A Metabolomic Profile of Aging Derived from a Large Data Set
In the realm of aging research, the analysis of extensive omics data sets is advancing rapidly. One approach involves the creation of aging clocks, utilizing machine learning to combine omics data and determine biological age. Concurrently, researchers are exploring correlations between individual biomarkers from blood samples and aging and mortality. With countless metabolites circulating in the body, there is potential for discovering superior biomarkers for various applications.
The plasma metabolome reflects dynamic biological signals that convey personal health status. Metabolomic biomarkers have shown promise in predicting disease and mortality risks. Leveraging low-cost, high-throughput NMR metabolomic profiling and routine blood tests, identifying and quantifying aging-related biomarkers could revolutionize personalized health monitoring and anti-aging strategies.
This study unveils the largest aging-related metabolomic profile yet, examining 325 NMR biomarkers from 250,341 individuals in the UK Biobank. Identification of 54 aging-related metabolomic biomarkers capable of predicting all-cause mortality sheds light on potential anti-aging targets and aids in understanding aging-related diseases. Advanced analysis of lipoprotein-related biomarkers through NMR profiling provides valuable insights into the role of lipid metabolism in aging processes.
Link: https://doi.org/10.1038/s41467-024-52310-9
