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Glomerular hyperfiltration (GH) has been reported to be higher in women with polycystic ovary syndrome (PCOS) and is an independent risk factor for renal function deterioration, metabolic, and cardiovascular disease. The aim of this study was to determine GH in type A PCOS subjects and to identify whether inflammatory markers, markers of CKD, renal tubule injury markers, and complement system proteins were associated. In addition, a secondary cohort study was performed to determine if the eGFR had altered over time. In this comparative cross-sectional analysis, demographic, metabolic, and proteomic data from Caucasian women aged 18–40 years from a PCOS Biobank (137 with PCOS, 97 controls) was analyzed. Slow Off-rate Modified Aptamer (SOMA)-scan plasma protein measurement was undertaken for inflammatory proteins, serum markers of chronic kidney disease (CKD), tubular renal injury markers, and complement system proteins. A total of 44.5% of the PCOS cohort had GH (eGFR ≥ 126 mL/min/1.73 m2 (n = 55)), and 12% (n = 17) eGFR ≥ 142 mL/min/1.73 m2 (super-GH(SGH)). PCOS-GH women were younger and had lower creatinine and urea versus PCOS-nonGH. C-reactive protein (CRP), white cell count (WCC), and systolic blood pressure (SBP) were higher in PCOS versus controls, but CRP correlated only with PCOS-SGH alone. Complement protein changes were seen between controls and PCOS-nonGH, and decay-accelerator factor (DAF) was decreased between PCOS-nonGH and PCOS-GSGH (p < 0.05). CRP correlated with eGFR in the PCOS-SGH group, but not with other inflammatory or complement parameters. Cystatin-c (a marker of CKD) was reduced between PCOS-nonGH and PCOS-GSGH (p < 0.05). No differences in tubular renal injury markers were found. A secondary cohort notes review of the biobank subjects 8.2–9.6 years later showed a reduction in eGFR: controls −6.4 ± 12.6 mL/min/1.73 m2 (−5.3 ± 11.5%; decrease 0.65%/year); PCOS-nonGH −11.3 ± 13.7 mL/min/1.73 m2 (−9.7 ± 12.2%; p < 0.05, decrease 1%/year); PCOS-GH (eGFR 126–140 mL/min/17.3 m2) −27.1 ± 12.8 mL/min/1.73 m2 (−19.1 ± 8.7%; p < 0.0001, decrease 2%/year); PCOS-SGH (eGFR ≥ 142 mL/min/17.3 m2) −33.7 ± 8.9 mL/min/17.3 m2 (−22.8 ± 6.0%; p < 0.0001, decrease 3.5%/year); PCOS-nonGH eGFR versus PCOS-GH and PCOS-SGH, p < 0.001; no difference PCOS-GH versus PCOS-SGH. GH was associated with PCOS and did not appear mediated through tubular renal injury; however, cystatin-c and DAF were decreased, and CRP correlated positively with PCOS-SGH, suggesting inflammation may be involved at higher GH. There were progressive eGFR decrements for PCOS-nonGH, PCOS-GH, and PCOS-SGH in the follow-up period which, in the presence of additional factors affecting renal function, may be clinically important in the development of CKD in PCOS. [ABSTRACT FROM AUTHOR]

Introduction: Some but not all women with polycystic ovary syndrome (PCOS) develop the metabolic syndrome (MS). The objective of this study was to determine if a subset of women with PCOS had higher androgen levels predisposing them to MS and whether routinely measured hormonal parameters impacted the metabolic syndrome score (siMS). Methods: We included data from a discovery (PCOS clinic data) and a replication cohort (Hull PCOS Biobank) and utilized eight routinely measured hormonal parameters in our clinics (free androgen index [FAI], sex hormone‐binding globulin, dehydroepiandrosterone sulphate (DHEAS), androstenedione, luteinizing hormone [LH], follicular stimulating hormone, anti‐Müllerian hormone and 17 hydroxyprogesterone [17‐OHP]) to perform a K‐means clustering (an unsupervised machine learning algorithm). We used NbClust Package in R to determine the best number of clusters. We estimated the siMS in each cluster and used regression analysis to evaluate the effect of hormonal parameters on SiMS. Results: The study consisted of 310 women with PCOS (discovery cohort: n = 199, replication cohort: n = 111). The cluster analysis identified two clusters in both the discovery and replication cohorts. The discovery cohort identified a larger cluster (n = 137) and a smaller cluster (n = 62), with 31% of the study participants. Similarly, the replication cohort identified a larger cluster (n = 74) and a smaller cluster (n = 37) with 33% of the study participants. The smaller cluster in the discovery cohort had significantly higher levels of LH (7.26 vs. 16.1 IU/L, p <.001), FAI (5.21 vs. 9.22, p <.001), androstenedione (3.93 vs. 7.56 nmol/L, p <.001) and 17‐OHP (1.59 vs. 3.12 nmol/L, p <.001). These findings were replicated in the replication cohort. The mean (±SD) siMS score was higher in the smaller cluster, 3.1 (±1.1) versus 2.8 (±0.8); however, this was not statistically significant (p =.20). In the regression analysis, higher FAI (β =.05, p =.003) and androstenedione (β =.03, p =.02) were independently associated with a higher risk of SiMS score, while higher DHEAS levels were associated with a lower siMS score (β = −.07, p =.03) Conclusion: We identified a subset of women in our PCOS cohort with significantly higher LH, FAI, and androstenedione levels. We show that higher levels of androstenedione and FAI are associated with a higher siMS, while higher DHEAS levels were associated with lower siMS. [ABSTRACT FROM AUTHOR]