Hyperandrogenism is a common reason for consultation, the causes of which can range from common conditions (PCOS) to rarer conditions with major genetic implications (NC21OHD). It is characterized by elevated levels of circulating androgens, mainly testosterone. This excess of androgens usually manifests clinically as increased male-pattern hair growth and, less specifically, acne and alopecia. Its prevalence is estimated at between 6 and 12% in women of reproductive age, and its incidence is increasing. It is also responsible for infertility. As a reminder, infertility is a major public health issue and affects more and more couples around the world. The investigators therefore wish to develop innovative tools to improve the diagnosis and management of hyperandrogenism
Hyperandrogenism is a common reason for consultation, the causes of which can range from common conditions (PCOS) to rarer conditions with major genetic implications (NC21OHD). It is characterized by elevated levels of circulating androgens, mainly testosterone. This excess of androgens usually manifests clinically as increased male-pattern hair growth and, less specifically, acne and alopecia. Its prevalence is estimated at between 6 and 12% in women of reproductive age, and its incidence is increasing. It is also responsible for infertility. As a reminder, infertility is a major public health issue and affects more and more couples around the world. The investigators therefore wish to develop innovative tools to improve the diagnosis and management of hyperandrogenism
Study Type
OBSERVATIONAL
Enrollment
800
collection of data from medical records over a period of 5 years
Use of machine learning models combined with metabolomics to distinguish between different causes of hyperandrogenism
Time frame: 5 years
Use of metabolomics to improve the management of patients with hyperandrogenism
Time frame: 5 years
Use of metabolomics to predict CYP21A2 genotyping results
Time frame: 5 years
Study of the impact of anti-androgenic hormone therapy on the predictive capabilities of the model
Time frame: 5 years
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