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NCT Number: NCT07253454

Combined Use of Machine Learning and Metabolomics 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

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Key information

Conditions

Hyperandrogenism 46, XX Disorders of Sex Development Adrenogenital Syndrome Congenital Abnormalities Congenital, Hereditary, and Neonatal Diseases and Abnormalities Disorders of Sex Development Endocrine System Diseases Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Gonadal Disorders Male Urogenital Diseases Urogenital Abnormalities Urogenital Diseases

Age range

16 year–45 year

Sex eligibility

Female

Study type

Observational

About this study

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

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Patients of childbearing age (16 to 45 years old)
  • Suffering from hyperandrogenism
  • Established etiological diagnosis with elimination of differential diagnoses
  • Informed and not opposed to the collection of their data for the purposes of the study

Exclusion criteria

  • Pregnancy
  • Patients under legal protection measures

Treatment and study plan

Data Collection

Other

collection of data from medical records over a period of 5 years

Primary outcomes

  1. Use of machine learning models combined with metabolomics to distinguish between different causes of hyperandrogenism

    Time frame: 5 years

Secondary outcomes

  1. Use of metabolomics to improve the management of patients with hyperandrogenism

    Time frame: 5 years

  2. Use of metabolomics to predict CYP21A2 genotyping results

    Time frame: 5 years

  3. Study of the impact of anti-androgenic hormone therapy on the predictive capabilities of the model

    Time frame: 5 years

Study contacts

Contact information is provided by the study sponsor or research team.

Anne Pr BACHELOT

CONTACT

[email protected]

0033 01 42 16 02 46

Sponsors and collaborators

Lead sponsor

Assistance Publique - Hôpitaux de Paris

Other

Registry information

Acronym: HYPERMETABO

Important dates

Study start
2026
Primary completion
2040
Study completion
2040
First posted
Nov 28, 2025
Registry last updated
Dec 4, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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