Precision diabetes care through advanced clinical assessment biomarkers artificial intelligence and personalized therapeutics

There is a great heterogeneity in disease characteristics, progression, manifestations, and treatment efficacy that make population-level strategies based on diabetes mellitus ineffective. Precision medicine provides ways to tackle this disparity by combining clinical, genetic, molecular, metabolic and behavioural data with the goal of personalized diagnosis, risk stratification or choosing treatment. This review covers a comprehensive and clinically relevant assessment of personalized diabetes care and the emerging research areas of the associated biomarkers, genomics, transcriptomics, proteomics, metabolomics, epigenomics, pharmacogenomics, nutrigenomics, continuous glucose monitoring, wearable technologies, digital health and artificial intelligence-based decision support systems. Molecular characterization, patient stratification, personalised pharmacotherapy, lifestyle intervention, and long-term digital monitoring is integrated with these technologies rather than being considered separate entities. The main focus is on the clinical and/or translational implications of these strategies, such as their current evidence strength and their limitations, population diversity, external validation, cost-effectiveness, data interoperability, privacy and healthcare implementation. Combining the multi-omics and real-time digital information with traditional clinical evaluation could make it possible to better manage diabetes in a more predictive, adaptive, patient-centred manner. Yet, future validation, standardized analysis tools, access, and tools to be designed into standard clinical practice are still vital to bringing precision diabetes strategies from novel technologies into practical care.