{"paper_id":"1528d94a-a6ec-421a-99c4-1ac4d0b90633","body_text":"1 \nRunning title: Endometriosis-PMOS overlap & infertility \nArticle title: Menstrual symptoms and infertility odds among individuals with co-occurring \nendometriosis and polyendocrine metabolic ovarian syndrome \nSophia Werdin, PhDa, Zifan Wang, PhDa, Eliana Huffman, MPHa, Anne Marie Z Jukic, PhDb, Allen J \nWilcox, MD, PhD b, Sanaa Suharwardy, MD c, Jukka-Pekka Onnela , ScDd, Brent A Coull, PhD a,d, \nMichelle A Williams, ScDe,f, Shruthi Mahalingaiah, MDa,g \naDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA \nbEpidemiology Branch, Division of Intramural Research, National Institute of Environmental Health \nSciences, National Institutes of Health, Research Triangle Park, Durham, NC \ncHealth, Apple Inc, Cupertino, CA \ndDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA \neDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA \nfDepartment of Epidemiology and Population Health, Stanford School of Medicine, Stanford, CA \ngDepartment of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, MA \nCorresponding author: \nSophia Werdin, PhD \nDepartment of Environmental Health \nHarvard T.H. Chan School of Public Health \n665 Huntington Avenue, Boston, MA 02115, USA \nPhone: +1 617 992 5240 \nEmail: swerdin@hsph.harvard.edu \nArticle type: Cohort study \nFunding statement: The Apple Women’s Health Study was funded by Apple Inc. The funding source \nprovided platforms and software for the collection and management of the data and participated in the \nreview of the manuscript. It played no role in the design and conduct of this analysis, interpretation of \nthe data, nor in the decision to submit the manuscript for publication. The co-author from Apple (S.S.) \nreviewed the manuscript for use of company name and accuracy and contributed to revising/editing the \nmanuscript. Any views expressed in this material are those of the authors and not of Apple Inc. This \nresearch was supported in part by the Intramural Research Program of the NIH, National Institute of \nEnvironmental Health Sciences . The contributions of the NIH authors are considered Works of the \nUnited States Government. The findings and conclusions presented in this paper are those of the authors \nand do not necessarily reflect the views of the NIH or the U.S. Department of Health an d Human \nServices. \nConflict of interest statement: S.S. owns Apple Inc. stock and is employed by Apple Inc. S.M. has \nconsulted for DexCom, Ferring, and EMD Serono. Other co-authors have no conflict of interest. There \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 14, 2026. ; https://doi.org/10.64898/2026.10.13.26365449doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\n 2 \nwere no other financial relationships with any organizations that might have an interest in the submitted \nwork in the previous 3 years and no other relationships or activities that could appear to have influenced \nthe submitted work. \nCRediT Authorship Contribution Statement: \nSophia Werdin : Conceptualization, Formal analysis , Investigation, Methodology, Visualization, \nWriting – original draft, Writing – review & editing; Zifan Wang: Writing – review & editing; Eliana \nHuffman: Writing – review & editing; Anne Marie Z Jukic: Writing – review & editing; Allen J Wilcox: \nWriting – review & editing ; Sanaa Suharwardy: Writing – review & editing ; Jukka-Pekka Onnela: \nSupervision, Writing – review & editing ; Brent A Coull : Supervision, Writing – review & editing ; \nMichelle A Williams : Supervision, Writing – review & editing ; Shruthi Mahalingaiah : \nConceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Writing \n– review & editing \nAttestation statement s: Data regarding any of the subjects in the study has not been previously  \npublished unless specified. Data will be made available to the editors of the journal for review or query \nupon request. \nData sharing statement: Aggregated data that support the findings of this study may be available upon \nreasonable request to the corresponding author. Any request for data will be evaluated and responded to \nin a manner consistent with policies intended to protect participant confidentiality and language in the \nstudy protocol and informed consent form. \nWord count: 346 words for abstract and 3,471 words for text \nCapsule: Co-occurring endometriosis and polyendocrine metabolic ovarian syndrome affected 1 in 50 \nparticipants and was associated with supra-additive infertility risk  and poorer health , suggesting a \ndistinct subpopulation needing tailored multidisciplinary care. \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 14, 2026. ; https://doi.org/10.64898/2026.10.13.26365449doi: medRxiv preprint \n\n 3 \nStructured abstract \nObjective: To study the prevalence of co-occurring endometriosis and polyendocrine metabolic ovarian \nsyndrome (PMOS), estimate infertility odds , and characterize menstrual symptom profiles associated \nwith this combined phenotype in a population cohort. \nDesign: Prospective digital cohort in the United States (US). \nSubjects: 90,714 consented participants enrolled between November 2019 and June 2025 who \ncompleted relevant surveys; a subset of 25,152 participants with menstrual cycle logs linked to monthly \nsurveys contributed to symptom analyses. \nExposure: Participants were classified by self-reported physician  diagnosis into four groups : \nendometriosis alone, PMOS alone, co-occurring endometriosis and PMOS (endometriosis+PMOS), or \nneither condition (reference group). \nMain Outcome Measures: (1) Odds of self-reported physician-diagnosed infertility (ever/never, cross-\nsectional), estimated  using multivariable logistic regression adjusted for age, race/ethnicity, and \nsubjective socioeconomic status; (2) number of adverse menstrual symptoms logged per cycle, assessed \nusing negative binomial mixed -effects models adjusted for age, race/ethnicity, and hormon al \ncontraceptive use.  \nResults: Among 90,714 participants, the  mean age was 35.1 years (standard deviation: 11.6) and the \nmajority identified as non-Hispanic White (72%). Prevalence estimates were 11% for PMOS alone, 6% \nfor endometriosis alone, and 2% for endometriosis+PMOS. Individuals with  endometriosis+PMOS \nmore frequently reported unemployment, low socioeconomic status, active smoking, poorer subjective \nhealth, early menarche, current irregular cycles, and a broad range of comorbidities. Infertility odds were \nhighest for endometriosis+PMOS (odds ratio: 12.49 [95% confidence interval: 11.13–13.99]), followed \nby PMOS alone (6.12 [5.69–6.59]) and endometriosis alone (4.31 [3.93–4.72]), with the combined effect \nexceeding additive expectations (relative excess risk due to interaction : 3.05 [1.65–4.46]). Individuals \nwith endometriosis+PMOS logged 11% more symptoms per cycle than those with neither condition \n(incidence rate ratio:  1.11 [1.04–1.19]) and had the highest odds of logging pelvic pain , fatigue, and \nsleep changes, among other symptoms. \nConclusion: In this large  US digital cohort, co -occurring endometriosis and PMOS affected 1 in 50 \nparticipants and was associated with markedly elevated infertility odds exceeding additive expectations, \nand a heightened menstrual symptom burden. Several risk factors and health impacts characteristic of \neach condition appeared amplified in individuals with both  conditions, suggesting a subgroup with \ncompounded clinical complexity and greater care needs. \nKeywords: endometriosis, polyendocrine metabolic ovarian syndrome , infertility, symptom \nassessment, mobile applications \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 14, 2026. ; https://doi.org/10.64898/2026.10.13.26365449doi: medRxiv preprint \n\n 4 \nIntroduction \nEndometriosis and polyendocrine metabolic ovarian syndrome (PMOS; the updated name for polycystic \novary syndrome proposed in 2026 (1)) are among the most prevalent chronic multisystem conditions \naffecting reproductive-age individuals. While each condition is underpinned by a distinct and complex \npathophysiology, both are leading causes of female infertility (2) and share risk factors and long-term \ndisease outcomes (3). PMOS affects an estimated 8 –13% of menstruating individuals globally, with \nprevalence varying by diagnostic criteria (4). Driven by dysregulation  of neuroendocrine, insulin, \nandrogen, and ovarian hormone pathways, PMOS may manifest metabolic, reproductive, \ncardiovascular, psychological, and dermatologic features (5–7). Common symptoms include infrequent \nor prolonged menstrual cycles, hirsutism, and acne  (1). Endometriosis affects approximately 10% of \nreproductive-age women, is characterized by the growth of endometrial -like tissue outside the uterus , \nand commonly presents with chronic pelvic pain, dysmenorrhea, and dyspareunia (8). Both conditions \nare substantially underdiagnosed, with diagnostic delays often spanning years attributable to factors \nincluding normalization of symptoms , the complexity of diagnostic criteria, and limited awareness \namong patients and clinicians alike (9–11). \nPMOS and endometriosis are often considered unrelated or diametric disorders (12–15). While both \nconditions are thought to arise from alterations in prenatal testosterone exposure and dysregulation of \nthe hypothalamic-pituitary-ovarian axis, they occupy opposite ends of that spectrum  (13,15–18). In \nPMOS, elevated gonadotropin -releasing hormone pulse frequency is thought to drive a luteinizing \nhormone-dominant pattern , promoting follic ular arrest, anovulation, and an androgen -dominant \nhormonal environment  (19–21). Conversely, in endometriosis, reduced gonadotropin-releasing \nhormone pulse frequency is thought to cause a follicle-stimulating hormone-dominant pattern, favoring \nfollicular growth, ovulation, and an estrogen -dominant hormonal environment (13,22–24). These \nopposing disturbances in steroid balance shape fundamentally different patterns of ovarian function and \ndistinct hormonal milieus in each condition (12), and their divergent pathophysiology suggests that co-\noccurrence within the same individual would be unlikely.  \nHowever, a growing body of evidence  has documented the biologically unique phenotype of co -\noccurring endometriosis and PMOS and has begun to characterize the  clinical profile  of affected \nindividuals (3,25). In a US operative cohort, Schliep et al. (3) found that patients with co-occurring \nendometriosis and PMOS had the highest rates of subfertility and chronic pelvic pain compared to those \nwith one condition alone or neither condition. An analysis of a US inpatient sample database further \ndemonstrated that individuals with co-occurring endometriosis and PMOS had higher rates of pregnancy \ncomplications than those with PMOS alone (14). Despite these emerging findings , e vidence on the \ncombined phenotype remains largely derived from small clinical samples focused on individuals with \ninfertility, with limited research examining its broader health effects. While menstrual symptoms and \ninfertility risk have been well-characterized in each condition individually, estimates of their combined \neffects in population-based samples have yet to be established . Generating such insights could help \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 14, 2026. ; https://doi.org/10.64898/2026.10.13.26365449doi: medRxiv preprint \n\n 5 \ninform clinical recognition of this unique phenotype, reduce diagnostic delay, and guide more tailored \nmanagement strategies for individuals presenting with features of both conditions.  Accordingly, this \nstudy aimed to quantify  the prevalence of co -occurrence, estimate infertility odds , and characterize \nmenstrual symptom profiles among individuals with co-occurring endometriosis and PMOS in a large, \ndigital US cohort. \nMaterials and Methods \nStudy design and population \nWe analyzed  self-reported health data from  the Apple Women’s Health Study  (AWHS), a large, \nprospective, US-based digital cohort enrolling participants since November 2019. Participants must \nreside in the US , be at least 18 years old (19 in Alabama and Nebraska, 21 in Puerto Rico) , have \nmenstruated at least once , be the sole user of  an iPhone/iCloud account, be able to communicate in \nEnglish, and provide informed consent (26). All participants actively enrolled in the study and explicitly \nopted in to share their data for research purposes.  Detailed study procedures have  been described \npreviously (26). The AWHS was  approved by the Institutional Review  Board at Advarra (CIRB \n#PRO00037562) and registered on ClinicalTrials.gov (NCT04196595). \nAt enrollment, participants completed surveys on demographics, medical and reproductive history, and \nlifestyle behaviors, with monthly follow-up surveys collecting contextual data on the previous menstrual \ncycle, including hormonal contraceptive use (26). Participants could also link mobile menstrual cycle \nlogs and clinical health records (CHR) to the study. For this analysis, menstrual cycle logging data were \nrestricted to the Apple Health app, where standardized symptom categories were consistently available \n(27). We included  participants enrolled through June 9 , 2025, who  completed baseline surveys on  \ndemographics and medical history . For symptom analyses, we further restricted t he sample t o \nparticipants who did not report a history of hysterectomy or bilateral oophorectomy, logged at least one \nmenstrual symptom  through March 1, 2025 , and completed the  corresponding monthly survey \n(Supplemental figure 1). \nSociodemographic characteristics, medical history, and hormonal contraceptive use \nSociodemographic characteristics and medical history were self-reported at enrollment, including age, \nrace/ethnicity, subjective socioeconomic status ( SES), body mass index (BMI ; calculated from self -\nreported height and weight ), and physician -diagnosed gynecologic condition s, including  infertility \n(“Have you ever been diagnosed with any of the following by a doctor or other care provider? ”). \nSubjective SES was assessed using the MacArthur Scale of Subjective Social Status, a validated single-\nitem social ladder measure in which  participants ranked themselves relative to others (28). Parity was \ndefined as the number of self-reported pregnancies lasting ≥24 weeks of gestation. Participants were \nclassified into four mutually exclusive  groups: endometriosis alone, PMOS alone, co-occurring \nendometriosis and PMOS, or neither condition. As data collection preceded the PMOS nomenclature \nupdate (1), participants self-reported a physician diagnosis of polycystic ovary syndrome. Throughout \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 14, 2026. ; https://doi.org/10.64898/2026.10.13.26365449doi: medRxiv preprint \n\n 6 \nthis paper, polycystic ovary syndrome diagnoses have been mapped to the updated PMOS label.  To \nvalidate self-reported diagnoses, we evaluated a subset of participants who shared their CHR data and \nassessed condition co -occurrence in this clinically confirmed subset.  In symptom analyses, age  at \nsymptom logging  was used rather than  baseline age . Both a ge at symptom logging  and hormonal \ncontraceptive use were defined at the cycle level to capture within-person variation.  \nMenstrual cycles and symptoms \nSymptom analyses included bleeding days and symptoms logged in the Apple Health app. Each of 23 \nprespecified adverse symptoms could be logged once per day, yielding binary day-level indicators. Only \npost-enrollment entries linked to logged bleeding dates and  corresponding monthly survey responses \nwere included. A menstrual cycle was defined as one or more consecutive bleeding days followed by at \nleast two non-bleeding days, with the first bleeding day marking cycle onset (29). Cycles shorter than \n10 or longer than 90 days were excluded as unlikely to represent natural cycles (30). \nStatistical analyses \nBaseline characteristics were summarized descriptively. Infertility odds across condition groups were \nestimated using multivariable logistic regression adjusted for age, race/ethnicity, and subjective SES. \nTo account for nonlinear associations between age and infertility, age was modeled using natural cubic \nsplines with three interior knots (at the 25th, 50th, and 75th percentiles), with the number of knots selected \nbased on Akaike Information Criterion comparison . As a secondary analysis, the model was \nreparameterized using two binary indicators for endometriosis and PMOS status and their interaction \nterm to decompose the main effect of each condition and assess additive interaction via the relative \nexcess risk due to interaction (RERI = OREndometriosis+PMOS – OREndometriosis alone – ORPMOS alone + 1). A further \nsecondary analysis stratified infertility by baseline parity, distinguishing primary (parity =  0) from \nsecondary infertility (parity ≥1). Since infertility diagnosis and parity were both assessed at baseline , \ntemporal ordering could not be established and related findings should be considered exploratory. \nMenstrual-symptom prevalence was defined as the proportion of participants logging each symptom at \nleast once across all included cycles. For each menstrual symptom, univariable mixed -effects logistic \nregression with a subject-specific random intercept estimated odds of logging that symptom in a cycle \nby condition group. Overall menstrual symptom burden per cycle was examined using negative binomial \nmixed-effects regression with a  subject-specific random intercept and a  log link to account for \noverdispersed count data , adjusting for condition group, age at symptom logging,  race/ethnicity, and \nhormonal contraceptive use. Individuals with neither condition served as the reference in all analyses. \nGiven minimal missing data across survey variables (≤2% per variable), complete case analyses were \nperformed. Data analysis was performed in R version 4.1.2. \nResults \nBaseline sociodemographic characteristics \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 14, 2026. ; https://doi.org/10.64898/2026.10.13.26365449doi: medRxiv preprint \n\n 7 \nThe primary analytic sample comprised 90,714 participants with a mean age of 35.1 years (SD = 11.6; \nTable 1). Most identified as non -Hispanic White (72% ), were highly educated (28% college degree , \n22% graduate-level education), and were employed (72%). Endometriosis alone was reported by 6% (n \n= 5,635), PMOS alone by 11% (n = 9,695), and endometriosis+PMOS by 2% (n = 1,872) (Supplemental \nfigure 2). Infertility was reported by  5% (n = 4,676). Endometriosis alone had the highest mean age  \n(40.5 years, SD = 12.2), while PMOS alone had the lowest (33.8 years, SD = 9.2). Endometriosis alone \nand endometriosis+PMOS had the highest proportions of non-Hispanic White individuals (78% and \n76%, respectively) and highest rates of unemployment or inability to work (15% and 18%, respectively), \nwhile s ubjective SES was lowest in the endometriosis+PMOS group. PMOS alone had the highest \nprevalence of obesity (60%), followed by endometriosis+PMOS (54%) and endometriosis alone (36%). \nOf the 90,714 participants in the primary analytic sample, 17,165 individuals (19%) contributed CHR \ndata. In this subsample, self-reported prevalences were consistently higher than clinically confirmed \ndiagnoses (endometriosis alone: 7% vs. 2%, PMOS alone: 12% vs. 6%, endometriosis+PMOS: 3% vs. \n0.3%, infertility: 6% vs. 1%). \nReproductive history, medical history, and lifestyle behaviors \nEndometriosis+PMOS had the highest prevalence of nearly all adverse reproductive characteristics and \ncomorbidities examined (Figure 1). Early menarche, irregular cycles, and hormone use for menstrual-\ncycle management and fertility were most common with endometriosis+PMOS and PMOS alone. The \ncombined group and endometriosis alone had the highest prevalence of other gynecologic conditions \nand surgeries. This pattern extended to broader medical history, with the endometriosis+PMOS group \nhaving particularly elevated rates of chronic fatigue syndrome (19%  vs. 4% ), pelvic inflammatory \ndisease (12% vs. 2%), and fibromyalgia (19% vs. 4%) relative to individuals with neither condition . \nParticipants with endometriosis+PMOS also reported the poorest subjective health,  the highest \nfrequency of feeling overly sleepy during the day, and the highest rates of active smoking (Supplemental \ntable 1).  \nAssociation between condition groups and infertility \nAfter adjusting for age, race/ethnicity, and subjective SES, all three condition groups were associated \nwith significantly elevated infertility odds compared to those with neither condition (Table 2). Infertility \nodds were highest for endometriosis+PMOS (OR = 12.49, 95% CI: 11.13–13.99), followed by PMOS \nalone (OR = 6.12, 95% CI: 5. 69–6.59) and endometriosis alone (OR = 4.31, 95% CI: 3.93–4.72). The \ncombined effect exceeded additive expectations (RERI = 3.05, 95% CI: 1. 65–4.46), indicating supra-\nadditive interaction. Among covariates, higher subjective SES and non-Hispanic White race/ethnicity \nwere associated with increased infertility odds.  In the parity -stratified analysis, 2,032 infertile \nparticipants (44%) were classified as having primary and 2,484 (53%) as secondary infertility ; 160 \nparticipants (3%) could not be classified due to missing parity data. The pattern of elevated infertility \nodds was consistent across subtypes, with endometriosis+PMOS showing the highest odds for both \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 14, 2026. ; https://doi.org/10.64898/2026.10.13.26365449doi: medRxiv preprint \n\n 8 \nprimary (OR = 1 4.19, 95% CI: 1 1.87–16.92) and secondary infertility (OR = 11. 34, 95% CI: 9.69–\n13.24). \nMenstrual symptoms \nThe analytic sample for menstrual symptom s comprised 25,152 participants  (PMOS alone: 2,775;  \nendometriosis alone: 1,103;  endometriosis+PMOS: 375;  neither condition : 20,899 ), contributing \n171,549 menstrual cycles and 648,255 symptom entries. The most commonly  logged symptom was \nabdominal cramps, reported by 65% of participants at least once, followed by fatigue (42%), bloating \n(39%), and acne (39%). Logging of pelvic pain showed the largest effect sizes across condition groups, \nwith the highest odds in the endometriosis+PMOS group (OR = 3.07, 95% CI: 2.32–4.05), followed by \nendometriosis alone (OR = 2.50, 95% CI: 2.12–2.95) and PMOS alone (OR = 1.38, 95% CI: 1.22–1.56). \nAdditional symptoms with significantly higher odds of logging  in the combined group compared to \nindividuals with neither condition included chills, dry skin, hair loss, bladder incontinence,  nausea, \nfatigue, and sleep changes (Supplemental table 2). After adjusting for age, race/ethnicity, and hormonal \ncontraceptive use, all condition groups logged significantly more symptoms per cycle than participants \nwith neither condition (Table 3). The increased symptom burden was comparable for endometriosis \nalone (IRR = 1.14, 95% CI: 1.09–1.18) and endometriosis+PMOS (IRR = 1.11, 95% CI: 1.04–1.19), and \nmodestly smaller for PMOS alone (IRR = 1.06, 95% CI: 1.03–1.08). \nDiscussion \nIn this large US digital cohort of over 90,000 reproductive-age individuals, co-occurring endometriosis \nand PMOS affected 2% of participants and was associated with markedly elevated infertility odds, a \ndistinct menstrual symptom pattern, and a broadly poorer health profile compared to those with either \ncondition alone or neither condition. Infertility odds exceeded the sum of individual condition effects , \nindicating supra-additive interaction. Participants with endometriosis+PMOS had the highest prevalence \nof all examined health conditions , including chronic fatigue syndrome, fibromyalgia, and pelvic \ninflammatory disease , together with  the poorest subjective health. Prospectively logged menstrual \nsymptoms showed an elevated symptom burden in the combined group and the highest odds of logging \npelvic pain, fatigue, and sleep changes, among other symptoms. Together, these findings indicate that \nindividuals with comorbid endometriosis and PMOS may constitute a clinically distinct subpopulation \nfacing a disproportionate health burden, underscoring the need for tailored diagnostic approaches and \nmultidisciplinary management strategies. \nDespite a considerably smaller population cohort of 127 women investigated by Schliep et al. (3), and \nslightly differing prevalence estimates for endometriosis alone (10% vs. 6%) and PMOS alone (8% vs. \n11%), the prevalence estimate for co-occurring endometriosis+PMOS matched our finding of 2%. Other \nstudies examining the prevalence of endometriosis among individuals with PMOS have reported wide-\nranging estimates, including 1% among patients with PMOS who delivered (14), 8% among women \nwith clomiphene citrate-resistant PMOS undergoing laparoscopic ovarian drilling (31), and 74% among \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 14, 2026. ; https://doi.org/10.64898/2026.10.13.26365449doi: medRxiv preprint \n\n 9 \nwomen with PMOS and infertility or pelvic pain who underwent laparoscopy (25), reflecting substantial \nheterogeneity across study populations. In our population-based sample, 16% of individuals with self -\nreported PMOS also reported endometriosis - an estimate that, given the larger sample size, may provide \na useful reference point within this wide range of clinical estimates. \nConsistent with previous work (3,12,14,25), individuals with endometriosis+PMOS were more likely to \nidentify as non-Hispanic White, had a lower mean BMI than those with PMOS alone but a higher BMI \nthan those with endometriosis alone, and mo re frequently reported early menarche . The observed \nsociodemographic differences in disease and infertility prevalence by race/ethnicity and subjective SES \nmay partly reflect differential healthcare access rather than true differences in disease burden , with \nunmeasured social determinants of health potentially contributing to underdiagnosis in certain groups \n(32,33). The intermediate BMI in the combined group may reflect competing metabolic influences, \nwhere insulin resistance, hyperandrogenism, and obesity characteristic of PMOS (34) may be partially \noffset by the lower BMI and altered metabolic signaling  associated with endometriosis (35). Early \nmenarche can be linked to greater lifetime exposure to ovarian hormones and has been associated with \nincreased risk of endometriosis (36,37), with some studies also suggesting a link to PMOS (38,39). The \nhigher prevalence of early menarche with endometriosis+PMOS may therefore reflect a shared \nhormonal vulnerability or cumulative risk effec t, potentially accelerating the pathological processes \nunderlying both conditions. \nOur study extends previous work by demonstrating a broadly poorer health profile across reproductive \nand medical history domains, suggesting that co-occurrence may constitute a distinct and more severe \nphenotype with its own clinical trajectory.  PMOS is a multisystem condition with well -established \nmetabolic, reproductive, and psychological manifestations, including hypertension, cardiovascular \ndisease, sleep apnea, depression, and anxiety (5,40). Consistent with this evidence, both PMOS groups \nshowed elevated prevalence of these features compared to individuals with endometriosis alone or \nneither condition. Notably, the combined group showed an even greater burden of nearly all PMOS -\nassociated multisystem features than the PMOS alone group, except for obesity and irregular menstrual \ncycle history. Rates of gynecologic surgeries, including hysterectomy and oophorectomy, were closer \nto those with endometriosis alone, suggesting that the surgical burden in co -occurring \nendometriosis+PMOS is driven predominantly by endometriosis. The higher burden of comorbidities  \nmay contribute to, and likely interacts with,  the highest rates of unemployment, lowest subjective \nsocioeconomic status, and poorest self -reported health observed in th e combined group. Given well-\ndocumented diagnostic delays for endometriosis and PMOS individually (10,11), clinician awareness \nof the combined phenotype is particularly important to facilitate timely recognition and more intensive \nmonitoring of patients presenting with features of both conditions. Our findings underscore the need for \nmultidisciplinary care models that reflect the systemic health burden  affected individuals face, with \nroutine screening for common comorbidities, including fatigue -related disorders  and pelvic \ninflammatory disease, particularly warranted.  \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 14, 2026. ; https://doi.org/10.64898/2026.10.13.26365449doi: medRxiv preprint \n\n 10 \nIn an operative cohort of 473 women, Schliep et al. (3) found that a history of subfertility was associated \nwith a tenfold higher adjusted probability of having co-occurring endometriosis and PMOS compared \nwith neither condition, which is broadly consistent with the eightfold higher unadjusted prevalence of \ninfertility observed in the combined group in our sample . After adjusting for relevant covariates, we \nfound 12-fold higher odds of infertility in the co-occurring group compared to individuals with neither \ncondition, markedly exceeding the odds observed for either condition alone and  surpassing additive \nexpectations. These findings have direct clinical implications , including that proactive fertility \ncounseling should be integrated into routine care for patients with both endometriosis and PMOS. This \ncould include early discussions regarding fertility preservation strategies (e.g., oocyte cryopreservation), \nparticularly for younger individuals who have not yet completed their reproductive plans but face a \ndisproportionately high risk of infertility. Furthermore, individuals with co-occurring endometriosis and \nPMOS may be at risk of  being evaluated and managed within a single diagnostic framework - either \nendometriosis or PMOS - rather than both. As each condition carries distinct diagnostic and therapeutic \nimplications (41,42), recognizing co-occurrence may directly inform treatment selection and potentially \nimprove assisted reproductive technology outcomes for those seeking fertility care , though dedicated \nresearch in this area is warranted. \nIn the menstrual symptom subsample of 25,152 participants, all condition groups were associated with \na higher symptom burden compared to those with neither condition . Individuals with \nendometriosis+PMOS showed the highest odds of logging pelvic pain, chills, dry skin, hair loss, bladder \nincontinence, nausea, fatigue, and sleep changes, suggesting a distinct and broad symptom profile. The \nmarkedly elevated odds of logging pelvic pain with endometriosis+PMOS compared to endometriosis \nalone may reflect additive nociceptive or inflammatory mechanisms arising from the co-occurrence with \nPMOS. The presence of  symptoms such as hair loss, dry skin, and bladder incontinence points to \nsystemic involvement extending beyond the reproductive tract, potentially reflecting the interplay of \nhormonal dysregulation, immune dysfunction, and chronic inflammation characterist ic of both \nconditions (1,43). These findings highlight the value of prospectively logged symptom data in capturing \nthe full breadth of disease burden, and suggest that individuals with co -occurring endometriosis and \nPMOS may benefit from symptom-specific clinical attention beyond standard gynecologic care. \nStrengths and Limitations \nWith 1,872 participants reporting both endometriosis and PMOS, the AWHS offers a considerably larger \nand more population-representative sample than prior studies relying on smaller clinical samples of \nwomen with infertility or undergoing laparoscopy. Beyond broad survey-based data, prospective mobile \nsymptom logging provided insights into menstrual symptom burden and minimized recall bias.  Key \nlimitations include potential surveillance bias, whereby diagnosed gynecologic conditions and infertility \nmay reinforce each other’s detection, potentially leading to a relative overestimation of infertility burden \nacross condition groups. More broadly, individuals with two diagnoses may have undergone more \nintensive clinical workups, potentially contributing to greater detection of unrelated conditions and the \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 14, 2026. ; https://doi.org/10.64898/2026.10.13.26365449doi: medRxiv preprint \n\n 11 \nhigher comorbidity burden observed in the endometriosis+PMOS group. All conditions were based on \nself-reported physician diagnoses, introducing potential misc lassification. Cross-validation with CHR \ndata demonstrated consistently higher self-reported than clinically confirmed prevalences, which may \nreflect both over-reporting and underdiagnosis in clinical records. As individuals typically accumulate \nmultiple health records over their lifetime, the available CHR data likely capture an incomplete \ndiagnostic history, contributing to lower concordance with self -reported conditions. S elf-reported \nendometriosis and PMOS have been shown to be  valid proxies for population-based research (44,45), \nand our prevalence estimates are consistent with population-based literature (4,46,47), lending support \nto their validity. As symptom logs reflect both physiological experience and app engagement (48), true \nsymptom absence could not be distinguished from non-logging, potentially underestimating symptom \nburden. However, as this limitation applies equally across groups, it s impact on between -group \ndifferences is likely minimal . Finally, overrepresentation of non-Hispanic White and highly educated \nindividuals in the AWHS cohort (26) limits generalizability to the broader US menstruating population.  \nConclusions \nIn this  large, population -based US cohort , co -occurring endometriosis and PMOS affected 2% of \nparticipants and was associated  with a  poorer health profile than either condition alone across \nreproductive and clinical domains. Individuals with  endometriosis+PMOS had markedly elevated \ninfertility odds reflecting compounded risk beyond th e sum of individual condition effects, alongside \nthe highest burden of comorbid mental and physical health conditions, the poorest subjective health, and \na distinct menstrual symptom profile. Clinicians should be aware of this high -risk phenotype and \nconsider evaluating for both conditions in individuals presenting with features of either. Dedicated \nresearch into the sequence of condition onset, specific risk factors, and long-term health trajectories is \nneeded to inform clinical guidelines and improve care for this population.  \nAcknowledgment \nThe AWHS team would like to thank the study participants for consenting and contributing to the \nadvancement of women’s health research.  We would also like to thank Carrie Sarcione and Kayley \nAbrams for their project management support. \nList of abbreviations \nAWHS  Apple Women’s Health Study \nBMI  Body mass index \nCHR  Clinical health record \nPMOS  Polyendocrine metabolic ovarian syndrome \nRERI  Relative excess risk due to interaction \nSES  Socioeconomic status \nUS  United States \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 14, 2026. ; https://doi.org/10.64898/2026.10.13.26365449doi: medRxiv preprint \n\n 12 \n \nReferences \n1. Teede HJ, Khomami MB, Morman R, Laven JSE, Joham AE, Costello MF, et al. Polyendocrine \nmetabolic ovarian syndrome, the new name for polycystic ovary syndrome: a multistep global \nconsensus process. 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Li K, Urteaga I, Wiggins CH, Druet A, Shea A, Vitzthum VJ, et al. Characterizing physiological \nand symptomatic variation in menstrual cycles using self-tracked mobile-health data. NPJ Digit \nMed. 2020;3:79. doi:10.1038/s41746-020-0269-8 PubMed PMID: 32509976; PubMed Central \nPMCID: PMC7250828. \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 14, 2026. ; https://doi.org/10.64898/2026.10.13.26365449doi: medRxiv preprint \n\n 16 \nFigure \n \nFigure 1. Prevalence ratios for reproductive history, gynecologic conditions and surgeries, and \nother health conditions by condition group, relative to individuals with neither condition \n(reference group indicated by dashed line at 1x). The x-axis is displayed on a log scale. PMOS = \npolyendocrine metabolic ovarian syndrome; Endometriosis+PMOS = co-occurring endometriosis and \nPMOS. \n \n \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 14, 2026. ; https://doi.org/10.64898/2026.10.13.26365449doi: medRxiv preprint \n\n 17 \nTables \nTable 1. Demographic and anthropometric characteristics at baseline by condition group. \n Study sample  \n(n = 90,714) \nPMOS alone \n(n = 9,695) \nEndometriosis \nalone (n = 5,635) \nEndometriosis & \nPMOS (n = 1,872) \nNeither condition \n(n = 73,512) \nAge in years, mean (SD) 35.1 (11.6) 33.8 (9.2) 40.5 (12.2) 36.9 (10.1) 34.8 (11.7) \nRace/ethnicity, n (%) \nNon-Hispanic White 65,314 (72.0) 6,867 (70.8) 4,390 (77.9) 1,426 (76.2) 52,631 (71.6) \nNon-Hispanic Black 4,723 (5.2) 392 (4.0) 253 (4.5) 65 (3.5) 4,013 (5.5) \nAsian 2,878 (3.2) 340 (3.5) 73 (1.3) 23 (1.2) 2,442 (3.3) \nHispanic 6,333 (7.0) 767 (7.9) 245 (4.3) 81 (4.3) 5,240 (7.1) \nOther/Multiple races 11,191 (12.3) 1,311 (13.5) 650 (11.5) 268 (14.3) 8,962 (12.2) \nMissing 275 (0.3) 18 (0.2) 24 (0.4) 9 (0.5) 224 (0.3) \nEducation level, n (%) \nHigh school or below 15,868 (17.5) 1,401 (14.5) 933 (16.6) 295 (15.8) 13,239 (18.0) \nSome college 29,273 (32.3) 3,458 (35.7) 2,055 (36.5) 816 (43.6) 22,944 (31.2) \nCollege degree 25,619 (28.2) 2,746 (28.3) 1,400 (24.8) 452 (24.1) 21,021 (28.6) \nGraduate school 19,565 (21.6) 2,065 (21.3) 1,232 (21.9) 305 (16.3) 15,963 (21.7) \nMissing 389 (0.4) 25 (0.3) 15 (0.3) 4 (0.2) 345 (0.5) \nPrimary employment, n (%) \nEmployed 64,960 (71.6) 7,211 (74.4) 3,827 (67.9) 1,249 (66.7) 52,673 (71.7) \nFamily care 5,742 (6.3) 633 (6.5) 410 (7.3) 148 (7.9) 4,551 (6.2) \nIn school 8,452 (9.3) 716 (7.4) 285 (5.1) 96 (5.1) 7,355 (10.0) \nNot working (unable/ \nunemployed) 8,928 (9.8) 1,003 (10.3) 820 (14.6) 338 (18.1) 6,767 (9.2) \nRetired 2,088 (2.3) 76 (0.8) 262 (4.6) 28 (1.5) 1,722 (2.3) \nMissing 544 (0.6) 56 (0.6) 31 (0.6) 13 (0.7) 444 (0.6) \nSubjective socioeconomic statusa, n (%) \nLow 25,472 (28.1) 3,091 (31.9) 1,701 (30.2) 718 (38.4) 19,962 (27.2) \nMedium 37,692 (41.6) 4,172 (43.0) 2,284 (40.5) 777 (41.5) 30,459 (41.4) \nHigh 27,331 (30.1) 2,413 (24.9) 1,636 (29.0) 373 (19.9) 22,909 (31.2) \nMissing 219 (0.2) 19 (0.2) 14 (0.2) 4 (0.2) 182 (0.2) \nBody mass index, n (%) \nUnderweight (<18.5) 2,493 (2.7) 128 (1.3) 153 (2.7) 37 (2.0) 2,175 (3.0) \nHealthy weight (18.5 to \n<25)  30,193 (33.3) 1,688 (17.4) 1,850 (32.8) 368 (19.7) 26,287 (35.8) \nOverweight (25 to <30)  22,679 (25.0) 1,880 (19.4) 1,544 (27.4) 424 (22.6) 18,831 (25.6) \nObesity (≥30) 33,286 (36.7) 5,766 (59.5) 1,998 (35.5) 1,001 (53.5) 24,521 (33.4) \nMissing or unrealistic \nvalue (<10 or >70) 2,063 (2.3) 233 (2.4) 90 (1.6) 42 (2.2) 1,698 (2.3) \nPMOS: polyendocrine metabolic ovarian syndrome; SD: Standard deviation \na Based on the MacArthur Scale of Subjective Social Status (Low = 0-3, Medium = 4-5, High = 6-9) \n \n \n \n \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 14, 2026. ; https://doi.org/10.64898/2026.10.13.26365449doi: medRxiv preprint \n\n 18 \nTable 2. Multivariable logistic regression analysis of infertility odds by gynecologic condition group, \nincluding additive interaction decomposition for co-occurring endometriosis and polyendocrine metabolic \novarian syndrome. \n \n OR 95% CI P-value \nPrimary analysis (four-category exposure) \nCondition group (reference: Neither condition) \nPolyendocrine metabolic ovarian syndrome alone 6.12 5.69; 6.59 <0.001 \nEndometriosis alone 4.31 3.93; 4.72 <0.001 \nCo-occurring endometriosis and polyendocrine metabolic \novarian syndrome \n12.49 11.13; 13.99 <0.001 \nBaseline age in years (modeled as splines, 3 interior knots) - - - \nRace/ethnicity (reference: Non-Hispanic White) \nNon-Hispanic Black 0.82 0.70; 0.95 0.009 \nAsian 0.58 0.46; 0.72 <0.001 \nHispanic 0.65 0.56; 0.75 <0.001 \nOther/multiple races/ethnicities 0.91 0.82; 1.00 0.056 \nSubjective socioeconomic status (reference: Low)    \nMedium 1.16 1.07; 1.26 <0.001 \nHigh 1.37 1.26; 1.49 <0.001 \n \nSecondary analysis (interaction decomposition) \nPolyendocrine metabolic ovarian syndrome (main effect) 6.12 5.69; 6.59 \nEndometriosis (main effect) 4.31 3.93; 4.72 \nEndometriosis × Polyendocrine metabolic ovarian syndrome \n(interaction term) 0.47 0.41; 0.55 \nEndometriosis + Polyendocrine metabolic ovarian syndrome \n(joint effect) 12.49 11.13; 13.99 \nRelative Excess Risk due to Interaction 3.05 1.65; 4.46 \nComplete case analysis of 90,224 participants; those with missing data on any model covariate were \nexcluded. Covariate estimates in the secondary analysis were identical to those in the primary analysis. OR: \nodds ratio, CI: confidence interval \n \n \n \nTable 3. Negative binomial mixed-effects regression analysis of menstrual symptom burden per cycle by \ngynecologic condition group. \n IRR 95% CI P-value \nCondition group (reference: Neither condition) \nPolyendocrine metabolic ovarian syndrome alone 1.06 1.03; 1.08 <0.001 \nEndometriosis alone 1.14 1.09; 1.18 <0.001 \nCo-occurring endometriosis and polyendocrine metabolic \novarian syndrome 1.11 1.04; 1.19 0.003 \nAge in years 0.99 0.99; 1.00 <0.001 \nRace/ethnicity (reference: Non-Hispanic White) \nNon-Hispanic Black 0.97 0.94; 1.01 0.171 \nAsian 0.95 0.91; 1.00 0.039 \nHispanic 0.94 0.91; 0.97 <0.001 \nOther/multiple races/ethnicities 1.02 0.99; 1.05 0.133 \nHormone use in previous month (reference: No) \nYes 0.99 0.98; 1.01 0.372 \nComplete case analysis; the model was fitted on 175,822 cycle-level observations from 25,085 \nparticipants. Those with missing data on any model covariate were excluded.  \nIRR: incidence rate ratio, CI: confidence interval \n \n \n \n \n . CC-BY-NC-ND 4.0 International licenseIt is made available under a \nperpetuity. \n is the author/funder, who has granted medRxiv a license to display the preprint in(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted October 14, 2026. ; https://doi.org/10.64898/2026.10.13.26365449doi: medRxiv preprint","source_license":"CC0","license_restricted":false}