Characterizing the Clinical and Genetic Spectrum of Polycystic Ovary Syndrome in Electronic Health Records

Abstract

Context: Polycystic ovary syndrome (PCOS) is one of the leading causes of infertility, yet current diagnostic criteria are ineffective at identifying patients whose symptoms reside outside strict diagnostic criteria. As a result, PCOS is underdiagnosed and its etiology is poorly understood.

Objective: We aim to characterize the phenotypic spectrum of PCOS clinical features within and across racial and ethnic groups.

Methods: We developed a strictly defined PCOS algorithm (PCOSkeyword-strict) using the International Classification of Diseases, ninth and tenth revisions and keywords mined from clinical notes in electronic health records (EHRs) data. We then systematically relaxed the inclusion criteria to evaluate the change in epidemiological and genetic associations resulting in 3 subsequent algorithms (PCOScoded-broad, PCOScoded-strict, and PCOSkeyword-broad). We evaluated the performance of each phenotyping approach and characterized prominent clinical features observed in racially and ethnically diverse PCOS patients.

Results: The best performance came from the PCOScoded-strict algorithm, with a positive predictive value of 98%. Individuals classified as cases by this algorithm had significantly higher body mass index (BMI), insulin levels, free testosterone values, and genetic risk scores for PCOS, compared to controls. Median BMI was higher in African American females with PCOS compared to White and Hispanic females with PCOS.

Conclusions: PCOS symptoms are observed across a severity spectrum that parallels the continuous genetic liability to PCOS in the general population. Racial and ethnic group differences exist in PCOS symptomology and metabolic health across different phenotyping strategies.

Keywords: electronic health record; hormones; phenotyping; polycystic ovary syndrome; polygenic risk scores.

Figure 1.

Polycystic ovary syndrome (PCOS) algorithm pipeline. This schematic illustrates the algorithm development steps for the controls and the 4 PCOS algorithms. The Venn diagram illustrates how the algorithm data sets are nested within one another. Each color corresponds with one of the algorithm data sets. ICD, International Classification of Diseases, 9th and 10th revision.

Figure 2.

Laboratory measurements for polycystic ovary syndrome (PCOS) algorithm cases and controls. Box plots of A, body mass index (BMI) measurements; B, insulin levels; C, estradiol levels; and D, free testosterone levels for PCOS algorithm cases and controls. Colored boxes represent races. Boxes represent the individuals with lab measurements in the 25th and 75th percentile. Lines above and below the boxes represent the 95th and 5th quartiles. Lines within each box mark the median. Wilcoxon rank sum tests were performed between algorithm cases and controls and brackets display the P values of the statistical test.

Figure 3.

Comparison of laboratory measurements between White (W), African American (AA), and Hispanic (HIS) polycystic ovary syndrome (PCOS) algorithm cases and controls. Box plots of A, body mass index (BMI) measurements; B, insulin levels; C, estradiol levels; and D, free testosterone levels for W, AA, and HIS PCOS cases and controls. Colors correspond with each race. Boxes represent the 25th and 75th quartiles. Lines above and below the boxes represent the 95th and fifth quartiles. Lines within each box mark the median. Wilcoxon rank sum tests were performed between races within the PCOS cases and control data sets. Brackets display the P values of the statistical test.

Figure 4.

Race-stratified laboratory measurements for polycystic ovary syndrome (PCOS) algorithm cases and controls. Box plots of A, body mass index (BMI) measurements; B, insulin levels; C, estradiol levels; and D, free testosterone levels of race stratified PCOS algorithm data sets. Each color corresponds with a race. Boxes represent the 25th and 75th quartiles. Lines above and below the boxes represent the 95th and fifth quartiles. Lines within each box mark the median. Wilcoxon rank sum tests were performed between algorithm cases and controls within each race. Brackets display the P values of the statistical test. CB, coded broad; CN, controls; CS, coded strict; RB, keyword broad; RS, keyword strict.

Figure 5.

Genetic validation of polycystic ovary syndrome (PCOS) algorithms. Logistic regressions were performed between PCOS case status and PCOS polygenic risk scores (PCOS_PRS). The regression models were adjusted for median age and the first 10 principal components. P values are displayed for each regression model. Colors correspond to the genotyped sample used in the analysis. OR, odds ratio.