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2023 Grantee: Wansu Chen, PhD
Kaiser Permanente Southern California Department of Research & Evaluation
Research Project: Recent-onset Hyperglycemia and the Risk of Pancreatic Cancer: An Enhancement of the ENDPAC Model
Award: 2023 Pancreatic Cancer Action Network Catalyst Research Award funded in memory of Daniel J. Kobasic
Award Period: October 1, 2022 – October 1, 2023
Amount: $225,000
Biographical Highlights
Dr. Wansu Chen is a Research Scientist at Kaiser Permanente Southern California Department of Research & Evaluation. Dr. Chen’s primary focus is on the application of biomedical informatics methods in clinical research, particularly devoting her efforts to the early detection of pancreatic cancer. In this field, Dr. Chen has applied machine learning approaches to develop and to validate risk prediction models for pancreatic cancer. Additionally, Dr. Chen has been leading efforts on using radiomic analyses of images of the pancreas. This promising work focuses on quantifying pancreatic features and then leveraging them to predict pancreatic cancer.
Dr. Chen previously received an Early Detection Research Project Grant from PanCAN in 2021.
Project Overview
Dr. Chen’s project will complement PanCAN’s Early Detection Initiative, which is exploring the connection between changes in blood sugar and weight, and pancreatic cancer in patients aged 50 to 85. The Early Detection Initiative currently uses changes in a person’s fasting blood glucose (sugar) level as a parameter to calculate what is known as an ENDPAC score. ENDPAC stands for Enriching New-Onset Diabetes for Pancreatic Cancer, which is a model that Dr. Suresh Chari, principal investigator of the Early Detection Initiative, developed when he was at Mayo Clinic to predict the risk of pancreatic cancer in patients with new-onset diabetes or prediabetes. In the original study that validated the ENDPAC model, researchers identified patients at high risk for pancreatic cancer primarily by changes in their fasting blood glucose levels.
However, major health systems have increasingly shifted practice towards use of A1C measurement for the diagnosis and management of diabetes in routine clinical practice. One reason for this shift is because A1c was included as a parameter for diagnosing and managing diabetes in the diagnostic criteria released by the American Diabetes Association in 2010. Another challenge of the glucose-based model is the need for a confirmatory abnormal lab test or diabetes treatment which significantly limits the size of the population that may benefit from screening. In the current study, a detailed analysis is planned to increase our understanding of how fasting blood glucose and A1c values relate back to the pancreatic cancer risk prediction model used in the Early Detection Initiative, as well as reexamining the blood sugar or A1c levels used to identify patients for the study.
Previous work by Dr. Chen’s group has shown that new-onset elevated blood sugar based on changes in A1c levels can be an effective way to measure patients’ pancreatic cancer risk. Now, the team seeks to understand how using A1c as a parameter to calculate ENDPAC scores could improve our ability to identify patients who are most at risk for pancreatic cancer and further enhance the risk prediction model used for the Early Detection Initiative.
