Tamm-Horsfall protein biosensors for management of nephrolithiasis

Nephrolithiasis is a common urological condition characterized by kidney stone formation and complex metabolic imbalances. Uromodulin (Tamm-Horsfall protein, THP) is the principal urinary glycoprotein and a promising risk-stratifying biomarker and long-term follow-up marker for nephrolithiasis. This review examines the dual action of THP in stone disease. The normal glycosylated form of THP can inhibit the formation of calcium oxalate crystals, whereas structurally modified or hypoglycosylated THP can promote crystal adhesion and aggregation. We provide a critical comparison between conventional immunoassays and emerging THP biosensors with respect to analytical performance, selectivity, and preanalytical requirements for the measurement of urine with reasonable reliability. Electrochemical and optical biosensors, as well as nanomaterials, are increasingly being used in biosensors, with graphene, gold nanoparticles (AuNPs), and tantalum oxide. Non-Faradaic impedance (Ta₂O₅-passivated interdigitated electrodes) has been demonstrated to detect a 0.5 ng/mL LOD in artificial urine, and printed impedance sensors and AuNP-based lateral-flow formats have a typical LOD of 25 to 80 ng/mL and have been demonstrated using minimally processed urine (e.g., centrifugation and/or dilution), instead of crude samples. Since urinary THP is usually in the mg L⁻¹ range, sub-ng/mL sensitivity cannot be understood in standard terms of detectability, but in terms of special use-cases (e.g., highly diluted samples, low-THP phenotypes, or quantitative low-end monitoring), it can be detected. In conclusion, we examined several significant limitations to translation, including biological variability (such as hydration status, infection, and circadian variations), the influence of the matrix, the lack of calibration and traceability, and the imperative for prospective clinical validation. Although microfluidics and digital presentation facilitate point-of-care tracking, the integration of AI/ML is not yet prevalent.