ABSTRACT
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with poor survival. Novel biomarkers are urgently needed to improve the outcome through early detection. Here, we aimed to discover novel biomarkers for early PDAC detection using multi-omics profiling in pre-diagnostic plasma samples biobanked after routine health examinations. A nested case-control study within the Northern Sweden Health and Disease Study was designed. Pre-diagnostic plasma samples from 37 future PDAC patients collected within 2.3 years before diagnosis and 37 matched healthy controls were included. We analyzed metabolites using liquid chromatography mass spectrometry and gas chromatography mass spectrometry, microRNAs by HTG edgeseq, proteins by multiplex proximity extension assays, as well as three clinical biomarkers using milliplex technology. Supervised and unsupervised multi-omics integration were performed as well as univariate analyses for the different omics types and clinical biomarkers. Multiple hypothesis testing was corrected using Benjamini-Hochberg's method and a false discovery rate (FDR) below 0.1 was considered statistically significant. Carbohydrate antigen (CA) 19-9 was associated with PDAC risk (OR [95 % CI] = 3.09 [1.31-7.29], FDR = 0.03) and increased closer to PDAC diagnosis. Supervised multi-omics models resulted in poor discrimination between future PDAC cases and healthy controls with obtained accuracies between 0.429-0.500. No single metabolite, microRNA, or protein was differentially altered (FDR < 0.1) between future PDAC cases and healthy controls. CA 19-9 levels increase up to two years prior to PDAC diagnosis but extensive multi-omics analysis including metabolomics, microRNAomics and proteomics in this cohort did not identify novel early biomarkers for PDAC.
ABSTRACT
Background: Pancreatic ductal adenocarcinoma (pancreatic cancer) is often detected at late stages resulting in poor overall survival. To improve survival, more patients need to be diagnosed early when curative surgery is feasible. We aimed to identify circulating metabolites that could be used as early pancreatic cancer biomarkers. Methods: We performed metabolomics by liquid and gas chromatography-mass spectrometry in plasma samples from 82 future pancreatic cancer patients and 82 matched healthy controls within the Northern Sweden Health and Disease Study (NSHDS). Logistic regression was used to assess univariate associations between metabolites and pancreatic cancer risk. Least absolute shrinkage and selection operator (LASSO) logistic regression was used to design a metabolite-based risk score. We used receiver operating characteristic (ROC) analyses to assess the discriminative performance of the metabolite-based risk score. Results: Among twelve risk-associated metabolites with a nominal P value <0.05, we defined a risk score of three metabolites [indoleacetate, 3-hydroxydecanoate (10:0-OH), and retention index (RI): 2,745.4] using LASSO. A logistic regression model containing these three metabolites, age, sex, body mass index (BMI), smoking status, sample date, fasting status, and carbohydrate antigen 19-9 (CA 19-9) yielded an internal area under curve (AUC) of 0.784 [95% confidence interval (CI): 0.714-0.854] compared to 0.681 (95% CI: 0.597-0.764) for a model without these metabolites (P value =0.007). Seventeen metabolites were significantly associated with pancreatic cancer survival [false discovery rate (FDR) <0.1]. Conclusions: Indoleacetate, 3-hydroxydecanoate (10:0-OH), and RI: 2,745.4 were identified as the top candidate biomarkers for early detection. However, continued efforts are warranted to determine the usefulness of these metabolites as early pancreatic cancer biomarkers.
ABSTRACT
OBJECTIVE: To determine the risk association between fasting glucose levels and pancreatic cancer using systematically collected prediagnostic blood glucose samples. METHODS: Prospective nested case-control study of participants from the Northern Sweden Health and Disease Study, including 182 cases that developed pancreatic cancer and four matched controls per case. Blood glucose levels collected up to 24 years before pancreatic cancer diagnosis were analyzed. The association between fasting glucose levels and pancreatic cancer risk was determined using unconditional and conditional logistic regression models. The association between fasting glucose and the time to pancreatic cancer diagnosis, tumor stage and survival was determined using likelihood-ratio test, t-test and log rank test. RESULTS: The unadjusted risk of developing pancreatic cancer increased with increasing fasting glucose levels (OR 1.30, 95% CI 1.05-1.60, P = .015). Impaired fasting glucose (≥6.1 mmol/L) was associated with an adjusted risk of 1.77 for developing pancreatic cancer (95% CI 1.05-2.99, P = .032). In subgroup analysis, fasting glucose levels were associated with an increased risk in never-smokers (OR 4.02, 95% CI 1.26-12.77, P = .018) and non-diabetics (OR 3.08, 95% CI 1.08-8.79, P = .035) (non-significant for interaction). The ratio between fasting glucose and BMI was higher among future pancreatic cancer patients and an increased ratio was associated with elevated risk of pancreatic cancer (OR 1.66, 95% CI 1.04-2.66, P = .034). Fasting glucose levels were not associated with TNM stage at diagnosis or survival. CONCLUSIONS: High fasting glucose is associated with an increased risk of being diagnosed with pancreatic cancer.
