Metabolic and hormonal heterogeneity in polyendocrine metabolic ovarian syndrome in a South Indian cohort identified by k-means clustering

CONTEXT: In polyendocrine metabolic ovarian syndrome (PMOS), conventional Rotterdam phenotypes may not adequately capture the multidimensional hormonal and metabolic heterogeneity of the disorder. AIMS: To identify clinically meaningful PMOS subtypes using a data-driven clustering approach among infertile South Indian women. METHODS: A cross-sectional study was conducted among 196 infertile women diagnosed with PMOS (Rotterdam criteria) at a fertility centre in South India. Unsupervised k-means clustering was performed and cluster stability was assessed using Jaccard bootstrapping. KEY RESULTS: Three subtypes were identified: gonadotrophin predominant, body mass index (BMI) predominant, and intermediate. The gonadotrophin predominant cluster exhibited intermediate BMI with higher LH and FSH concentrations and the highest LH/FSH ratio. The BMI predominant cluster demonstrated the highest BMI with a relatively low LH/FSH ratio, while the intermediate cluster showed lean-to-normal BMI with the lowest LH, FSH, and LH/FSH ratio. Significant differences were observed between clusters in systolic and diastolic blood pressure and endometrial thickness. No significant between-cluster differences were found for anti-Müllerian hormone, estradiol, prolactin, thyroid-stimulating hormone and haemoglobin A1c. CONCLUSIONS: Data-driven clustering using BMI, LH, and FSH identified three PMOS subtypes, demonstrating heterogeneity beyond the conventional Rotterdam phenotypic classification. IMPLICATIONS: The identified subtypes enhance understanding of clinical heterogeneity of PMOS and provide a basis for potential application in personalized patient management.