ENDO1000 - A UK-wide Endometriosis Research Project

NCT07240883 | Recruiting

• 1 other identifier: 101157146
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

Brief Summary

Approximately 1.5 million people in the UK, and 200 million globally live with endometriosis, and it is estimated to affect 1 in 10 women of reproductive age. Endometriosis is a chronic pain condition where the lining of the uterus (endometrium) grows in areas outside the uterus. This can cause patients a range of symptoms including severe pain, fatigue, irregular periods, infertility and gastrointestinal symptoms. There is a clear unmet need for early diagnosis and more effective pain management for people who suffer from endometriosis. Endometriosis is a heterogeneous disease in terms of symptoms, trajectory, and indicated therapeutic course, which highlights the need to develop personalised/targeted approaches for effective longitudinal management which are acceptable and accessible to patients and their health care team. The goal of this study is to accelerate discovery and advance data-driven research into endometriosis diagnosis and treatment by collecting large, multimodal, longitudinal data. To achieve this goal, the investigators plan to deliver a longitudinal cohort study. Approximately 3000 UK individuals with endometriosis will be invited over a 24-month period to self-report symptoms that include, pain, menstrual cycle, painkiller use, sleep, exercise, diet and bowel habits via a bespoke ENDO1000 mobile app. A cohort of 1000 women will be asked to complete more in-depth questionnaires asking about endometriosis history, quality of life and treatments. This cohort of women will be asked to take self-collected biological samples (blood, urine, saliva, vaginal swab and faeces). The investigators will look at, for example, inflammatory markers, examine the microbiome and may look at DNA, with permission to see if there are any markers that can help with diagnosis or treatment of endometriosis. At the same time participants will be asked to wear a smartwatch which will monitor, for example, temperature, ambient light, sleep patterns and movement. All this will help the investigators to build a picture over time of the participant's endometriosis symptoms, treatments and what may cause symptoms to flare which in turn could lead to more patient led treatments.

Conditions

Endometriosis

Keywords

Endometriosis; Pelvic Pain; Longitudinal; Mobile app

Outcome Measures

Primary Outcomes (1)

• To generate a unique biomedical endometriosis resource with detailed clinical and lifestyle phenotyping.

  This study aims to characterise overall endometriosis symptom severity. This is a multifaceted objective to cover the breadth of symptoms of endometriosis and there is not a single scale that captures this that could be assigned as "primary outcome measure". We aim to define a new gold standard, on the basis of the largest longitudinal multimodal endometriosis study ever conducted. We envisage the resulting clinical score will be derived by combining different symptoms as experienced by patients and combining different data modalities. Specifically, we will elicit responses where participants self-quantify: pain (EHP-30), fatigue (BFI), sleep problems (PSQI and PROMIS), bowel habits (NHS questionnaire), diet (FFQ), epidemiological and genetic data (WERF EPHect Patient Questionnaire). Additional inputs will be provided by biological samples and data from a wrist-worn wearable sensor, from which we have validated algorithms to characterise physical activity, sleep and diurnal rhythm.

  5 years

Secondary Outcomes (1)

• To use the biomedical endometriosis resource to address fundamental questions in our understanding of endometriosis.

  5 years

Interventions

Sample and data collection BIOLOGICAL

We will collect data via a bespoke mobile application which will include; pain, fatigue, sleep patterns, diet, bowel habits, exercise, painkiller use, hormone use from participants with endometriosis. In a subset of the participants, we will collect biological samples which will be self collected at home, that will include, blood, saliva, urine and faeces. At time of sample collected, we will also ask a selection of validated questionnaires to be completed.

Eligibility Criteria

• Age: 16 Years+
• Sex: female
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Anyone living within the UK with a self-reported diagnosis of endometriosis

You may qualify if:

• Aged 16 or over
• Participants who confirm that they have received a clinical diagnosis of endometriosis (based on MRI, ultrasound or laparoscopy) within the last 10 years
• Living within the UK
• Willing and able to consent to installing and using the mobile ENDO1000 app on their smartphone or use our web-based equivalent.

You may not qualify if:

• Pregnant
• Known severe coagulation disorder
• Known active Hepatitis B/C and/or HIV (due to Royal Mail restrictions on biospecimen postage)

Sponsors & Collaborators

• Clinical Hospital Centre Zagreb: collaborator
• Endometriosis UK: collaborator
• University of Edinburgh: lead

Study Sites (1)

University of Edinburgh

Edinburgh, United Kingdom

RECRUITING

Biospecimen

Retention: SAMPLES WITH DNA

Blood, urine, saliva, faeces

MeSH Terms

Conditions

Endometriosis; Pelvic Pain

Interventions

Data Collection

Condition Hierarchy (Ancestors)

Genital Diseases, Female; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Genital Diseases; Pain; Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Epidemiologic Methods; Investigative Techniques; Health Care Evaluation Mechanisms; Quality of Health Care; Health Care Quality, Access, and Evaluation; Public Health; Environment and Public Health

Study Officials

• Andrew W Horne, PhD, MB ChB

  University of Edinburgh

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Ann M Doust, DipN

CONTACT

+4478910643488; ann.doust@ed.ac.uk

Priscilla Fernandez, BSc, BM

CONTACT

priscilla.fernandez@ed.ac.uk

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 2 Years
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 5, 2025
• First Posted: November 21, 2025
• Study Start: December 5, 2025
• Primary Completion (Estimated): December 31, 2030
• Study Completion (Estimated): December 31, 2030
• Last Updated: January 9, 2026

Record last verified: 2026-01

Locations

GB (1)