Polycystic Ovary SyndromeDAISy-PCOSNCT03911297
DAISy-PCOS Phenome Study - Dissecting Androgen Excess and Metabolic Dysfunction in Polycystic Ovary Syndrome
An observational study of Polycystic Ovary Syndrome, run by Imperial College London at 1 site in United Kingdom.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 70 years, women only
- Conditions the study is for
- Polycystic Ovary Syndrome
- What is being tested?
- Women with polycystic ovary syndrome
- How many people
- 1,000 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 14 Aug 2019to 1 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 70 years
- Women only
- No healthy volunteers
- Adult, Older adult
Study population: Women with a new diagnosis of Polycystic ovary syndrome aged 18-70 who are treatment naive
Who may be able to join
- Women with a suspected diagnosis of polycystic ovary syndrome
- Age range 18-70 years
- Ability to provide informed consent
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United Kingdom1 site, 1 recruiting
- Recruiting
Wellcome Trust Clinical Research Facility
Birmingham, West Midlands, B15 2TT
Eka Melson, 07852146611, ekamelson@hotmail.com
About this study
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
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.
Read the 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
- Being tested
- Women with polycystic ovary syndrome
- Keywords
- polycystic ovary syndrome, androgens, steroids, metabolic risk, prediction, stratified medicine
- Medical subject headings
- Polycystic Ovary Syndrome
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Women with polycystic ovary syndromeOther
Prospective cohort study in women with polycystic ovary syndrome to identify the risk of developing metabolic disease
What is being measured
The primary outcome is the main question the study is designed to answer.
Metabolic risk
Metabolic-risk prediction model would be made from a machine learning algorithm where the study team would be able to enter phenome and metabolome data of patient with a new diagnosis of PCOS. With this model, the study team would be able to stratify the women with PCOS into their risk of metabolic disease hence personalise the management of the condition
Measured: 5 years
Secondary outcomes (2)
Dissect the severity and pattern of androgen excess in development of metabolic disease
The study team would assess how pattern of androgen excess in each phenotype relates to their risk of metabolic disease
Measured: 5 years
Eligibility for other PCOS-related studies
Participants will be screened for their eligibility to enroll in other PCOS-related research studies
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Apr 2019
- Study start14 Aug 2019
- Primary completion31 Dec 2026 (estimated)
- Study completion1 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Polycystic Ovary SyndromeStudies of the same condition, open and under way.
- More trials run by Imperial College LondonThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Imperial College London
- Sponsor type
- University, hospital or other
- 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, Royal College of Surgeons, Ireland, Imperial College Healthcare NHS Trust, Hull University Teaching Hospitals NHS Trust, King's College Hospital NHS Trust
- Responsible party
- Sponsor
- Investigators
- Wiebke Arlt, Principal investigator, University of Birmingham
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03911297
- Sponsor's protocol ID
- 19HH5264
- Other IDs
- WT209492/Z/17/Z
- Acronym
- DAISy-PCOS
- Status last verified
- Dec 2024
- First submitted
- 8 Apr 2019
- Last updated
- 5 Dec 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 5 Dec 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT03911297
- Acronym / sponsor ID
- DAISy-PCOS / 19HH5264
- Conditions
- Polycystic Ovary Syndrome
- Keywords
- polycystic ovary syndrome, androgens, steroids, metabolic risk, prediction, stratified medicine
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Imperial College LondonUniversity, hospital or other
- 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, Royal College of Surgeons, Ireland, Imperial College Healthcare NHS Trust, Hull University Teaching Hospitals NHS Trust, King's College Hospital NHS Trust
- Study start
- 14 Aug 2019 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 1 Dec 2027 (estimated)
- First posted
- 11 Apr 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 to 70 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Women with a new diagnosis of Polycystic ovary syndrome aged 18-70 who are treatment naive
Key inclusion criteria (3 listed)
- Women with a suspected diagnosis of polycystic ovary syndrome
- Age range 18-70 years
- Ability to provide informed consent
Key exclusion criteria (8 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Women with polycystic ovary syndromeProspective cohort study in women with polycystic ovary syndrome to identify the risk of developing metabolic disease | Other | – |
Primary outcome
Metabolic risk
Time frame: 5 years
Metabolic-risk prediction model would be made from a machine learning algorithm where the study team would be able to enter phenome and metabolome data of patient with a new diagnosis of PCOS. With this model, the study team would be able to stratify the women with PCOS into their risk of metabolic disease hence personalise the management of the condition
2 secondary outcomes
Dissect the severity and pattern of androgen excess in development of metabolic disease
Time frame: 5 years
Eligibility for other PCOS-related studies
Time frame: 3 years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Dec 2024.
- Individual participant dataSharing planned: No
Related trials for Polycystic Ovary Syndrome
Recruiting
The Effects of the Mediterranean Diet and Synbiotics in Polycystic Ovary Syndrome
PCOS (Polycystic Ovary Syndrome)
Recruiting
Brown Adipose Tissue Modulation in Polycystic Ovary Syndrome Using Infrared Thermography
Polycystic Ovary Syndrome (PCOS)
Active, not recruiting
Mindfulness-Based Education For Women With Polycystic Ovary Syndrome
Polycystic Ovary Syndrome (PCOS), Menstrual Symptoms
Not yet recruitingObservational study
Chinese Overweight/Obesity Cohort: Metabolic & Comorbidity Risks
Obesity & Overweight, Diabetes Mellitus, Type 2
RecruitingPhase 1
Safety of Cultured Allogeneic Adult Umbilical Cord or Placental Derived Mesenchymal Stem Cells for PCOS
Polycystic Ovary Syndrome (PCOS), Polycystic Ovary Syndrome
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Wiebke Arlt principal investigator
University of Birmingham
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Polycystic Ovary Syndrome, run by Imperial College London at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 8 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 5 Dec 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Eka Melson (+447852146611, e.melson@bham.ac.uk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Metabolic risk (time frame: 5 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 5 Dec 2024.