Glasgow Caledonian University

3

Hamish Innes

OFHS240108

AIM: To develop and evaluate prognostic clinical prediction models to help identify members of the UK general population at risk of ill-health and death from chronic liver disease, including cancer of the liver. Research questions: 1) What is the frequency of death and ill-health from chronic liver disease (in terms of prevalence and incidence) in the general UK population. 2) What genetic factors are associated with development of advanced chronic liver disease? 3) What are the main non-genetic predictors of liver disease? In particular, in relation to (i.e. socio-demographic factors/inequalities, lifestyle factors and blood biomarkers). 4) How accurately can risk scores combining information on multiple genetic markers (i.e. polygenic scores) predict risk of ill-health or death from chronic liver disease? 5) What is the predictive element of non-genetic biomarkers for assessing illness and death from chronic liver disease and what is the incremental value of polygenic scores?Chronic liver disease (CLD) is a leading cause of premature death in the UK. The two main risk factors for CLD - obesity and alcohol use - are both extremely common in the UK general population and other Western countries. Thus, given the size of the “at-risk” population, it is crucial that limited diagnostic resources/interventions are targeted effectively and efficiently. However, current care pathways for CLD are suboptimal, generating many false positives (i.e. focusing scarce resource on low risk patients) and false negatives (i.e. diverting resource away from high risk individuals). It is unclear if genetic information could help to improve risk stratification. In this project, models integrating genetic and non-genetic information will be developed to predict individualised risk of severe liver disease. This will allow health systems to manage “atrisk” patients in a way that is commensurate to their individualised risk of severe disease

The aim of this research is to reduce late diagnosis of chronic liver disease and liver cancer, a key area of unmet need. We will develop prognostic prediction models and evaluate how well these models distinguish people who go onto develop serious liver disease from those who do not. A key priority will be the integration of genetic and non-genetic information, of which the latter will include socio-demographic, comorbidities (e.g. mental health) deprivation and lifestyle factors and data on prescribed medications. Overall, the goal is to develop new tools that will enable health systems to target interventions/services at individuals who are most likely to develop severe disease

Public Health Research

02/02/2025

07/04/2024

TRE