Assessing the androgenic and metabolic heterogeneity in polycystic ovary syndrome using cluster analysis. (20th November 2022)

• Record Type: Journal Article
• Authors: Deshmukh, Harshal; Akbar, Shahzad; Bhaiji, Amira; Saeed, Yamna; Shah, Najeeb; Adeleke, Kazeem; Papageorgiou, Maria; Atkin, Stephen; Sathyapalan, Thozhukat
• Is Part Of: Clinical endocrinology. Volume 98:Number 3(2023)
• Page Start: 400
• Page End: 406
• Publication Date: 2022-11-20
• Subjects: androstenedione -- clusters -- DHEAS -- FAI -- ovary -- PCOS; Endocrinology -- Periodicals; 616.4005
• DOI: 10.1111/cen.14847
• Languages: English
• ISSNs: 0300-0664

Abstract

Abstract: 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.