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Approved Research

The interaction effects of genetic susceptibility and life exposures on human metabolomics and liver diseases risk and related mortality

Principal Investigator: Dr Ci Song
Approved Research ID: 85248
Approval date: May 19th 2022

Lay summary

Liver disease is increasing in the UK and around the world and is now a major cause of reduced quality of life and early death. Many liver diseases are linked to alcohol excess, obesity, diabetes, unhealthy lifestyle factors and air pollution and are the result of damage over many years. Current attempts to tackle liver disease in its early phases are limited by a lack of epidemiological data and poor understanding of the natural history of these diseases in the general population. Chronic liver diseases have been shown to run in families, suggesting that there is a genetic cause in addition to environmental influences. Recently, there is increasing interests to generate polygenic scores by combining these single-nucleotide polymorphisms (SNPs) to reveal the overall effect of genetic architecture on common diseases, such as body mass index (BMI)-related polygenic scores in association with cardiovascular disease, diabetes mellitus, and obesity-related cancers (e.g. hepatocarcinoma, pancreatic cancer, colorectal cancer, etc.). The effect of these metabolism-related SNPs in liver disease is unknown. To our knowledge, few studies have paid attention to the interaction between life exposures and genetic susceptibility in the development of liver diseases. On the other hand, metabolomics is useful tool to study life exposures and human health because it potentially measures intermediate phenotypes that integrate lifestyle exposures, genotype, and other host factors. In this study, we will construct polygenic scores for SNPs and analyze polygenic scores modify the effects of life exposures on the occurrence of liver diseases. The extent of the metabolomics signatures in mediating the associations between life exposures, SNP scores, and metabolic diseases will further be explored.

Lastly, we will use the above results to generate a liver diseases risk and mortality prediction model to establish new strategies for extending longevity.

We estimate that we will complete the majority of data analysis and finish the publication of the relevant results in 36 months. Our study will help to promote the understanding of liver diseases, and the model could be used in identifying groups of individuals who are at high risk of severe liver diseases and more likely to benefit from interventions.