Plasma proteomics reveals biomarkers and undulating changes of metabolic aging

Large-scale proteomics enables identification of biomarkers and undulations of metabolic aging. This study aimed to develop a metabolic age (MA) and identify proteomic biomarkers and their undulating changes during metabolic aging. Using UK Biobank data, MA was developed from mortality-associated metabolomic profiles (nuclear magnetic resonance platform) in 203,491 participants. Associations between 2923 plasma proteins (Olink Explore 3072 platform) and metabolic aging phenotypes, including MA, telomere length, frailty index, incident type 2 diabetes (T2D), cardiovascular disease (CVD), and mortality, were examined in 24,920 participants via Cox proportional hazards or linear models. Differential expression – sliding window analysis captured protein waves during metabolic aging in 7092 participants. MA improved predictions of mortality, CVD, and T2D beyond conventional risk factors (Cindex up to 0.786) and correlated strongly with chronological age (Spearman’s r: 0.876). Sixty proteins were associated with all metabolic aging phenotypes. Among them, GDF15, PLAUR, TNFRSF10A, TNFRSF10B, IFI30, HGF, WFDC2, COL6A3, PIGR, IGFBP4, and EDA2R ranked within the top 20 for at least four phenotypes based on P values. Pathway analysis highlighted symbiont entry into host cell and cytokinecytokine receptor interaction in metabolic aging. Proteins showed undulating changes during metabolic aging, with three peaks at 44, 51, and 63 years. MA-protein trajectories clustered into three groups. Groups 1 and 3 exhibited linear increases with MA, whereas group 2 showed nonlinear increases. In conclusion, identification of plasma proteomic biomarkers and their undulating changes in metabolic aging provides a critical foundation for developing clinical markers and precision interventions to prevent accelerated metabolic aging.