After obtaining an engineering degree from the French School of Statistics and Information Analysis (ENSAI), Lauric Ferrat completed a PhD in Mathematics at the University of Exeter, United Kingdom. While he appreciated the theoretical depth and intellectual challenge of mathematics, it also sparked a growing interest in applying his skills to biomedical research, where he could contribute directly to understanding and improving human health.  

He joined the team of Professor Richard Oram, a clinician specialising in Type 1 Diabetes (T1D), where they combined their expertise to develop predictive models for T1D. Their work was recognised among the top 10 genetic discoveries of 2021 by the American Journal of Human Genetics, and Dr Ferrat was recently awarded a Rising Star presentation at IDS 2024.

He has recently joined the laboratory of Professor Timothy Frayling, where he is further developing his expertise in genetics and working under his mentorship to progress towards an independent research career.

RESEARCH AIMS

Dr Ferrat’s long-term research goal is to help reduce the burden of both type 1 and type 2 diabetes. He draws on his background in mathematics and statistics to analyse large-scale datasets, including those from cohort studies and biobanks, in order to identify key risk factors associated with disease outcomes. He contributes to several international initiatives focused on the early detection of type 1 diabetes. These efforts aim to identify children at high risk of developing the condition through a combination of autoantibody testing and genetic profiling.

EXPERTISE

• Prediction of Type 1 Diabetes

• Genetics of Type 1 and Type 2 Diabetes

• Mathematics and Statistics

• Machine Learning

KEY PUBLICATIONS

1.Ferrat, L.A., Templeman, E.L., Steck, A.K. et al. Type 1 diabetes prediction in autoantibody-positive individuals: performance, time and money matter. Diabetologia (2025). https://doi.org/10.1007/s00125-025-06434-2

2. You L, Ferrat LA, Oram RA, et al. Identification of type 1 diabetes risk phenotypes using an outcome-guided clustering analysis. Diabetologia. 2024;67(11):2507–2517. https://doi.org/10.1007/s00125-024-06246-wresearchgate.net

3. Oram RA, Sharp SA, Ferrat LA, et al. Utility of diabetes type–specific genetic risk scores for the classification of diabetes type among multiethnic youth. Diabetes Care. 2022;45(5):1124–1131. https://doi.org/10.2337/dc20-2872

4. Ferrat LA, Vehik K, Sharp SA, et al. A combined risk score enhances prediction of type 1 diabetes among susceptible children. Nat Med. 2020;26(8):1247–1255. https://doi.org/10.1038/s41591-020-0930-4