Risk scores for prediction of diabetes and its complications

background

Diabetes, albeit largely preventable, it is one of the leading causes of death and disability worldwide, with an estimated 529 million of people living with diabetes in 2021. Polygenic risk scores (PRSs) can potentially enhance risk stratification and prevention for chronic diseases, but the complementary value of such scores for predicting differential risk for diabetes and its many complications is uncertain. Studies suggest the superiority of genetic instruments for enhancing prediction of disease-specific risk when added to established risk factors. However, the value of such algorithms for predicting and differentiating risk for diabetes and its specific complications at a population level is unknown. Furthermore, the implications of using traditional versus novel omics biomarkers versus genetic information to guide preventive and therapeutic interventions in diabetes and its complications are unknown. The UK Biobank (UKB) offers an unique platform to comparatively asses the relevance of traditional and genetic risk scores to diabetes and its many complications among 1 million adults with different morbidity profiles, and a wealth of data on genotype, lifestyle and biological measurements, biochemistry, NMR metabolomics and proteomic assays, and long follow-up for causes of hospitalization and mortality. 

The aims of the project include: 

1. To derive and validate PRSs for diabetes and related complications (e.g, macro vs micro-vascular vs. neurological vs. infective), using external genome-wide association studies and cohort-specific instruments
2. To assess the additional value of the different tools for predicting non-fatal or fatal events from diabetic complications in the cohort populations, including the additive value of novel metabolites and proteomic biomarkers
3. To assess the implications of the different predictive tools for prevention and treatment of diabetic complications between UK and other populations.

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

The student will gain experience in genetic epidemiology and analysis of large-scale prospective data. They will develop skills in conducting systematic literature reviews, analytical techniques, research planning, statistical programming, data analysis, and presentation skills. The student will be supported to publish peer-reviewed papers during their DPhil.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

Training might be provided as needed. Attendance at seminars, workshops and courses provided by the Department and University will also be encouraged. There will be opportunities to present research work at relevant international/national conferences. 

prospective student

The ideal candidate will have a Master's degree in statistics/genetic epidemiology/biomedical or life sciences, and good proficiency with programing analyses in R, STATA,  Python or SAS packages.