Ignite Creation Date:
2025-12-25 @ 12:17 AM
Ignite Modification Date:
2025-12-30 @ 10:49 AM
Study NCT ID:
NCT04873258
Status:
RECRUITING
Last Update Posted:
2025-07-23
First Post:
2021-04-20
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Development of a Non-invasive Screening Tool to Predict Metabolic Dysfunction-associated Steatotic Liver Disease
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D005234', 'term': 'Fatty Liver'}, {'id': 'D009765', 'term': 'Obesity'}], 'ancestors': [{'id': 'D008107', 'term': 'Liver Diseases'}, {'id': 'D004066', 'term': 'Digestive System Diseases'}, {'id': 'D050177', 'term': 'Overweight'}, {'id': 'D044343', 'term': 'Overnutrition'}, {'id': 'D009748', 'term': 'Nutrition Disorders'}, {'id': 'D009750', 'term': 'Nutritional and Metabolic Diseases'}, {'id': 'D001835', 'term': 'Body Weight'}, {'id': 'D012816', 'term': 'Signs and Symptoms'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 2000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2019-09-27', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-07', 'completionDateStruct': {'date': '2027-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-07-18', 'studyFirstSubmitDate': '2021-04-20', 'studyFirstSubmitQcDate': '2021-04-29', 'lastUpdatePostDateStruct': {'date': '2025-07-23', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2021-05-05', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2027-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Establishment of Classification Tool for Use in Clinical Trials', 'timeFrame': 'Study duration (1 year)', 'description': 'The success of the final classification tool (as measured by area under the receiver operator curve (AUROC). The initial measure to generate the dataset will be the presence or absence of MASLD on liver USS.'}], 'secondaryOutcomes': [{'measure': 'Normal LFT Range in NAFLD patients', 'timeFrame': 'Study duration (1 year)', 'description': "The normal range of LFT's in patients with known NAFLD"}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Fatty liver', 'MASLD'], 'conditions': ['Healthy', 'Fatty Liver', 'MASLD', 'MASLD - Metabolic Dysfunction-Associated Steatotic Liver Disease', 'Obesity and Obesity-related Medical Conditions']}, 'descriptionModule': {'briefSummary': 'A generic screening study to establish structural and/or functional baselines of specific organs.', 'detailedDescription': 'Fatty liver disease is a common condition (25% of the population) which can lead to liver inflammation, liver scarring and even liver cancer. Clinical trials are often performed in healthy volunteers, who may have underlying fatty liver without knowledge of it. In clinical trials fatty liver can both mean volunteers have abnormal liver tests, preventing them joining the trial, as well as more likely to have a possible liver drug reaction, causing volunteers to withdraw from a clinical trial of a new drug.\n\nThe principal objective of the study is to develop a clinical scoring tool that can accurately predict fatty liver disease in study volunteers, without the need for invasive tests (such as a tissue biopsy).\n\nWe aim to recruit initially 2000 volunteers to this study, both healthy volunteers and patients with known MASLD.\n\nVolunteers will attend the unit to undergo all assessments on one day. Once consent is given with a study research physician, bloods will be taken and body measurements made (including BMI, weight, waist circumference). A full medical history and physical examination will then be performed by the research physician.\n\nBioimpedance body composition analysis will then be performed on an ACUNIQ device. Finally ultrasound of the liver and fibroscan will be performed. Once all assessments are complete the study volunteer will be discharged from the unit.\n\nOnce all results are finalised, analysis will be performed on all the data to create a clinical score to predict the presence of MASLD, both with statistical and machine learning methods.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '80 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Adult male or female volunteers for other clinical trials taking place at the study unit.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Male or female volunteers aged ≥18 to ≤80 years at the date of signing the informed consent.\n2. Willingness and ability to provide written, personally signed, and dated informed consent, in accordance with the latest ICH Good Clinical Practice (GCP) Guidelines and applicable regulations.\n3. An understanding, ability and willingness to fully comply with project procedures and restrictions.\n\nFor PART B only:\n\n1\\. With a known history of MASLD as evidenced either of:\n\n1. GP diagnosis on HCF\n2. Documented Fibroscan or liver US demonstrating MASLD\n\nExclusion Criteria:\n\n1. Known alcoholic liver disease, history of cirrhosis of any other cause (metabolic, viral hepatitis or other)\n2. Any other significant previous liver pathology (liver malignancy, portal hypertension, infiltrative liver disease)\n3. Alcohol consumption \\>30 units per week\n4. An Implanted cardiac devices'}, 'identificationModule': {'nctId': 'NCT04873258', 'acronym': 'MASLD', 'briefTitle': 'Development of a Non-invasive Screening Tool to Predict Metabolic Dysfunction-associated Steatotic Liver Disease', 'organization': {'class': 'INDUSTRY', 'fullName': 'Richmond Pharmacology Limited'}, 'officialTitle': 'Development of a Non-invasive Screening Tool to Predict Metabolic Dysfunction-associated Steatotic Liver Disease (MASLD) in Volunteers on Clinical Trials Utilising Machine-learning and Bioimpedance Vector Analysis', 'orgStudyIdInfo': {'id': 'C19030'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Patients with MASLD', 'description': 'Patients with known MASLD', 'interventionNames': ['Diagnostic Test: Bioimpedence Vector Analysis']}, {'label': 'Healthy volunteers', 'description': 'Patients without any known health issues', 'interventionNames': ['Diagnostic Test: Bioimpedence Vector Analysis']}], 'interventions': [{'name': 'Bioimpedence Vector Analysis', 'type': 'DIAGNOSTIC_TEST', 'description': 'Bioimpedence vector analysis', 'armGroupLabels': ['Healthy volunteers', 'Patients with MASLD']}]}, 'contactsLocationsModule': {'locations': [{'zip': 'SE1 1YR', 'city': 'London', 'state': 'London', 'status': 'RECRUITING', 'country': 'United Kingdom', 'contacts': [{'name': 'James Rickard', 'role': 'CONTACT', 'email': 'j.rickard@richmondpharmacology.com'}, {'name': 'Jorg Taubel, MD FFPM', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Richmond Pharmacology Ltd. 1a Newcomen St, London Bridge', 'geoPoint': {'lat': 51.50853, 'lon': -0.12574}}], 'centralContacts': [{'name': 'James Rickard', 'role': 'CONTACT', 'email': 'grants@richmondresearchinstitute.org', 'phone': '+44 (0)20 7042 5800'}], 'overallOfficials': [{'name': 'Jorg Taubel, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Richmond Pharmacology Limited'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Richmond Research Institute', 'class': 'INDUSTRY'}, 'collaborators': [{'name': 'Richmond Pharmacology Limited', 'class': 'INDUSTRY'}], 'responsibleParty': {'type': 'SPONSOR'}}}}