A circulating 12-protein score identified long-term risk of diabetic macrovascular complications and may support earlier risk detection. In a study published in Diabetes, Obesity and Metabolism, an integrated proteomic approach also highlighted biologic pathways linked to vascular disease progression in diabetes.

The analysis combined proteome-wide Mendelian randomization, Cox proportional hazards regression, and proteome-wide association study methods to prioritize candidate proteins. A machine learning-derived protein risk score was then developed from selected proteins. Functional enrichment, multi-omics integration, longitudinal trajectory modeling, and phenome-wide Mendelian randomization analyses were also performed.

A total of 43 proteins were prioritized and clustered in pathways related to immune-inflammatory cascades and extracellular matrix remodeling. The resulting 12-protein panel showed robust discrimination for diabetic macrovascular complications with an area under the curve (AUC) of 0.793. Performance remained stable across 15 years and provided clear risk stratification.

Longitudinal analyses indicated that protein perturbations emerged 10 to 12 years before clinical onset. Multi-omics integration showed synchronized associations with cardiometabolic dysregulation and cardiac structural changes. Phenome-wide Mendelian randomization supported pleiotropic associations across multiple disease categories and provided causal support for several prioritized proteins.

The findings suggest that integrated proteomic profiling may help identify future macrovascular risk in diabetes while improving understanding of underlying disease pathways.