Proteome atlas for mechanistic discovery and risk prediction of diabetic retinopathy

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract

• Effect Size: Top proteins: plexin B2, growth differentiation factor 15, and renin

• Primary Outcome: 668 protein associations with diabetic retinopathy identified

• Nnt Or Sensitivity: Parsimonious 3-protein panel achieves comparable predictive performance to full proteomic profile

• Confidence Interval: Not reported in abstract

Clinical Application

Proteomic testing may be feasible in specialist centres but cost-effectiveness needs evaluation Could complement existing RACGP diabetes management guidelines and PBS-funded DR screening programs, particularly for high-risk patients Adults with diabetes or prediabetes requiring DR screening

Abstract

Proteomics offers an unprecedented opportunity to characterize and predict diabetic retinopathy (DR) with minimal invasiveness. Here we examine this in 10,873 individuals with (pre)diabetes from two ethnically distinct cohorts. By simultaneous profiling of ~3000 proteins, we identify 668 associations with mechanistically plausible directionality that constitute a comprehensive DR proteomic landscape with linkages to retinal tomographic structure and genetic predisposition, pointing to established and novel biological pathways conferring DR risk. Integrating DR proteomic profile markedly improves predictive performance beyond clinical and genetic predictors, with plexin B2, growth differentiation factor 15, and renin emerging as top proteins validated across cohorts and linked to retinal microvascular degeneration in Guangzhou Diabetic Eye Study (GDES) based on SS-OCTA. A parsimonious panel of these three proteins alone achieves comparable performance in predicting DR development and progression, while renin is confirmed as a causal promoter through genetic analyses. Our findings highlight the potential of large-scale proteomics in elucidating DR pathogenesis and advancing biomarker discovery, with broad implications for early detection and intervention.

References

1. 1.Yang, S., Xin, Z., Xiong, R., Zhu, Z., Li, H., Chen, Y., Zhong, Z., Du, L., Zhu, L. Z., Shang, X., Huang, W., Zhang, L., Chen, S., He, C., Tan, S., He, M., Congdon, N., Jonas, J. B., Tham, Y.-C., Cheng, C.-Y., & Wang, W. (2025). Proteome atlas for mechanistic discovery and risk prediction of diabetic retinopathy. Nature Communications. https://doi.org/10.1007/s00125-020-05134-3