Breakthrough Aging Metabolomics Score Predicts Mortality Better Than Traditional Methods

Discover how a new metabolomics aging marker outperforms conventional metrics in predicting short-term mortality, opening the door to personalized health insights and early disease intervention.

Study: Metabolomic profile of biological aging in 250,341 individuals from the UK Biobank. Image source: ArtemisDiana / Shutterstock

In a recent study published in the journal Nature communication, Researchers from China investigated nuclear magnetic resonance (NMR) biomarkers associated with aging. They developed a longitudinal metabolomic aging index and a metabolomic aging score to predict the risk of disease and all-cause mortality. They identified 54 representative aging-related biomarkers with different hazard ratios, including GlycA, which had the highest hazard ratio (1.25 per SD) for all-cause mortality. The study also revealed 439 potential biomarker-disease causal pairs through multivariate Mendelian randomization and colocalization analysis, leading to the creation of a metabolomic aging score that better predicts short-term mortality risk.

Background

Aging is a complex biological process that leads to declines in physiological functions and increases the risk of frailty, disease, and mortality. In 2017, age-related conditions contributed to more than half of the global health burden in adults. Advances in omics technologies have accelerated research into biological ageing, leading to the development of ageing clocks that predict both chronological age and adverse health outcomes. This study highlights the utility of metabolomics, particularly through innovations in high-throughput NMR analysis and machine learning, for population-scale ageing research and disease prediction. The comprehensive NMR metabolomics data and health information of the United Kingdom (UK) Biobank are a key resource for the advancement of metabolomics-based ageing research. In this study, researchers investigated ageing-related biomarkers and examined their predictive power for mortality. In addition, they developed a new metabolomic ageing score and derived a personalized metabolomic ageing index.

About the study

The UK Biobank dataset included 249 metabolomic biomarkers (168 in absolute concentrations and 81 derived ratios) from approximately 250,341 participants. An additional 76 biomarker ratios were calculated to complement the existing data and quality control measures were applied. The Cox proportional hazards LASSO model was used to identify biomarkers associated with ageing.

A GWAS of 325 biomarkers was conducted in a subset of approximately 95,000 individuals to identify genetic variants associated with biomarkers. Genetic correlations and pleiotropic effects were analyzed, and multiple aging metrics (e.g., frailty index, leukocyte telomere length) were compared with metabolomic aging scores. Multivariate Mendelian randomization analysis (MVMR) assessed potential causal relationships between metabolomic biomarkers and 20 aging-related diseases. Different statistical methods were used (e.g., MVMR-IVW, MVMR-Egger), and colocalization analysis examined shared genetic variants between biomarkers and diseases.

Results and discussion

The study identified 54 metabolomic biomarkers associated with biological aging, including amino acids, ketone bodies, fatty acids, lipoproteins, and inflammation-related markers. GlycA, a biomarker of systemic inflammation, showed the highest hazard ratio (1.25 per SD) for all-cause mortality. Most biomarkers correlated significantly with various aging metrics, such as chronological age, frailty index, and leukocyte telomere length. While GlycA was associated with higher odds of frailty, three biomarkers of polyunsaturated fatty acids were associated with lower odds of frailty. In addition, some lipoprotein-related biomarkers showed negative associations with cardiovascular disease. A total of 439 potential causal associations were identified between 213 NMR biomarkers and 20 aging-related diseases, with 14 pairs reaching Bonferroni-adjusted significance. Chronic kidney disease (CKD) had the most candidate biomarkers. Key disease-associated biomarkers included glucose for type 2 diabetes (T2D) and creatinine for CKD. Some biomarkers served as common risk or protective factors across multiple conditions, while colocalization analysis revealed pleiotropic variants affecting different biomarkers and diseases.

In addition, a new metabolomic aging score based on 54 representative NMR biomarkers was developed, which correlated strongly with MetaboHealth and moderately with chronological age and frailty index. It showed strong predictive performance for all-cause mortality during the follow-up periods, particularly in the age group 51–60 years, where it significantly outperformed chronological age. The score was the most accurate among the aging metrics, particularly for short-term mortality risk, while it was comparable to chronological age for 10-year prediction but less accurate for 15-year prediction. The study also developed a metabolomic aging rate, derived from longitudinal data, offering a more personalized assessment of aging. The metabolomic aging score was effective in predicting disease risk, particularly for conditions with dysregulated metabolic pathways, and outperformed other aging metrics for diseases such as type 2 diabetes and chronic kidney disease. Differences in metabolomic aging scores distinguished early-onset, other-onset, and disease-free prospective groups, with significant findings for diseases such as T2D and hypertension. Additionally, 40 pro-aging and anti-aging biomarkers were identified, showing distinct patterns based on metabolomic aging rates.

Although the study is strengthened by its large scale, it is limited by the narrow age range of participants, underrepresentation of disadvantaged groups, and potential variability in the predictive power of the plasma metabolome across diseases. Interpretation of cause-effect relationships in the study also requires caution.

Application

In summary, this study offers the most comprehensive metabolomic profile associated with biological aging to date. It introduces a metabolomic aging score that can predict short-term mortality and disease risk, outperforming other aging markers in specific contexts. However, this score is not intended as a definitive measure of biological aging. Instead, it reflects the signal of aging at the metabolome level. Future studies could potentially combine this score with other aging markers, such as proteomic and epigenetic data, to further improve our understanding of aging.