ORANGE: A Machine Learning Approach for Modeling Tissue-Specific Aging from Transcriptomic Data

Abstract

Despite aging being a fundamental biological process which profoundly influences health and disease, the interplay between tissue-specific aging and mortality remains underexplored. This study applies machine learning on GTEx transcriptomic data to model tissue-specific biological ages across 12 different types of tissues and introduces an age-gap metric to quantify deviations from the chronological age. Our best models achieve an average RMSE of 6.44 years and an average R2 of 0.64. Age-gap statistics reveal significant tissue-specific aging patterns, identifying extreme agers and correlations between extreme aging and mortality. About 20% of subjects are found to exhibit extreme aging in one tissue, while 1% show multi-organ aging. These findings greatly emphasize the role of transcriptomics in aging research and its implications for health and longevity.