Recently, the team led by Prof. An Pan and Prof. Tingting Geng from the School of Public Health, Huazhong University of Science and Technology, has made new progress in the proteomic characterization of metabolic aging. Their study, entitled “Plasma Proteomics Reveals Biomarkers and Undulating Changes in Metabolic Aging”, was published in Research. The findings highlight core plasma proteins associated with metabolic aging and identify critical periods of dramatic proteomic changes during the aging process. This provides a scientific basis for developing biomarkers of metabolic aging and designing personalized interventions for healthy aging.
Research Background
Global population aging has become an increasingly severe challenge. Advancing age is accompanied by a decline in metabolic function, and accelerated metabolic aging significantly increases the risk of cardiometabolic diseases and mortality. Therefore, slowing metabolic aging is crucial for preventing cardiometabolic diseases and promoting healthy aging. In recent years, breakthroughs in proteomics have opened new avenues for identifying novel biomarkers and pathways of metabolic aging.
Research Progress
Based on a prospective population cohort, this study constructed a measure of metabolic biological age using metabolomic data from 203,491 participants. Metabolic biological age serves as an indicator of metabolic aging. The results showed that metabolic biological age improved the prediction of mortality, cardiovascular disease, and type 2 diabetes risk beyond traditional risk factors (Figure 1). Moreover, metabolic biological age was closely associated with other aging features such as telomere shortening and frailty (Figure 2).
Figure 1. Predictive power of metabolic biological age for cardiometabolic diseases and mortality
Figure 2. Associations of metabolic biological age with cardiovascular disease, telomere shortening, and frailty
In a subset of 24,920 participants with both metabolomic and proteomic data, the researchers identified 60 proteins associated with all metabolic aging phenotypes (including metabolic biological age, telomere length, frailty index, cardiovascular disease, type 2 diabetes, and mortality). Among them, proteins such as GDF15, PLAUR, TNFRSF10A, TNFRSF10B, IFI30, HGF, WFDC2, COL6A3, PIGR, IGFBP4, and EDA2R ranked among the top 20 across at least four phenotypes. These proteins represent core components of metabolic aging, primarily involved in biological processes such as inflammation and immune responses, apoptosis, fibrosis, and regulation of metabolic homeostasis.
Furthermore, plasma proteins exhibited undulating fluctuations during metabolic aging. Peaks in protein expression were observed at metabolic biological ages of 44, 51, and 63 years (Figure 3). These time points may represent critical windows in metabolic aging, suggesting potential periods for targeted metabolic interventions to delay aging and prevent cardiometabolic diseases.
Figure 3. Differential expression of plasma proteins during metabolic aging
Future Outlook
The metabolic biological age constructed in this study can accurately predict mortality and cardiometabolic disease risk, offering clinical significance for population health risk stratification and early disease prevention. The identification of key proteomic biomarkers and their dynamic fluctuations provides important clues for understanding biological pathways of metabolic aging and for developing biomarkers and intervention targets to delay metabolic aging.
Future research should validate these findings across diverse ethnic groups and age ranges, while integrating experimental studies to elucidate underlying biological mechanisms and establish causal relationships. Moreover, precise intervention strategies targeting critical proteins and age-specific windows should be explored to advance early personalized prevention of cardiometabolic diseases and promote healthy aging.
