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Integrative Proteome- and Phenome-Wide Assessment Uncovers Causal Protein Drivers and Drug Targets for Heterogeneous Kidney Diseases

Triozzi, J. L.; Mamak, F.; Wilson, O. D.; Chen, H.-C.; Yu, Z.; Gravel-Pucillo, K.; Ferolito, B. R.; Cho, K.; Gaziano, J. M.; Muralidhar, S.; Ikizler, T. A.; Robinson-Cohen, C.; Giri, A.; Tao, R.; Pereira, A. C.; Hung, A. M.

2025-10-10 genetic and genomic medicine
10.1101/2025.10.09.25337438 medRxiv
Show abstract

Interpreting proteomic associations with chronic kidney disease (CKD) is challenging due to the diseases clinical heterogeneity and complex overlap with systemic conditions. We present a framework that identifies causal circulating protein drivers of CKD and delineates their subtype- specific and systemic effects using electronic health record (EHR) data at biobank scale. Using proteome-wide Mendelian randomization, we instrumented cis-acting protein quantitative trait loci for 2,807 circulating proteins and tested them against detailed, EHR-based kidney function outcomes in 464,631 Million Veteran Program participants. Proteins were mapped to nine kidney disease subtypes defined by genome-wide association meta-analyses from the Million Veteran Program, UK Biobank, and FinnGen. Phenome-wide association studies across 1,020 traits distinguished renal versus extra-renal associations. This integrative strategy prioritizes 93 proteins with proteome-wide significance for kidney outcomes, demonstrates subtype-specific relevance, exposes systemic associations, and maps therapeutic targets to nominate candidates for drug development and repurposing.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.