Last updated:
ID:
785434
Start date:
13 June 2025
Project status:
Current
Principal investigator:
Mr Yong Jiang
Lead institution:
Guangdong Provincial People's Hospital, China

Research Questions: Pancreatic cancer is one of the most aggressive cancers, with a poor prognosis due to late-stage diagnosis. Early detection when the cancer is still resectable is critical. Recent research suggests pancreatogenic diabetes (T3cDM), a diabetes subtype caused by pancreatic damage, could be an early warning signal for pancreatic cancer. Clinical evidence shows that 80% of pancreatic cancer patients develop hyperglycemia 2-3 years before diagnosis, with most cases meeting diagnostic criteria for T3cDM.
Objective: Our study will leverage UK Biobank’s genomic data to investigate genetic variants specific to T3cDM and their association with pancreatic cancer. By uncovering the biological links, including genetic risk loci and molecular markers, we aim to establish the foundation for using T3cDM as an early detection marker. These findings could help identify high-risk individuals and enable early intervention during treatable stages. This study builds on our previous work using UK Biobank data to innovate further.
Scientific Rationale: Pancreatic cancer has a five-year survival rate below 10%, primarily due to late-stage diagnosis, with 85% of cases deemed unresectable at presentation. The lack of effective screening underscores the urgency of early detection strategies. Pancreatogenic diabetes (T3cDM), resulting from pancreatic dysfunction rather than insulin resistance, has emerged as a potential early sign. Evidence indicates that 50-80% of pancreatic cancer patients exhibit glucose metabolism abnormalities, with new-onset diabetes (NODM) increasing cancer risk 2- to 7-fold within 2-3 years. Notably, approximately 1% of NODM cases progress to pancreatic cancer within three years, supporting T3cDM as a paraneoplastic manifestation. This study seeks to identify genetic variants and metabolic alterations associated with T3cDM to refine early detection strategies and improve risk stratification in high-risk diabetic populations.