Research questions:
Diabetic complications-including cardiovascular disease, nephropathy, neuropathy, and retinopathy-are major contributors to disability, reduced quality of life, and premature mortality among individuals with diabetes. Early identification of individuals at high risk is essential for targeted prevention, yet existing clinical prediction tools rely on limited risk factors.
Objectives:
(1) integrate clinical, biochemical, lifestyle, and genetic data to identify major predictors of diabetic complications;
(2) apply advanced statistical and machine learning methods to build and validate predictive models;
(3) explore the potential roles of ageing-related pathways, particularly genes such as p16 and p21, in the development and progression of diabetic complications.
Scientific rationale:
The scientific rationale for this study arises from several critical gaps in current knowledge. First, the biological mechanisms driving the progression of diabetic complications remain incompletely understood, particularly regarding the contribution of cellular senescence and ageing-related pathways. Emerging evidence suggests that ageing markers-such as p16 and p21-may accelerate vascular and metabolic dysfunction, yet their relevance at the population level has not been fully explored.
Second, most existing prediction models rely on limited clinical variables and are unable to capture the complex interactions among metabolic, lifestyle, and genetic factors. This limitation contributes to poor generalizability and insufficient accuracy for use in precision prevention.
The UK Biobank combined longitudinal health outcomes, comprehensive phenotype data, biomarker measurements, genetic profiles, and lifestyle information for over 500,000 participants provides the scale and depth necessary to investigate complex risk patterns and mechanistic pathways. Integrating these data enables identification of novel determinants, assessment of ageing-related mechanisms.
