Abstract Urinary proteomics has swiftly emerged as a formidable tool for the identification of non-invasive biomarkers and the surveillance of diseases. The progression in high-resolution mass spectrometry and data-independent acquisition techniques has facilitated the urinary proteome in providing detailed insights into intricate pathophysiological processes impacting multiple organ systems. This review synthesizes the recent advancements in urinary proteomics, detailing the analytical methodologies utilized, the challenges associated with standardization, and the normalization strategies crucial to the discipline. We undertake a comparative analysis of data-dependent and data-independent acquisition methodologies and examine their complementary roles in clinical workflows for biomarker discovery and translation. Additionally, we highlight both common and disease-specific proteomic signatures across a spectrum of disorders, including oncological, renal, cardiovascular, metabolic, and neurodegenerative diseases. We also investigate the role of artificial intelligence and multi-omics integration in supporting predictive modeling. Lastly, we discuss the ongoing developments in regulatory and implementation frameworks, such as data privacy regulations and clinical validation standards, that are positioning urinary proteomics as a key component of preventive and precision medicine.
Meng et al. (2026) studied this question.