Working...
ClinicalTrials.gov
ClinicalTrials.gov Menu
Trial record 43 of 562 for:    "Polycystic Ovary Syndrome"

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

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
ClinicalTrials.gov Identifier: NCT03911297
Recruitment Status : Not yet recruiting
First Posted : April 11, 2019
Last Update Posted : June 7, 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

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.

Condition or disease Intervention/treatment
Polycystic Ovary Syndrome Other: Women with polycystic ovary syndrome

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.


Layout table for study information
Study Type : Observational
Estimated Enrollment : 1000 participants
Observational Model: Cohort
Time Perspective: Prospective
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
Estimated Study Start Date : June 1, 2019
Estimated Primary Completion Date : June 1, 2021
Estimated Study Completion Date : April 1, 2024



Intervention Details:
  • Other: Women with polycystic ovary syndrome
    Prospective cohort study in women with polycystic ovary syndrome to identify the risk of developing metabolic disease


Primary Outcome Measures :
  1. 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


Secondary Outcome Measures :
  1. 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

  2. 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


Biospecimen Retention:   Samples With DNA
DNA will be stored in a Human tissue act approved site and ethics for a potential future analysis


Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


Layout table for eligibility information
Ages Eligible for Study:   18 Years to 70 Years   (Adult, Older Adult)
Sexes Eligible for Study:   Female
Gender Based Eligibility:   Yes
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Women with a new diagnosis of Polycystic ovary syndrome aged 18-70 who are treatment naive
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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03911297


Contacts
Layout table for location contacts
Contact: Eka Melson +447852146611 e.melson@bham.ac.uk

Sponsors and Collaborators
University of Birmingham
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
Layout table for investigator information
Principal Investigator: Wiebke Arlt University of Birmingham

Layout table for additonal information
Responsible Party: University of Birmingham
ClinicalTrials.gov Identifier: NCT03911297     History of Changes
Other Study ID Numbers: RG 18-033
WT209492/Z/17/Z ( Other Grant/Funding Number: Wellcome Trust )
First Posted: April 11, 2019    Key Record Dates
Last Update Posted: June 7, 2019
Last Verified: April 2019
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

Layout table for additional information
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No

Keywords provided by University of Birmingham:
polycystic ovary syndrome
androgens
steroids
metabolic risk
prediction
stratified medicine

Additional relevant MeSH terms:
Layout table for MeSH terms
Polycystic Ovary Syndrome
Syndrome
Hyperandrogenism
Disease
Pathologic Processes
Ovarian Cysts
Cysts
Neoplasms
Ovarian Diseases
Adnexal Diseases
Genital Diseases, Female
Gonadal Disorders
Endocrine System Diseases
46, XX Disorders of Sex Development
Disorders of Sex Development
Urogenital Abnormalities
Adrenogenital Syndrome
Congenital Abnormalities
Androgens
Hormones
Hormones, Hormone Substitutes, and Hormone Antagonists
Physiological Effects of Drugs