Decreased Serum MG53 Levels Are Associated with SHBG and Androgen Excess in Women with Polycystic Ovary Syndrome

Decreased Serum MG53 Levels Are Associated with SHBG and Androgen Excess in Women with Polycystic Ovary Syndrome | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Decreased Serum MG53 Levels Are Associated with SHBG and Androgen Excess in Women with Polycystic Ovary Syndrome Enes Serhat Coşkun, Fatma Ketenci Gencer, Süleyman Salman, Nilhan Nurlu, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8007485/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Polycystic ovary syndrome (PCOS) is a common endocrine disorder characterized by hormonal and metabolic abnormalities. Mitsugumin-53 (MG53), a multifunctional E3 ubiquitin ligase, is implicated in insulin signaling and oxidative stress regulation, yet its role in PCOS remains unclear. This study aimed to investigate serum MG53 levels in women with PCOS and explore their associations with hormonal, metabolic, and ovarian parameters. Methods: This case–control study included 64 women with PCOS and 64 healthy controls with comparable age and BMI. Serum MG53 concentrations were measured using ELISA. Hormonal, metabolic, and ultrasonographic variables were analyzed, and correlations were assessed using Spearman’s rank analysis. Independent predictors of MG53 were identified via multiple linear regression, and diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis. Results: Serum MG53 levels were significantly lower in women with PCOS compared to controls (220.8 ± 288.9 vs. 335.2 ± 392.6 pg/mL, p = 0.001). MG53 showed a positive correlation with SHBG (ρ = 0.274, p = 0.010) and negative correlations with hirsutism (ρ = –0.463, p < 0.001) and follicle count (ρ = –0.223, p = 0.034). In multivariable analysis, SHBG remained an independent positive determinant of MG53, while hirsutism and follicle count were independent negative predictors. ROC analysis indicated modest diagnostic accuracy for PCOS (AUC = 0.667, 95% CI: 0.569–0.762). Conclusions: Serum MG53 levels are reduced in PCOS and independently associated with SHBG and androgen excess, suggesting a potential link between metabolic regulation and ovarian dysfunction. Although MG53 alone has limited diagnostic value, a model combining MG53 with hormonal and metabolic parameters may improve PCOS prediction, representing a novel direction for future research. Trial registration: ClinicalTrials.gov identifier: NCT07094776. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Endocrinology Health sciences/Medical research Polycystic ovary syndrome MG53 Mitsugumin-53 SHBG Androgen excess Biomarker Metabolic dysfunction Insulin resistance Case–control study Figures Figure 1 Figure 2 Background Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting 6–20% of women of reproductive age and characterized by oligo/anovulation, hyperandrogenism, and polycystic ovarian morphology [1, 2] . Beyond its reproductive manifestations, PCOS represents a complex metabolic condition strongly associated with insulin resistance, obesity, dyslipidemia, type 2 diabetes (T2D), and cardiovascular risk [1-4] . Chronic low-grade inflammation, oxidative stress, and stromal fibrosis also contribute to its multifactorial pathophysiology [3, 4] . Accordingly, molecules that regulate insulin signaling and cellular stress responses may play pivotal roles in the disorder. Mitsugumin-53 (MG53, also known as TRIM72) is an E3 ubiquitin ligase originally identified as a skeletal muscle membrane repair protein. Recent studies have expanded its biological relevance to metabolic and cardiometabolic diseases, showing context-dependent effects on insulin sensitivity, lipid metabolism, and inflammatory regulation [5-8]. Elevated circulating MG53 levels have been described in type 2 diabetes and obesity, whereas experimental models of metabolic syndrome have reported decreased MG53 expression associated with impaired tissue repair and mitochondrial stress [6-8]. These bidirectional effects have led to MG53 being characterized as a “double-edged sword” molecule influencing both regenerative and metabolic processes [5, 7]. MG53 modulates several pathways relevant to PCOS pathophysiology, including insulin signaling, PI3K/AKT/mTOR activation, oxidative stress regulation, and inflammatory signaling through NF-κB and inflammasome modulation [9, 10]. Furthermore, its roles in mitochondrial homeostasis and fibrosis are of particular interest given that ovarian stromal fibrosis and mitochondrial dysfunction in granulosa cells are key features of PCOS [9, 10]. Despite these mechanistic parallels, no prior clinical study has evaluated circulating MG53 levels in women with PCOS. Given these observations, we hypothesized that MG53 may be involved in the metabolic and ovarian alterations observed in PCOS. Therefore, this study aimed to compare serum MG53 concentrations between women with PCOS and healthy controls, and assess correlations between MG53 levels and hormonal, metabolic, and ovarian parameters. To the best of our knowledge, this is the first clinical study to evaluate circulating MG53 levels in women with PCOS. Elucidating the potential role of MG53 in PCOS may offer new insight into the intersection between reproductive and metabolic dysfunction and identify novel targets for future therapeutic research. Methods Study Design and Participants This prospective, observational case–control study was conducted at the Department of Obstetrics and Gynecology, Gaziosmanpaşa Training and Research Hospital. Participants were consecutively recruited from eligible women who presented to the clinic. Written informed consent was obtained from all participants prior to inclusion in the study. The study was carried out over a three-month period following approval by the Haseki Training and Research Hospital Non-Interventional Clinical Research Ethics Committee (Approval date: June 18, 2025; Decision No: 69–2025). The research was conducted in accordance with the principles of the Declaration of Helsinki and was registered in the ClinicalTrials.gov database (Registration No: NCT07094776). A total of 128 women were enrolled, including 64 patients diagnosed with polycystic ovary syndrome (PCOS) and 64 age- and body mass index (BMI)–matched healthy controls. The diagnosis of PCOS was established according to the Rotterdam criteria (2003), requiring the presence of at least two of the following: oligo/anovulation, clinical or biochemical hyperandrogenism, and polycystic ovarian morphology on ultrasonography. Before confirming the diagnosis of PCOS, hypothyroidism, hyperthyroidism, hyperprolactinemia, late-onset congenital adrenal hyperplasia, and androgen-secreting tumors were excluded through appropriate biochemical testing. The control group consisted of women with regular menstrual cycles who attended the clinic for routine gynecological examination, had no sonographic evidence of polycystic ovaries, and had no known endocrine or metabolic disorders. The two groups were comparable in terms of age and BMI distribution. Inclusion and Exclusion Criteria Women aged 18–45 years who were diagnosed with PCOS according to the Rotterdam criteria and provided written informed consent were included in the study. Exclusion criteria were as follows: pregnancy or lactation; acute or chronic systemic disease; diabetes mellitus; thyroid dysfunction; hepatic or renal failure; history of malignancy; use of hormonal therapy or metformin within the past six months; presence of chronic inflammatory or autoimmune disorders; use of steroids or medications affecting insulin sensitivity; a body weight change exceeding 10% within the past three months; and engagement in professional athletic activity. Clinical and Ultrasonographic Evaluation Clinical data including age, height, weight, body mass index (BMI), age at menarche, menstrual cycle pattern, Ferriman–Gallwey score, presence of acne, smoking and alcohol habits, physical activity level, infertility, and history of abortion were recorded for all participants.Regular smoking and alcohol use were defined as consistent consumption within the preceding three months. All ultrasonographic evaluations were performed by the same experienced investigator using the same ultrasound device (Mindray Resona R9) equipped with a transvaginal probe. Ovarian volume and follicle count were assessed separately for each ovary. To minimize measurement bias, all examinations were conducted by a single operator. Blood Sampling and Storage of Serum Samples Venous blood samples were collected from all participants during the early follicular phase (cycle days 2–4), in the morning hours after an overnight fast of at least 8 hours. The samples were centrifuged at 3000 rpm for 10 minutes to separate the serum. Two aliquots of serum were obtained from each participant and stored at –80 °C until analysis. All samples were kept under single freeze–thaw conditions, and hemolyzed or lipemic specimens were excluded from the study. Hormonal and Metabolic Measurements Serum levels of follicle-stimulating hormone (FSH), luteinizing hormone (LH), total testosterone, sex hormone-binding globulin (SHBG), anti-Müllerian hormone (AMH), prolactin, thyroid-stimulating hormone (TSH), glucose, insulin, total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglycerides, C-reactive protein (CRP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and erythrocyte sedimentation rate were measured using standard laboratory methods. Hormonal assays were performed using the electrochemiluminescence immunoassay (ECLIA) technique. Insulin resistance was calculated using the homeostasis model assessment (HOMA-IR) formula: HOMA-IR = [fasting glucose (mg/dL) × fasting insulin (µIU/mL)] / 405. Measurement of MG53 Levels Serum MG53 concentrations were measured using a commercial Human TRIM72 (Mitsugumin-53) ELISA kit (Sinogeneclon Co., Ltd., China; Cat. No: SG-13990). The kit sensitivity was 5 pg/mL, with a measurement range of 25–1600 pg/mL, and intra-assay and inter-assay coefficients of variation of <8% and <10%, respectively. All samples were analyzed in duplicate, and only plates with a coefficient of variation (%CV) ≤10 were accepted. The analyses were performed in a blinded manner by laboratory personnel who were unaware of the participants’ group assignments. Statistical Analysis Sample size estimation was performed based on an a priori power analysis using a two-tailed Student’s t-test to compare serum MG53 levels between two independent groups. Since no prior data were available on MG53 concentrations in PCOS, a moderate effect size (Cohen’s d = 0.50) was assumed according to Cohen’s convention, with a significance level (α) of 0.05 and power (1–β) of 0.80. The analysis indicated that a minimum of 64 participants per group (total n = 128) was required to achieve the desired statistical power. All statistical analyses were performed using IBM SPSS Statistics for Windows, Version 26.0 (IBM Corp., Armonk, NY, USA). Data distribution was assessed with the Shapiro–Wilk test. Normally distributed variables were expressed as mean ± standard deviation (SD), whereas non-normally distributed variables were presented as median (minimum–maximum). Group comparisons were performed using the independent samples t-test for normally distributed variables and the Mann–Whitney U test for non-normally distributed variables. Categorical variables were analyzed with the Chi-square or Fisher’s exact test, as appropriate. Associations between serum MG53 (TRIM72) levels and clinical, hormonal, and metabolic parameters were evaluated using Spearman’s rank correlation analysis. The predictive ability of MG53 for PCOS was examined through Receiver Operating Characteristic (ROC) curve analysis, and the area under the curve (AUC) with 95% confidence intervals (CI) and p-values were calculated. A multiple linear regression analysis was performed to identify independent determinants of MG53 concentrations. Variables showing significant correlations with MG53 in univariate analysis were entered into the model as independent predictors. Age and body mass index (BMI) were included as covariates to control for potential confounding, given their established influence on metabolic and hormonal parameters. The regression analysis was performed using the enter method, and model fitness was evaluated with R², adjusted R², and F statistics. Non-normally distributed variables were analyzed using appropriate non-parametric tests without data transformation. No correction for multiple testing was applied, as the analyses were exploratory. All statistical tests were two-tailed, and a p value < 0.05 was considered statistically significant. There were no missing data. Measurement bias was minimized through a single-center design, the use of standardized procedures, and blinded laboratory analyses. This study was designed, conducted, and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. All methodological steps were presented in compliance with the STROBE checklist recommendations. Results Demographic and Clinical Characteristics A total of 128 women were included in the study (64 with PCOS and 64 healthy controls). The groups were comparable in age (24.31 ± 4.94 vs. 24.91 ± 4.97 years, p = 0.463), whereas women with PCOS had a significantly higher body mass index (BMI) than controls (26.31 ± 5.00 kg/m² vs. 23.50 ± 4.11 kg/m², p = 0.0008). Maximum ovarian volume and bilateral follicle counts were markedly increased in the PCOS group ( p < 0.001 for all). Menstrual irregularity (81.2% vs. 15.6%), hirsutism (92.2% vs. 46.9%), and acne (50.0% vs. 18.8%) were also more frequent among PCOS patients (all p 0.05).(Table 1 ) Table 1 Comparison of Demographic and Clinical Characteristics Between PCOS and Control Groups Variable PCOS (Mean ± SD / Median [IQR] / n (%)) Control (Mean ± SD / Median [IQR] / n (%)) p-value Age (years) 24.31 ± 4.94 (23 [20–28]) 24.91 ± 4.97 (24 [21–28]) 0.463 Body Mass Index, kg/m² 26.31 ± 5.00 (26.3 [22.4–29.1]) 23.50 ± 4.11 (23.3 [20.7–25.1]) 0.0008 Age at Menarche (years) 12.95 ± 1.59 (13 [ 12 – 14 ]) 12.55 ± 1.30 (12 [ 12 – 13 ]) 0.064 Maximum Ovarian Volume, cm³ 12.40 ± 4.61 (11.8 [9.0–15.6]) 5.92 ± 2.23 (5.4 [4.3–7.1]) < 0.001 Left Ovarian Follicle Count 10.58 ± 2.50 (10.5 [8–12.25]) 7.11 ± 1.38 (7 [ 6 – 8 ]) < 0.001 Right Ovarian Follicle Count 10.66 ± 2.39 (12 [ 9 – 12 ]) 6.97 ± 1.57 (7 [ 6 – 8 ]) < 0.001 Menstrual Irregularity (Oligomenorrhea/Amenorrhea) 52 (81.2%) 10 (15.6%) < 0.001 Hirsutism, n (%) 59 (92.2%) 30 (46.9%) < 0.001 Acne, n (%) 32 (50.0%) 12 (18.8%) 0.0002 History of Pregnancy, n (%) 14 (21.9%) 14 (21.9%) 1.000 History of Miscarriage, n (%) 4 (6.2%) 4 (6.2%) 1.000 History of Infertility, n (%) 8 (12.5%) 6 (9.4%) 0.778 Current Smoking, n (%) 23 (35.9%) 18 (28.1%) 0.449 Alcohol Consumption, n (%) 11 (17.2%) 10 (15.6%) 1.000 Non-parametric variables were analyzed using the Mann–Whitney U test, and categorical variables using Fisher’s Exact test. Data are presented as Mean ± SD (Median [IQR]) or n (%). p < 0.05 was considered statistically significant. Hormonal and Metabolic Parameters Compared with healthy controls, women with PCOS showed significant alterations in hormonal and metabolic parameters (Table 2 ). Serum TRIM72/MG53 levels were significantly lower in the PCOS group (220.84 ± 288.88 pg/mL) than in controls (335.19 ± 392.59 pg/mL, p = 0.001). In contrast, LH, LH/FSH ratio, total testosterone, AMH, fasting glucose, insulin, HOMA-IR, and ALT were significantly higher in PCOS patients (all p < 0.05). SHBG ( p = 0.001) and FSH ( p = 0.040) levels were lower in the PCOS group. No significant intergroup differences were found in lipid profile (total, HDL, LDL cholesterol, triglycerides), inflammatory markers (CRP, ESR), or thyroid and hepatic enzymes other than ALT ( p > 0.05 for all). Table 2 Comparison of TRIM72/MG53 and related hormonal and metabolic parameters between PCOS and control groups. Variable PCOS (Mean ± SD (Median [IQR])) Control (Mean ± SD (Median [IQR])) p TRIM72/MG53 (pg/mL) 220.84 ± 288.88 (124.40 [104.25–202.15]) 335.19 ± 392.59 (206.40 [131.62–316.57]) 0.001 LH (IU/L) 7.43 ± 3.66 (6.55 [4.70–9.14]) 5.95 ± 6.09 (4.94 [3.73–6.17]) < 0.001 LH/FSH ratio 1.18 ± 0.54 (1.04 [0.80–1.43]) 0.79 ± 0.42 (0.67 [0.51–0.95]) < 0.001 Total testosterone (µg/L) 0.32 ± 0.12 (0.32 [0.23–0.38]) 0.23 ± 0.07 (0.21 [0.17–0.29]) < 0.001 Sex hormone-binding globulin (SHBG, nmol/L) 64.16 ± 39.03 (58.10 [38.27–70.33]) 77.03 ± 31.08 (75.00 [57.70–90.28]) 0.001 Anti-Müllerian hormone (AMH, ng/mL) 7.99 ± 4.33 (6.77 [5.64–9.73]) 3.71 ± 2.47 (3.65 [1.66–5.02]) < 0.001 HOMA-IR 2.81 ± 2.17 (2.46 [1.87–3.13]) 2.22 ± 1.64 (2.04 [1.36–2.50]) 0.003 ALT (U/L) 18.19 ± 7.46 (16.00 [13.00–23.00]) 16.72 ± 14.91 (14.00 [11.00–19.00]) 0.016 Glucose (mg/dL) 91.09 ± 7.35 (91.00 [86.62–95.37]) 86.56 ± 10.93 (89.00 [79.00–93.00]) 0.019 Insulin (mU/L) 12.34 ± 8.13 (11.21 [8.00–14.20]) 10.48 ± 7.45 (9.95 [6.18–12.17]) 0.030 FSH (IU/L) 6.36 ± 1.26 (6.22 [5.52–7.17]) 7.73 ± 4.87 (6.67 [5.71–8.57]) 0.040 LDL cholesterol (mg/dL) 110.69 ± 51.32 (102.50 [83.00–125.00]) 95.70 ± 24.43 (97.50 [78.75–106.25]) 0.080 Erythrocyte sedimentation rate (mm/h) 9.66 ± 8.11 (7.00 [4.00–12.25]) 7.19 ± 5.61 (6.00 [2.00–9.00]) 0.122 AST (U/L) 17.28 ± 3.77 (17.00 [15.00–19.00]) 16.84 ± 4.95 (15.00 [14.00–19.00]) 0.152 Total cholesterol (mg/dL) 183.98 ± 47.04 (179.50 [148.75–207.50]) 173.30 ± 31.25 (173.00 [152.75–196.00]) 0.298 HDL cholesterol (mg/dL) 56.47 ± 14.03 (55.50 [47.75–62.25]) 58.67 ± 14.46 (56.50 [48.00–65.25]) 0.418 C-reactive protein (CRP, mg/L) 5.71 ± 6.15 (4.00 [1.00–8.00]) 4.70 ± 5.30 (3.00 [1.00–5.33]) 0.433 Thyroid-stimulating hormone (TSH, mIU/L) 1.72 ± 0.96 (1.35 [1.16–1.96]) 1.94 ± 1.42 (1.48 [1.18–2.09]) 0.457 Prolactin (ng/mL) 12.07 ± 5.26 (11.29 [8.19–15.44]) 12.25 ± 6.12 (10.50 [8.22–14.07]) 0.740 Triglycerides (mg/dL) 103.50 ± 54.21 (93.50 [66.25–121.75]) 101.17 ± 53.16 (88.00 [60.50–131.25]) 0.782 Data are presented as Mean ± SD (Median [IQR]). p values were calculated using the Mann–Whitney U test. Correlations of Serum MG53 with Clinical and Biochemical Parameters Spearman’s rank analysis identified several significant correlations between circulating MG53 (TRIM72) levels and clinical or biochemical variables (Table 3 ). MG53 levels correlated positively with HDL cholesterol (ρ = 0.299, p < 0.001) and sex hormone–binding globulin (SHBG) (ρ = 0.274, p = 0.002). Negative correlations were observed with C-reactive protein (CRP) (ρ = −0.197, p = 0.026), hirsutism (ρ = −0.463, p < 0.001), presence of PCOS (ρ = −0.289, p = 0.001), maximum ovarian volume (ρ = −0.239, p = 0.007), and follicle count (ρ = −0.223, p = 0.012). Thus, lower MG53 concentrations were associated with more severe hyperandrogenic features and increased ovarian morphological indices, whereas higher MG53 levels corresponded to a more favorable metabolic and inflammatory profile. Table 3 Significant Spearman correlations between circulating MG53 (TRIM72) levels and clinical, hormonal, and metabolic parameters Parameter Spearman’s ρ p -value HDL cholesterol 0.299 < 0.001 Sex hormone-binding globulin 0.274 0.002 C-reactive protein −0.197 0.026 Hirsutism −0.463 < 0.001 PCOS −0.289 0.001 Maximum Ovarian Volume −0.239 0.007 Ovarian follicle count −0.223 0.012 Data are presented as Spearman’s correlation coefficients (ρ) and corresponding p -values. Correlations were analyzed using Spearman’s rank test. Only statistically significant associations ( p < 0.05) are displayed. Multiple Linear Regression Analysis A multiple linear regression model was constructed to identify independent determinants of serum MG53 levels, adjusting for age and BMI as potential confounders (Table 4 ). The overall model was significant ( F (9,118) = 3.10, p = 0.003), explaining 18.2% of the variance in MG53 concentrations (adjusted R² = 0.123). SHBG was an independent positive predictor of MG53 (β = 2.52, p = 0.010), whereas hirsutism (β = −186.53, p = 0.018) and ovarian follicle count (β = −34.10, p = 0.047) were independent negative predictors. No other variables, including HDL cholesterol, CRP, PCOS status, ovarian volume, age, or BMI, remained significant after adjustment. Table 4 Multiple linear regression analysis showing independent predictors of circulating MG53 (TRIM72) levels Variable β (Coefficient) SE t p -value 95% CI (Lower–Upper) Variable β (Coefficient) HDL cholesterol 0.48 2.32 0.21 0.836 −4.11–5.07 HDL (mg/dL) 0.48 Sex hormone-binding globulin (SHBG) 2.52 0.96 2.63 0.010 0.62–4.42 SHBG (nmol/L) 2.52 C-reactive protein −3.62 5.52 −0.66 0.513 −14.56–7.31 CRP (mg/L) −3.62 Hirsutism −186.53 77.98 −2.39 0.018 −340.94 – −32.11 Hirsutism (0 = absent, 1 = present) −186.53 PCOS 80.32 96.30 0.83 0.406 −110.37–271.02 PCOS (0 = absent, 1 = present) 80.32 Maximum Ovarian Volume 3.13 8.95 0.35 0.727 −14.59–20.85 Ovarian volume (cm³) 3.13 Ovarian Follicle Count −34.10 16.99 −2.01 0.047 −67.74 – −0.46 Ovarian follicle count −34.10 Age 9.09 6.27 1.45 0.150 −3.32–21.50 Age (years) 9.09 Body Mass Index 10.82 7.35 1.47 0.143 −3.73–25.37 BMI (kg/m²) 10.82 Model statistics n = 128, R² = 0.182, Adjusted R² = 0.123 , F (9,118) = 3.10 , p (model) = 0.003 Regression coefficients (β) are presented with standard errors and 95% confidence intervals. Age and BMI were included as covariates to control for potential demographic and anthropometric confounding. Receiver Operating Characteristic (ROC) Analysis ROC curve analysis demonstrated that serum MG53 (TRIM72) had a moderate discriminative capacity for identifying PCOS. The area under the curve (AUC) was 0.667 (95% CI: 0.569–0.762), indicating fair diagnostic accuracy. A cut-off value of 131.1 pg/mL provided the optimal balance between sensitivity (57.8%) and specificity (75.0%). Based on this threshold, serum MG53 concentrations below 131 pg/mL were associated with an increased likelihood of PCOS, whereas higher values were more consistent with non-PCOS profiles. As shown in the boxplot (Fig. 2 ), MG53 concentrations were distinctly lower in women with PCOS compared with healthy controls, supporting its potential role as a biomarker candidate for the disorder. (Fig. 1 – 2 ) Discussion This study presents the first clinical evidence demonstrating that serum Mitsugumin-53 (MG53, also known as TRIM72) levels are significantly decreased in women with polycystic ovary syndrome (PCOS). Moreover, MG53 levels were closely associated with both metabolic and ovarian parameters. MG53 showed a positive correlation with sex hormone-binding globulin (SHBG) and HDL cholesterol, and a negative correlation with hirsutism score, follicle count, and ovarian volume. These findings suggest that MG53, an E3 ubiquitin ligase involved in membrane repair and metabolic regulation, may play a previously unrecognized role in the pathophysiology of PCOS. MG53 has been mainly studied in the context of metabolic and cardiometabolic disorders [ 5 – 8 ]. Increased MG53 levels have been reported in type 2 diabetes and obesity, where MG53 impairs insulin signaling by promoting ubiquitination and degradation of the insulin receptor and IRS-1, and acts as a glucose-sensitive myokine that regulates systemic insulin response [ 5 , 6 ]. Conversely, decreased MG53 levels have been observed in metabolic syndrome and tissue injury models, which are associated with reduced membrane repair capacity and enhanced oxidative stress [ 8 , 11 , 12 ]. The reduction of MG53 in PCOS may reflect a complex metabolic–inflammatory phenotype characterized by insulin resistance, chronic inflammation, mitochondrial dysfunction, and fibrotic remodeling [ 3 , 4 ]. These findings imply that MG53 may be involved not only in glucose metabolism but also in inflammatory and regenerative processes relevant to PCOS. MG53 has been implicated in the regulation of endoplasmic reticulum (ER) stress and tissue remodeling.In diabetic wound models, suppression of TRIM72 alleviated ER stress and enhanced repair capacity, indicating that excessive MG53 activation may impair regeneration in certain pathological contexts [ 13 ].Furthermore, MG53 regulates fibrosis and cellular proliferation through the PI3K/AKT/mTOR signaling pathway.Experimental evidence shows that MG53 overexpression enhances hepatic stellate cell activation and extracellular matrix deposition, supporting its profibrotic role in liver injury models.Consistently, PCOS ovaries exhibit increased TGF-β expression and collagen accumulation, reflecting fibrotic remodeling of the ovarian stroma [ 3 , 4 ].Thus, dysregulated MG53 signaling may influence ovarian extracellular matrix dynamics in a similar manner. MG53 can also modulate inflammatory responses by interacting with the NF-κB and inflammasome signaling pathways. In infectious models, MG53 overexpression was shown to suppress host immunity against Candida albicans by inhibiting NF-κB activation and inflammasome assembly[ 14 ]. Beyond its protein-level effects, MG53 expression is also regulated epitranscriptomically through m6A RNA methylation mediated by YTHDF2, which influences oxidative stress and inflammatory responses [ 15 ]. In the context of PCOS, reduced MG53 levels may therefore contribute to the heightened inflammatory and oxidative milieu characteristic of the disorder [ 4 , 16 ]. Collectively, these multifaceted and context-dependent actions illustrate the dual nature of MG53, which can exert either protective or detrimental effects depending on cellular and metabolic conditions. This context-specific behavior has led to its characterization as a “double-edged sword” in the literature[ 17 ]. Mitochondrial dysfunction in granulosa cells has been implicated in impaired folliculogenesis and reduced oocyte quality [ 3 ].Given that MG53 supports mitochondrial integrity and energy homeostasis[ 11 , 12 , 15 , 18 ], lower MG53 levels may contribute to cellular stress and disrupted follicular development in PCOS.Recent evidence also indicates that MG53 can activate the Nrf2/HO-1 antioxidant pathway through its interaction with Annexin A1 [ 18 ] and enhance mitochondrial repair in neuronal models [ 12 ], further supporting its role in maintaining cellular homeostasis.The negative correlations between MG53 and both follicle count and ovarian volume in the present study support this hypothesis. No significant correlations were observed between MG53 and AMH or gonadotropins, suggesting that MG53 may be more closely related to stromal metabolic activity than to ovarian reserve per se. The positive association between MG53 and SHBG highlights the protein’s link to metabolic and hormonal balance. Low SHBG levels are closely associated with hyperandrogenism and insulin resistance [ 3 , 4 ]. Parallel changes in MG53 and SHBG may therefore reflect shared regulatory mechanisms involving hepatic function and inflammatory status. The positive correlation with HDL cholesterol and the negative correlation with CRP further support MG53’s role in maintaining metabolic–inflammatory homeostasis [ 7 , 8 , 14 ]. The observed positive association between MG53 and SHBG may represent a key metabolic–hormonal interface in PCOS. SHBG is synthesized primarily in the liver and binds circulating androgens, limiting their bioavailability. Reduced SHBG concentrations lead to higher levels of free testosterone, which intensifies clinical hyperandrogenism, including hirsutism and acne [ 3 , 4 ]. MG53, through its regulatory effects on hepatic insulin signaling and the PI3K/AKT/mTOR pathway [ 19 ], may indirectly influence SHBG synthesis. In insulin-resistant states, elevated insulin suppresses hepatic SHBG production; therefore, decreased MG53 expression could potentiate this suppression by impairing insulin sensitivity. Conversely, adequate MG53 activity may support hepatic metabolic stability and favor SHBG synthesis, thereby mitigating androgen excess. Additionally, MG53’s modulation of inflammatory and oxidative stress pathways via NF-κB and Nrf2 signaling [ 10 , 14 , 15 , 18 ] could further affect hepatic transcriptional control of SHBG. These interactions suggest that MG53 deficiency may act as a permissive factor for androgen-driven manifestations in PCOS through a combined effect on SHBG production, insulin resistance, and inflammatory tone. The negative correlation between MG53 and hirsutism found in this study supports this mechanistic link. Collectively, these findings point toward MG53 as a potential upstream regulator of the hepatic–endocrine crosstalk governing androgen bioavailability in PCOS. In multiple linear regression analysis, SHBG remained an independent positive determinant of MG53, whereas hirsutism and follicle count were independent negative predictors. Age and body mass index (BMI) were included as covariates because both are potential confounding factors known to influence metabolic markers. This approach enabled a clearer assessment of the independent relationships between hormonal, metabolic, and morphological parameters and MG53. Receiver operating characteristic (ROC) analysis revealed that MG53 had a modest diagnostic accuracy for predicting PCOS (AUC = 0.667, 95% CI: 0.569–0.762; sensitivity 57.8%, specificity 75.0). These findings suggest that MG53 alone has limited diagnostic value but may serve as an adjunct biomarker when combined with hormonal and metabolic parameters [ 7 ]. In future research, an integrated predictive model incorporating MG53 together with hormonal and metabolic indicators could be developed to improve diagnostic accuracy for PCOS. Such an approach may represent a promising direction for subsequent studies. The strengths of this study include a homogeneous study population, standardized sampling during the follicular phase, and blinded laboratory analyses. Nevertheless, the cross-sectional design precludes causal inference, and the sample size may limit subgroup analyses. Tissue-level MG53 expression was not assessed; thus, mechanistic and translational studies exploring MG53 expression in ovarian stroma and granulosa cells are warranted. Future investigations employing immunohistochemical or molecular approaches could further clarify the reproductive relevance of MG53. Conclusions This study demonstrates that serum MG53 levels are significantly reduced in women with PCOS and are associated with hormonal, metabolic, and ovarian morphological features. These findings suggest that MG53 may represent a novel molecular link between metabolic dysregulation and ovarian dysfunction in PCOS. As the first clinical study to evaluate circulating MG53 levels in PCOS, our findings emphasize that lower MG53 concentrations are positively correlated with SHBG levels and inversely related to clinical hyperandrogenism and ovarian morphological parameters. Although MG53 alone shows limited diagnostic power (AUC = 0.667), values below the identified cut-off (131 pg/mL) may support the diagnosis of PCOS in conjunction with established hormonal and metabolic markers. These results reinforce the potential of MG53 as an adjunct biomarker reflecting both metabolic and androgenic aspects of the syndrome. Further clinical and mechanistic studies evaluating the potential impact of MG53 on infertility prognosis, oocyte quality, and treatment responsiveness may open new avenues for understanding the interplay between reproductive and metabolic health. Abbreviations AUC area under the curve AMH anti-Müllerian hormone ALT alanine aminotransferase AST aspartate aminotransferase BMI body mass index CRP C-reactive protein ECLIA electrochemiluminescence immunoassay ER endoplasmic reticulum FSH follicle-stimulating hormone HDL high-density lipoprotein HOMA-IR homeostasis model assessment of insulin resistance IRS-1 insulin receptor substrate-1 LDL low-density lipoprotein LH luteinizing hormone MG53 Mitsugumin-53 mTOR mammalian target of rapamycin NF-κB nuclear factor kappa-light-chain-enhancer of activated B cells PCOS polycystic ovary syndrome ROC receiver operating characteristic SD standard deviation SE standard error SHBG sex hormone-binding globulin STROBE Strengthening the Reporting of Observational Studies in Epidemiology TG triglyceride TGF-β transforming growth factor beta TSH thyroid-stimulating hormone WHR waist-to-hip ratio. Declarations Ethics approval and consent to participate The study protocol was approved by the Gaziosmanpaşa Training and Research Hospital Ethics Committee (approval date: June 18, 2025; approval number: 25). Written informed consent was obtained from all participants prior to enrollment. Consent for publication Not applicable. Availability of data and materials The dataset supporting the conclusions of this article is included as an additional file in Excel format (dataset.xlsx). Competing interests The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors’ contributions ESC conceived and designed the study, collected data, and drafted the manuscript. FKG, SS, and NN contributed to data interpretation and biochemical analyses. HBB, SK, GYT, EÇ, ŞÜ, FT, and RG contributed to clinical data collection, statistical analysis, and manuscript revision. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank the staff of the Department of Obstetrics and Gynecology and the Department of Biochemistry at Gaziosmanpaşa Training and Research Hospital for their support and collaboration during this study. References Azziz, R. Polycystic ovary syndrome. Obstet. Gynecol. 132 (2), 321–336 (2018). Teede, H. J. et al. Recommendations from the 2023 international evidence-based guideline for the assessment and management of polycystic ovary syndrome. Eur. J. Endocrinol. 189 (2), G43–G64 (2023). Dumesic, D. A. et al. Scientific statement on the diagnostic criteria, epidemiology, pathophysiology, and molecular genetics of polycystic ovary syndrome. Endocr. Rev. 36 (5), 487–525 (2015). González, F. Inflammation in polycystic ovary syndrome: underpinning of insulin resistance and ovarian dysfunction. Steroids 77 (4), 300–305 (2012). Song, R. et al. Central role of E3 ubiquitin ligase MG53 in insulin resistance and metabolic disorders. Nature 494 (7437), 375–379 (2013). Wu, H-K. et al. Glucose-sensitive myokine/cardiokine MG53 regulates systemic insulin response and metabolic homeostasis. Circulation 139 (7), 901–914 (2019). Bianchi, C. et al. MG53 does not mark cardiovascular risk and all-cause mortality in subjects with type 2 diabetes: a prospective, observational study. Diabetes Res. Clin. Pract. 204 , 110916 (2023). Yanık Çolak, S. et al. The Relationship Between Serum MG53 Levels and the Presence of Metabolic Syndrome and Its Components. Medicina 61 (4), 582 (2025). Wang, Y-F., An, Z-Y., Li, J-W., Dong, Z-K. & Jin, W-L. MG53/TRIM72: multi-organ repair protein and beyond. Front. Physiol. 15 , 1377025 (2024). Gumpper-Fedus, K. et al. MG53 preserves mitochondrial integrity of cardiomyocytes during ischemia reperfusion-induced oxidative stress. Redox Biol. 54 , 102357 (2022). Liggett, M. R. et al. Treatment with MG53 ameliorates traumatic brain injury–associated acute kidney injury. Journal Trauma. Acute Care Surgery :101097. (2023). Bulgart, H. R., Lopez Perez, M. A. & Weisleder, N. Enhancing Membrane Repair Using Recombinant MG53/TRIM72 (rhMG53) Reduces Neurotoxicity in Alzheimer’s Disease Models. Biomolecules 15 (3), 418 (2025). Peng, L. et al. Suppression of TRIM72-mediated endoplasmic reticulum stress facilitates FOXM1 promotion of diabetic ulcer healing. Wound Repair. Regeneration . 33 (1), e13247 (2025). Tan, W. et al. Trim72 is a major host factor protecting against lethal Candida albicans infection. PLoS Pathog. 20 (11), e1012747 (2024). Li, Z., Li, K. & Zhao, J. YTHDF2 mediates the protective effects of MG53 on myocardial infarction injury via recognizing the m6A modification of MG53. J. Cardiothorac. Surg. 20 (1), 121 (2025). Gumpper, K. et al. Recombinant human MG53 protein preserves mitochondria integrity in cardiomyocytes during ischemia reperfusion-induced oxidative stress. bioRxiv 2020:2020.2002. 2006.936278. Zhang, Y., Wu, H-K., Lv, F-X. & Xiao, R-P. MG53 is a double-edged sword for human diseases. Sheng li xue bao:[Acta Physiol. Sinica] . 68 (4), 505–516 (2016). Zhang, L., Chen, P., Han, P., Fu, H. & Sun, B. Overexpression of Annexin A1 Inhibits Pyroptosis and Improves Dry Eye Signs by Regulating the TRIM72/Nrf2/HO-1 Signaling Pathway. Archivum Immunologiae et Ther. Experimentalis 73 (1). (2025). Shamsan, E. et al. The role of PI3k/AKT signaling pathway in attenuating liver fibrosis: a comprehensive review. Front. Med. 11 , 1389329 (2024). Additional Declarations No competing interests reported. Supplementary Files DataSet.xlsx Cite Share Download PDF Status: Published Journal Publication published 13 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 25 Feb, 2026 Reviews received at journal 25 Feb, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviews received at journal 16 Jan, 2026 Reviewers agreed at journal 23 Dec, 2025 Reviewers invited by journal 30 Nov, 2025 Editor assigned by journal 30 Nov, 2025 Editor invited by journal 14 Nov, 2025 Submission checks completed at journal 05 Nov, 2025 First submitted to journal 05 Nov, 2025 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. 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1","display":"","copyAsset":false,"role":"figure","size":63771,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eROC Curve of Serum MG53 (TRIM72) Levels for Prediction of PCOS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8007485/v1/d327bd549de846bdab0557b5.png"},{"id":97271533,"identity":"2a5c4aaf-210c-4380-9b8d-07d9bbefee22","added_by":"auto","created_at":"2025-12-02 15:02:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":268456,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eComparison of Serum MG53 (TRIM72)Levels Between PCOS and Control Groups\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8007485/v1/b6c578cb6a68fb237d9ca5f5.png"},{"id":107353622,"identity":"932ae09f-644d-4e35-88c0-57eca85de0c9","added_by":"auto","created_at":"2026-04-20 16:26:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":880204,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8007485/v1/486caa6c-cb73-4c06-ba0f-1fa34904095f.pdf"},{"id":97368464,"identity":"062a448b-5c47-48db-aad7-50704e5f6dd9","added_by":"auto","created_at":"2025-12-03 16:22:19","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":36113,"visible":true,"origin":"","legend":"","description":"","filename":"DataSet.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8007485/v1/8633f56bdf736821e6f5ded3.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Decreased Serum MG53 Levels Are Associated with SHBG and Androgen Excess in Women with Polycystic Ovary Syndrome","fulltext":[{"header":"Background","content":"\u003cp\u003ePolycystic ovary syndrome (PCOS) is a common endocrine disorder affecting 6–20% of women of reproductive age and characterized by oligo/anovulation, hyperandrogenism, and polycystic ovarian morphology\u0026nbsp;\u003cstrong\u003e[1, 2]\u003c/strong\u003e. Beyond its reproductive manifestations, PCOS represents a complex metabolic condition strongly associated with insulin resistance, obesity, dyslipidemia, type 2 diabetes (T2D), and cardiovascular risk\u0026nbsp;\u003cstrong\u003e[1-4]\u003c/strong\u003e. Chronic low-grade inflammation, oxidative stress, and stromal fibrosis also contribute to its multifactorial pathophysiology\u0026nbsp;\u003cstrong\u003e[3, 4]\u003c/strong\u003e. Accordingly, molecules that regulate insulin signaling and cellular stress responses may play pivotal roles in the disorder.\u003c/p\u003e\n\u003cp\u003eMitsugumin-53 (MG53, also known as TRIM72) is an E3 ubiquitin ligase originally identified as a skeletal muscle membrane repair protein. Recent studies have expanded its biological relevance to metabolic and cardiometabolic diseases, showing context-dependent effects on insulin sensitivity, lipid metabolism, and inflammatory regulation [5-8]. Elevated circulating MG53 levels have been described in type 2 diabetes and obesity, whereas experimental models of metabolic syndrome have reported decreased MG53 expression associated with impaired tissue repair and mitochondrial stress [6-8]. These bidirectional effects have led to MG53 being characterized as a “double-edged sword” molecule influencing both regenerative and metabolic processes [5, 7].\u003c/p\u003e\n\u003cp\u003eMG53 modulates several pathways relevant to PCOS pathophysiology, including insulin signaling, PI3K/AKT/mTOR activation, oxidative stress regulation, and inflammatory signaling through NF-κB and inflammasome modulation [9, 10]. Furthermore, its roles in mitochondrial homeostasis and fibrosis are of particular interest given that ovarian stromal fibrosis and mitochondrial dysfunction in granulosa cells are key features of PCOS [9, 10]. Despite these mechanistic parallels, no prior clinical study has evaluated circulating MG53 levels in women with PCOS.\u003c/p\u003e\n\u003cp\u003eGiven these observations, we hypothesized that MG53 may be involved in the metabolic and ovarian alterations observed in PCOS. Therefore, this study aimed to compare serum MG53 concentrations between women with PCOS and healthy controls, and assess correlations between MG53 levels and hormonal, metabolic, and ovarian parameters. To the best of our knowledge, this is the first clinical study to evaluate circulating MG53 levels in women with PCOS. Elucidating the potential role of MG53 in PCOS may offer new insight into the intersection between reproductive and metabolic dysfunction and identify novel targets for future therapeutic research.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eStudy Design and Participants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis prospective, observational case\u0026ndash;control study was conducted at the Department of Obstetrics and Gynecology, Gaziosmanpaşa Training and Research Hospital. Participants were consecutively recruited from eligible women who presented to the clinic. Written informed consent was obtained from all participants prior to inclusion in the study. The study was carried out over a three-month period following approval by the Haseki Training and Research Hospital Non-Interventional Clinical Research Ethics Committee (Approval date: June 18, 2025; Decision No: 69\u0026ndash;2025). The research was conducted in accordance with the principles of the Declaration of Helsinki and was registered in the ClinicalTrials.gov database (Registration No: NCT07094776).\u003c/p\u003e\n\u003cp\u003eA total of 128 women were enrolled, including 64 patients diagnosed with polycystic ovary syndrome (PCOS) and 64 age- and body mass index (BMI)\u0026ndash;matched healthy controls. The diagnosis of PCOS was established according to the Rotterdam criteria (2003), requiring the presence of at least two of the following: oligo/anovulation, clinical or biochemical hyperandrogenism, and polycystic ovarian morphology on ultrasonography.\u003c/p\u003e\n\u003cp\u003eBefore confirming the diagnosis of PCOS, hypothyroidism, hyperthyroidism, hyperprolactinemia, late-onset congenital adrenal hyperplasia, and androgen-secreting tumors were excluded through appropriate biochemical testing.\u003c/p\u003e\n\u003cp\u003eThe control group consisted of women with regular menstrual cycles who attended the clinic for routine gynecological examination, had no sonographic evidence of polycystic ovaries, and had no known endocrine or metabolic disorders. The two groups were comparable in terms of age and BMI distribution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eInclusion and Exclusion Criteria\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWomen aged 18\u0026ndash;45 years who were diagnosed with PCOS according to the Rotterdam criteria and provided written informed consent were included in the study.\u003c/p\u003e\n\u003cp\u003eExclusion criteria were as follows: pregnancy or lactation; acute or chronic systemic disease; diabetes mellitus; thyroid dysfunction; hepatic or renal failure; history of malignancy; use of hormonal therapy or metformin within the past six months; presence of chronic inflammatory or autoimmune disorders; use of steroids or medications affecting insulin sensitivity; a body weight change exceeding 10% within the past three months; and engagement in professional athletic activity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClinical and Ultrasonographic Evaluation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical data including age, height, weight, body mass index (BMI), age at menarche, menstrual cycle pattern, Ferriman\u0026ndash;Gallwey score, presence of acne, smoking and alcohol habits, physical activity level, infertility, and history of abortion were recorded for all participants.Regular smoking and alcohol use were defined as consistent consumption within the preceding three months.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll ultrasonographic evaluations were performed by the same experienced investigator using the same ultrasound device (Mindray Resona R9) equipped with a transvaginal probe. Ovarian volume and follicle count were assessed separately for each ovary. To minimize measurement bias, all examinations were conducted by a single operator.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBlood Sampling and Storage of Serum Samples\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVenous blood samples were collected from all participants during the early follicular phase (cycle days 2\u0026ndash;4), in the morning hours after an overnight fast of at least 8 hours. The samples were centrifuged at 3000 rpm for 10 minutes to separate the serum.\u003c/p\u003e\n\u003cp\u003eTwo aliquots of serum were obtained from each participant and stored at \u0026ndash;80 \u0026deg;C until analysis. All samples were kept under single freeze\u0026ndash;thaw conditions, and hemolyzed or lipemic specimens were excluded from the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHormonal and Metabolic Measurements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum levels of follicle-stimulating hormone (FSH), luteinizing hormone (LH), total testosterone, sex hormone-binding globulin (SHBG), anti-M\u0026uuml;llerian hormone (AMH), prolactin, thyroid-stimulating hormone (TSH), glucose, insulin, total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglycerides, C-reactive protein (CRP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and erythrocyte sedimentation rate were measured using standard laboratory methods.\u003c/p\u003e\n\u003cp\u003eHormonal assays were performed using the electrochemiluminescence immunoassay (ECLIA) technique. Insulin resistance was calculated using the homeostasis model assessment (HOMA-IR) formula: \u003cstrong\u003eHOMA-IR = [fasting glucose (mg/dL) \u0026times; fasting insulin (\u0026micro;IU/mL)] / 405.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMeasurement of MG53 Levels\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerum MG53 concentrations were measured using a commercial Human TRIM72 (Mitsugumin-53) ELISA kit (Sinogeneclon Co., Ltd., China; Cat. No: SG-13990). The kit sensitivity was 5 pg/mL, with a measurement range of 25\u0026ndash;1600 pg/mL, and intra-assay and inter-assay coefficients of variation of \u0026lt;8% and \u0026lt;10%, respectively.\u003c/p\u003e\n\u003cp\u003eAll samples were analyzed in duplicate, and only plates with a coefficient of variation (%CV) \u0026le;10 were accepted. The analyses were performed in a blinded manner by laboratory personnel who were unaware of the participants\u0026rsquo; group assignments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSample size estimation was performed based on an a priori power analysis using a two-tailed Student\u0026rsquo;s t-test to compare serum MG53 levels between two independent groups. Since no prior data were available on MG53 concentrations in PCOS, a moderate effect size (Cohen\u0026rsquo;s d = 0.50) was assumed according to Cohen\u0026rsquo;s convention, with a significance level (\u0026alpha;) of 0.05 and power (1\u0026ndash;\u0026beta;) of 0.80. The analysis indicated that a minimum of 64 participants per group (total n = 128) was required to achieve the desired statistical power.\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using IBM SPSS Statistics for Windows, Version 26.0 (IBM Corp., Armonk, NY, USA). Data distribution was assessed with the Shapiro\u0026ndash;Wilk test. Normally distributed variables were expressed as mean \u0026plusmn; standard deviation (SD), whereas non-normally distributed variables were presented as median (minimum\u0026ndash;maximum). Group comparisons were performed using the independent samples t-test for normally distributed variables and the Mann\u0026ndash;Whitney U test for non-normally distributed variables. Categorical variables were analyzed with the Chi-square or Fisher\u0026rsquo;s exact test, as appropriate.\u003c/p\u003e\n\u003cp\u003eAssociations between serum MG53 (TRIM72) levels and clinical, hormonal, and metabolic parameters were evaluated using Spearman\u0026rsquo;s rank correlation analysis. The predictive ability of MG53 for PCOS was examined through Receiver Operating Characteristic (ROC) curve analysis, and the area under the curve (AUC) with 95% confidence intervals (CI) and p-values were calculated.\u003c/p\u003e\n\u003cp\u003eA multiple linear regression analysis was performed to identify independent determinants of MG53 concentrations. Variables showing significant correlations with MG53 in univariate analysis were entered into the model as independent predictors. Age and body mass index (BMI) were included as covariates to control for potential confounding, given their established influence on metabolic and hormonal parameters. The regression analysis was performed using the enter method, and model fitness was evaluated with R\u0026sup2;, adjusted R\u0026sup2;, and F statistics.\u003c/p\u003e\n\u003cp\u003eNon-normally distributed variables were analyzed using appropriate non-parametric tests without data transformation. No correction for multiple testing was applied, as the analyses were exploratory. All statistical tests were two-tailed, and a p value \u0026lt; 0.05 was considered statistically significant. There were no missing data. Measurement bias was minimized through a single-center design, the use of standardized procedures, and blinded laboratory analyses.\u003c/p\u003e\n\u003cp\u003eThis study was designed, conducted, and reported in accordance with the \u003cem\u003eStrengthening the Reporting of Observational Studies in Epidemiology (STROBE)\u003c/em\u003e guidelines. All methodological steps were presented in compliance with the STROBE checklist recommendations.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eDemographic and Clinical Characteristics\u003c/h2\u003e\u003cp\u003eA total of 128 women were included in the study (64 with PCOS and 64 healthy controls). The groups were comparable in age (24.31\u0026thinsp;\u0026plusmn;\u0026thinsp;4.94 vs. 24.91\u0026thinsp;\u0026plusmn;\u0026thinsp;4.97 years, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.463), whereas women with PCOS had a significantly higher body mass index (BMI) than controls (26.31\u0026thinsp;\u0026plusmn;\u0026thinsp;5.00 kg/m\u0026sup2; vs. 23.50\u0026thinsp;\u0026plusmn;\u0026thinsp;4.11 kg/m\u0026sup2;, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0008). Maximum ovarian volume and bilateral follicle counts were markedly increased in the PCOS group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all). Menstrual irregularity (81.2% vs. 15.6%), hirsutism (92.2% vs. 46.9%), and acne (50.0% vs. 18.8%) were also more frequent among PCOS patients (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant differences were observed in age at menarche, reproductive history, or lifestyle factors such as smoking and alcohol use (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Demographic and Clinical Characteristics Between PCOS and Control Groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCOS (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD / Median [IQR] / n (%))\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD / Median [IQR] / n (%))\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.31\u0026thinsp;\u0026plusmn;\u0026thinsp;4.94 (23 [20\u0026ndash;28])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.91\u0026thinsp;\u0026plusmn;\u0026thinsp;4.97 (24 [21\u0026ndash;28])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody Mass Index, kg/m\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.31\u0026thinsp;\u0026plusmn;\u0026thinsp;5.00 (26.3 [22.4\u0026ndash;29.1])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.50\u0026thinsp;\u0026plusmn;\u0026thinsp;4.11 (23.3 [20.7\u0026ndash;25.1])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge at Menarche (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59 (13 [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30 (12 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum Ovarian Volume, cm\u0026sup3;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.40\u0026thinsp;\u0026plusmn;\u0026thinsp;4.61 (11.8 [9.0\u0026ndash;15.6])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.92\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23 (5.4 [4.3\u0026ndash;7.1])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft Ovarian Follicle Count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.58\u0026thinsp;\u0026plusmn;\u0026thinsp;2.50 (10.5 [8\u0026ndash;12.25])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38 (7 [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRight Ovarian Follicle Count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.66\u0026thinsp;\u0026plusmn;\u0026thinsp;2.39 (12 [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57 (7 [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMenstrual Irregularity (Oligomenorrhea/Amenorrhea)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (81.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (15.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHirsutism, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59 (92.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (46.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcne, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (18.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of Pregnancy, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (21.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (21.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of Miscarriage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of Infertility, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (12.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (9.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.778\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent Smoking, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (35.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (28.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.449\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol Consumption, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (17.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (15.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eNon-parametric variables were analyzed using the Mann\u0026ndash;Whitney U test, and categorical variables using Fisher\u0026rsquo;s Exact test. Data are presented as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (Median [IQR]) or n (%). p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eHormonal and Metabolic Parameters\u003c/h2\u003e\u003cp\u003eCompared with healthy controls, women with PCOS showed significant alterations in hormonal and metabolic parameters (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Serum TRIM72/MG53 levels were significantly lower in the PCOS group (220.84\u0026thinsp;\u0026plusmn;\u0026thinsp;288.88 pg/mL) than in controls (335.19\u0026thinsp;\u0026plusmn;\u0026thinsp;392.59 pg/mL, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). In contrast, LH, LH/FSH ratio, total testosterone, AMH, fasting glucose, insulin, HOMA-IR, and ALT were significantly higher in PCOS patients (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). SHBG (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and FSH (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040) levels were lower in the PCOS group. No significant intergroup differences were found in lipid profile (total, HDL, LDL cholesterol, triglycerides), inflammatory markers (CRP, ESR), or thyroid and hepatic enzymes other than ALT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of TRIM72/MG53 and related hormonal and metabolic parameters between PCOS and control groups.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePCOS (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (Median [IQR]))\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (Median [IQR]))\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTRIM72/MG53 (pg/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e220.84\u0026thinsp;\u0026plusmn;\u0026thinsp;288.88 (124.40 [104.25\u0026ndash;202.15])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e335.19\u0026thinsp;\u0026plusmn;\u0026thinsp;392.59 (206.40 [131.62\u0026ndash;316.57])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLH (IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e7.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.66 (6.55 [4.70\u0026ndash;9.14])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e5.95\u0026thinsp;\u0026plusmn;\u0026thinsp;6.09 (4.94 [3.73\u0026ndash;6.17])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLH/FSH ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54 (1.04 [0.80\u0026ndash;1.43])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42 (0.67 [0.51\u0026ndash;0.95])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal testosterone (\u0026micro;g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 (0.32 [0.23\u0026ndash;0.38])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 (0.21 [0.17\u0026ndash;0.29])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex hormone-binding globulin (SHBG, nmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e64.16\u0026thinsp;\u0026plusmn;\u0026thinsp;39.03 (58.10 [38.27\u0026ndash;70.33])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e77.03\u0026thinsp;\u0026plusmn;\u0026thinsp;31.08 (75.00 [57.70\u0026ndash;90.28])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnti-M\u0026uuml;llerian hormone (AMH, ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e7.99\u0026thinsp;\u0026plusmn;\u0026thinsp;4.33 (6.77 [5.64\u0026ndash;9.73])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47 (3.65 [1.66\u0026ndash;5.02])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHOMA-IR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e2.81\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17 (2.46 [1.87\u0026ndash;3.13])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64 (2.04 [1.36\u0026ndash;2.50])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e18.19\u0026thinsp;\u0026plusmn;\u0026thinsp;7.46 (16.00 [13.00\u0026ndash;23.00])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e16.72\u0026thinsp;\u0026plusmn;\u0026thinsp;14.91 (14.00 [11.00\u0026ndash;19.00])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e91.09\u0026thinsp;\u0026plusmn;\u0026thinsp;7.35 (91.00 [86.62\u0026ndash;95.37])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e86.56\u0026thinsp;\u0026plusmn;\u0026thinsp;10.93 (89.00 [79.00\u0026ndash;93.00])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsulin (mU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e12.34\u0026thinsp;\u0026plusmn;\u0026thinsp;8.13 (11.21 [8.00\u0026ndash;14.20])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e10.48\u0026thinsp;\u0026plusmn;\u0026thinsp;7.45 (9.95 [6.18\u0026ndash;12.17])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFSH (IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e6.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26 (6.22 [5.52\u0026ndash;7.17])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e7.73\u0026thinsp;\u0026plusmn;\u0026thinsp;4.87 (6.67 [5.71\u0026ndash;8.57])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL cholesterol (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e110.69\u0026thinsp;\u0026plusmn;\u0026thinsp;51.32 (102.50 [83.00\u0026ndash;125.00])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e95.70\u0026thinsp;\u0026plusmn;\u0026thinsp;24.43 (97.50 [78.75\u0026ndash;106.25])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eErythrocyte sedimentation rate (mm/h)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e9.66\u0026thinsp;\u0026plusmn;\u0026thinsp;8.11 (7.00 [4.00\u0026ndash;12.25])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e7.19\u0026thinsp;\u0026plusmn;\u0026thinsp;5.61 (6.00 [2.00\u0026ndash;9.00])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e17.28\u0026thinsp;\u0026plusmn;\u0026thinsp;3.77 (17.00 [15.00\u0026ndash;19.00])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e16.84\u0026thinsp;\u0026plusmn;\u0026thinsp;4.95 (15.00 [14.00\u0026ndash;19.00])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.152\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e183.98\u0026thinsp;\u0026plusmn;\u0026thinsp;47.04 (179.50 [148.75\u0026ndash;207.50])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e173.30\u0026thinsp;\u0026plusmn;\u0026thinsp;31.25 (173.00 [152.75\u0026ndash;196.00])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.298\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL cholesterol (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e56.47\u0026thinsp;\u0026plusmn;\u0026thinsp;14.03 (55.50 [47.75\u0026ndash;62.25])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e58.67\u0026thinsp;\u0026plusmn;\u0026thinsp;14.46 (56.50 [48.00\u0026ndash;65.25])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.418\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-reactive protein (CRP, mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e5.71\u0026thinsp;\u0026plusmn;\u0026thinsp;6.15 (4.00 [1.00\u0026ndash;8.00])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e4.70\u0026thinsp;\u0026plusmn;\u0026thinsp;5.30 (3.00 [1.00\u0026ndash;5.33])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.433\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThyroid-stimulating hormone (TSH, mIU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96 (1.35 [1.16\u0026ndash;1.96])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42 (1.48 [1.18\u0026ndash;2.09])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.457\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProlactin (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e12.07\u0026thinsp;\u0026plusmn;\u0026thinsp;5.26 (11.29 [8.19\u0026ndash;15.44])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e12.25\u0026thinsp;\u0026plusmn;\u0026thinsp;6.12 (10.50 [8.22\u0026ndash;14.07])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.740\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e103.50\u0026thinsp;\u0026plusmn;\u0026thinsp;54.21 (93.50 [66.25\u0026ndash;121.75])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e101.17\u0026thinsp;\u0026plusmn;\u0026thinsp;53.16 (88.00 [60.50\u0026ndash;131.25])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.782\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eData are presented as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (Median [IQR]). p values were calculated using the Mann\u0026ndash;Whitney U test.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCorrelations of Serum MG53 with Clinical and Biochemical Parameters\u003c/h2\u003e\u003cp\u003eSpearman\u0026rsquo;s rank analysis identified several significant correlations between circulating MG53 (TRIM72) levels and clinical or biochemical variables (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). MG53 levels correlated positively with HDL cholesterol (ρ\u0026thinsp;=\u0026thinsp;0.299, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and sex hormone\u0026ndash;binding globulin (SHBG) (ρ\u0026thinsp;=\u0026thinsp;0.274, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Negative correlations were observed with C-reactive protein (CRP) (ρ = \u0026minus;0.197, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026), hirsutism (ρ = \u0026minus;0.463, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), presence of PCOS (ρ = \u0026minus;0.289, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), maximum ovarian volume (ρ = \u0026minus;0.239, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), and follicle count (ρ = \u0026minus;0.223, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012). Thus, lower MG53 concentrations were associated with more severe hyperandrogenic features and increased ovarian morphological indices, whereas higher MG53 levels corresponded to a more favorable metabolic and inflammatory profile.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSignificant Spearman correlations between circulating MG53 (TRIM72) levels and clinical, hormonal, and metabolic parameters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpearman\u0026rsquo;s ρ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL cholesterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex hormone-binding globulin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-reactive protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHirsutism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum Ovarian Volume\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOvarian follicle count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eData are presented as Spearman\u0026rsquo;s correlation coefficients (ρ) and corresponding\u003c/em\u003e p\u003cem\u003e-values. Correlations were analyzed using Spearman\u0026rsquo;s rank test. Only statistically significant associations (\u003c/em\u003ep\u0026thinsp;\u003cem\u003e\u0026lt;\u0026thinsp;0.05) are displayed.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eMultiple Linear Regression Analysis\u003c/h2\u003e\u003cp\u003eA multiple linear regression model was constructed to identify independent determinants of serum MG53 levels, adjusting for age and BMI as potential confounders (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The overall model was significant (\u003cem\u003eF\u003c/em\u003e(9,118)\u0026thinsp;=\u0026thinsp;3.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), explaining 18.2% of the variance in MG53 concentrations (adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.123). SHBG was an independent positive predictor of MG53 (β\u0026thinsp;=\u0026thinsp;2.52, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010), whereas hirsutism (β = \u0026minus;186.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) and ovarian follicle count (β = \u0026minus;34.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047) were independent negative predictors. No other variables, including HDL cholesterol, CRP, PCOS status, ovarian volume, age, or BMI, remained significant after adjustment.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultiple linear regression analysis showing independent predictors of circulating MG53 (TRIM72) levels\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ (Coefficient)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI (Lower\u0026ndash;Upper)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eβ (Coefficient)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL cholesterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;4.11\u0026ndash;5.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHDL (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex hormone-binding globulin (SHBG)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.62\u0026ndash;4.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSHBG (nmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-reactive protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;3.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.513\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;14.56\u0026ndash;7.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCRP (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;3.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHirsutism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;186.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;2.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;340.94 \u0026ndash; \u0026minus;32.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHirsutism (0\u0026thinsp;=\u0026thinsp;absent, 1\u0026thinsp;=\u0026thinsp;present)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;186.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e96.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;110.37\u0026ndash;271.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePCOS (0\u0026thinsp;=\u0026thinsp;absent, 1\u0026thinsp;=\u0026thinsp;present)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e80.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum Ovarian Volume\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;14.59\u0026ndash;20.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOvarian volume (cm\u0026sup3;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOvarian Follicle Count\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e\u0026minus;34.10\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e16.99\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e\u0026minus;2.01\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e0.047\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003e\u0026minus;67.74 \u0026ndash; \u0026minus;0.46\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOvarian follicle count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;34.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;3.32\u0026ndash;21.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;3.73\u0026ndash;25.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e10.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eModel statistics\u003c/strong\u003e\u003cp\u003en\u0026thinsp;\u003cem\u003e=\u0026thinsp;128, R\u0026sup2; = 0.182, Adjusted R\u0026sup2; = 0.123\u003c/em\u003e, F\u003cem\u003e(9,118)\u0026thinsp;=\u0026thinsp;3.10\u003c/em\u003e, p\u003cem\u003e(model)\u0026thinsp;=\u0026thinsp;0.003 Regression coefficients (β) are presented with standard errors and 95% confidence intervals.\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eAge and BMI were included as covariates to control for potential demographic and anthropometric confounding.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eReceiver Operating Characteristic (ROC) Analysis\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eROC curve analysis demonstrated that serum MG53 (TRIM72) had a moderate discriminative capacity for identifying PCOS. The area under the curve (AUC) was 0.667 (95% CI: 0.569\u0026ndash;0.762), indicating fair diagnostic accuracy. A cut-off value of 131.1 pg/mL provided the optimal balance between sensitivity (57.8%) and specificity (75.0%). Based on this threshold, serum MG53 concentrations below 131 pg/mL were associated with an increased likelihood of PCOS, whereas higher values were more consistent with non-PCOS profiles. As shown in the boxplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), MG53 concentrations were distinctly lower in women with PCOS compared with healthy controls, supporting its potential role as a biomarker candidate for the disorder. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study presents the first clinical evidence demonstrating that serum Mitsugumin-53 (MG53, also known as TRIM72) levels are significantly decreased in women with polycystic ovary syndrome (PCOS). Moreover, MG53 levels were closely associated with both metabolic and ovarian parameters. MG53 showed a positive correlation with sex hormone-binding globulin (SHBG) and HDL cholesterol, and a negative correlation with hirsutism score, follicle count, and ovarian volume. These findings suggest that MG53, an E3 ubiquitin ligase involved in membrane repair and metabolic regulation, may play a previously unrecognized role in the pathophysiology of PCOS.\u003c/p\u003e\u003cp\u003eMG53 has been mainly studied in the context of metabolic and cardiometabolic disorders [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Increased MG53 levels have been reported in type 2 diabetes and obesity, where MG53 impairs insulin signaling by promoting ubiquitination and degradation of the insulin receptor and IRS-1, and acts as a glucose-sensitive myokine that regulates systemic insulin response [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Conversely, decreased MG53 levels have been observed in metabolic syndrome and tissue injury models, which are associated with reduced membrane repair capacity and enhanced oxidative stress [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The reduction of MG53 in PCOS may reflect a complex metabolic\u0026ndash;inflammatory phenotype characterized by insulin resistance, chronic inflammation, mitochondrial dysfunction, and fibrotic remodeling [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These findings imply that MG53 may be involved not only in glucose metabolism but also in inflammatory and regenerative processes relevant to PCOS.\u003c/p\u003e\u003cp\u003eMG53 has been implicated in the regulation of endoplasmic reticulum (ER) stress and tissue remodeling.In diabetic wound models, suppression of TRIM72 alleviated ER stress and enhanced repair capacity, indicating that excessive MG53 activation may impair regeneration in certain pathological contexts [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].Furthermore, MG53 regulates fibrosis and cellular proliferation through the PI3K/AKT/mTOR signaling pathway.Experimental evidence shows that MG53 overexpression enhances hepatic stellate cell activation and extracellular matrix deposition, supporting its profibrotic role in liver injury models.Consistently, PCOS ovaries exhibit increased TGF-β expression and collagen accumulation, reflecting fibrotic remodeling of the ovarian stroma [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].Thus, dysregulated MG53 signaling may influence ovarian extracellular matrix dynamics in a similar manner. MG53 can also modulate inflammatory responses by interacting with the NF-κB and inflammasome signaling pathways. In infectious models, MG53 overexpression was shown to suppress host immunity against Candida albicans by inhibiting NF-κB activation and inflammasome assembly[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Beyond its protein-level effects, MG53 expression is also regulated epitranscriptomically through m6A RNA methylation mediated by YTHDF2, which influences oxidative stress and inflammatory responses [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In the context of PCOS, reduced MG53 levels may therefore contribute to the heightened inflammatory and oxidative milieu characteristic of the disorder [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Collectively, these multifaceted and context-dependent actions illustrate the dual nature of MG53, which can exert either protective or detrimental effects depending on cellular and metabolic conditions. This context-specific behavior has led to its characterization as a \u0026ldquo;double-edged sword\u0026rdquo; in the literature[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMitochondrial dysfunction in granulosa cells has been implicated in impaired folliculogenesis and reduced oocyte quality [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].Given that MG53 supports mitochondrial integrity and energy homeostasis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], lower MG53 levels may contribute to cellular stress and disrupted follicular development in PCOS.Recent evidence also indicates that MG53 can activate the Nrf2/HO-1 antioxidant pathway through its interaction with Annexin A1 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and enhance mitochondrial repair in neuronal models [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], further supporting its role in maintaining cellular homeostasis.The negative correlations between MG53 and both follicle count and ovarian volume in the present study support this hypothesis. No significant correlations were observed between MG53 and AMH or gonadotropins, suggesting that MG53 may be more closely related to stromal metabolic activity than to ovarian reserve per se.\u003c/p\u003e\u003cp\u003eThe positive association between MG53 and SHBG highlights the protein\u0026rsquo;s link to metabolic and hormonal balance. Low SHBG levels are closely associated with hyperandrogenism and insulin resistance [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Parallel changes in MG53 and SHBG may therefore reflect shared regulatory mechanisms involving hepatic function and inflammatory status. The positive correlation with HDL cholesterol and the negative correlation with CRP further support MG53\u0026rsquo;s role in maintaining metabolic\u0026ndash;inflammatory homeostasis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe observed positive association between MG53 and SHBG may represent a key metabolic\u0026ndash;hormonal interface in PCOS. SHBG is synthesized primarily in the liver and binds circulating androgens, limiting their bioavailability. Reduced SHBG concentrations lead to higher levels of free testosterone, which intensifies clinical hyperandrogenism, including hirsutism and acne [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. MG53, through its regulatory effects on hepatic insulin signaling and the PI3K/AKT/mTOR pathway [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], may indirectly influence SHBG synthesis. In insulin-resistant states, elevated insulin suppresses hepatic SHBG production; therefore, decreased MG53 expression could potentiate this suppression by impairing insulin sensitivity. Conversely, adequate MG53 activity may support hepatic metabolic stability and favor SHBG synthesis, thereby mitigating androgen excess.\u003c/p\u003e\u003cp\u003eAdditionally, MG53\u0026rsquo;s modulation of inflammatory and oxidative stress pathways via NF-κB and Nrf2 signaling [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] could further affect hepatic transcriptional control of SHBG. These interactions suggest that MG53 deficiency may act as a permissive factor for androgen-driven manifestations in PCOS through a combined effect on SHBG production, insulin resistance, and inflammatory tone. The negative correlation between MG53 and hirsutism found in this study supports this mechanistic link. Collectively, these findings point toward MG53 as a potential upstream regulator of the hepatic\u0026ndash;endocrine crosstalk governing androgen bioavailability in PCOS.\u003c/p\u003e\u003cp\u003eIn multiple linear regression analysis, SHBG remained an independent positive determinant of MG53, whereas hirsutism and follicle count were independent negative predictors. Age and body mass index (BMI) were included as covariates because both are potential confounding factors known to influence metabolic markers. This approach enabled a clearer assessment of the independent relationships between hormonal, metabolic, and morphological parameters and MG53. Receiver operating characteristic (ROC) analysis revealed that MG53 had a modest diagnostic accuracy for predicting PCOS (AUC\u0026thinsp;=\u0026thinsp;0.667, 95% CI: 0.569\u0026ndash;0.762; sensitivity 57.8%, specificity 75.0). These findings suggest that MG53 alone has limited diagnostic value but may serve as an adjunct biomarker when combined with hormonal and metabolic parameters [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In future research, an integrated predictive model incorporating MG53 together with hormonal and metabolic indicators could be developed to improve diagnostic accuracy for PCOS. Such an approach may represent a promising direction for subsequent studies.\u003c/p\u003e\u003cp\u003eThe strengths of this study include a homogeneous study population, standardized sampling during the follicular phase, and blinded laboratory analyses. Nevertheless, the cross-sectional design precludes causal inference, and the sample size may limit subgroup analyses. Tissue-level MG53 expression was not assessed; thus, mechanistic and translational studies exploring MG53 expression in ovarian stroma and granulosa cells are warranted. Future investigations employing immunohistochemical or molecular approaches could further clarify the reproductive relevance of MG53.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrates that serum MG53 levels are significantly reduced in women with PCOS and are associated with hormonal, metabolic, and ovarian morphological features. These findings suggest that MG53 may represent a novel molecular link between metabolic dysregulation and ovarian dysfunction in PCOS. As the first clinical study to evaluate circulating MG53 levels in PCOS, our findings emphasize that lower MG53 concentrations are positively correlated with SHBG levels and inversely related to clinical hyperandrogenism and ovarian morphological parameters. Although MG53 alone shows limited diagnostic power (AUC\u0026thinsp;=\u0026thinsp;0.667), values below the identified cut-off (131 pg/mL) may support the diagnosis of PCOS in conjunction with established hormonal and metabolic markers. These results reinforce the potential of MG53 as an adjunct biomarker reflecting both metabolic and androgenic aspects of the syndrome. Further clinical and mechanistic studies evaluating the potential impact of MG53 on infertility prognosis, oocyte quality, and treatment responsiveness may open new avenues for understanding the interplay between reproductive and metabolic health.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003earea under the curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAMH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eanti-M\u0026uuml;llerian hormone\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ealanine aminotransferase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003easpartate aminotransferase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eC-reactive protein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eECLIA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eelectrochemiluminescence immunoassay\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eER\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eendoplasmic reticulum\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFSH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003efollicle-stimulating hormone\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHDL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehigh-density lipoprotein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHOMA-IR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehomeostasis model assessment of insulin resistance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIRS-1\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003einsulin receptor substrate-1\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLDL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elow-density lipoprotein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eluteinizing hormone\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMG53\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMitsugumin-53\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003emTOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emammalian target of rapamycin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNF-κB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enuclear factor kappa-light-chain-enhancer of activated B cells\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCOS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epolycystic ovary syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ereceiver operating characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003estandard deviation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003estandard error\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSHBG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esex hormone-binding globulin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSTROBE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStrengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etriglyceride\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTGF-β\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etransforming growth factor beta\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTSH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ethyroid-stimulating hormone\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWHR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ewaist-to-hip ratio.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Gaziosmanpaşa Training and Research Hospital Ethics Committee (approval date: June 18, 2025; approval number: 25). Written informed consent was obtained from all participants prior to enrollment.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe dataset supporting the conclusions of this article is included as an additional file in Excel format (dataset.xlsx).\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eESC conceived and designed the study, collected data, and drafted the manuscript. FKG, SS, and NN contributed to data interpretation and biochemical analyses. HBB, SK, GYT, E\u0026Ccedil;, Ş\u0026Uuml;, FT, and RG contributed to clinical data collection, statistical analysis, and manuscript revision. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the staff of the Department of Obstetrics and Gynecology and the Department of Biochemistry at Gaziosmanpaşa Training and Research Hospital for their support and collaboration during this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAzziz, R. Polycystic ovary syndrome. \u003cem\u003eObstet. Gynecol.\u003c/em\u003e \u003cb\u003e132\u003c/b\u003e (2), 321\u0026ndash;336 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTeede, H. J. et al. Recommendations from the 2023 international evidence-based guideline for the assessment and management of polycystic ovary syndrome. \u003cem\u003eEur. J. Endocrinol.\u003c/em\u003e \u003cb\u003e189\u003c/b\u003e (2), G43\u0026ndash;G64 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDumesic, D. A. et al. Scientific statement on the diagnostic criteria, epidemiology, pathophysiology, and molecular genetics of polycystic ovary syndrome. \u003cem\u003eEndocr. Rev.\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e (5), 487\u0026ndash;525 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGonz\u0026aacute;lez, F. Inflammation in polycystic ovary syndrome: underpinning of insulin resistance and ovarian dysfunction. \u003cem\u003eSteroids\u003c/em\u003e \u003cb\u003e77\u003c/b\u003e (4), 300\u0026ndash;305 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong, R. et al. Central role of E3 ubiquitin ligase MG53 in insulin resistance and metabolic disorders. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e494\u003c/b\u003e (7437), 375\u0026ndash;379 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu, H-K. et al. Glucose-sensitive myokine/cardiokine MG53 regulates systemic insulin response and metabolic homeostasis. \u003cem\u003eCirculation\u003c/em\u003e \u003cb\u003e139\u003c/b\u003e (7), 901\u0026ndash;914 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBianchi, C. et al. MG53 does not mark cardiovascular risk and all-cause mortality in subjects with type 2 diabetes: a prospective, observational study. \u003cem\u003eDiabetes Res. Clin. Pract.\u003c/em\u003e \u003cb\u003e204\u003c/b\u003e, 110916 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYanık \u0026Ccedil;olak, S. et al. The Relationship Between Serum MG53 Levels and the Presence of Metabolic Syndrome and Its Components. \u003cem\u003eMedicina\u003c/em\u003e \u003cb\u003e61\u003c/b\u003e (4), 582 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, Y-F., An, Z-Y., Li, J-W., Dong, Z-K. \u0026amp; Jin, W-L. MG53/TRIM72: multi-organ repair protein and beyond. \u003cem\u003eFront. Physiol.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 1377025 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGumpper-Fedus, K. et al. MG53 preserves mitochondrial integrity of cardiomyocytes during ischemia reperfusion-induced oxidative stress. \u003cem\u003eRedox Biol.\u003c/em\u003e \u003cb\u003e54\u003c/b\u003e, 102357 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiggett, M. R. et al. Treatment with MG53 ameliorates traumatic brain injury\u0026ndash;associated acute kidney injury. \u003cem\u003eJournal Trauma. Acute Care Surgery\u003c/em\u003e :101097. (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBulgart, H. R., Lopez Perez, M. A. \u0026amp; Weisleder, N. Enhancing Membrane Repair Using Recombinant MG53/TRIM72 (rhMG53) Reduces Neurotoxicity in Alzheimer\u0026rsquo;s Disease Models. \u003cem\u003eBiomolecules\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (3), 418 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeng, L. et al. Suppression of TRIM72-mediated endoplasmic reticulum stress facilitates FOXM1 promotion of diabetic ulcer healing. \u003cem\u003eWound Repair. Regeneration\u003c/em\u003e. \u003cb\u003e33\u003c/b\u003e (1), e13247 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTan, W. et al. Trim72 is a major host factor protecting against lethal Candida albicans infection. \u003cem\u003ePLoS Pathog.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (11), e1012747 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, Z., Li, K. \u0026amp; Zhao, J. YTHDF2 mediates the protective effects of MG53 on myocardial infarction injury via recognizing the m6A modification of MG53. \u003cem\u003eJ. Cardiothorac. Surg.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (1), 121 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGumpper, K. et al. Recombinant human MG53 protein preserves mitochondria integrity in cardiomyocytes during ischemia reperfusion-induced oxidative stress. \u003cem\u003ebioRxiv\u003c/em\u003e 2020:2020.2002. 2006.936278.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, Y., Wu, H-K., Lv, F-X. \u0026amp; Xiao, R-P. MG53 is a double-edged sword for human diseases. \u003cem\u003eSheng li xue bao:[Acta Physiol. Sinica]\u003c/em\u003e. \u003cb\u003e68\u003c/b\u003e (4), 505\u0026ndash;516 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, L., Chen, P., Han, P., Fu, H. \u0026amp; Sun, B. Overexpression of Annexin A1 Inhibits Pyroptosis and Improves Dry Eye Signs by Regulating the TRIM72/Nrf2/HO-1 Signaling Pathway. \u003cem\u003eArchivum Immunologiae et Ther. Experimentalis\u003c/em\u003e \u003cb\u003e73\u003c/b\u003e(1). (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShamsan, E. et al. The role of PI3k/AKT signaling pathway in attenuating liver fibrosis: a comprehensive review. \u003cem\u003eFront. Med.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 1389329 (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Polycystic ovary syndrome, MG53, Mitsugumin-53, SHBG, Androgen excess, Biomarker, Metabolic dysfunction, Insulin resistance, Case–control study","lastPublishedDoi":"10.21203/rs.3.rs-8007485/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8007485/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground:\u003c/p\u003e\n\u003cp\u003ePolycystic ovary syndrome (PCOS) is a common endocrine disorder characterized by hormonal and metabolic abnormalities. Mitsugumin-53 (MG53), a multifunctional E3 ubiquitin ligase, is implicated in insulin signaling and oxidative stress regulation, yet its role in PCOS remains unclear. This study aimed to investigate serum MG53 levels in women with PCOS and explore their associations with hormonal, metabolic, and ovarian parameters.\u003c/p\u003e\n\u003cp\u003eMethods:\u003c/p\u003e\n\u003cp\u003eThis case–control study included 64 women with PCOS and 64 healthy controls with comparable age and BMI. Serum MG53 concentrations were measured using ELISA. Hormonal, metabolic, and ultrasonographic variables were analyzed, and correlations were assessed using Spearman’s rank analysis. Independent predictors of MG53 were identified via multiple linear regression, and diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis.\u003c/p\u003e\n\u003cp\u003eResults:\u003c/p\u003e\n\u003cp\u003eSerum MG53 levels were significantly lower in women with PCOS compared to controls (220.8 ± 288.9 vs. 335.2 ± 392.6 pg/mL, p = 0.001). MG53 showed a positive correlation with SHBG (ρ = 0.274, p = 0.010) and negative correlations with hirsutism (ρ = –0.463, p \u0026lt; 0.001) and follicle count (ρ = –0.223, p = 0.034). In multivariable analysis, SHBG remained an independent positive determinant of MG53, while hirsutism and follicle count were independent negative predictors. ROC analysis indicated modest diagnostic accuracy for PCOS (AUC = 0.667, 95% CI: 0.569–0.762).\u003c/p\u003e\n\u003cp\u003eConclusions:\u003c/p\u003e\n\u003cp\u003eSerum MG53 levels are reduced in PCOS and independently associated with SHBG and androgen excess, suggesting a potential link between metabolic regulation and ovarian dysfunction. Although MG53 alone has limited diagnostic value, a model combining MG53 with hormonal and metabolic parameters may improve PCOS prediction, representing a novel direction for future research.\u003c/p\u003e\n\u003cp\u003eTrial registration:\u003c/p\u003e\n\u003cp\u003eClinicalTrials.gov identifier: NCT07094776.\u003c/p\u003e","manuscriptTitle":"Decreased Serum MG53 Levels Are Associated with SHBG and Androgen Excess in Women with Polycystic Ovary Syndrome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 15:02:42","doi":"10.21203/rs.3.rs-8007485/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-26T03:22:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-25T09:35:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268132314606306907153290577628535113248","date":"2026-01-28T11:47:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-16T13:38:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316419017375297884182886593767966230239","date":"2025-12-23T08:24:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-01T01:55:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-01T01:53:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-14T08:44:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-05T09:50:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-05T08:38:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c1c1c9f9-5b57-4e84-a667-9ebf03757454","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":58850070,"name":"Health sciences/Biomarkers"},{"id":58850071,"name":"Health sciences/Diseases"},{"id":58850072,"name":"Health sciences/Endocrinology"},{"id":58850073,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-04-20T16:25:54+00:00","versionOfRecord":{"articleIdentity":"rs-8007485","link":"https://doi.org/10.1038/s41598-026-48800-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-13 15:57:43","publishedOnDateReadable":"April 13th, 2026"},"versionCreatedAt":"2025-12-02 15:02:42","video":"","vorDoi":"10.1038/s41598-026-48800-z","vorDoiUrl":"https://doi.org/10.1038/s41598-026-48800-z","workflowStages":[]},"version":"v1","identity":"rs-8007485","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8007485","identity":"rs-8007485","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}