Moreland Russell, Arredondo Abigail, Dhasmana Anupam, Dhasmana Swati, Shabir Shabia, Siddiqua Asfia, Banerjee Bonny, Yallapu Murali M, Behrman Stephen W, Chauhan Subhash C, Khan Sheema
Department of Immunology and Microbiology, School of Medicine, University of Texas Rio Grande Valley, McAllen, TX, United States.
South Texas Center of Excellence in Cancer Research, School of Medicine, The University of Texas Rio Grande Valley, McAllen, TX, United States.
Front Pharmacol. 2025 May 23;16:1543112. doi: 10.3389/fphar.2025.1543112. eCollection 2025.
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.
新发糖尿病（NOD）已成为胰腺癌（PC）潜在的早期指标，因此需要一种完善的临床方法来进行风险评估和早期检测。本研究讨论了在理解NOD与PC关系方面的关键差距，并提出了一种多方面的方法来加强早期检测和风险评估。我们提出了一个评估NOD患者的全面临床工作流程，纳入生物标志物发现、基因筛查和人工智能驱动的成像技术，以改善PC风险分层。虽然现有模型考虑了代谢因素，但它们往往忽略了可能影响疾病发展的种系遗传易感性。我们建议整合种系基因检测，以识别携带癌症易感基因（CSG）致病变异的个体，从而实现有针对性的监测和预防性干预。为了推进早期检测，生物标志物发现研究必须纳入多样化的患者群体，并采用多组学方法，包括基因组学、蛋白质组学和代谢组学。标准化的样本采集和基于人工智能的预测模型可以优化风险评估，从而制定个性化的筛查策略。为确保可重复性，多中心研究方法对于验证生物标志物并将其与临床数据整合以开发强大的预测模型至关重要。这种多学科策略将内分泌学家、肿瘤学家、遗传学家和数据科学家联合起来，有可能彻底改变NOD-PC风险评估，加强早期检测，并为基于精准医学的干预措施铺平道路。预期影响包括改善早期检测、提高预测准确性，以及开发有针对性的干预措施以降低PC风险。
