Analysis of large omics data sets in the context of aging and mortality proceeds apace in the research community. On the one hand there is the production of aging clocks, algorithmic combinations of omics data generated via machine learning, in the attempt to produce a useful measure of biological age. On the other hand there are related analyses such as the one noted here, in which researchers attempt to correlate specific individual biomarkers obtained from a blood sample to age and mortality. Many, many metabolites circulate in the body, and it is certainly possible that some of these are better biomarkers for specific uses than the present consensus choices.
The plasma metabolome carries dynamic biological signals reflecting personal health status. Previous studies have demonstrated the potential of metabolomic biomarkers for disease and mortality risk prediction. With the availability of low-cost, standardized, high-throughput nuclear magnetic resonance (NMR) metabolomic profiling and the promotion of blood tests during medical checkups, the identification and quantification of aging-related metabolomic biomarkers hold potential for personalized health monitoring and anti-aging interventions.
Here, we present the largest aging-related metabolomic profile to date based on 325 NMR biomarkers from 250,341 individuals from the UK Biobank. A subset of 54 aging-related representative metabolomic biomarkers were identified based on their ability to predict all-cause mortality. These aging-related biomarkers are involved in diverse biological functions and metabolic pathways, which might serve as potential anti-aging intervention targets and facilitate further exploration of the mechanism of aging-related diseases. High-resolution analysis of the refined composition and structure of multiple lipoprotein-related biomarkers, enabled by NMR profiling, contributes greatly to unraveling the roles of lipid metabolism in the process of aging.
Link: https://doi.org/10.1038/s41467-024-52310-9
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