Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer

Targeting early diagnosis and treatment of pancreatic cancer among the diabetic population: a comprehensive review of biomarker screening strategies

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by challenging early diagnosis, limited therapeutic options, and a poor prognosis. Diabetes mellitus, marked by altered glucose metabolism, has emerged as a significant risk factor for PDAC development, highlighting a complex, bidirectional pathogenic relationship. This review systematically examines the intricate interactions between diabetes and PDAC, emphasizing their shared pathophysiological mechanisms. A comprehensive understanding of these mechanisms can inform the development of targeted therapeutic strategies, potentially improving patient outcomes by concurrently managing diabetes and pancreatic cancer. We further evaluate current biomarker screening approaches for PDAC within diabetic subpopulations, assess the effectiveness of screening programs among high-risk groups, and propose practical strategies for the early identification and monitoring of PDAC. Early detection in diabetic individuals through targeted biomarker screening followed by timely therapeutic intervention may significantly reduce mortality, improve survival rates, and extend patient longevity. In conclusion, an integrated approach combining early diagnosis, targeted treatments, and a detailed understanding of the underlying pathogenesis represents the most promising strategy for enhancing clinical outcomes and survival among diabetic patients diagnosed with pancreatic cancer.

The exposome and pancreatic cancer, lifestyle and environmental risk factors for PDAC

Pancreatic cancer (PC), particularly pancreatic ductal adenocarcinoma (PDAC), is a significant global health issue with high mortality rates. PDAC, though only 3 % of cancer diagnoses, causes 7 % of cancer deaths due to its severity and asymptomatic early stages. Risk factors include lifestyle choices, environmental exposures, and genetic predispositions. Conditions like new-onset type 2 diabetes and chronic pancreatitis also contribute significantly. Modifiable risk factors include smoking, alcohol consumption, non-alcoholic fatty pancreatic disease (NAFPD), and obesity. Smoking and heavy alcohol consumption increase PC risk, while NAFPD and obesity, particularly central adiposity, contribute through chronic inflammation and insulin resistance. Refined sugar and sugar-sweetened beverages (SSBs) are also linked to increased PC risk, especially among younger individuals. Hormonal treatments and medications like statins, aspirin, and metformin have mixed results on PC risk, with some showing protective effects. The gut microbiome influences PC through the gut-pancreas axis, with disruptions leading to inflammation and carcinogenesis. Exposure to toxic substances, including heavy metals and chemicals, is associated with increased PC risk. Glycome changes, such as abnormal glycosylation patterns, are significant in PDAC development and offer potential for early diagnosis. Interactions between environmental and genetic factors are crucial in PDAC susceptibility. Genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs) linked to PDAC, but gene-environment interactions remain largely unexplored. Future research should focus on polygenic risk scores (PRS) and large-scale studies to better understand these interactions and their impact on PDAC risk.

Can AI Be Useful in the Early Detection of Pancreatic Cancer in Patients with New-Onset Diabetes?

Pancreatic cancer is one of the most lethal neoplasms. Despite considerable research conducted in recent decades, not much has been achieved to improve its survival rate. That may stem from the lack of effective screening strategies in increased pancreatic cancer risk groups. One population that may be appropriate for screening is new-onset diabetes (NOD) patients. Such a conclusion stems from the fact that pancreatic cancer can cause diabetes several months before diagnosis. The most widely used screening tool for this population, the ENDPAC (Enriching New-Onset Diabetes for Pancreatic Cancer) model, has not achieved satisfactory results in validation trials. This provoked the first attempts at using artificial intelligence (AI) to create larger, multi-parameter models that could better identify the at-risk population, which would be suitable for screening. The results shown by the authors of these trials seem promising. Nonetheless, the number of publications is limited, and the downfalls of using AI are not well highlighted. This narrative review presents a summary of previous publications, recent advancements and feasible solutions for effective screening of patients with NOD for pancreatic cancer.