Peking University First Hospital

In 24,638 UK Biobank participants with prediabetes, LASSO and elastic net selection of nuclear magnetic resonance metabolomic markers yielded 16 biomarkers, and k-means clustering in the training set (12,322 participants) identified low-, intermediate-, and high-metabolic-risk subtypes that showed progressively higher risks of type 2 diabetes, cardiovascular disease, and chronic kidney disease in the validation set (12,316 participants), with diet-quality associations differing across subtypes and Mendelian randomization suggesting potential causal links for several metabolomic biomarkers.

In 24,638 UK Biobank participants with prediabetes, LASSO and elastic net selection of nuclear magnetic resonance metabolomic markers yielded 16 biomarkers, and k-means clustering in the training set (12,322 participants) identified low-, intermediate-, and high-metabolic-risk subtypes that showed progressively higher risks of type 2 diabetes, cardiovascular disease, and chronic kidney disease in the validation set (12,316 participants), with diet-quality associations differing across subtypes and Mendelian randomization suggesting potential causal links for several metabolomic biomarkers.

This review states that, with improved computational resources and advances in artificial intelligence, current digital twins (DT) can integrate multi-omics data through hybrid mechanism- and data-driven models, enabling personalized simulation of patients, organs, and cells, which makes DT application in drug evaluation and drug repurposing discovery possible; the authors review current and emerging DT applications across the drug evaluation continuum, propose a staged development roadmap, and further highlight pivotal challenges that must be addressed to realize DT's full potential in drug evaluation, while noting that practical and regulatory issues have also emerged amid rapid development.

This review states that, with improved computational resources and advances in artificial intelligence, current digital twins (DT) can integrate multi-omics data through hybrid mechanism- and data-driven models, enabling personalized simulation of patients, organs, and cells, which makes DT application in drug evaluation and drug repurposing discovery possible; the authors review current and emerging DT applications across the drug evaluation continuum, propose a staged development roadmap, and further highlight pivotal challenges that must be addressed to realize DT's full potential in drug evaluation, while noting that practical and regulatory issues have also emerged amid rapid development.