The hidden burden of infertility: multimorbidity patterns and longitudinal disease trajectories in a population-based cohort of 2 million women

The hidden burden of infertility: multimorbidity patterns and longitudinal disease trajectories in a population-based cohort of 2 million women | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The hidden burden of infertility: multimorbidity patterns and longitudinal disease trajectories in a population-based cohort of 2 million women Jing Tan, Yulong Jia, Xuehong Liu, Hao Jiang, Yiquan Xiong, Lehana Thabane, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7308896/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Infertility is increasingly recognized not only as a reproductive concern but also as a potential sentinel of systemic health vulnerability in women. Yet, the broader multimorbidity patterns and temporal progression of diseases surrounding infertility remain poorly understood. In this large-scale retrospective cohort study, we analyzed electronic health records from 2,064,482 women in Tianjin, China, diagnosed with infertility and infertility-related gynecological disorders between 2014 and 2023. We constructed multimorbidity networks using partial correlation and multimorbidity coefficients (MMC), and applied disease trajectory modeling to map temporal patterns before and after infertility diagnosis. Among all patients, 94.6% exhibited multimorbidity, with the number of distinct diagnoses peaking at age 40 and rising sharply after 50. Non-random disease clusters emerged across gynecological and systemic conditions, including gastrointestinal, musculoskeletal, cardiovascular, and endocrine disorders. We identified a sequential progression from pelvic inflammatory disorders (N70) to structural ovarian abnormalities (N83) and then to benign ovarian neoplasms (D27), indicating a reproductive-to-systemic transition. Notably, inflammatory diseases of the vagina and vulva (N76, MMC=1.17) and oligomenorrhea (N91, MMC=0.78) were associated with the highest multimorbidity burden. Infertility significantly elevated the risk of subsequent hyperlipidemia (E78), heart failure (I50), and pregnancy-related complications (O24), with antecedent diagnoses occurring on average 0.6 years before and downstream conditions appearing one year after infertility diagnosis. This study provides the first comprehensive mapping of multimorbidity and disease progression in women with infertility, positioning infertility as an early-life marker of multisystem dysregulation. These findings underscore the need for life course-oriented, integrated care strategies in women’s health that bridge reproductive and systemic medicine. Health sciences/Diseases/Reproductive disorders/Infertility Health sciences/Health care/Public health/Epidemiology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Main Women's health has become a global health priority in the context of achieving the World Health Organization (WHO) Sustainable Development Goal (SDG) 3 and SDG 5 1 . Across the female lifespan, dynamic fluctuations in sex hormones—from adolescence through menopause—shape distinct physiological trajectories 2-5 . These hormonal changes extend beyond reproductive functions, profoundly influencing immune regulation, metabolic homeostasis, cardiovascular physiology, and neuroendocrine signaling 5-7 . For instance, estrogen enhance both innate and adaptive immunity, which may underlie women’s higher susceptibility to autoimmune disorders, while androgens typically exert immunosuppressive and cardioprotective effects 6,8 . These complex endocrine-immune-metabolic interactions have brought renewed attention to the growing burden of multimorbidity and its progression over time. Infertility exemplifies the intersection between reproductive and systemic health. Affecting more than 10% of reproductive-aged women globally 9 , infertility—once viewed narrowly as a reproductive concern—is now increasingly recognized as a potential early marker and contributor to broader physiological dysregulation 10-12 . Accumulating evidence suggests that infertility and related gynecological disorders (e.g., polycystic ovary syndrome, endometriosis, chronic pelvic inflammation) often co-occur with systemic conditions such as metabolic, cardiovascular, and autoimmune diseases, forming non-random multimorbidity clusters 13-15 . Despite this recognition, critical knowledge gaps remain. Most multimorbidity research focuses on elderly populations or high-income countries 16-20 , overlooking the hormonally dynamic and rapidly shifting health profiles of women in low- and middle-income settings. Furthermore, the temporal evolution, directional relationships, and cumulative burden of multimorbidity among women with infertility remain poorly characterized 21-23 , and their longitudinal impact has yet to be mapped in large populations. To address this gap, we conducted a large-scale retrospective cohort study using electronic health records from over 2 million women in China, diagnosed with infertility and infertility-related gynecological conditions. Through multimorbidity analysis and disease trajectory modeling, we aimed to: (1) identify multimorbidity clusters specifically associated with infertility and infertility-related gynecological diseases; and (2) elucidate non-random multimorbidity patterns across the female lifespan; (3) map disease trajectories explicitly related to infertility. These findings offer crucial insights into the systemic health implications of infertility, providing a foundation for targeted early interventions and comprehensive long-term care strategies tailored to women's health. Results Baseline characteristics and healthcare utilization Among the 2,064,482 included patients, 1,953,818 (94.6%) were identified as having multimorbidity. The mean age of the overall cohort was 34.2 years (standard deviation [SD]: 9.3), with 42.2% aged 25–35 years. The median follow-up duration was 5.3 years (Table 1). The distribution of the 18 infertility and infertility-related gynecological diseases is presented in Supplement Table 1, with menstrual disorders (N91 and N92) being the most prevalent. Patients with multimorbidity exhibited substantially higher healthcare utilization compared to those with a single condition. Specifically, they had a higher median number of outpatient visits (16 [7, 34] vs. 1 [1, 1]), laboratory tests per visit (13 [5, 28] vs. 2 [1, 4]), cumulative laboratory tests (141 [46, 321] vs. 5 [2, 29]), prescription records per visit (26[10, 55] vs. 2[1, 3]), and total prescription records (62 [21, 140] vs. 3 [1, 11]). These findings highlight the elevated medical burden associated with multimorbidity in this population. [Table 1 is here] Age-related patterns in multimorbidity Figure 1 shows age-specific trends in the average number of diagnosed conditions per individual. The number of gynecological conditions increased steadily with age and plateaued after 40 years. For all medical conditions, the average number of diagnoses also peaked around 40 years, remained stable for the next decade, and increased again after 50 years. Interestingly, the proportion of patients with high multimorbidity (≥6 conditions) was similar between the 36–45 and 46–55 age groups, indicating a substantial disease burden already emerging in early to mid-adulthood. Supplement Figure 1 and Supplement Table 2 list 200 most common binary multimorbidity pairs. The most frequent pairing was between other inflammatory of vagina and vulva (N76) and absent, scanty and rare menstruation (N91). The female infertility- related gynecological diseases were commonly associated with pregnancy-related care (O26), acute upper respiratory infections (J02, J06), gastritis and duodenitis (K29), dorsalgia (M54), spondylosis (M47), hypertrophy of breast (N62), anaemias (D64), and hypertension (I10). [Figure 1 is here] Multimorbidity patterns and temporal disease trajectory analysis In this study, we focused on 18 infertility-related gynecological diseases as primary conditions of interest. Based on these, we constructed an extended set of 39 gynecological diseases, which included the original 18 target conditions and 21 additional gynecologic diagnoses observed in the cohort. This broader group reflected a comprehensive profile of gynecological comorbidities potentially relevant to infertility, encompassing inflammatory disorders, structural abnormalities, hormonal imbalances, and benign or malignant tumors. The additional 21 conditions were not pre-specified but emerged from empirical co-occurrence patterns in the data, enabling us to assess multimorbidity not only within infertility-specific conditions but across a full spectrum of gynecologic disease. We then performed multimorbidity analysis across four levels (Figure 2): Gynecological-level multimorbidity: Examined comorbidity patterns among 39 gynecological diseases—including 18 infertility-related conditions and 21 additional gynecological diagnoses—within women diagnosed with one or more of the infertility-related diseases. Whole-disease-level multimorbidity : Assessed the interrelationships among all 1,017 ICD-10-coded diagnoses across body systems to characterize broader multimorbidity patterns in women with infertility-related gynecological diseases, beyond their reproductive conditions. System-level multimorbidity : Quantified the aggregated strength of multimorbidity between each of the 18 infertility-related diseases and broader disease categories (e.g., cardiovascular, endocrine, metabolic, psychiatric), as classified by ICD-10 chapters. Temporal disease trajectories : Modeled the chronological progression of disease before and after infertility diagnosis, capturing the directionality, strength, and timing of transitions across the 1,017 conditions to map longitudinal disease trajectories. [Figure 2 is here] 1. Gynecological-level associations Figure 3 and Supplement Table 3 present partial correlation coefficients (ρ) among 39 gynecological conditions, revealing several notable non-random associations. Inflammatory disease of the cervix uteri (N72) was positively associated with dysplasia of the cervix uteri (N87, ρ=0.12), which in turn exhibited a stronger correlation with carcinoma in situ of cervix uteri (D06, ρ=0.32). Similarly, salpingitis and oophoritis ovarian (N70) was associated with noninflammatory disorders of ovary, fallopian tube and broad ligament (N83, ρ=0.08), which further demonstrated a moderate correlation with benign neoplasm of ovary (D27, ρ=0.22). These results suggest a potential pathological continuum. In addition, a significant association was observed between ovarian dysfunction (E28) and female infertility (N97, ρ = 0.12), reflecting their shared endocrine and reproductive pathophysiology. These patterns suggest a pathological continuum among gynecological inflammatory, structural, and neoplastic diseases. Strong clustering was also observed among both malignant and benign tumors of the female genital tract (C50-C57, D05-D27, D39), indicating possible shared developmental or etiological mechanisms. Notably, the five gynecological conditions with the highest multimorbidity coefficients (MMCs) were other inflammatory diseases of the vagina and vulva (N76), inflammatory diseases of the cervix uteri (N72), noninflammatory disorders of the fallopian tube and broad ligament (N83), cervical dysplasia (N87), and endometriosis (N80) (Supplement Table 4 and Supplement Figure 2). [Figure 3 is here] 2. Whole-disease-level associations At the whole-disease level, Supplement Table 5 and Supplement Figure 3 present multimorbidity networks with partial correlations greater than 0.15. The strongest observed association was between neoplasms of uncertain behavior of lymphoid, hematopoietic, and related tissue (D47) and other osteochondropathies (M93, ρ=0.85). Most high-correlation pairs were within the same physiological system. For instance, myeloid leukaemia (C92) showed a robust correlation with Leukaemia of unspecified cell type (C95, ρ=0.51), consistent with shared pathological mechanisms within hematologic malignancies. The MNA (Multimorbidity Network App see http://152.136.125.122:3838/) can be used to explore multimorbidity networks for some index diseases. The top 10 conditions with the highest MMC across all diseases included: other disorders of fluid, electrolyte and acid-base balance (E87), disorders of glycoprotein metabolism (E77), other aplastic anaemias (D61), osteoporosis without pathological fracture (M81), other diseases of liver (K76), maternal care for other conditions predominantly related to pregnancy (O26), other dermatitis (L30), essential (primary) hypertension (I10), gastritis and duodenitis (K29), and chronic ischaemic heart disease (I25) (supplement table 6 and supplement figure 4). Together, these findings underscore the systemic, multi-organ nature of high-burden multimorbidity across metabolic, cardiovascular, hepatic, hematologic, and pregnancy-related domains (Supplement Table 6 and Supplement Figure 4). 3. System-level associations At the system level, Figure 4 and Supplement Table 7 summarize the associations between the 18 categories of infertility-related gynecological diseases and systemic diseases diagnosed prior to their onset. The strongest multimorbidity coefficients were consistently observed with genitourinary diseases (N00–N99), with MMCs exceeding 0.2 across all 18 gynecological categories. Additionally, high MMCs (MMC>0.2) were observed between certain infectious and parasitic diseases (A00-B99) and dysplasia of cervix uteri (N87), as well as between metabolic disorders (E00-E90) and ovarian dysfunction (E28), suggesting possible upstream influences or shared etiologies. Post-diagnosis associations, shown in Figure 4 and Supplementary Table 8, revealed similar patterns: infertility-related gynecological diseases were most strongly associated with subsequent genitourinary diseases (N00–N99). Notably, menstrual irregularities (N91) showed a strong association with pregnancy-related complications (O00-O29), with an MMC of 0.78, indicating a possible predictive relationship between menstrual dysfunction and future obstetric complications. [Figure 4 is here] 4. Disease trajectory and temporal analysis Figure 5 and Supplementary Table 9 present the disease trajectory network analysis preceding infertility diagnosis, identifying a series of conditions significantly associated with increased infertility risk. These included several endocrine and metabolic disorders-such as other disorders of pancreatic endocrinology (E16 RR=1.60), hyperpituitarism (E22, RR=1.61) and ovarian dysfunction (E28, RR=1.82) - as well as nutritional and metabolic deficiencies, including unspecified protein-energy malnutrition (E46, RR = 1.25) and disorders of amino acid metabolism (E72, RR = 1.51). Inflammatory pelvic conditions also emerged as strong antecedents, notably salpingitis and oophoritis (N70, RR = 1.53) and chlamydia-related infections (A74, RR = 2.58). Reproductive complications such as habitual miscarriage (N96, RR = 4.22), spontaneous abortion (O03, RR = 1.68), and abnormal products of conception (O02, RR = 1.79) further indicated early reproductive health disruptions linked to later infertility. Following a diagnosis of infertility, women exhibited elevated risks for a wide range of systemic diseases. These included additional endocrine, nutritional and metabolic diseases (E02, E03, E34, E11, E14, E77-E83), pregnancy-related complications (O00, O03, O13, O14, O16, O20, O24, O26, O28), genitourinary conditions (N61, N85, N94, N96,), cardiovascular diseases (I10, I20, I24, I50, I70), and musculoskeletal disorders (M10, M25, M51, M81, M93). Together, these findings illustrate a progressive and multisystem disease burden emerging after the onset of infertility. Temporal analysis revealed that antecedent conditions were diagnosed, on average, 0.6 years before infertility. Notably, disorders such as pancreatic endocrinology (E16) and tubal/ovarian infections (N70) occurred approximately 1.8 years earlier. Post-infertility diagnoses typically occurred about 1 year later, with maternal hypertension (O16) representing the most delayed outcome at 2.5 years. One particularly extended trajectory spanned over a decade, beginning with infertility and followed by hypothyroidism (E03), then progressing to cardiovascular conditions including angina pectoris (I20), acute myocardial ischemia (I24), heart failure (I50), and atherosclerosis (I70), highlighting the long-term systemic impact of infertility (Figure 5 and supplement table 9). [Figure 5 is here] Discussion Main findings To our knowledge, this is the first large-scale and comprehensive investigation of multimorbidity patterns and disease trajectories among Chinese women, leveraging electronic medical records from over 2 million patients. We observed that the average number of diagnosed conditions per individual peaked around age 40, remained stable for a decade, and then increased markedly after age 50. The most prevalent binary multimorbidity combination was other inflammation of vagina and vulva (N76) with menstrual disorders (N91 or N92). Female infertility-related gynecological diseases were frequently observed in conjunction with gastritis and duodenitis (K29), dorsalgia (M54), spondylosis (M47), hypertrophy of breast (N62), and hypertension(I10), underscoring the systemic nature of female reproductive morbidity. Our findings provide novel evidence supporting a sequential pathway linking gynecological inflammation, structural reproductive disorders, and gynecological neoplasms. For example, salpingitis and oophoritis (N70) were associated with noninflammatory adnexal disorders (N83), which in turn were linked to benign ovarian tumors (D27); similarly, cervical inflammation (N72) was linked to cervical dysplasia (N87), which further progressed to carcinoma in situ (D06). Among the 39 gynecological conditions analyzed, inflammatory disorders of the vagina and vulva (N76) exhibited the highest multimorbidity burden (MMC = 1.17). Across the broader disease network, strong associations were observed within physiological systems, with metabolic and cardiovascular disorders showing particularly high burdens. Notably, diseases of the genitourinary system (N00-N99) demonstrated consistently strong bidirectional associations with all 18 infertility-related gynecological conditions (MMC > 0.2). In addition, high MMC values were identified between certain infectious and parasitic diseases (A00-B99) and cervical dysplasia (N87), and between metabolic disorders (E00-E90) and ovarian endocrine dysfunction (E28). Particularly, oligomenorrhea (N91) showed the strongest non-random association (MMC = 0.78) with maternal conditions related to pregnancy (O00-O29), suggesting a robust clinical link between menstrual irregularities and adverse pregnancy outcomes. Trajectory analysis further revealed that female infertility is preceded by distinct gynecological risk factors, including ovarian dysfunction (E28), tubal or ovarian infections (N70), and habitual miscarriage (N96). On average, antecedent conditions occurred 0.6 years before the infertility diagnosis. Infertility subsequently increased the risk of endocrine and metabolic diseases, maternal complications during pregnancy, and genitourinary disorders, which were in turn associated with future cardiovascular, musculoskeletal, and other systemic diseases. Notably, we identified a long-term trajectory extending over a decade—from female infertility to hypothyroidism (E03), and subsequently to major cardiovascular conditions such as angina (I20), myocardial ischemia (I24), heart failure (I50), and atherosclerosis (I70)—reflecting the long-term and systemic health risks associated with infertility. Comparison with other studies and potential explanations This study addresses a major gap in the understanding of multimorbidity patterns among reproductive-age women by utilizing a large, population-level dataset. Unlike many previous studies that focused on either physical or mental health conditions alone 19,24 , or limited disease domains such as ischemic heart disease or depression 25,26 , our analysis included a wide spectrum of 1,017 medical conditions across all major disease systems. This inclusive approach provides a more complete picture of the complexity and burden of multimorbidity among reproductive-age women. Using electronic medical records, we found that 94.6% of women in our cohort had multimorbidity. While prior studies have reported a broad range in multimorbidity prevalence—from 6.4% to 98% 19,27,28 —one study that considered only 308 conditions reported a prevalence of 75.1% among women. Direct comparison across studies is limited by differences in disease definitions, population characteristics, and the number of conditions considered 29 . Consistent with prior literature 30,31 , we also found that patients with multimorbidity utilized significantly more healthcare services than those with a single condition, underscoring the challenge of optimizing care for this population. Our age-specific findings refine previous knowledge by identifying a plateau in multimorbidity between ages 40 and 50 and a distinct peak in gynecological multimorbidity around age 40. This deviates from the commonly assumed linear increase with age 19,30,32-34 and may reflect perimenopausal hormonal transitions that influence susceptibility to both reproductive and systemic conditions. These results support the need for proactive screening and early intervention strategies before the age of 40. The observed progression from inflammatory gynecological diseases to non-inflammatory conditions and neoplasms echoes earlier studies linking pelvic inflammatory disease to increased cancer risk 35-37 , Mechanistic research has also shown that chronic inflammation may promote tumorigenesis via immune modulation 38 . Among the gynecological conditions examined, N76 was associated with the highest MMC, reinforcing the need for early detection and management of chronic gynecological inflammation. We also observed that menstrual disorders—particularly oligomenorrhea (N91)—were strongly associated with maternal complications. Prior studies have shown similar links between menstrual irregularities and risks of premature mortality, type 2 diabetes, and cardiovascular disease 39-41 . These findings suggest that women with menstrual disorders may require closer monitoring during pregnancy and long-term follow-up. Additionally, the strong associations between infertility and metabolic disorders, such as hypothyroidism, diabetes, and obesity, align with prior evidence on hormonal dysregulation and disrupted ovulatory function 42 . These links underscore the central role of the hypothalamic–pituitary–ovarian axis in coordinating reproductive and systemic health. The disease trajectory analysis confirmed several well-established infertility risk factors—including ovarian dysfunction, tubal infections, and miscarriage—as well as identified novel potential contributors such as pancreatic endocrine disorders (E16), protein-energy malnutrition (E46), and amino acid metabolism disorders (E72), which warrant further investigation. Our findings are consistent with large-scale studies from China and recent systematic reviews on infertility 43,44 . We also noted that endometriosis may contribute to infertility through tubal damage, likely due to diagnostic delays 45 . Collectively, our results suggest that female infertility serves not only as a reproductive diagnosis but also as an early indicator of systemic vulnerability. It was strongly associated with a range of metabolic, cardiovascular, and genitourinary disorders—many of which were diagnosed at different time points across the life course. These findings are in line with data from the Women’s Health Initiative 46-48 , which showed similar links between infertility and cardiovascular outcomes. Disruption of hormonal homeostasis—particularly ovarian function—may thus act as both a trigger and amplifier of long-term multimorbidity risk. These results highlight the importance of integrated, hormone-sensitive care approaches for women. Implications Although our findings were derived from a Chinese population, they are likely generalizable to other middle-income settings undergoing demographic and epidemiological transitions. This study provides critical evidence for multiple stakeholders, including clinicians, researchers, health policymakers, and guideline developers. In clinical practice, the increasing prevalence of multimorbidity among reproductive-age women demands more nuanced care approaches. Current clinical guidelines, largely based on randomized trials that exclude multimorbid patients, may be insufficiently applicable. Our results support the integration of multimorbidity into clinical decision-making, especially in gynecology, endocrinology, and primary care. Identifying sex- and age-specific multimorbidity clusters can help tailor care pathways, optimize treatment priorities, and reduce adverse interactions. Embedding multimorbidity insights into electronic health records could support early risk stratification and targeted screening. For instance, recognizing diagnostic histories that include ovarian dysfunction, metabolic abnormalities, and cardiovascular risk may prompt multidisciplinary referral or proactive monitoring. Gynecological visits—especially those for menstrual disorders or infertility—should be leveraged as opportunities to assess broader systemic health, including thyroid function, glucose metabolism, and cardiovascular profiles. Moreover, reproductive conditions such as infertility and menstrual disorders may serve as sentinel indicators of wider physiological disruption. Their documentation warrants not only reproductive management but also longitudinal health planning and surveillance. For reproductive endocrinologists and obstetricians, our findings emphasize the importance of preconception risk assessment, endocrine optimization, and chronic disease prevention. For internists and primary care physicians, they highlight the broader relevance of reproductive health signals. Finally, our results provide a data-driven foundation for risk-based models of care, multimorbidity-informed clinical guidelines, and personalized health strategies. As fertility rates decline and chronic diseases increase worldwide, translating multimorbidity insights into actionable care strategies will be essential to improving health outcomes for women across the life course. Strengths and limitations This study offers several notable strengths. First, it leverages a large and diverse cohort of over 2 million women, encompassing 1,017 distinct conditions—far surpassing prior studies in both scale and granularity—and enabling a comprehensive mapping of multimorbidity patterns. Second, we systematically assessed non-random associations among 39 gynecological diseases, all 1,017 conditions, and 18 gynecological-systemic disease pairs, thereby providing a robust empirical basis for clinical insight and mechanistic research. Third, we introduce a novel disease trajectories modeling approach that incorporates chronological ordering of diagnoses, offering new opportunities to infer potential causal pathways. Fourth, we identified several previously underrecognized infertility-associated conditions, such as pancreatic endocrine disorders (E16), protein-energy malnutrition (E46), amino acid metabolism disorders (E72), as well as a long-term progression from infertility to hypothyroidism (E03) and subsequently to cardiovascular outcomes (I20, I24, I50, I70), advancing understanding of infertility’s broader systemic implications. Several limitations should be considered. First, asymptomatic conditions or those managed exclusively in outpatient settings may be underrepresented in electronic health records, which could lead to underestimation of certain condition frequencies or associations. Nevertheless, the large sample size, the inclusion of both outpatient and inpatient data across multiple hospitals, and the consistency of observed patterns with established disease relationships support the robustness of our findings. Second, although the dataset captures nearly all secondary and tertiary hospital encounters in Tianjin, data from primary care or from outside the region may be missing, potentially resulting in incomplete patient histories. This limitation is inherent to most regional EHR-based studies but is unlikely to systematically bias the identification of major multimorbidity patterns. Third, while our cohort represents the largest female multimorbidity dataset in China, regional differences in healthcare access and socioeconomic context may affect generalizability. However, Tianjin’s predominantly Han Chinese population is broadly representative of the national demographic profile, minimizing concerns related to ethnic heterogeneity. Methods Ethics This hospital-based cohort study was approved by the Ethics Committee of West China Hospital, Sichuan University, China (Approval No. 2024−1821). Given the retrospective nature of the study, which relied on electronic medical records (EMRs), the requirement for informed consent from patients was waived. All necessary approvals were also obtained from the National Natural Science Foundation of China, which supported this research. The definition of multimorbidity Multimorbidity is traditionally defined as the coexistence of two or more chronic conditions in the same individual 49 . However, to more accurately capture the impact of multimorbidity on clinical management and shared disease pathways, we adopted an expanded definition. Specifically, we defined multimorbidity as the presence of two or more distinct conditions occurring in the same individual at any point in time, regardless of whether these conditions were concurrent 19 . We identified these conditions through patients’ cumulative medical histories, as even conditions separated in time may share underlying pathological mechanisms. This broader definition has important clinical implications: prior health conditions, even if no longer active, may influence current diagnostic decisions, therapeutic choices, and health outcomes. Moreover, it allows for a more comprehensive understanding of the developmental trajectories and causal relationships underlying multimorbidity, many of which unfold progressively over time. Study design and population The cohort was established in Tianjin, a province-level municipality in northern China directly administered by the central government. Capitalizing on Tianjin’s integrated healthcare big data infrastructure, we constructed a women’s reproductive lifespan database by aggregating electronic health records (EHRs) from both outpatient and inpatient services across 43 public tertiary hospitals (defined as those with >500 beds) and 39 public secondary hospitals (defined as those with 100 to 499 beds). These hospitals constitute 88% of Tianjin’s public tertiary (47) and secondary (46) hospitals, most non-included hospitals were military facilities and other non-maternal-and-child health specialty hospitals, ensuring high representativeness for our study population. Together, these included hospitals deliver approximately 76% of inpatient care and 70% of outpatient services across Tianjin 50 . In this study, we included all female patients aged 18 to 55 years (n=2,064,482) who received an incident diagnosis of one of 18 infertility-related gynecological conditions between January 1, 2014, and December 31, 2023. Eligible individuals were defined as those with at least one outpatient diagnosis or hospital discharge coded with one of the following 18 ICD-10 codes: E28, N70, N71, N72, N73, N80, N83, N85, N87, N88, N91, N92, N93, N97, D25, D26, D27, D06. These codes were selected based on diagnostic criteria outlined in a 2021 JAMA review of female infertility 44 . For each eligible patient, we retrieved complete longitudinal clinical records across the entire study period, enabling detailed analyses of disease onset, comorbidity accumulation, and multimorbidity trajectories. At present, the database contains 118,122,044 diagnostic records, 229,448,601 prescription records, 427,857,370 laboratory test records, 7,730,268 imaging examination records, 5,664,172 physical examination records, and 8,254,619 surgical records. The structure, quality, and management of this database have been detailed in our previous publication. Medical conditions identification and selection Medical conditions were identified based on both primary and secondary diagnoses, using level-3 ICD-10 codes. All diagnostic codes from Chapters I to XV of the ICD-10 were included, while all codes from Chapters XVI to XXII were excluded in order to maintain consistency with our focus on general medical and gynecological morbidity. Finally, 1,017 distinct diseases were selected from the database for inclusion in our study (Appendix table S1). We identified 39 gynecological diseases, including inflammatory and non-inflammatory diseases, tumors and infertility-related conditions. These were diagnosed using the following ICD-10 codes: C51-C57, D06, D07, D25-D28, D39, E28, N70-N73, N75, N76 and N80-N97. We excluded certain codes such as N74 and N77, as these represent gynecological conditions secondary to diseases classified elsewhere in the ICD-10 system, and thus do not reflect primary gynecological diagnoses. All conditions were classified according to their three-digit ICD-10 code. To avoid duplicate counting of chronic conditions, we assigned the date of the first recorded diagnosis as the official onset date for each condition of interest. Subsequent repeated diagnoses of the same condition were not counted separately. Statistical analysis We first conducted descriptive analyses to characterize the overall study population and subgroups stratified by presence or absence of multimorbidity. Categorical variables were presented as frequencies and percentages, while continuous variables such as age and healthcare utilization (inpatient and outpatient visits) were summarized using means with standard deviations or medians with interquartile ranges, depending on data distribution. To examine the patterns and progression of multimorbidity, we employed two analytical approaches: multimorbidity network analysis and disease trajectory modeling. These methods enabled both cross-sectional mapping of disease associations and longitudinal tracking of disease progression across the reproductive lifespan. Multimorbidity analysis by frequency We first assessed the overall burden of multimorbidity by calculating the cumulative number of medical conditions per individual. This was stratified by age to examine trends in disease accumulation across different life stages. To further explore common multimorbidity patterns, we tabulated the 200 most frequent binary multimorbidity pairs—defined as two distinct conditions occurring in the same individual at any time point. Multimorbidity analysis by non-random association We employed the partial correlation coefficient to identify gynecological-level, whole-disease-level, system-level multimorbidity patterns, a method previously proposed to quantify the strength of association between two health conditions while adjusting for the influence of all other conditions in the dataset. This coefficient is derived from the Pearson correlation and allows for identification of independent associations in high-dimensional comorbidity data. A positive partial correlation coefficient (ρ > 0) suggests that two conditions co-occur more frequently than would be expected by random chance, with larger values indicating stronger associations. The partial correlation formula for two health conditions i and j, adjusted for a third condition k, denoted as 𝜌 𝑖𝑗 | 𝑘 is as follows: We further conducted multimorbidity network analyses separately for gynecological-level and whole-disease-level multimorbidity. In these networks, nodes represented individual health conditions, while edges represented partial correlation coefficients between condition pairs. A pre-specified threshold for the partial correlation coefficient was applied to filter the edges, balancing interpretability with visual clarity. To enhance accessibility and exploration, we developed interactive online tools to present the full network analysis results. All network analyses were performed using the igraph package in R (version 4.3.1). We next applied the multimorbidity coefficient (MMC) approach for gynecological-level and whole-disease-level multimorbidity, previously introduced to quantify the overall multimorbidity strength of a given condition among gynecological-level and whole-disease-level conditions separately. The MMC is defined as the sum of all positive partial correlation coefficients between a target condition and all other health conditions. For condition i, the MMC formula is as follows: We also applied the MMC approach for system-level multimorbidity, we calculated partial correlation coefficients between each of the 18 infertility-related gynecological diseases and each of all other diagnosed conditions—distinguishing those that occurred before or after the index diagnosis. MMC values were then computed for diseases grouped within the same ICD-10 chapter (i.e., disease system), enabling system-level analysis of multimorbidity strength. These relationships were visualized using Sankey diagrams, which illustrated the strength and direction of associations between the 18 target conditions and various disease systems. Disease trajectory network analysis To explore the impact of diseases progression for female infertility across the entire life cycle, we conducted disease trajectory network analysis for female infertility across the 1,017 diseases utilizing data from this cohort study and mapped them into a longitudinal disease trajectory network. Diagnosis pairs (D1-D2), where D1 denotes the exposure and D2 denotes the outcome, were identified based on the chronological order of initial diagnosis. Inverse Probability of Treatment Weighting (IPTW) was applied to control for baseline confounders (age and diagnostic variables, including ICD-10 codes: E28, N70, N71, N72, N73, N80, N83, N85, N87, N88, N91, N92, N93, N97, D25, D26, D27, D06). Weighted logistic regression models were then used to estimate relative risk (RR) and corresponding p-values for each disease pair. Disease trajectories with statistically significant and positively associated RRs were retained for network construction. The final disease trajectory network was visualized using Cytoscape (version 3.10.2), with nodes representing disease conditions and directed edges indicating the chronological progression from D1 to D2. Temporal disease trajectories analysis Building on the disease trajectory network for female infertility, we conducted a temporal analysis to quantify the average age interval between disease onset within each significant diagnosis pairs (D1→D2). For each validated D1–D2 pair, we extracted individual-level data and calculated the weighted average age at diagnosis for both D1 and D2. These averages were computed using inverse probability weighting, adjusting for the same set of baseline confounders as in the network analysis. The weighted average age was defined as: Age = . The interval between the two weighted averages was used to infer the temporal distance between diagnoses. All temporal trajectory analyses and visualizations were implemented in R (version 4.3.1). Declarations Data availability This database is developed by the Chinese Evidence-based Medicine Center, affiliated with West China Hospital, Sichuan University. Access to the data requires joint approval from both the Chinese Evidence-Based Medicine Center and the Tianjin Health Care Big Data Ltd. Qualified research institutions may apply for data access by submitting a formal request that includes (1) a project proposal, and (2) an analytical plan approved by an accredited Ethical Review Board in Tianjin. Data access is subject to final approval by both data custodians. Contributors YR, JT, and XS conceived and designed the study. YR, JT, and XS obtained the funding. YR and YLJ contributed to the statistical analysis. YR, JT, YLJ, and SX contributed to data interpretation. YR and YLJ drafted the manuscript. JT and SX critically revised the manuscript. All authors reviewed and interpreted the results, commented on the paper, contributed to revisions, and read and approved the final version. Competing interests The authors declare no competing interests. Acknowledgments We would like to acknowledge funding from National Key R&D Program of China (NO. 2024YFC3505800), National Science Fund for Distinguished Young Scholars (Grant No. 82225049), National Natural Science Foundation of China (NO. 82474334, NO. 82474335, NO. 72177132), the Key R&D Projects of Sichuan Provincial Department of Science and Technology (NO. 2024YFFK0174, 2024YFFK0152), 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (Grant No. ZYYC24010 and No. ZYGD23004), Special fund for traditional Chinese medicine of Sichuan Provincial Administration of Traditional Chinese Medicine (Grant No. 2024zd023). References Vogel B, Acevedo M, Appelman Y, et al. The Lancet women and cardiovascular disease Commission: reducing the global burden by 2030. Lancet . Jun 19 2021;397(10292):2385-2438. doi:10.1016/s0140-6736(21)00684-x Patwardhan V, Gil GF, Arrieta A, et al. 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Beijing. https://www.nhc.gov.cn/mohwsbwstjxxzx/tjtjnj/202501/8193a8edda0f49df80eb5a8ef5e2547c.shtml Table Table 1 Baseline characteristics and healthcare utilization in the longitudinal database Variables All patients Patients with a single disease Patients with multimorbidity ( ≥ 2 conditions) No. patients 2064482 110664 1953818 Number of health service visit 54457409 205779 54251630 Inpatients visit, median (interquartile range) 1570740 1[1, 2] 2031 1[1, 1] 1568709 1[1, 2] Outpatients visit, median (interquartile range) 52886669 15[6, 33] 203748 1[1, 1] 52682921 16[7, 34] Median length of follow-up (years) 5.3 4.1 5.4 Median number of laboratory tests per visit 12[5, 27] 2[1, 4] 13[5, 28] Median number of laboratory tests records per capita 133[41, 313] 5[2, 29] 141[46, 321] Median number of prescription records per visit 25[9, 54] 2[1, 3] 26[10, 55] Median number of prescriptions per capita 60[20, 137] 3[1, 11] 62[21, 140] Age, Mean (SD) 34.2（9.3） 31.5（9.3） 34.4（9.3） <18, N (%) 5473 (0.3) 91 (0.1) 5382 (0.3) [18-25], N (%) 371380（18.0） 35844（32.4） 335536（17.2） (25-35], N (%) 871551（42.2） 40756（36.8） 830795（42.5） (35-45], N (%) 497435（24.1） 22057（19.9） 475378（24.3） (45-55], N (%) 318643（15.4） 11916（10.8） 306727（15.7） Additional Declarations There is NO Competing Interest. Supplementary Files SupplementFigure0805.docx Supplement Figure SupplementTable0805.docx Supplement Table AppendixTableS1.xlsx Appendix Table S1 Cite Share Download PDF Status: Under Review 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-7308896","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":497121225,"identity":"06529322-6fde-44eb-9ad2-aa31f2dc066d","order_by":0,"name":"Jing 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medical conditions per individual. \u003c/strong\u003e(Trends in the average number of diagnosed conditions with age (Figure 1 left); Proportion of patients with multimorbidity across age groups (Figure 1 right))\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7308896/v1/b2b4816a383d9200189d9da9.png"},{"id":90891701,"identity":"84ce59ee-8717-4064-b68c-6efe4b8193d6","added_by":"auto","created_at":"2025-09-09 11:12:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":501123,"visible":true,"origin":"","legend":"\u003cp\u003eOverall design framework for multimorbidity networks and longitudinal disease progression among female patients diagnosed one of 18 infertility-related gynecological conditions\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7308896/v1/78b4cf5524f1af808b76cde1.png"},{"id":90892409,"identity":"b0b0022e-3a1d-402b-8536-382d683e9982","added_by":"auto","created_at":"2025-09-09 11:20:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":187166,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultimorbidity networks correlation matrix for 39 gynecological diseases. (Left) Network of disease pairs with partial correlation coefficients ≥ 0.05; (Right) Heatmap of partial correlations among 39 gynecological diseases. \u003c/strong\u003e\u0026nbsp;(Red labels indicate the 18 infertility-related categories, black labels indicate other gynecological conditions)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7308896/v1/f44f60dcdb2d58417ca270d0.png"},{"id":90892407,"identity":"0c5f3435-9ee7-4ac0-8a64-fca13af4d935","added_by":"auto","created_at":"2025-09-09 11:20:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":943934,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations between the 18 infertility-related gynecological diseases and systemic diseases diagnosed before (left) and after (right) the index gynecological diagnoses.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7308896/v1/d7b9845fc9c2a8529d18d3d4.png"},{"id":90894079,"identity":"f9c4b7d3-b45d-444c-991c-5ec62390ace9","added_by":"auto","created_at":"2025-09-09 11:28:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":541620,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDisease trajectory network and temporal patterns of comorbidity before and after the diagnosis of female infertility\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7308896/v1/62758eed3346e6bf24f0a575.png"},{"id":90895878,"identity":"9c03fdc6-a5bc-4230-beaa-5334bd02f6a3","added_by":"auto","created_at":"2025-09-09 11:36:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3138364,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7308896/v1/600e0413-2e5f-4806-8c84-17d0260fcad4.pdf"},{"id":90891709,"identity":"1f1cd51b-5c23-4665-826e-2d0c4d5a9f25","added_by":"auto","created_at":"2025-09-09 11:12:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1039256,"visible":true,"origin":"","legend":"Supplement Figure","description":"","filename":"SupplementFigure0805.docx","url":"https://assets-eu.researchsquare.com/files/rs-7308896/v1/0aae12b12afe60441d91a54d.docx"},{"id":90891703,"identity":"ee6148e9-c0f7-42b9-a6fb-2607246d97dc","added_by":"auto","created_at":"2025-09-09 11:12:21","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":185675,"visible":true,"origin":"","legend":"\u003cp\u003eSupplement Table\u003c/p\u003e","description":"","filename":"SupplementTable0805.docx","url":"https://assets-eu.researchsquare.com/files/rs-7308896/v1/b93a5106d238a621f5b3d108.docx"},{"id":90891705,"identity":"15eed248-7f8c-496e-b30e-ed22e861eb3f","added_by":"auto","created_at":"2025-09-09 11:12:21","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":48076,"visible":true,"origin":"","legend":"\u003cp\u003eAppendix Table S1\u003c/p\u003e","description":"","filename":"AppendixTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7308896/v1/e35a122df74004ed2ad3c37f.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"The hidden burden of infertility: multimorbidity patterns and longitudinal disease trajectories in a population-based cohort of 2 million women","fulltext":[{"header":"Main","content":"\u003cp\u003eWomen's health has become a global health priority in the context of achieving the World Health Organization (WHO) Sustainable Development Goal (SDG) 3 and SDG 5 \u003csup\u003e1\u003c/sup\u003e. Across the female lifespan, dynamic fluctuations in sex hormones—from adolescence through menopause—shape distinct physiological trajectories\u0026nbsp;\u003csup\u003e2-5\u003c/sup\u003e. These hormonal changes extend beyond reproductive functions, profoundly influencing immune regulation, metabolic homeostasis, cardiovascular physiology, and neuroendocrine signaling\u0026nbsp;\u003csup\u003e5-7\u003c/sup\u003e. For instance, estrogen enhance both innate and adaptive immunity, which may underlie women’s higher susceptibility to autoimmune disorders, while androgens typically exert immunosuppressive and cardioprotective effects\u0026nbsp;\u003csup\u003e6,8\u003c/sup\u003e. These complex endocrine-immune-metabolic interactions have brought renewed attention to the growing burden of multimorbidity and its progression over time.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInfertility exemplifies the intersection between reproductive and systemic health. Affecting more than 10% of reproductive-aged women globally \u003csup\u003e9\u003c/sup\u003e, infertility—once viewed narrowly as a reproductive concern—is now increasingly recognized as a potential early marker and contributor to broader physiological dysregulation\u003csup\u003e10-12\u003c/sup\u003e. Accumulating evidence suggests that infertility and related gynecological disorders (e.g., polycystic ovary syndrome, endometriosis, chronic pelvic inflammation) often co-occur with systemic conditions such as metabolic, cardiovascular, and autoimmune diseases, forming non-random multimorbidity clusters\u0026nbsp;\u003csup\u003e13-15\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite this recognition, critical knowledge gaps remain. Most multimorbidity research focuses on elderly populations or high-income countries \u003csup\u003e16-20\u003c/sup\u003e, overlooking the hormonally dynamic and rapidly shifting health profiles of women in low- and middle-income settings. Furthermore, the temporal evolution, directional relationships, and cumulative burden of multimorbidity among women with infertility remain poorly characterized\u0026nbsp;\u003csup\u003e21-23\u003c/sup\u003e, and their longitudinal impact has yet to be mapped in large populations.\u003c/p\u003e\n\u003cp\u003eTo address this gap, we conducted a large-scale retrospective cohort study using electronic health records from over 2 million women in China, diagnosed with infertility and infertility-related gynecological conditions. Through multimorbidity analysis and disease trajectory modeling, we aimed to: (1) identify multimorbidity clusters specifically associated with infertility and infertility-related gynecological diseases; and (2) elucidate non-random multimorbidity patterns across the female lifespan; (3) map disease trajectories explicitly related to infertility. These findings offer crucial insights into the systemic health implications of infertility, providing a foundation for targeted early interventions and comprehensive long-term care strategies tailored to women's health.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics and healthcare utilization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 2,064,482 included patients, 1,953,818 (94.6%) were identified as having multimorbidity. The mean age of the overall cohort was 34.2 years (standard deviation [SD]: 9.3), with 42.2% aged 25\u0026ndash;35 years. The median follow-up duration was 5.3 years (Table 1). The distribution of the 18 infertility and infertility-related gynecological diseases is presented in Supplement Table 1, with menstrual disorders (N91 and N92) being the most prevalent.\u003c/p\u003e\n\u003cp\u003ePatients with multimorbidity exhibited substantially higher healthcare utilization compared to those with a single condition. Specifically, they had a higher median number of outpatient visits (16 [7, 34] vs. 1 [1, 1]), laboratory tests per visit (13 [5, 28] vs. 2 [1, 4]), cumulative laboratory tests (141 [46, 321] vs. 5 [2, 29]), prescription records per visit (26[10, 55] vs. 2[1, 3]), and total prescription records (62 [21, 140] vs. 3 [1, 11]). These findings highlight the elevated medical burden associated with multimorbidity in this population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Table 1 is here]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAge-related patterns in multimorbidity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 shows age-specific trends in the average number of diagnosed conditions per individual. The number of gynecological conditions increased steadily with age and plateaued after 40 years. For all medical conditions, the average number of diagnoses also peaked around 40 years, remained stable for the next decade, and increased again after 50 years. Interestingly, the proportion of patients with high multimorbidity (\u0026ge;6 conditions) was similar between the 36\u0026ndash;45 and 46\u0026ndash;55 age groups, indicating a substantial disease burden already emerging in early to mid-adulthood.\u003c/p\u003e\n\u003cp\u003eSupplement Figure 1 and Supplement Table 2 list 200 most common binary multimorbidity pairs. The most frequent pairing was between other inflammatory of vagina and vulva (N76) and absent, scanty and rare menstruation (N91). The female infertility- related gynecological diseases were commonly associated with pregnancy-related care (O26), acute upper respiratory infections (J02, J06), gastritis and duodenitis (K29), dorsalgia (M54), spondylosis (M47), hypertrophy of breast (N62), anaemias (D64), and hypertension (I10).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Figure 1 is here]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultimorbidity patterns and temporal disease trajectory analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we focused on 18 infertility-related gynecological diseases as primary conditions of interest. Based on these, we constructed an extended set of 39 gynecological diseases, which included the original 18 target conditions and 21 additional gynecologic diagnoses observed in the cohort. This broader group reflected a comprehensive profile of gynecological comorbidities potentially relevant to infertility, encompassing inflammatory disorders, structural abnormalities, hormonal imbalances, and benign or malignant tumors. The additional 21 conditions were not pre-specified but emerged from empirical co-occurrence patterns in the data, enabling us to assess multimorbidity not only within infertility-specific conditions but across a full spectrum of gynecologic disease. We then performed multimorbidity analysis across four levels (Figure 2):\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eGynecological-level\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003emultimorbidity:\u003c/strong\u003e Examined comorbidity patterns among 39 gynecological diseases\u0026mdash;including 18 infertility-related conditions and 21 additional gynecological diagnoses\u0026mdash;within women diagnosed with one or more of the infertility-related diseases.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWhole-disease-level\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;multimorbidity\u003c/strong\u003e: Assessed the interrelationships among all 1,017 ICD-10-coded diagnoses across body systems to characterize broader multimorbidity patterns in women with infertility-related gynecological diseases, beyond their reproductive conditions.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSystem-level\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;multimorbidity\u003c/strong\u003e: Quantified the aggregated strength of multimorbidity between each of the 18 infertility-related diseases and broader disease categories (e.g., cardiovascular, endocrine, metabolic, psychiatric), as classified by ICD-10 chapters.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTemporal disease trajectories\u003c/strong\u003e: Modeled the chronological progression of disease before and after infertility diagnosis, capturing the directionality, strength, and timing of transitions across the 1,017 conditions to map longitudinal disease trajectories.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e[Figure 2 is here]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e1. Gynecological-level associations\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3 and Supplement Table 3 present partial correlation coefficients (\u0026rho;) among 39 gynecological conditions, revealing several notable non-random associations. Inflammatory disease of the cervix uteri (N72) was positively associated with dysplasia of the cervix uteri (N87, \u0026rho;=0.12), which in turn exhibited a stronger correlation with carcinoma in situ of cervix uteri (D06, \u0026rho;=0.32). Similarly, salpingitis and oophoritis ovarian (N70) was associated with noninflammatory disorders of ovary, fallopian tube and broad ligament (N83, \u0026rho;=0.08), which further demonstrated a moderate correlation with benign neoplasm of ovary (D27,\u0026nbsp;\u0026rho;=0.22). These results suggest a potential pathological continuum. In addition, a significant association was observed between ovarian dysfunction (E28) and female infertility (N97, \u0026rho; = 0.12), reflecting their shared endocrine and reproductive pathophysiology.\u003c/p\u003e\n\u003cp\u003eThese patterns suggest a pathological continuum among gynecological inflammatory, structural, and neoplastic diseases. Strong clustering was also observed among both malignant and benign tumors of the female genital tract (C50-C57, D05-D27, D39), indicating possible shared developmental or etiological mechanisms. Notably, the five gynecological conditions with the highest multimorbidity coefficients (MMCs) were other inflammatory diseases of the vagina and vulva (N76), inflammatory diseases of the cervix uteri (N72), noninflammatory disorders of the fallopian tube and broad ligament (N83), cervical dysplasia (N87), and endometriosis (N80) (Supplement Table 4 and Supplement Figure 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Figure 3 is here]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2. Whole-disease-level associations\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt the whole-disease level, Supplement Table 5 and Supplement Figure 3 present multimorbidity networks with partial correlations greater than 0.15. The strongest observed association was between neoplasms of uncertain behavior of lymphoid, hematopoietic, and related tissue (D47) and other osteochondropathies (M93, \u0026rho;=0.85). Most high-correlation pairs were within the same physiological system. For instance, myeloid leukaemia (C92) showed a robust correlation with Leukaemia of unspecified cell type (C95, \u0026rho;=0.51), consistent with shared pathological mechanisms within hematologic malignancies. The MNA (Multimorbidity Network App see http://152.136.125.122:3838/) can be used to explore multimorbidity networks for some index diseases.\u003c/p\u003e\n\u003cp\u003eThe top 10 conditions with the highest MMC across all diseases included: other disorders of fluid, electrolyte and acid-base balance (E87), disorders of glycoprotein metabolism (E77), other aplastic anaemias (D61), osteoporosis without pathological fracture (M81), other diseases of liver (K76), maternal care for other conditions predominantly related to pregnancy (O26), other dermatitis (L30), essential (primary) hypertension (I10), gastritis and duodenitis (K29), and chronic ischaemic heart disease (I25) (supplement table 6 and supplement figure 4). Together, these findings underscore the systemic, multi-organ nature of high-burden multimorbidity across metabolic, cardiovascular, hepatic, hematologic, and pregnancy-related domains (Supplement Table 6 and Supplement Figure 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3. System-level associations\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt the system level, Figure 4 and Supplement Table 7 summarize the associations between the 18 categories of infertility-related gynecological diseases and systemic diseases diagnosed prior to their onset. The strongest multimorbidity coefficients were consistently observed with genitourinary diseases (N00\u0026ndash;N99), with MMCs exceeding 0.2 across all 18 gynecological categories. Additionally, high MMCs (MMC\u0026gt;0.2) were observed between certain infectious and parasitic diseases (A00-B99) and dysplasia of cervix uteri (N87), as well as between metabolic disorders (E00-E90) and ovarian dysfunction (E28), suggesting possible upstream influences or shared etiologies.\u003c/p\u003e\n\u003cp\u003ePost-diagnosis associations, shown in Figure 4 and Supplementary Table 8, revealed similar patterns: infertility-related gynecological diseases were most strongly associated with subsequent genitourinary diseases (N00\u0026ndash;N99). Notably, menstrual irregularities (N91) showed a strong association with pregnancy-related complications (O00-O29), with an MMC of 0.78, indicating a possible predictive relationship between menstrual dysfunction and future obstetric complications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Figure 4 is here]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Disease trajectory and temporal analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 5 and Supplementary Table 9 present the disease trajectory network analysis preceding infertility diagnosis, identifying a series of conditions significantly associated with increased infertility risk. These included several endocrine and metabolic disorders-such as other disorders of pancreatic endocrinology (E16 RR=1.60), hyperpituitarism (E22, RR=1.61) and ovarian dysfunction (E28, RR=1.82) - as well as nutritional and metabolic deficiencies, including unspecified protein-energy malnutrition (E46, RR = 1.25) and disorders of amino acid metabolism (E72, RR = 1.51). Inflammatory pelvic conditions also emerged as strong antecedents, notably salpingitis and oophoritis (N70, RR = 1.53) and chlamydia-related infections (A74, RR = 2.58). Reproductive complications such as habitual miscarriage (N96, RR = 4.22), spontaneous abortion (O03, RR = 1.68), and abnormal products of conception (O02, RR = 1.79) further indicated early reproductive health disruptions linked to later infertility.\u003c/p\u003e\n\u003cp\u003eFollowing a diagnosis of infertility, women exhibited elevated risks for a wide range of systemic diseases. These included additional endocrine, nutritional and metabolic diseases (E02, E03, E34, E11, E14, E77-E83), pregnancy-related complications (O00, O03, O13, O14, O16, O20, O24, O26, O28), genitourinary conditions (N61, N85, N94, N96,), cardiovascular diseases (I10, I20, I24, I50, I70), and musculoskeletal disorders (M10, M25, M51, M81, M93). Together, these findings illustrate a progressive and multisystem disease burden emerging after the onset of infertility.\u003c/p\u003e\n\u003cp\u003eTemporal analysis revealed that antecedent conditions were diagnosed, on average, 0.6 years before infertility. Notably, disorders such as pancreatic endocrinology (E16) and tubal/ovarian infections (N70) occurred approximately 1.8 years earlier. Post-infertility diagnoses typically occurred about 1 year later, with maternal hypertension (O16) representing the most delayed outcome at 2.5 years. One particularly extended trajectory spanned over a decade, beginning with infertility and followed by hypothyroidism (E03), then progressing to cardiovascular conditions including angina pectoris (I20), acute myocardial ischemia (I24), heart failure (I50), and atherosclerosis (I70), highlighting the long-term systemic impact of infertility (Figure 5 and supplement table 9).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Figure 5 is here]\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eMain findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo our knowledge, this is the first large-scale and comprehensive investigation of multimorbidity patterns and disease trajectories among Chinese women, leveraging electronic medical records from over 2 million patients. We observed that the average number of diagnosed conditions per individual peaked around age 40, remained stable for a decade, and then increased markedly after age 50. The most prevalent binary multimorbidity combination was other inflammation of vagina and vulva (N76) with menstrual disorders (N91 or N92). Female infertility-related gynecological diseases were frequently observed in conjunction with gastritis and duodenitis (K29), dorsalgia (M54), spondylosis (M47), hypertrophy of breast (N62), and hypertension(I10), underscoring the systemic nature of female reproductive morbidity.\u003c/p\u003e\n\u003cp\u003eOur findings provide novel evidence supporting a sequential pathway linking gynecological inflammation, structural reproductive disorders, and gynecological neoplasms. For example, salpingitis and oophoritis (N70) were associated with noninflammatory adnexal disorders (N83), which in turn were linked to benign ovarian tumors (D27); similarly, cervical inflammation (N72) was linked to cervical dysplasia (N87), which further progressed to carcinoma in situ (D06). Among the 39 gynecological conditions analyzed, inflammatory disorders of the vagina and vulva (N76) exhibited the highest multimorbidity burden (MMC = 1.17). Across the broader disease network, strong associations were observed within physiological systems, with metabolic and cardiovascular disorders showing particularly high burdens.\u003c/p\u003e\n\u003cp\u003eNotably, diseases of the genitourinary system (N00-N99) demonstrated consistently strong bidirectional associations with all 18 infertility-related gynecological conditions (MMC \u0026gt; 0.2). In addition, high MMC values were identified between certain infectious and parasitic diseases (A00-B99) and cervical dysplasia (N87), and between metabolic disorders (E00-E90) and ovarian endocrine dysfunction (E28). Particularly, oligomenorrhea (N91) showed the strongest non-random association (MMC = 0.78) with maternal conditions related to pregnancy (O00-O29), suggesting a robust clinical link between menstrual irregularities and adverse pregnancy outcomes.\u003c/p\u003e\n\u003cp\u003eTrajectory analysis further revealed that female infertility is preceded by distinct gynecological risk factors, including ovarian dysfunction (E28), tubal or ovarian infections (N70), and habitual miscarriage (N96). On average, antecedent conditions occurred 0.6 years before the infertility diagnosis. Infertility subsequently increased the risk of endocrine and metabolic diseases, maternal complications during pregnancy, and genitourinary disorders, which were in turn associated with future cardiovascular, musculoskeletal, and other systemic diseases. Notably, we identified a long-term trajectory extending over a decade—from female infertility to hypothyroidism (E03), and subsequently to major cardiovascular conditions such as angina (I20), myocardial ischemia (I24), heart failure (I50), and atherosclerosis (I70)—reflecting the long-term and systemic health risks associated with infertility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison with other studies and potential explanations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study addresses a major gap in the understanding of multimorbidity patterns among reproductive-age women by utilizing a large, population-level dataset. Unlike many previous studies that focused on either physical or mental health conditions alone \u003csup\u003e19,24\u003c/sup\u003e, or limited disease domains such as ischemic heart disease or depression \u003csup\u003e25,26\u003c/sup\u003e, our analysis included a wide spectrum of 1,017 medical conditions across all major disease systems. This inclusive approach provides a more complete picture of the complexity and burden of multimorbidity among reproductive-age women.\u003c/p\u003e\n\u003cp\u003eUsing electronic medical records, we found that 94.6% of women in our cohort had multimorbidity. While prior studies have reported a broad range in multimorbidity prevalence—from 6.4% to 98% \u003csup\u003e19,27,28\u003c/sup\u003e—one study that considered only 308 conditions reported a prevalence of 75.1% among women. Direct comparison across studies is limited by differences in disease definitions, population characteristics, and the number of conditions considered\u0026nbsp;\u003csup\u003e29\u003c/sup\u003e.\u0026nbsp;Consistent with prior literature\u0026nbsp;\u003csup\u003e30,31\u003c/sup\u003e, we also found that patients with multimorbidity utilized significantly more healthcare services than those with a single condition, underscoring the challenge of optimizing care for this population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur age-specific findings refine previous knowledge by identifying a plateau in multimorbidity between ages 40 and 50 and a distinct peak in gynecological multimorbidity around age 40. This deviates from the commonly assumed linear increase with age \u003csup\u003e19,30,32-34\u003c/sup\u003e and may reflect perimenopausal hormonal transitions that influence susceptibility to both reproductive and systemic conditions. These results support the need for proactive screening and early intervention strategies before the age of 40.\u003c/p\u003e\n\u003cp\u003eThe observed progression from inflammatory gynecological diseases to non-inflammatory conditions and neoplasms echoes earlier studies linking pelvic inflammatory disease to increased cancer risk \u003csup\u003e35-37\u003c/sup\u003e, Mechanistic research has also shown that chronic inflammation may promote tumorigenesis via immune modulation \u003csup\u003e38\u003c/sup\u003e. Among the gynecological conditions examined, N76 was associated with the highest MMC, reinforcing the need for early detection and management of chronic gynecological inflammation.\u003c/p\u003e\n\u003cp\u003eWe also observed that menstrual disorders—particularly oligomenorrhea (N91)—were strongly associated with maternal complications. Prior studies have shown similar links between menstrual irregularities and risks of premature mortality, type 2 diabetes, and cardiovascular disease \u003csup\u003e39-41\u003c/sup\u003e. These findings suggest that women with menstrual disorders may require closer monitoring during pregnancy and long-term follow-up. Additionally, the strong associations between infertility and metabolic disorders, such as hypothyroidism, diabetes, and obesity, align with prior evidence on hormonal dysregulation and disrupted ovulatory function \u003csup\u003e42\u003c/sup\u003e. These links underscore the central role of the hypothalamic–pituitary–ovarian axis in coordinating reproductive and systemic health.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe disease trajectory analysis confirmed several well-established infertility risk factors—including ovarian dysfunction, tubal infections, and miscarriage—as well as identified novel potential contributors such as pancreatic endocrine disorders (E16), protein-energy malnutrition (E46), and amino acid metabolism disorders (E72), which warrant further investigation. Our findings are consistent with large-scale studies from China and recent systematic reviews on infertility \u003csup\u003e43,44\u003c/sup\u003e. We also noted that endometriosis may contribute to infertility through tubal damage, likely due to diagnostic delays \u003csup\u003e45\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCollectively, our results suggest that female infertility serves not only as a reproductive diagnosis but also as an early indicator of systemic vulnerability. It was strongly associated with a range of metabolic, cardiovascular, and genitourinary disorders—many of which were diagnosed at different time points across the life course. These findings are in line with data from the Women’s Health Initiative \u003csup\u003e46-48\u003c/sup\u003e, which showed similar links between infertility and cardiovascular outcomes. Disruption of hormonal homeostasis—particularly ovarian function—may thus act as both a trigger and amplifier of long-term multimorbidity risk. These results highlight the importance of integrated, hormone-sensitive care approaches for women.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough our findings were derived from a Chinese population, they are likely generalizable to other middle-income settings undergoing demographic and epidemiological transitions. This study provides critical evidence for multiple stakeholders, including clinicians, researchers, health policymakers, and guideline developers.\u003c/p\u003e\n\u003cp\u003eIn clinical practice, the increasing prevalence of multimorbidity among reproductive-age women demands more nuanced care approaches. Current clinical guidelines, largely based on randomized trials that exclude multimorbid patients, may be insufficiently applicable. Our results support the integration of multimorbidity into clinical decision-making, especially in gynecology, endocrinology, and primary care. Identifying sex- and age-specific multimorbidity clusters can help tailor care pathways, optimize treatment priorities, and reduce adverse interactions.\u003c/p\u003e\n\u003cp\u003eEmbedding multimorbidity insights into electronic health records could support early risk stratification and targeted screening. For instance, recognizing diagnostic histories that include ovarian dysfunction, metabolic abnormalities, and cardiovascular risk may prompt multidisciplinary referral or proactive monitoring. Gynecological visits—especially those for menstrual disorders or infertility—should be leveraged as opportunities to assess broader systemic health, including thyroid function, glucose metabolism, and cardiovascular profiles.\u003c/p\u003e\n\u003cp\u003eMoreover, reproductive conditions such as infertility and menstrual disorders may serve as sentinel indicators of wider physiological disruption. Their documentation warrants not only reproductive management but also longitudinal health planning and surveillance. For reproductive endocrinologists and obstetricians, our findings emphasize the importance of preconception risk assessment, endocrine optimization, and chronic disease prevention. For internists and primary care physicians, they highlight the broader relevance of reproductive health signals.\u003c/p\u003e\n\u003cp\u003eFinally, our results provide a data-driven foundation for risk-based models of care, multimorbidity-informed clinical guidelines, and personalized health strategies. As fertility rates decline and chronic diseases increase worldwide, translating multimorbidity insights into actionable care strategies will be essential to improving health outcomes for women across the life course.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study offers several notable strengths. First, it leverages a large and diverse cohort of over 2 million women, encompassing 1,017 distinct conditions—far surpassing prior studies in both scale and granularity—and enabling a comprehensive mapping of multimorbidity patterns. Second, we systematically assessed non-random associations among 39 gynecological diseases, all 1,017 conditions, and 18 gynecological-systemic disease pairs, thereby providing a robust empirical basis for clinical insight and mechanistic research. Third, we introduce a novel disease trajectories modeling approach that incorporates chronological ordering of diagnoses, offering new opportunities to infer potential causal pathways. Fourth, we identified several previously underrecognized infertility-associated conditions, such as pancreatic endocrine disorders (E16), protein-energy malnutrition (E46), amino acid metabolism disorders (E72), as well as a long-term progression from infertility to hypothyroidism (E03) and subsequently to cardiovascular outcomes (I20, I24, I50, I70), advancing understanding of infertility’s broader systemic implications.\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be considered. First, asymptomatic conditions or those managed exclusively in outpatient settings may be underrepresented in electronic health records, which could lead to underestimation of certain condition frequencies or associations. Nevertheless, the large sample size, the inclusion of both outpatient and inpatient data across multiple hospitals, and the consistency of observed patterns with established disease relationships support the robustness of our findings. Second, although the dataset captures nearly all secondary and tertiary hospital encounters in Tianjin, data from primary care or from outside the region may be missing, potentially resulting in incomplete patient histories. This limitation is inherent to most regional EHR-based studies but is unlikely to systematically bias the identification of major multimorbidity patterns. Third, while our cohort represents the largest female multimorbidity dataset in China, regional differences in healthcare access and socioeconomic context may affect generalizability. However, Tianjin’s predominantly Han Chinese population is broadly representative of the national demographic profile, minimizing concerns related to ethnic heterogeneity.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis hospital-based cohort study was approved by the Ethics Committee of West China Hospital, Sichuan University, China (Approval No. 2024\u0026minus;1821). Given the retrospective nature of the study, which relied on electronic medical records (EMRs), the requirement for informed consent from patients was waived. All necessary approvals were also obtained from the National Natural Science Foundation of China, which supported this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe definition of multimorbidity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultimorbidity is traditionally defined as the coexistence of two or more chronic conditions in the same individual \u003csup\u003e49\u003c/sup\u003e. However, to more accurately capture the impact of multimorbidity on clinical management and shared disease pathways, we adopted an expanded definition. Specifically, we defined multimorbidity as the presence of two or more distinct conditions occurring in the same individual at any point in time, regardless of whether these conditions were concurrent \u003csup\u003e19\u003c/sup\u003e. We identified these conditions through patients\u0026rsquo; cumulative medical histories, as even conditions separated in time may share underlying pathological mechanisms. This broader definition has important clinical implications: prior health conditions, even if no longer active, may influence current diagnostic decisions, therapeutic choices, and health outcomes. Moreover, it allows for a more comprehensive understanding of the developmental trajectories and causal relationships underlying multimorbidity, many of which unfold progressively over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy design and population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cohort was established in Tianjin, a province-level municipality in northern China directly administered by the central government. Capitalizing on Tianjin\u0026rsquo;s integrated healthcare big data infrastructure, we constructed a women\u0026rsquo;s reproductive lifespan database by aggregating electronic health records (EHRs) from both outpatient and inpatient services across 43 public tertiary hospitals (defined as those with \u0026gt;500 beds) and 39 public secondary hospitals (defined as those with 100 to 499 beds). These hospitals constitute 88% of Tianjin\u0026rsquo;s public tertiary (47) and secondary (46) hospitals, most non-included hospitals were military facilities and other non-maternal-and-child health specialty hospitals, ensuring high representativeness for our study population. Together, these included hospitals deliver approximately 76% of inpatient care and 70% of outpatient services across Tianjin \u003csup\u003e50\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn this study, we included all female patients aged 18 to 55 years (n=2,064,482) who received an incident diagnosis of one of 18 infertility-related gynecological conditions between January 1, 2014, and December 31, 2023. Eligible individuals were defined as those with at least one outpatient diagnosis or hospital discharge coded with one of the following 18 ICD-10 codes: E28, N70, N71, N72, N73, N80, N83, N85, N87, N88, N91, N92, N93, N97, D25, D26, D27, D06. These codes were selected based on diagnostic criteria outlined in a 2021 JAMA review of female infertility \u003csup\u003e44\u003c/sup\u003e. For each eligible patient, we retrieved complete longitudinal clinical records across the entire study period, enabling detailed analyses of disease onset, comorbidity accumulation, and multimorbidity trajectories. At present, the database contains 118,122,044 diagnostic records, 229,448,601 prescription records, 427,857,370 laboratory test records, 7,730,268 imaging examination records, 5,664,172 physical examination records, and 8,254,619 surgical records. The structure, quality, and management of this database have been detailed in our previous publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMedical conditions identification and selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMedical conditions were identified based on both primary and secondary diagnoses, using level-3 ICD-10 codes. All diagnostic codes from Chapters I to XV of the ICD-10 were included, while all codes from Chapters XVI to XXII were excluded in order to maintain consistency with our focus on general medical and gynecological morbidity. Finally, 1,017 distinct diseases were selected from the database for inclusion in our study (Appendix table S1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe identified 39 gynecological diseases, including inflammatory and non-inflammatory diseases, tumors and infertility-related conditions. These were diagnosed using the following ICD-10 codes: C51-C57, D06, D07, D25-D28, D39, E28, N70-N73, N75, N76 and N80-N97. We excluded certain codes such as N74 and N77, as these represent gynecological conditions secondary to diseases classified elsewhere in the ICD-10 system, and thus do not reflect primary gynecological diagnoses.\u003c/p\u003e\n\u003cp\u003eAll conditions were classified according to their three-digit ICD-10 code. To avoid duplicate counting of chronic conditions, we assigned the date of the first recorded diagnosis as the official onset date for each condition of interest. Subsequent repeated diagnoses of the same condition were not counted separately.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe first conducted descriptive analyses to characterize the overall study population and subgroups stratified by presence or absence of multimorbidity. Categorical variables were presented as frequencies and percentages, while continuous variables such as age and healthcare utilization (inpatient and outpatient visits) were summarized using means with standard deviations or medians with interquartile ranges, depending on data distribution.\u003c/p\u003e\n\u003cp\u003eTo examine the patterns and progression of multimorbidity, we employed two analytical approaches: multimorbidity network analysis and disease trajectory modeling. These methods enabled both cross-sectional mapping of disease associations and longitudinal tracking of disease progression across the reproductive lifespan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultimorbidity analysis by frequency\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe first assessed the overall burden of multimorbidity by calculating the cumulative number of medical conditions per individual. This was stratified by age to examine trends in disease accumulation across different life stages. To further explore common multimorbidity patterns, we tabulated the 200 most frequent binary multimorbidity pairs\u0026mdash;defined as two distinct conditions occurring in the same individual at any time point.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultimorbidity analysis by non-random association\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employed the partial correlation coefficient to identify gynecological-level, whole-disease-level, system-level multimorbidity patterns, a method previously proposed to quantify the strength of association between two health conditions while adjusting for the influence of all other conditions in the dataset. This coefficient is derived from the Pearson correlation and allows for identification of independent associations in high-dimensional comorbidity data. A positive partial correlation coefficient (\u0026rho; \u0026gt; 0) suggests that two conditions co-occur more frequently than would be expected by random chance, with larger values indicating stronger associations. The partial correlation formula for two health conditions i and j, adjusted for a third condition k, denoted as\u0026nbsp;𝜌\u003csub\u003e𝑖𝑗\u003c/sub\u003e\u003csub\u003e|\u003c/sub\u003e\u003csub\u003e𝑘\u003c/sub\u003e is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"205\" height=\"76\" src=\"data:image/png;base64,R0lGODlhNAFyAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABQA0AVEAhgAAAAAAAAEAAAEBAAAAOgEAOgAAZgEAZgAA/wA6OgA6ZgE6ZgA6kAA6jwE6kABmtgFmtQBmtQD//zoAADoBADsBADsBAToAOjoBOjsBOjo6ADo6kDpmkDpmtjqQ2zqQ2jqP2mYAAGYBAGcBAWYBAWY6AGY6AWc7AWZmOmZmZmaQtmaQ22aP2ma222a12ma2/2a2/ma1/pA6AJA6AZA7AZBmOpC224+225Db/5Da/5Da/o/a/rZmALdmALZnAbZmOrZnO7aQZrbb/7Xa/rb/27b//7b+/7X+/rX+/9uQOtuROtuQZtuQZ9u2Ztu2kNu2kdv//9r//9r+//+2Zv+3Zv/bkP/bkf7bkP/btv/bt///tv//t///2/7/2/7+2////wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwf/gACCg4SFhoeIiYqLjI2Oj5AAWDUTAQQdUJGaj5OVl5mbg52WmKGmp6ipqqusqlMTDJlNAQ+tpq+xALO1t7CytLbBwsPExahVvoJcMgQ4xozIuQDLzaHRoNQ5z9vc3d6mPNXKMgEv34bhzuPloemDywEw5/P09c/X7+TmgjwGRYVT2B0KGKCgwYMFxRnCt24fgH7/oCVbJ48QFQEV7WncyFFRQA+EtIQQt0zaIIjDPoYcqa5kJnj+BgYAOUgkAW2EehgwMm1GgJ0dgwqdx0OgIIb0ijoEgLTQMl7ojDKFFUURlxlQh2rdKmxZzJMz7XmNyC8sIpE0ncr4WjYtoi0i/z5wnUtXFVKGSQouJei21d2JeY1WueAsMK+/JgFcDCBXkBUM2pQIAFa3smWPBVsIcjJBAVkASdgKCri0FUHNADh7JhSabGsAIhUIGZ15c+fPgi5mVHKAJ1zZl4MLF5Q3hYaEKkCdTAxadKvixy0lL8TDJI8HXJpcQE08gHHkygnx5imox4Mu2rkPX093rCK06Jivcp8IviCRCdWNc46oB4OqAMCVEE7sFciVSPIRgkxp9gWDYHgLTeDQgguulGAhcDUGgBUUwFBFhwaGqBUyWR2ShEJHSTgMiYucqJ+LImXFIiMc7lZADlqIUKKIPNaTV2noGEDEDw65OMyPi/QzpP851WUC0RKaIcmIEjcO4l9VOhkBZY9cEoXiQBMQoF6Tw7jjUZiaPSUIFiUQsAI/X/b3nzJYCZJFm292qWdlMe4ZoI5+BroeFz/EKSKhN+k5yieC1lWUAvpx2UMAkOqJyy87FkMQQpwa2uinPTJETaSglmrqOWZOo8+prLZ6z0SqGoUSIaTBFgKQ3XCq66689urrr8AGK+ywxBZr7LHIDkubWza1JANzKInkEEy4uWotq0opCCsj0hKiZiOb9urpteR2RF9bkHRbUwh9letuoIiBEtheBdGk7lGEgVZQpu/2W+Bptq02yGu0CnRvc//ENpu/DIcIHXjUMUfwwddltx3/R/Akq/HGHHfs8ccg+3ruIQ0uB8q9+FlCaj3LtNvwy++FcGGKDLK77oQSVsjRwTD3jMiMiRiprUP3whgCv+fo7PPShkiZiJJEDqzQvWQ+qd48g63M9NKpIvKKmISQed+t4/DCppsbTTHu1mw70ufNdU3BX9t0M4JopDxrlcTMdfcdNqWk5j2U2H4Xni7ZdF1n+OJ6fsv44yG2DPnkBQpO+eVzKT1oENEB5zcXnBekwBB0Z21gUZohMzfTPAjgwoYVGHBE22prfZnicNrONQQnFaAD7aunVoLLW81aSBIK2HCOE8PffsDsiCSxwA397g3hOAQob4h2us/z9iHM890K/zXaF6IdgTuPEMEiTpjQgBTuEr6ufGyqHJRLVq1VrYMyX19/ouaiAQPgtwgu0OB55cKdt/RXiEkQAAU/2d88RnaW/hWDgg6EIFAwRgPZuW0E77uW4wahkkL8IDkiCZ43uNCPhTHCaYeowgauJ4gl5CkRJSTECaGQQvJohAs+OADpHpGEAcRAhDLoCwVXosJudG0RDwpaAOSTlw7kL3g95AgPfBcJLYCQgK3iGdBIFoImsmJRpaCNQ3iAK+qsLS+JyQvSUjTHLG4DjWCcghFPkpEkFWAH1tKcvog3NjO6Ihm7WMdB1tYdQnZHGnJ8oVkquEFjTKECA9TFANY3DRoghP8Av2tEEUEQyHz9rY2FlKBfJjKqUwRkjo+EghxpSB2pGMKOxbACJgloQFCmQo+cbFXtFri2FKqSFamCByob0ZToBSABtKAlMbuHS2JsMZSdDMARj2GBEApTNK2sTxmPGS5ejQspyjxFMw/Bwgg2IpyU9OEhFuMrAB5Cl5lUhie3qc5ugvFU1ptmzAzpyknaipGLWGfTnjlFaeajmGWUpzD0qCHYjMCX3PQmthIDTzISFBxSQYrxmLktE0UTjg5VFUQrWSYB8BN2+SThHh9RBX+6SoFgWaYxL8hAsKQFf9TCDCxnGcsk2bIQ1QyGAQ2AhLAJoKL8QKABfwI9r23SVSP/bGTMEKpOWCm0bC0y6EIfEB6iStGRNkFfMPAJRrYWkAbBjN4ASNkqyRVijNurl6Zqk5rbxMyRDxlXJA3xA8r8bAJznAVjNDWAALwOAE+ggAKqWp8RQBUdf3RV3paIjE4t0xQPk05Z9TII0xkGqRb0mhUR8YOrqYU/nf3kZ0Mhme+I1hB6XKxjMvC7IgYgrhbV6KkEuSyx9DRobHmNwo53VNOI9RtLbSoj9MhP3szOi6M73kxbZTpDLLEbUVQE4SqWnlumdhjf5YYWSBBTRVj3JBDwQhMy8NiQfNFawySj+IiBV/OmJWVxSm8rwnsOXQL3aQ4goBcTAkinHJCyrJKb6yrJNw8YxlBFNBOkao45PmaUzxuSeWllNaTLGFzBAiJ2ggUmS66AKoJ5sw3GE5umEKNlZQrJW14JYmyLazaixIOgkg68GNcpTM9UoOMAhOS3HqgxKRdWY5uQT5LghzyPCa5tVHYqYUucsudraZKB2fDUNh5UuZOcxMIJCMACYXJACEUxiV0xVzn1+Qw/00oinQvEBSBw0Wd5+Yrl9pw4ASgAmz3DD02IS+hGV9id3XW0pCdIDpBIeNKYPkdAmnHpTHt6G/B4gIs/TWr+hkkD+y21qtuxr1W72hadBeyrZ03E59L61ofjMa6DEwgAOw==\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003eWe further conducted multimorbidity network analyses separately for gynecological-level and whole-disease-level multimorbidity. In these networks, nodes represented individual health conditions, while edges represented partial correlation coefficients between condition pairs. A pre-specified threshold for the partial correlation coefficient was applied to filter the edges, balancing interpretability with visual clarity. To enhance accessibility and exploration, we developed interactive online tools to present the full network analysis results. All network analyses were performed using the igraph package in R (version 4.3.1).\u003c/p\u003e\n\u003cp\u003eWe next applied the multimorbidity coefficient (MMC) approach for gynecological-level and whole-disease-level multimorbidity, previously introduced to quantify the overall multimorbidity strength of a given condition among gynecological-level and whole-disease-level conditions separately. The MMC is defined as the sum of all positive partial correlation coefficients between a target condition and all other health conditions. For condition i, the MMC formula is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"197\" height=\"63\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003eWe also applied the MMC approach for system-level multimorbidity, we calculated partial correlation coefficients between each of the 18 infertility-related gynecological diseases and each of all other diagnosed conditions\u0026mdash;distinguishing those that occurred before or after the index diagnosis. MMC values were then computed for diseases grouped within the same ICD-10 chapter (i.e., disease system), enabling system-level analysis of multimorbidity strength. These relationships were visualized using Sankey diagrams, which illustrated the strength and direction of associations between the 18 target conditions and various disease systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisease trajectory network analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the impact of diseases progression for female infertility across the entire life cycle, we conducted disease trajectory network analysis for female infertility across the 1,017 diseases utilizing data from this cohort study and mapped them into a longitudinal disease trajectory network. Diagnosis pairs (D1-D2), where D1 denotes the exposure and D2 denotes the outcome, were identified based on the chronological order of initial diagnosis. Inverse Probability of Treatment Weighting (IPTW) was applied to control for baseline confounders (age and diagnostic variables, including ICD-10 codes: E28, N70, N71, N72, N73, N80, N83, N85, N87, N88, N91, N92, N93, N97, D25, D26, D27, D06).\u003c/p\u003e\n\u003cp\u003eWeighted logistic regression models were then used to estimate relative risk (RR) and corresponding p-values for each disease pair. Disease trajectories with statistically significant and positively associated RRs were retained for network construction. The final disease trajectory network was visualized using Cytoscape (version 3.10.2), with nodes representing disease conditions and directed edges indicating the chronological progression from D1 to D2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTemporal disease trajectories analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBuilding on the disease trajectory network for female infertility, we conducted a temporal analysis to quantify the average age interval between disease onset within each significant diagnosis pairs (D1\u0026rarr;D2). For each validated D1\u0026ndash;D2 pair, we extracted individual-level data and calculated the weighted average age at diagnosis for both D1 and D2. These averages were computed using inverse probability weighting, adjusting for the same set of baseline confounders as in the network analysis. The weighted average age was defined as: Age =\u003cimg width=\"83\" height=\"49\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;. The interval between the two weighted averages was used to infer the temporal distance between diagnoses. All temporal trajectory analyses and visualizations were implemented in R (version 4.3.1).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis database is developed by the Chinese Evidence-based Medicine Center, affiliated with West China Hospital, Sichuan University. Access to the data requires joint approval from both the Chinese Evidence-Based Medicine Center and the Tianjin Health Care Big Data Ltd. Qualified research institutions may apply for data access by submitting a formal request that includes (1) a project proposal, and (2) an analytical plan approved by an accredited Ethical Review Board in Tianjin. Data access is subject to final approval by both data custodians.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYR, JT, and XS conceived and designed the study. YR, JT, and XS obtained the funding. YR and YLJ contributed to the statistical analysis. YR, JT, YLJ, and SX contributed to data interpretation. YR and YLJ drafted the manuscript. JT and SX critically revised the manuscript. All authors reviewed and interpreted the results, commented on the paper, contributed to revisions, and read and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge funding from National Key R\u0026amp;D Program of China (NO. 2024YFC3505800), National Science Fund for Distinguished Young Scholars (Grant No. 82225049), National Natural Science Foundation of China (NO. 82474334, NO. 82474335, NO. 72177132), the Key R\u0026amp;D Projects of Sichuan Provincial Department of Science and Technology (NO. 2024YFFK0174, 2024YFFK0152), 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (Grant No. ZYYC24010 and No. ZYGD23004), Special fund for traditional Chinese medicine of Sichuan Provincial Administration of Traditional Chinese Medicine (Grant No. 2024zd023).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVogel B, Acevedo M, Appelman Y, et al. The Lancet women and cardiovascular disease Commission: reducing the global burden by 2030. \u003cem\u003eLancet\u003c/em\u003e. Jun 19 2021;397(10292):2385-2438. doi:10.1016/s0140-6736(21)00684-x\u003c/li\u003e\n\u003cli\u003ePatwardhan V, Gil GF, Arrieta A, et al. Differences across the lifespan between females and males in the top 20 causes of disease burden globally: a systematic analysis of the Global Burden of Disease Study 2021. \u003cem\u003eLancet Public Health\u003c/em\u003e. May 2024;9(5):e282-e294. doi:10.1016/s2468-2667(24)00053-7\u003c/li\u003e\n\u003cli\u003eArnold AP, Klein SL, McCarthy MM, Mogil JS. Male-female comparisons are powerful in biomedical research - don\u0026apos;t abandon them. \u003cem\u003eNature\u003c/em\u003e. May 2024;629(8010):37-40. doi:10.1038/d41586-024-01205-2\u003c/li\u003e\n\u003cli\u003eWitt A, Womersley K, Strachan S, Hirst J, Norton R. 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Nov 24 2023;14(1):7689. doi:10.1038/s41467-023-43569-5\u003c/li\u003e\n\u003cli\u003eZhu Y, Edwards D, Mant J, Payne RA, Kiddle S. Characteristics, service use and mortality of clusters of multimorbid patients in England: a population-based study. \u003cem\u003eBMC Med\u003c/em\u003e. Apr 10 2020;18(1):78. doi:10.1186/s12916-020-01543-8\u003c/li\u003e\n\u003cli\u003eHypp\u0026ouml;nen E, Mulugeta A, Zhou A, Santhanakrishnan VK. A data-driven approach for studying the role of body mass in multiple diseases: a phenome-wide registry-based case-control study in the UK Biobank. \u003cem\u003eLancet Digit Health\u003c/em\u003e. Jul 2019;1(3):e116-e126. doi:10.1016/s2589-7500(19)30028-7\u003c/li\u003e\n\u003cli\u003eBarnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. \u003cem\u003eLancet\u003c/em\u003e. Jul 7 2012;380(9836):37-43. doi:10.1016/s0140-6736(12)60240-2\u003c/li\u003e\n\u003cli\u003eBisquera A, Gulliford M, Dodhia H, et al. Identifying longitudinal clusters of multimorbidity in an urban setting: A population-based cross-sectional study. \u003cem\u003eLancet Reg Health Eur\u003c/em\u003e. Apr 2021;3:100047. doi:10.1016/j.lanepe.2021.100047\u003c/li\u003e\n\u003cli\u003eLin HW, Tu YY, Lin SY, et al. Risk of ovarian cancer in women with pelvic inflammatory disease: a population-based study. \u003cem\u003eLancet Oncol\u003c/em\u003e. Sep 2011;12(9):900-4. doi:10.1016/s1470-2045(11)70165-6\u003c/li\u003e\n\u003cli\u003eJonsson S, Jonsson H, Lundin E, H\u0026auml;ggstr\u0026ouml;m C, Idahl A. Pelvic inflammatory disease and risk of epithelial ovarian cancer: a national population-based case-control study in Sweden. \u003cem\u003eAm J Obstet Gynecol\u003c/em\u003e. Jan 2024;230(1):75.e1-75.e15. doi:10.1016/j.ajog.2023.09.094\u003c/li\u003e\n\u003cli\u003eFalconer H, Yin L, Salehi S, Altman D. Association between pelvic inflammatory disease and subsequent salpingectomy on the risk for ovarian cancer. \u003cem\u003eEur J Cancer\u003c/em\u003e. Mar 2021;145:38-43. doi:10.1016/j.ejca.2020.11.046\u003c/li\u003e\n\u003cli\u003eCoussens LM, Werb Z. Inflammation and cancer. \u003cem\u003eNature\u003c/em\u003e. Dec 19-26 2002;420(6917):860-7. doi:10.1038/nature01322\u003c/li\u003e\n\u003cli\u003eWang YX, Stuart JJ, Rich-Edwards JW, et al. Menstrual Cycle Regularity and Length Across the Reproductive Lifespan and Risk of Cardiovascular Disease. \u003cem\u003eJAMA Netw Open\u003c/em\u003e. Oct 3 2022;5(10):e2238513. doi:10.1001/jamanetworkopen.2022.38513\u003c/li\u003e\n\u003cli\u003eWang YX, Shan Z, Arvizu M, et al. 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Mar 2018;125(4):432-441. doi:10.1111/1471-0528.14966\u003c/li\u003e\n\u003cli\u003eCarson SA, Kallen AN. Diagnosis and Management of Infertility: A Review. \u003cem\u003eJama\u003c/em\u003e. Jul 6 2021;326(1):65-76. doi:10.1001/jama.2021.4788\u003c/li\u003e\n\u003cli\u003eHorne AW, Missmer SA. Pathophysiology, diagnosis, and management of endometriosis. \u003cem\u003eBmj\u003c/em\u003e. Nov 14 2022;379:e070750. doi:10.1136/bmj-2022-070750\u003c/li\u003e\n\u003cli\u003eParikh NI, Jeppson RP, Berger JS, et al. Reproductive Risk Factors and Coronary Heart Disease in the Women\u0026apos;s Health Initiative Observational Study. \u003cem\u003eCirculation\u003c/em\u003e. May 31 2016;133(22):2149-58. doi:10.1161/circulationaha.115.017854\u003c/li\u003e\n\u003cli\u003eMurugappan G, Leonard SA, Farland LV, et al. Association of infertility with atherosclerotic cardiovascular disease among postmenopausal participants in the Women\u0026apos;s Health Initiative. \u003cem\u003eFertil Steril\u003c/em\u003e. May 2022;117(5):1038-1046. doi:10.1016/j.fertnstert.2022.02.005\u003c/li\u003e\n\u003cli\u003eLau ES, Wang D, Roberts M, et al. Infertility and Risk of Heart Failure in the Women\u0026apos;s Health Initiative. \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e. Apr 26 2022;79(16):1594-1603. doi:10.1016/j.jacc.2022.02.020\u003c/li\u003e\n\u003cli\u003eWHO. Multimorbidity. Geneva: World Health Organization, 2016. http://apps.who.int/iris/handle/10665/252275 (accessed June 11, 2022)\u003c/li\u003e\n\u003cli\u003eNational Health Commission of the People\u0026rsquo;s Republic of China. China health statistics yearbook (2023). Beijing. https://www.nhc.gov.cn/mohwsbwstjxxzx/tjtjnj/202501/8193a8edda0f49df80eb5a8ef5e2547c.shtml\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Baseline characteristics and healthcare utilization in the longitudinal database\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"108%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll patients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients with a single disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients with multimorbidity (\u003c/strong\u003e\u003cstrong\u003e\u0026ge;\u003c/strong\u003e\u003cstrong\u003e2 conditions)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eNo. patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e2064482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e110664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1953818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eNumber of health service visit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e54457409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e205779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e54251630\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eInpatients visit, median (interquartile range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e1570740\u003c/p\u003e\n \u003cp\u003e1[1, 2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e2031\u003c/p\u003e\n \u003cp\u003e1[1, 1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1568709\u003c/p\u003e\n \u003cp\u003e1[1, 2]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eOutpatients visit, median (interquartile range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e52886669\u003c/p\u003e\n \u003cp\u003e15[6, 33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e203748\u003c/p\u003e\n \u003cp\u003e1[1, 1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e52682921\u003c/p\u003e\n \u003cp\u003e16[7, 34]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eMedian length of follow-up (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eMedian number of laboratory tests per visit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e12[5, 27]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e2[1, 4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e13[5, 28]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eMedian number of laboratory tests records per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e133[41, 313]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e5[2, 29]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e141[46, 321]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eMedian number of prescription records per visit\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e25[9, 54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e2[1, 3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e26[10, 55]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eMedian number of prescriptions per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e60[20, 137]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e3[1, 11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e62[21, 140]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eAge, Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e34.2（9.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e31.5（9.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e34.4（9.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u0026lt;18, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e5473 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e91 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e5382 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e[18-25], N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e371380（18.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e35844（32.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e335536（17.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e(25-35], N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e871551（42.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e40756（36.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e830795（42.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e(35-45], N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e497435（24.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e22057（19.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e475378（24.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e(45-55], N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e318643（15.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e11916（10.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e306727（15.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7308896/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7308896/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Infertility is increasingly recognized not only as a reproductive concern but also as a potential sentinel of systemic health vulnerability in women. Yet, the broader multimorbidity patterns and temporal progression of diseases surrounding infertility remain poorly understood. In this large-scale retrospective cohort study, we analyzed electronic health records from 2,064,482 women in Tianjin, China, diagnosed with infertility and infertility-related gynecological disorders between 2014 and 2023. We constructed multimorbidity networks using partial correlation and multimorbidity coefficients (MMC), and applied disease trajectory modeling to map temporal patterns before and after infertility diagnosis. Among all patients, 94.6% exhibited multimorbidity, with the number of distinct diagnoses peaking at age 40 and rising sharply after 50. Non-random disease clusters emerged across gynecological and systemic conditions, including gastrointestinal, musculoskeletal, cardiovascular, and endocrine disorders. We identified a sequential progression from pelvic inflammatory disorders (N70) to structural ovarian abnormalities (N83) and then to benign ovarian neoplasms (D27), indicating a reproductive-to-systemic transition. Notably, inflammatory diseases of the vagina and vulva (N76, MMC=1.17) and oligomenorrhea (N91, MMC=0.78) were associated with the highest multimorbidity burden. Infertility significantly elevated the risk of subsequent hyperlipidemia (E78), heart failure (I50), and pregnancy-related complications (O24), with antecedent diagnoses occurring on average 0.6 years before and downstream conditions appearing one year after infertility diagnosis. This study provides the first comprehensive mapping of multimorbidity and disease progression in women with infertility, positioning infertility as an early-life marker of multisystem dysregulation. These findings underscore the need for life course-oriented, integrated care strategies in women’s health that bridge reproductive and systemic medicine.","manuscriptTitle":"The hidden burden of infertility: multimorbidity patterns and longitudinal disease trajectories in a population-based cohort of 2 million women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 11:12:16","doi":"10.21203/rs.3.rs-7308896/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7b5bde96-8243-4bfa-a30e-2a8096c743d5","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":52809060,"name":"Health sciences/Diseases/Reproductive disorders/Infertility"},{"id":52809061,"name":"Health sciences/Health care/Public health/Epidemiology"}],"tags":[],"updatedAt":"2025-09-09T11:12:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 11:12:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7308896","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7308896","identity":"rs-7308896","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}