Background: Patients with new-onset diabetes are known to be at a higher risk of developing pancreatic cancer. The Enriching New-Onset Diabetes for Pancreatic Cancer (ENDPAC) model was recently developed to identify new-onset diabetics with this higher risk. Further validation is needed before the ENDPAC model is implemented as part of a screening program to identify pancreatic cancer.
Methods: A retrospective case-control study was performed; a cohort of patients with new-onset diabetes was identified using hemoglobin A1c. Patients were scored by the ENDPAC model and then divided based on whether pancreatic cancer was diagnosed after the diagnosis of diabetes. The performance of the model was assessed globally and at different cutoffs.
Results: There were 6254 controls and 48 cases of pancreatic cancer. Bivariate analysis showed that patients with pancreatic cancer lost weight before diagnosis while controls gained weight (-0.93 kg/m2 vs. 0.45 kg/m2, p < 0.00∗). Cases had a more significant increase in their HbA1C from one year before (1.3% vs. 0.82%, p = 0.02). Smoking and pancreatitis rates were higher in cases compared to controls (p < 0.00∗). The area under the curve (AUC) of the ENDPAC model was 0.72. A score >1 was the optimal cutoff. At this cutoff, the sensitivity was 56%, specificity was 75%, and pancreatic cancer prevalence increased from 0.78% at baseline to 1.7%.
Conclusion: The ENDPAC model was validated in an independent cohort of patients with new-onset diabetes.
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http://dx.doi.org/10.1016/j.pan.2021.02.001 | DOI Listing |
Ann Surg Oncol
January 2025
Hepato-Pancreato-Biliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Discov Oncol
January 2025
Department of Laboratory, the Second Hospital of Shanxi Medical University, No. 382, Wuyi Road, Taiyuan, 030001, Shanxi, People's Republic of China.
Background: Pancreatic cancer (PAC) has a complex tumor immune microenvironment, and currently, there is a lack of accurate personalized treatment. Establishing a novel consensus machine learning driven signature (CMLS) that offers a unique predictive model and possible treatment targets for this condition was the goal of this study.
Methods: This study integrated multiple omics data of PAC patients, applied ten clustering techniques and ten machine learning approaches to construct molecular subtypes for PAC, and created a new CMLS.
mSphere
January 2025
State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ningning Liu works in the field of fungal infection and cancer progression, with a particular focus on the mechanism of host-pathogen interaction. In this mSphere of influence article, he reflects on how papers entitled "The fungal mycobiome promotes pancreatic oncogenesis via activation of MBL," by B. Aykut, S.
View Article and Find Full Text PDFJ Hepatobiliary Pancreat Sci
January 2025
Department of Gastroenterology, Shizuoka General Hospital, Shizuoka, Japan.
Cureus
January 2025
Hepato-Pancreato-Biliary (HPB) Unit, University Hospital Southampton NHS Foundation Trust, Southampton, GBR.
Background The relationship between physical activity and incident pancreatic cancer is poorly defined, and the evidence to date is inconsistent, largely due to small sample sizes and insufficient incident outcomes. Using the UK Biobank cohort dataset, the association between physical activity levels at recruitment and incident pancreatic ductal adenocarcinoma (PDAC) at follow-up was analysed. Method Physical activity, the key exposure, was quantified using Metabolic Equivalent Task (MET) values and categorised into walking, moderate, and vigorous activity.
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