Correlation of PGP9.5 and TGF-β1 Levels in Menstrual Blood with Dysmenorrhea Severity in Adenomyosis Patients: A Cross-Sectional Study

Adenomyosis is a common gynecological disorder characterized by invasion of endometrial tissue into the myometrium, often presenting with severe dysmenorrhea. The pathophysiology involves neurogenesis and inflammation, with protein gene product 9.5 (PGP9.5) as a nerve fiber marker and transforming growth factor-beta 1 (TGF-β 1) as an inflammatory mediator. Menstrual blood represents an easily obtainable, non-invasive alternative to conventional tissue-based biomarker assessment.

To analyze the association between PGP9.5 and TGF-β 1 levels in menstrual blood with dysmenorrhea severity in adenomyosis patients, and to evaluate their diagnostic performance.

This cross-sectional study included 72 women aged 20– 45 years (36 with adenomyosis confirmed by histopathology and 36 controls) at Dr. Hasan Sadikin Hospital and Bandung Kiwari Hospital, Indonesia, from September 2025 to January 2026. Menstrual blood samples were collected using menstrual cups on the second or third day of menstruation. PGP9.5 and TGF-β 1 levels were measured using ELISA. Dysmenorrhea severity was assessed using the Visual Analog Scale (VAS). Statistical analyses included Mann–Whitney test, Spearman correlation, and ROC curve analysis.

PGP9.5 levels were significantly higher in adenomyosis patients compared to controls (median 335,77 vs 223,77 ng/L, p< 0.001), as were TGF-β 1 levels (median 608,92 vs 379,43 ng/mL, p< 0.001). VAS scores were also significantly higher in the adenomyosis group (median 9.00 vs 0.50, p< 0.001). Significant positive correlations across the overall study population were observed between PGP9.5 and VAS (r = 0.562, p < 0.001), TGF-β 1 and VAS (r = 0.581, p < 0.001), and between PGP9.5 and TGF-β 1 (r = 0.878, p < 0.001). ROC analysis demonstrated excellent diagnostic performance for PGP9.5 and TGF-β 1 with AUC of 0.943 and 0.942, with optimal cut-offs of 260.43 ng/L for PGP9.5 and 482.95 ng/mL for TGF-β 1, yielding sensitivity and specificity of 91.7% for both markers.

PGP9.5 and TGF-β 1 levels in menstrual blood are significantly elevated in adenomyosis patients and positively correlate with dysmenorrhea severity. These findings provide proof of concept that menstrual blood PGP9.5 and TGF-β 1 may serve as non-invasive biomarkers for adenomyosis.

adenomyosis, dysmenorrhea, PGP9.5, TGF-β 1, menstrual blood, biomarkers, neurogenesis, fibrogenesis

Adenomyosis is a benign gynecological disorder characterized by the presence of endometrial glands and stroma within the myometrium, accompanied by hypertrophy and hyperplasia of surrounding smooth muscle.1 The prevalence of adenomyosis ranges from 20% to 35% in reproductive-aged women, with increasing detection rates due to advances in imaging techniques such as transvaginal ultrasound (TVS) and magnetic resonance imaging (MRI).2 The most common clinical manifestations include abnormal uterine bleeding, chronic pelvic pain, and dysmenorrhea, which significantly impact quality of life.3 Dysmenorrhea in adenomyosis is often progressive and refractory to conventional analgesic therapy.4 The pathophysiology of pain in adenomyosis is multifactorial, involving abnormal uterine contractility, inflammatory mediators, vascular factors, and neurogenic mechanisms.5 Recent studies have demonstrated that adenomyotic lesions and eutopic endometrium from patients with pain exhibit higher densities of sensory nerve fibers compared to asymptomatic women.6 These nerve fibers, predominantly unmyelinated C-fibers, detect nociceptive stimuli and respond to inflammatory mediators, contributing to pain sensitization.7 Protein gene product 9.5 (PGP9.5), also known as ubiquitin carboxy-terminal hydrolase L1 (UCHL1), is a neuron-specific protein widely used as a pan-neuronal marker for detecting nerve fibers in tissue samples.8 Elevated expression of PGP9.5 has been reported in endometriotic lesions and is associated with pain severity in endometriosis.9 The mechanism of neurogenesis or local proliferation of nerve fibers around adenomyotic lesions is thought to be triggered by chronic inflammatory cascades that induce the release of mediators such as nerve growth factor (NGF), prostaglandins, and pro-inflammatory cytokines.10 Transforming growth factor-beta 1 (TGF-β1) is a pleiotropic cytokine involved in various biological processes including cell proliferation, differentiation, angiogenesis, and fibrosis.11 In the context of adenomyosis, TGF-β1 plays a crucial role in facilitating epithelial-mesenchymal transition (EMT), which is key to the invasion of endometrial cells into the myometrium.12 TGF-β1 also interacts with signaling pathways such as SMAD, Notch, and Wnt/β-catenin, which are known to be involved in adenomyosis pathogenesis and uterine tissue remodeling.13 Moreover, TGF-β1 has been linked to increased expression of pain mediators, with several studies showing that TGF-β1 can stimulate cyclooxygenase-2 (COX-2) and prostaglandin E2 (PGE2) expression, which are major mediators in nerve terminal sensitization and pain.14 Traditional assessment of endometrial biomarkers has relied on invasive procedures such as endometrial biopsy or hysterectomy specimens.15 In recent years, menstrual blood has emerged as an attractive non-invasive biological source that is easily obtainable and rich in epithelial cells, stromal cells, cytokines, and endometrial tissue fragments.16 Several studies have successfully detected PGP9.5 and TGF-β expression in menstrual blood, opening opportunities for the use of menstrual blood as a medium for molecular research.17 Despite growing evidence linking nerve fiber density and inflammatory mediators to pain in adenomyosis, limited data exist on the relationship between PGP9.5 and TGF-β1 levels in menstrual blood and dysmenorrhea severity in adenomyosis patients. PGP9.5 and TGF-β1 were selected because they represent two complementary biological mechanisms implicated in adenomyosis pathogenesis. PGP9.5 reflects neurogenesis and increased nerve fiber density, which are closely associated with dysmenorrhea, whereas TGF-β1 is a key mediator of inflammation, fibrosis, and epithelial–mesenchymal transition involved in lesion progression. Evaluating these biomarkers simultaneously may therefore provide insight into both the neurogenic and fibrotic components of adenomyosis and their potential utility as non-invasive menstrual blood biomarkers. No prior study has evaluated simultaneous menstrual blood levels of PGP9.5 and TGF-β1 as non-invasive biomarkers for adenomyosis diagnosis and pain severity, particularly in a histology-confirmed cohort. This study aimed to evaluate PGP9.5 and TGF-β1 levels in menstrual blood as potential non-invasive biomarkers for adenomyosis diagnosis and pain severity assessment.

Study Design and Setting This cross-sectional observational study was conducted at the Department of Obstetrics and Gynecology, Dr. Hasan Sadikin General Hospital and Bandung Kiwari Hospital, Bandung, Indonesia, from September 2025 to January 2026. The study protocol was approved by the Health Research Ethics Committee of Dr. Hasan Sadikin General Hospital (approval number: DP.04.03/D.XIV.4.4/2760/2025) and Bandung Kiwari Hospital (approval number: SE.61/KEP-RSUDBK/XI/2025). All participants provided written informed consent before enrollment. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Study Participants Women aged 20–45 years were recruited through consecutive sampling. The adenomyosis group consisted of patients diagnosed with adenomyosis using transvaginal ultrasound, according to the Morphological Uterus Sonographic Assessment (MUSA) criteria and confirmed by histopathological examination following surgical resection or hysterectomy. The control group comprised women without adenomyosis confirmed by transvaginal ultrasound. All participants received empirical dienogest therapy as part of routine clinical management consistent with the ESHRE recommendations for pain management. Inclusion criteria for both groups were: (1) age 20–45 years, (2) regular menstrual cycles (24–38 days), (3) normal body mass index (BMI 18.5–24.9 kg/m2), and (4) willingness to participate. Additional inclusion criteria for the control group included being married and having no adenomyosis or malignancy on transvaginal ultrasound. This criterion was applied because transvaginal ultrasonography (TVUS), which formed part of the diagnostic evaluation, is routinely performed only in married women at our institution in accordance with institutional clinical practice and local ethical considerations. Exclusion criteria included: (1) hormonal contraceptive use within the previous 6 months or intrauterine device use, (2) history of uterine surgery, (3) concurrent endometrial pathology (submucosal myoma types 0–3, endometrial polyps, or endometrial hyperplasia), (4) endometrioma or myoma detected on transvaginal ultrasound, (5) history of uterine malignancy, (6) abnormal uterine bleeding, (7) cardiovascular, cerebrovascular, or thromboembolic disease, (8) infections such as hydrosalpinx, endometritis, or chronic inflammatory disease, and (9) polycystic ovary syndrome. Participants were screened preoperatively using transvaginal ultrasonography (TVUS) to exclude ovarian endometrioma (OMA) and deep infiltrating endometriosis (DIE). Because superficial endometriosis cannot be reliably detected using routine preoperative TVUS, its presence could not be completely excluded before surgery and therefore represents a potential source of residual confounding. Sample Size Calculation Sample size was calculated using the correlation analysis formula: where Zα = 1.96 (95% confidence level), Zβ = 0.84 (80% power), and r = 0.527 based on previous literature. This yielded a minimum of 26 subjects per group. Following Gay and Diehl’s recommendation of 30 subjects per group for comparative studies, we recruited 36 subjects per group (total n=72). Menstrual Blood Collection and Processing Menstrual blood samples were collected using menstrual cups on the second or third day of menstruation. Participants were provided with sterile menstrual cups and instructions for proper use. The cups were inserted for 3 hours, after which menstrual blood was transferred to Eppendorf tubes (1 mL per tube) and immediately stored in cooler boxes at 2–8°C. Samples were transported to the Molecular Genetics Laboratory, Universitas Padjadjaran, within 1 hour of collection. Sample preparation involved the following steps: (1) coagulation—samples were transferred to anticoagulant-free containers and allowed to clot naturally at room temperature for 30–60 minutes without agitation; (2) clot retraction—samples were cooled at 4°C for 2 hours or overnight to induce clot contraction; (3) centrifugation—samples were centrifuged at 3000 rpm for 10 minutes at 4°C to obtain serum supernatant; (4) storage—serum was transferred to sterile tubes, aliquoted, and stored at −80°C until analysis. Biomarker Measurement PGP9.5 and TGF-β1 levels were measured using commercial ELISA kits (BIOENZY, China) according to the manufacturer’s protocols. Briefly, 50 μL of standard or 40 μL of sample plus 10 μL of specific antibody were added to microplate wells, followed by 50 μL of streptavidin-HRP. After 60 minutes of incubation at 37°C, plates were washed five times. Substrate solutions A and B (50 μL each) were added and incubated for 10 minutes at 37°C in the dark. The reaction was stopped with 50 μL of stop solution, and optical density was measured at 450 nm within 10 minutes. Concentrations were calculated from standard curves using four-parameter logistic regression. Dysmenorrhea Assessment Dysmenorrhea severity was assessed using the Visual Analog Scale (VAS), a validated 0–10 scale where 0 represents no pain and 10 represents the worst imaginable pain. Participants were asked to rate their pain intensity during the most recent menstrual period. Statistical Analysis Data were analyzed using SPSS version 26.0 (IBM Corporation, Armonk, NY, USA). Normality was assessed using the Shapiro–Wilk test. Continuous variables with non-normal distribution were presented as median (minimum-maximum) and compared using the Mann–Whitney U-test. Categorical variables were presented as frequencies and percentages and compared using chi-square or Fisher’s exact test as appropriate. Correlations between variables were assessed using Spearman’s rank correlation coefficient. Receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic performance, with area under the curve (AUC), optimal cut-off values, sensitivity, and specificity calculated. A p-value <0.05 was considered statistically significant.

Baseline Characteristics This study aimed to evaluate the correlation between PGP9.5 and TGF-β1 levels in menstrual blood and the severity of dysmenorrhea in patients with adenomyosis. A total of 72 participants (Table 1) were enrolled, consisting of patients who sought medical care or voluntarily underwent examination at Dr. Hasan Sadikin General Hospital and Bandung Kiwari Regional Hospital. | Table 1 Comparison of Patient Characteristics Between the Two Groups | This study population was divided into two groups: the adenomyosis group and the non-adenomyosis group. A cross-sectional design was employed. Data collected from participants included the severity of dysmenorrhea, obtained through structured interviews, as well as PGP9.5 and TGF-β1 levels measured using enzyme-linked immunosorbent assay (ELISA) from menstrual blood samples. Table 1 presents the baseline characteristics of subjects in both groups. In the adenomyosis group, the mean age was 32.42 years, and the median body mass index (BMI) was 24, with a range of 22.00–24.00. Regarding parity, 24 patients (66.7%) were nulliparous, 8 (22.2%) were primiparous, and 4 (11.1%) were multiparous. In the non-adenomyosis group, the mean age was 33.50 years, and the median BMI was 23.55, with a range of 18.30–24.70. Parity distribution in this group showed 5 subjects (13.9%) were nulliparous, 16 (44.4%) were primiparous, and 15 (41.7%) were multiparous. Statistical analysis demonstrated that the p-values for age, body mass index (BMI), and menstrual interval were greater than 0.05 (p > 0.05), indicating no statistically significant differences between the adenomyosis and non-adenomyosis groups. These findings suggest that the mean values of age, BMI, and menstrual interval were comparable across both groups. In contrast, the p-value for parity was less than 0.05 (p < 0.05), indicating a statistically significant difference in parity distribution between the two groups. Based on the comparative analysis of baseline characteristics, the two groups were generally comparable at baseline, with no significant differences observed in most variables. This indicates that the groups were sufficiently homogeneous and appropriate for further comparative and hypothesis-testing analyses, with the exception of parity, which may act as a potential confounding variable. As shown in Table 2, the statistical analysis revealed that the p-value for the pain score variable was less than 0.05 (p < 0.05), indicating a statistically significant difference. Therefore, it can be concluded that there was a significant difference in the mean pain scores between the adenomyosis and non-adenomyosis groups. | Table 2 Comparison of Pain Scores Between the Two Groups | Table 3 presents the comparison of PGP9.5 levels between the two groups. The statistical analysis showed that the p-value for PGP9.5 levels was less than 0.05 (p < 0.05), indicating a statistically significant difference. Therefore, it can be concluded that there was a significant difference in the mean PGP9.5 levels between the adenomyosis and non-adenomyosis groups. | Table 3 Comparison of PGP9.5 Levels Between the Two Groups | The statistical analysis presented in Table 4 showed that the p-value for TGF-β1 levels was less than 0.05 (p < 0.05), indicating a statistically significant difference. Thus, there was a significant difference in the mean TGF-β1 levels between the adenomyosis and non-adenomyosis groups. | Table 4 Comparison of TGF-β1 Levels Between the Two Groups | The statistical analysis in Table 5, performed using the Chi-square test, demonstrated that the p-value for the PGP9.5 cut-off variable was less than 0.05 (p < 0.05), indicating a statistically significant difference. This finding suggests a significant difference in the proportion of subjects based on the PGP9.5 cut-off levels between the adenomyosis and non-adenomyosis groups. | Table 5 Comparison of the Proportion and Association of PGP9.5 Cut-off Values Between the Adenomyosis and Non-Adenomyosis Groups | Furthermore, the diagnostic performance analysis showed a sensitivity and specificity of 91.7%, indicating excellent discriminatory ability. The positive predictive value (PPV) was 91.7%, reflecting a high probability that subjects with a positive test truly had the condition. Similarly, the negative predictive value (NPV) was 91.7%, indicating a strong ability to correctly identify subjects without the condition. The overall diagnostic accuracy was also 91.7%, demonstrating excellent performance of the test. The statistical analysis presented in Table 6, using the Chi-square test, showed that the p-value for the TGF-β1 cut-off variable was less than 0.05 (p < 0.05), indicating a statistically significant difference. This finding demonstrates a significant difference in the proportion of subjects based on the TGF-β1 cut-off levels between the adenomyosis and non-adenomyosis groups. | Table 6 Comparison of the Proportion and Association of TGF-β1 Cut-off Values Between the Adenomyosis and Non-Adenomyosis Groups | The diagnostic performance analysis revealed a sensitivity of 91.7% and a specificity of 91.7%, indicating excellent diagnostic strength. The positive predictive value (PPV) was 91.7%, reflecting a high probability that individuals with a positive test truly had the condition, while the negative predictive value (NPV) was also 91.7%, indicating a strong ability to correctly identify those without the condition. The overall diagnostic accuracy was 91.7%, demonstrating excellent performance of TGF-β1 as a diagnostic marker. The ROC curves demonstrated that PGP9.5 and TGF-β1 levels (Figure 1 and 2) have good diagnostic performance, as indicated by curves that deviate from the 50% diagonal line and approach the upper left corner. The areas under the curve (AUCs) for PGP9.5 and TGF-β1 obtained from ROC analysis were 94.3% and 94.2%, respectively, with a p-value of 0.001, indicating statistically significant discriminatory ability. AUCs of 0.94 for both markers indicate excellent discrimination between adenomyosis and non-adenomyosis, with balanced sensitivity and specificity of 91.7% at the optimal cut-off. | Figure 1 PGP9.5 cut-off value. | | Figure 2 TGF-β1 cut-off value. | Based on the ROC-derived estimates, the optimal cut-off value for PGP9.5 in this study was 260.43, yielding both a sensitivity and specificity of 91.7% (Figure 1). This indicates that 94.3% of patients with PGP9.5 levels above 260.43 are predicted to have adenomyosis. The optimal cut-off value for TGF-β1 was 482.95, also with a sensitivity and specificity of 91.7%, meaning that 94.2% of patients with TGF-β1 levels above 482.95 are predicted to have adenomyosis (Figure 2). The similarity between sensitivity and specificity suggests a balanced diagnostic performance, with comparable accuracy for ruling in and ruling out the disease. In Table 7, the correlation coefficient (r) between PGP9.5 levels and pain score was 0.562 with a p-value of 0.0001. This indicates a statistically significant, positive, and moderate correlation between PGP9.5 levels and pain severity. The correlation coefficient between TGF-β1 levels and pain score was 0.581 (p = 0.0001), also demonstrating a statistically significant, positive, and moderate association between TGF-β1 levels and pain severity. In contrast, the correlation coefficient between PGP9.5 and TGF-β1 levels was 0.878 (p = 0.0001), indicating a statistically significant, positive, and strong correlation between these two biomarkers. | Table 7 Correlation Analysis of PGP9.5 and TGF-β1 Levels with Pain Scores in the Overall Study Population | Table 8 presents the correlation analysis between PGP9.5 levels and pain scores in both groups. The correlation coefficient (r) between PGP9.5 levels and pain score in the adenomyosis group was −0.161 with a p-value of 0.349, and in the non-adenomyosis group r was −0.182 with a p-value of 0.288. Similarly, the correlation analysis between TGF-β1 levels and pain scores showed an r value of −0.033 (p = 0.849) in the adenomyosis group and −0.221 (p = 0.195) in the non-adenomyosis group. These results indicate that there was no statistically significant correlation between biomarker levels and pain scores in either group. | Table 8 Correlation Analysis of PGP9.5 and TGF-β1 Levels with Pain Scores in the Adenomyosis and Non-Adenomyosis Groups | In contrast, the correlation analysis between PGP9.5 and TGF-β1 levels in both groups demonstrated a strong and significant association. The correlation coefficient between PGP9.5 and TGF-β1 levels in the adenomyosis group was 0.880 with a p-value of 0.0001, while in the non-adenomyosis group r was 0.856 with a p-value of 0.0001. These findings indicate a strong, positive, and statistically significant correlation between PGP9.5 and TGF-β1 levels in both groups.

This study included 72 participants divided into two groups: an adenomyosis group and a non-adenomyosis group. The mean age of patients with adenomyosis was 32.42 years (SD 6.0), whereas in the non-adenomyosis group it was 32.28 years (SD 6.8). Previous studies have reported mean ages of 44.5 and 54.9 years, typically placing adenomyosis in the fourth to fifth decades of life. However, more recent evidence indicates a shift toward higher prevalence in younger women, consistent with improved imaging-based diagnosis in reproductive-age populations.18 This shift is largely attributable to the wider use of less invasive diagnostic modalities, particularly ultrasonography, which allows adenomyosis to be diagnosed without the need for surgical specimens. More recently, adenomyosis has even been identified in adolescents and young adults between 13 and 25 years of age. In contrast, a study by Fitrina et al conducted at Dr. Hasan Sadikin General Hospital in 2015–2016 reported that 70.7% of adenomyosis cases occurred in women older than 35 years, with a mean age of approximately 39 years.19 The median body mass index (BMI) in the adenomyosis group was 24, with a range of 18.5 to 24.8, whereas in the non-adenomyosis group it was 23.5, with a range of 18.3 to 24.7 (Table 1). One of the inclusion criteria in this study was a normal BMI, which explains the minimal difference between the two groups. Several studies have reported that women who are overweight or obese are more likely to develop adenomyosis, and that patients with adenomyosis have a higher prevalence of central obesity. This may be attributed to the estrogen-dependent nature of adenomyosis, which is often associated with metabolic syndrome. In central obesity, adipocytes act as an extragonadal source of aromatase, converting androstenedione to local estradiol, which may enhance proliferation of ectopic endometrial tissue within the myometrium, induce epithelial–mesenchymal transition (EMT) through TGF-β1, and reduce stromal cell apoptosis via progesterone resistance. In the adenomyosis group, 24 women (66.7%) were nulliparous, 8 (22.2%) were primiparous, and 4 (11.0%) were multiparous. In contrast, in the non-adenomyosis group, 5 women (13.9%) were nulliparous, 16 (44.4%) were primiparous, and 15 (41.7%) were multiparous. Historically, adenomyosis has been associated with multiparity; however, more recent data indicate that it is increasingly diagnosed in infertile women of reproductive age. Several studies have reported that adenomyosis is linked to reduced implantation rates, lower clinical and ongoing pregnancy rates, and higher first-trimester miscarriage rates, particularly in women undergoing assisted reproductive treatments.20,21 This finding helps explain why, in a fertility clinic population, the proportion of nulliparous women is higher in the adenomyosis group than in the non-adenomyosis group. Adenomyosis contributes to infertility, and affected patients are therefore more likely to have never conceived. Adenomyosis has a detrimental impact on female fertility. Adenomyosis also adversely affects in vitro fertilization–embryo transfer (IVF-ET) outcomes, being associated with reduced implantation, clinical pregnancy, ongoing pregnancy, and live birth rates, as well as increased miscarriage rates. In addition, adenomyosis has been linked to unfavorable obstetric outcomes, including preterm birth and premature rupture of membranes. Recent meta-analytic evidence suggests that adenomyosis is associated with an approximately 30% reduction in the likelihood of achieving pregnancy.22 Infertility in women with adenomyosis is closely related to the disease’s underlying etiology and pathogenesis. Several reports have highlighted the involvement of inflammatory mediators, extracellular matrix–remodeling enzymes, neuroangiogenic factors, growth factors, and sex steroid receptors in the pathophysiological processes of adenomyosis. The tissue injury and repair (TIAR) mechanism is thought to be activated in response to repeated microtrauma, leading to a self-perpetuating cycle that promotes disease progression. TGF-β1 as an Inflammatory and Fibrotic Marker Transvaginal sonography is superior to transabdominal ultrasound for the diagnosis of adenomyosis, as it offers higher sensitivity and more accurate lesion characterization. In women undergoing hysterectomy, the sensitivity of TVS has been reported to reach 81.1–85.5%, while three-dimensional (3D) TVS provides optimal visualization of the junctional zone. The most specific ultrasonographic finding is the presence of myometrial cysts, whereas a heterogeneous myometrium is the most sensitive feature on two-dimensional (2D) TVS. A meta-analysis reported a sensitivity of 72% and a specificity of 81% when TVS findings were compared with histopathology. However, ultrasound performs less well in assessing lesion extent (57%) and depth of myometrial involvement (23%), even though hyperechoic islands, subendometrial projections, and subendometrial lines are considered direct signs with the highest predictive value.23 The transvaginal ultrasound appearance of adenomyosis also reflects its underlying pathogenesis, including fibrosis resulting from an inflammatory cascade that contributes to pain. These processes enhance the expression of COX-2 and PGE2 and lead to excessive aromatase activity, thereby increasing local estradiol concentrations. Overexpression of estrogen receptor-α and estrogen receptor-β further accelerates endometrial proliferation, angiogenesis, and invagination into the myometrium, giving rise to the characteristic lesions. Estrogen receptor-α also increases oxytocin and oxytocin receptor expression, triggering uterine hyperperistalsis, tissue injury and repair (TIAR), and chronic uterine damage. This sequence of events parallels fibrotic processes in other organs, with upregulation of TGF-β1 activating epithelial cells to secrete profibrotic factors, promote fibroblast proliferation, and enhance pain sensitization.12,24–28 An increase in TGF-β1 levels was observed in this study. The mean TGF-β1 concentration differed significantly between the adenomyosis and non-adenomyosis groups, with a median value of 608.92 in the 36 women with adenomyosis and 379.43 in the non-adenomyosis group. To the best of our knowledge, this is the first study to quantify TGF-β1 using an ELISA-based assay in menstrual blood from patients with adenomyosis. A previous study by Effendi et al conducted at Dr. Mohammad Hoesin Hospital, Palembang, measured TGF-β1 levels in menstrual blood from patients with endometriosis, demonstrating the feasibility of this approach but limiting its application to endometriosis.29 Building on this concept, the present study extends the use of menstrual blood ELISA for TGF-β1 assessment to adenomyosis, underscoring the novelty of our work. Menstrual blood represents a complex biological fluid composed not only of peripheral blood but also of shed endometrial tissue, inflammatory exudates, extracellular fluid, immune cells, and cellular debris generated during cyclic endometrial breakdown. During menstruation, tissue desquamation and cellular shedding release locally expressed proteins, including TGF-β1 and neuronal proteins such as PGP9.5, into the menstrual effluent. In adenomyosis, repeated TIAR, chronic inflammation, increased vascular permeability, and extensive remodeling of the eutopic and ectopic endometrium further facilitate the release of these biomarkers into menstrual blood. Consequently, menstrual blood reflects the local uterine microenvironment more directly than peripheral blood and provides a biologically plausible, minimally invasive source for biomarker assessment in adenomyosis.28,30 The elevated TGF-β1 levels detected by ELISA in menstrual blood in this study suggest disruption of the normal endometrial injury and repair process. In a physiological menstrual cycle, endometrial repair proceeds without scar formation, making it a unique regenerative process compared with other tissues, and begins with endometrial desquamation and continues until bleeding ceases. The human endometrium is a dynamic, steroid-dependent tissue, characterized by proliferative growth under estrogen influence and cellular differentiation in response to progesterone. Endometrial stromal fibroblasts play a pivotal role in early implantation and pregnancy maintenance, as well as in tissue desquamation and hemostasis when pregnancy does not occur. Elevated TGF-β1 levels in menstrual blood may reflect impaired endometrial regeneration and aberrant remodeling.28,31,32 TGF-β1 is strongly implicated in driving the fibrotic processes that underlie adenomyotic lesions and consequent uterine enlargement. As a key cytokine in fibrogenesis, TGF-β1 is upregulated in response to repeated tissue injury and repair (TIAR), which in adenomyosis leads to chronic inflammation and sustained overproduction of TGF-β1. This, in turn, stimulates fibroblasts to differentiate into myofibroblasts and promotes excessive extracellular matrix (ECM) deposition. Accumulation of ECM results in fibrosis and contributes to the formation and progression of adenomyotic lesions. In addition to its role in fibrosis, TGF-β1 is also involved in epithelial–mesenchymal transition (EMT), enabling endometrial epithelial cells to invade the myometrium and establish adenomyotic foci. Higher TGF-β1 levels in menstrual blood among women with adenomyosis are a logical reflection of more intense and extensive fibrotic activity in this group.28,30 PGP9.5 as a Neurogenic Marker Tissue trauma involved in the formation of adenomyotic lesions due to disruption of the endometrial–myometrial interface (EMI) is thought to induce neurogenesis. Elevated TGF-β levels in injured tissue, particularly following platelet activation, promote Schwann cell migration across bridging structures, thereby creating a permissive substrate for successful nerve regeneration and tissue repair. Tumor necrosis factor-alpha produced by recruited mast cells, together with other mediators, further drives neurogenesis and angiogenesis, ultimately contributing to pain generation. Neurogenesis in both adenomyosis and endometriosis is characterized by increased density of PGP9.5-positive nerve fibers in the functional and basal layers of the endometrium, which correlates with menstruation-related pain. In menstrual blood, detection of PGP9.5 likely reflects the release of neuronal fragments from proliferating nerve fibers exposed to chronic inflammatory stimuli.33 This is consistent with the PGP9.5 findings in the present study. The comparison of PGP9.5 levels between the two groups showed a statistically significant difference, with a median PGP9.5 concentration of 335.77 in the adenomyosis group and 223.77 in the non-adenomyosis group. Thus, a significant elevation of PGP9.5 was observed in women with adenomyosis. PGP9.5 is a biological neuronal marker commonly used for the detection of nerve fibers in the endometrium by immunohistochemistry. In various studies, the presence of these nerve fibers has been implicated in the generation of pain in both endometriosis and adenomyosis. Consistent with the present findings, Tokushige et al reported that the functional layer of the endometrium in women with endometriosis stained positively for PGP9.5, whereas no such staining was observed in the functional layer of women without endometriosis. These results indicate that numerous fine, unmyelinated sensory nerve fibers are present within the functional layer of eutopic endometrium in affected women, which helps explain the abundant PGP9.5-positive neurons detected near vascularized sites. Most of these fibers are unmyelinated sensory C fibers responsible for transmitting dull, throbbing, and diffuse pain and are localized within the functional layer of the endometrium.34 Bulletti et al also suggested a possible role of these nerve fibers in pain generation, with a higher density of nerve fibers observed in the functional and basal layers compared with the myometrium.35 A study from Indonesia by Setiawan et al, specifically investigating adenomyosis using menstrual blood samples, also demonstrated PGP9.5 expression in women with adenomyosis.36 Clinical Implications PGP9.5 and TGF-β1 levels in menstrual blood demonstrated excellent diagnostic performance in distinguishing women with adenomyosis from those without, with nearly identical cut-off values and a very robust test profile. Biologically, this high diagnostic accuracy is consistent with current pathogenetic concepts, whereby adenomyosis is characterized by enhanced nociceptive neurogenesis (captured by PGP9.5) and activation of the TGF-β/SMAD pathway, which drives EMT, fibroblast-to-myofibroblast transition (FMT), and myometrial fibrosis. The study by Setiawan et al showed that PGP9.5 expression in adenomyotic tissue was significantly higher than in controls and identified a histoscore cut-off with good sensitivity and specificity, which is in line with the menstrual blood PGP9.5 cut-off values observed in the present study.36 The findings of this study are also consistent with those of Effendi et al in Palembang, which demonstrated significantly higher TGF-β1 levels in the disease group. In that study, ROC curve analysis identified an optimal cut-off of approximately 515 ng/mL with high sensitivity and specificity for discriminating endometriosis from non-endometriosis. These data further support that TGF-β1 levels in menstrual blood are a stable and robust biomarker for the presence of estrogen-dependent inflammatory disease, such as endometriosis and adenomyosis.29 The AUC values observed in Tables 5 and 6 reflect not only strong statistical performance but also a close linkage between tissue-level disease burden and the expression of PGP9.5 and TGF-β1, which can be quantified noninvasively in menstrual blood. These findings support the candidacy of menstrual blood–based PGP9.5 and TGF-β1 as promising fluid biomarkers for the diagnosis of adenomyosis. Correlation Between Biomarker Dysmenorrhea is one of the main symptoms reported by women with adenomyosis. In this study, the median menstrual pain score in the adenomyosis group was 9, whereas in the non-adenomyosis group it was 1, with a range of 0 to 6. This marked difference may be explained by the shared pathogenic mechanisms between endometriosis and adenomyosis, which involve complex interactions among immunologic, genetic, hormonal, and environmental factors. Menstrual pain and pelvic pain in adenomyosis are thought to result from increased prostaglandin production and a higher density of nociceptive nerve fibers. In addition, uterine hyperperistalsis and upregulation of oxytocin receptors in women with adenomyosis further contribute to heightened uterine contractility and pain. The proposed pathophysiological pathways linking the biomarkers to pain are shown in Figure 3.37 PGP9.5 and TGF-β1 levels in menstrual blood showed a significant, positive association with dysmenorrhea scores in the overall study population. This positive correlation indicates that higher expression of neuronal fiber markers and fibrotic–inflammatory mediators in menstrual blood parallels increasing menstrual pain intensity, reflecting the underlying chronic inflammatory cascade and neuroangiogenesis described within the TIAR, EMT/FMT, and fibrogenesis framework in adenomyosis. These findings are consistent with evidence that TGF-β1 plays a central role in inducing EMT, driving fibroblast-to-myofibroblast activation, stimulating CTGF and collagen production, and promoting fibrosis of adenomyotic lesions, which in turn contributes to chronic pain through increased tissue stiffness and nociceptor sensitization.27,28,38 The very strong correlation between PGP9.5 and TGF-β1 across all samples reinforces existing evidence that inflammation and activation of the TGF-β/SMAD pathway occur in parallel with peripheral neurogenesis and increased density of nociceptive nerve fibers in both eutopic and ectopic endometrium. This is in line with reports of elevated NGF, PGP9.5, and other neuronal markers in women with endometriosis or adenomyosis who present with pain, supporting a tight coupling between fibrotic–inflammatory signaling and neurogenic remodeling in these conditions.12,39 PGP9.5 and TGF-β1 are not exclusively expressed in adenomyosis. PGP9.5 is detectable in the endometrium and myometrium of women both with and without adenomyosis or endometriosis.30 By contrast, TGF-β1 is physiologically expressed in normal endometrium, particularly during the late secretory phase and menstruation, as part of the tissue repair process.31,32 PGP9.5 and TGF-β1 expression increases in parallel with activation of TIAR pathways, fibrosis, and neuroangiogenesis within adenomyotic lesions. The correlation analysis between menstrual blood PGP9.5 and TGF-β1 levels and dysmenorrhea scores in the overall cohort biologically maps the transition of these biomarkers from physiological to pathological levels across the spectrum of menstrual pain severity (Figure 3). The lack of a statistically significant difference in mean PGP9.5 and TGF-β1 levels with respect to dysmenorrhea severity may be explained by several factors. In the adenomyosis group, VAS scores clustered at the upper end of the scale (median 9). Because almost all patients experienced severe pain, there were essentially no participants with mild pain within this group, making it difficult to determine whether lower biomarker levels are associated with a linear reduction in pain intensity. Pain in adenomyosis is not solely determined by nerve fiber density (as reflected by PGP9.5) or inflammation and fibrosis (as reflected by TGF-β1). Experimental and clinical evidence indicates that dysmenorrhea is also influenced by other mechanisms, including elevated prostaglandin levels, excessive myometrial contractions, and mechanical compression from adenomyotic lesions. Pain is a multifactorial and inherently subjective perception. Although PGP9.5 reflects neurogenesis and TGF-β1 reflects inflammatory and fibrotic pathways, additional contributors—such as COX-2 pathway activity and psychological factors—also influence VAS scores, which may explain why single protein levels do not correlate linearly with pain intensity.37,40–42 There is a possibility that once PGP9.5 or TGF-β1 levels reach a certain threshold, such as the cut-off values identified in this study, sensory nerves are already maximally activated. Mechsner et al reported that both endometriosis and adenomyosis are characterized by increased densities of sensory nerve fibers, supporting the concept of a ceiling effect whereby further biomarker elevation does not translate into proportionally higher pain scores.43 However, pain intensity does not necessarily increase in direct proportion to the number of nerve fibers, but rather to the degree of sensitization of those fibers.38,44 In addition to peripheral sensitization, central sensitization may also contribute to the dissociation between biomarker levels and pain severity. Persistent nociceptive input from adenomyotic lesions can induce functional changes within the central nervous system, resulting in amplification of pain processing even when peripheral inflammatory or neurogenic activity remains relatively stable Consequently, once central sensitization has developed, subjective pain intensity may no longer increase in parallel with local PGP9.5 or TGF-β1 concentrations, providing an additional explanation for the absence of a significant linear correlation between biomarker levels and VAS scores within the adenomyosis group. Similarly, Zhang et al, in a study of abdominal wall endometriosis, also reported no significant correlation (p > 0.05) between nerve fiber density and VAS pain scores.45 Lertvikool et al did identify a correlation in cases of adenomyosis; however, this relationship was not strictly linear.39 TGF-β1 is better recognized as a mediator of long-term fibrosis than of acute pain. In the pathogenesis of adenomyosis, TGF-β1 promotes epithelial–mesenchymal transition, leading to increased stiffness of the myometrial tissue. The absence of a direct correlation between TGF-β1 levels and VAS scores in the adenomyosis group in this study suggests that the principal role of this cytokine is more likely related to tissue remodeling and lesion fibrosis rather than direct stimulation of nociceptive pathways.28

This study has several limitations. First, women with adenomyosis could not be asked to discontinue analgesics or dienogest because of the severity of their pain, although these medications may have influenced the measured levels of PGP9.5 and TGF-β1. Second, despite preoperative screening for ovarian endometrioma and deep infiltrating endometriosis, concomitant superficial endometriosis could not be completely excluded and may have acted as a confounding factor. Information regarding concomitant superficial endometriosis was not collected as a predefined study variable; therefore, subgroup analysis of isolated adenomyosis versus concurrent disease could not be performed. All participants received empirical dienogest therapy as part of routine clinical management; the potential influence of hormonal therapy on biomarker expression could not be eliminated and thus, no untreated comparison group was available. Analgesic use also may have influenced the measured concentrations of PGP9.5 and TGF-β1 and should therefore be considered when interpreting the findings. In addition, a significant imbalance in parity was observed between the adenomyosis and non-adenomyosis groups. Because parity has been associated with the development of adenomyosis, it may have acted as a potential confounding factor and should be considered when interpreting the findings. Future studies with larger sample sizes and multivariable analyses are warranted to further evaluate the potential confounding effect of parity. Future Directions Further studies are warranted to compare PGP9.5 and TGF-β1 levels in eutopic and ectopic endometrial tissue in order to validate and extend the findings of the present study. Future research should also evaluate whether reductions in menstrual blood levels of PGP9.5 and TGF-β1 can serve as biomarkers of therapeutic response, particularly as indicators of successful medical treatment in alleviating pain in women with adenomyosis.

This study demonstrates that PGP9.5 and TGF-β1 levels in menstrual blood are significantly elevated in patients with adenomyosis compared with controls and exhibit excellent diagnostic performance for identifying adenomyosis. Across the overall study population, both biomarkers showed moderate positive correlations with dysmenorrhea severity, whereas these correlations were not significant within the adenomyosis subgroup alone. The strong correlation between PGP9.5 and TGF-β1 supports the close interaction between neurogenic remodeling and fibrotic pathways in adenomyosis. These findings suggest that menstrual blood PGP9.5 and TGF-β1 are promising non-invasive biomarkers for adenomyosis diagnosis, while their relationship with pain severity in established disease warrants further investigation. Data Sharing Statement The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. Acknowledgments We thank all participants who contributed to this study. We acknowledge the staff of the Molecular Genetics Laboratory, Universitas Padjadjaran, for their technical assistance. Author Contributions All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Disclosure The authors declare no conflicts of interest in this work.

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