(Source-news-medical.net)
A groundbreaking study conducted by researchers from China and published in Nature Communications has introduced a more accurate method for predicting mortality risk through nuclear magnetic resonance (NMR) biomarkers associated with aging. The team developed a new metabolomic aging score and rate, surpassing traditional metrics in forecasting disease risk and all-cause mortality. By identifying 54 biomarkers linked to aging, the researchers created a predictive tool with heightened accuracy. One particular biomarker, GlycA, displayed the highest hazard ratio (1.25 per SD) for mortality. The study uncovered 439 potential causal relationships between biomarkers and diseases, further emphasizing the significance of this new score in predicting short-term mortality.
Background on Aging and Omics Technologies
Aging is a complex biological process that leads to a decline in physiological functions, increasing the risk of disease and mortality. In 2017, conditions related to aging were responsible for over half of the global health burden among adults. Recent advancements in omics technologies, including high-throughput NMR analysis and machine learning, have accelerated research into biological aging. This has paved the way for the development of aging clocks that predict chronological age and adverse health outcomes.
The UK Biobank, a comprehensive database of NMR metabolomics data and health information, was instrumental in this study. Researchers analyzed aging biomarkers, examining their predictive capacity for mortality and developing a personalized metabolomic aging rate, offering a more nuanced assessment of an individual’s aging process.
Study Findings and Implications Metabolomic Aging Score
The study identified key biomarkers related to aging, including amino acids, fatty acids, lipoproteins, and inflammation markers like GlycA. These biomarkers correlated significantly with aging metrics such as the frailty index and leukocyte telomere length. Notably, GlycA was associated with a higher risk of frailty, while certain polyunsaturated fatty acids were linked to a reduced risk. The team discovered 439 potential causal relationships between biomarkers and 20 aging-related diseases, with chronic kidney disease (CKD) emerging as the condition with the most candidate biomarkers. Glucose was identified as a key biomarker for type 2 diabetes (T2D), and creatinine for CKD.
A novel metabolomic aging score, derived from 54 representative biomarkers, outperformed chronological age in predicting short-term mortality, particularly in individuals aged 51–60. The study also highlighted differences in the aging score among disease-free individuals, early-onset, and those with other-onset conditions. Moreover, 40 pro-aging and anti-aging biomarkers were identified, providing new insights into the patterns of aging.
This study represents a significant advance in aging research by providing a comprehensive metabolomic profile linked to biological aging. The newly developed metabolomic aging score offers improved accuracy in predicting short-term mortality and disease risk. However, it is not intended as a definitive measure of biological aging but as a reflection of the aging signal at the metabolome level. Future research could enhance this score by integrating other aging metrics, such as proteomic and epigenetic data, to deepen our understanding of the aging process and improve prediction methods.
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