Recruiting

Observational Study

Sponsor:

Shandong University

Code:

NCT06124391

Conditions

Polycystic Ovary Syndrome

Eligibility Criteria

Sex: Female

Age: 18 - 45

Healthy Volunteers: Accepted

Interventions

Long-term follow-up

Study Details

Brief summary:

To classify subtypes of Polycystic Ovary Syndrome (PCOS) using machine-learning algorithms, and compare the reproductive and metabolic characteristics and IVF outcomes across these identified subtypes.

Conditions

Polycystic Ovary Syndrome

Study ID

NCT06124391

Start date

Jan 1, 2021

Status verified date

Nov, 2023

Completion date

Dec 30, 2025

Anticipated

Primary completion date

Dec 30, 2025

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: Female

Age: 18 - 45

Healthy Volunteers: Accepted

Inclusion Criteria:

  • PCOS patients diagnosed using the Rotterdam criteria, which requires the presence of at least two of the following:

1. Menstrual Irregularities: A menstrual cycle length of fewer than 21 days or more than 35 days, and/or fewer than eight cycles per year.
2. Hyperandrogenism: Defined either by an elevated total testosterone level (as per local laboratory criteria) or by a modified Ferriman-Gallwey (mFG) score of 5 or higher.
3. Polycystic Ovaries on Ultrasound: Presence of 12 or more follicles measuring 2-9 mm in diameter in each ovary and/or an ovarian volume exceeding 10 mL.

Exclusion Criteria:

Patients with congenital adrenal hyperplasias, androgen-secreting tumours, or Cushing's syndrome) will be excluded.

Study Design

Enrollment

50000 participants

Anticipated

Interventions and Outcome Measures

Arms

HA-PCOS

Patients were classified into each PCOS subtype based on our machine-learning classification model. The feature of the HA-PCOS group is hyperandrogenism.

OB-PCOS

Patients were classified into each PCOS subtype based on our machine-learning classification model. The feature of the OB-PCOS group is overweight/obesity.

SHBG-PCOS

Patients were classified into each PCOS subtype based on our machine-learning classification model. The feature of the SHBG-PCOS group is the high level of serum SHBG.

LH-PCOS

Patients were classified into each PCOS subtype based on our machine-learning classification model. The feature of the LH-PCOS group is the high level of LH and AMH.

Interventions

Long-term follow-up

Participants diagnosed with PCOS were not subjected to any specific intervention post-diagnosis. Instead, they were followed up after 6.5 years to assess various outcomes related to PCOS and associated conditions.

Primary outcome measure

  • Persistence of PCOS Diagnosis [ Time Frame: At the 6.5-year follow-up visit. ]
  • Changes in PCOS Subtype [ Time Frame: At the 6.5-year follow-up visit. ]
  • Body Mass Index [ Time Frame: At the 6.5-year follow-up visit. ]
  • Non-Alcoholic Fatty Liver Disease (NAFLD) [ Time Frame: At the 6.5-year follow-up visit. ]
  • Hypertension [ Time Frame: At the 6.5-year follow-up visit. ]
  • Type 2 Diabetes Mellitus (T2DM) [ Time Frame: At the 6.5-year follow-up visit. ]
  • Dyslipidemia [ Time Frame: At the 6.5-year follow-up visit. ]
  • Total live birth rate [ Time Frame: From the diagnosis of PCOS (at the time of enrollment) until a follow-up period of 6.5 years. ]
  • Clinical pregnancy rate [ Time Frame: From the diagnosis of PCOS (at the time of enrollment) until a follow-up period of 6.5 years. ]
  • Pregnancy loss rate [ Time Frame: From the diagnosis of PCOS (at the time of enrollment) until a follow-up period of 6.5 years. ]
  • Maternal and neonatal complications [ Time Frame: From the diagnosis of PCOS (at the time of enrollment) until a follow-up period of 6.5 years. ]

Central Contacts and Locations

Central contacts

Zi-jiang Chen

86-531-85187856chenzijiang@hotmail.com

Locations

Penn State College of Medicine

Recruiting

Hershey, Pennsylvania, United States, 17033

Contacts

Richard Legro

rsl1@psu.edu

More Information

Sponsor

Shandong University

Last update posted

Nov 30, 2023

Last verified

Nov, 2023

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-10. This information was provided to ClinicalTrials.gov by Shandong University on 2023-11-30.