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This study investigates the potential of a parallel dual secondary column-dual detection two-dimensional comprehensive GC platform (GC×2GC-MS/FID) for metabolic profiling and fingerprinting of mouse urine. Samples were obtained from a murine model that mimics a typical unhealthy Western diet featuring both high fat and sugar (HFHS) intake, which induces obesity, dyslipidemia, and insulin resistance. Urines collected at different steps of the study were used to obtain pivotal and comparative data on the presence and relative distributions of early markers of metabolic disease. The data elaboration and interpretation work-flow includes an advanced untargeted fingerprinting approach, with peak-region features to locate relevant features to be quantified by external standard calibration. The reliability of untargeted fingerprinting is confirmed by quantitative results on selected relevant features that showed percentages of variations consistent with those observed by comparing raw data qu antitative descriptors (2D peak-region volumes and percent of response). Analytes that were up-regulated with % of variation ranging from 30 to 1000, included pyruvic acid, glycerol, fructose, galactose, glucose, lactic acid, mannitol and valine. Down-regulation was evidenced for malonic acid, succinic acid, alanine, glycine, and creatinine. Advanced fingerprinting also is demonstrated for effectively evaluating individual variations during experiments, thus representing a promising tool for personalized intervention studies. In this context, it is interesting to observe that informative features that were not discriminant for the entire population may be relevant for individuals.