Background:
Our earlier work comparing cardiometabolic health in multi-ethnic populations of UK, Singapore and Japan have revealed differences that are dependent on age and ancestry. We also observed that a substantial number of health conditions co-occur, forming cases of multimorbidity. This is supported by reports of poorer health outcomes for ethnic minorities in the UK. Yet, it is unknown how the risk factors for multimorbidity are different across ethnicities, and whether they can be explained by genetic ancestry or their environment.
Hypotheses:
1. Differences in the incidence of multiple diseases (cardiorespiratory, metabolic, neurological and cancer) is dependent on age and genetically-informed ancestry.
2. Disease risk and the likelihood of multimorbidity onset can be effectively characterized using multimodal learning approaches that integrate clinical history, multi-omics data, medical imaging, and demographics.
3. A model capturing biological risk factors and health outcome inequities will demonstrate robust prediction performance across individuals of different age groups and ancestries.
4. Modifiable factors (healthcare, medication, diet, physical activity, lifestyle choices, etc.) can serve as early intervention strategies to reduce risk of multimorbidity.
