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Approved Research

Environment, genotype, AI-derived phenotype, and the risk of cardiovascular diseases, metabolic diseases, and chronic kidney disease

Principal Investigator: Professor Luxia Zhang
Approved Research ID: 90018
Approval date: November 9th 2022

Lay summary

Chronic non-communicable diseases (NCDs) have been a major global challenge. In recent decades, environmental factors have been recognised as an important cause for NCDs burden. Based on data from China National Survey of Chronic Kidney Disease (CKD), our research group found that environmental factors including particulate matter (for example, PM2.5 and PM1) and gaseous pollutants (for example, NO2 and ozone) were the potential risk factors for a high prevalence of CKD. However, whether these environmental risk factors affect the genetic susceptibility in the development of CKD as well as other common NCDs remains unclear.

In addition to genetic factors and environment, NCDs are also associated with other factors such as phenotypes [for example, body mass index (BMI), ethnicity, and blood type]. These observed phenotypes can interact with both genetic factors and environment and result in the development of NCDs. In recent decades, computational phenotype (for example, information from electrocardiograph and medical imaging) is a newly emerging research topic related to both artificial intelligence (AI) research community and medical research community and can be treated as a supplementary for the observed phenotype.

Hence, in this 36-months project we aim to investigate the effects of interactions between three potential causes of NCDs including environment, genetic factors, and computational phenotypes on the development of common NCDs. Results from the project will further our understanding of the effect of environment in the aetiology of common NCDs, particularly from the gene-environment interaction view.