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SAT-024 Investigating Racial and Ethnic Comorbidity Patterns of Polycystic Ovary Syndrome
Polycystic ovary syndrome (PCOS) is a highly heterogenous reproductive endocrine disorder that affects up to 15% of women and is one of the leading causes of infertility. However, its genetic etiology remains poorly understood. Additionally, PCOS patients have a greater risk of having metabolic diso...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7208200/ http://dx.doi.org/10.1210/jendso/bvaa046.727 |
Sumario: | Polycystic ovary syndrome (PCOS) is a highly heterogenous reproductive endocrine disorder that affects up to 15% of women and is one of the leading causes of infertility. However, its genetic etiology remains poorly understood. Additionally, PCOS patients have a greater risk of having metabolic disorders, such as insulin resistance and cardiovascular diseases, but it is estimated that up to 75% of women remain undiagnosed. Delayed treatment and care can exacerbate comorbid conditions and be detrimental to high risk populations like African American and Hispanic women. We aim to characterize genetic and environmental variables contributing to PCOS and understand its shared etiological features with metabolic disorders. To do this, we developed two algorithms to identify diverse PCOS patients using medical records. The broad algorithm used a combination of PCOS-related billing codes (Code Based) and identified a large dataset (N = 8,340) who exhibited diverse PCOS symptoms, while the strict algorithm required PCOS keywords in addition to billing codes (Regex Based). The strict algorithm identified a smaller cohort of patients (N = 4,593) who exhibited more classically diagnoseable PCOS characteristics according to Rotterdam and NIH criteria. Using both datasets, we tested PCOS case status against 1,853 phenotypes in the medical database using a logistic regression model and identified comorbidity patterns for women of European and African descent. We observed that European descent women consistently had more distinct phenotypes associated with PCOS case status than African American women. Next, we examined the interacting effects of self-reported race on PCOS case status and found four significant phenotypes (p < 6.25e-4) in our Regex Based algorithm. African American women with PCOS had greater odds of being diagnosed with “Early onset of delivery” (p = 1.3e-4, OR = 1.86), “Hereditary hemolytic anemias” (p =1.8e-4, OR = 0.65), and “Other hereditary hemolytic anemias” (p = 3.7e-04, OR = 0.90). Meanwhile, European descent women had greater odds of being diagnosed with “Hypertensive chronic kidney disease” (p = 1.7e-04, OR = 0.68). Results show that European and African American women have unique metabolic comorbidity patterns and it may also indicate that clinical PCOS diagnostic standards vary between these groups with possible disparity-causing effects. |
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