The analysis will investigate associations between genetic, clinical, and lifestyle factors and the onset of diabetes mellitus and its complications in the UK Biobank. We will apply multivariable regression, survival analysis, and machine learning models to quantify these associations and develop risk prediction tools.
Research questions
Which genetic, environmental, and lifestyle factors are associated with incident diabetes mellitus and its major complications in the UK Biobank cohort?
How do clinical biomarkers (e.g. fasting glucose, HbA1c, lipid profiles) relate to diabetes risk and progression to complications?
What is the contribution of modifiable lifestyle factors (physical activity, diet, smoking, alcohol) to prevention or worsening of diabetes and its complications?
Objectives
Identify genetic variants and clinical biomarkers linked to higher risk of diabetes and its complications.
Quantify the effects of lifestyle factors on diabetes incidence and subsequent complications.
Examine interactions between genetic, environmental, and clinical factors in shaping diabetes risk.
Develop and validate prediction models to identify individuals at high risk of diabetes and its complications.
Scientific rationale
Diabetes mellitus is a common, multifactorial disease and a major cause of cardiovascular disease, kidney failure, neuropathy, and visual impairment. Its occurrence and progression are driven by both inherited susceptibility and modifiable factors, particularly obesity, physical inactivity, and unhealthy diet. The UK Biobank, with its large sample size, long-term follow-up, and rich phenotypic and genotypic information, offers a unique resource to disentangle these contributions. By integrating genetic, biomarker, and lifestyle data, this project aims to improve understanding of diabetes aetiology, identify high!risk groups, and inform targeted prevention and early intervention strategies.