Biomarker-driven risk stratification and early intervention in acute kidney injury: a comprehensive review from early warning to clinical response

Acute kidney injury (AKI) is a prevalent clinical syndrome in critically ill patients and is associated with adverse outcomes. Early detection has long relied on functional indicators, such as serum creatinine and urine output, which are inherently delayed. Recently, a growing body of biomarkers reflecting tubular structural injury, cellular stress and cell cycle arrest, inflammatory and immune activation, as well as metabolic and oxidative stress, has demonstrated utility in detecting subclinical kidney injury before overt functional deterioration. These biomarkers provide a novel biological basis for early AKI warning and risk stratification. Advances in continuous monitoring, time-series analysis, and artificial intelligence-based methods, the integration of multidimensional biological signals with dynamic clinical information has driven a paradigm shift in AKI early-warning research from static prediction toward dynamic risk assessment. This review synthesizes the mechanistic stratification of AKI biomarkers and their translational pathways within a closed-loop framework of early warning and clinical response. It focuses on integrated applications in Kidney Disease: Improving Global Outcomes-guided intervention strategies, individualized hemodynamic optimization, and multidisciplinary collaborative management. Furthermore, it analyzes challenges relating to standardized implementation, clinical heterogeneity, and real-world translation. The clinical value of AKI biomarkers extends beyond early risk identification; they function as triggers for structured clinical intervention pathways, facilitating a systematic linkage between risk assessment and therapeutic response, which promotes a transition of AKI management toward a mechanism-driven and prospective care paradigm.