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Since ancient times, physicians have studied the properties of urine to provide clues to the nature of the their patients’ maladies. For example, ancient physicians knew that urine from patients with diabetes is sweet to the taste. The discoveries of such properties were obviously made before disease mechanisms were understood. In modern times, clinicians have added many measurements of urinary composition to provide data for clinical decision-making, relying on growing knowledge of physiology and pathophysiology to identify candidate chemical biomarkers that have been validated for clinical use.Now, aided by methods capable of quantifying thousands of proteins in a urine sample, urinary biomarker discovery has gone “open loop,” allowing the identity of urine proteins that have no known relation to a disease as potential biomarkers for that disease. In such studies, correlation is sought rather than causation. Although this open-loop approach has great promise for accelerated urine biomarker discovery, the lack of connection to pathophysiologic knowledge is a significant limitation.The shift to an open-loop (“unbiased”) approach has been driven largely by the development of modern mass spectrometry techniques that are capable of identifying and quantifying proteins and peptides in large numbers.1 Indeed, recent studies from various laboratories identified more than 1000 proteins present in various biochemical fractions of urine, including urinary exosomes (for a listing of urinary proteins, go to “Urinary Exosome Protein Database” via any internet search engine).2–6At this point, organizations such the Human Kidney and Urine Proteome Project7 and the European Network for Urine and Kidney Proteomics8 have assembled largely for the purpose of promoting efforts in urinary protein biomarker discovery, and articles addressing various aspects of urinary biomarker discovery have been published. It can only be a short time before large-scale studies evaluating the efficacy of new candidate urinary …

Common Sense Approaches to Urinary Biomarker Study Design