Min Li, Xiaohui Liao, Jingyang Ran
Acute kidney injury (AKI) is a prevalent critical clinical condition primarily characterized by a sharp deterioration of renal function within 48 hours. Accumulating clinical evidence has demonstrated that AKI predisposes patients to multisystem complications, among which renal-brain axis dysfunction is well recognized. This condition manifests as delirium, coma and stroke, also elevates the risk of chronic cognitive impairment, dementia and depression. Such complications not only increase short-term all-cause mortality but also severely impair the long-term quality of life in survivors, thereby placing a substantial socioeconomic burden on global healthcare systems. Current mechanistic investigations into AKI-associated brain dysfunction have predominantly centered on canonical pathological pathways, including systemic inflammatory response, oxidative stress, hippocampal neuronal injury, uremic toxin accumulation and others. Emerging evidence indicates that insulin resistance (IR) is highly prevalent in AKI patients and closely correlated with the pathophysiological progression of AKI. We will discuss the specific role of IR in AKI-induced cognitive impairment. Additionally, this review highlights the prospective application of combined multimodal neuroimaging and machine learning algorithms. By integrating readily available multidimensional clinical data, the establishment of early risk prediction models for AKI-related brain dysfunction can facilitate timely identification and precise intervention for high-risk populations. Furthermore, we elaborate the therapeutic potential of targeted anti-inflammatory strategies and natural bioactive compounds in ameliorating AKI-associated brain dysfunction. Collectively, this review provides novel insights for the development of optimized, safe and effective clinical interventions in the future.