Plasma Proteomics Reveals Biomarkers and Undulating Changes in Metabolic Aging

Area of Science:

• Proteomics
• Metabolomics
• Aging Research

Background:

• Metabolic aging is a key determinant of healthspan and disease risk.
• Identifying reliable biomarkers for metabolic aging is crucial for developing targeted interventions.

Purpose of the Study:

• To develop a metabolic age (MA) prediction model using metabolomic data.
• To identify plasma proteomic biomarkers associated with metabolic aging and its phenotypes.
• To characterize the dynamic, undulating changes of these proteins over the lifespan.

Main Methods:

• Developed MA from mortality-associated metabolomic profiles in 203,491 UK Biobank participants.
• Examined associations between 2,923 plasma proteins and metabolic aging phenotypes in 24,920 participants.
• Utilized differential expression-sliding window analysis to capture protein waves during aging in 7,092 participants.

Main Results:

• MA improved prediction of mortality, cardiovascular disease, and type 2 diabetes (C-index up to 0.786) and correlated strongly with chronological age (r=0.876).
• Sixty proteins were associated with all metabolic aging phenotypes, including GDF15, PLAUR, and TNFRSF10A/B.
• Proteins exhibited undulating changes, with peaks at ages 44, 51, and 63, and MA-protein trajectories clustered into linear and nonlinear groups.

Conclusions:

• The developed MA model and identified plasma proteomic biomarkers offer valuable tools for assessing metabolic age.
• Undulating protein changes during aging highlight dynamic biological processes and potential intervention points.
• These findings provide a foundation for clinical markers and precision interventions to combat accelerated metabolic aging.