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

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ClinicalTrials.gov Identifier: NCT03911297
Recruitment Status : Recruiting
First Posted : April 11, 2019
Last Update Posted : October 2, 2019
Sponsor:
Collaborators:
University Hospital Birmingham NHS Foundation Trust
Birmingham Women's and Children's NHS Foundation Trust
University Hospital of Wales
University Hospitals Coventry and Warwickshire NHS Trust
Royal Infirmary of Edinburgh
The Leeds Teaching Hospitals NHS Trust
St Mary's NHS Trust
Barts & The London NHS Trust
Information provided by (Responsible Party):
University of Birmingham

Tracking Information
First Submitted Date April 8, 2019
First Posted Date April 11, 2019
Last Update Posted Date October 2, 2019
Actual Study Start Date August 14, 2019
Estimated Primary Completion Date June 1, 2021   (Final data collection date for primary outcome measure)
Current Primary Outcome Measures
 (submitted: April 8, 2019)
Metabolic risk [ Time Frame: 5 years ]
Metabolic-risk prediction model would be made from a machine learning algorithm where we would be able to enter phenoma and metabolome data of patient with a new diagnosis of PCOS. With this model, we would be able to stratify the women with PCOS into their risk of metabolic disease hence personalise the management of the condition
Original Primary Outcome Measures Same as current
Change History Complete list of historical versions of study NCT03911297 on ClinicalTrials.gov Archive Site
Current Secondary Outcome Measures
 (submitted: April 8, 2019)
  • Dissect the severity and pattern of androgen excess in development of metabolic disease [ Time Frame: 5 years ]
    We would assess how pattern of androgen excess in each phenotype relates to their risk of metabolic disease
  • Eligibility for other PCOS-related studies [ Time Frame: 3 years ]
    Participants will be screened for their eligibility to enroll in other PCOS-related research studies
Original Secondary Outcome Measures Same as current
Current Other Pre-specified Outcome Measures Not Provided
Original Other Pre-specified Outcome Measures Not Provided
 
Descriptive Information
Brief Title DAISy-PCOS Phenome Study - Dissecting Androgen Excess and Metabolic Dysfunction in Polycystic Ovary Syndrome
Official Title Dissecting Androgen Excess and Metabolic Dysfunction for an Integrated Systems Approach to Polycystic Ovary Syndrome Through the Assessment of Detailed Phenome and Metabolome Data
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.

Study Type Observational
Study Design Observational Model: Cohort
Time Perspective: Prospective
Target Follow-Up Duration Not Provided
Biospecimen Retention:   Samples With DNA
Description:
DNA will be stored in a Human tissue act approved site and ethics for a potential future analysis
Sampling Method Non-Probability Sample
Study Population Women with a new diagnosis of Polycystic ovary syndrome aged 18-70 who are treatment naive
Condition Polycystic Ovary Syndrome
Intervention Other: Women with polycystic ovary syndrome
Prospective cohort study in women with polycystic ovary syndrome to identify the risk of developing metabolic disease
Study Groups/Cohorts Not Provided
Publications * Not Provided

*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Recruitment Information
Recruitment Status Recruiting
Estimated Enrollment
 (submitted: April 8, 2019)
1000
Original Estimated Enrollment Same as current
Estimated Study Completion Date April 1, 2024
Estimated Primary Completion Date June 1, 2021   (Final data collection date for primary outcome measure)
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
Sex/Gender
Sexes Eligible for Study: Female
Gender Based Eligibility: Yes
Ages 18 Years to 70 Years   (Adult, Older Adult)
Accepts Healthy Volunteers No
Contacts
Contact: Eka Melson +447852146611 e.melson@bham.ac.uk
Listed Location Countries United Kingdom
Removed Location Countries  
 
Administrative Information
NCT Number NCT03911297
Other Study ID Numbers RG 18-033
WT209492/Z/17/Z ( Other Grant/Funding Number: Wellcome Trust )
Has Data Monitoring Committee Yes
U.S. FDA-regulated Product
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
IPD Sharing Statement
Plan to Share IPD: No
Responsible Party University of Birmingham
Study Sponsor University of Birmingham
Collaborators
  • University Hospital Birmingham NHS Foundation Trust
  • Birmingham Women's and Children's NHS Foundation Trust
  • University Hospital of Wales
  • University Hospitals Coventry and Warwickshire NHS Trust
  • Royal Infirmary of Edinburgh
  • The Leeds Teaching Hospitals NHS Trust
  • St Mary's NHS Trust
  • Barts & The London NHS Trust
Investigators
Principal Investigator: Wiebke Arlt University of Birmingham
PRS Account University of Birmingham
Verification Date April 2019