Revisiting ESR in PCOS: an easily obtainable link to immune and metabolic dysregulation

This retrospective case-control study involved 2,006 PCOS women, and 10,038 normal ovulating women (serving as controls) who were not receiving therapy at the first visit of the Reproductive Medicine Center of the First Affiliated Hospital of Wenzhou Medical University. The study protocol received ethical approval from the local Ethics Committee (KY2025-R174) and adhered to the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants before their clinical evaluations. PCOS diagnosis followed the Rotterdam criteria. In the present cohort, all women in the PCOS group fulfilled two specific Rotterdam features: polycystic ovarian morphology on ultrasound and oligo-/anovulation. Clinical or biochemical hyperandrogenism, when present, was recorded but was not required for inclusion. Other endocrine disorders that may mimic PCOS, including congenital adrenal hyperplasia and Cushing’s syndrome, were excluded. The control group comprised women receiving IUI/IVF treatment solely for male or tubal factors during the same period. By rigorously excluding those with ovulatory, endocrine, or metabolic disorders, this well-characterized cohort serves as a reliable proxy for normal ovulatory women. Participants met stringent inclusion criteria: (1) reproductive age (20–45 years); (2) intact bilateral ovaries; (3) nonsmoking status; (4) absence of regular alcohol consumption; (5) euthyroid individuals. Exclusion criteria included: (1) malignancies; (2) pelvic inflammatory disease or endometriosis (confirmed by transvaginal ultrasonography/laparoscopy); (3) systemic inflammatory conditions (e.g., tuberculosis, systemic lupus erythematosus); (4) hematologic/hepatic/renal dysfunction; (5) recent use (< 3 months) of oral contraceptives, glucocorticoids, lipid-lowering, antiobesity, antidiabetes, estrogenic/anti-androgenic medications, or antihypertensive agents. Body mass index (BMI) was calculated as body weight (kg)/height (m) 2 . Subjects were categorized by Chinese BMI criteria: underweight (< 18.5 kg/m 2 ), normal (18.5–23.9 kg/m 2 ), overweight (24.0–27.9 kg/m 2 ), and obese (≥ 28 kg/m 2 ) [ 24 ]. Blood samples were collected in the morning between 8 a.m. and 9 a.m. after an overnight fast. All biochemical and endocrine parameters were assessed concurrently during a standardized basal window (the early follicular phase). For patients with regular menstrual cycles, fasting blood samples were obtained on 2nd-5th day of spontaneous menses. For PCOS patients experiencing oligo/amenorrhea, samples were collected either on 2nd-5th day following progestin-induced withdrawal bleeding, a standard and validated clinical proxy for the spontaneous early follicular phase, or during an amenorrheic phase strictly confirmed as non-luteal by serum progesterone levels < 2 ng/mL. Biochemical parameters, including fasting blood glucose (FBG), total cholesterol (TC), creatinine, blood urea nitrogen (BUN), aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transpeptidase (γ-GGT), estimated glomerular filtration rate (eGFR), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), thyroid stimulating hormone (TSH), free triiodothyronine (FT3), and free thyroxine (FT4) were measured by an autoanalyzer [AU5800, Beckman, USA]. Prolactin (PRL), follicle-stimulating hormone (FSH), luteinizing hormone (LH), estradiol (E 2 ), and total testosterone (TT) were quantified using an autoimmunoassay analyzer [Unicel Dxl 800, Beckman Coulter, USA], according to manufacturer’s instructions. Specifically, the analytical sensitivity of the assay for TT was 0.35 nmol/L. The intra-assay coefficient of variation (CV) was < 4.0%, and the inter-assay CV was < 7.5%. Because ESR may be physiologically elevated during menstruation, blood samples for ESR measurement were collected 5–7 days later, during the non-menstrual follicular phase. ESR was measured using an automated ESR analyzer (TEST1TH, SIRE ANALYTICAL SYSTEMS) in accordance with the manufacturer’s instructions and the standard procedures of the clinical laboratory. For baseline characteristics, continuous variables were expressed as mean ± standard deviation (SD) and categorical variables were expressed as frequency and percentage. Continuous variables with a normal distribution were compared using the independent samples t-test, while those with a non-normal distribution were compared using the Mann-Whitney U test. Univariable and multivariable linear regression analyses were used to examine the associations between various clinical parameters and continuous ESR levels. Furthermore, logistic regression models were employed to evaluate the associations with categorical outcomes, including the presence of hyper-ESR (> 20 mm/h) and specific metabolic dysregulations. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the discriminatory ability of clinical parameters (BMI, TT, LH/FSH, TG, ESR, and PCOS status), both as single markers and in multivariable prediction models, for predefined metabolic-endocrine abnormalities and PCOS prevalence. The area under the curve (AUC) was calculated to quantify the performance of these parameters in distinguishing participants with and without each condition. Statistical analyses were performed with R software (version 4.2.3) and SPSS (version 25.0). P values less than 0.05 were deemed statistically significant.

The study enrolled 12,044 reproductive-aged women, comprising 2,006 women with PCOS and 10,038 normo-ovulatory controls. As shown in Table  1 , no significant differences were observed between groups in multiple metabolic and endocrine parameters (E 2 , FBG, creatinine, BUN, creatinine/BUN ratio, eGFR, TSH, and FT4). However, PCOS women demonstrated elevated cardiovascular risk (higher SBP and DBP), subclinical hepatorenal dysfunction (increased uric acid, AST, and γ-GGT), and an adverse lipid profile (higher TC, TG, LDL-C, and lower HDL-C). Endocrine and ovarian evaluations confirmed characteristic PCOS perturbations: lower PRL, elevated LH/FSH ratio and TT, as well as a marked increase in antral follicle count (AFC). Notably, ESR was significantly increased in PCOS women, highlighting chronic immune dysregulation as a key pathophysiological feature. Table 1 General characteristics of included participants Non-PCOS ( n  = 10,038) PCOS ( n  = 2,006) P value Effect size (Cohen‘s d) Age (y) 31.38 ± 4.58 29.00 ± 3.49 < 0.001 0.56 Infertility duration (y) 4.10 ± 3.05 3.97 ± 2.52 0.092 0.05 AFC (n) 15.17 ± 4.18 29.25 ± 4.51 < 0.001 3.32 BMI (kg/m 2 ) < 18.5, n (%) 18.5–23.9, n (%) 24.0-27.9, n (%) ≥ 28.0, n (%) 21.50 ± 2.88 1287 (12.82%) 7611 (75.82%) 1024 (10.20%) 116 (1.16%) 22.94 ± 3.64 177 (8.82%) 1306 (65.10%) 434 (21.64%) 89 (4.44%) < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.46 SBP (mmHg) 113.66 ± 11.34 116.12 ± 11.64 < 0.001 0.21 DBP (mmHg) 72.17 ± 7.45 73.91 ± 7.88 < 0.001 0.23 PRL (mIU/mL) 287.15 ± 104.00 273.90 ± 103.58 < 0.001 0.13 FSH (IU/L) 8.13 ± 2.36 6.75 ± 1.57 < 0.001 0.65 LH (IU/L) 4.67 ± 2.02 6.76 ± 3.20 < 0.001 0.84 LH/FSH 0.60 ± 0.28 1.03 ± 0.51 < 0.001 1.11 E 2 (pmol/L) 149.60 ± 64.16 148.58 ± 61.37 0.514 0.02 TT (nmol/L) 1.26 ± 2.33 1.65 ± 0.58 < 0.001 0.21 FBG (mmol/L) 5.31 ± 0.48 5.32 ± 0.58 0.486 0.02 Uric acid (µmol/L) 275.45 ± 59.39 313.58 ± 75.24 < 0.001 0.59 Creatinine (µmol/L) 49.24 ± 7.09 49.54 ± 6.91 0.085 0.04 BUN (mmol/L) 4.18 ± 1.03 4.18 ± 1.02 0.953 0.00 Creatinine/BUN ratio 21.08 ± 5.74 21.11 ± 5.80 0.850 0.01 ESR (mm/h) 14.60 ± 9.66 16.03 ± 9.95 < 0.001 0.15 AST (U/L) 19.09 ± 8.70 21.75 ± 11.19 < 0.001 0.28 γ-GGT (U/L) 16.25 ± 12.48 22.25 ± 20.79 < 0.001 0.38 eGFR 74.00 ± 12.92 74.48 ± 12.43 0.128 0.04 TC (mmol/L) 4.44 ± 0.79 4.64 ± 0.84 < 0.001 0.25 TG (mmol/L) 1.06 ± 0.76 1.42 ± 1.01 < 0.001 0.42 HDL-C (mmol/L) 1.46 ± 0.31 1.39 ± 0.33 < 0.001 0.22 LDL-C (mmol/L) 2.46 ± 0.65 2.63 ± 0.70 < 0.001 0.25 TSH (mIU/L) 2.26 ± 3.43 2.22 ± 2.56 0.617 0.01 FT4 (pmol/L) 11.37 ± 1.80 11.30 ± 2.04 0.137 0.04 FT3 (pmol/L) 4.95 ± 0.79 5.07 ± 1.13 < 0.001 0.13 Data are mean ± SD for continuous variables and number (%) for categorical variables Abbreviations: AFC antral follicle count, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, LH luteinizing hormone, FSH follicle stimulating hormone, TT total testosterone, FBG fasting blood glucose, PRL prolactin, E 2 estradiol, BUN blood urea nitrogen, ESR erythrocyte sedimentation rate, AST aspartate aminotransferase, γ-GGT gamma-glutamyl transpeptidase, eGFR estimated glomerular filtration rate, TC total cholesterol, TG triglyceride, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, TSH thyroid stimulating hormone, FT3 free triiodothyronine, FT4 free thyroxine General characteristics of included participants BMI (kg/m 2 ) < 18.5, n (%) 18.5–23.9, n (%) 24.0-27.9, n (%) ≥ 28.0, n (%) 21.50 ± 2.88 1287 (12.82%) 7611 (75.82%) 1024 (10.20%) 116 (1.16%) 22.94 ± 3.64 177 (8.82%) 1306 (65.10%) 434 (21.64%) 89 (4.44%) < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 Data are mean ± SD for continuous variables and number (%) for categorical variables Abbreviations: AFC antral follicle count, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, LH luteinizing hormone, FSH follicle stimulating hormone, TT total testosterone, FBG fasting blood glucose, PRL prolactin, E 2 estradiol, BUN blood urea nitrogen, ESR erythrocyte sedimentation rate, AST aspartate aminotransferase, γ-GGT gamma-glutamyl transpeptidase, eGFR estimated glomerular filtration rate, TC total cholesterol, TG triglyceride, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, TSH thyroid stimulating hormone, FT3 free triiodothyronine, FT4 free thyroxine Further subgroup analyses within the PCOS cohort revealed distinct metabolic-endocrine profiles by weight and androgen status. Compared with normal-weight (NW) PCOS women, overweight/obese (OW/OB) PCOS women exhibited systemic multi-organ dysregulation, encompassing endocrine disturbances (lower PRL, FSH, LH, TSH and FT3, but higher TT), hepatorenal dysfunction (elevated uric acid, AST, γ-GGT, creatinine alongside reduced eGFR), dyslipidemia (higher TC, TG, LDL-C, and lower HDL-C), hyperglycemia, elevated blood pressure, as well as a longer infertility duration (Supplementary Table 1). In parallel, hyperandrogenic (HA) PCOS women showed a distinct phenotype characterized by exacerbated metabolic burden (higher BMI, uric acid, AST, γ-GGT, and LDL-C) and heightened endocrine activity (elevated LH, LH/FSH ratio, E 2 , FT4, and FT3; Supplementary Table 2). Importantly, ESR levels were significantly elevated in both OW/OB and HA subgroups compared with their respective counterparts, indicating that obesity and hyperandrogenemia each contribute to heightened inflammatory status in PCOS (Supplementary Tables 1, 2). Subsequent analyses defined hyper-ESR as an ESR exceeding 20 mm/h, the established and validated upper limit of the normal clinical reference range for non-pregnant adult women at our institutional clinical laboratory (and aligns with standard clinical guidelines) [ 25 ]. Even among subjects with ESR ≤ 20 mm/h, PCOS women exhibited higher ESR levels than controls (Fig. 1 a). Despite no significant difference in ESR levels among those with hyper-ESR (Fig. 1 b), the prevalence of hyper-ESR was significantly higher in the PCOS group (Fig. 1 c). Fig. 1 ESR levels and hyper-ESR prevalence in PCOS women and controls across age and BMI classes. ( a ) ESR levels in PCOS women and controls without hyper-ESR. ( b ) ESR levels in PCOS women and controls with hyper-ESR. ( c ) Prevalence of hyper-ESR in PCOS women and controls. ( d ) ESR levels in PCOS women and controls grouped by age. ( e ) Age-related trends in ESR for controls and PCOS groups. Lines represent the estimated mean ESR across ages, with shaded areas indicating 95% CI. ( f ) Prevalence of hyper-ESR in PCOS women and controls grouped by age. ( g ) ESR levels in PCOS women and controls grouped by BMI. ( h ) Relationship between BMI and ESR in controls and PCOS populations. Lines depict the estimated mean ESR across BMI, and shaded regions denote 95% CI. ( i ) Prevalence of hyper-ESR in PCOS women and controls grouped by BMI. ( j ) ESR levels in PCOS women stratified by TT levels. ( k ) Prevalence of hyper-ESR in PCOS women stratified by TT levels. ( l ) ESR levels in PCOS women stratified by BMI. ( m ) Prevalence of hyper-ESR in PCOS women stratified by BMI ESR levels and hyper-ESR prevalence in PCOS women and controls across age and BMI classes. ( a ) ESR levels in PCOS women and controls without hyper-ESR. ( b ) ESR levels in PCOS women and controls with hyper-ESR. ( c ) Prevalence of hyper-ESR in PCOS women and controls. ( d ) ESR levels in PCOS women and controls grouped by age. ( e ) Age-related trends in ESR for controls and PCOS groups. Lines represent the estimated mean ESR across ages, with shaded areas indicating 95% CI. ( f ) Prevalence of hyper-ESR in PCOS women and controls grouped by age. ( g ) ESR levels in PCOS women and controls grouped by BMI. ( h ) Relationship between BMI and ESR in controls and PCOS populations. Lines depict the estimated mean ESR across BMI, and shaded regions denote 95% CI. ( i ) Prevalence of hyper-ESR in PCOS women and controls grouped by BMI. ( j ) ESR levels in PCOS women stratified by TT levels. ( k ) Prevalence of hyper-ESR in PCOS women stratified by TT levels. ( l ) ESR levels in PCOS women stratified by BMI. ( m ) Prevalence of hyper-ESR in PCOS women stratified by BMI Stratification of PCOS patients (Table  2 ) demonstrated that those with hyper-ESR presented aggravated multi-system pathology, including elevated cardiovascular risk (higher SBP and DBP), prolonged infertility duration, exacerbated metabolic derangements (increased BMI, FBG, uric acid, AST, and γ-GGT), as well as pro-atherogenic lipid profile (higher LDL-C, TG, TC and lower HDL-C). Thus, ESR elevation may serve as an integrative biomarker associated with concurrent disturbances across reproductive, metabolic, cardiovascular, and endocrine axes in PCOS. Table 2 General characteristics of PCOS women with or without hyper-ESR ESR ≤ 20 mm/h ( n  = 1,451) ESR > 20 mm/h ( n  = 555) P Effect size (Cohen’s d) Age (y) 28.94 ± 3.48 29.13 ± 3.53 0.276 0.05 Infertility duration (y) 3.89 ± 2.48 4.19 ± 2.63 0.016 0.12 AFC (n) 29.60 ± 4.62 28.35 ± 4.06 < 0.001 0.28 BMI (kg/m 2 ) < 18.5, n (%) 18.5–23.9, n (%) 24.0-27.9, n (%) ≥ 28.0, n (%) 22.56 ± 3.45 143 (9.86%) 867 (59.75%) 331 (22.81%) 110 (7.58%) 23.94 ± 3.90 34 (6.13%) 273 (49.19%) 161 (29.01%) 87 (15.68%) < 0.001 0.008 < 0.001 0.004 < 0.001 0.39 SBP (mmHg) 115.37 ± 11.55 118.06 ± 11.64 < 0.001 0.23 DBP (mmHg) 73.51 ± 7.79 74.95 ± 8.03 < 0.001 0.18 PRL (mIU/mL) 276.21 ± 102.81 267.78 ± 105.42 0.109 -0.08 FSH (IU/L) 6.80 ± 1.59 6.61 ± 1.54 0.015 -0.12 LH (IU/L) 6.83 ± 3.28 6.57 ± 2.98 0.108 -0.08 LH/FSH 1.03 ± 0.51 1.03 ± 0.51 0.888 0.00 E 2 (pmol/L) 148.67 ± 61.51 148.32 ± 61.04 0.910 -0.01 TT (nmol/L) 1.64 ± 0.56 1.67 ± 0.64 0.240 0.05 FBG (mmol/L) 5.31 ± 0.57 5.37 ± 0.62 0.040 0.10 Uric acid (µmol/L) 306.67 ± 72.19 331.66 ± 79.99 < 0.001 0.34 Creatinine (µmol/L) 49.61 ± 6.81 49.34 ± 7.16 0.444 -0.04 BUN (mmol/L) 4.18 ± 1.03 4.17 ± 1.00 0.816 -0.01 Creatinine/BUN ratio 21.11 ± 5.73 21.13 ± 5.96 0.947 0.00 AST (U/L) 20.73 ± 9.38 24.42 ± 14.60 < 0.001 0.33 γ-GGT (U/L) 20.58 ± 19.59 26.61 ± 23.10 < 0.001 0.29 eGFR 74.31 ± 12.15 74.91 ± 13.16 0.336 0.05 TC (mmol/L) 4.57 ± 0.80 4.82 ± 0.91 < 0.001 0.30 TG (mmol/L) 1.32 ± 0.91 1.67 ± 1.20 < 0.001 0.35 HDL-C (mmol/L) 1.41 ± 0.33 1.34 ± 0.33 < 0.001 -0.21 LDL-C (mmol/L) 2.57 ± 0.66 2.77 ± 0.75 < 0.001 0.29 TSH (mIU/L) 2.21 ± 2.92 2.24 ± 1.19 0.837 0.01 FT4 (pmol/L) 11.31 ± 1.85 11.27 ± 2.45 0.669 -0.02 FT3 (pmol/L) 5.08 ± 1.21 5.07 ± 0.88 0.869 -0.01 Data are mean ± SD for continuous variables and number (%) for categorical variables Abbreviations: AFC antral follicle count, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, LH luteinizing hormone, FSH follicle stimulating hormone, TT total testosteron, FBG fasting blood glucose, PRL prolactin, E 2 estradiol, BUN blood urea nitrogen, ESR erythrocyte sedimentation rate, AST aspartate aminotransferase, γ-GGT gamma-glutamyl transpeptidase, eGFR estimated glomerular filtration rate, TC total cholesterol, TG triglyceride, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, TSH thyroid stimulating hormone, FT3 free triiodothyronine, FT4 free thyroxine General characteristics of PCOS women with or without hyper-ESR BMI (kg/m 2 ) < 18.5, n (%) 18.5–23.9, n (%) 24.0-27.9, n (%) ≥ 28.0, n (%) 22.56 ± 3.45 143 (9.86%) 867 (59.75%) 331 (22.81%) 110 (7.58%) 23.94 ± 3.90 34 (6.13%) 273 (49.19%) 161 (29.01%) 87 (15.68%) < 0.001 0.008 < 0.001 0.004 < 0.001 Data are mean ± SD for continuous variables and number (%) for categorical variables Abbreviations: AFC antral follicle count, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, LH luteinizing hormone, FSH follicle stimulating hormone, TT total testosteron, FBG fasting blood glucose, PRL prolactin, E 2 estradiol, BUN blood urea nitrogen, ESR erythrocyte sedimentation rate, AST aspartate aminotransferase, γ-GGT gamma-glutamyl transpeptidase, eGFR estimated glomerular filtration rate, TC total cholesterol, TG triglyceride, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, TSH thyroid stimulating hormone, FT3 free triiodothyronine, FT4 free thyroxine Given the known influence of BMI and age on ESR, we performed stratified analyses. Both control and PCOS women showed a modest age-related rise in ESR levels (Fig.  1 d, e). PCOS patients consistently demonstrated elevated ESR levels (Fig.  1 d, e) and higher prevalence of hyper-ESR (Fig.  1 f) across all age strata. BMI-stratified analysis revealed a positive correlation between BMI and ESR levels in both groups (Fig.  1 g, h). Notably, normal-weight PCOS women showed significantly higher ESR levels compared to their BMI-matched controls, whereas no such difference was observed in underweight, overweight, or obese individuals (Fig.  1 g). ESR rose with BMI in both groups, with a steeper slope in the PCOS cohort (Fig.  1 h). Hyper-ESR prevalence also increased progressively with BMI, with the most pronounced difference observed in normal-weight subjects (Fig.  1 i). Considering the heterogeneity of PCOS phenotypes, further sub-stratification by androgen levels and BMI was performed. Among PCOS women with hyperandrogenism or overweight/obesity, both ESR levels and hyper-ESR prevalence were significantly elevated (Fig.  1 j-m). Multivariable logistic regression analysis identified distinct factors associated with hyper-ESR risk across populations. In PCOS women, hyper-ESR was primarily related to atherogenic dyslipidemia, specifically elevated TC (aOR 1.38, 95% CI 1.23–1.55, P  < 0.001), TG (aOR 1.27, 95% CI 1.14–1.41, P  < 0.001), and LDL-C (aOR 1.38, 95% CI 1.19–1.59, P  < 0.001; Fig.  2 a). Conversely, hyper-ESR in controls and the combined cohort involved multiple domains after adjustment for age and BMI, involving endocrine dysregulation [elevated LH/FSH ratio (controls: aOR 1.51, 95% CI 1.28–1.78, P  < 0.001; combined cohort: aOR 1.32, 95% CI 1.18–1.48, P  < 0.001)], metabolic dysfunction (increased FBG [controls: aOR 1.22, 95% CI 1.10–1.34, P <  0.001; combined cohort: aOR 1.18, 95% CI 1.08–1.28, P  < 0.001) and eGFR (controls: 1.67, 95% CI 1.16–2.42, P =  0.006; combined cohort: aOR 1.69, 95% CI 1.21–2.35, P =  0.002)], and lipid perturbations [higher TC (controls: aOR 1.39, 95% CI 1.31–1.47, P  < 0.001; combined cohort: aOR 1.39, 95% CI 1.32–1.47, P  < 0.001), TG (controls: aOR 1.16, 95% CI 1.24–1.32, P  < 0.001; combined cohort: aOR 1.26, 95% CI 1.19–1.33, P  < 0.001), LDL-C (controls: aOR 1.53, 95% CI 1.42–1.64, P  < 0.001; combined cohort: aOR 1.50, 95% CI 1.41–1.60, P  < 0.001), and reduced HDL-C (controls: aOR 0.77, 95% CI 0.65–0.90, P =  0.002; combined cohort: aOR 0.76, 95% CI 0.66–0.89, P <  0.001)] (Fig.  2 b, c). This population-specific risk architecture suggests fundamental pathophysiological divergence: PCOS-related immune dysregulation is primarily linked to lipid-centric pathways, whereas hyper-ESR in PCOS involves integrated endocrine-metabolic dysregulation. Fig. 2 Logistic regression analysis of clinical and biochemical parameters associated with hyper-ESR status among populations. Forest plot showing associations between clinical parameters and hyper-ESR risk in PCOS women ( a ), controls ( b ), and combined subjects ( c ). Blue lines indicate results from the unadjusted model, whereas red lines indicate results from the model adjusted for age and BMI Logistic regression analysis of clinical and biochemical parameters associated with hyper-ESR status among populations. Forest plot showing associations between clinical parameters and hyper-ESR risk in PCOS women ( a ), controls ( b ), and combined subjects ( c ). Blue lines indicate results from the unadjusted model, whereas red lines indicate results from the model adjusted for age and BMI Logistic regression analysis identified several parameters that may contribute to PCOS development. Key endocrine indicators, such as elevated LH/FSH (aOR 18.19, 95% CI 15.48–21.37, P  < 0.001) and TT (aOR 1.83, 95% CI 1.67–2.01, P  < 0.001), exhibited positive associations, underscoring neuroendocrine dysregulation as a principal contributor. Metabolic markers also showed significant correlations, including elevated TG (aOR 1.48, 95% CI 1.38–1.57), TC (aOR 1.38, 95% CI 1.29–1.46), LDL-C levels (aOR 1.37, 95% CI 1.270–1.476) and reduced HDL-C (aOR 0.82, 95% CI 0.68–0.97, all P  < 0.001). Notably, although continuous ESR levels showed only a negligible association (aOR 1.01, 95% CI 1.00-1.01, P  = 0.004), hyper-ESR status was associated with a modest increase in PCOS risk (aOR 1.19, 95% CI 1.06–1.33, P  = 0.004; Fig.  3 a). To further investigate whether the associations differ by ESR status, we stratified the cohort into normal-ESR and hyper-ESR subgroups. In both strata, elevated LH/FSH ratio (normal-ESR: aOR 20.73, 95% CI 17.18–25.10, P  < 0.001; hyper-ESR: aOR 12.54, 95% CI 9.32–17.06, P  < 0.001), TT (normal-ESR: aOR 1.67, 95% CI 1.51–1.86, P  < 0.001; hyper-ESR: aOR 2.36, 95% CI 1.96–2.83, P  < 0.001), and adverse lipid profiles [higher TC (normal-ESR cohort: aOR 1.39, 95% CI 1.29–1.49, P  < 0.001; hyper-ESR cohort: aOR 1.32, 95% CI 1.18–1.48, P  < 0.001), TG (normal-ESR cohort: aOR 1.52, 95% CI 1.41–1.65, P  < 0.001; hyper-ESR cohort: aOR 1.35, 95% CI 1.22–1.50, P  < 0.001), and LDL-C (normal-ESR cohort: aOR 1.42, 95% CI 1.30–1.56, P  < 0.001; hyper-ESR cohort: aOR 1.22, 95% CI 1.07–1.39, P  < 0.001)] remained significant risk factors for PCOS (Supplementary Fig. 1). Importantly, ESR status significantly modified the association between TT and PCOS, with a greater correlation of TT in the hyper-ESR population (OR_int 1.39, 95% CI_int 1.12–1.72, P _int = 0.002; Supplementary Fig. 1). Fig. 3 Predictors of PCOS incidence. ( a ) Forest plot showing associations between clinical parameters and PCOS risk. Blue lines indicate results from the unadjusted model, whereas red lines indicate results from the model adjusted for age and BMI. ( b ) ROC curve comparison of single and combined biomarkers for predicting PCOS incidence Predictors of PCOS incidence. ( a ) Forest plot showing associations between clinical parameters and PCOS risk. Blue lines indicate results from the unadjusted model, whereas red lines indicate results from the model adjusted for age and BMI. ( b ) ROC curve comparison of single and combined biomarkers for predicting PCOS incidence ROC analysis corroborated these findings, demonstrating that a composite model of TG, TT, and LH/FSH ratio achieved optimal discriminative capacity for PCOS (AUC 0.83, 95% CI 0.82–0.84, P  < 0.001). Incorporating ESR failed to enhance this model’s predictive performance (ΔAUC < 0.01, P  = 0.597) (Fig.  3 b, Supplementary Table 3). Collectively, this discrepancy suggests a threshold effect, whereby only ESR elevation beyond the normal clinical range confers a detectable, though still limited, incremental risk for PCOS. Inflammation is known to be mechanistically linked to various pathological conditions, including metabolic and endocrine disturbances. Spearman correlation analysis showed that ESR was positively correlated with AST ( r  = 0.19), γ-GGT ( r  = 0.26), uric acid ( r  = 0.20), TC ( r  = 0.21), TG ( r  = 0.23), and LDL-C ( r  = 0.21, all P  < 0.001), but negatively associated with AFC ( r =-0.10, P  < 0.001). Notably, these correlations were consistently more pronounced in PCOS subjects (Fig.  4 and Supplementary Table 4). Fig. 4 Significant correlations between ESR and AST, γ-GGT, uric acid, TC, TG, and LDL-C in controls (blue), PCOS women (red), and all the participants (gray). The regression lines for each group are presented, with the 95% CI shown as shaded regions Significant correlations between ESR and AST, γ-GGT, uric acid, TC, TG, and LDL-C in controls (blue), PCOS women (red), and all the participants (gray). The regression lines for each group are presented, with the 95% CI shown as shaded regions Logistic regression analyses revealed distinct factors associated with dyslipidemia across cohorts. In PCOS women, FBG (aOR 1.23, 95% CI 1.02–1.48, P  = 0.029) and eGFR (aOR 3.58, 95% CI 1.63–7.85, P =  0.001) were independently linked to dyslipidemia (Fig.  5 a). When stratifying PCOS women by androgen status, eGFR remained a significant correlate in the non-HA subgroup (aOR 4.31, 95% CI 1.86–9.99, P  = 0.001), whereas modest correlates in the HA subgroup included ESR (aOR 1.09, 95% CI 1.01–1.18, P  = 0.023), creatinine/BUN ratio (aOR 0.87, 95% CI 0.77-1.00, P  = 0.046), AST (aOR 1.09, 95% CI 1.00-1.19, P  = 0.045), γ-GGT (aOR 1.10, 95% CI 1.02–1.19, P  = 0.010; Supplementary Fig. 2). Formal interaction tests, however, revealed no significant differences between the two subgroups (Supplementary Fig. 2), suggesting that hyperandrogenemia exerts a weak modifying effect. In controls, dyslipidemia was inversely associated with BUN (aOR 0.89, 95% CI 0.85–0.93, P  < 0.001) and positively correlated with FT3 (aOR 1.11, 95% CI 1.04–1.17, P =  0.001; Fig.  5 b). In the combined cohort, neuroendocrine dysregulation (LH/FSH: aOR 1.23, 95% CI 1.11–1.37, P  < 0.001), impaired glucose metabolism (FBG: aOR 1.11, 95% CI 1.03–1.21, P =  0.010), and altered renal function (eGFR: aOR 1.52, 95% CI 1.12–2.06, P =  0.007; BUN: aOR 0.90, 95% CI 0.86–0.93, P  < 0.001) emerged as cross-cutting risk factors, highlighting shared metabolic-endocrine pathways (Fig.  5 c). Notably, hyper-ESR status consistently demonstrated a significant association with dyslipidemia across all three cohorts (PCOS: aOR 1.54, 95% CI 1.22–1.93, P  < 0.001; controls: aOR 1.42, 95% CI 1.28–1.56, P  < 0.001; combined cohort: aOR 1.45, 95% CI 1.32–1.59, P  < 0.001; Fig.  5 a-c). Fig. 5 Risk factors of lipid metabolism disorder identified through logistic regression analysis. Forest plot showing associations between clinical parameters and lipid dysregulation risk in PCOS women ( a ), controls ( b ), and combined subjects ( c ). Blue lines indicate results from the unadjusted model, whereas red lines indicate results from the model adjusted for age and BMI Risk factors of lipid metabolism disorder identified through logistic regression analysis. Forest plot showing associations between clinical parameters and lipid dysregulation risk in PCOS women ( a ), controls ( b ), and combined subjects ( c ). Blue lines indicate results from the unadjusted model, whereas red lines indicate results from the model adjusted for age and BMI Comprehensive ROC analyses revealed a consistent hierarchy for dyslipidemia discrimination. Notably, BMI emerged as the strongest single predictor across all cohorts (PCOS: AUC 0.72, 95% CI 0.70–0.74; controls: AUC 0.68, 95% CI 0.67–0.69; combined cohort: AUC 0.69, 95% CI 0.68–0.70, all P  < 0.001). ESR consistently showed intermediate discriminative ability (AUC range 0.57–0.60), significantly outperforming TT (AUC range: 0.52–0.69; Fig.  6 a-c, Supplementary Table 5). In women with PCOS, the combination of BMI and ESR demonstrated the strongest predictive capacity (AUC 0.73, 95% CI 0.71–0.75, P  < 0.001), significantly improving over models incorporating TT or individual parameters ( P  < 0.01; Fig.  6 a, Supplementary Table 5). However, this synergistic effect was absent in controls (Fig.  6 b, Supplementary Table 5). Adding ESR, TT, or PCOS status to BMI failed to provide meaningful incremental value beyond BMI alone (Fig.  6 b-c). Collectively, these findings position chronic immune dysregulation as clinically relevant to lipid dysregulation, yet subordinate to central metabolic drivers represented by BMI. Fig. 6 Single and combined biomarkers for predicting dyslipidemia across populations. ROC curves evaluating the predictive performance of clinical markers for dyslipidemia in PCOS women ( a ), controls ( b ), and all subjects ( c ) Single and combined biomarkers for predicting dyslipidemia across populations. ROC curves evaluating the predictive performance of clinical markers for dyslipidemia in PCOS women ( a ), controls ( b ), and all subjects ( c ) Multivariable analyses revealed both population-specific and shared pathways that may contribute to hyperandrogenemia. In PCOS women, LH/FSH ratio exhibited the strongest association (aOR 2.21, 95% CI 1.72–2.85; P  < 0.001), followed by atherogenic dyslipidemia markers: LDL-C (aOR 1.29, 95% CI 1.10–1.52, P =  0.002), TC (aOR 1.21, 95% CI 1.06–1.38, P =  0.005), and paradoxically HDL-C (aOR 1.44, 95% CI 1.01–2.05, P =  0.042). And elevated FT3 (aOR 1.23, 95% CI 1.04–1.46, P =  0.014) suggested thyroid-androgen crosstalk (Supplementary Fig. 3a). Of note, LH/FSH ratio remained significant in both NW (aOR 3.66, 95% CI 2.31–5.79, P <  0.001) and OW/OB (aOR 2.37, 95% CI 1.30–4.33, P =  0.005) subgroups, albeit with an attenuated effect in the latter (aOR_int 0.62, 95% CI_int 0.29–1.31, P  = 0.213; Supplementary Fig. 3b). In controls, endocrine mediators were weaker, where LH/FSH was still significant but less impactful (aOR 1.97, 95% CI 1.69–2.29, P  < 0.001), and HDL-C (aOR 1.17, 95% CI 1.02–1.34, P =  0.026) emerged as a modulator (Supplementary Fig. 4a). The combined cohort confirmed universal pathways: LH/FSH was the cornerstone predictor (aOR 2.69, 95% CI 2.38–3.03, P  < 0.001), complemented by consistent lipid-androgen interactions (LDL-C: aOR 1.17, 95% CI 1.10–1.24, P  < 0.001; TC: aOR 1.14, 95% CI 1.09–1.19, P  < 0.001; HDL-C: aOR 1.16, 95% CI 1.03–1.32, P  < 0.018), renal protection (eGFR: aOR 0.67, 95% CI 0.50–0.89, P =  0.006), and thyroid modulation (FT3: aOR 1.14, 95% CI 1.08–1.20, P  < 0.001) (Supplementary Fig. 4b). These findings position LH/FSH as a trans-population biomarker and delineate how PCOS modulates neuroendocrine-lipid crosstalk. Despite comprehensive modeling, either individual or combined parameters (BMI, ESR, TG, LH/FSH, PCOS status) showed limited discriminative capacity for hyperandrogenemia (AUC range 0.50–0.63 across models; Supplementary Fig. 5a-c, Supplementary Table 6). Although the model did not achieve an ideal predictive performance, the strong association between LH/FSH and hyperandrogenemia warrants further investigation into underlying mechanisms.

Polycystic ovary syndrome (PCOS) represents one of the most prevalent endocrine disorders among women of reproductive age, affecting approximately 15% of this population worldwide [ 1 ]. This complex condition presents as a constellation of interrelated reproductive and metabolic disturbances, leading to significant clinical manifestations, including anovulatory infertility, obesity, insulin resistance, and increased risk of cardiovascular diseases [ 2 – 4 ]. While the precise etiology remains incompletely understood, accumulating evidence implicates immune dysregulation as a pivotal contributor to PCOS pathogenesis [ 5 , 6 ]. Studies have demonstrated elevated inflammatory infiltrates within the ovary [ 7 ] and endometrium [ 8 ] of PCOS patients, which correlate with ovulatory dysfunction and unfavorable pregnancy outcomes such as recurrent pregnancy loss and placental insufficiency [ 9 , 10 ]. Concurrently, systemic immune dysregulation is evidenced by an imbalance between pro- and anti-inflammatory cytokines, including interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), C-reactive protein (CRP), and interleukin-1β (IL-1β) [ 11 – 13 ], collectively underscoring inflammation’s integral role in PCOS pathophysiology. Despite these well-documented inflammatory changes, clinical assessment of inflammation in PCOS remains challenging. CRP and erythrocyte sedimentation rate (ESR) are established inflammatory markers, yet their nonspecific nature and susceptibility to various physiological influences limit their diagnostic utility [ 14 , 15 ]. ESR, which measures the rate of erythrocyte aggregation in anticoagulated blood, is primarily associated with fibrinogen-mediated rouleaux formation during inflammation [ 16 ]. Epidemiological studies have characterized age- and sex-related variations in ESR values [ 17 ]. Critically, elevated ESR levels have also been linked to specific diseases, including rheumatoid arthritis, systemic lupus erythematosus, and ischemic stroke [ 18 – 20 ]. However, data remain scarce regarding physiological ESR ranges in healthy reproductive-aged women. Current inflammatory assessment in PCOS often relies on expensive or technically demanding assays for specific cytokines and acute-phase proteins [ 21 – 23 ]. Nevertheless, population-level data on ESR distribution in reproductive-aged women with PCOS, particularly in the absence of comorbidities, remain limited. This knowledge gap is particularly critical given ESR’s retained utility in monitoring dysregulated immunity, as seen in PCOS, a condition characterized by features of immune dysregulation. Given its low cost, wide availability, and routine clinical use, ESR may offer a practical window into systemic inflammatory status. Therefore, we investigated ESR levels in reproductive-aged women with PCOS and normo-ovulatory controls without major comorbidities, and explored their relationships with metabolic and endocrine parameters.

To conclude, this study establishes that chronic immune dysregulation, reflected by elevated ESR, is a component of PCOS pathophysiology, persisting across all age/BMI strata independently of comorbidities. Key findings reveal ESR’s significant correlation with atherogenic dyslipidemia (TC, TG, LDL-C) even after BMI adjustment, positioning immune dysfunction as integral to PCOS metabolic-endocrine dysregulation. Clinically, despite its non-specificity and poor diagnostic performance that limit utility for risk prediction, ESR serves as a cost-effective severity biomarker for monitoring and guiding anti-inflammatory interventions in assisted reproduction.

This large-scale study demonstrates that PCOS is characterized by chronic immune dysregulation, as evidenced by significantly elevated ESR levels in reproductive-aged women without systemic comorbidities. These elevations persisted across all age and BMI strata, with PCOS women exhibiting a higher prevalence of hyper-ESR compared to controls. Crucially, we identified BMI-independent positive correlations between ESR and atherogenic lipid markers (TC, TG, LDL-C), and demonstrated endocrine (LH/FSH, TT) and lipid markers (TC, TG, HDL-C, LDL-C) as independent correlates of hyper-ESR. These findings underscore immune dysregulation as an integral component of PCOS pathophysiology. Notably, hyper-ESR status exhibited modest but statistically significant associations with both PCOS and dyslipidemia risk across populations. In our cohort, the modest predictive value of ESR contrasted with the strong performance of BMI, indicating that central metabolic factors dominate dyslipidemia risk. The observed low-grade inflammation is likely a downstream consequence following the well-established cascade: obesity leading to insulin resistance, subsequently driving dyslipidemia and systemic inflammation. While elevated ESR is significantly associated with an adverse metabolic profile in PCOS, its incremental predictive value for dyslipidemia beyond traditional markers like BMI is limited. Therefore, ESR should be viewed as a modest, supplementary indicator of metabolic inflammation rather than a standalone clinical predictor. Prior evidence links obesity to systemic inflammation as reflected by elevated CRP levels [ 26 , 27 ], suggesting that immune dysfunction integrates with lipid dysregulation beyond adipose-mediated mechanisms. In the context of PCOS, recent studies have further implicated adipokine dysregulation, including lipocalin-2, monocyte chemoattractant protein-1 (MCP-1), and retinol-binding protein 4 (RBP4), in the pathogenesis of chronic inflammation and insulin resistance [ 28 ]. Extending these observations, our data demonstrate significant positive correlations between ESR and atherogenic lipid markers (TC, TG, and LDL-C), positioning ESR as a clinically accessible parameter that is associated with the integration of immune and metabolic disturbances. This link is biologically plausible, as cholesterol accumulation could potentially contribute to a pro-inflammatory phenotype in innate immune cells [ 29 ]. Notably, across most PCOS subtypes, elevated lipids (TC, TG, LDL-C) remained significant risk factors for hyper-ESR. In overweight and obese individuals, ESR levels were universally elevated regardless of PCOS status, indicating that obesity-mediated inflammation may far exceed the additional inflammatory burden conferred by PCOS itself, thereby rendering the differences between these subgroups negligible. Furthermore, overweight and obese PCOS patients are more likely to receive active metabolic interventions, which may partially lower their inflammatory status. Crucially, our identification of the serum testosterone levels as an important modulator of hyper-ESR may help to elucidate the connection between PCOS-related immune dysfunction and its core reproductive features. Subgroup analyses further enriched our understanding of inflammatory-metabolic crosstalk in PCOS, with ESR status significantly modifying the association between TT and PCOS. Furthermore, it is important to note that the observed association between total testosterone and inflammatory markers could be secondary to changes in sex hormone-binding globulin (SHBG). Because SHBG production is suppressed by insulin resistance [ 30 ] and inflammation [ 31 ], a reduction in SHBG could alter total testosterone levels without necessarily reflecting a primary hyperandrogenic state [ 32 ]. Although not directly measured in this study, proposed mechanisms for this observation may be mediated by both adipose-dependent and adipose-independent mechanisms. First, hyperandrogenism promotes visceral adiposity [ 33 ], a key source of pro-inflammatory cytokines such as IL-6 and TNF-α, which stimulate hepatic production of acute-phase reactants like fibrinogen, a major determinant of ESR [ 34 ]. Second, testosterone has been shown to directly enhance macrophage activation [ 35 ] and increase the abundance of circulating neutrophils [ 36 ] in PCOS women and animal models, thereby amplifying immune cascades and promoting erythrocyte aggregation. Third, androgen excess may impair endothelial function through oxidative stress mechanisms [ 37 , 38 ], contributing to microvascular inflammation reflected in ESR elevation. This androgen-inflammation nexus was further substantiated by LH/FSH-hyperandrogenemia relationships in stratified analyses, positioning neuroendocrine dysregulation as an upstream correlate of inflammatory-metabolic cascades. Clinically, ESR quantification offers a cost-effective and rapid tool for monitoring inflammatory burden in PCOS, particularly valuable in resource-limited settings. The BMI-independent positive correlations between ESR and atherogenic lipid markers (TC, TG, and LDL-C) suggest that elevated ESR may serve as a red flag for concurrent dyslipidemia, prompting targeted lipid screening even in lean PCOS women. The lifelong persistence of ESR elevation mandates continuous cardiovascular surveillance even after reproductive concerns resolve, especially given our observation of elevated blood pressure and atherogenic lipids in PCOS women with hyper-ESR. Critically, in assisted reproductive technology (ART) cycles, where immune dysfunction impairs endometrial receptivity, our findings may have potential implications for future clinical strategies, such as the use of ESR thresholds to guide personalized anti-inflammatory interventions (e.g., adjuvant glucocorticoids). However, this strictly requires further validation through randomized controlled trials, despite preliminary evidence suggesting improved oocyte yield and pregnancy rates in PCOS patients receiving anti-inflammatory therapy [ 39 – 42 ]. This study benefits from a large, well-phenotyped cohort of 12,044 reproductive-aged women, with standardized quantification of ESR and metabolic-endocrine parameters using automated platforms, ensuring analytical reliability and generalizability to clinical populations. Comprehensive adjustment for age and BMI through stratified analyses, coupled with exclusion of systemic comorbidities, enhanced isolation of PCOS-specific effects. The focus on ESR, a low-cost, potential supplementary biomarker, prioritizes translatability to resource-limited settings, distinguishing this study from prior investigations reliant on expensive, specialized assays. However, several limitations warrant consideration. First, regarding the study design and population, the cross-sectional nature precludes any causal inferences between inflammation and metabolic dysregulation. Furthermore, single-center recruitment, the use of clinic-based IVF/IUI patients who cannot be fully regarded as healthy community controls, and the underrepresentation of severely obese individuals (1.7% of the cohort) introduce inherent selection biases that limit the broader generalizability of our findings. Second, ESR is inherently a nonspecific composite marker influenced by various factors (e.g., fibrinogen levels, immunoglobulins, and erythrocyte morphology) [ 14 ]. Crucially, our endocrine profiling relied on automated immunoassays for TT; while standard in routine clinical practice, this method lacks the precision of the gold-standard liquid chromatography-tandem mass spectrometry (LC-MS/MS), particularly at the low testosterone concentrations typical in females. Coupled with the absence of SHBG measurements, which precludes calculation of the free androgen index (FAI), our interpretation of androgen-driven inflammation remains partially incomplete. Moreover, the large ORs observed for eGFR should be interpreted with caution, as eGFR is strongly influenced by BMI, hydration status, and muscle mass, which were not fully adjusted in our models. Third, although we adjusted for major covariates, residual confounding from unmeasured factors (specific indices of insulin resistance) cannot be completely ruled out. Importantly, the high statistical significance ( P 12,000), whereas the actual magnitude of these associations (effect sizes) remains modest. Therefore, our findings should be interpreted as demonstrating a statistically detectable biological link between low-grade inflammation and dyslipidemia, rather than establishing ESR as a dominant clinical risk factor. Despite these constraints, the large sample size positions ESR as an accessible and cost-effective supplementary biomarker in PCOS management, meriting further validation through longitudinal, prospective multi-omics studies.

Supplementary Material 1. Supplementary Material 1.