Athina Spiliopoulou (Affiliate)
Using statistical methods to answer questions on the pathogenesis, progression and treatment of disease.
Research in a Nutshell
I am a Chancellor's Fellow in Data-Driven Innovation at the Usher Institute for Population health Sciences and Informatics. I have a background in machine learning and genetic epidemiology. I am motivated by technology solutions to real problems or suboptimal decision making and I aspire to translate my research into innovative open-source software tools.
My research goal is to address questions about the pathogenesis, progression and treatment of disease by developing and applying state-of-the-art statistical methods to perform predictive and causal inferences. A key component of my work is the analysis of large-scale, multi-source and high-dimensional datasets, combining genetic and biomarker data (omics) with electronic or conventionally collected health records.
My main focus is on autoimmune diseases, and much of my recent work has been on two precision medicine objectives:
(1) Prediction of treatment response in rheumatoid arthritis: biologic therapies have revolutionised the outlook for severe rheumatoid arthritis, but there is substantial variability in response to treatment among rheumatoid arthritis patients for all classes of drugs. This has spurred efforts to discover predictors of response, as early effective therapy is consistently shown to improve long-term outcomes in rheumatoid arthritis.
(2) Risk stratification for type 1 diabetes and its complications: it is now recognised that type 1 diabetes --previously seen as a singe disease-- is heterogeneous, with some people retaining residual capacity to secrete insulin. Identifying genetic determinants and biomarkers of type 1 diabetes and diabetic complications can help us understand the underlying pathogenic processes and better inform decisions for preventing, managing and treating complications.
* MATURA consortium: An MRC-funded collaboration including academic and industry partners from the UK. The aim of the consortium is to find and develop tests that will allow better targeting of available treatments in rheumatoid arthritis.
* The Scottish Early Rheumatoid Arthritis Study group: national collaboration of arthritis researchers between the Universities of Aberdeen, Dundee, Edinburgh and Glasgow, NHS Scotland, Healthcare Improvement Scotland, the Chief Scientist’s Office Scotland and Pfizer Ltd.
* The Scottish Diabetes Research Network Type 1 Bioresource group: national collaboration with diabetes researchers across Scottish Universities and NHS clinicians and centres across Scotland.
Machine learning, genetic epidemiology, precision medicine, omics datasets, autoimmune diseases