Aging has a significant impact on chronic diseases and mortality rates. As people age, the incidence of these diseases increases; however, chronological age may not fully reflect individuals’ biological aging processes. Omic data allow for a more accurate prediction of these processes. The rate of biological aging plays a critical role in determining an individual’s risk of developing major chronic diseases and early mortality.
Argentieri and colleagues aimed to develop a proteomic aging clock based on the plasma proteins to measure biological age and predict age-related diseases, multimorbidity, and mortality risk. In the study, 2,897 plasma proteins were analyzed, and 204 proteins were identified that could accurately predict chronological age. The proteomic age clock was rigorously validated across diverse populations with varying genetic backgrounds, which demonstrated its efficacy as a robust predictive tool for a wide spectrum of age-related diseases. Proteomic age was linked to the prevalence of 18 major chronic diseases, multimorbidity, and all-cause mortality risk, along with age-related measurements of biological, physical, and cognitive functions.
The study developed a proteomic age clock using plasma proteins to estimate biological age and assess risks of age-related diseases, multimorbidity, and mortality. This clock was developed on a platform of 2,897 plasma proteins using data from 45,441 participants in the UK Biobank. A total of 204 proteins were identified that accurately predicted chronological age, with a Pearson correlation coefficient (r) of 0.94, demonstrating strong accuracy in estimating age
The researchers showed that proteomic aging has a significant impact on age-related measures such as physical frailty, cognitive function, and physical performance. Additionally, the relationship between proteomic aging and the risk of all-cause mortality and common diseases was examined after adjusting for variables such as chronological age, sex, smoking status, physical activity, socioeconomic factors, and clinical risk factors. The proteomic age clock model was trained using machine learning methods, and the gradient boosting model was preferred for its superior generalizability in predicting proteomic age in independent datasets.
The LightGBM model provided the second-best model accuracy among the UK Biobank test set and demonstrated significant superiority in the Chinese Kadoorie Biobank (CKB) and FinnGen datasets. During the research process, statistical methods such as linear/logistic regression, Cox proportional hazards models, and functional enrichments were used. Additionally, analyses were conducted to identify a subset of proteins capable of predicting chronological age and to explore these protein-protein interaction networks. The proteomic age clock successfully predicted chronological age in the UK Biobank, CKB, and FinnGen datasets with correlation coefficients of 0.94 r, 0.92 r, and 0.94 r, respectively.In conclusion, the study’s findings highlight the potential of plasma proteomics as a powerful tool for measuring biological age and predicting age-related diseases in diverse populations. It also provides new evidence on the relationship between proteomic aging and various aging-related phenotypes and disease risks. The findings not only offer new perspectives for future research and healthcare applications but also lay the groundwork for innovative approaches in personalized medicine and early diagnosis strategies aimed at preventing and treating age-related diseases. Expanding the use of proteomic data in biomedical research holds great potential to improve the accuracy of health assessments and guide targeted treatment applications.
Author: Tuana Yaman
Editor: Nida Dereli Çalışkan
Reference:
Argentieri, M. A., Xiao, S., Bennett, D., Winchester, L., Nevado-Holgado, A. J., Ghose, U., Albukhari, A., Yao, P., Mazidi, M., Lv, J., Millwood, I., Fry, H., Rodosthenous, R. S., Partanen, J., Zheng, Z., Kurki, M., Daly, M. J., Palotie, A., Adams, C. J., … Van Duijn, C. M. (2024). Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations. Nature Medicine. https://doi.org/10.1038/s41591-024-03164-7
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