A review of lifestyle, metabolic risk factors, and blood‐based biomarkers for early diagnosis of pancreatic ductal adenocarcinoma. Issue 2 (17th January 2019)

• Record Type: Journal Article
• Authors: Pang, Yuanjie; Holmes, Michael V; Chen, Zhengming; Kartsonaki, Christiana
• Is Part Of: Journal of gastroenterology and hepatology. Volume 34:Issue 2(2019)
• Page Start: 330
• Page End: 345
• Publication Date: 2019-01-17
• Subjects: biomarkers -- early diagnosis -- metabolomics -- pancreatic ductal adenocarcinoma -- proteomics -- risk factors; Gastroenterology -- Periodicals; Digestive organs -- Diseases -- Periodicals; Liver -- Diseases -- Periodicals; Gastroenterology -- Periodicals; Liver Diseases -- Periodicals; 616.33
• DOI: 10.1111/jgh.14576
• Languages: English
• ISSNs: 0815-9319

Abstract

Abstract: We aimed to review the epidemiologic literature examining lifestyle and metabolic risk factors, and blood‐based biomarkers including multi‐omics (genomics, proteomics, and metabolomics) and to discuss how these predictive markers can inform early diagnosis of pancreatic ductal adenocarcinoma (PDAC). A search of the PubMed database was conducted in June 2018 to review epidemiologic studies of (i) lifestyle and metabolic risk factors for PDAC, genome‐wide association studies, and risk prediction models incorporating these factors and (ii) blood‐based biomarkers for PDAC (conventional diagnostic markers, metabolomics, and proteomics). Prospective cohort studies have reported at least 20 possible risk factors for PDAC, including smoking, heavy alcohol drinking, adiposity, diabetes, and pancreatitis, but the relative risks and population attributable fractions of individual risk factors are small (mostly < 10%). High‐throughput technologies have continued to yield promising genetic, metabolic, and protein biomarkers in addition to conventional biomarkers such as carbohydrate antigen 19‐9. Nonetheless, most studies have utilized a hospital‐based case–control design, and the diagnostic accuracy is low in studies that collected pre‐diagnostic samples. Risk prediction models incorporating lifestyle and metabolic factors as well as other clinical parameters have shown good discrimination and calibration. Combination of traditional risk factors, genomics, and blood‐based biomarkers can help identify high‐risk populations and inform clinical decisions. Multi‐omics investigations can provide valuable insights into disease etiology, but prospective cohort studies that collect pre‐diagnostic samples and validation in independent studies are warranted.