Researchers in China have developed two metrics that show that accelerated metabolic aging is associated with unfavorable lifestyle factors and increased risks for chronic diseases and death.
Jun Pu, MD, PhD, from Shanghai Jiao Tong University, and colleagues say that their findings “support plasma metabolomics as a scalable and clinically interpretable framework for studies of disease prevention, early detection, healthy aging, and aging-related heterogeneity across the life course.”
They add: “The value of these metabolomic aging metrics lies not only in chronological-age prediction itself but more in the clinically relevant information carried by deviations from age-expected metabolic profiles.”
In recent years, researchers have sought to identify biomarkers of aging—such as epigenetic markers and changes in metabolic signatures—and integrate them into models called aging clocks, but large-scale plasma metabolomic studies that cover the full human life span are scarce.
In one of the most comprehensive studies to date of metabolic aging, Pu and co-authors used nano-assisted laser desorption/ionization mass spectrometry to profile plasma metabolomes from 18,476 participants aged one to 96 years across six centers in China.
This identified 56 metabolites that were linked to age-associated shifts across the lifespan. The metabolites represented several biologically relevant domains, including collagen turnover, amino acid and one-carbon metabolism, γ-glutamyl-cycle redox metabolism, renal and muscle turnover, and host-microbiome co-metabolism.
The researchers combined the metabolites with machine learning to develop a model that predicted metabolomic age. They then quantified their key metric—metabolomic age acceleration (mAA)—as the difference between metabolomic age and chronological age.
Pu and co-authors report in Science Translational Medicine that participants with high mAA (defined as the upper quartile) had significantly higher risks for developing chronic diseases, such as fatty liver disease hypertension, and diabetes, as well as higher all-cause mortality, than those with low mAA (lower quartile).
Furthermore, high mAA was associated with unfavorable lifestyles factors such as smoking, shift work, and unhealthy diet, whereas low mAA was associated with mixed grain intake, and exercise participation.
When the researchers looked at within-person changes in mAA over time, they found that increased physical activity and reduced fried-food intake were associated with a favorable change in mAA, whereas new secondhand-smoke exposure, higher preserved-food intake, rising depressive symptoms, and transition to overweight or obesity were associated with an unfavorable change in mAA.
The team also examined a cohort of individuals with chronic disease at baseline and uncovered a second metabolic metric called age deviation that quantified individualized departures from age-expected mortality risk inferred from plasma metabolites.
They found that higher age deviation was associated with worse clinical health status, greater disease burden, and higher multimorbidity.
Finally, the team showed that mAA and age deviation exhibited stronger associations with all-cause mortality and better discriminative performance than established composite health measures including the frailty index and the American Heart Association’s Life’s Essential 8 cardiovascular health metric, as well as routine blood biomarkers, supporting their added value as metabolomics-derived predictors of mortality risk.
Ju et al. conclude that their findings “may help identify individuals at elevated risk before overt clinical disease and provide prognostic risk stratification in patients with established disease.”
However, they caution that the as cohort primarily comprised Chinese participants, further validation in populations with diverse ethnic and geographic backgrounds will be needed.
The authors also note that their age prediction framework is available to the public via a web interface.
