Activity Tracking and Whole-Body MRI For The Prediction of Individual Wellness -Comparative Analysis with Chinese Cohorts
Approved Research ID: 78730
Approval date: February 1st 2022
Chronic diseases such as diabetes, cardiovascular diseases, and cancers develop through multiple culmination of processes including lifestyle, dietary, environmental and genetic factors. The development of diseases occur on a continuum with the final stages resulting in detectable disease, and patients having symptoms. There are several stages prior to this, that we can detect early changes in the body, in which if intervention can be targeted, can be more effective at disease prevention and cure. This study will examine the early changes based on body MRI quantitative measurements and physical activity measurements. These changes can be subtle and will require comparison with population reference ranges which can now be calculated based on the data collected by the UK Biobank.
Our project aims to derive population normative range using the UK biobank data and the local Chinese cohorts with data collected in the same manner and to combine the various quantitative parameters for assessing individual wellness.
The idea is we may be able to create biomarkers for the cardiovascular, cancer-related and all-cause mortality prediction. The impact to the broader population will be that there will be a more accurate method of screening of subclinical disease detection based on these non-invasive data points opening a window for serial monitoring during the intervention to improve health and individual wellness. Accurate and reproducible biomarkers are desirable not only to improve how we can measure health but also in assisting large scale clinical trials. We anticipate the findings to significantly contribute to the advancement of preventative medicine. The project duration is expected to be 36 months.
With a wealth of data available and quantifiable on medical imaging such as MRI scans, it is possible to derive new quantitative biomarkers such as body composition, liver fat composition, organ volumetry, bone intensity analysis, and muscle volumetry and fatty infiltration, etc. Whilst normative reference ranges for these emerging biomarkers are being established, we also know that there are population differences based on not only gender, age, but also ethnicity. The goal of this project is to develop a non-invasive tool for assessing an individual's wellness combining the various data types/modalities available in the UK Biobank, including the MRI imaging data and physical activity tracking data.
The aims of our project are as follows:
1) to compare the derived normative values for these quantifiable biomarkers with those of retrospective and prospectively collected Chinese cohorts using similar imaging protocols and activity tracker methods.
2) to combine these biomarkers for personalised cardiovascular, cancer-related and all-cause mortality prediction.
In our exploration of the data, we decided to incorporate additional modalities and outcomes.
To be more specific, these include DXA imaging and NMR metabolomics as inputs into prediction models.
For prediction, we have expanded to include outcomes such as hepatic steatosis and metabolic syndrome, as well as more broadly encompassing age-related diseases. These are defined more broadly to include diseases such as Heart Failure, COPD, Lung Cancer, Cerebral Stroke, Liver Disease, ASCVD, T2D and Metabolic Syndrome (including hepatic steatosis). These will be included as individual incidents and composite outcomes for our study.