Improving the reliability of polygenic risk score-based prediction for cardiovascular and renal complications across ancestries in type 2 diabetes using Mondrian Cross-Conformal Prediction

Polygenic risk scores (PRS) developed in European populations often show reduced predictive performance in non-European populations, limiting their clinical utility. This lack of transferability across ancestries remains a major challenge in genomic medicine and raises concerns about health equity. We aimed to evaluate whether uncertainty-aware prediction, implemented through Mondrian Cross-Conformal Prediction, improves the performance and reliability of polygenic risk score-based predictions across ancestries for nephropathy, stroke, and myocardial infarction in individuals with type 2 diabetes in a multi-ethnic cohort. We leveraged Mondrian Cross-Conformal Prediction (MCCP), an uncertainty quantification framework, combined with logistic regression applied to a multi-polygenic risk score (multiPRS) to predict the risk of nephropathy, stroke, and myocardial infarction in individuals with type 2 diabetes. Two training frameworks were evaluated: one using 4,098 individuals with type 2 diabetes of European ancestry from the ADVANCE trial for training and 17,574 White British, 1,145 South Asian, and 749 African UK Biobank participants for testing; and another using the 17,574 White British UK Biobank participants for training and the South Asian and African participants for testing. Logistic regression provided robust baseline performance across populations. On top of this baseline, MCCP did not improve performance but added capabilities absent from probability-based stratification: for each individual, it issued a prediction together with an explicit confidence and credibility level; it allowed a tolerated error level to be set in advance and delivered prediction sets respecting it in the majority of settings; and it flagged individuals for whom no reliable prediction could be made. Applying MCCP to PRS-based prediction thus enables uncertainty-aware risk stratification and improves the reliability of risk prediction across ancestries, providing a more equitable framework for clinical use.Author summary: Type 2 diabetes is a major global health concern, often leading to serious complications such as heart disease, stroke, and kidney failure. Identifying individuals at high risk of developing these complications is essential for improving prevention and treatment strategies. Polygenic risk scores, which use genetic information to estimate disease risk, have shown promise but are typically developed using data from individuals of European ancestry. As a result, their performance is often reduced in other populations, raising concerns about their clinical applicability and fairness. In this study, we showed that incorporating a method that accounts for prediction uncertainty can improve the reliability of genetic risk prediction across diverse populations. Using data from large clinical and population-based cohorts, we show that this approach allows the acceptable error level to be set in advance and helps identify individuals for whom the model is less certain. This additional information may support more informed clinical decision-making. Our findings highlight a potential strategy to improve the equitable use of genetic risk prediction in multi-ethnic populations.