Aging significantly impacts the onset of chronic diseases such as heart disease, stroke, diabetes, and cancer. However, the timing and severity of these conditions can vary greatly among individuals. Traditionally, chronological age has been used to estimate biological aging, but this method may not always be accurate. This study is groundbreaking as it is the first to validate a proteomic age clock across diverse populations, offering a more reliable tool to predict age-related diseases and mortality. Proteomic data, which reflect an individual’s biological functioning, can provide a more accurate measure of biological aging compared to DNA methylation (DNAm) clocks, which have been used before but mainly in smaller or less diverse groups.
About the Study
Researchers gathered data from three large biobank cohorts: the United Kingdom Biobank (UKB), China Kadoorie Biobank (CKB), and FinnGen. They developed and validated a proteomic age clock using the Olink Explore 3072 platform. This clock estimates biological age based on specific protein levels, which can differ from chronological age. The difference between biological and chronological age, known as “ProtAgeGap,” was examined for its relationship with aging, frailty, and disease.
The study included 45,441 participants from the UKB (ages 39–71 years, 54% women), 3,977 from the CKB (ages 30–78 years, 54% women), and 1,990 from FinnGen (ages 19–78 years, 52% women). Researchers processed and normalized proteomic data across these cohorts, analyzing 2,897 proteins after quality control. They used a gradient-boosting model (LightGBM) to predict chronological age, outperforming other machine-learning models. A minimal predictive model, ProtAge20, was created by identifying the 20 most important proteins, maintaining high accuracy. This model was validated with fivefold cross-validation in the UKB and then applied to the CKB and FinnGen cohorts to calculate ProtAgeGap. Various statistical methods were used for analysis, including regression models, survival analysis, and protein interaction visualization.
Results and Discussion
During the 11–16 year follow-up, mortality rates were 10.6% in CKB, 36% in UKB, and 1% in FinnGen. The study identified 204 aging-related proteins, with stable associations between age and these proteins over time.
ProtAgeGap was found to correlate strongly with biological aging markers and clinical outcomes. It predicted multimorbidity, 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). It also showed associations with cancer risks, such as breast cancer (HR = 1.12), lung cancer (HR = 1.09), and prostate cancer (HR = 1.08). ProtAgeGap correlated with various biological aging markers, including telomere length and insulin-like growth factor-1, and measures of cognitive and physical function. Sensitivity analyses, including subsets like non-smokers and normal-weight individuals, supported these findings.
The proteomic age clock primarily reflects proteins involved in a range of biological functions, such as extracellular matrix interactions, immune response, hormone regulation, reproduction, and neuronal development. Unlike DNAm clocks, this proteomic clock highlights new aging-related proteins and offers additional insights into aging biomarkers. The study benefits from using gradient-boosting models, which can better handle nonlinear associations and interactions between proteins, enhancing generalizability. However, the study’s reliance on the Olink Explore 3072 platform limits protein coverage, and the absence of DNAm data restricts direct comparisons with DNAm age clocks.
Conclusion
This study’s proteomic age clock offers a robust method for predicting biological aging and understanding age-related diseases, frailty, and mortality. The findings suggest that plasma proteomics is a reliable way to measure biological age, potentially guiding drug development, new interventions, or lifestyle changes to reduce premature mortality and delay major age-related health conditions.
