A new cognitive clock matching phenotypic and epigenetic ages

Cognitive abilities decline with age, yet quantitative biomarkers and their relations to biological clocks remain insufficiently defined. This study analyzes three cognitive tests—shade differentiation (campimetry), arithmetic correctness evaluation, and reversed letter detection—to identify age-associated cognitive indices. Using subsets of these indices, the authors construct a machine learning–based Cognitive Clock that predicts chronological age with a mean absolute error (MAE) of 8.62 years. Notably, the Cognitive Clock predicts epigenetic and phenotypic ages with even better accuracy. Correlations are observed between cognitive, phenotypic, and epigenetic age accelerations, indicating a strong link between cognitive performance and an individual's aging status.