Abstract
New-onset diabetes (NOD) has emerged as a potential early indicator of pancreatic cancer (PC), necessitating a refined clinical approach for risk assessment and early detection. This study discusses critical gaps in understanding the NOD-PC relationship and proposes a multifaceted approach to enhance early detection and risk assessment. We present a comprehensive clinical workflow for evaluating NOD patients, incorporating biomarker discovery, genetic screening, and AI-driven imaging to improve PC risk stratification. While existing models consider metabolic factors, they often overlook germline genetic predispositions that may influence disease development. We propose integrating germline genetic testing to identify individuals carrying pathogenic variants in cancer-susceptibility genes (CSGs), enabling targeted surveillance and preventive interventions. To advance early detection, biomarker discovery studies must enroll diverse patient populations and utilize multi-omics approaches, including genomics, proteomics, and metabolomics. Standardized sample collection and AI-based predictive modeling can refine risk assessment, allowing for personalized screening strategies. To ensure reproducibility, a multicenter research approach is essential for validating biomarkers and integrating them with clinical data to develop robust predictive models. This multidisciplinary strategy, uniting endocrinologists, oncologists, geneticists, and data scientists, holds the potential to revolutionize NOD-PC risk assessment, enhance early detection, and pave the way for precision medicine-based interventions. The anticipated impact includes improved early detection, enhanced predictive accuracy, and the development of targeted interventions to mitigate PC risk.