Analysis of dysmenorrhea-related factors in adenomyosis and development of a risk prediction model

Our study identifies multiple deliveries, menorrhagia, pelvic inflammatory disease, and family history of adenomyosis as significant risk factors for dysmenorrhea in adenomyosis patients, while older age at onset, infrequent menstruation, and regular exercise are protective factors. The developed risk prediction model effectively stratifies high-risk populations, facilitating early intervention and management strategies to mitigate dysmenorrhea in clinical practice.

A cross-sectional survey was conducted on 1636 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). 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 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. 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.

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. 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 [ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{x }\pm S$$\end{document} x ¯ ± S , n (%), M (P25, P75)] Clinical Characteristics All Patients (n = 1636) Dysmenorrhea (n = 1003) Non-Dysmenorrhea (n = 633) P In age at onset (years) 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 Clinical characteristics [ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{x }\pm S$$\end{document} x ¯ ± S , n (%), M (P25, P75)] 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 [ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{x }\pm S$$\end{document} x ¯ ± S , n (%), M ( P25, P75 )] 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)  < 0.001 Family history of EM 82 (5.01) 57 (5.68) 25 (3.95) 0.118 Characteristics of past medical history and family history in AM patients [ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{x }\pm S$$\end{document} x ¯ ± S , n (%), M ( P25, P75 )] There were no statistically significant differences (P > 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 [ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{x }\pm S$$\end{document} x ¯ ± S , n (%), M ( P25, P75 )] 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 Characteristics of lifestyle habits [ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{x }\pm S$$\end{document} x ¯ ± S , n (%), M ( P25, P75 )] 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 [ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{x }\pm S$$\end{document} x ¯ ± S , n (%), M (P25, P75)] 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 Treatment status [ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{x }\pm S$$\end{document} x ¯ ± S , n (%), M (P25, P75)] 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 Multivariate logistic regression analysis 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 6 Nomogram for predicting the probability of dysmenorrhea in AM Table 7 ROC curve 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 Table 9 Calibration plot Nomogram for predicting the probability of dysmenorrhea in AM ROC curve Scoring formula for probability of dysmenorrhea in AM Calibration plot

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 [ 34 ], 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 [ 35 ], 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.

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 preventing 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. By identifying and monitoring risk factors, we seek to prevent or mitigate the development of dysmenorrhea in AM. Furthermore, this study aims to establish a nomogram model based on identified risk factors to predict dysmenorrhea in adenomyosis. The model will facilitate risk stratification, enabling early prevention, detection, and treatment of the disease through quantified scoring.