbd-results/ Acknowledgements The authors would like to extend their thanks to the Institute for Health Metrics and Evaluation (IHME) and the Global Burden of Disease study collaborations. Author contributions Liu Junxiu : Methodology, Visualization. Huang Chengzi : Writing. Jiao Jun : Supervision. Ma Yingxiu : Methodology. Sun Yue : Data reduction. Chao Lan : Review and editing. Funding This study was funded by the China Postdoctoral Science Foundation (2023M742109) Competing interests The authors declare no competing interests. References Carson SA, Kallen AN. Diagnosis and Management of Infertility: A Review. JAMA. 2021;326(1):65–76. Revised. 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome (PCOS). Human reproduction (Oxford, England) 2004, 19(1):41–47. Chen W, Pang Y. Metabolic Syndrome and PCOS: Pathogenesis and the Role of Metabolites. Metabolites 2021, 11(12). Balen AH, Morley LC, Misso M, Franks S, Legro RS, Wijeyaratne CN, Stener-Victorin E, Fauser BC, Norman RJ, Teede H. The management of anovulatory infertility in women with polycystic ovary syndrome: an analysis of the evidence to support the development of global WHO guidance. Hum Reprod Update. 2016;22(6):687–708. Franks S, Stark J, Hardy K. Follicle dynamics and anovulation in polycystic ovary syndrome. Hum Reprod Update. 2008;14(4):367–78. Yusuf ANM, Amri MF, Ugusman A, Hamid AA, Wahab NA, Mokhtar MH. Hyperandrogenism and Its Possible Effects on Endometrial Receptivity: A Review. Int J Mol Sci 2023, 24(15). Jiang NX, Zhao WJ, Shen HR, Du DF, Li XL. Hyperinsulinemia impairs decidualization via AKT-NR4A1 signaling: new insight into polycystic ovary syndrome (PCOS)-related infertility. J ovarian Res. 2024;17(1):31. Yan H, Wang L, Zhang G, Li N, Zhao Y, Liu J, Jiang M, Du X, Zeng Q, Xiong D et al. Oxidative stress and energy metabolism abnormalities in polycystic ovary syndrome: from mechanisms to therapeutic strategies. Reproductive biology and endocrinology: RB&E 2024, 22(1):159. Palomba S, Santagni S, Falbo A, La Sala GB. Complications and challenges associated with polycystic ovary syndrome: current perspectives. Int J women's health. 2015;7:745–63. Qin JZ, Pang LH, Li MJ, Fan XJ, Huang RD, Chen HY. Obstetric complications in women with polycystic ovary syndrome: a systematic review and meta-analysis. Reproductive biology and endocrinology: RB&E 2013, 11:56. Teede HJ, Misso ML, Costello MF, Dokras A, Laven J, Moran L, Piltonen T, Norman RJ. Recommendations from the international evidence-based guideline for the assessment and management of polycystic ovary syndrome. Fertil Steril. 2018;110(3):364–79. Sun B, Ma Y, Li L, Hu L, Wang F, Zhang Y, Dai S, Sun Y. Factors Associated with Ovarian Hyperstimulation Syndrome (OHSS) Severity in Women With Polycystic Ovary Syndrome Undergoing IVF/ICSI. Front Endocrinol. 2020;11:615957. Riestenberg C, Jagasia A, Markovic D, Buyalos RP, Azziz R. Health Care-Related Economic Burden of Polycystic Ovary Syndrome in the United States: Pregnancy-Related and Long-Term Health Consequences. J Clin Endocrinol Metab. 2022;107(2):575–85. Moss KM, Doust J, Copp T, Homer H, Mishra GD. Fertility treatment pathways and births for women with and without polycystic ovary syndrome-a retrospective population linked data study. Fertil Steril. 2024;121(2):314–22. Casals G, Fábregues F, Pavesi M, Arroyo V, Balasch J. Conservative medical treatment of ovarian hyperstimulation syndrome: a single center series and cost analysis study. Acta Obstet Gynecol Scand. 2013;92(6):686–91. Infante-Cano M, García-Muñoz C, Matias-Soto J, Pineda-Escobar S, Villar-Alises O, Martinez-Calderon J. The prevalence and risk of anxiety and depression in polycystic ovary syndrome: an overview of systematic reviews with meta-analysis. Arch Women Ment Health 2024. Golabi P, Paik JM, AlQahtani S, Younossi Y, Tuncer G, Younossi ZM. Burden of non-alcoholic fatty liver disease in Asia, the Middle East and North Africa: Data from Global Burden of Disease 2009–2019. J Hepatol. 2021;75(4):795–809. Paik JM, Kabbara K, Eberly KE, Younossi Y, Henry L, Younossi ZM. Global burden of NAFLD and chronic liver disease among adolescents and young adults. Hepatology (Baltimore MD). 2022;75(5):1204–17. Su Z, Zou Z, Hay SI, Liu Y, Li S, Chen H, Naghavi M, Zimmerman MS, Martin GR, Wilner LB, et al. Global, regional, and national time trends in mortality for congenital heart disease, 1990–2019: An age-period-cohort analysis for the Global Burden of Disease 2019 study. EClinicalMedicine. 2022;43:101249. 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: Int J Obstet Gynecol 2025. Das Gupta P. Standardization and decomposition of rates from cross-classified data. Genus. 1994;50(3–4):171–96. Teede HJ, Misso ML, Costello MF, Dokras A, Laven J, Moran L, Piltonen T, Norman RJ. Recommendations from the international evidence-based guideline for the assessment and management of polycystic ovary syndrome. Hum Reprod (Oxford England). 2018;33(9):1602–18. Teede HJ, Misso ML, Costello MF, Dokras A, Laven J, Moran L, Piltonen T, Norman RJ. Erratum. Recommendations from the international evidence-based guideline for the assessment and management of polycystic ovary syndrome. Hum Reprod (Oxford England). 2019;34(2):388. Legro RS, Arslanian SA, Ehrmann DA, Hoeger KM, Murad MH, Pasquali R, Welt CK. Diagnosis and treatment of polycystic ovary syndrome: an Endocrine Society clinical practice guideline. J Clin Endocrinol Metab. 2013;98(12):4565–92. Okoth K, Chandan JS, Marshall T, Thangaratinam S, Thomas GN, Nirantharakumar K, Adderley NJ. Association between the reproductive health of young women and cardiovascular disease in later life: umbrella review. BMJ (Clinical Res ed). 2020;371:m3502. Liu X, Zhang J, Wang S. Global, regional, and national burden of infertility attributable to PCOS, 1990–2019. Hum Reprod (Oxford England). 2024;39(1):108–18. Lim SS, Norman RJ, Davies MJ, Moran LJ. The effect of obesity on polycystic ovary syndrome: a systematic review and meta-analysis. Obes reviews: official J Int Association Study Obes. 2013;14(2):95–109. Moran LJ, Hutchison SK, Norman RJ, Teede HJ. Lifestyle changes in women with polycystic ovary syndrome. Cochrane Database Syst Rev 2011(7):Cd007506. Srnovršnik T, Virant-Klun I, Pinter B. Polycystic Ovary Syndrome and Endocrine Disruptors (Bisphenols, Parabens, and Triclosan)-A Systematic Review. Life (Basel Switzerland) 2023, 13(1). Parker J, O'Brien C, Hawrelak J, Gersh FL. Polycystic Ovary Syndrome: An Evolutionary Adaptation to Lifestyle and the Environment. Int J Environ Res Public Health 2022, 19(3). Zeng X, Xie YJ, Liu YT, Long SL, Mo ZC. Polycystic ovarian syndrome: Correlation between hyperandrogenism, insulin resistance and obesity. Clin Chim Acta. 2020;502:214–21. Rodriguez Paris V, Solon-Biet SM, Senior AM, Edwards MC, Desai R, Tedla N, Cox MJ, Ledger WL, Gilchrist RB, Simpson SJ, et al. Defining the impact of dietary macronutrient balance on PCOS traits. Nat Commun. 2020;11(1):5262. Chaurasiya D, Gupta A, Chauhan S, Patel R, Chaurasia V. Age, period and birth cohort effects on prevalence of obesity among reproductive-age women in India. SSM - Popul health. 2019;9:100507. Shafei AE, Nabih ES, Shehata KA, Abd Elfatah ESM, Sanad ABA, Marey MY, Hammouda A, Mohammed MMM, Mostafa R, Ali MA. Prenatal Exposure to Endocrine Disruptors and Reprogramming of Adipogenesis: An Early-Life Risk Factor for Childhood Obesity. Child Obes (Print). 2018;14(1):18–25. Palioura E, Diamanti-Kandarakis E. Polycystic ovary syndrome (PCOS) and endocrine disrupting chemicals (EDCs). Reviews Endocr metabolic disorders. 2015;16(4):365–71. Mills G, Badeghiesh A, Suarthana E, Baghlaf H, Dahan MH. Polycystic ovary syndrome as an independent risk factor for gestational diabetes and hypertensive disorders of pregnancy: a population-based study on 9.1 million pregnancies. Hum Reprod (Oxford England). 2020;35(7):1666–74. Inhorn MC, Patrizio P. Infertility around the globe: new thinking on gender, reproductive technologies and global movements in the 21st century. Hum Reprod Update. 2015;21(4):411–26. Dyer SJ, Abrahams N, Hoffman M, van der Spuy ZM. Infertility in South Africa: women's reproductive health knowledge and treatment-seeking behaviour for involuntary childlessness. Hum Reprod (Oxford England). 2002;17(6):1657–62. Shafaghi M, Ahmadinezhad GS, Karimi FZ, Mazloum SR, Golbar Yazdi HZ, Afiat M. The effect of supportive counseling on self-esteem of infertile women after in vitro fertilization (IVF) failure: a randomized controlled trial study. BMC Psychol. 2024;12(1):408. Katyal N, Poulsen CM, Knudsen UB, Frederiksen Y. The association between psychosocial interventions and fertility treatment outcome: A systematic review and meta-analysis. Eur J Obstet Gynecol Reprod Biol. 2021;259:125–32. Global incidence. prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet (London England). 2024;403(10440):2133–61. Tables Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files supplementtable.docx supplementfigure.docx Table1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6881341","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486170214,"identity":"16958f0f-cc1b-4e00-a894-13fc02d5d67f","order_by":0,"name":"Junxiu Liu","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Junxiu","middleName":"","lastName":"Liu","suffix":""},{"id":486170216,"identity":"bbd89ef5-465e-499f-995f-b9a2438d264a","order_by":1,"name":"Chengzi Huang","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Chengzi","middleName":"","lastName":"Huang","suffix":""},{"id":486170218,"identity":"3682a11f-9dc9-40e9-a06d-348525274c45","order_by":2,"name":"Jun Jiao","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Jiao","suffix":""},{"id":486170221,"identity":"b0cb9fec-3ce0-4093-a879-bd4cab76b3fb","order_by":3,"name":"Yingxiu Ma","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Yingxiu","middleName":"","lastName":"Ma","suffix":""},{"id":486170222,"identity":"59440c2d-5660-4140-9858-6e4d537c1b54","order_by":4,"name":"yue sun","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"prefix":"","firstName":"yue","middleName":"","lastName":"sun","suffix":""},{"id":486170223,"identity":"9b1c1d3d-1eb0-4110-92c4-00953deddfe3","order_by":5,"name":"Lan Chao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYDACCSB+wMAgB2IfADIYG6CC+LUkMDAYg7UkkKIlEaSSgSgt/LObHz5IqLmT3i92+CHQljrZDQeYD97mYbDLw2nJnWPGBgnHnuXOnJ1mANRy2HjDAbZkax6G5GJcWgwkEswkEtgO5264nQDSciBxwwEeM2keIKMBp5b0bxIJ/w6n299O/wByGFAL/zcCWnLMJBLbDicYSOeAbGEG2cKGV4vEjZxig8S+w4YzbucUHEgwOGw88zCbseUcg2ScWvhnpG988OHbYXn+2embP3yoqJPtO9788MabCjucWtDdCcTMMMYoGAWjYBSMArIBAK+FXUkQC04BAAAAAElFTkSuQmCC","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Lan","middleName":"","lastName":"Chao","suffix":""}],"badges":[],"createdAt":"2025-06-12 14:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6881341/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6881341/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87103125,"identity":"5e5a70af-f847-469d-9a87-3c968683d096","added_by":"auto","created_at":"2025-07-19 13:38:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":485882,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe joinpoint analysis for infertility due to PCOS by SDI during 1990–2021 among women of reproductive age. \u003c/strong\u003e(A-B) Infertility. (C-D) Primary infertility. (E-F) Secondary infertility.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6881341/v1/358307e095e0f7a9920d88d9.png"},{"id":87103365,"identity":"dc152c7a-84b3-40f3-abf3-62ea47f030a3","added_by":"auto","created_at":"2025-07-19 13:46:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":915477,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeographic distribution of ASPR of PCOS-related infertility across 204 countries in 2021.\u003c/strong\u003e (A) Infertility. (B) Primary infertility. (C) Secondary infertility. Blue indicates the lowest values of ASPR. Red indicates the highest values of ASPR. The red to blue gradient effectively highlights regional differences in ASPR levels.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6881341/v1/d4e4f76e6f9f2505910502c9.png"},{"id":87103369,"identity":"1308ed96-73bd-4e0f-9ffe-4a1b1b39c19b","added_by":"auto","created_at":"2025-07-19 13:46:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":889159,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between ASPR and national SDI levels.\u003c/strong\u003e All correlation analyses showed statistical significance (p \u0026lt; 0.001). (A) Infertility. (B) Primary infertility. (C) Secondary infertility.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6881341/v1/b67cbe33b1ffa24a18b877c5.png"},{"id":87103127,"identity":"d8616978-b245-4a77-9cae-a9c6ba1bc42a","added_by":"auto","created_at":"2025-07-19 13:38:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":511976,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAge, period and birth cohort effects on infertility due to the prevalence of PCOS across SDI quintiles.\u003c/strong\u003e (A) Annual percentage change for each specific age group illustrated by annual change. (B) Period effects illustrated by the period relative risk. (C) Birth cohort effects illustrated by the cohort relative risk. (D) Age effects illustrated by the fitted longitudinal age-­specific rates.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6881341/v1/3263dedf360a5bf1ccaa2f67.png"},{"id":87103178,"identity":"cb0594c8-766b-49ad-ae3f-b3f485a2ccf4","added_by":"auto","created_at":"2025-07-19 13:38:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":370682,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal trend in the number of prevalence cases and ASPRs for infertility due to PCOS from 1990 to 2044. \u003c/strong\u003e(A) Number of cases. (B) ASPRs.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6881341/v1/592f2c3224356d4a5e9f630e.png"},{"id":87103134,"identity":"3f37d423-5a15-43b5-b423-e3618434369c","added_by":"auto","created_at":"2025-07-19 13:38:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":177602,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecomposition of case growth from 1990 to 2021: contributions from population growth, aging, and epidemiological transition. \u003c/strong\u003eThe black dots indicate the net total change in disease burden, aligning with the sum of both components.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6881341/v1/3c503945346533483859fd93.png"},{"id":87103373,"identity":"477947f6-0ce2-4432-9c8c-45c30ca3912d","added_by":"auto","created_at":"2025-07-19 13:46:48","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":304782,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrends in health inequality for PCOS-related infertility: Slope Index of Inequality (SII) and Concentration Index (CI), 1990–2021.\u003c/strong\u003e (A, B) Infertility. (C, D) Primary infertility. (E, F) Secondary infertility.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6881341/v1/f20d1de0b84413407b8fe1f0.png"},{"id":93356008,"identity":"6d68d85f-3152-4327-a34a-d28c987ffe20","added_by":"auto","created_at":"2025-10-13 01:16:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4675479,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6881341/v1/25ddc505-a25d-44a7-bdad-b491bd01b04c.pdf"},{"id":87103136,"identity":"7c032e72-cf5d-4d2e-8fa8-cad560e17325","added_by":"auto","created_at":"2025-07-19 13:38:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":383437,"visible":true,"origin":"","legend":"","description":"","filename":"supplementtable.docx","url":"https://assets-eu.researchsquare.com/files/rs-6881341/v1/50e57acb92c646cf4b132f3c.docx"},{"id":87103144,"identity":"b8d9e66d-862e-4fa4-b33f-31cb6b7e2e50","added_by":"auto","created_at":"2025-07-19 13:38:47","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14814290,"visible":true,"origin":"","legend":"","description":"","filename":"supplementfigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-6881341/v1/a2ad4446df1f804895c6141e.docx"},{"id":87103152,"identity":"7a24dc41-a5b9-4bb2-9c65-23ac76e360d1","added_by":"auto","created_at":"2025-07-19 13:38:48","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":25091,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6881341/v1/ac877d8e74baaacdcb000a20.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global Trends and Burden of Infertility Attributable to Polycystic Ovary Syndrome, 1990–2021: A Comprehensive Analysis from the Global Burden of Disease Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInfertility is defined as the failure to conceive after at least one year of regular, unprotected sexual intercourse. It is a globally recognized reproductive health concern, affecting approximately one in eight women of reproductive age[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among the various causes of infertility, polycystic ovary syndrome (PCOS) stands out as one of the most common endocrine-metabolic disorders in women of reproductive age, with a global prevalence of 10–13%. PCOS is primarily characterized by three clinical features: polycystic ovarian morphology, menstrual irregularities, and hyperandrogenism[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Notably, PCOS is not only a reproductive disorder but also a multisystem metabolic syndrome frequently associated with significant insulin resistance (IR), obesity, and dyslipidemia. These pathological changes interact through a complex endocrine network, collectively exacerbating metabolic disturbances and reproductive dysfunction in affected individuals[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFrom a reproductive perspective, PCOS is the leading cause of anovulatory infertility, accounting for approximately 70–80% of infertility cases in women[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The underlying mechanisms of infertility in PCOS are multifactorial. First, ovulatory dysfunction primarily arises from dysregulation of the hypothalamic–pituitary–ovarian (HPO) axis, wherein abnormal luteinizing hormone (LH) pulsatility, hyperinsulinemia, and excessive local ovarian androgens impair follicular development and prevent the selection of a dominant follicle[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Second, elevated androgen and insulin levels negatively affect endometrial receptivity, thereby impairing embryo implantation[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Additionally, diminished oocyte quality is a critical contributing factor, with oxidative stress and mitochondrial dysfunction in granulosa cells potentially compromising oocyte maturation capacity[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Studies have shown that women with PCOS have a significantly higher risk of spontaneous miscarriage compared to non-PCOS populations, which may be linked to insulin resistance, fibrinolytic abnormalities, and impaired endometrial function[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Even when pregnancy is achieved, the risk of gestational complications such as gestational diabetes and preeclampsia remains substantially elevated[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Long-term management of PCOS typically involves lifestyle modifications (e.g., weight loss), insulin-sensitizing agents (e.g., metformin), and ovulation induction therapies[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For patients with refractory anovulation, assisted reproductive technologies (ART), such as in vitro fertilization (IVF), can improve pregnancy outcomes, although attention must be paid to the risk of ovarian hyperstimulation syndrome (OHSS)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe health and socioeconomic burden of PCOS-related infertility has attracted increasing attention. From a healthcare economics perspective, patients often face considerable financial costs associated with ovulation induction drugs (e.g., clomiphene citrate, letrozole) and ART procedures such as IVF[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. One study reported that approximately 46% of PCOS patients undergoing ovulation induction required further ART, with complications like OHSS further increasing medical expenditures[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, meta-analyses indicate that 40–60% of women with PCOS-related infertility experience symptoms of anxiety and depression, significantly higher than in the general population. These psychological burdens are closely associated with prolonged treatment durations, social expectations, and self-image concerns[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite the profound impact of PCOS, effective strategies for managing PCOS-related infertility remain limited. There are notable gaps in research and public health interventions, including: (1) a lack of systematic global and regional disease burden assessments; (2) insufficient analysis of temporal trends, particularly regarding age–period–cohort effects; (3) inadequate quantification and elucidation of the driving forces behind the increasing burden of PCOS-related infertility. Addressing these gaps requires enhanced research investment to better understand the epidemiological features of PCOS-related infertility, identify key sociodemographic risk factors, and improve healthcare services for affected populations. Therefore, the present study utilizes data from the 2021 Global Burden of Disease (GBD) database to comprehensively evaluate prevalence trends of PCOS-related infertility, providing critical evidence for epidemiological research. This study aims to identify current shortcomings in prevention, management, and treatment strategies, and to highlight deficiencies or areas in need of adjustment—particularly in high-prevalence age groups or regions. Ultimately, it offers evidence-based support for policy formulation and the optimization of healthcare strategies, aiming to improve reproductive health outcomes in women with PCOS and underscore the necessity of prioritizing PCOS in the women's health agenda.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eEthics Approval\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe data used in this study were obtained from the publicly available GBD database. The GBD project has obtained ethics approval from the University of Washington Institutional Review Board (IRB), and all data are anonymized and aggregated to ensure compliance with ethical standards. Our study is a secondary analysis of de-identified data and does not involve any direct interaction with human subjects. Therefore, separate IRB approval for this study was not required. Please note that the ethical standards of the GBD project and the use of de-identified data ensure the ethical integrity of our research.\u003c/p\u003e\n\u003cp\u003e\u003cb\u003eData Sources\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study utilized data from the Global Burden of Disease Study 2021 (GBD 2021), coordinated by the Institute for Health Metrics and Evaluation (IHME). The GBD provides standardized estimates of incidence, prevalence, mortality, and disability-adjusted life years (DALYs) for 371 diseases and injuries across 204 countries and territories from 1990 to 2021. Data were derived from over 100,000 sources, including systematic reviews, hospital and outpatient records, health surveys, insurance claims, and vital registration systems. Disease estimates were produced using the DisMod-MR 2.1 Bayesian meta-regression tool to correct for measurement bias, data sparsity, and inter-source heterogeneity, ensuring comparability across time and geography.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCase Definition and Study Population\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePCOS and infertility were defined using ICD-9 and ICD-10 codes. Specifically, PCOS was identified using ICD-10 code E28.2, while female infertility was defined using codes N97–N98.9. Data were extracted for women of reproductive age (15–49 years), consistent with GBD-defined population groups. The study population was stratified by age (in 5-year groups), world regions (21 GBD regions), and country-level SDI. Infertility was categorized into primary infertility (no prior pregnancy despite 12 months of unprotected intercourse) and secondary infertility (inability to conceive following a previous pregnancy), as defined by GBD 2021.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAge Standardization and Temporal Trend Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePrevalence data were age-standardized using the GBD global standard population to ensure comparability over time and between regions[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Subsequently, Joinpoint regression modeling was employed to analyze the temporal trends in age-standardized prevalence rates (ASPR)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This model fits a segmented linear regression to the logarithmic values of ASPR and calculates the annual percent change (APC) and average annual percent change (AAPC) to characterize the trend over a specified time period. Using the Grid Search Method (GSM), the model automatically identifies significant turning points (joinpoints) that reflect changes in trend direction, aiming to minimize the sum of squared errors in the fit. The statistical significance of each candidate joinpoint is evaluated via Monte Carlo permutation tests, determining the optimal number of joinpoints (with a maximum of five). Within each segmented time interval, the model estimates the APC to quantify the rate of change in ASPR. The AAPC is then used to summarize the overall average trend over the study period. A positive AAPC indicates an increasing trend in ASPR, while a negative AAPC suggests a decreasing trend. This method provides a quantitatively robust and statistically supported analysis of epidemiological trends in PCOS-related infertility, offering valuable insights for future research and informing the development of public health policies.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAge–Period–Cohort Model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study employed the Age–Period–Cohort (APC) model to systematically assess the epidemiological trends of infertility related to polycystic ovary syndrome (PCOS). The APC model decomposes changes in disease burden into three independent effects: the influence of biological aging (age effect), temporal changes in external social and environmental factors (period effect), and the characteristics specific to different birth cohorts (cohort effect). This framework facilitates a deeper understanding of the dynamic patterns underlying the epidemiology of PCOS-related infertility. The APC model enables the estimation of both local drift and net drift parameters. Local drift represents the age-specific annual percentage change in prevalence, whereas net drift reflects the overall temporal trend across all age groups. The model also generates longitudinal age curves to depict the age-specific distribution of PCOS-related infertility. Additionally, rate ratios (RR) for period and cohort effects are calculated to assess the relative risk of specific time periods or birth cohorts compared with a designated reference group[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. An RR greater than 1 indicates a higher relative risk than the reference, while an RR less than 1 suggests a lower relative risk.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBAPC Model Projection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study utilized the Bayesian Age–Period–Cohort (BAPC) model to project the long-term burden of infertility attributable to polycystic ovary syndrome (PCOS). The BAPC model simultaneously incorporates age, period, and cohort effects, thereby offering a comprehensive depiction of the temporal evolution and intergenerational dynamics of disease burden[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. By applying smoothing techniques to historical data, the BAPC model effectively reduces the influence of random fluctuations and noise, enhancing the stability and accuracy of the forecasts. During model construction, prior distributions for parameters were defined based on data from previous Global Burden of Disease (GBD) studies. The Markov Chain Monte Carlo (MCMC) method was employed for iterative simulation to generate the posterior distributions of the parameters. Model outputs included projections of the prevalence and ASPR of PCOS-related infertility for future periods, accompanied by corresponding 95% credible intervals (CrI) to reflect the uncertainty in the forecasts. Based on the predictions derived from the BAPC model, this study provides a forward-looking assessment of trends in the burden of PCOS-related infertility up to the year 2045. These projections offer valuable data support and decision-making references for policy formulation and the strategic allocation of health resources.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDecomposition Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe further conducted a decomposition analysis to assess the driving factors behind changes in the burden of infertility attributable to PCOS from 1990 to 2021. This method decomposes overall changes into contributions from demographic drivers (e.g., population growth, aging) and epidemiological changes, thereby clarifying the roles of population dynamics and risk transitions in shaping disease burden trends. By comparing actual observations with counterfactual baseline scenarios, we identified key contributors to global changes in PCOS-related infertility burden. This framework provides policymakers with quantitative, evidence-based insights to support targeted intervention design and optimized health system planning [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eCross-Country Inequality Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo assess how inequality influences the distribution of infertility burden due to PCOS across countries, this study conducted a cross-national health inequality analysis using two key metrics: the Slope Index of Inequality (SII) and the Concentration Index (CI). These indicators respectively quantify absolute and relative disparities in PCOS-related burden along the gradient of the Sociodemographic Index (SDI). The SII was estimated using a weighted linear regression model and reflects the absolute difference in the ASPR of PCOS-related infertility between countries with the lowest and highest SDI levels. This metric compares the burden of infertility across countries ranked by SDI.\u003c/p\u003e\u003cp\u003eIn contrast, the CI is used to evaluate relative inequality. Based on the Lorenz curve, the horizontal axis represents the cumulative percentage of the population ranked by ascending SDI, while the vertical axis represents the cumulative share of the infertility burden attributed to PCOS. The area between the concentration curve and the line of perfect equality (the 45-degree diagonal) reflects the degree of inequality. A negative CI indicates that the burden is concentrated in low-SDI countries, whereas a positive CI suggests that high-SDI countries bear a greater share of the burden.\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003e\u003cb\u003eGlobal and Temporal Trends in PCOS-Related Infertility (1990–2021)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFrom 1990 to 2021, the global burden of infertility attributable to polycystic ovary syndrome (PCOS) showed a persistent upward trend. The age-standardized prevalence rate (ASPR) increased from 475.54 to 638.15 per 100,000 women (Table\u0026nbsp;1, the end of this article), with AAPC of 0.96% (95% CI: 0.92–1.01%). Notably, secondary infertility rose faster than primary infertility, with AAPCs of 1.12% and 0.62%, respectively. These findings indicate that secondary infertility is becoming an increasingly prominent contributor to the overall burden of PCOS-related infertility.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTrends by SDI Regions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDisparities emerged across SDI levels (Table\u0026nbsp;1, Fig.\u0026nbsp;1). High-SDI regions exhibited the highest ASPRs, particularly in high-income Asia Pacific, Western Europe, and North America. However, these regions also demonstrated relatively modest growth. A unique U-shaped trend was observed in high-SDI countries—an initial decline during 2000–2010 followed by a marked rebound after 2010 (e.g., APC = 2.98% during 2016–2021). In contrast, middle and low-middle SDI regions exhibited sustained high-growth trajectories (AAPC = 1.72% in middle-SDI regions), potentially driven by increasing obesity, urbanization, and limited healthcare access.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAge-Specific Burden and Infertility Subtype Patterns\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePCOS-related infertility displayed a unimodal age distribution, peaking at ages 25–39 across SDI strata (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). However, the age composition differed between subtypes: primary infertility cases clustered in younger age groups (20–29), whereas secondary infertility was more common in women aged 35–44 (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB–C). The ratio of secondary to primary infertility increased with age and peaked at 35–39 years in most SDI groups, except in high-SDI regions where the ratio rose steadily across all ages (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpatial Distribution and correlation with SDI\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSubstantial geographic variation was observed in both ASPR and AAPC across 204 countries in 2021 (Fig.\u0026nbsp;2; Figure S3; Table S3). High-burden countries were concentrated in high-income regions, including Italy (2345.40 per 100,000), Japan (1700.92), and New Zealand (1469.73). In contrast, lower ASPRs were reported in countries such as Albania (67.52), Bosnia and Herzegovina (72.54), and North Macedonia (74.19), mostly in South Asia, Central Africa, and Central Asia. Equatorial Guinea exhibited the fastest increase in ASPR (AAPC = 2.77%), followed by Peru and the Maldives. The smallest increases were seen in Malawi, Burundi, and Brazil.\u003c/p\u003e\u003cp\u003eCorrelation analysis confirmed a significant positive association between ASPR and national SDI levels for total, primary, and secondary infertility (R = 0.45, 0.48, and 0.38, respectively; all p \u0026lt; 0.001) (Fig.\u0026nbsp;3), suggesting both socioeconomic gradient and detection effects.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAge–Period–Cohort (APC) Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAPC modeling revealed that age, period, and cohort effects jointly influenced PCOS-related infertility trends (Fig.\u0026nbsp;4; Tables S4–S7). Local drift analysis showed increasing annual prevalence across all age groups, with the most pronounced rise in the 15–19 age group (1.19%, 95% CI: 1.06–1.32) and the smallest in the 45–49 group (0.65%, 95% CI: 0.46–0.83) (Fig.\u0026nbsp;4A). Period effects demonstrated a global increase in relative risk, rising from baseline in 1992–1996 to 1.28 in 2017–2021, with the most pronounced increases seen in middle-SDI countries (Fig.\u0026nbsp;4B). Cohort effects were also increasing, especially among individuals born between 1997–2006 (global RR = 1.35, 95% CI: 1.29–1.40), suggesting rising vulnerability in younger generations (Fig.\u0026nbsp;4C). The age effect peaked at 35–44 years, then declined sharply in the oldest age group (Fig.\u0026nbsp;4D).\u003c/p\u003e\u003cp\u003e\u003cb\u003eFuture Projections Using Bayesian APC Model (2021–2045)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBayesian APC model projections (Fig.\u0026nbsp;5; Table S8) predict that total PCOS-related infertility cases will rise from 12.5\u0026nbsp;million in 2021 to nearly 20\u0026nbsp;million by 2045. ASPR is expected to increase to 876.38 per 100,000 (95% CI: 347.02–1405.74). Primary infertility cases are projected to reach 5.25\u0026nbsp;million (ASPR = 228.90), while secondary infertility is projected to account for over 15.9\u0026nbsp;million cases (ASPR = 683.23), with a more pronounced growth rate. These forecasts suggest that secondary infertility will become the dominant form of PCOS-related infertility and require targeted intervention planning.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDeterminants of Change: Decomposition Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDecomposition analysis (Fig.\u0026nbsp;6; Table S9) identified the key drivers of increased PCOS-related infertility burden from 1990 to 2021. Globally, population growth accounted for 55.3% of the increase, followed by epidemiological transitions (43.7%) and population aging (0.95%). In low-SDI regions, population growth was the predominant contributor (71.0%), while in high-middle SDI regions, epidemiological factors—such as changes in lifestyle, reproductive behavior, and environmental exposure—were dominant (up to 81.8%). Aging contributed minimally and showed subtype-specific effects: suppressive for primary infertility but promotive for secondary infertility.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHealth Inequality Trends\u003c/b\u003e\u003c/p\u003e\u003cp\u003eInequality analysis demonstrated widening absolute disparities(Tables S10–S11; Fig.\u0026nbsp;7). The Slope Index of Inequality (SII) increased from 257.95 (1990) to 324.13 (2021), indicating a growing gap in disease burden between countries of different SDI levels. Meanwhile, the Concentration Index (CI) declined from 0.25 to 0.19, suggesting a relative shift in burden toward lower-SDI countries. This redistribution may reflect a convergence in risk exposures, greater diagnostic reach, and the global diffusion of lifestyle-related risk factors.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePolycystic ovary syndrome (PCOS) is a leading endocrine disorder contributing to female infertility, responsible for approximately 6–15% of cases among reproductive-aged women[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Its multifactorial pathophysiology—encompassing hypothalamic–pituitary–ovarian (HPO) axis dysfunction, insulin resistance, and chronic low-grade inflammation—leads to ovulatory disturbances and luteal phase insufficiency. The clinical manifestations of PCOS, such as hyperandrogenism and menstrual irregularities, significantly impair fertility. While treatment strategies include lifestyle modifications, ovulation induction agents (e.g., clomiphene citrate, letrozole), insulin sensitizers (e.g., metformin), and assisted reproductive technologies (ART) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], their effectiveness varies, and some patients remain refractory to standard regimens. Moreover, long-term metabolic risks, including type 2 diabetes and cardiovascular disease, add further complexity to management[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Against this backdrop, assessing global epidemiological trends in PCOS-related infertility is essential to guide policy and clinical interventions.\u003c/p\u003e\u003cp\u003eOur findings, based on the GBD 2021 data, indicate a steadily increasing global burden of infertility attributable to PCOS over the past three decades. The ASPR rose from 475.54 per 100,000 in 1990 to 638.15 per 100,000 in 2021, with AAPC of 0.96%. This upward trajectory aligns with prior studies, including that of Liu et al., which reported a rising ASPR between 1990 and 2019 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The comparatively higher estimates in our study may be due to updated datasets and expanded methodologies. The burden has grown most rapidly in middle- and low-middle SDI regions, likely due to increasing obesity, unhealthy lifestyles, environmental exposures, and endocrine-disrupting chemicals[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e–\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSecondary infertility showed a faster rate of increase (AAPC = 1.12%) compared to primary infertility (AAPC = 0.62%), underscoring a pressing challenge in reproductive health management. The acceleration of secondary infertility may reflect the cumulative impact of metabolic deterioration post-pregnancy, progressive ovarian dysfunction, and insufficient postpartum care. This finding highlights a critical gap in managing recurrent infertility in PCOS patients, even amid ART advancements.\u003c/p\u003e\u003cp\u003eJoinpoint and APC analyses further revealed regional disparities. Although high-SDI regions exhibited higher ASPR levels, their growth rates were lower, potentially due to superior healthcare infrastructure, early diagnosis, and broader ART access. However, the post-2010 rebound in ASPR in these regions may indicate emerging challenges, including delayed childbearing, cumulative metabolic burden, and increasing environmental risk exposures[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Regionally, high-income Asia Pacific, Western Europe, and North America reported the highest ASPRs, while Central and Eastern Europe and Central Asia reported the lowest. These differences likely stem from disparities in obesity prevalence, healthcare access, and diagnostic practice[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAge-specific analysis showed that PCOS-related infertility peaks between ages 25–39, aligning with peak reproductive years. APC modeling revealed increasing period and cohort effects, particularly in middle- and low-SDI regions. Factors such as adolescent overweight, sedentary behavior, and widespread exposure to endocrine disruptors likely contribute[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. While the age effect peaked at 35–44 years and declined thereafter, this trend may reflect improved fertility management and evolving reproductive planning.\u003c/p\u003e\u003cp\u003eForecasts from the Bayesian Age–Period–Cohort (BAPC) model predict a sustained rise in PCOS-related infertility through 2045, with secondary infertility projected to increase more rapidly. These results emphasize the urgency of regionally tailored interventions. In low-SDI areas, cost-effective tools—such as simplified endocrine panels or portable ultrasound—could reduce diagnostic delays. Integrating PCOS into national chronic disease programs, following a tiered care model, may optimize outcomes. The application of digital health tools, including mobile apps and telehealth, can further streamline diagnosis and monitoring.\u003c/p\u003e\u003cp\u003eDecomposition analysis revealed that population growth contributed 55.3% of the global increase in PCOS-related infertility cases—up to 71.0% in low-SDI regions. In contrast, epidemiological transitions (e.g., lifestyle, environment) drove the rise in high-middle-SDI areas, accounting for 81.8% of the burden. Environmental risks are becoming more prominent, particularly in middle-SDI countries. Exposure to endocrine-disrupting chemicals like bisphenol A has been implicated in PCOS pathogenesis[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], highlighting the need for interventions that address both demographic and environmental determinants of health. In low-SDI countries, the combination of rapid growth in the reproductive-age population and inadequate primary healthcare capacity creates a dual burden. Additionally, PCOS increases the risk of pregnancy complications such as gestational diabetes and hypertensive disorders, particularly in resource-limited settings[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHealth inequality remains a critical concern. High-SDI countries currently bear a larger absolute burden, as reflected in the rising Slope Index of Inequality (SII). However, the decreasing Concentration Index (CI) indicates a diffusion of burden to lower-SDI regions, likely driven by urbanization, lifestyle convergence, and expanding healthcare access. Structural determinants—such as gender norms, stigma, and limited medical access—remain major barriers in many low-income countries[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. While efforts to expand policy coverage have shown some success (e.g., reduced SII), systemic disparities in diagnostic access persist. Structural reforms—such as expanding public insurance and investing in primary care capacity—are essential to reduce these gaps.\u003c/p\u003e\u003cp\u003eFrom a public health perspective, the findings underscore the need for enhanced awareness, early diagnosis, and effective PCOS management strategies. Lifestyle interventions, particularly those targeting weight management, are critical in high-SDI countries with high obesity prevalence. Integrating fertility counseling into routine care and providing psychosocial support, especially for women experiencing secondary infertility, can alleviate emotional stress and improve outcomes[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn summary, PCOS-related infertility is a growing global challenge characterized by persistent growth and widening inequalities. Without comprehensive, multi-tiered strategies adapted to each SDI context, this burden is likely to worsen, particularly in middle- and low-income regions. Coordinated efforts in early screening, reproductive education, and healthcare system strengthening are essential to mitigate this rising threat and improve reproductive health equity worldwide.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStrengths and Limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study has several strengths. It integrates the most up-to-date and comprehensive global data from GBD 2021, covering 204 countries and territories over three decades. Compared to previous studies, we employed a broader range of advanced methods—including Bayesian Age–Period–Cohort (BAPC) modeling, decomposition analysis, and cross-national inequality assessment—to capture both temporal trends and structural disparities. Stratification by SDI and inclusion of inequality metrics allowed us to explore the complex interplay between socioeconomic development and PCOS-related infertility. The analysis also distinguishes between primary and secondary infertility, highlighting their divergent trajectories and policy implications. The prediction of a continuing rise in secondary infertility provides valuable foresight for targeted interventions.\u003c/p\u003e\u003cp\u003eNonetheless, the study has limitations. Estimates rely on modeled data, and input data remain sparse for many low- and middle-income countries, which may lead to underestimation of the actual burden[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Diagnostic criteria for PCOS vary by region and over time (e.g., NIH vs. Rotterdam definitions), potentially affecting prevalence comparability[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Additionally, we did not differentiate PCOS phenotypes, limiting insights into disease heterogeneity. Social and environmental determinants—such as diet, stress, pollution, and healthcare access—were also not included, restricting the scope of causal inference. Future studies should incorporate phenotype-specific data and multidimensional contextual variables to improve precision in prevention and intervention strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides a comprehensive assessment of the global burden and trends of infertility attributable to PCOS. Despite improvements in diagnostic capacity, the absolute burden has continued to rise over the past three decades—particularly among older reproductive-age women and in low- and middle-income countries. PCOS-related infertility is emerging as a major global public health concern, with distinct challenges across different SDI levels. Low-SDI regions require strengthened primary healthcare and health education. Middle-SDI regions should prioritize integration of reproductive and chronic disease management, while high-SDI regions must address the growing pressure of delayed childbearing. Our predictive modeling offers a scientific basis for policymaking and resource planning but highlights the need for dynamic updates using real-time data. Effective burden control will depend on promoting early screening and intervention, improving public awareness, and enhancing PCOS diagnostic capacity at the primary care level. Future research should evaluate the impact of national policy responses, encourage data sharing, and foster global collaboration to advance sustainable PCOS management and improve reproductive health outcomes worldwide.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are included within the manuscript and Methods. Data of the GBD study are publicly available at https://vizhub.healthdata.org/gbd-results/\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to extend their thanks to the Institute for Health Metrics and Evaluation (IHME) and the Global Burden of Disease study collaborations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLiu Junxiu\u003c/strong\u003e: Methodology, Visualization. \u003cstrong\u003eHuang Chengzi\u003c/strong\u003e: Writing. \u003cstrong\u003eJiao Jun\u003c/strong\u003e: Supervision. \u003cstrong\u003eMa Yingxiu\u003c/strong\u003e: Methodology. \u003cstrong\u003eSun Yue\u003c/strong\u003e: Data reduction. \u003cstrong\u003eChao Lan\u003c/strong\u003e: Review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the China Postdoctoral Science Foundation (2023M742109)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCarson SA, Kallen AN. Diagnosis and Management of Infertility: A Review. JAMA. 2021;326(1):65\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRevised. 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome (PCOS). \u003cem\u003eHuman reproduction (Oxford, England)\u003c/em\u003e 2004, 19(1):41\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen W, Pang Y. Metabolic Syndrome and PCOS: Pathogenesis and the Role of Metabolites. Metabolites 2021, 11(12).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBalen AH, Morley LC, Misso M, Franks S, Legro RS, Wijeyaratne CN, Stener-Victorin E, Fauser BC, Norman RJ, Teede H. The management of anovulatory infertility in women with polycystic ovary syndrome: an analysis of the evidence to support the development of global WHO guidance. Hum Reprod Update. 2016;22(6):687\u0026ndash;708.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFranks S, Stark J, Hardy K. Follicle dynamics and anovulation in polycystic ovary syndrome. Hum Reprod Update. 2008;14(4):367\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYusuf ANM, Amri MF, Ugusman A, Hamid AA, Wahab NA, Mokhtar MH. Hyperandrogenism and Its Possible Effects on Endometrial Receptivity: A Review. Int J Mol Sci 2023, 24(15).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang NX, Zhao WJ, Shen HR, Du DF, Li XL. Hyperinsulinemia impairs decidualization via AKT-NR4A1 signaling: new insight into polycystic ovary syndrome (PCOS)-related infertility. J ovarian Res. 2024;17(1):31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan H, Wang L, Zhang G, Li N, Zhao Y, Liu J, Jiang M, Du X, Zeng Q, Xiong D et al. Oxidative stress and energy metabolism abnormalities in polycystic ovary syndrome: from mechanisms to therapeutic strategies. \u003cem\u003eReproductive biology and endocrinology: RB\u0026amp;E\u003c/em\u003e 2024, 22(1):159.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePalomba S, Santagni S, Falbo A, La Sala GB. Complications and challenges associated with polycystic ovary syndrome: current perspectives. Int J women's health. 2015;7:745\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQin JZ, Pang LH, Li MJ, Fan XJ, Huang RD, Chen HY. Obstetric complications in women with polycystic ovary syndrome: a systematic review and meta-analysis. \u003cem\u003eReproductive biology and endocrinology: RB\u0026amp;E\u003c/em\u003e 2013, 11:56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTeede HJ, Misso ML, Costello MF, Dokras A, Laven J, Moran L, Piltonen T, Norman RJ. Recommendations from the international evidence-based guideline for the assessment and management of polycystic ovary syndrome. Fertil Steril. 2018;110(3):364\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSun B, Ma Y, Li L, Hu L, Wang F, Zhang Y, Dai S, Sun Y. Factors Associated with Ovarian Hyperstimulation Syndrome (OHSS) Severity in Women With Polycystic Ovary Syndrome Undergoing IVF/ICSI. Front Endocrinol. 2020;11:615957.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRiestenberg C, Jagasia A, Markovic D, Buyalos RP, Azziz R. Health Care-Related Economic Burden of Polycystic Ovary Syndrome in the United States: Pregnancy-Related and Long-Term Health Consequences. J Clin Endocrinol Metab. 2022;107(2):575\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoss KM, Doust J, Copp T, Homer H, Mishra GD. Fertility treatment pathways and births for women with and without polycystic ovary syndrome-a retrospective population linked data study. Fertil Steril. 2024;121(2):314\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCasals G, F\u0026aacute;bregues F, Pavesi M, Arroyo V, Balasch J. Conservative medical treatment of ovarian hyperstimulation syndrome: a single center series and cost analysis study. Acta Obstet Gynecol Scand. 2013;92(6):686\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInfante-Cano M, Garc\u0026iacute;a-Mu\u0026ntilde;oz C, Matias-Soto J, Pineda-Escobar S, Villar-Alises O, Martinez-Calderon J. The prevalence and risk of anxiety and depression in polycystic ovary syndrome: an overview of systematic reviews with meta-analysis. Arch Women Ment Health 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGolabi P, Paik JM, AlQahtani S, Younossi Y, Tuncer G, Younossi ZM. Burden of non-alcoholic fatty liver disease in Asia, the Middle East and North Africa: Data from Global Burden of Disease 2009\u0026ndash;2019. J Hepatol. 2021;75(4):795\u0026ndash;809.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaik JM, Kabbara K, Eberly KE, Younossi Y, Henry L, Younossi ZM. Global burden of NAFLD and chronic liver disease among adolescents and young adults. Hepatology (Baltimore MD). 2022;75(5):1204\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSu Z, Zou Z, Hay SI, Liu Y, Li S, Chen H, Naghavi M, Zimmerman MS, Martin GR, Wilner LB, et al. Global, regional, and national time trends in mortality for congenital heart disease, 1990\u0026ndash;2019: An age-period-cohort analysis for the Global Burden of Disease 2019 study. EClinicalMedicine. 2022;43:101249.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen 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: Int J Obstet Gynecol 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDas Gupta P. Standardization and decomposition of rates from cross-classified data. Genus. 1994;50(3\u0026ndash;4):171\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTeede HJ, Misso ML, Costello MF, Dokras A, Laven J, Moran L, Piltonen T, Norman RJ. Recommendations from the international evidence-based guideline for the assessment and management of polycystic ovary syndrome. Hum Reprod (Oxford England). 2018;33(9):1602\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTeede HJ, Misso ML, Costello MF, Dokras A, Laven J, Moran L, Piltonen T, Norman RJ. Erratum. Recommendations from the international evidence-based guideline for the assessment and management of polycystic ovary syndrome. Hum Reprod (Oxford England). 2019;34(2):388.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLegro RS, Arslanian SA, Ehrmann DA, Hoeger KM, Murad MH, Pasquali R, Welt CK. Diagnosis and treatment of polycystic ovary syndrome: an Endocrine Society clinical practice guideline. J Clin Endocrinol Metab. 2013;98(12):4565\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOkoth K, Chandan JS, Marshall T, Thangaratinam S, Thomas GN, Nirantharakumar K, Adderley NJ. Association between the reproductive health of young women and cardiovascular disease in later life: umbrella review. BMJ (Clinical Res ed). 2020;371:m3502.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu X, Zhang J, Wang S. Global, regional, and national burden of infertility attributable to PCOS, 1990\u0026ndash;2019. Hum Reprod (Oxford England). 2024;39(1):108\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLim SS, Norman RJ, Davies MJ, Moran LJ. The effect of obesity on polycystic ovary syndrome: a systematic review and meta-analysis. Obes reviews: official J Int Association Study Obes. 2013;14(2):95\u0026ndash;109.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoran LJ, Hutchison SK, Norman RJ, Teede HJ. Lifestyle changes in women with polycystic ovary syndrome. Cochrane Database Syst Rev 2011(7):Cd007506.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSrnovršnik T, Virant-Klun I, Pinter B. Polycystic Ovary Syndrome and Endocrine Disruptors (Bisphenols, Parabens, and Triclosan)-A Systematic Review. Life (Basel Switzerland) 2023, 13(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParker J, O'Brien C, Hawrelak J, Gersh FL. Polycystic Ovary Syndrome: An Evolutionary Adaptation to Lifestyle and the Environment. Int J Environ Res Public Health 2022, 19(3).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZeng X, Xie YJ, Liu YT, Long SL, Mo ZC. Polycystic ovarian syndrome: Correlation between hyperandrogenism, insulin resistance and obesity. Clin Chim Acta. 2020;502:214\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRodriguez Paris V, Solon-Biet SM, Senior AM, Edwards MC, Desai R, Tedla N, Cox MJ, Ledger WL, Gilchrist RB, Simpson SJ, et al. Defining the impact of dietary macronutrient balance on PCOS traits. Nat Commun. 2020;11(1):5262.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChaurasiya D, Gupta A, Chauhan S, Patel R, Chaurasia V. Age, period and birth cohort effects on prevalence of obesity among reproductive-age women in India. SSM - Popul health. 2019;9:100507.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShafei AE, Nabih ES, Shehata KA, Abd Elfatah ESM, Sanad ABA, Marey MY, Hammouda A, Mohammed MMM, Mostafa R, Ali MA. Prenatal Exposure to Endocrine Disruptors and Reprogramming of Adipogenesis: An Early-Life Risk Factor for Childhood Obesity. Child Obes (Print). 2018;14(1):18\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePalioura E, Diamanti-Kandarakis E. Polycystic ovary syndrome (PCOS) and endocrine disrupting chemicals (EDCs). Reviews Endocr metabolic disorders. 2015;16(4):365\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMills G, Badeghiesh A, Suarthana E, Baghlaf H, Dahan MH. Polycystic ovary syndrome as an independent risk factor for gestational diabetes and hypertensive disorders of pregnancy: a population-based study on 9.1 million pregnancies. Hum Reprod (Oxford England). 2020;35(7):1666\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInhorn MC, Patrizio P. Infertility around the globe: new thinking on gender, reproductive technologies and global movements in the 21st century. Hum Reprod Update. 2015;21(4):411\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDyer SJ, Abrahams N, Hoffman M, van der Spuy ZM. Infertility in South Africa: women's reproductive health knowledge and treatment-seeking behaviour for involuntary childlessness. Hum Reprod (Oxford England). 2002;17(6):1657\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShafaghi M, Ahmadinezhad GS, Karimi FZ, Mazloum SR, Golbar Yazdi HZ, Afiat M. The effect of supportive counseling on self-esteem of infertile women after in vitro fertilization (IVF) failure: a randomized controlled trial study. BMC Psychol. 2024;12(1):408.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKatyal N, Poulsen CM, Knudsen UB, Frederiksen Y. The association between psychosocial interventions and fertility treatment outcome: A systematic review and meta-analysis. Eur J Obstet Gynecol Reprod Biol. 2021;259:125\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGlobal incidence. prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet (London England). 2024;403(10440):2133\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Polycystic ovary syndrome, infertility, epidemiology, global burden of disease, health inequality","lastPublishedDoi":"10.21203/rs.3.rs-6881341/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6881341/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: To assess the global trends, geographic disparities, and sociodemographic determinants of infertility attributable to polycystic ovary syndrome from 1990 to 2021, using data from a large-scale global health database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign\u003c/strong\u003e: A population-based observational study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubjects\u003c/strong\u003e: Women aged 15 to 49 years diagnosed with infertility due to polycystic ovary syndrome in 204 countries and territories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExposure (for observational studies)\u003c/strong\u003e: The burden and prevalence of infertility associated with polycystic ovary syndrome, analyzed by age, time period, birth cohort, and sociodemographic index levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMain Outcome Measures\u003c/strong\u003e: Age-standardized prevalence rates of infertility related to polycystic ovary syndrome, stratified by primary and secondary infertility, age group, world region, and sociodemographic index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Between 1990 and 2021, the global age-standardized prevalence of infertility due to polycystic ovary syndrome increased from 475.54 to 638.15 per 100,000 women. Secondary infertility increased at a faster rate than primary infertility. The highest burden was observed in high-income regions, but the most rapid increases occurred in low- and middle-income regions. The peak age-specific burden occurred in women aged 25 to 39 years. Time period and birth cohort effects both showed rising trends, particularly in younger generations in lower-income settings. Decomposition analysis attributed the rising burden to population growth and changing epidemiological patterns. Inequality analysis revealed widening absolute disparities and a shifting burden toward lower-income countries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Infertility related to polycystic ovary syndrome has increased steadily over the past three decades, with growing disparities between countries. Future policies should prioritize early diagnosis, targeted interventions, and expanded reproductive care, particularly in lower-resource settings, to mitigate this rising global health burden.\u003c/p\u003e","manuscriptTitle":"Global Trends and Burden of Infertility Attributable to Polycystic Ovary Syndrome, 1990–2021: A Comprehensive Analysis from the Global Burden of Disease Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-19 13:38:38","doi":"10.21203/rs.3.rs-6881341/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8d6de1b3-ff99-4704-b38c-32506034618a","owner":[],"postedDate":"July 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T01:08:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-19 13:38:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6881341","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6881341","identity":"rs-6881341","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}