This is evident from a recent study published in the journal Naturopathy, researchers developed a proteomic age clock that uses plasma proteins to predict biological age and associated health risks. They found that this clock accurately predicts age and is associated with the risk of serious chronic diseases, multimorbidity and mortality among different population groups.
Study: Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations. Image credits: kiehlord / Shutterstock
Background
Aging is a key factor in the onset of chronic diseases such as heart disease, stroke, diabetes and cancer, although the timing and severity vary from person to person. Although chronological age is often used to estimate biological aging, it may not be an accurate surrogate measure. This study is important because it is the first to validate a proteomic age clock for large and diverse populations, providing a robust tool to predict age-related diseases and mortality. More accurate estimates can be achieved using ‘omics data’, which reflect an individual’s biological functioning. Biological aging influences the risk of chronic diseases, disability and the demand for health care. Although deoxyribonucleic acid methylation clocks (DNAm) have previously been used to measure biological age, protein levels may provide more direct insights into aging mechanisms. Although previous studies have developed proteomic age clocks to predict disease risk and mortality, none have done so in large, diverse populations. Therefore, in the current study, researchers addressed this gap by developing and validating a proteomic age clock for different populations and assessing its predictive power for chronic disease risk, mortality, and aging-related traits.
About the study
In the current study, data were obtained from three large biobank cohorts: United Kingdom Biobank (UKB), China Kadoorie Biobank (CKB) and FinnGen. The researchers developed and validated a proteomic age clock via the Olink Explore 3072 platform. The clock could predict a person’s biological age based on the expression levels of specific proteins, which may differ from their chronological age. The difference, called ‘ProtAgeGap’, was analyzed to investigate its relationship with aging, frailty and disease.
A total of 45,441 participants from UKB (age 39–71 years, 54% female), 3,977 from CKB (age 30–78 years, 54% female) and 1,990 from FinnGen (age 19–78 years, 52% female) were included. Proteomic data were processed and normalized across cohorts, with 2,897 proteins selected for analysis after quality control. A gradient boosting model (LightGBM) was used, which outperformed other machine learning models in predicting chronological age. Recursive feature elimination helped identify the top 20 proteins, creating a minimal predictive model (ProtAge20) that maintained high accuracy. The model was trained and validated using fivefold cross-validation in the UKB and applied to the CKB and FinnGen cohorts to calculate the ProtAgeGap. Statistical analysis included the use of linear or logistic regression, Cox proportional hazard models, functional enrichment analysis, Shapley additive explanations (SHAP) interaction analysis, Kaplan-Meier survival analysis, and protein-protein interaction (PPI) network visualization.
Athe UKB participants were split into training and test sets in a ratio of 70:30. In the training set, a LightGBM model was trained to predict chronological age using 2,897 plasma proteins and five-fold cross-validation. We identified 204 proteins relevant for predicting chronological age using the Boruta feature selection algorithm and retrained a refined LightGBM model using these 204 proteins, which was then evaluated in the UKB test set. bIndependent data from the CKB and FinnGen were used for further independent validation of the proteomic age clock model. cProtein-predicted age (ProtAge) was calculated in the full UKB sample using fivefold cross-validation and LightGBM. ProtAgeGap was calculated as the difference between ProtAge and chronological age. We used linear and logistic regression to test the associations between ProtAgeGap and a comprehensive panel of biological aging markers and measures of frailty and physical/cognitive status. Furthermore, we used Cox proportional hazards models to test the associations between ProtAgeGap and mortality, 14 common diseases, and 12 cancers. Most association analyzes were performed only in the UKB, due to the smaller sample size in the CKB and the lack of cases in FinnGen. Figure made with BioRender.com.
Results and discussion
During the 11–16 year follow-up period, there were 10.6%, 36%, and 1% deaths in the CKB, UKB, and FinnGen cohorts, respectively. A total of 204 aging-related proteins were identified, and the associations between age and these proteins were found to be stable over time.
ProtAgeGap was found to correlate with biological aging markers and clinical outcomes. It turned out to be a strong predictor of the risk of multimorbidity and all-cause mortality (hazard ratio [HR] = 1.15 per year ProtAgeGap), and 14 non-cancer diseases, including Alzheimer’s disease (HR = 1.11), chronic kidney disease (HR = 1.14), and type 2 diabetes (HR = 1.13). In addition, ProtAgeGap also showed associations with cancer risk, including breast cancer (HR = 1.12), lung cancer (HR = 1.09) and prostate cancer (HR = 1.08). ProtAgeGap was also found to be associated with several biological aging markers (e.g. telomere length, insulin-like growth factor-1) and measures of cognitive and physical functioning. Sensitivity analyzes including nonsmokers and normal weight subjects confirmed these associations.
According to the study, the proteomic age clock is largely influenced by proteins involved in diverse biological functions, such as extracellular matrix interactions, immune response and inflammation, hormone regulation, reproduction, neuronal development and differentiation. The proteomic clock showed limited overlap with DNAm clocks, highlighting novel aging-related proteins and providing additional insights into aging biomarkers. The study is strengthened by the use of gradient boosting models that allow for nonlinear associations and interactions between proteins, providing better generalizability compared to other models. However, the study is limited by the exclusive use of the Olink Explore 3072 platform, which limits protein coverage, and the lack of DNAm data for direct comparisons with DNAm age clocks.
Conclusion
In conclusion, the proteomic age clock developed in this study provides a robust prediction system for biological aging that can provide insights into age-related diseases, frailty, and mortality mechanisms. The study suggests that plasma proteomics is a reliable method for measuring biological age, guiding drug targets, new interventions or lifestyle changes to potentially reduce premature mortality and delay the onset of major age-related health problems.