| Title: | Activity Tracking and Whole-Body MRI For The Prediction of Individual Wellness -Comparative Analysis with Chinese Cohorts |
| Lead Institution: | University of Hong Kong |
| Principal investigator: | Dr Varut Vardhanabhuti |
Application 78730
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About
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.3 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 8973 | Machine learning prediction of hepatic steatosis using body composition parameters: A UK Biobank Study | Delbert Almerick T. Boncan (+4) | 2024 | npj Aging |
| 9690 | Metabolic biomarkers using nuclear magnetic resonance metabolomics assay for the prediction of aging-related disease risk and mortality: a prospective, longitudinal, observational, cohort study based on the UK Biobank | Jie Lian (+1) | 2023 | GeroScience |
| 10260 | Towards visceral fat estimation at population scale: correlation of visceral adipose tissue assessment using three-dimensional cross-sectional imaging with BIA, DXA, and single-slice CT | Benjamin Chan (+3) | 2023 | Frontiers in Endocrinology |
Enabling scientific discoveries that improve human health