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RationaleThe profiling of natural urinary peptides is a valuable indicator of kidney condition. While front‐end separation limits the speed of peptidomic profiling, MS1‐based results suffer from limited peptide coverage and specificity. Clinical studies on chronic kidney disease require an effective strategy to balance the trade‐off between identification depth and throughput.MethodsCKD273, a urinary proteome classifier associated with chronic kidney disease, in samples from diabetic nephropathy patients was profiled in parallel using capillary electrophoresis–mass spectrometry (CE–MS), liquid chromatography with mass spectrometry (LC–MS), and matrix‐assisted laser desorption/ionization–mass spectrometry (MALDI‐MS). Through cross‐comparison of results from MS1 of unfractionated peptides and elution‐time‐resolved MS1 as well as MS/MS in LC– and CE–MS approaches, we evaluated the contribution of false‐positive identification to MS1‐based identification and quantitation, and analyzed the benefit of front‐end separation in terms of accuracy and efficiency.ResultsIn LC– and CE–MS, although MS1 data resulted in higher number of identifications than MS/MS, elution‐time‐dependent analysis revealed extensive interference by non‐CKD273 peptides, which would contribute up to 50% to quantitation if they are not separated from genuine CKD273 peptides. In the absence of separation, MS1 data resulted in lower numbers of identifications and abundance pattern that significantly deviated from those by liquid chromatography with tandem mass spectrometry (LC–MS/MS) or capillary electrophoresis with tandem mass spectrometry (CE–MS/MS). CE showed higher identification efficiency even when less sample was used or achieved faster separation.ConclusionsTo ensure the reliability of MS1‐based urinary peptide profiling, front‐end separation should not be omitted, and elution time should be used in addition to intact mass for identification. Including MS/MS in data acquisition does not compromise the speed or identification number, while benefiting data reliability by providing real‐time sequence verification.

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