The interaction effects of genetic susceptibility and life exposures on human metabolomics and liver cancer risk and related mortality
Principal Investigator
Name
Ci Song
Degrees
M.D.
Institution
Nanjing Medical University
Position Title
lecturer
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-929
Initial CDAS Request Approval
Mar 2, 2022
Title
The interaction effects of genetic susceptibility and life exposures on human metabolomics and liver cancer risk and related mortality
Summary
Liver cancer is increasing 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 cancer. The extent of the metabolomics signatures in mediating the associations between life exposures, SNP scores, and metabolic diseases will further be explored.
A combination of PRS, environmental factors and clinical factors had shown good performance in identification of high-risk populations for breast cancer and prostate cancer.Therefore, we will construct a PRS for liver cancer, and evaluate its effectiveness in the UK Biobank as well as the PLCO cohort study respectively. At last, we plan to develop a liver cancer risk prediction model based on the PRS, metabolites and lifestyle behaviors based on the UK Biobank, and evaluated its application value in the PLCO cohort .
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.
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 cancer. The extent of the metabolomics signatures in mediating the associations between life exposures, SNP scores, and metabolic diseases will further be explored.
A combination of PRS, environmental factors and clinical factors had shown good performance in identification of high-risk populations for breast cancer and prostate cancer.Therefore, we will construct a PRS for liver cancer, and evaluate its effectiveness in the UK Biobank as well as the PLCO cohort study respectively. At last, we plan to develop a liver cancer risk prediction model based on the PRS, metabolites and lifestyle behaviors based on the UK Biobank, and evaluated its application value in the PLCO cohort .
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.
Aims
This project aims to:
(i) Unravel the interaction effect of life exposures (e.g., smoking, obesity, lack of physical activity, alcohol drinking, dietary pattern, and air pollution) and genetic factors on the development of liver diseases including non-alcoholic fatty liver disease (NAFLD), liver cirrhosis, and liver cancer;
(ii) Identify life exposures or genetic susceptibility-related metabolomics signatures, and investigate their association with liver diseases;
(iii) Reveal the risk prediction ability of the identified signatures for liver cancer risk and related mortality.
Collaborators
Hongbing Shen, Professor of Epidemiology, Nanjing Medical University
Meng Zhu, Professor of Epidemiology, Nanjing Medical University