For multivariate analysis, data sets were imported into the software SIMCA version 14 (Umetrics). Unit variance scaling was applied to give metabolite features with low and high variation between samples equal importance, and log transformation was applied to shift data towards Gaussian distribution. Principal component analysis (PCA) was initially applied to all variables extracted using XCMS (Supplementary Fig. 3). Subsequently was orthogonal partial least squares-discriminant analysis (OPLS-DA) (Bylesjo et al. 2006) with default SIMCA settings was used to identify metabolite feature patterns discriminating PDAC and CP. The features with the highest discriminatory power between classes were selected based on the variable importance for the projection (VIP) plot. Features significant with a 95% confidence interval, based on the cross- validation, were selected for further analysis. For the discovery cohort, the feature selection was done in the same way with the additional criterion that the VIP-values should be >1.5.