Could a simple blood test reveal how well someone is aging A team of researchers led by Wolfram Weckwerth from the University of Vienna Austria and Nankai University China has combined advanced metabolomics with cutting edge machine learning and a novel network modeling tool to uncover the key molecular processes underlying active aging Their study published in the Nature Journal npj Systems Biology and Applications identifies aspartate as a dominant biomarker of physical fitness and maps the dynamic interactions that support healthier aging

It has long been known that exercise protects mobility and lowers the risk of chronic disease Yet the precise molecular processes that translate physical activity into healthier aging remained poorly understood The researchers set out to answer a simple but powerful question Can we see the benefits of an active lifestyle in elderly individuals directly in the blood and pinpoint the molecules that matter most

From fitness tests to blood fingerprints a Body Activity Index and a Metabolomics Index Researchers first synthesized a single Body Activity Index BAI by applying canonical correlation analysis to scores from walking distance chair rise tests handgrip strength and balance assessments This composite physical performance metric captures endurance strength and coordination in one robust measure Independently a Metabolomics Index was derived from blood concentrations of 35 small molecule metabolites Across 263 samples from older adults these two indices showed a Pearson correlation coefficient of 085 p less than 1 times 10 to the minus 19 demonstrating that the molecular signature in blood mirrors the composite measure of physical fitness

Machine learning highlights active and less active groups and their metabolic signature To capture complex non linear patterns the researchers trained five different machine learning models ranging from simple statistical approaches Generalized Linear Model GLM to more advanced methods such as boosted decision trees Gradient Boosting Machine GBM XGBoost and a deep learning autoencoder network Each model was tuned with repeated cross checks double cross validation and tested on independent data to ensure robust performance Both boosting methods GBM and XGBoost achieved high accuracy distinguishing active from less active participants in over 91 percent of cases area under the curve AUC greater than 091 Across all five algorithms eight metabolites consistently emerged as predictors of activity level aspartate proline fructose malic acid pyruvate valine citrate and ornithine Among them aspartate stood out by a factor of two to three confirming its central role as a molecular marker of active aging

Network rewiring revealed by COVRECON Correlation alone cannot explain why certain molecules are linked to fitness To uncover the underlying mechanisms the team applied COVRECON a data driven modeling tool In simple terms COVRECON looks at how metabolites vary together and then reconstructs the network of biochemical interactions between them Mathematically this involved estimating a differential Jacobian matrix a way of identifying which enzymatic connections change most between active and less active groups This analysis revealed two well known enzymes aspartate aminotransferase AST and alanine aminotransferase ALT as central hubs in the network Both are standard markers in clinical liver panels but here they emerged as indicators of how activity reshapes metabolism Importantly the predictions were confirmed by routine blood tests over the six month study period AST and ALT fluctuated more strongly in active participants than in their less active peers suggesting greater metabolic flexibility in liver and muscle function

Implications for brain health and dementia Aspartate is more than a simple metabolic intermediate in the brain it also serves as a precursor of neurotransmitters activating NMDA receptors that are essential for learning and memory This dual role provides a possible link between physical fitness and cognitive health Independent studies have shown that low AST and ALT levels in midlife or an elevated AST ALT ratio are associated with increased risk of Alzheimers disease and age related cognitive decline By demonstrating that physical activity drives dynamic changes in aspartate metabolism and in the plasticity of these two enzymes the present study points to a molecular bridge between muscle liver health and brain resilience These findings suggest a simple message physical activity helps in preserving strength and mobility and may also contribute to protecting the brain from dementia through measurable shifts in amino acid based signaling pathways

Source: https://www.news-medical.net/news/20250930/Molecular-fingerprint-predicts-physical-fitness-in-older-adults.aspx