Diagnosis of Polycystic Ovary Syndrome: Which Criteria to Use and When?

Abstract

Current diagnostic criteria for polycystic ovary syndrome (PCOS) are based on expert opinion. This article reviews the rationale for and the limitations of these criteria as well as which criteria to use and when. The insights provided into PCOS pathogenesis by modern genetic analyses and the promise of objective data mining approaches for biologically relevant disease classification are discussed.

Keywords: Diagnostic criteria; Disease classification; Hyperandrogenism; PCOS; Polycystic ovary syndrome.

Figure 1:. Comparing Components of Different Diagnostic Criteria.

NIH PCOS must include both HA+OD, +/− PCOM. There are no endocrine or metabolic differences between phenotypes A and B; there is no need to assess PCOM in NIH PCOS. Rotterdam Criteria requires at least 2 out of the 3 criteria to be present, adding 2 new phenotypes HA+PCOM and OD+PCOM. The prevalence of PCOS increases from ~7% to 15% when including the two non-NIH Rotterdam phenotypes.

Figure 2:. Principal component analysis plot of quantitative traits for a genotyped PCOS clustering cohort showing a biologically driven method of PCOS classification.

Women with PCOS were clustered into distinct groups—metabolic, reproductive, and indeterminate—based on BMI, fasting insulin, fasting glucose, DHEAS, T, FSH, LH, SHBG, and genotype data. The relative magnitude and direction of trait correlations with the principal components are shown with black arrows. PC=principal component; Ins0= fasting insulin; Glu0= fasting glucose. (From Dapas M, Lin FTJ, Nadkarni GN, et al. Distinct subtypes of polycystic ovary syndrome with novel genetic associations: An unsupervised, phenotypic clustering analysis. PLoS Med. 2020;17(6):e1003132; with permission.)