Proteomic insights of peripheral artery disease across the glycemic spectrum: a prospective cohort study
Metabolism, 2026
Yu H., Zhang J., Qian F., Zhu K., Qiu Z., Li R., Li L., Wang Y., Guo T., Zhao S., Che J., Tang Z., Li R., Xu K., Franco O., Geng T., Pan A., Liu G.
| Disease area | Application area | Sample type | Products |
|---|---|---|---|
Metabolic Diseases CVD | Patient Stratification | Plasma |  Olink Explore 3072/384 |
Abstract
Background
Peripheral artery disease (PAD) risk varies substantially across glycemic states, but glycemic status-specific proteomic features of PAD remain poorly characterized. This study aimed to provide comprehensive proteomic insights into PAD risk across the glycemic spectrum.
Methods
We included 43,875 UK Biobank participants, categorized into normoglycemia, prediabetes, and type 2 diabetes (T2D). Associations between 2920 plasma proteins and PAD were assessed using Cox regression models. PAD-related proteins underwent pathway enrichment and protein-protein interaction (PPI) analyses, and protein predictors were selected via least absolute shrinkage and selection operator models. The differential expression-sliding window analysis identified proteomic changes across the glycemic continuum.
Results
We identified 558 proteins associated with PAD risks, and those proteins were predominantly involved in pathways related to immune system regulation, inflammatory processes, and vascular remodeling. Two major PPI networks were identified, centered on tumor necrosis factor in normoglycemic participants and T-cell surface glycoprotein CD4 in those with T2D. The integration of protein predictors or derived protein risk scores into the clinical model significantly improved PAD prediction performance, achieving a maximum C-index of 0.834. Two proteomic peaks were revealed at glycated hemoglobin levels of 37 and 42 mmol/mol (5.5% and 6.0%), at which 11 and 4 proteins, respectively, showed potential causal associations with PAD.
Conclusion
This study revealed glycemic state-specific proteomic features of PAD risk. These findings suggest the involvement of innate immunity in normoglycemia, and adaptive immune dysregulation with chronic inflammation in T2D. Integrating proteomic data also improved PAD risk prediction. Further validation is warranted.