Abstract
OBJECTIVE: To develop and externally validate a two-stage machine learning framework that integrates polygenic risk and clinical variables for early identification of individuals at risk of developing type 2 diabetes. METHODS: We conducted a prospective prediction study using data from the All of Us Research Program for model development and the UK Biobank for external validation. Two models were constructed. Stage 1 used gradient boosted decision trees (XGBoost) with cross validation, automated hyperparameter optimisation and class weighting to predict 5-year incident type 2 diabetes using demographic, clinical and polygenic predictors. Stage 2 incorporated glycated haemoglobin or fasting glucose measurements to refine risk estimates. Model interpretation used SHapley Additive exPlanations values and permutation importance, and logistic regression and random forest models served as comparators. Discrimination of all models was compared using the DeLong test. RESULTS: The Stage 1 model achieved an area under the receiver operating characteristic curve (AUROC) of 0.81 in All of Us and 0.82 in UK Biobank, performing significantly better than the phenotype-only model in UK Biobank (DeLong p=1.05×10⁻⁷⁶). Higher polygenic risk quartiles were associated with increased incidence of type 2 diabetes in both cohorts (global χ(2) p<0.001). The Stage 2 model achieved AUROC values of 0.78 in All of Us and 0.77 in UK Biobank. Subgroup performance was consistent across sex and ancestry groups, with CIs reported. Cost analysis suggested potential net savings compared with the American Diabetes Association test. CONCLUSION: A two-stage machine learning framework that integrates genetic and clinical information can support personalised screening for type 2 diabetes across diverse populations. The approach demonstrated robust performance across cohorts and offers a practical structure for early risk identification.