Flow injection-traveling-wave ion mobility-mass spectrometry (FI-TWIM-MS) was applied to the nontargeted metabolic profiling of serum extracts from 61 prostate-cancer (PCa) patients and 42 controls with an analysis speed of 6 min per sample, including a 3 min wash run. Comprehensive data mining of the mobility-mass domain was used to discriminate species with various charge states and filter matrix salt-cluster ions. Specific criteria were developed to ensure correct grouping of adducts, in-source fragments, and impurities in the data set. Endogenous metabolites were identified with high confidence using FI-TWIM-MS/MS and collision-cross-section (CCS) matching with chemical standards or CCS databases. PCa patient samples were distinguished from control samples with good accuracies (88.3-89.3%), sensitivities (88.5-90.2%), and specificity (88.1%) using supervised multivariate classification methods. Although largely underutilized in metabolomics studies, FI-TWIM-MS proved advantageous in terms of analysis speed, separation of ions in complex mixtures, improved signal-to-noise ratio, and reduction of spectral congestion. Results from this study showcase the potential of FI-TWIM-MS as a high-throughput metabolic-profiling tool for large-scale metabolomics studies.