Serum and urine samples from patients with type 2 diabetes mellitus and control samples were analyzed by UPLC-TOF-MS; fast and slow separation gradients were compared using both positive and negative ionization modes. The resulting data were analyzed using partial least squares discriminant analysis (PLS-DA), and models were developed to differentiate between patient and control samples. The models were evaluated using external test sets to classify their predictive ability. Under both fast and slow gradient conditions, the PLS-DA models generated using serum samples were more robust than those generated using urine samples, and the positive ionization mode produced better differentiation and higher classification rates than negative ionization mode. In addition, fast gradient conditions were found to have a comparable ability for differentiation to slow gradient conditions.