Exploring the Kidney-Brain Crosstalk: Biomarkers for Early Detection of Kidney Injury-Related Alzheimer's Disease

J Inflamm Res. 2025 Jan 18:18:827-846. doi: 10.2147/JIR.S499343. eCollection 2025.

Abstract

Background: The phenomenon of "kidney-brain crosstalk" has stimulated scholarly inquiry into the correlations between kidney injury (KI) and Alzheimer's disease (AD). Nonetheless, the precise interactions and shared mechanisms between KI and AD have yet to be fully investigated. The primary goal of this study was to investigate the link between KI and AD, with a specific focus on identifying diagnostic biomarkers for KI-related AD.

Methods: The first step of the present study was to use Mendelian randomization (MR) analysis to investigate the link between KI and AD, followed by verification of in vivo and in vitro experiments. Subsequently, bioinformatics and machine learning techniques were used to identify biomarkers for KI-associated ferroptosis-related genes (FRGs) in AD, which were validated in following experiments. Moreover, the relationship between hub biomarkers and immune infiltration was assessed using CIBERSORT, and the potential drugs or small molecules associated with the core biomarkers were identified via the DGIdb database.

Results: MR analysis showed that KI may be a risk factor for AD. Experiments showed that the combination of D-galactose and aluminum chloride was found to induce both KI and AD, with ferroptosis emerging as a bridge to facilitate crosstalk between KI and AD. Besides, we identified EGFR and RELA have significant diagnostic value. These biomarkers are associated with NK_cells_resting and B_cells_memory and could be targeted for intervention in KI-related AD by treating gefitinib and plumbagin.

Conclusion: Our study elucidates that ferroptosis may be an important pathway for kidney-brain crosstalk. Notably, gefitinib and plumbagin may be therapeutic candidates for intervening in KI-associated AD by targeting EGFR and RELA.

Keywords: Alzheimer’s disease; bioinformatics; ferroptosis; kidney injury; kidney-brain crosstalk; machine learning.

Grants and funding

The National Natural Science Foundation of China (No. 82201220), the Natural Science Foundation of Jiangsu Province (No. BK20190149), the Postdoctoral Science Foundation of China (No. 2020M681669), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX24_2248) all provided support for the work.