Integrated multi-omics decodes the AKI-to-CKD transition: from ensemble discovery to structure-guided translational targeting
Journal of Translational Medicine, 2026
Cao X., Xiao Y., Wang Y., Huang H., Kong W., Chen Z., Zheng Y., Li J., Li E., Gong Y., Yuan Q., Ge H., Xiao X.
| Disease area | Application area | Sample type | Products |
|---|---|---|---|
Nephrology | Pathophysiology Patient Stratification | Plasma |  Olink Explore 3072/384 |
Abstract
Background
The transition from acute kidney injury (AKI) to chronic kidney disease (CKD) constitutes a major global health burden. Identifying causal drivers remains challenging due to proteomic complexity and confounding biases. We aimed to construct a discovery framework to identify actionable targets driving this pathological conversion.
Methods
Among 53,014 UK Biobank participants with Olink proteomic data, 2,532 AKI patients (410 progression events) were identified and included in the subsequent predictive modeling. We developed a ‘triple-orthogonal screening strategy’, integrating an ensemble machine learning framework (six algorithms including LASSO and RF-RFE), Cox proportional hazards models, and two-sample Mendelian randomization (MR) to filter 2,923 proteins. Findings were validated via single-cell transcriptomics and a mouse model of ischemia-reperfusion injury characterized by paired bulk RNA-sequencing and TMT-based quantitative proteomics.
Results
We identified 25 key proteins possessing both statistical prognostic value and genetic support. An ensemble prediction model based on these features achieved an ROC-AUC of 0.939. Using LGALS3 as a paradigm, multi-omics validation revealed a unique ‘transcriptional-translational discordance’—characterized by sustained protein accumulation despite transcriptional silence—during the maladaptive repair phase (day 14). Furthermore, structural profiling identified a hotspot on LGALS3 (residues Glu185 and Gln187), where the binding pocket for the inhibitor olitigaltin and lactose (anhydrous) spatially converges with high-scoring B-cell epitopes.
Conclusions
We decoded the prioritized proteomic landscape of the AKI-to-CKD transition. This study establishes a framework integrating multi-layer prioritization with structure-guided validation, identifying pathogenic drivers characterized by transcriptional-translational discordance. This workflow provides atomic-level coordinates for precision therapeutic intervention against renal fibrosis.