Risk scores for prediction of diabetes and its complications

Background

Diabetes, albeit largely preventable, 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 risk of diabetes and its many complications, when added to established risk factors, is uncertain. Furthermore, the relevance of novel ‘omic biomarkers and genetic information using conventional versus hypothesis-free Machine Learning approaches may reveal new insights for prevention and treatment of diabetes and its complications. The UK Biobank (UKB) offers a unique platform to comparatively assess the relevance of traditional and novel risk scores to predict diabetes and its complications among 0.5 million adults with various morbidity profiles and a wealth of data on genotype, lifestyle, biological measurements, biochemistry, metabolomics, 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 its specific complications (e.g. macro vs micro-vascular vs. neurological vs. infective), using external and cohort-specific genetic information
2. To assess the additional value of the different tools for predicting non-fatal or fatal events from various diabetic complications within the cohort, including genetic, metabolite and proteomic biomarkers or machine learning techniques
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 skills 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 will 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 Bachelor’s or Master’s degree in statistics/genetic epidemiology/biomedical or life sciences, and strong computational and  statistical programming skills.