Estimation of biological aging based on T-cell differentiation trajectories: emerging and future avenues.

The relationship between disease onset and chronological age varies between individuals, driving the need for a more accurate, universal biomarker of biological aging. Among the emerging alternatives, the immune system represents a universally shared, complex system that consistently shows aging-related decline across diverse individuals. Specifically, T-cell dynamics, capturing both thymic involution and lifelong antigenic exposure, provide insights into immune system aging. Although existing aging clocks, such as those based on DNA methylation (i.e., Horvath's and GrimAge), offer valuable predictions of biological age and disease risk, these methods are often limited in ability and cost to reflect real-time immune function. We also explore cutting-edge techniques to measure T-cell states, such as flow cytometry, single-cell omics, cytometry by time-of-flight (CyTOF), and the potential of non-invasive retinal imaging, but these techniques also face these limitations. To account for the challenges with the above-mentioned methods, we propose the naive-to-exhausted T-cell ratio as a promising, quantifiable metric of immune aging. The conceptual framework benchmarks the naive-to-exhausted T-cell ratio against established epigenetic clocks, generating an "immune age curve" that offers clinicians and researchers a practical approach to integrate immune aging assessments into clinical and preventative care. To test our hypothesis, we conducted survival association analysis based on naive-to-exhausted T-cell levels across all major cancers (including adrenal carcinoma (HR = 0.19 (CI [0.082, 0.44]),