Risk prediction models for macrovascular complications in type 2 diabetes mellitus: from traditional risk scores to multimodal intelligent prediction.

Macrovascular complications are the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). Early risk identification and accurate risk stratification are essential for both primary and secondary cardiovascular prevention. With the rapid development of electronic health records, medical imaging, multi-omics technologies, and artificial intelligence (AI), risk prediction models for diabetic macrovascular complications have evolved from traditional statistical approaches to machine learning-, deep learning-, and multimodal AI-driven models. However, despite substantial improvements in predictive performance, current models remain limited by heterogeneous target populations, inconsistent outcome definitions, insufficient external validation, limited clinical interpretability, and inadequate real-world implementation, thereby restricting their clinical translation. This review summarizes the evolution, data sources, modeling methodologies, clinical applications, and future perspectives of risk prediction models for macrovascular complications in T2DM. We first describe the transition from conventional cardiovascular risk scores, including the Framingham Risk Score, to diabetes-specific models such as the UKPDS Risk Engine, ADVANCE, RECODe, and SCORE2-Diabetes, followed by the emergence of AI-based prediction models. We then review the major data sources used in contemporary prediction models, including clinical characteristics, laboratory biomarkers, vascular function and imaging, multi-omics profiles, and wearable device-derived data. Furthermore, we compare the strengths and limitations of traditional statistical methods, machine learning, deep learning, and multitask learning with respect to predictive performance, interpretability, and clinical applicability, and discuss the major challenges related to study design, external validation, data quality, algorithmic bias, and real-world implementation. Finally, we propose that future risk prediction models for diabetic macrovascular complications will evolve from single-source data to multimodal data integration, from static risk estimation to dynamic risk trajectories, from single outcomes to multiple interconnected cardiovascular outcomes, from prognosis prediction to individualized treatment benefit prediction, and from standalone algorithms to continuously learning clinical decision support systems. Emerging technologies, including foundation models, large language models, digital twins, causal inference, and continuous learning frameworks, are expected to drive the transition from Risk Prediction to Clinical Decision and ultimately Patient Benefit, thereby advancing precision prevention and individualized management of diabetes.