Improving the diagnosis and screening of familial hypercholesterolaemia

Abstract

Familial hypercholesterolaemia (FH) is a prevalent inherited disorder that leads to significantly elevated low-density lipoprotein cholesterol (LDL-C) due to compromised clearance of LDL-C from the bloodstream. This leads to a heightened risk of cardiovascular diseases (CVD). Despite advances in treatment, the majority of individuals with FH remain undiagnosed, highlighting the need for improved detection and screening strategies. This thesis addresses key aspects of FH detection, from its genetic and pathophysiological mechanisms to global screening approaches, with a focus on Northern Ireland.

We begin by exploring the genetic underpinnings of FH, particularly the mutations that impair LDL-C clearance and the consequences of lifelong elevated LDL-C levels. We review the application of machine learning models in the development of FH diagnostics. We analyse global and local screening programs, comparing national efforts. This analysis includes a critical evaluation of the NHS Long Term Plan’s 25% FH detection target.

We develop a microsimulation model to simulate FH inheritance amongst populations in Northern Ireland and explore effective detection strategies. This model is rigorously parameterised and assessed for stability with parameter space sensitivity analysis. By modelling universal screening of 1-2-year-olds and FAMCAT screening of 20-39 year olds, both with cascade screening, this study provides insights into how detection can be improved for better health outcomes and cost-effectiveness. We highlight the critical importance of early detection, particularly through family-based screening, in nationwide FH detection strategies. We develop the microsimulation model and screening strategy simulator as a free to use and open-source tool available at:https://mybinder.org/v2/gh/chrisp123/FH_Screener.git/HEAD?urlpath=voila

These results contribute to the global discourse on FH detection and treatment.

Date of Award	Apr 2025
Original language	English
Sponsors	Department for the Economy
Supervisor	Steven Watterson (Supervisor), Haiying Wang (Supervisor), Huiru (Jane) Zheng (Supervisor) & Taranjit Singh Rai (Supervisor)