Clinical trial NCT06014567: Twins MR Imaging Study
Official title: Characterising Biomarkers of Disease and Ageing in Twins Using Magnetic Resonance Imaging
Study type: OBSERVATIONAL
Overall status: ENROLLING_BY_INVITATION
Conditions: Aging
Keywords: Magnetic resonance imaging (MRI)
Twins
Lead sponsor: King's College London
Sponsor class: OTHER
Collaborators: name: Guy's and St Thomas' NHS Foundation Trust
First posted: 2023-08-28
Enrollment: 2500
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: TwinsUK Biobank longitudinal registry twin volunteers.

Brief summary: This study aims to create a comprehensive Magnetic Resonance Imaging data resource in twins aged 18 years and older. The data will be used alone or in conjunction with existing data to explore organ-specific ageing and twin-pair differences related to ageing and disease.

Detailed description: BACKGROUND Diverse interactions between environmental, genetic, and epigenetic factors shape individual paths of ageing and disease. In young adulthood, twins are highly similar in most organ structures, but their similarity decreases over time as they are progressively exposed to different environments. Twin studies provide an ideal way to investigate how the body ages and how age-related diseases (such as Alzheimer's disease, heart failure, fatty liver disease, and cancer) develop. Magnetic Resonance Imaging (MRI) is a type of scan that produces detailed images of the inside of the body. MRI enables non-invasive and safe visualisation of age and disease-related changes in the body, often long before clinical symptoms are perceived. MRI-derived biological features/biomarkers can be used to understand the influence of environmental exposures on ageing and disease.

PARTICIPANTS 2500 adult (age 18 and older) volunteers of TwinsUK, the UK's largest adult twin Biobank and the most clinically detailed globally, will be invited to participate. Interested individuals will complete screening to confirm study eligibility and their safety for MRI scanning. Eligible participants will complete informed consent prior to undertaking research MRI scans of their brain, spine, heart, abdomen and parts of their skeleton and muscles. 350 twins with differences in their data will be invited to repeat the MRI scans about two years later to explore longitudinal multi-organ imaging correlates of key environmental exposures based on cross-sectional signals and existing literature.

PROCEDURES Two 45-minute MRI scans of (1) the head and spine and (2) the abdomen to the upper thigh.

OUTCOMES Imaging-derived measures of morphology and function of the brain, spine, abdominal organs and musculoskeletal tissue. These measures will be used to identify twin pair differences related to ageing and disease. A comprehensive repository of MRI data will be created using protocols harmonised with the UK biobank. This will facilitate subsequent linkage with concurrently acquired samples and historic exposome data in longitudinal twin cohort.

Eligibility criteria: Inclusion Criteria:

* Identical or non-identical twins
* Gender: Male or Female
* Age: 18 years or above at the time of recruitment
* Must be able to understand the study information and provide informed consent
* Participation in the TwinsUK Biobank

Exclusion Criteria:

* Pregnancy
* Unable to tolerate MRI Procedures, e.g., claustrophobia, severe anxiety
* Contraindication to MRI Scanning, e.g., presence of non-MR conditional implants
* Any additional condition declared voluntarily by the participant that the principal investigator (PI) deems likely to affect the study's outcome.

Primary outcomes:
measure: BrainAGE score
timeFrame: At time of scan (duration 45 mins)
measure: BodyAGE score
timeFrame: At time of scan (duration 45 mins)
Secondary outcomes:
Other outcomes:
Locations:
city: London
country: United Kingdom
facility: King's College London
Study officials:
affiliation: King's College London
name: Claire Steves
role: STUDY_CHAIR
References:
citation: Cole JH, Poudel RPK, Tsagkrasoulis D, Caan MWA, Steves C, Spector TD, Montana G. Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage. 2017 Dec;163:115-124. doi: 10.1016/j.neuroimage.2017.07.059. Epub 2017 Jul 29.
citation: Linge J, Borga M, West J, Tuthill T, Miller MR, Dumitriu A, Thomas EL, Romu T, Tunon P, Bell JD, Dahlqvist Leinhard O. Body Composition Profiling in the UK Biobank Imaging Study. Obesity (Silver Spring). 2018 Nov;26(11):1785-1795. doi: 10.1002/oby.22210. Epub 2018 May 22.