← Back to Clinical Trials
Recruiting NCT03911297

NCT03911297 DAISy-PCOS Phenome Study - Dissecting Androgen Excess and Metabolic Dysfunction in Polycystic Ovary Syndrome

◆ AI Clinical Summary
Plain-language summary for patients
Clinical Trial Summary
NCT ID NCT03911297
Status Recruiting
Phase
Sponsor Imperial College London
Condition Polycystic Ovary Syndrome
Study Type OBSERVATIONAL
Enrollment 1,000 participants
Start Date 2019-08-14
Primary Completion 2026-12-31

Eligibility & Interventions

Sex Female only
Min Age 18 Years
Max Age 70 Years
Study Type OBSERVATIONAL
Interventions
Women with polycystic ovary syndrome

Eligibility Fast-Check

Enter your details for a quick preliminary check. This does not replace medical advice.

What to Expect as a Participant

This is an observational study. You will not receive an experimental treatment; researchers will collect data based on your existing condition or standard treatment.

This trial targets 1,000 participants in total. It began in 2019-08-14 with a primary completion date of 2026-12-31.

⚠ This information is for research awareness only. Always consult your physician before joining any clinical trial. Participation is voluntary and you may withdraw at any time.

Brief Summary

Polycystic ovary syndrome (PCOS) affects 10% of all women and usually presents with irregular menstrual periods and difficulties conceiving. However, PCOS is also a lifelong metabolic disorder and affected women have an increased risk of type 2 diabetes, high blood pressure, and heart disease. Increased blood levels of male hormones, also termed androgens, are found in most PCOS patients. Androgen excess appears to impair the ability of the body to respond to the sugar-regulating hormone insulin (=insulin resistance). The investigator has found that fat tissue of PCOS patients overproduces androgens and that this can result in a build-up of toxic fat, which increases insulin resistance and could cause liver damage. In a large cohort of women registered in a GP database, the study team have found that androgen excess increases the risk of fatty liver disease. The aim is to identify those women with PCOS who are at the highest risk of developing metabolic disease, which would allow for early detection and potentially prevention of type 2 diabetes, high blood pressure, fatty liver and cardiovascular disease. The investigator will assess clinical presentation, androgen production and metabolic function in women with PCOS to use similarities and differences in these parameters for the identification of subsets (=clusters) of women who are at the highest risk of metabolic disease. The investigator will do this by using a standardised set of questions to scope PCOS-related signs and symptoms and the patient's medical history and measure body composition and blood pressure. This standardised recording of a patient's clinical presentation (=clinical phenotype) is called Phenome analysis. The investigator will collect blood and urine samples for the systematic measurement of steroid hormones including a very detailed androgen profile (=steroid metabolome analysis) and of thousands of substances produced by human metabolism (=global metabolome analysis). Phenome and metabolome data will then undergo integrated computational analysis for the detection of clusters predictive of metabolic risk.

Eligibility Criteria

Inclusion Criteria: * Women with a suspected diagnosis of polycystic ovary syndrome * Age range 18-70 years * Ability to provide informed consent Exclusion Criteria: * Pregnancy or breastfeeding at the time of planned recruitment * History of significant renal (eGFR\<30) or hepatic impairment (AST or ALT \>two-fold above ULN; pre-existing bilirubinaemia \>1.2 ULN) * Any other significant disease or disorder that, in the opinion of the Investigator, may either put the participant at risk because of participation in the study, or may influence the result of the study, or the participant's ability to participate in the study. * Participants who have participated in another research study involving an investigational medicinal product in the 12 weeks preceding the planned recruitment * Glucocorticoid use via any route within the last six months * Current intake of drugs known to impact upon steroid or metabolic function or intake of such drugs during the six months preceding the planned recruitment * Use of oral or transdermal hormonal contraception in the three months preceding the planned recruitment * Use of contraceptive implants in the twelve months preceding the planned recruitment

Contact & Investigator

Central Contact

Eka Melson

✉ e.melson@bham.ac.uk

📞 +447852146611

Principal Investigator

Wiebke Arlt

PRINCIPAL INVESTIGATOR

University of Birmingham

Frequently Asked Questions

Who can join the NCT03911297 clinical trial?

This trial is open to female participants only, aged 18 Years or older, up to 70 Years, studying Polycystic Ovary Syndrome. Full inclusion and exclusion criteria are listed in the Eligibility Criteria section. Always confirm your eligibility with the research team before applying.

Is NCT03911297 currently recruiting?

Yes, NCT03911297 is actively recruiting participants. Contact the research team at e.melson@bham.ac.uk for enrollment information.

Where is the NCT03911297 trial being conducted?

This trial is being conducted at Birmingham, United Kingdom.

Who is sponsoring the NCT03911297 clinical trial?

NCT03911297 is sponsored by Imperial College London. The principal investigator is Wiebke Arlt at University of Birmingham. The trial plans to enroll 1,000 participants.

Related Trials

ClinicalMetric — Independent clinical trial intelligence platform. Not affiliated with NIH, ClinicalTrials.gov, the U.S. FDA, or any pharmaceutical company, hospital, or clinical research organization. Trial data is sourced from ClinicalTrials.gov for informational purposes only and does not constitute medical advice. Do not make any treatment, enrollment, or health decisions based solely on information found here — always consult a qualified healthcare professional. Full Disclaimer  ·  Last Reviewed: September 2026  ·  Data Methodology