Analysis of Dysmenorrhea-Related Factors in Adenomyosis and Development of a Risk Prediction Model

Analysis of Dysmenorrhea-Related Factors in Adenomyosis and Development of a Risk Prediction Model | 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 Research Article Analysis of Dysmenorrhea-Related Factors in Adenomyosis and Development of a Risk Prediction Model Yudan Fu, Xin Wang, Xinchun Yang, Ruihua zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4998744/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Mar, 2025 Read the published version in Archives of Gynecology and Obstetrics → Version 1 posted 5 You are reading this latest preprint version Abstract Objective To explore factors related to dysmenorrhea in adenomyosis and construct a risk prediction model. Methods A cross-sectional survey involving 1636 adenomyosis patients from 37 hospitals nationwide (November 2019 - February 2022) was conducted. Data on demographics, disease history, menstrual and reproductive history, and treatment history was collect.Patients were categorized into dysmenorrhea and non-dysmenorrhea groups. Multivariate logistic regression analyzed factors influencing dysmenorrhea, and a risk prediction model was created using a nomogram. The model's performance was evaluated through ROC curve analysis, C-index, Hosmer-Lemeshow test, and bootstrap method The nomogram function was used to establish a nomogram model. The model was evaluated using the area under the ROC curve (AUC), C-index, Hosmer-Lemeshow goodness-of-fit test, and bootstrap method. Patients were scored based on the nomogram, and high-risk groups were delineated. Results Dysmenorrhea was present in 61.31% (1003/1636) of the patients. Univariate analysis showed significant differences (P < 0.05) between groups in age at onset, course of disease, oligomenorrhea, menorrhagia, number of deliveries, pelvic inflammatory disease, family history of adenomyosis, exercise, and excessive menstrual fatigue. Significant factors included menorrhagia, multiple deliveries, pelvic inflammatory disease, and family history of adenomyosis as risk factors. Older age at onset, oligomenorrhea, and exercise were identified as protective factors. The model's accuracy, discrimination, and reliability were acceptable, and a risk score > 88.5 points indicated a high-risk group. Conclusion Dysmenorrhea is prevalent among adenomyosis patients. Identifying and mitigating risk factors, while leveraging protective factors, can aid in prevention and management. The developed model effectively predicts dysmenorrhea risk, facilitating early intervention and treatment. Adenomyosis Dysmenorrhea Related Factors Logistic Clinical Prediction Model 1 Introduction Adenomyosis (AM) is a refractory, benign, estrogen-dependent disease characterized by the invasion of endometrium tissue into the myometrium and stroma. It affects 10–57% of women of reproductive age and commonly presents with dysmenorrhea, pelvic pain, abnormal uterine bleeding, and infertility( 1 – 3 ). Symptoms are recurrent and persistent, with long-term, progressively worsening pain, difficult-to-treat infertility, and significant economic burdens leading to depression and anxiety in patients, impacting their physical and mental health, social relationships, and work ability.Research has reported that the public health costs of endometriosis (EM) and adenomyosis are high and can be comparable to those of chronic diseases like diabetes and rheumatoid arthritis( 4 ). Among the AM population, 63.0% present with dysmenorrhea or pelvic pain as the main symptoms, 72% of patients require painkillers, and 37.6% use themlong-term( 5 ). Dysmenorrhea can significantly impacts women of reproductive age at different stages, resulting in high absenteeism, decreased learning quality and work capacity, poor sleep quality, and mental health issues( 6 – 8 ). Research has found that dysmenorrhea can negatively impact women's emotional regulation and even cause anxiety or depression( 9 ). Other studies have pointed out that anxiety and depression can further exacerbate the progression of endometriosis-related diseases. Furthermore, long-term dysmenorrhea can increase women's pain sensitivity and the likelihood of developing other chronic pain conditions( 10 ). Therefore, exploring the related factors of dysmenorrhea in AM and constructing a risk prediction model can aid in preventingpreventing and controlling the condition. Current research has noted that associations between dysmenorrhea and factors such as age, smoking, early menarche, prolonged menstruation, heavy menstrual flow, high BMI, alcohol consumption, nulliparity, and family history( 11 – 14 ). This study aims to explore dysmenorrhea-related factors in AM through a cross-sectional survey, prevent or mitigate the development of dysmenorrhea by preventing and monitoring risk factors, and establish a nomogram model to predict dysmenorrhea in adenomyosis based on these risk factors, achieving risk stratification and early prevention, detection, and treatment of the disease through quantified scores. 2 Methods 2.1 Study Population and Data Collection A cross-sectional survey was conducted on 1,636 AM patients treated from November 2019 to February 2022, covering 37 hospitals across 18 provinces, municipalities, and autonomous regions, including Beijing, Anhui, Sichuan, Fujian, Guangdong, Guangxi, Xinjiang, Hainan, Hebei, Henan, Shandong, Heilongjiang, Jiangsu, Jiangxi, and Yunnan. Diagnosed as AM by clinical specialists based on symptoms, signs, and auxiliary examination results, patients were divided into a dysmenorrhea group and a non-dysmenorrhea group, with 1,003 patients in the dysmenorrhea group and 633 in the non-dysmenorrhea group. Diagnostic criteria: The diagnostic criteria for adenomyosis were established based on the "Guidelines for the Diagnosis and Treatment of Endometriosis (2015 Edition)"( 15 ). Inclusion criteria: Clinical symptoms and auxiliary examination results (ultrasound, MRI) consistent with AM; women of reproductive age; complete clinical data. Exclusion criteria were pregnancy, menopause, malignant tumors, liver or kidney dysfunction, and history of hysterectomy. All patients signed informed consent forms, and this study was approved by the Ethics Committee of Guang'anmen Hospital (cross-sectional survey study approval number 2020-040-KY). 2.2 Clinical Data Collection A cross-sectional survey study was conducted, and questionnaires were completed based on clinical case data. The questionnaire content included general patient information, past medical history, family history, personal lifestyle habits, and AM treatment status. ( 1 ) General Information Including the patient's age, course of disease, menstrual history, and reproductive history. Menstrual history includes menstrual cycle and menstrual volume. Reproductive history includes sexual history, number of deliveries, and history of miscarriage. A menstrual cycle 35 days as infrequent menstruation, and a self-perceived heavy menstrual flow affecting quality of life as menorrhagia( 16 ). ( 2 ) Past Medical History and Family History Past medical history includes whether the patient has had uterine polyps, endometrial hyperplasia, benign ovarian cysts, premature ovarian failure, pelvic inflammatory disease, thyroid disease, breast disease, etc. Family history includes whether the patient's immediate relatives have endometriosis or adenomyosis. ( 3 ) Personal Lifestyle Habits Including whether the patient smokes, drinks alcohol, stays up late, exercises, and whether they engage in sexual activity, intense exercise, overwork, or exposure to cold during menstruation. ( 4 ) AM Treatment Status Whether they have received treatments such as progestogens, oral contraceptives, mifepristone, Mirena, or androgen drugs. 2.3 Statistical Methods Data analysis was performed using SPSS 25.0. For univariate analysis, count data were expressed as composition ratio (%), and the chi-square test was used to compare the composition ratios between groups. Measurement data that conformed to normal distribution were expressed as mean ± standard deviation (x̄ ± S), and the t-test was used to compare the means between groups. Measurement data that did not conform to normal distribution were expressed as M (P25, P75), and the Mann-Whitney U rank-sum test was used to compare the medians between groups. Items with P < 0.05 in the univariate analysis were included in the multivariate logistic regression analysis model. Continuous variables need to pass the Box-Tidwell test for the linearity assumption of the logit and diagnose collinearity between independent variables using Tolerance or Variance Inflation Factor (VIF), excluding factors that cause collinearity. Binary logistic regression analysis was conducted with dysmenorrhea as the test variable. The overall significance of the model was tested using the − 2 log-likelihood ratio test, and the model's goodness-of-fit was assessed using the Hosmer-Lemeshow test. The odds ratio (OR) and 95% confidence interval (CI) were calculated as measures of risk, with P < 0.05 indicating statistical significance. The obtained data were analyzed using R 4.3.3 software, validated with the rms package, and a nomogram was drawn. Patients were scored based on the risk scores indicated by the nomogram. The ROC curve was drawn using the pROC package in R, and the cut-off value was calculated to evaluate the discrimination. The calibration plot was drawn using the rms and rmda packages to evaluate the model's calibration. Internal validation was performed using the bootstrap method (1000 samples) with the rms package to obtain the C-index after secondary validation. 3 Results 3.1 Basic Information Among the 1,636 patients, 1,003 were in the dysmenorrhea group, with an incidence rate of 61.31%, and an average age of (38.89 ± 6.50) years. There were 633 patients in the non-dysmenorrhea group, with an average age of (39.75 ± 6.49) years. Of all patients, 59.78% (978/1636) had diffuse adenomyosis, 23.96% (392/1636) had adenomyoma, and 16.26% (266/1636) had both diffuse adenomyosis and adenomyoma. 3.2 Clinical Characteristics There were no statistically significant differences (P > 0.05) between the dysmenorrhea and non-dysmenorrhea groups in frequent menstruation, sexual history, delivery history, and miscarriage history. There were statistically significant differences (P < 0.05) in age at onset, course of disease, infrequent menstruation, menorrhagia, and number of deliveries between the two groups. See Table 1 for details. Table 1 Clinical Characteristics Clinical Characteristics All Patients (n = 1636) Dysmenorrhea(n = 1003) Non-Dysmenorrhea(n = 633) P in age at onset(Year) 37(31,42) 36(31,41) 37(33,42) <0.001 course of disease 12(2,42) 13(2,46) 11(2,36) 0.030 frequent menstruation 28(1.71) 17(1.69) 11(1.74) 0.948 infrequent menstruation 107(6.54) 54(5.38) 53(8.37) 0.017 menorrhagia 543(33.19) 353 (35.19) 190(30.02) 0.030 sexual history 1567(95.78) 959(95.61) 608(96.05) 0.668 delivery history 1074(65.65) 674(67.20) 400(63.19) 0.096 number of deliveries 1(0,1) 1(0,1) 1(0,1) 0.047 miscarriage history 887(54.22) 540(53.84) 347(54.82) 0.698 3.3 Characteristics of Past Medical History and Family History The proportion of AM dysmenorrhea patients with pelvic inflammatory disease and a family history of AM was significantly higher than in the non-dysmenorrhea group, with statistically significant differences (P < 0.05). There were no statistically significant differences between the two groups in the distribution of endometrial polyps, endometrial hyperplasia, benign ovarian cysts, premature ovarian failure, thyroid disease, breast disease, and family history of endometriosis (P > 0.05). See Table 2 for details. Table 2 Characteristics of Past Medical History and Family History in AM Patients Characteristics All Patients (n = 1636) Dysmenorrhea(n = 1003) Non-Dysmenorrhea(n = 633) P endometrial polyps 205(12.53) 114(11.37) 91(14.38) 0.073 endometrial hyperplasia 88(5.38) 48(4.79) 40(6.32) 0.181 benign ovarian cysts 133(8.13) 78(7.78) 55(8.69) 0.511 premature ovarian failure 35(2.14) 18(1.79) 17(2.69) 0.225 pelvic inflammatory disease 182(11.12) 133(13.26) 49(7.74) 0.001 thyroid disease 240(14.67) 137(13.66) 103(16.27) 0.146 breast disease 378(23.11) 232(23.13) 146(23.06) 0.975 family history of AM 124(7.58) 96(9.57) 28(4.42) 0.05) between the AM dysmenorrhea group and the non-dysmenorrhea group in smoking, drinking, staying up late, sexual activity during menstruation, intense exercise during menstruation, or exposure to cold during menstruation. The dysmenorrhea group had significantly higher instances of overwork during menstruation and significantly lower instances of exercise compared to the non-dysmenorrhea group (P < 0.05). See Table 3 for details. Table 3 Characteristics of Lifestyle Habits Characteristics of Lifestyle Habits All Patients (n = 1636) Dysmenorrhea(n = 1003) Non-Dysmenorrhea(n = 633) P smoking 40(2.44) 22(2.19) 18(2.84) 0.407 drinking 55(3.36) 39(3.89) 16(2.53) 0.137 staying up late 880(53.79) 551(54.94) 329(51.97) 0.242 exercise 300(18.34) 162(16.15) 138(21.80) 0.004 sexual activity during menstruation 11(0.67) 4(0.40) 7(1.11) 0.163 intense exercise during menstruation 35(2.14) 26(2.59) 9(1.42) 0.111 overwork during menstruation 276(16.87) 185(18.44) 91(14.38) 0.032 exposure to cold during menstruation 147(8.99) 95(9.47) 52(8.21) 0.387 3.5 Treatment Status of Adenomyosis In terms of treatment status, there were no significant differences (P > 0.05) between the AM dysmenorrhea group and the non-dysmenorrhea group in the use of mifepristone, oral progestogen drugs, combined oral contraceptives, levonorgestrel-releasing intrauterine system (LNG-IUS), or androgen derivatives. See Table 4 for details. Table 4 Treatment Status Treatment Status of Adenomyosis All Patients (n = 1636) Dysmenorrhea(n = 1003) Non-Dysmenorrhea(n = 633) P mifepristone 24(1.47) 15(1.50) 9(1.42) 0.904 oral progestogen drugs 55(3.36) 40(3.99) 15(2.37) 0.077 combined oral contraceptives 118(7.21) 69(6.88) 49(7.74) 0.512 LNG⁃IUS 138(8.44) 88(8.77) 50(7.90) 0.535 androgen derivatives 16(0.98) 10(1.00) 6(0.95) 0.922 3.6 Multivariate Logistic Analysis Multivariate logistic regression analysis was conducted using the statistically significant and clinically relevant indicators from the univariate analysis (age at onset, course of disease, number of deliveries, infrequent menstruation, menorrhagia, pelvic inflammatory disease, family history of AM, exercise, and overwork during menstruation) as independent variables, and whether AM patients developed dysmenorrhea as the dependent variable. The results showed that age at onset (OR = 0.964, 95%CI: 0.947–0.981), number of deliveries (OR = 1.271, 95%CI: 1.088–1.486), infrequent menstruation (OR = 0.556, 95%CI: 0.371–0.835), menorrhagia (OR = 1.299, 95%CI: 1.039–1.624), pelvic inflammatory disease (OR = 1.716, 95%CI: 1.206–2.440), family history of AM (OR = 2.230, 95%CI: 1.427–3.484), and exercise (OR = 0.697, 95%CI: 0.537–0.905) were statistically significant in the multivariate logistic regression analysis (P < 0.05). More deliveries, menorrhagia, pelvic inflammatory disease, and family history of AM were risk factors for AM dysmenorrhea, while older age at onset, infrequent menstruation, and exercise were protective factors for AM dysmenorrhea. See Table 5 for details. Table 5 Multivariate Logistic Regression Analysis Variables B Wald P OR 95%CI age at onset -0.037 15.986 <0.001 0.964 0.947–0.981 course of disease 0.000 0.018 0.892 1.000 0.997–1.003 number of deliveries 0.240 9.099 0.003 1.271 1.088–1.486 infrequent menstruation -0.586 8.016 0.005 0.556 0.371–0.835 menorrhagia 0.262 5.280 0.022 1.299 1.039–1.624 pelvic inflammatory disease 0.540 9.022 0.003 1.716 1.206–2.440 family history of AM 0.802 12.397 < 0.001 2.230 1.427–3.484 exercise -0.361 7.328 0.007 0.697 0.537–0.905 overwork during menstruation 0.203 1.995 0.158 1.225 0.924–1.624 3.7 Establishment, Validation, and Evaluation of the Clinical Prediction Model The prediction factors identified by multivariate logistic analysis were used to establish a clinical prediction model for individualized adenomyosis dysmenorrhea risk using R, presented in the form of a nomogram (Table 6 ). This study further plotted the receiver operating characteristic curve (ROC curve), calibration plot, and clinical decision curve analysis (DCA curve) to evaluate the model's discrimination, calibration, and clinical validity. This study further plotted the receiver operating characteristic curve (ROC curve), calibration plot, and clinical decision curve analysis (DCA curve) to evaluate the model's discrimination, calibration, and clinical validity. The AUC value obtained from the ROC curve was 0.630, the maximum Youden index was 0.156, the corresponding risk score cut-off value was 88.5 points, and the cut-off value was 0.601 (Table 7 ), with the model's C-index calculated to be 0.627. Therefore, when the total risk score for the adenomyosis population is ≥ 88.5 points, they are considered a high-risk group for developing dysmenorrhea. The risk scoring formula in this study is shown in Table 8 . The calibration plot results indicated that the model line fit well with the standard line (Table 9 ). Additionally, this study used the bootstrap internal validation method for secondary validation of the nomogram model, with the C-index calculated to be 0.619, indicating acceptable discrimination. The above results collectively indicate that the model has good predictive ability. Table 8 Scoring Formula for Probability of Dysmenorrhea in AM Factors Points age at onset(X1) Age 60 is scored as 0, for each year the onset age decreases from 60, the score increases by 11/5. number of deliveries(X2) 3: 43 2: 29 1: 14 0: 0 infrequent menstruation(X3) Yes: 36 No: 0 Menorrhagia(X4) Yes: 16 No: 0 pelvic inflammatory disease(X5) Yes: 33 No: 0 family history of AM(X6) Yes: 51 No: 0 Exercise(X7) No: 22 Yes: 0 Total points(TP) TP = X1 + X2 + X3 + X4 + X5 + X6 + X7 4 Discussion The results of this study indicate that among the 1,636 AM patients, 61.31% had dysmenorrhea, demonstrating a high incidence rate. In a population-based cohort study in the United States from 2006 to 2015( 17 ), the incidence of dysmenorrhea in adenomyosis was 60.3%, consistent with our results, indicating that AM-related dysmenorrhea is a significant public health concern. The results of this study confirmed that menorrhagia, concurrent pelvic inflammatory disease, family history of AM, and multiple deliveries are risk factors for dysmenorrhea in AM. Previous studies based on imaging and pathological examinations reported that about one-third of patients were asymptomatic( 18 – 19 ).Menorrhagia is one of the primary symptoms of AM. The possible mechanism is the invasion of the endometrium into the myometrium and stroma, causing improper uterine contractions during menstruation, increased endometrial surface area, excessive prostaglandin secretion, and excessive estrogen, leading to increased menstrual volume( 20 ). The results of this study indicate that menorrhagia is a risk factor for dysmenorrhea in AM, possibly related to increased uterine contraction intensity, increased prostaglandins, increased tissue pressure, local ischemia, and inflammatory response. This study suggests that pelvic inflammatory disease is related to dysmenorrhea in AM patients, possibly associated with local inflammation, tissue damage, extensive adhesions, hyperplasia, scar formation, and pelvic congestion. Symptoms may include lower abdominal heaviness and pain, worsened by fatigue and around menstruation, easily triggering dysmenorrhea. The mechanism may be related to the imbalance between pro-inflammatory and anti-infective factors( 21 ). Family history of AM is a risk factor for AM dysmenorrhea. It may be due to genetic factors, and it may also be because women adopt similar behaviors or lifestyles from relatives, increasing the risk of dysmenorrhea. Nilufer et al. conducted a meta-analysis using 24 GWAS datasets from multiple countries and regions, finding that endometriosis-related pelvic pain may be associated with genetically mediated increased neural sensitization. As similar diseases, AM and EM are reasonably suspected to have similar genetic potentials( 22 ). The relationship between the number of deliveries and adenomyosis has received extensive research attention. During pregnancy, trophoblast cells continuously invade the myometrium, increasing the risk of disrupting the junctional zone( 23 ). With an increase in the number of pregnancies, the damage to the endometrium-myometrium junctional zone increases, enlarging the lesion area, stimulating more prostaglandins, inflammatory factors, and other substances, easily triggering dysmenorrhea. Binary logistic regression analysis results indicated that older age at onset, infrequent menstruation, and exercise are protective factors for AM-related dysmenorrhea. This study explored the relationship between age at onset and dysmenorrhea, finding that older age at onset may be a protective factor for AM. Clinical studies have also indicated that the risk of dysmenorrhea decreases by 0.97 times with increasing age( 24 ). The possible reason is that with age, the pain threshold increases and sensitivity to pain decreases. In this study, a long menstrual cycle (≥ 30 days) was a protective factor for dysmenorrhea. The possible reason is that the duration of dysmenorrhea is relatively short, and the long interval between menstruations reduces pain sensitivity. However, some studies have reached opposite conclusions. A meta-analysis of 77 articles suggested that a long menstrual cycle increases the duration of prostaglandin secretion, thereby increasing the severity and duration of dysmenorrhea( 25 ). Other studies have found no significant difference in menstrual cycle between adenomyosis patients with no to mild dysmenorrhea and those with moderate to severe dysmenorrhea( 26 ). The relationship between menstrual cycle and AM-related dysmenorrhea has not yet been conclusively determined and requires further research. Prostaglandins stimulate uterine muscle contractions, causing dysmenorrhea. Studies have reported that high levels of stress inhibit LH and FSH, further damaging follicle development, affecting progesterone release, and thus affecting prostaglandin synthesis, leading to dysmenorrhea( 27 – 29 ). Stress may also influence PG synthesis by affecting adrenaline and cortisol release, impacting muscle contractions and causing dysmenorrhea( 30 ). Exercise is a protective factor for dysmenorrhea. Moderate exercise can relieve patient stress and reduce the synthesis of prostaglandins in women( 31 ). If intense exercise becomes a source of stress, the severity of dysmenorrhea may increase. Some studies have reported that regular intense exercise may increase the severity of dysmenorrhea, possibly related to the increased frequency, intensity of exercise, and bodily sensitivity, which may also be related to the occupational characteristics of the surveyed population( 32 ). Adenomyosis, as a chronic disease, seriously affects health services and the socio-economy. Domestic experts( 33 ) have pointed out that adenomyosis should be managed with a long-term management approach. Based on the characteristics of the disease at different stages, a hierarchical management model should be adopted to achieve phased prevention and treatment. Based on the risk factors and protective factors for dysmenorrhea in adenomyosis identified in this study, primary management goals should be achieved for women of reproductive age. At the stage where susceptible individuals are exposed to risk factors but have not yet developed the disease, appropriate measures should be taken to prevent or delay the occurrence of dysmenorrhea in adenomyosis. The following management recommendations are proposed: initiate health education for adenomyosis early to improve awareness of the disease; guide susceptible individuals to improve their lifestyle by adjusting diet, daily routine, emotions, and exercise habits to enhance disease resistance, and fully utilize the advantages of traditional Chinese medicine; and ensure early diagnosis and treatment. For adenomyosis patients meeting the minimum diagnostic criteria, early intervention is needed even if dysmenorrhea has not yet occurred. Previous studies have often been limited to populations diagnosed with AM through surgery or pathology reports, which are prone to underdiagnosis and selection bias. This study, however, is based on gynecological patients visiting outpatient clinics, diagnosed according to symptoms and ultrasound or MRI reports, with a large sample size and high data reliability, making the epidemiological results closer to the real world. The main limitation of this study is that it is a cross-sectional survey, which cannot accurately establish causality. Additionally, variables such as depression, anxiety, diet, and exercise were not quantified. Future research will further improve experimental design and deepen the study of AM dysmenorrhea. In summary, this study found the distribution of adenomyosis dysmenorrhea through a cross-sectional survey and conducted logistic regression analysis. The results showed that dysmenorrhea is a common symptom in AM patients. A long disease course, multiple deliveries, menorrhagia, concurrent pelvic inflammatory disease, and family history of AM are risk factors for dysmenorrhea in AM, while older age at onset, infrequent menstruation, and exercise are protective factors. Furthermore, this study established a nomogram model for predicting the probability of dysmenorrhea in AM, used to stratify high- and low-risk populations, develop effective health strategies, alleviate patient suffering, and improve quality of life. Declarations Authors Contributions YuDan Fu, Xin Wang, XinChun Yang contributed to literature review, essay writing, data extraction, data analysis and picture drawing. RuiHua Zhao contributed to the project innovation and paper submission. All authors have read and approved the final version of the manuscript. Funding This work was supported by the Science and technology innovation project of Chinese Academy of Traditional Chinese Medicine (CI2022C003). Availability of data and materials All the data used to support the findings of this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate The study was approved by the Ethics Committee of Guang'anmen Hospital of China Academy of Chinese Medical Sciences, approval number: 2020-040-KY. All patients signed informed consent forms. Consent for publication Not applicable. Conflict of interest statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Che, X., Wang, X., Zhang, Y., Li, H., & Zhu, S. (2020). A new trick for an old dog: The application of mifepristone in the treatment of adenomyosis. J. Cell. Mol. Med., 24, 1724-1737. https://doi.org/10.1111/jcmm.14866 Guo, S. W. (2020). The pathogenesis of adenomyosis vis-à-vis endometriosis. J. Clin. 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Gynecol., 95, 688-691. https://doi.org/10.1016/S0002-7844(99)00659-6 Bourdon, M., Santulli, P., Jeljeli, M., et al. (2021). Immunological changes associated with adenomyosis: a systematic review. Hum. Reprod. Update, 27, 108-129. https://doi.org/10.1093/humupd/dmaa038 Rahmioglu, N., Mortlock, S., Ghiasi, M., et al. (2023). The genetic basis of endometriosis and comorbidity with other pain and inflammatory conditions. Nat. Genet., 55, 423-436. https://doi.org/10.1038/s41588-023-01323- Uduwela, A. S., Perera, M. A. K., Aiqing, L., et al. (2000). Endometrial-myometrial interface: relationship to adenomyosis and changes in pregnancy. Obstet. Gynecol. Surv., 55, 390-400. https://doi.org/10.1097/00006254-200006000-00025 Jang, I. A., Kim, M. Y., Lee, S. R., Jeong, K. A., & Chung, H. W. (2013). Factors related to dysmenorrhea among Vietnamese and Vietnamese marriage immigrant women in South Korea. Obstet. Gynecol. Sci., 56, 242-248. https://doi.org/10.5468/ogs.2013.56.4.242 Mitsuhashi, R., Sawai, A., Kiyohara, K., Shiraki, H., & Nakata, Y. (2022). Factors associated with the prevalence and severity of menstrual-related symptoms: A systematic review and meta-analysis. Int. J. Environ. Res. Public Health, 20, 569. https://doi.org/10.3390/ijerph200100569 Li, Q., Huang, J., Zhang, X. Y., Feng, W. W., & Hua, K. Q. (2021). Dysmenorrhea in patients with adenomyosis: A clinical and demographic study. J. Gynecol. Obstet. Hum. Reprod., 50, 101761. https://doi.org/10.1016/j.jogoh.2020.101761 Wang, L., Wang, X., Wang, W., et al. (2004). Stress and dysmenorrhoea: a population based prospective study. Occup. Environ. Med., 61, 1021-1026. https://doi.org/10.1136/oem.2003.012302 Christiani, D. C., Niu, T., & Xu, X. (1995). Occupational stress and dysmenorrhea in women working in cotton textile mills. Int. J. Occup. Environ. Health, 1, 9-15. https://doi.org/10.1179/oeh.1995.1.1.9 Dehnavi, Z. M., Jafarnejad, F., & Kamali, Z. (2018). The effect of aerobic exercise on primary dysmenorrhea: A clinical trial study. J. Educ. Health Promot., 7, 3. https://doi.org/10.4103/jehp.jehp_79_17 Casey, M. L., MacDonald, P. C., & Mitchell, M. D. (1985). Despite a massive increase in cortisol secretion in women during parturition, there is an equally massive increase in prostaglandin synthesis. A paradox?. J. Clin. Invest., 75, 1852-1857. https://doi.org/10.1172/JCI111899 Martínez, M. E., Heddens, D., Earnest, D. L., et al. (1999). Physical activity, body mass index, and prostaglandin E2 levels in rectal mucosa. J. Natl. Cancer Inst., 91, 950-953. https://doi.org/10.1093/jnci/91.11.950 Metheny, W. P., & Smith, R. P. (1989). The relationship among exercise, stress, and primary dysmenorrhea. J. Behav. Med., 12, 569-586. https://doi.org/10.1007/BF00844826 Wang, S. X., Cui, P. F., & Zhang, J. J. (2024). Expert consensus on the three-level management of adenomyosis. Pract. J. Gynecol. Obstet., 40, 106-111. Table 6,7 and 9 Table 6,7 and 9 are available in the Supplementary Files section. Supplementary Files Table67and9.docx Cite Share Download PDF Status: Published Journal Publication published 17 Mar, 2025 Read the published version in Archives of Gynecology and Obstetrics → Version 1 posted Reviewers agreed at journal 23 Oct, 2024 Reviewers invited by journal 11 Oct, 2024 Editor invited by journal 08 Oct, 2024 Editor assigned by journal 18 Sep, 2024 First submitted to journal 17 Sep, 2024 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. 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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-4998744","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":364974225,"identity":"50d81dbf-d8bd-4208-9b8d-9000b4730b6a","order_by":0,"name":"Yudan Fu","email":"","orcid":"https://orcid.org/0009-0001-0309-3519","institution":"China Academy of Traditional Chinese Medicine Guanganmen Hospital: China Academy of Chinese Medical Sciences Guang'anmen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yudan","middleName":"","lastName":"Fu","suffix":""},{"id":364974226,"identity":"2123d0f7-92b8-4cec-a584-0da82deca397","order_by":1,"name":"Xin Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Wang","suffix":""},{"id":364974227,"identity":"cced7931-8ccb-495f-9973-042cf298246c","order_by":2,"name":"Xinchun Yang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xinchun","middleName":"","lastName":"Yang","suffix":""},{"id":364974228,"identity":"c2ff0a1b-7ecf-43e9-a8f1-3f580caa6400","order_by":3,"name":"Ruihua zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYDACCQiZwMDAfPDBxz82JGlhSzac2ZBGtBYGoBYeM2nehsOEdcjPbn74uLLNIs/g+AFjA94d5+36p50xk2CouYNTC+OcY8aGZ9skig3OJCQ+kDxzO3nG7RyglmPPcGphlkgwk2xsk0jccCDhsIEB2+1kBpAWRjwuZJNI/wbRcv5hm0QC27lkeUJaeCRyoLbcSGaTONh2wM6AkBYJiZxiw4ZzEokzbzxjNmw4k5xgeDut2CLhGG4t8jPSNz5sKKtL7Duf//Hxnwo7e7nbyRtvfKghIrRhILGBgcMEFLPEA3sGBvbHH0jRMQpGwSgYBcMeAAArOV3DWjFX/wAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Ruihua","middleName":"","lastName":"zhao","suffix":""}],"badges":[],"createdAt":"2024-08-29 15:21:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4998744/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4998744/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00404-025-07967-y","type":"published","date":"2025-03-17T15:57:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79120411,"identity":"0864d0b4-852f-4314-95da-23896c7de604","added_by":"auto","created_at":"2025-03-24 16:07:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":922497,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4998744/v1/1c786172-68a7-43e7-8fc1-1489e1b1d553.pdf"},{"id":66640617,"identity":"bd877dc9-5e56-40c5-9da2-63bd8d4f20f0","added_by":"auto","created_at":"2024-10-15 06:05:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":207250,"visible":true,"origin":"","legend":"","description":"","filename":"Table67and9.docx","url":"https://assets-eu.researchsquare.com/files/rs-4998744/v1/137fd7c0a4b94bb08a76f156.docx"}],"financialInterests":"","formattedTitle":"Analysis of Dysmenorrhea-Related Factors in Adenomyosis and Development of a Risk Prediction Model","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAdenomyosis (AM) is a refractory, benign, estrogen-dependent disease characterized by the invasion of endometrium tissue into the myometrium and stroma. It affects 10\u0026ndash;57% of women of reproductive age and commonly presents with dysmenorrhea, pelvic pain, abnormal uterine bleeding, and infertility(\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSymptoms are recurrent and persistent, with long-term, progressively worsening pain, difficult-to-treat infertility, and significant economic burdens leading to depression and anxiety in patients, impacting their physical and mental health, social relationships, and work ability.Research has reported that the public health costs of endometriosis (EM) and adenomyosis are high and can be comparable to those of chronic diseases like diabetes and rheumatoid arthritis(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the AM population, 63.0% present with dysmenorrhea or pelvic pain as the main symptoms, 72% of patients require painkillers, and 37.6% use themlong-term(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Dysmenorrhea can significantly impacts women of reproductive age at different stages, resulting in high absenteeism, decreased learning quality and work capacity, poor sleep quality, and mental health issues(\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Research has found that dysmenorrhea can negatively impact women's emotional regulation and even cause anxiety or depression(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Other studies have pointed out that anxiety and depression can further exacerbate the progression of endometriosis-related diseases. Furthermore, long-term dysmenorrhea can increase women's pain sensitivity and the likelihood of developing other chronic pain conditions(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Therefore, exploring the related factors of dysmenorrhea in AM and constructing a risk prediction model can aid in preventingpreventing and controlling the condition. Current research has noted that associations between dysmenorrhea and factors such as age, smoking, early menarche, prolonged menstruation, heavy menstrual flow, high BMI, alcohol consumption, nulliparity, and family history(\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aims to explore dysmenorrhea-related factors in AM through a cross-sectional survey, prevent or mitigate the development of dysmenorrhea by preventing and monitoring risk factors, and establish a nomogram model to predict dysmenorrhea in adenomyosis based on these risk factors, achieving risk stratification and early prevention, detection, and treatment of the disease through quantified scores.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Population and Data Collection\u003c/h2\u003e \u003cp\u003eA cross-sectional survey was conducted on 1,636 AM patients treated from November 2019 to February 2022, covering 37 hospitals across 18 provinces, municipalities, and autonomous regions, including Beijing, Anhui, Sichuan, Fujian, Guangdong, Guangxi, Xinjiang, Hainan, Hebei, Henan, Shandong, Heilongjiang, Jiangsu, Jiangxi, and Yunnan. Diagnosed as AM by clinical specialists based on symptoms, signs, and auxiliary examination results, patients were divided into a dysmenorrhea group and a non-dysmenorrhea group, with 1,003 patients in the dysmenorrhea group and 633 in the non-dysmenorrhea group.\u003c/p\u003e \u003cp\u003eDiagnostic criteria: The diagnostic criteria for adenomyosis were established based on the \"Guidelines for the Diagnosis and Treatment of Endometriosis (2015 Edition)\"(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Inclusion criteria: Clinical symptoms and auxiliary examination results (ultrasound, MRI) consistent with AM; women of reproductive age; complete clinical data.\u003c/p\u003e \u003cp\u003eExclusion criteria were pregnancy, menopause, malignant tumors, liver or kidney dysfunction, and history of hysterectomy.\u003c/p\u003e \u003cp\u003eAll patients signed informed consent forms, and this study was approved by the Ethics Committee of Guang'anmen Hospital (cross-sectional survey study approval number 2020-040-KY).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinical Data Collection\u003c/h2\u003e \u003cp\u003eA cross-sectional survey study was conducted, and questionnaires were completed based on clinical case data. The questionnaire content included general patient information, past medical history, family history, personal lifestyle habits, and AM treatment status.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) General Information\u003c/p\u003e \u003cp\u003eIncluding the patient's age, course of disease, menstrual history, and reproductive history. Menstrual history includes menstrual cycle and menstrual volume. Reproductive history includes sexual history, number of deliveries, and history of miscarriage. A menstrual cycle\u0026thinsp;\u0026lt;\u0026thinsp;21 days is defined as frequent menstruation, a cycle\u0026thinsp;\u0026gt;\u0026thinsp;35 days as infrequent menstruation, and a self-perceived heavy menstrual flow affecting quality of life as menorrhagia(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Past Medical History and Family History\u003c/p\u003e \u003cp\u003ePast medical history includes whether the patient has had uterine polyps, endometrial hyperplasia, benign ovarian cysts, premature ovarian failure, pelvic inflammatory disease, thyroid disease, breast disease, etc. Family history includes whether the patient's immediate relatives have endometriosis or adenomyosis.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Personal Lifestyle Habits\u003c/p\u003e \u003cp\u003eIncluding whether the patient smokes, drinks alcohol, stays up late, exercises, and whether they engage in sexual activity, intense exercise, overwork, or exposure to cold during menstruation.\u003c/p\u003e \u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) AM Treatment Status\u003c/p\u003e \u003cp\u003eWhether they have received treatments such as progestogens, oral contraceptives, mifepristone, Mirena, or androgen drugs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical Methods\u003c/h2\u003e \u003cp\u003eData analysis was performed using SPSS 25.0. For univariate analysis, count data were expressed as composition ratio (%), and the chi-square test was used to compare the composition ratios between groups. Measurement data that conformed to normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x̄ \u0026plusmn; S), and the t-test was used to compare the means between groups. Measurement data that did not conform to normal distribution were expressed as M (P25, P75), and the Mann-Whitney U rank-sum test was used to compare the medians between groups. Items with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariate analysis were included in the multivariate logistic regression analysis model. Continuous variables need to pass the Box-Tidwell test for the linearity assumption of the logit and diagnose collinearity between independent variables using Tolerance or Variance Inflation Factor (VIF), excluding factors that cause collinearity. Binary logistic regression analysis was conducted with dysmenorrhea as the test variable. The overall significance of the model was tested using the \u0026minus;\u0026thinsp;2 log-likelihood ratio test, and the model's goodness-of-fit was assessed using the Hosmer-Lemeshow test. The odds ratio (OR) and 95% confidence interval (CI) were calculated as measures of risk, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating statistical significance. The obtained data were analyzed using R 4.3.3 software, validated with the rms package, and a nomogram was drawn. Patients were scored based on the risk scores indicated by the nomogram. The ROC curve was drawn using the pROC package in R, and the cut-off value was calculated to evaluate the discrimination. The calibration plot was drawn using the rms and rmda packages to evaluate the model's calibration. Internal validation was performed using the bootstrap method (1000 samples) with the rms package to obtain the C-index after secondary validation.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e3.1 Basic Information\u003c/h2\u003e\n \u003cp\u003eAmong the 1,636 patients, 1,003 were in the dysmenorrhea group, with an incidence rate of 61.31%, and an average age of (38.89\u0026thinsp;\u0026plusmn;\u0026thinsp;6.50) years. There were 633 patients in the non-dysmenorrhea group, with an average age of (39.75\u0026thinsp;\u0026plusmn;\u0026thinsp;6.49) years. Of all patients, 59.78% (978/1636) had diffuse adenomyosis, 23.96% (392/1636) had adenomyoma, and 16.26% (266/1636) had both diffuse adenomyosis and adenomyoma.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e3.2 Clinical Characteristics\u003c/h2\u003e\n \u003cp\u003eThere were no statistically significant differences (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between the dysmenorrhea and non-dysmenorrhea groups in frequent menstruation, sexual history, delivery history, and miscarriage history. There were statistically significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in age at onset, course of disease, infrequent menstruation, menorrhagia, and number of deliveries between the two groups. See Table \u003cspan\u003e1\u003c/span\u003e for details.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eClinical Characteristics\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img172897102928.png\"\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClinical Characteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAll Patients\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1636)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDysmenorrhea(n\u0026thinsp;=\u0026thinsp;1003)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-Dysmenorrhea(n\u0026thinsp;=\u0026thinsp;633)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ein age at onset(Year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(31,42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36(31,41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(33,42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecourse of disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(2,42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(2,46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(2,36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efrequent menstruation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28(1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003einfrequent menstruation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107(6.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54(5.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53(8.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emenorrhagia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e543(33.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e353 (35.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190(30.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esexual history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1567(95.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e959(95.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e608(96.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edelivery history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1074(65.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e674(67.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e400(63.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enumber of deliveries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emiscarriage history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e887(54.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e540(53.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e347(54.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e3.3 Characteristics of Past Medical History and Family History\u003c/h2\u003e\n \u003cp\u003eThe proportion of AM dysmenorrhea patients with pelvic inflammatory disease and a family history of AM was significantly higher than in the non-dysmenorrhea group, with statistically significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There were no statistically significant differences between the two groups in the distribution of endometrial polyps, endometrial hyperplasia, benign ovarian cysts, premature ovarian failure, thyroid disease, breast disease, and family history of endometriosis (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). See Table \u003cspan\u003e2\u003c/span\u003e for details.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCharacteristics of Past Medical History and Family History in AM Patients \u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1728971029.png\"\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAll Patients\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1636)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDysmenorrhea(n\u0026thinsp;=\u0026thinsp;1003)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-Dysmenorrhea(n\u0026thinsp;=\u0026thinsp;633)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eendometrial polyps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e205(12.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114(11.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91(14.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eendometrial hyperplasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88(5.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48(4.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40(6.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebenign ovarian cysts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133(8.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78(7.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55(8.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epremature ovarian failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35(2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18(1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17(2.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epelvic inflammatory disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e182(11.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133(13.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49(7.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ethyroid disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240(14.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e137(13.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103(16.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebreast disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e378(23.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e232(23.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146(23.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efamily history of AM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124(7.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96(9.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28(4.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efamily history of EM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82(5.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57(5.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25(3.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e3.4 Characteristics of Lifestyle Habits\u003c/h2\u003e\n \u003cp\u003eThere were no statistically significant differences (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between the AM dysmenorrhea group and the non-dysmenorrhea group in smoking, drinking, staying up late, sexual activity during menstruation, intense exercise during menstruation, or exposure to cold during menstruation. The dysmenorrhea group had significantly higher instances of overwork during menstruation and significantly lower instances of exercise compared to the non-dysmenorrhea group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). See Table \u003cspan\u003e3\u003c/span\u003e for details.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCharacteristics of Lifestyle Habits \u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img172897102986.png\"\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics of Lifestyle Habits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAll Patients\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1636)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDysmenorrhea(n\u0026thinsp;=\u0026thinsp;1003)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-Dysmenorrhea(n\u0026thinsp;=\u0026thinsp;633)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40(2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22(2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18(2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55(3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39(3.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16(2.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003estaying up late\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e880(53.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e551(54.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e329(51.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eexercise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e300(18.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e162(16.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138(21.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esexual activity during menstruation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11(0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4(0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7(1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eintense exercise during menstruation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35(2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26(2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9(1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eoverwork during menstruation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e276(16.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e185(18.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91(14.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eexposure to cold during menstruation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e147(8.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95(9.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52(8.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e3.5 Treatment Status of Adenomyosis\u003c/h2\u003e\n \u003cp\u003eIn terms of treatment status, there were no significant differences (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between the AM dysmenorrhea group and the non-dysmenorrhea group in the use of mifepristone, oral progestogen drugs, combined oral contraceptives, levonorgestrel-releasing intrauterine system (LNG-IUS), or androgen derivatives. See Table \u003cspan\u003e4\u003c/span\u003e for details.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eTreatment Status\u0026nbsp;\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1728971028.png\"\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatment Status of Adenomyosis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAll Patients\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1636)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDysmenorrhea(n\u0026thinsp;=\u0026thinsp;1003)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-Dysmenorrhea(n\u0026thinsp;=\u0026thinsp;633)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emifepristone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24(1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15(1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9(1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eoral progestogen drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55(3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40(3.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15(2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecombined oral contraceptives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118(7.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69(6.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49(7.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNG⁃IUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138(8.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88(8.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50(7.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eandrogen derivatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16(0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10(1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6(0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.6 Multivariate Logistic Analysis\u003c/h2\u003e\n \u003cp\u003eMultivariate logistic regression analysis was conducted using the statistically significant and clinically relevant indicators from the univariate analysis (age at onset, course of disease, number of deliveries, infrequent menstruation, menorrhagia, pelvic inflammatory disease, family history of AM, exercise, and overwork during menstruation) as independent variables, and whether AM patients developed dysmenorrhea as the dependent variable.\u003c/p\u003e\n \u003cp\u003eThe results showed that age at onset (OR\u0026thinsp;=\u0026thinsp;0.964, 95%CI: 0.947\u0026ndash;0.981), number of deliveries (OR\u0026thinsp;=\u0026thinsp;1.271, 95%CI: 1.088\u0026ndash;1.486), infrequent menstruation (OR\u0026thinsp;=\u0026thinsp;0.556, 95%CI: 0.371\u0026ndash;0.835), menorrhagia (OR\u0026thinsp;=\u0026thinsp;1.299, 95%CI: 1.039\u0026ndash;1.624), pelvic inflammatory disease (OR\u0026thinsp;=\u0026thinsp;1.716, 95%CI: 1.206\u0026ndash;2.440), family history of AM (OR\u0026thinsp;=\u0026thinsp;2.230, 95%CI: 1.427\u0026ndash;3.484), and exercise (OR\u0026thinsp;=\u0026thinsp;0.697, 95%CI: 0.537\u0026ndash;0.905) were statistically significant in the multivariate logistic regression analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). More deliveries, menorrhagia, pelvic inflammatory disease, and family history of AM were risk factors for AM dysmenorrhea, while older age at onset, infrequent menstruation, and exercise were protective factors for AM dysmenorrhea. See Table \u003cspan\u003e5\u003c/span\u003e for details.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMultivariate Logistic Regression Analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWald\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage at onset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.947\u0026ndash;0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecourse of disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.997\u0026ndash;1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enumber of deliveries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.088\u0026ndash;1.486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003einfrequent menstruation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.371\u0026ndash;0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emenorrhagia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.039\u0026ndash;1.624\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epelvic inflammatory disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.206\u0026ndash;2.440\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efamily history of AM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.427\u0026ndash;3.484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eexercise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.537\u0026ndash;0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eoverwork during menstruation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.924\u0026ndash;1.624\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e3.7 Establishment, Validation, and Evaluation of the Clinical Prediction Model\u003c/h2\u003e\n \u003cp\u003eThe prediction factors identified by multivariate logistic analysis were used to establish a clinical prediction model for individualized adenomyosis dysmenorrhea risk using R, presented in the form of a nomogram (Table \u003cspan\u003e6\u003c/span\u003e). This study further plotted the receiver operating characteristic curve (ROC curve), calibration plot, and clinical decision curve analysis (DCA curve) to evaluate the model\u0026apos;s discrimination, calibration, and clinical validity. This study further plotted the receiver operating characteristic curve (ROC curve), calibration plot, and clinical decision curve analysis (DCA curve) to evaluate the model\u0026apos;s discrimination, calibration, and clinical validity. The AUC value obtained from the ROC curve was 0.630, the maximum Youden index was 0.156, the corresponding risk score cut-off value was 88.5 points, and the cut-off value was 0.601 (Table \u003cspan\u003e7\u003c/span\u003e), with the model\u0026apos;s C-index calculated to be 0.627. Therefore, when the total risk score for the adenomyosis population is \u0026ge;\u0026thinsp;88.5 points, they are considered a high-risk group for developing dysmenorrhea. The risk scoring formula in this study is shown in Table \u003cspan\u003e8\u003c/span\u003e. The calibration plot results indicated that the model line fit well with the standard line (Table \u003cspan\u003e9\u003c/span\u003e). Additionally, this study used the bootstrap internal validation method for secondary validation of the nomogram model, with the C-index calculated to be 0.619, indicating acceptable discrimination. The above results collectively indicate that the model has good predictive ability.\u003c/p\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 8\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eScoring Formula for Probability of Dysmenorrhea in AM\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003ePoints\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage at onset(X1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eAge 60 is scored as 0, for each year the onset age decreases from 60, the score increases by 11/5.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enumber of deliveries(X2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3: 43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2: 29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: 14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0: 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003einfrequent menstruation(X3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes: 36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo: 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMenorrhagia(X4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes: 16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo: 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epelvic inflammatory disease(X5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes: 33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo: 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efamily history of AM(X6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes: 51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo: 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExercise(X7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo: 22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes: 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal points(TP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eTP\u0026thinsp;=\u0026thinsp;X1\u0026thinsp;+\u0026thinsp;X2\u0026thinsp;+\u0026thinsp;X3\u0026thinsp;+\u0026thinsp;X4\u0026thinsp;+\u0026thinsp;X5\u0026thinsp;+\u0026thinsp;X6\u0026thinsp;+\u0026thinsp;X7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab9\" border=\"1\"\u003e\u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe results of this study indicate that among the 1,636 AM patients, 61.31% had dysmenorrhea, demonstrating a high incidence rate. In a population-based cohort study in the United States from 2006 to 2015(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), the incidence of dysmenorrhea in adenomyosis was 60.3%, consistent with our results, indicating that AM-related dysmenorrhea is a significant public health concern.\u003c/p\u003e \u003cp\u003eThe results of this study confirmed that menorrhagia, concurrent pelvic inflammatory disease, family history of AM, and multiple deliveries are risk factors for dysmenorrhea in AM. Previous studies based on imaging and pathological examinations reported that about one-third of patients were asymptomatic(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).Menorrhagia is one of the primary symptoms of AM. The possible mechanism is the invasion of the endometrium into the myometrium and stroma, causing improper uterine contractions during menstruation, increased endometrial surface area, excessive prostaglandin secretion, and excessive estrogen, leading to increased menstrual volume(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The results of this study indicate that menorrhagia is a risk factor for dysmenorrhea in AM, possibly related to increased uterine contraction intensity, increased prostaglandins, increased tissue pressure, local ischemia, and inflammatory response. This study suggests that pelvic inflammatory disease is related to dysmenorrhea in AM patients, possibly associated with local inflammation, tissue damage, extensive adhesions, hyperplasia, scar formation, and pelvic congestion. Symptoms may include lower abdominal heaviness and pain, worsened by fatigue and around menstruation, easily triggering dysmenorrhea. The mechanism may be related to the imbalance between pro-inflammatory and anti-infective factors(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Family history of AM is a risk factor for AM dysmenorrhea. It may be due to genetic factors, and it may also be because women adopt similar behaviors or lifestyles from relatives, increasing the risk of dysmenorrhea. Nilufer et al. conducted a meta-analysis using 24 GWAS datasets from multiple countries and regions, finding that endometriosis-related pelvic pain may be associated with genetically mediated increased neural sensitization. As similar diseases, AM and EM are reasonably suspected to have similar genetic potentials(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The relationship between the number of deliveries and adenomyosis has received extensive research attention. During pregnancy, trophoblast cells continuously invade the myometrium, increasing the risk of disrupting the junctional zone(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). With an increase in the number of pregnancies, the damage to the endometrium-myometrium junctional zone increases, enlarging the lesion area, stimulating more prostaglandins, inflammatory factors, and other substances, easily triggering dysmenorrhea.\u003c/p\u003e \u003cp\u003eBinary logistic regression analysis results indicated that older age at onset, infrequent menstruation, and exercise are protective factors for AM-related dysmenorrhea. This study explored the relationship between age at onset and dysmenorrhea, finding that older age at onset may be a protective factor for AM. Clinical studies have also indicated that the risk of dysmenorrhea decreases by 0.97 times with increasing age(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The possible reason is that with age, the pain threshold increases and sensitivity to pain decreases. In this study, a long menstrual cycle (\u0026ge;\u0026thinsp;30 days) was a protective factor for dysmenorrhea. The possible reason is that the duration of dysmenorrhea is relatively short, and the long interval between menstruations reduces pain sensitivity. However, some studies have reached opposite conclusions. A meta-analysis of 77 articles suggested that a long menstrual cycle increases the duration of prostaglandin secretion, thereby increasing the severity and duration of dysmenorrhea(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Other studies have found no significant difference in menstrual cycle between adenomyosis patients with no to mild dysmenorrhea and those with moderate to severe dysmenorrhea(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The relationship between menstrual cycle and AM-related dysmenorrhea has not yet been conclusively determined and requires further research. Prostaglandins stimulate uterine muscle contractions, causing dysmenorrhea. Studies have reported that high levels of stress inhibit LH and FSH, further damaging follicle development, affecting progesterone release, and thus affecting prostaglandin synthesis, leading to dysmenorrhea(\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Stress may also influence PG synthesis by affecting adrenaline and cortisol release, impacting muscle contractions and causing dysmenorrhea(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Exercise is a protective factor for dysmenorrhea. Moderate exercise can relieve patient stress and reduce the synthesis of prostaglandins in women(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). If intense exercise becomes a source of stress, the severity of dysmenorrhea may increase. Some studies have reported that regular intense exercise may increase the severity of dysmenorrhea, possibly related to the increased frequency, intensity of exercise, and bodily sensitivity, which may also be related to the occupational characteristics of the surveyed population(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdenomyosis, as a chronic disease, seriously affects health services and the socio-economy. Domestic experts(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) have pointed out that adenomyosis should be managed with a long-term management approach. Based on the characteristics of the disease at different stages, a hierarchical management model should be adopted to achieve phased prevention and treatment. Based on the risk factors and protective factors for dysmenorrhea in adenomyosis identified in this study, primary management goals should be achieved for women of reproductive age. At the stage where susceptible individuals are exposed to risk factors but have not yet developed the disease, appropriate measures should be taken to prevent or delay the occurrence of dysmenorrhea in adenomyosis. The following management recommendations are proposed: initiate health education for adenomyosis early to improve awareness of the disease; guide susceptible individuals to improve their lifestyle by adjusting diet, daily routine, emotions, and exercise habits to enhance disease resistance, and fully utilize the advantages of traditional Chinese medicine; and ensure early diagnosis and treatment. For adenomyosis patients meeting the minimum diagnostic criteria, early intervention is needed even if dysmenorrhea has not yet occurred.\u003c/p\u003e \u003cp\u003ePrevious studies have often been limited to populations diagnosed with AM through surgery or pathology reports, which are prone to underdiagnosis and selection bias. This study, however, is based on gynecological patients visiting outpatient clinics, diagnosed according to symptoms and ultrasound or MRI reports, with a large sample size and high data reliability, making the epidemiological results closer to the real world. The main limitation of this study is that it is a cross-sectional survey, which cannot accurately establish causality. Additionally, variables such as depression, anxiety, diet, and exercise were not quantified. Future research will further improve experimental design and deepen the study of AM dysmenorrhea.\u003c/p\u003e \u003cp\u003eIn summary, this study found the distribution of adenomyosis dysmenorrhea through a cross-sectional survey and conducted logistic regression analysis. The results showed that dysmenorrhea is a common symptom in AM patients. A long disease course, multiple deliveries, menorrhagia, concurrent pelvic inflammatory disease, and family history of AM are risk factors for dysmenorrhea in AM, while older age at onset, infrequent menstruation, and exercise are protective factors. Furthermore, this study established a nomogram model for predicting the probability of dysmenorrhea in AM, used to stratify high- and low-risk populations, develop effective health strategies, alleviate patient suffering, and improve quality of life.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYuDan Fu, Xin Wang,\u0026nbsp;XinChun Yang\u0026nbsp;contributed to literature review, essay writing, data extraction, data analysis and picture drawing. RuiHua Zhao contributed to the project innovation and paper submission. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Science and technology innovation project of Chinese Academy of Traditional Chinese Medicine (CI2022C003).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data used to support the findings of this study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of Guang\u0026apos;anmen Hospital of China Academy of Chinese Medical Sciences, approval number: 2020-040-KY. All patients signed informed consent forms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch3\u003eConflict of interest statement\u003c/h3\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChe, X., Wang, X., Zhang, Y., Li, H., \u0026amp; Zhu, S. (2020). A new trick for an old dog: The application of mifepristone in the treatment of adenomyosis. J. Cell. Mol. Med., 24, 1724-1737. https://doi.org/10.1111/jcmm.14866\u003c/li\u003e\n\u003cli\u003eGuo, S. W. (2020). The pathogenesis of adenomyosis vis-\u0026agrave;-vis endometriosis. J. Clin. Med., 9, 485. https://doi.org/10.3390/jcm9020485\u003c/li\u003e\n\u003cli\u003eGordts, S., Grimbizis, G., \u0026amp; Campo, R. (2018). Symptoms and classification of uterine adenomyosis, including the place of hysteroscopy in diagnosis. Fertil. Steril., 109, 380-388.e1. https://doi.org/10.1016/j.fertnstert.2018.01.013\u003c/li\u003e\n\u003cli\u003eSimoens, S., Dunselman, G., Dirksen, C., et al. (2012). The burden of endometriosis: costs and quality of life of women with endometriosis and treated in referral centres. Hum. Reprod., 27, 1292-1299. https://doi.org/10.1093/humrep/des073\u003c/li\u003e\n\u003cli\u003eYu, O., Schulze-Rath, R., Grafton, J., Hansen, K., Scholes, D., \u0026amp; Reed, S. D. (2020). Adenomyosis incidence, prevalence and treatment: United States population-based study 2006-2015. Am. J. Obstet. Gynecol., 223, 94.e1-94.e10. https:// doi.org/10.1016/j.ajog.2020.01.016 \u003c/li\u003e\n\u003cli\u003eZannoni, L., Giorgi, M., Spagnolo, E., et al. (2014). Dysmenorrhea, absenteeism from school, and symptoms suspicious for endometriosis in adolescents. J. Pediatr. Adolesc. 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(2021). Immunological changes associated with adenomyosis: a systematic review. Hum. Reprod. Update, 27, 108-129. https://doi.org/10.1093/humupd/dmaa038\u003c/li\u003e\n\u003cli\u003eRahmioglu, N., Mortlock, S., Ghiasi, M., et al. (2023). The genetic basis of endometriosis and comorbidity with other pain and inflammatory conditions. Nat. Genet., 55, 423-436. https://doi.org/10.1038/s41588-023-01323-\u003c/li\u003e\n\u003cli\u003eUduwela, A. S., Perera, M. A. K., Aiqing, L., et al. (2000). Endometrial-myometrial interface: relationship to adenomyosis and changes in pregnancy. Obstet. Gynecol. Surv., 55, 390-400. https://doi.org/10.1097/00006254-200006000-00025\u003c/li\u003e\n\u003cli\u003eJang, I. A., Kim, M. Y., Lee, S. R., Jeong, K. A., \u0026amp; Chung, H. W. (2013). Factors related to dysmenorrhea among Vietnamese and Vietnamese marriage immigrant women in South Korea. Obstet. Gynecol. Sci., 56, 242-248. https://doi.org/10.5468/ogs.2013.56.4.242\u003c/li\u003e\n\u003cli\u003eMitsuhashi, R., Sawai, A., Kiyohara, K., Shiraki, H., \u0026amp; Nakata, Y. (2022). Factors associated with the prevalence and severity of menstrual-related symptoms: A systematic review and meta-analysis. Int. J. Environ. Res. Public Health, 20, 569. https://doi.org/10.3390/ijerph200100569\u003c/li\u003e\n\u003cli\u003eLi, Q., Huang, J., Zhang, X. Y., Feng, W. W., \u0026amp; Hua, K. Q. (2021). Dysmenorrhea in patients with adenomyosis: A clinical and demographic study. J. Gynecol. Obstet. Hum. Reprod., 50, 101761. https://doi.org/10.1016/j.jogoh.2020.101761\u003c/li\u003e\n\u003cli\u003eWang, L., Wang, X., Wang, W., et al. (2004). Stress and dysmenorrhoea: a population based prospective study. Occup. Environ. Med., 61, 1021-1026. https://doi.org/10.1136/oem.2003.012302\u003c/li\u003e\n\u003cli\u003eChristiani, D. C., Niu, T., \u0026amp; Xu, X. (1995). 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Cancer Inst., 91, 950-953. https://doi.org/10.1093/jnci/91.11.950\u003c/li\u003e\n\u003cli\u003eMetheny, W. P., \u0026amp; Smith, R. P. (1989). The relationship among exercise, stress, and primary dysmenorrhea. J. Behav. Med., 12, 569-586. https://doi.org/10.1007/BF00844826\u003c/li\u003e\n\u003cli\u003eWang, S. X., Cui, P. F., \u0026amp; Zhang, J. J. (2024). Expert consensus on the three-level management of adenomyosis. Pract. J. Gynecol. Obstet., 40, 106-111.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 6,7 and 9 ","content":"\u003cp\u003eTable 6,7 and 9 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"archives-of-gynecology-and-obstetrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arch","sideBox":"Learn more about [Archives of Gynecology and Obstetrics](https://www.springer.com/journal/404)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/arch/default.aspx","title":"Archives of Gynecology and Obstetrics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Adenomyosis, Dysmenorrhea, Related Factors, Logistic, Clinical Prediction Model","lastPublishedDoi":"10.21203/rs.3.rs-4998744/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4998744/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo explore factors related to dysmenorrhea in adenomyosis and construct a risk prediction model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional survey involving 1636 adenomyosis patients from 37 hospitals nationwide (November 2019 - February 2022) was conducted. Data on demographics, disease history, menstrual and reproductive history, and treatment history was collect.Patients were categorized into dysmenorrhea and non-dysmenorrhea groups. Multivariate logistic regression analyzed factors influencing dysmenorrhea, and a risk prediction model was created using a nomogram. The model's performance was evaluated through ROC curve analysis, C-index, Hosmer-Lemeshow test, and bootstrap method The nomogram function was used to establish a nomogram model. The model was evaluated using the area under the ROC curve (AUC), C-index, Hosmer-Lemeshow goodness-of-fit test, and bootstrap method. Patients were scored based on the nomogram, and high-risk groups were delineated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDysmenorrhea was present in 61.31% (1003/1636) of the patients. Univariate analysis showed significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between groups in age at onset, course of disease, oligomenorrhea, menorrhagia, number of deliveries, pelvic inflammatory disease, family history of adenomyosis, exercise, and excessive menstrual fatigue. Significant factors included menorrhagia, multiple deliveries, pelvic inflammatory disease, and family history of adenomyosis as risk factors. Older age at onset, oligomenorrhea, and exercise were identified as protective factors. The model's accuracy, discrimination, and reliability were acceptable, and a risk score\u0026thinsp;\u0026gt;\u0026thinsp;88.5 points indicated a high-risk group.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eDysmenorrhea is prevalent among adenomyosis patients. Identifying and mitigating risk factors, while leveraging protective factors, can aid in prevention and management. The developed model effectively predicts dysmenorrhea risk, facilitating early intervention and treatment.\u003c/p\u003e","manuscriptTitle":"Analysis of Dysmenorrhea-Related Factors in Adenomyosis and Development of a Risk Prediction Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-15 05:56:30","doi":"10.21203/rs.3.rs-4998744/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-10-23T15:15:26+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-11T13:10:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Archives of Gynecology and Obstetrics","date":"2024-10-08T16:26:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-18T13:06:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Archives of Gynecology and Obstetrics","date":"2024-09-17T06:09:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"archives-of-gynecology-and-obstetrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arch","sideBox":"Learn more about [Archives of Gynecology and Obstetrics](https://www.springer.com/journal/404)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/arch/default.aspx","title":"Archives of Gynecology and Obstetrics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"11f348c7-02b0-48e7-8081-0c1fbd48cc77","owner":[],"postedDate":"October 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-24T15:59:58+00:00","versionOfRecord":{"articleIdentity":"rs-4998744","link":"https://doi.org/10.1007/s00404-025-07967-y","journal":{"identity":"archives-of-gynecology-and-obstetrics","isVorOnly":false,"title":"Archives of Gynecology and Obstetrics"},"publishedOn":"2025-03-17 15:57:11","publishedOnDateReadable":"March 17th, 2025"},"versionCreatedAt":"2024-10-15 05:56:30","video":"","vorDoi":"10.1007/s00404-025-07967-y","vorDoiUrl":"https://doi.org/10.1007/s00404-025-07967-y","workflowStages":[]},"version":"v1","identity":"rs-4998744","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4998744","identity":"rs-4998744","version":["v1"]},"buildId":"B-jG_2CBjPDmsCi4Wdhf-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}