Menstrual dysfunction in PCOS: primarily linked to hyperinsulinemia over dysglycemia

Menstrual dysfunction in PCOS: primarily linked to hyperinsulinemia over dysglycemia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Menstrual dysfunction in PCOS: primarily linked to hyperinsulinemia over dysglycemia Lijing Wang, Nianjun Su, Juan Huang, Shihan Wang, Yang Liao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7936158/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective While insulin resistance (IR) underlies reduced ovulation in polycystic ovary syndrome (PCOS), the relative contributions of hyperinsulinemia versus dysglycemia to anovulatory dysfunction remain unclear. This study quantitatively assessed the relationship between menstrual disturbance and both insulin and glucose parameters in women with PCOS. Methods This retrospective cross-sectional study included 408 women diagnosed with PCOS. Participants were categorized into three groups based on self-reported menstrual cycle length: Eumenorrhea (26–34 days), Oligomenorrhea (35–90 days), and Amenorrhea (> 90 days). All participants underwent a standardized 75g oral glucose tolerance test (OGTT). Fasting plasma glucose (FPG) and insulin (FINS), along with 2-hour post-load glucose (2hPG) and insulin (2hINS) levels were measured. Insulin resistance was assessed using the Homeostatic Model Assessment (HOMA-IR). Statistical analyses compared metabolic parameters across groups using ANOVA/chi-square tests and assessed relationships using multivariable linear regression. Results Increasing menstrual cycle length was significantly associated with elevated body mass index (BMI), waist-to-hip ratio (WHR), FPG, 2hPG, FINS, 2hINS, alanine aminotransferase (ALT), low-density lipoprotein cholesterol (LDL-C), androstenedione, luteinizing hormone/follicle-stimulating hormone ratio (LH/FSH), LH, prolactin, and free androgen index (FAI) (all P < 0.05). Conversely, sex hormone-binding globulin (SHBG) and high-density lipoprotein cholesterol (HDL-C) levels decreased significantly with greater menstrual disturbance (all P < 0.05). The prevalence of insulin resistance (IR) was significantly higher in women with amenorrhea compared to those with eumenorrhea (79.9% vs. 44.6%; P < 0.001), demonstrating a progressive increase in IR risk with worsening menstrual dysfunction (Ptrend < 0.001). Following adjustment for BMI and WHR, the amenorrhea group demonstrated persistently elevated FINS (β = 2.55, 95% CI 0.47 to 4.63; P = 0.017) and 2hINS (β = 37.24, 95% CI 8.13 to 66.35; P = 0.013). No statistically significant differences in FPG or 2hPG levels were observed between the groups. Conclusion In women with PCOS, menstrual disturbance severity is independently associated with hyperinsulinemia, not dysglycemia. These findings suggest that hyperinsulinemia, rather than glucose levels, represents a key biomarker determining the severity of menstrual dysfunction in PCOS. Polycystic ovary syndrome insulin resistance menstrual dysfunction hyperinsulinemia dysglycemia Figures Figure 1 Introduction Polycystic Ovary Syndrome (PCOS) is a common, heterogeneous endocrine disorder characterised by oligomenorrhea, hyperandrogenism, and polycystic ovarian morphology ( 1 , 2 ). Epidemiological studies indicate that 60–80% of women with PCOS exhibit concomitant insulin resistance (IR) ( 3 , 4 ), a metabolic disturbance associated with long-term reproductive complications, including ovulatory dysfunction, and metabolic sequelae such as type 2 diabetes mellitus. Compensatory hyperinsulinemia emerges during early insulin resistance, timely intervention at this stage may potentially ameliorate many PCOS manifestations ( 5 ). However, current clinical practice predominantly relies on glycaemic testing to identify endocrine-metabolic disturbances in PCOS. Accumulating evidence suggests that dysglycemia likely represents a terminal manifestation of metabolic dysfunction, whereas hyperinsulinemia may constitute a primary antecedent pathophysiological driver ( 6 – 9 ). Despite its pathophysiological plausibility, this hypothesis requires robust validation through large-scale clinical studies. Menstrual dysfunction is highly prevalent in PCOS, manifesting primarily as oligo-ovulation or anovulation ( 10 ). Substantial evidence links prolonged menstrual cycles with increased risks of chronic comorbidities, including diabetes mellitus ( 4 ), cardiovascular disease ( 11 ), specific malignancies ( 12 ), and elevated all-cause mortality ( 13 ). Among PCOS features, menstrual disturbances exhibit the most robustly documented associations with adverse metabolic and reproductive sequelae. Prospective research indicates that IR and hyperinsulinemia—rather than hyperandrogenism (HA)—influence the severity of menstrual dysfunction in PCOS ( 14 ). Supporting this, a retrospective cross-sectional study demonstrates that IR severity increases proportionally with the degree of menstrual irregularity in affected individuals ( 15 ). Collectively, these observations suggest that menstrual dysfunction may serve as a useful clinical indicator of underlying metabolic derangements in PCOS. However, prior investigations examining relationships between menstrual dysfunction and metabolic parameters frequently combined insulin and glucose measurements in analyses, thereby failing to disentangle their independent associations. Therefore, we conducted a cross-sectional study to examine the specific relationships between menstrual dysfunction and IR, insulin, and glucose levels in women with PCOS. This study aimed to analyse the independent contributions of these key metabolic parameters to establish refined criteria for early identification of IR in this population. Such stratification could facilitate timely intervention, thereby mitigating the risk of subsequent metabolic and reproductive sequelae. Materials and Methods Study Participants A cohort of 408 patients with PCOS was recruited from the Reproductive Medicine Clinic at Zhaoqing Maternal and Child Health Care Hospital between July 2022 and December 2024. PCOS diagnosis was based on the 2003 Rotterdam criteria, requiring at least two of the following: 1) oligo-ovulation or anovulation, 2) clinical and/or biochemical hyperandrogenism (HA), or 3) ultrasonographic polycystic ovarian morphology ( 16 ). The diagnosis was confirmed following exclusion of related disorders, including congenital adrenal hyperplasia, Cushing's syndrome, and androgen-secreting tumours. Ethical approval for this study was granted by the Ethics Committee of Zhaoqing Maternal and Child Health Care Hospital (Approval No. 20241112001), with waiver of written informed consent for retrospective data collection. Methods During outpatient consultations, all participants provided detailed clinical data, including personal information, medical history, menstrual patterns, relevant family history, clinical hyperandrogenism features, and metabolic disorder profiles. Comprehensive physical examinations followed, comprising anthropometric measurements (height, weight, waist circumference, hip circumference) and transvaginal pelvic ultrasonography for antral follicle count (AFC). Polycystic ovarian morphology (PCOM) was defined as the presence of ≥ 12 antral follicles (2–9 mm diameter) per ovary and/or ovarian volume > 10 mL in either ovary ( 17 ). Based on self-reported menstrual cycle length, participants were stratified into three groups: eumenorrhea (26–34 days), oligomenorrhea (35–90 days), and amenorrhea (> 90 days) ( 18 ). Baseline fasting blood samples were obtained during days 2–5 of the menstrual cycle (or via random sampling for amenorrheic subjects) for biochemical and hormonal analyses. Participants fasted for 8–12 hours (water permitted) prior to sampling. Glucose, homocysteine (HCY), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) levels were measured using an automated biochemistry analyser. Androstenedione (A4) and sex hormone-binding globulin (SHBG) were quantified by automated chemiluminescent immunoassay. Serum concentrations of follicle-stimulating hormone (FSH), luteinizing hormone (LH), estradiol (E2), total testosterone (TT), progesterone (P4), insulin, and thyroid-stimulating hormone (TSH) were assessed using chemiluminescent microparticle immunoassay. The free androgen index (FAI) was calculated as: FAI = [TT (nmol/L) × 100] / SHBG (nmol/L). Assessment of Insulin Resistance The oral glucose tolerance test (OGTT) is regarded as the current gold standard for evaluating glucose metabolism, measuring venous plasma glucose and insulin levels under fasting conditions and 2 hours post-glucose load. Insulin resistance was assessed using the homeostasis model assessment (HOMA-IR), calculated as: HOMA-IR = [fasting glucose (mmol/L) × fasting insulin (mU/L)] / 22.5 ( 19 ). A HOMA-IR value ≥ 2.6 was defined as indicative of insulin resistance ( 20 ). Covariate Selection and Handling of Missing Values The choice of potential confounders was derived from previous literature ( 14 , 15 , 18 , 21 ). Missing data were handled according to the Predictive Model Development Guidelines ( 22 ). For continuous variables with normal distribution, mean imputation was applied; for non-normally distributed continuous variables, median imputation was used. Categorical variables were imputed using the mode. Statistical Analysis Descriptive statistics were expressed as mean ± standard error of the mean (SEM). Continuous variables across groups were compared using analysis of variance (ANOVA). Categorical variables were analysed by χ² test or Fisher's exact test, as appropriate. Covariate-adjusted logistic regression models (for BMI and WHR) examined associations between menstrual cycle groups and HOMA-IR-defined insulin resistance. Linear regression analyses assessed relationships between menstrual cycle groups and fasting glucose and insulin levels. Statistical significance was defined as a two-sided p-value < 0.05. Analyses were performed using R statistical software (v4.2.2; R Core Team, Vienna, Austria) and the Free Statistics analysis platform (v2.1; Beijing, China). Results 1. Baseline characteristics of PCOS patients stratified by menstrual dysfunction severity The cohort comprised 408 PCOS patients stratified into menstrual cycle groups: eumenorrhea (26–34 days; 20.34%), oligomenorrhea (35–90 days; 46.81%), and amenorrhea (>90 days; 32.84%). Increasing severity of menstrual dysfunction was associated with progressive metabolic and endocrine alterations (Table 1). Significant linear trends were observed for elevations in: BMI, WHR, FPG, 2hPG, FINS, 2hINS, ALT, LDL-C, A4, LH, LH/FSH, Prolactin and FAI (all P<0.05). Conversely, SHBG and HDL-C demonstrated significant inverse trends with increasing dysfunction severity (both P0.05). 2. Insulin resistance characteristics by menstrual dysfunction severity The overall prevalence of insulin resistance (IR) among PCOS patients was 61% (Table 2), with significant between-group differences across menstrual dysfunction severity strata (P<0.001). Prevalence rates were 44.6% in the eumenorrhea group, 55.0% in oligomenorrhea, and 79.9% in amenorrhea. Trend analysis demonstrated a stepwise increase in IR prevalence with escalating menstrual dysfunction severity (Ptrend<0.001). In unadjusted models, the amenorrhea group exhibited 4.93-fold higher odds of IR versus the eumenorrhea group (OR=4.93, 95% CI: 2.69–9.02). After adjustment for BMI and WHR, this association was markedly attenuated (aOR=1.88, 95% CI: 0.85–4.20) and lost statistical significance (P=0.121). 3. Association between menstrual dysfunction severity and glucose metabolism parameters As detailed in Table 3 and Figure 1, progressive menstrual dysfunction demonstrated significant associations with dysglycemia. In unadjusted models, FPG exhibited an increasing trend with worsening menstrual irregularity (P=0.020). The 2hPG was significantly elevated in the amenorrhea group versus the eumenorrhea group (β=1.00, 95% CI: 0.42–1.59, P=0.001). FINS levels were 7.10 mU/L higher in the amenorrhea group compared to the eumenorrhea group (95% CI: 4.71–9.48, P<0.001), while 2hINS levels increased by 128% (135.34 vs. 59.30 mU/L; β=73.32, 95% CI: 45.20–101.44, P<0.001). After adjustment for BMI and WHR: FPG differences became non-significant (amenorrhea vs eumenorrhea: β=0.04, 95% CI -0.14–0.23; P=0.636), 2hPG differences attenuated to non-significance (β=0.31, 95% CI -0.3–0.92; P=0.325). FINS remained significantly elevated in amenorrhea (β=2.55, 95% CI 0.47–4.63; P=0.017), 2hINS differences retained significance (β=37.24, 95% CI 8.13–66.35; P=0.013). 4. Association between menstrual dysfunction severity and androgen parameters TT levels showed no significant differences across menstrual dysfunction subgroups (P=0.172; Table 1). In contrast, FAI demonstrated significant between-group variation (P=0.022). Androstenedione exhibited progressive elevation with worsening menstrual irregularity (P<0.001). SHBG levels were 30% lower in the amenorrhea group than in the eumenorrhea group (P<0.001). Discussion This analysis of 408 PCOS patients demonstrated significant positive correlations between menstrual dysfunction severity (stratified by cycle length) and elevated BMI, WHR, and IR. Following adjustment for BMI and WHR, progressive menstrual dysfunction—from eumenorrhea to amenorrhea—retained significant associations with hyperinsulinaemia, but not with glucose levels. These findings align with the insulin-centric model proposed by Parker et al. (5), while further elucidating insulin's direct pathophysiological contribution to menstrual disturbances in PCOS. The amenorrhea group demonstrated significantly higher HOMA-IR values versus the eumenorrhea group. The 2hINS levels were markedly elevated in amenorrhea (median: 135.34 mU/L) compared to eumenorrhea (59.30 mU/L), representing a 128% increase. Crucially, this association remained statistically significant after BMI and WHR adjustment. These findings substantiate insulin resistance severity as an independent determinant of menstrual cycle disturbances, extending Parker et al.'s hypothesis that dynamic insulin resistance drives menstrual dysfunction in PCOS (5). Although the amenorrhea group exhibited elevated fasting and postprandial glucose levels, these associations attenuated to non-significance following BMI and WHR adjustment. This suggests hyperglycemia may represent either a secondary manifestation of abdominal adiposity (23) or advanced-stage PCOS pathophysiology (24,25). In contrast, our cohort likely reflects an early compensatory phase characterized by predominant hyperinsulinaemia. Following adjustment for BMI and WHR, this study demonstrated significant independent associations between menstrual dysfunction severity and hyperinsulinaemia—but not hyperglycaemia. This dissociation aligns with insulin resistance's dual-pathway disruption of ovulatory function. Ovarian Pathway: Hyperinsulinaemia directly amplifies ovarian theca-cell androgen synthesis while suppressing hepatic SHBG production. This dual action elevates bioavailable testosterone, impairing dominant follicle selection and ovulation (26,27). Neuroendocrine Pathway: Insulin resistance modulates hypothalamic GnRH pulse frequency via central signalling, exacerbating the LH/FSH ratio imbalance, such dysregulation further disrupts folliculogenesis (28). Critically, compensatory hyperinsulinaemia preserves euglycaemia through pancreatic β-cell adaptation. This physiological compensation explains the attenuated association between glucose homeostasis and menstrual disturbances despite profound insulin dysregulation (24,25). Although serum TT levels did not differ significantly across menstrual dysfunction subgroups, the FAI demonstrated statistically significant intergroup variation. This suggests that hyperandrogenaemia likely contributes indirectly to menstrual cycle abnormalities. As a sensitive biomarker of bioactive androgen activity, elevated FAI primarily reflects a downstream consequence of insulin resistance—mediated by suppressed hepatic SHBG synthesis, which increases free testosterone bioavailability—rather than acting as an independent primary determinant of menstrual dysfunction severity (29,30). This study establishes an association between menstrual dysfunction and insulin resistance, with amenorrheic patients displaying the most pronounced metabolic derangements: significantly elevated HOMA-IR indices, concomitant increases in ALT and LDL-C, alongside reduced HDL-C. These findings position menstrual dysfunction as a clinical indicator for timely identification of high-risk metabolic phenotypes in PCOS (14,15,18). Given insulin resistance's role as a central pathogenic driver, we recommend integrating lifestyle interventions and insulin-sensitizing agents into first-line management strategies (31,32), rather than focusing exclusively on glycemic control (5). This study has several notable strengths. First, we adjusted for confounding effects of key anthropometric indices (WHR). Second, we differentiated the independent associations of menstrual dysfunction with insulin versus glucose homeostasis parameters. However, limitations should be acknowledged. The cross-sectional design precludes causal inferences regarding insulin resistance and menstrual phenotypes. Phenotypic stratification of PCOS subtypes was not conducted. Additionally, dynamic assessment of insulin resistance was unavailable. Future studies should employ prospective cohort designs incorporating dynamic evaluation techniques (e.g., hyperinsulinemic-euglycemic clamp, insulin tolerance test [kITT]) to establish causal metabolic pathways. Conclusion This study demonstrates that the severity of menstrual dysfunction in PCOS patients correlates with hyperinsulinemia, independent of dysglycemia. These findings necessitate systematic screening for insulin resistance and early intervention in PCOS patients with menstrual abnormalities. Abbreviations PCOS, Polycystic ovary syndrome; BMI, Body mass index; WHR, Waist-to-hip ratio; TSH, Thyroid stimulating hormone; HCY, Homocysteine; FPG, Fasting plasma glucose; FINS, Fasting insulin; HOMA-IR, Homeostatic Model Assessment of Insulin Resistance; 2hPG, 2-hour post-load glucose; 2hINS, 2-hour post-load insulin; ALT, Aminotransferase; TG, Triglyceride; TC, Total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, High-density lipoprotein cholesterol; A4, Androstenedione; SHBG, Sex hormone binding globulin; FSH, Follicle stimulating hormone; LH, Luteinizing hormone; E2, Estradiol; TT, Total testosterone; PRL, Prolactin; P4, Progesterone; FAI, Free androgen index. Declarations Ethics approval and consent to participate Ethical approval for this study was granted by the Ethics Committee of Zhaoqing Maternal and Child Health Care Hospital (Approval No. 20241112001) Consent for publication All authors agree to publish Data availability The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Funding There is no funding for the project. Author contributions Lijing Wang conceived and designed the study, conducted data curation, investigation, formal analysis, writing – review; Nianjun Su, Juan Huang, Shihan Wang conducted investigation, analysis, software, data curation, writing – original draft; Yang Liao conducted project administration, supervision, validation, and approved the final version for publication; All authors agree to be responsible for all aspects of the work. 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Variables Total (n = 408) Eumenorrhea (n = 83) Oligomenorrhea(n = 191) Amenorrhea (n = 134) p Age (y) 29.53 ± 4.30 29.96 ± 4.32 29.57 ± 4.00 29.19 ± 4.67 0.432 BMI (Kg/m 2 ) 23.04 ± 4.00 22.36 ± 4.14 22.06 ± 3.31 24.85 ± 4.22 < 0.001 WHR 0.84 ± 0.05 0.83 ± 0.05 0.83 ± 0.05 0.87 ± 0.05 < 0.001 TSH(uIU/ml) 2.23 ± 1.32 1.95 ± 1.10 2.33 ± 1.32 2.26 ± 1.44 0.131 HCY(umol/L) 8.32 ± 1.69 8.11 ± 1.31 8.28 ± 1.77 8.53 ± 1.80 0.234 FPG (mmol/L) 5.41 ± 0.61 5.34 ± 0.65 5.35 ± 0.51 5.53 ± 0.70 0.014 FINS (mU/L) 15.57 ± 9.17 12.60 ± 7.01 13.95 ± 7.86 19.70 ± 10.59 < 0.001 HOMA-IR 3.83 ± 2.49 3.04 ± 1.81 3.40 ± 2.19 4.94 ± 2.88 < 0.001 2hPG (mmol/L) 7.34 ± 2.19 7.01 ± 1.92 7.02 ± 2.10 8.01 ± 2.32 < 0.001 2hINS (mU/L) 88.61 (49.31, 156.43) 59.30 (41.78, 111.76) 80.79 (47.78, 131.01) 135.34 (76.35, 212.55) < 0.001 ALT (U/L) 16.80 (12.70, 25.17) 14.10 (11.70, 21.60) 15.60 (12.05, 20.60) 20.75 (15.00, 33.90) < 0.001 TG (mmol/L) 1.26 ± 0.82 1.17 ± 0.66 1.19 ± 0.80 1.38 ± 0.91 0.117 TC(mmol/L) 4.82 ± 0.80 4.88 ± 0.93 4.82 ± 0.72 4.79 ± 0.82 0.774 LDL-C(mmol/L) 2.95 ± 0.71 2.98 ± 0.78 2.84 ± 0.62 3.07 ± 0.75 0.026 HDL-C(mmol/L) 1.52 ± 0.40 1.53 ± 0.38 1.65 ± 0.42 1.35 ± 0.33 < 0.001 A4(nmol/L) 5.53 ± 2.17 4.67 ± 1.90 5.50 ± 2.20 6.01 ± 2.14 < 0.001 SHBG(nmol/L) 33.94 (22.59, 53.33) 39.13 (26.98, 53.80) 36.60 (25.39, 56.09) 27.52 (18.87, 47.65) < 0.001 FSH(IU/L) 7.21 ± 1.70 7.59 ± 1.82 7.19 ± 1.69 7.03 ± 1.61 0.105 LH(IU/L) 8.91 ± 5.75 7.40 ± 5.94 8.59 ± 5.78 10.20 ± 5.37 0.004 TT(ng/ml) 0.41 ± 0.22 0.39 ± 0.24 0.39 ± 0.21 0.44 ± 0.23 0.172 E2(pg/ml) 42.25 ± 23.29 43.82 ± 36.23 41.90 ± 18.60 41.86 ± 19.80 0.833 P4(ng/ml) 0.37 (0.29, 0.51) 0.44 (0.29, 0.55) 0.36 (0.29, 0.51) 0.36 (0.28, 0.50) 0.253 PRL(ng/ml) 19.43 (12.56, 26.38) 20.29 (12.01, 28.48) 21.20 (14.65, 27.40) 16.60 (11.88, 22.62) 0.020 LH/FSH 1.28 ± 0.83 1.00 ± 0.80 1.22 ± 0.80 1.51 ± 0.83 < 0.001 FAI 3.34 (2.01, 6.37) 3.07 (1.61, 5.45) 3.08 (1.99, 5.39) 4.51 (2.27, 7.98) 0.022 Note: Data are presented as mean ± standard deviation (SD) for continuous variables with normal distribution or as median (IQR) for continuous variables that did not show a normal distribution, and categorical variables are reported as no. (%). BMI, Body mass index; WHR, Waist-to-hip ratio; TSH, Thyroid stimulating hormone; HCY, Homocysteine; FPG, Fasting plasma glucose; FINS, Fasting insulin; HOMA-IR, Homeostatic Model Assessment of Insulin Resistance; 2hPG, 2-hour post-load glucose; 2hINS, 2-hour post-load insulin; ALT, Aminotransferase; TG, Triglyceride; TC, Total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, High-density lipoprotein cholesterol; A4, Androstenedione; SHBG, Sex hormone binding globulin; FSH, Follicle stimulating hormone; LH, Luteinizing hormone; E2, Estradiol; TT, Total testosterone; PRL, Prolactin; P4, Progesterone; FAI, Free androgen index. Table 2. Logistic multivariate analysis of HOMA-IR and Menstrual dysfunction severity. Variable n.total n.event_% Model 1 OR(95%CI) P Model 2 OR(95%CI) P Eumenorrhea 83 37 (44.6) 1(Ref) 1(Ref) Oligomenorrhea 191 105 (55.0) 1.52 (0.90~2.55) 0.114 1.43 (0.72~2.84) 0.305 Amenorrhea 134 107 (79.9) 4.93 (2.69~9.02) <0.001 1.88 (0.85~4.20) 0.121 Trend.test 408 249 (61.0) 2.25 (1.67~3.02) <0.001 1.37 (0.92~2.05) 0.122 Model 1: crude model Model 2: adjusted for BMI+WHR Table 3. Linear regression analysis of glucose metabolism parameters and menstrual dysfunction severity. Variable n Model 1 OR(95%CI) P Model 2 OR(95%CI) P FPG (mmol/L) Eumenorrhea 83 0(Ref) 0(Ref) Oligomenorrhea 191 0.01 (-0.14~0.17) 0.856 0 (-0.17~0.17) 0.972 Amenorrhea 134 0.20 (0.03~0.37) 0.020 0.04 (-0.14~0.23) 0.636 2hPG (mmol/L) Eumenorrhea 83 0(Ref) 0(Ref) Oligomenorrhea 191 0.02 (-0.54~0.57) 0.955 0.04 (-0.51~0.60) 0.875 Amenorrhea 134 1.00 (0.42~1.59) 0.001 0.31 (-0.30~0.92) 0.325 FINS (mU/L) Eumenorrhea 83 0(Ref) 0(Ref) Oligomenorrhea 191 1.35 (-0.90~3.59) 0.240 1.39 (-0.51~3.29) 0.153 Amenorrhea 134 7.10 (4.71~9.48) <0.001 2.55 (0.47~4.63) 0.017 2hINS (mU/L) Eumenorrhea 83 0(Ref) 0(Ref) Oligomenorrhea 191 8.92 (-17.54~35.39) 0.509 11.92 (-14.68~38.53) 0.38 Amenorrhea 134 73.32 (45.2~101.44) <0.001 37.24 (8.13~66.35) 0.013 Model 1: crude model Model 2: adjusted for BMI+WHR FPG, Fasting plasma glucose; 2hPG, 2-hour post-load glucose; FINS, Fasting insulin; 2hINS, 2-hour post-load insulin Additional Declarations No competing interests reported. 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Nianjun","middleName":"","lastName":"Su","suffix":""},{"id":545947530,"identity":"385a2087-6bc9-4a1f-bf85-7619270b171c","order_by":2,"name":"Juan Huang","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Huang","suffix":""},{"id":545947531,"identity":"457e8fb7-e279-40a4-876a-0f599f0dbf63","order_by":3,"name":"Shihan Wang","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shihan","middleName":"","lastName":"Wang","suffix":""},{"id":545947535,"identity":"51ff54cb-db47-478e-aef9-35eaa11d4428","order_by":4,"name":"Yang 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17:13:34","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":113480,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7936158/v1/6cb1edc682e050a8d128f0a2.html"},{"id":96205867,"identity":"17624bda-03a6-4fff-b538-e03e1e86a0c0","added_by":"auto","created_at":"2025-11-18 17:13:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":134597,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots illustrating the association between the severity of menstrual dysfunction and glucose metabolism indicators.\u003c/p\u003e\n\u003cp\u003e(A) Severity of menstrual dysfunction vs. fasting plasma glucose (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e(B) Severity of menstrual dysfunction vs. 2-hour post-load glucose (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e(C) Severity of menstrual dysfunction vs. fasting insulin (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e(D) Severity of menstrual dysfunction vs. 2-hour post-load insulin (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eFPG, Fasting plasma glucose; 2hPG, 2-hour post-load glucose; FINS, Fasting insulin; 2hINS, 2-hour post-load insulin\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7936158/v1/3d0afd39ac4ecd4c625b84f9.png"},{"id":100373434,"identity":"60499c0d-e23f-435c-b113-91364c9d1c7d","added_by":"auto","created_at":"2026-01-16 08:14:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":932064,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7936158/v1/ba171c60-f0a0-432d-a35e-50cd47fc1b7a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Menstrual dysfunction in PCOS: primarily linked to hyperinsulinemia over dysglycemia","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePolycystic Ovary Syndrome (PCOS) is a common, heterogeneous endocrine disorder characterised by oligomenorrhea, hyperandrogenism, and polycystic ovarian morphology (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Epidemiological studies indicate that 60\u0026ndash;80% of women with PCOS exhibit concomitant insulin resistance (IR) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), a metabolic disturbance associated with long-term reproductive complications, including ovulatory dysfunction, and metabolic sequelae such as type 2 diabetes mellitus. Compensatory hyperinsulinemia emerges during early insulin resistance, timely intervention at this stage may potentially ameliorate many PCOS manifestations (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, current clinical practice predominantly relies on glycaemic testing to identify endocrine-metabolic disturbances in PCOS. Accumulating evidence suggests that dysglycemia likely represents a terminal manifestation of metabolic dysfunction, whereas hyperinsulinemia may constitute a primary antecedent pathophysiological driver (\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Despite its pathophysiological plausibility, this hypothesis requires robust validation through large-scale clinical studies.\u003c/p\u003e\u003cp\u003eMenstrual dysfunction is highly prevalent in PCOS, manifesting primarily as oligo-ovulation or anovulation (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Substantial evidence links prolonged menstrual cycles with increased risks of chronic comorbidities, including diabetes mellitus (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), cardiovascular disease (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), specific malignancies (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), and elevated all-cause mortality (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Among PCOS features, menstrual disturbances exhibit the most robustly documented associations with adverse metabolic and reproductive sequelae. Prospective research indicates that IR and hyperinsulinemia\u0026mdash;rather than hyperandrogenism (HA)\u0026mdash;influence the severity of menstrual dysfunction in PCOS (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Supporting this, a retrospective cross-sectional study demonstrates that IR severity increases proportionally with the degree of menstrual irregularity in affected individuals (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Collectively, these observations suggest that menstrual dysfunction may serve as a useful clinical indicator of underlying metabolic derangements in PCOS. However, prior investigations examining relationships between menstrual dysfunction and metabolic parameters frequently combined insulin and glucose measurements in analyses, thereby failing to disentangle their independent associations.\u003c/p\u003e\u003cp\u003eTherefore, we conducted a cross-sectional study to examine the specific relationships between menstrual dysfunction and IR, insulin, and glucose levels in women with PCOS. This study aimed to analyse the independent contributions of these key metabolic parameters to establish refined criteria for early identification of IR in this population. Such stratification could facilitate timely intervention, thereby mitigating the risk of subsequent metabolic and reproductive sequelae.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Participants\u003c/h2\u003e\u003cp\u003eA cohort of 408 patients with PCOS was recruited from the Reproductive Medicine Clinic at Zhaoqing Maternal and Child Health Care Hospital between July 2022 and December 2024. PCOS diagnosis was based on the 2003 Rotterdam criteria, requiring at least two of the following: 1) oligo-ovulation or anovulation, 2) clinical and/or biochemical hyperandrogenism (HA), or 3) ultrasonographic polycystic ovarian morphology (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The diagnosis was confirmed following exclusion of related disorders, including congenital adrenal hyperplasia, Cushing's syndrome, and androgen-secreting tumours. Ethical approval for this study was granted by the Ethics Committee of Zhaoqing Maternal and Child Health Care Hospital (Approval No. 20241112001), with waiver of written informed consent for retrospective data collection.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMethods\u003c/h3\u003e\n\u003cp\u003eDuring outpatient consultations, all participants provided detailed clinical data, including personal information, medical history, menstrual patterns, relevant family history, clinical hyperandrogenism features, and metabolic disorder profiles. Comprehensive physical examinations followed, comprising anthropometric measurements (height, weight, waist circumference, hip circumference) and transvaginal pelvic ultrasonography for antral follicle count (AFC). Polycystic ovarian morphology (PCOM) was defined as the presence of \u0026ge;\u0026thinsp;12 antral follicles (2\u0026ndash;9 mm diameter) per ovary and/or ovarian volume\u0026thinsp;\u0026gt;\u0026thinsp;10 mL in either ovary (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Based on self-reported menstrual cycle length, participants were stratified into three groups: eumenorrhea (26\u0026ndash;34 days), oligomenorrhea (35\u0026ndash;90 days), and amenorrhea (\u0026gt;\u0026thinsp;90 days) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBaseline fasting blood samples were obtained during days 2\u0026ndash;5 of the menstrual cycle (or via random sampling for amenorrheic subjects) for biochemical and hormonal analyses. Participants fasted for 8\u0026ndash;12 hours (water permitted) prior to sampling. Glucose, homocysteine (HCY), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) levels were measured using an automated biochemistry analyser. Androstenedione (A4) and sex hormone-binding globulin (SHBG) were quantified by automated chemiluminescent immunoassay. Serum concentrations of follicle-stimulating hormone (FSH), luteinizing hormone (LH), estradiol (E2), total testosterone (TT), progesterone (P4), insulin, and thyroid-stimulating hormone (TSH) were assessed using chemiluminescent microparticle immunoassay. The free androgen index (FAI) was calculated as:\u003c/p\u003e\u003cp\u003eFAI = [TT (nmol/L) \u0026times; 100] / SHBG (nmol/L).\u003c/p\u003e\n\u003ch3\u003eAssessment of Insulin Resistance\u003c/h3\u003e\n\u003cp\u003eThe oral glucose tolerance test (OGTT) is regarded as the current gold standard for evaluating glucose metabolism, measuring venous plasma glucose and insulin levels under fasting conditions and 2 hours post-glucose load. Insulin resistance was assessed using the homeostasis model assessment (HOMA-IR), calculated as: HOMA-IR = [fasting glucose (mmol/L) \u0026times; fasting insulin (mU/L)] / 22.5 (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). A HOMA-IR value\u0026thinsp;\u0026ge;\u0026thinsp;2.6 was defined as indicative of insulin resistance (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eCovariate Selection and Handling of Missing Values\u003c/h3\u003e\n\u003cp\u003eThe choice of potential confounders was derived from previous literature (\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, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Missing data were handled according to the Predictive Model Development Guidelines (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). For continuous variables with normal distribution, mean imputation was applied; for non-normally distributed continuous variables, median imputation was used. Categorical variables were imputed using the mode.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM). Continuous variables across groups were compared using analysis of variance (ANOVA). Categorical variables were analysed by χ\u0026sup2; test or Fisher's exact test, as appropriate. Covariate-adjusted logistic regression models (for BMI and WHR) examined associations between menstrual cycle groups and HOMA-IR-defined insulin resistance. Linear regression analyses assessed relationships between menstrual cycle groups and fasting glucose and insulin levels. Statistical significance was defined as a two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Analyses were performed using R statistical software (v4.2.2; R Core Team, Vienna, Austria) and the Free Statistics analysis platform (v2.1; Beijing, China).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1. Baseline characteristics of PCOS patients stratified by menstrual dysfunction severity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cohort comprised 408 PCOS patients stratified into menstrual cycle groups: eumenorrhea (26\u0026ndash;34 days; 20.34%), oligomenorrhea (35\u0026ndash;90 days; 46.81%), and amenorrhea (\u0026gt;90 days; 32.84%). Increasing severity of menstrual dysfunction was associated with progressive metabolic and endocrine alterations (Table 1). Significant linear trends were observed for elevations in: BMI, WHR, FPG, 2hPG, FINS, 2hINS, ALT, LDL-C, A4, LH, LH/FSH, Prolactin and FAI (all P\u0026lt;0.05). Conversely, SHBG and HDL-C demonstrated significant inverse trends with increasing dysfunction severity (both P\u0026lt;0.05). No significant between-group differences existed for: Age, TC, TG, FSH, E2,TT or P4 (all P\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Insulin resistance characteristics by menstrual dysfunction severity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall prevalence of insulin resistance (IR) among PCOS patients was 61% (Table 2), with significant between-group differences across menstrual dysfunction severity strata (P\u0026lt;0.001). Prevalence rates were 44.6% in the eumenorrhea group, 55.0% in oligomenorrhea, and 79.9% in amenorrhea. Trend analysis demonstrated a stepwise increase in IR prevalence with escalating menstrual dysfunction severity (Ptrend\u0026lt;0.001). In unadjusted models, the amenorrhea group exhibited 4.93-fold higher odds of IR versus the eumenorrhea group (OR=4.93, 95% CI: 2.69\u0026ndash;9.02). After adjustment for BMI and WHR, this association was markedly attenuated (aOR=1.88, 95% CI: 0.85\u0026ndash;4.20) and lost statistical significance (P=0.121).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Association between menstrual dysfunction severity and glucose metabolism parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs detailed in Table 3 and Figure 1, progressive menstrual dysfunction demonstrated significant associations with dysglycemia. In unadjusted models, FPG exhibited an increasing trend with worsening menstrual irregularity (P=0.020). The 2hPG was significantly elevated in the amenorrhea group versus the eumenorrhea group (\u0026beta;=1.00, 95% CI: 0.42\u0026ndash;1.59, P=0.001). FINS levels were 7.10 mU/L higher in the amenorrhea group compared to the eumenorrhea group (95% CI: 4.71\u0026ndash;9.48, P\u0026lt;0.001), while 2hINS levels increased by 128% (135.34 vs. 59.30 mU/L; \u0026beta;=73.32, 95% CI: 45.20\u0026ndash;101.44, P\u0026lt;0.001). After adjustment for BMI and WHR: FPG differences became non-significant (amenorrhea vs eumenorrhea: \u0026beta;=0.04, 95% CI -0.14\u0026ndash;0.23; P=0.636), 2hPG differences attenuated to non-significance (\u0026beta;=0.31, 95% CI -0.3\u0026ndash;0.92; P=0.325). FINS remained significantly elevated in amenorrhea (\u0026beta;=2.55, 95% CI 0.47\u0026ndash;4.63; P=0.017), 2hINS differences retained significance (\u0026beta;=37.24, 95% CI 8.13\u0026ndash;66.35; P=0.013).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Association between menstrual dysfunction severity and androgen parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTT levels showed no significant differences across menstrual dysfunction subgroups (P=0.172; Table 1). In contrast, FAI demonstrated significant between-group variation (P=0.022). Androstenedione exhibited progressive elevation with worsening menstrual irregularity (P\u0026lt;0.001). SHBG levels were 30% lower in the amenorrhea group than in the eumenorrhea group (P\u0026lt;0.001).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis analysis of 408 PCOS patients demonstrated significant positive correlations between menstrual dysfunction severity (stratified by cycle length) and elevated BMI, WHR, and IR. Following adjustment for BMI and WHR, progressive menstrual dysfunction\u0026mdash;from eumenorrhea to amenorrhea\u0026mdash;retained significant associations with hyperinsulinaemia, but not with glucose levels. These findings align with the insulin-centric model proposed by Parker et al. (5), while further elucidating insulin\u0026apos;s direct pathophysiological contribution to menstrual disturbances in PCOS.\u003c/p\u003e\n\u003cp\u003eThe amenorrhea group demonstrated significantly higher HOMA-IR values versus the eumenorrhea group. The 2hINS levels were markedly elevated in amenorrhea (median: 135.34 mU/L) compared to eumenorrhea (59.30 mU/L), representing a 128% increase. Crucially, this association remained statistically significant after BMI and WHR adjustment. These findings substantiate insulin resistance severity as an independent determinant of menstrual cycle disturbances, extending Parker et al.\u0026apos;s hypothesis that dynamic insulin resistance drives menstrual dysfunction in PCOS (5).\u003c/p\u003e\n\u003cp\u003eAlthough the amenorrhea group exhibited elevated fasting and postprandial glucose levels, these associations attenuated to non-significance following BMI and WHR adjustment. This suggests hyperglycemia may represent either a secondary manifestation of abdominal adiposity (23) or advanced-stage PCOS pathophysiology (24,25). In contrast, our cohort likely reflects an early compensatory phase characterized by predominant hyperinsulinaemia.\u003c/p\u003e\n\u003cp\u003eFollowing adjustment for BMI and WHR, this study demonstrated significant independent associations between menstrual dysfunction severity and hyperinsulinaemia\u0026mdash;but not hyperglycaemia. This dissociation aligns with insulin resistance\u0026apos;s dual-pathway disruption of ovulatory function. Ovarian Pathway: Hyperinsulinaemia directly amplifies ovarian theca-cell androgen synthesis while suppressing hepatic SHBG production. This dual action elevates bioavailable testosterone, impairing dominant follicle selection and ovulation (26,27). Neuroendocrine Pathway: Insulin resistance modulates hypothalamic GnRH pulse frequency via central signalling, exacerbating the LH/FSH ratio imbalance, such dysregulation further disrupts folliculogenesis (28). Critically, compensatory hyperinsulinaemia preserves euglycaemia through pancreatic \u0026beta;-cell adaptation. This physiological compensation explains the attenuated association between glucose homeostasis and menstrual disturbances despite profound insulin dysregulation (24,25).\u003c/p\u003e\n\u003cp\u003eAlthough serum TT levels did not differ significantly across menstrual dysfunction subgroups, the FAI demonstrated statistically significant intergroup variation. This suggests that hyperandrogenaemia likely contributes indirectly to menstrual cycle abnormalities. As a sensitive biomarker of bioactive androgen activity, elevated FAI primarily reflects a downstream consequence of insulin resistance\u0026mdash;mediated by suppressed hepatic SHBG synthesis, which increases free testosterone bioavailability\u0026mdash;rather than acting as an independent primary determinant of menstrual dysfunction severity (29,30).\u003c/p\u003e\n\u003cp\u003eThis study establishes an association between menstrual dysfunction and insulin resistance, with amenorrheic patients displaying the most pronounced metabolic derangements: significantly elevated HOMA-IR indices, concomitant increases in ALT and LDL-C, alongside reduced HDL-C. These findings position menstrual dysfunction as a clinical indicator for timely identification of high-risk metabolic phenotypes in PCOS (14,15,18). Given insulin resistance\u0026apos;s role as a central pathogenic driver, we recommend integrating lifestyle interventions and insulin-sensitizing agents into first-line management strategies (31,32), rather than focusing exclusively on glycemic control (5).\u003c/p\u003e\n\u003cp\u003eThis study has several notable strengths. First, we adjusted for confounding effects of key anthropometric indices (WHR). Second, we differentiated the independent associations of menstrual dysfunction with insulin versus glucose homeostasis parameters. However, limitations should be acknowledged. The cross-sectional design precludes causal inferences regarding insulin resistance and menstrual phenotypes. Phenotypic stratification of PCOS subtypes was not conducted. Additionally, dynamic assessment of insulin resistance was unavailable. Future studies should employ prospective cohort designs incorporating dynamic evaluation techniques (e.g., hyperinsulinemic-euglycemic clamp, insulin tolerance test [kITT]) to establish causal metabolic pathways.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that the severity of menstrual dysfunction in PCOS patients correlates with hyperinsulinemia, independent of dysglycemia. These findings necessitate systematic screening for insulin resistance and early intervention in PCOS patients with menstrual abnormalities.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePCOS, Polycystic ovary syndrome; BMI, Body mass index; WHR, Waist-to-hip ratio; TSH, Thyroid stimulating hormone; HCY, Homocysteine; FPG, Fasting plasma glucose; FINS, Fasting insulin; HOMA-IR, Homeostatic Model Assessment of Insulin Resistance; 2hPG, 2-hour post-load glucose; 2hINS, 2-hour post-load insulin; ALT, Aminotransferase; TG, Triglyceride; TC, Total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, High-density lipoprotein cholesterol; A4, Androstenedione; SHBG, Sex hormone binding globulin; FSH, Follicle stimulating hormone; LH, Luteinizing hormone; E2, Estradiol; TT, Total testosterone; PRL, Prolactin; P4, Progesterone; \u0026nbsp;FAI, Free androgen index.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was granted by the Ethics Committee of Zhaoqing Maternal and Child Health Care Hospital (Approval No. 20241112001)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors agree to publish\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no funding for the project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLijing Wang conceived and designed the study, conducted data curation, investigation, formal analysis, writing \u0026ndash; review; Nianjun Su,\u0026nbsp;Juan Huang, Shihan Wang\u0026nbsp;conducted investigation, analysis, software,\u0026nbsp;data curation, writing \u0026ndash; original draft; \u0026nbsp;Yang Liao\u0026nbsp;conducted project administration, supervision, validation, and\u0026nbsp;approved the final version for publication; All authors agree to be responsible for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all the staff of the Reproductive Medicine Center of Zhaoqing Maternal and Child Health Care Hospital for their cooperation and support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNorman RJ, Dewailly D, Legro RS, Hickey TE. Polycystic ovary syndrome. The Lancet. 2007;370 (9588): 685-697.\u003c/li\u003e\n\u003cli\u003eJoham AE, Norman RJ, Stener-Victorin E, Legro RS, Franks S, et al. Polycystic ovary syndrome. Lancet Diabetes Endocrinol. 2022;10(9):668-680.\u003c/li\u003e\n\u003cli\u003eJeanes Y, Reeves S. Metabolic Consequences of Obesity and Insulin Resistance in Polycystic Ovary Syndrome: Diagnostic and Methodological Challenges. Nutr Res Rev. 2017;30(1):97-105.\u003c/li\u003e\n\u003cli\u003eWang YX, Shan Z, Arvizu M, Pan A, Manson JE, Missmer SA, et al. Associations of Menstrual Cycle Characteristics Across the Reproductive Life Span and Lifestyle Factors With Risk of Type 2 Diabetes. JAMA Netw Open. 2020;3(12):e2027928.\u003c/li\u003e\n\u003cli\u003eParker J ,Briden L ,Gersh FL. Recognizing the Role of Insulin Resistance in Polycystic Ovary Syndrome: A Paradigm Shift from a Glucose-Centric Approach to an Insulin-Centric Model. Journal of Clinical Medicine. 2025;14(12):4021-4021.\u003c/li\u003e\n\u003cli\u003eParker J. Pathophysiological Effects of Contemporary Lifestyle on Evolutionary-Conserved Survival Mechanisms in Polycystic Ovary Syndrome. Life. 2023;20;13(4):1056. \u003c/li\u003e\n\u003cli\u003eTab\u0026aacute;k AG, Jokela M, Akbaraly TN, Brunner EJ, Kivim\u0026auml;ki M, Witte DR. Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of type 2 diabetes: An analysis from the Whitehall II study. Lancet. 2009;27;373(9682):2215-21.\u003c/li\u003e\n\u003cli\u003eStener-Victorin E, Padmanabhan V, Walters KA, Campbell RE, Benrick A, Giacobini P, et al. Animal Models to Understand the Etiology and Pathophysiology of Polycystic Ovary Syndrome. Endocr. Rev. 2020;1;41(4):bnaa010. doi: 10.1210/endrev/bnaa010.\u003c/li\u003e\n\u003cli\u003eHe FF, Li YM. Role of gut microbiota in the development of insulin resistance and the mechanism underlying polycystic ovary syndrome: A review. J. Ovarian Res. 2020;17;13(1):73. \u003c/li\u003e\n\u003cli\u003eHarris HR, Titus LJ, Cramer DW, Terry KL. Long and Irregular Menstrual Cycles, Polycystic Ovary Syndrome, and Ovarian Cancer Risk in a Population-Based Case-Control Study. Int J Cancer. 2017;140(2):285-91.\u003c/li\u003e\n\u003cli\u003eWang YX, Stuart JJ, Rich-Edwards JW, Missmer SA, Rexrode KM, Farland LV, et al. Menstrual Cycle Regularity and Length Across the Reproductive Lifespan and Risk of Cardiovascular Disease. JAMA network open. 2022;5(10):e2238513-e2238513. \u003c/li\u003e\n\u003cli\u003eWang SW, Wang YX, Sandoval-Insausti H, Farland LV, Shifren JL, Zhang D , et al. Menstrual cycle characteristics and incident cancer: a prospective cohort study. Human reproduction (Oxford, England). 2021;37(2):341-351.\u003c/li\u003e\n\u003cli\u003eWang YX, Arvizu M, Rich-Edwards JW, Stuart JJ, Manson JE, Missmer SA, et al. Menstrual cycle regularity and length across the reproductive lifespan and risk of premature mortality: prospective cohort study. BMJ (Clinical research ed.). 2020;371 m3464-m3464.\u003c/li\u003e\n\u003cli\u003eEzeh U, Ezeh C, Pisarska MD, Azziz R. Menstrual dysfunction in polycystic ovary syndrome: association with dynamic state insulin resistance rather than hyperandrogenism. Fertil Steril. 2021;115(6):1557-1568.\u003c/li\u003e\n\u003cli\u003eLi XJ, Yang DD, Pan P, Aizziz R, Yang DZ, Cheng YX, et al. The Degree of Menstrual Disturbance Is Associated With the Severity of Insulin Resistance in PCOS. Frontiers in Endocrinology. 2022;13:873726-873726.\u003c/li\u003e\n\u003cli\u003eRotterdam ESHRE/ASRM-Sponsored PCOS consensus workshop group. Revised 2003 Consensus on Diagnostic Criteria and Long-Term Health Risks Related to Polycystic Ovary Syndrome (PCOS). Hum Reprod. 2004;19(1):41-7.\u003c/li\u003e\n\u003cli\u003eLegro RS, Kunselman AR, Dodson WC, Dunaif A. Prevalence and Predictors of Risk for Type 2 Diabetes Mellitus and Impaired Glucose Tolerance in Polycystic Ovary Syndrome: A Prospective, Controlled Study in 254 Affected Women. J Clin Endocrinol Metab. 1999;84(1):165-9.\u003c/li\u003e\n\u003cli\u003eEzeh U, Pisarska MD, Azziz R. Association of severity of menstrual dysfunction with hyperinsulinemia and dysglycemia in polycystic ovary syndrome. Hum Reprod. 2022;1;37(3):553-564.\u003c/li\u003e\n\u003cli\u003eMatthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis Model Assessment: Insulin Resistance and Beta-Cell Function From Fasting Plasma Glucose and Insulin Concentrations in Man. Diabetologia. 1985;28(7):412-9.\u003c/li\u003e\n\u003cli\u003eBonora E, Targher G, Alberiche M, Bonadonna RC, Saggiani F, Zenere MB, et al. Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity: studies in subjects with various degrees of glucose tolerance and insulin sensitivity. Diabetes care. 2000;23(1): 57-63. \u003c/li\u003e\n\u003cli\u003eZhang HL, Wang W, Zhao JM, Jiao PJ, Zeng L,Zhang H, et al. Relationship between body composition, insulin resistance, and hormonal profiles in women with polycystic ovary syndrome . Frontiers in Endocrinology. 2023;13;1085656-1085656. \u003c/li\u003e\n\u003cli\u003eCollins GS, Reitsma JB, Altman DG, Moons KGM. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement. Journal of Clinical Epidemiology. 2015;68(2):134-143.\u003c/li\u003e\n\u003cli\u003eNeeland IJ, Ross R, Despres JP, Matsuzawa YJ, Yamashita S, Shai I, et al. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement. Lancet Diabetes Endocrinol. 2019;7(9):715-725. \u003c/li\u003e\n\u003cli\u003eSamuel VT, Shulman GI. The pathogenesis of insulin resistance: integrating signaling pathways and substrate flux. J Clin Invest. 2016;126(1):12-22.\u003c/li\u003e\n\u003cli\u003eDumesic DA, Oberfield SE, Stener-Victorin E, Marshall JC, Laven JS, Legro RS. Scientific Statement on the Diagnostic Criteria, Epidemiology, Pathophysiology, and Molecular Genetics of Polycystic Ovary Syndrome. Endocr Rev. 2015;36(5):487-525. \u003c/li\u003e\n\u003cli\u003eDiamanti-Kandarakis E, Papavassiliou AG. Molecular mechanisms of insulin resistance in polycystic ovary syndrome. Trends Mol Med. 2006;12(7):324-32.\u003c/li\u003e\n\u003cli\u003eWinters SJ, Gogineni J, Karegar M, Scoggins C, Wunderlich CA, Baumgartner R, et al. Sex hormone-binding globulin gene expression and insulin resistance. The Journal of clinical endocrinology and metabolism. 2014;99(12):E2780-8. \u003c/li\u003e\n\u003cli\u003eMarshall JC, Dunaif A. Should all women with PCOS be treated for insulin resistance? Fertil Steril. 2012 ;97(1):18-22. \u003c/li\u003e\n\u003cli\u003eLegro RS, Arslanian SA, Ehrmann DA, Hoeger KM, Murad MH, Pasquali R, et al. Diagnosis and treatment of polycystic ovary syndrome: an Endocrine Society clinical practice guideline. J Clin Endocrinol Metab. 2013;98(12):4565-4592. \u003c/li\u003e\n\u003cli\u003eConway G, Dewailly D, Diamanti-Kandarakis E, Escobar-Morreale HF, Franks S, Gambineri A, et al. The polycystic ovary syndrome: a position statement from the European Society of Endocrinology. Eur J Endocrinol. 2014;171(4):P1-P29. \u003c/li\u003e\n\u003cli\u003eMoran LJ, Ko H, Misso M, Marsh K, Noakes M, Talbot M, et al. Dietary composition in the treatment of polycystic ovary syndrome: a systematic review to inform evidence-based guidelines. Hum Reprod Update. 2013;19(5):432. \u003c/li\u003e\n\u003cli\u003eMorley LC, Tang T, Yasmin E, Norman RJ, Balen AH. Insulin-sensitising drugs (metformin, rosiglitazone, pioglitazone, D-chiro-inositol) for women with polycystic ovary syndrome, oligo amenorrhoea and subfertility. Cochrane Database Syst Rev. 2017;29;11(11):CD003053. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1.Baseline characteristics of the study population.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(n = 408)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003eEumenorrhea (n = 83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003eOligomenorrhea(n = 191)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003eAmenorrhea\u003c/p\u003e\n \u003cp\u003e(n = 134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eAge (y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e29.53 \u0026plusmn; 4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e29.96 \u0026plusmn; 4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e29.57 \u0026plusmn; 4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e29.19 \u0026plusmn; 4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eBMI (Kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e23.04 \u0026plusmn; 4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e22.36 \u0026plusmn; 4.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e22.06 \u0026plusmn; 3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e24.85 \u0026plusmn; 4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eWHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e0.84 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e0.83 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e0.83 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e0.87 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eTSH(uIU/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e2.23 \u0026plusmn; 1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e1.95 \u0026plusmn; 1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e2.33 \u0026plusmn; 1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e2.26 \u0026plusmn; 1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eHCY(umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e8.32 \u0026plusmn; 1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e8.11 \u0026plusmn; 1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e8.28 \u0026plusmn; 1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e8.53 \u0026plusmn; 1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eFPG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e5.41 \u0026plusmn; 0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e5.34 \u0026plusmn; 0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e5.35 \u0026plusmn; 0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e5.53 \u0026plusmn; 0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eFINS (mU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e15.57 \u0026plusmn; 9.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e12.60 \u0026plusmn; 7.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e13.95 \u0026plusmn; 7.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e19.70 \u0026plusmn; 10.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e3.83 \u0026plusmn; 2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e3.04 \u0026plusmn; 1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e3.40 \u0026plusmn; 2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e4.94 \u0026plusmn; 2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003e2hPG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e7.34 \u0026plusmn; 2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e7.01 \u0026plusmn; 1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e7.02 \u0026plusmn; 2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e8.01 \u0026plusmn; 2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003e2hINS (mU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e88.61 (49.31, 156.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e59.30 (41.78, 111.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e80.79 (47.78, 131.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e135.34 (76.35, 212.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e16.80 (12.70, 25.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e14.10 (11.70, 21.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e15.60 (12.05, 20.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e20.75 (15.00, 33.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e1.26 \u0026plusmn; 0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e1.17 \u0026plusmn; 0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e1.19 \u0026plusmn; 0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e1.38 \u0026plusmn; 0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eTC(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e4.82 \u0026plusmn; 0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e4.88 \u0026plusmn; 0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e4.82 \u0026plusmn; 0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e4.79 \u0026plusmn; 0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eLDL-C(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e2.95 \u0026plusmn; 0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e2.98 \u0026plusmn; 0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e2.84 \u0026plusmn; 0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e3.07 \u0026plusmn; 0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eHDL-C(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e1.52 \u0026plusmn; 0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e1.53 \u0026plusmn; 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e1.65 \u0026plusmn; 0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e1.35 \u0026plusmn; 0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eA4(nmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e5.53 \u0026plusmn; 2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e4.67 \u0026plusmn; 1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e5.50 \u0026plusmn; 2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e6.01 \u0026plusmn; 2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eSHBG(nmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e33.94 (22.59, 53.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e39.13 (26.98, 53.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e36.60 (25.39, 56.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e27.52 (18.87, 47.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eFSH(IU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e7.21 \u0026plusmn; 1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e7.59 \u0026plusmn; 1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e7.19 \u0026plusmn; 1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e7.03 \u0026plusmn; 1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eLH(IU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e8.91 \u0026plusmn; 5.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e7.40 \u0026plusmn; 5.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e8.59 \u0026plusmn; 5.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e10.20 \u0026plusmn; 5.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eTT(ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e0.41 \u0026plusmn; 0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e0.39 \u0026plusmn; 0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e0.39 \u0026plusmn; 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e0.44 \u0026plusmn; 0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eE2(pg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e42.25 \u0026plusmn; 23.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e43.82 \u0026plusmn; 36.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e41.90 \u0026plusmn; 18.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e41.86 \u0026plusmn; 19.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eP4(ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e0.37 (0.29, 0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e0.44 (0.29, 0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e0.36 (0.29, 0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e0.36 (0.28, 0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003ePRL(ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e19.43 (12.56, 26.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e20.29 (12.01, 28.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e21.20 (14.65, 27.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e16.60 (11.88, 22.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eLH/FSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e1.28 \u0026plusmn; 0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e1.00 \u0026plusmn; 0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e1.22 \u0026plusmn; 0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e1.51 \u0026plusmn; 0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.2465%;\"\u003e\n \u003cp\u003eFAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e3.34 (2.01, 6.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3732%;\"\u003e\n \u003cp\u003e3.07 (1.61, 5.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.4296%;\"\u003e\n \u003cp\u003e3.08 (1.99, 5.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.0775%;\"\u003e\n \u003cp\u003e4.51 (2.27, 7.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.5%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Data are presented as mean \u0026plusmn; standard deviation (SD) for continuous variables with normal distribution or as median (IQR) for continuous variables that did not show a normal distribution, and categorical variables are reported as no. (%). BMI, Body mass index; WHR, Waist-to-hip ratio; TSH, Thyroid stimulating hormone; HCY, Homocysteine; FPG, Fasting plasma glucose; FINS, Fasting insulin; HOMA-IR, Homeostatic Model Assessment of Insulin Resistance; 2hPG, 2-hour post-load glucose; 2hINS, 2-hour post-load insulin; ALT, Aminotransferase; TG, Triglyceride; TC, Total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, High-density lipoprotein cholesterol; A4, Androstenedione; SHBG, Sex hormone binding globulin; FSH, Follicle stimulating hormone; LH, Luteinizing hormone; E2, Estradiol; TT, Total testosterone; PRL, Prolactin; P4, Progesterone; \u0026nbsp;FAI, Free androgen index.\u003c/p\u003e\n\u003cp\u003eTable 2. Logistic multivariate analysis of HOMA-IR and Menstrual dysfunction severity.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19.2446%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4317%;\"\u003e\n \u003cp\u003en.total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7482%;\"\u003e\n \u003cp\u003en.event_%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3669%;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6906%;\"\u003e\n \u003cp\u003eP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.5468%;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9712%;\"\u003e\n \u003cp\u003eP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19.2446%;\"\u003e\n \u003cp\u003eEumenorrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4317%;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7482%;\"\u003e\n \u003cp\u003e37 (44.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3669%;\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6906%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.5468%;\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9712%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19.2446%;\"\u003e\n \u003cp\u003eOligomenorrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4317%;\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7482%;\"\u003e\n \u003cp\u003e105 (55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3669%;\"\u003e\n \u003cp\u003e1.52 (0.90~2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6906%;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.5468%;\"\u003e\n \u003cp\u003e1.43 (0.72~2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9712%;\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19.2446%;\"\u003e\n \u003cp\u003eAmenorrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4317%;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7482%;\"\u003e\n \u003cp\u003e107 (79.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3669%;\"\u003e\n \u003cp\u003e4.93 (2.69~9.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6906%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.5468%;\"\u003e\n \u003cp\u003e1.88 (0.85~4.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9712%;\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 19.2446%;\"\u003e\n \u003cp\u003eTrend.test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4317%;\"\u003e\n \u003cp\u003e408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.7482%;\"\u003e\n \u003cp\u003e249 (61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3669%;\"\u003e\n \u003cp\u003e2.25 (1.67~3.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.6906%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.5468%;\"\u003e\n \u003cp\u003e1.37 (0.92~2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.9712%;\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1: crude model\u003c/p\u003e\n\u003cp\u003eModel 2: adjusted for BMI+WHR\u003c/p\u003e\n\u003cp\u003eTable 3. Linear regression analysis of glucose metabolism parameters and menstrual dysfunction severity.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.5379%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4848%;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.553%;\"\u003e\n \u003cp\u003eP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1591%;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9318%;\"\u003e\n \u003cp\u003eP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100%;\"\u003e\n \u003cp\u003eFPG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.5379%;\"\u003e\n \u003cp\u003eEumenorrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4848%;\"\u003e\n \u003cp\u003e0(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.553%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1591%;\"\u003e\n \u003cp\u003e0(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9318%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.5379%;\"\u003e\n \u003cp\u003eOligomenorrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4848%;\"\u003e\n \u003cp\u003e0.01 (-0.14~0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.553%;\"\u003e\n \u003cp\u003e0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1591%;\"\u003e\n \u003cp\u003e0 (-0.17~0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9318%;\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.5379%;\"\u003e\n \u003cp\u003eAmenorrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4848%;\"\u003e\n \u003cp\u003e0.20 (0.03~0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.553%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1591%;\"\u003e\n \u003cp\u003e0.04 (-0.14~0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9318%;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100%;\"\u003e\n \u003cp\u003e2hPG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.5379%;\"\u003e\n \u003cp\u003eEumenorrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4848%;\"\u003e\n \u003cp\u003e0(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.553%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1591%;\"\u003e\n \u003cp\u003e0(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9318%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.5379%;\"\u003e\n \u003cp\u003eOligomenorrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4848%;\"\u003e\n \u003cp\u003e0.02 (-0.54~0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.553%;\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1591%;\"\u003e\n \u003cp\u003e0.04 (-0.51~0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9318%;\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.5379%;\"\u003e\n \u003cp\u003eAmenorrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4848%;\"\u003e\n \u003cp\u003e1.00 (0.42~1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.553%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1591%;\"\u003e\n \u003cp\u003e0.31 (-0.30~0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9318%;\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100%;\"\u003e\n \u003cp\u003eFINS (mU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.5379%;\"\u003e\n \u003cp\u003eEumenorrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4848%;\"\u003e\n \u003cp\u003e0(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.553%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1591%;\"\u003e\n \u003cp\u003e0(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9318%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.5379%;\"\u003e\n \u003cp\u003eOligomenorrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4848%;\"\u003e\n \u003cp\u003e1.35 (-0.90~3.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.553%;\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1591%;\"\u003e\n \u003cp\u003e1.39 (-0.51~3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9318%;\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.5379%;\"\u003e\n \u003cp\u003eAmenorrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4848%;\"\u003e\n \u003cp\u003e7.10 (4.71~9.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.553%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1591%;\"\u003e\n \u003cp\u003e2.55 (0.47~4.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9318%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100%;\"\u003e\n \u003cp\u003e2hINS (mU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.5379%;\"\u003e\n \u003cp\u003eEumenorrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4848%;\"\u003e\n \u003cp\u003e0(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.553%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1591%;\"\u003e\n \u003cp\u003e0(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9318%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.5379%;\"\u003e\n \u003cp\u003eOligomenorrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4848%;\"\u003e\n \u003cp\u003e8.92 (-17.54~35.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.553%;\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1591%;\"\u003e\n \u003cp\u003e11.92 (-14.68~38.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9318%;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.5379%;\"\u003e\n \u003cp\u003eAmenorrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.33333%;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4848%;\"\u003e\n \u003cp\u003e73.32 (45.2~101.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.553%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1591%;\"\u003e\n \u003cp\u003e37.24 (8.13~66.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9318%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1: crude model\u003c/p\u003e\n\u003cp\u003eModel 2: adjusted for BMI+WHR\u003c/p\u003e\n\u003cp\u003eFPG, Fasting plasma glucose; 2hPG, 2-hour post-load glucose; FINS, Fasting insulin; 2hINS, 2-hour post-load insulin\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Polycystic ovary syndrome, insulin resistance, menstrual dysfunction, hyperinsulinemia, dysglycemia","lastPublishedDoi":"10.21203/rs.3.rs-7936158/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7936158/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eWhile insulin resistance (IR) underlies reduced ovulation in polycystic ovary syndrome (PCOS), the relative contributions of hyperinsulinemia versus dysglycemia to anovulatory dysfunction remain unclear. This study quantitatively assessed the relationship between menstrual disturbance and both insulin and glucose parameters in women with PCOS.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective cross-sectional study included 408 women diagnosed with PCOS. Participants were categorized into three groups based on self-reported menstrual cycle length: Eumenorrhea (26\u0026ndash;34 days), Oligomenorrhea (35\u0026ndash;90 days), and Amenorrhea (\u0026gt;\u0026thinsp;90 days). All participants underwent a standardized 75g oral glucose tolerance test (OGTT). Fasting plasma glucose (FPG) and insulin (FINS), along with 2-hour post-load glucose (2hPG) and insulin (2hINS) levels were measured. Insulin resistance was assessed using the Homeostatic Model Assessment (HOMA-IR). Statistical analyses compared metabolic parameters across groups using ANOVA/chi-square tests and assessed relationships using multivariable linear regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIncreasing menstrual cycle length was significantly associated with elevated body mass index (BMI), waist-to-hip ratio (WHR), FPG, 2hPG, FINS, 2hINS, alanine aminotransferase (ALT), low-density lipoprotein cholesterol (LDL-C), androstenedione, luteinizing hormone/follicle-stimulating hormone ratio (LH/FSH), LH, prolactin, and free androgen index (FAI) (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, sex hormone-binding globulin (SHBG) and high-density lipoprotein cholesterol (HDL-C) levels decreased significantly with greater menstrual disturbance (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The prevalence of insulin resistance (IR) was significantly higher in women with amenorrhea compared to those with eumenorrhea (79.9% vs. 44.6%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), demonstrating a progressive increase in IR risk with worsening menstrual dysfunction (Ptrend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Following adjustment for BMI and WHR, the amenorrhea group demonstrated persistently elevated FINS (β\u0026thinsp;=\u0026thinsp;2.55, 95% CI 0.47 to 4.63; P\u0026thinsp;=\u0026thinsp;0.017) and 2hINS (β\u0026thinsp;=\u0026thinsp;37.24, 95% CI 8.13 to 66.35; P\u0026thinsp;=\u0026thinsp;0.013). No statistically significant differences in FPG or 2hPG levels were observed between the groups.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIn women with PCOS, menstrual disturbance severity is independently associated with hyperinsulinemia, not dysglycemia. These findings suggest that hyperinsulinemia, rather than glucose levels, represents a key biomarker determining the severity of menstrual dysfunction in PCOS.\u003c/p\u003e","manuscriptTitle":"Menstrual dysfunction in PCOS: primarily linked to hyperinsulinemia over dysglycemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 17:13:30","doi":"10.21203/rs.3.rs-7936158/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"14abd0b9-d886-4cc6-9338-4d569aafc289","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-15T04:23:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 17:13:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7936158","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7936158","identity":"rs-7936158","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}