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

NCT ID: NCT03911297

Last Updated: 2024-12-05

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 1000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2019-08-14
• Study Completion Date: 2027-12-01

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.

Detailed Description

The investigator propose an innovative approach to solving the clinical problem at hand, the lack of identified measurable parameters one can use to predict the risk of future metabolic disease in women diagnosed with PCOS.The chosen approach is the standardised collection of phenome and metabolome data and their unbiased integration by machine learning analysis. Utilising the detailed results of the clinical phenome and metabolome analysis in the DAISy-PCOS Phenome Study cohort, The study will aim to identify distinct subsets (=clusters) of PCOS patients that share similar characteristics. This approach has previously been used by the team to successfully identify distinct steroid markers that can serve as a "malignant steroid fingerprint" in urine to distinguish benign from malignant tumours in patients with incidentally discovered adrenal masses. Similarly, The investigator have used unbiased analysis of steroid metabolome data to reveal that patients with aldosterone excess also overproduce glucocorticoids and that the latter explains the majority of metabolic disease risk observed in affected patients.

In the integrated analysis of the DAISy-PCOS phenome and metabolome data, The investigator will apply a variety of methods in the context of connectivity or centroid-based clustering and density estimation. Supervised relevance learning will give insight into markers, e.g. steroids, that are most decisive for the determination of cluster memberships. In addition, The investigator will use state-of-the-art visualisation and machine learning techniques based on adaptive similarity measures.the investigator will use integrative approaches, addressing the heterogeneous data from different sources as a whole, whilst considering data-driven adaptation of generative models for the underlying biological processes. The investigator will employ these approaches to characterise central phenotype clusters affecting large numbers of patients as the basis of personalised management including outcome prediction.

Conditions

Polycystic Ovary Syndrome

Keywords

polycystic ovary syndrome; androgens; steroids; metabolic risk; prediction; stratified medicine

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Interventions

Women with polycystic ovary syndrome

Prospective cohort study in women with polycystic ovary syndrome to identify the risk of developing metabolic disease

Intervention Type: OTHER

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

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 70 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: No

Sponsors

University Hospital Birmingham NHS Foundation Trust

OTHER

Sponsor Role: collaborator

Birmingham Women's and Children's NHS Foundation Trust

OTHER

Sponsor Role: collaborator

University Hospital of Wales

OTHER

Sponsor Role: collaborator

University Hospitals Coventry and Warwickshire NHS Trust

OTHER

Sponsor Role: collaborator

Royal Infirmary of Edinburgh

OTHER

Sponsor Role: collaborator

Royal College of Surgeons, Ireland

OTHER

Sponsor Role: collaborator

Imperial College Healthcare NHS Trust

OTHER

Sponsor Role: collaborator

Hull University Teaching Hospitals NHS Trust

OTHER_GOV

Sponsor Role: collaborator

King's College Hospital NHS Trust

OTHER

Sponsor Role: collaborator

Imperial College London

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Wiebke Arlt

Role: PRINCIPAL_INVESTIGATOR

University of Birmingham

Locations

Wellcome Trust Clinical Research Facility

Birmingham, West Midlands, United Kingdom

Site Status: RECRUITING

Countries

United Kingdom

Central Contacts

Eka Melson

Role: CONTACT

Phone: +447852146611

Email: e.melson@bham.ac.uk

Facility Contacts

Eka Melson

Role: primary

Other Identifiers

WT209492/Z/17/Z

Identifier Type: OTHER_GRANT

Identifier Source: secondary_id

19HH5264

Identifier Type: -

Identifier Source: org_study_id