A blood test could one day flag chronic diseases years before symptoms appear, by tracking how gene activity and chemical reactions in the body change over time. Doctors today often diagnose chronic diseases like diabetes or heart disease only after damage has occurred. The problem is that we don’t know which molecular changes are early warning signs and which are just normal variation. This project tracks 700 twins over seven years, measuring their gene expression and blood metabolites at three time points. Because twins share genetics but not identical environments, the researchers can untangle which changes are inherited and which are driven by ageing or lifestyle. If the team succeeds, it will lay the groundwork for personalised medicine where a simple blood sample reveals a person’s disease trajectory years in advance. That could shift healthcare from reactive treatment to early prevention, saving both lives and costs. The work is fundamentally about building the analytical tools and biological understanding needed to make sense of complex molecular data—a necessary step before any clinical test can be developed.
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New technologies are providing opportunities to measure health and disease in many novel ways. The data produced is complex and hard to decipher even by clinicians and health workers. This proposal will investigate how we can use modern molecular techniques which measure in blood the activity and expression of genes and the signatures of chemical reactions (metabolites) in the cell to help predict early disease. To do this we need to explore how global gene expression and metabolites alter over time and how these longitudinal changes along with other new molecular and genetic techniques (called omics) can be used to explore disease mechanisms and susceptibility in ageing populations. To explore the biology of "omic" variability, and to lay the foundation for the clinical integration of genetic and genomic data, we will investigate the longitudinal relationships of cellular and genomic phenotypes, including global gene expression and metabolites, in 700 twins over 7 years, measured at three time-periods. The study subjects derive from the TwinsUK cohort on whom there is already extensive clinical information and cross-sectional genetic and genomic data. Building on these existing data, and making use of the specific methodological opportunities and advantages afforded by the twin design, we will explore how these genomic traits track and vary over time, determine how such variation relates to underlying genetic variation, and explore the joint contribution of genetic and genomic data to disease risk and onset. We will also explore the potential value of monitoring changes in these and other situations within an integrated personalised medicine framework. We will use and develop new analysis approaches to integrate these complex data sets and suggest which changes might play a role in clinically relevant tratis and disease itself. This study will provide novel insights into disease understanding and stimulate larger-scale efforts to combine modern genetic and genomic data for clinical benefit in the future. These studies will pave the way for individualised medicine.
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