Refining Biological Aging Clocks: Harnessing Multi-Organ, Multi-Omics Data with Machine Learning - Goshu, Belay Sitotaw (BIRCU, 2025)
The image aging as a deeply personal story, where a gentle mirror reflects not just the years you’ve lived but the vibrant health pulsing through you, guided by cutting-edge aging clocks. This study dives into crafting and testing two aging clocks, basic and refined, using synthetic data from 1,200 unique souls across Groups A, B, and C. With 12 biomarkers as our guide, we shaped these clocks with linear and Ridge regression, achieving a heartwarming near-perfect match to biological age, missing by just 1.81 years on average, with an R² of 0.99 for both. In imagined clinical moments, a 30% dip in Delta Age after intervention and a strong bond between faster aging and lower therapy adherence (r = -0.587, p = 4.558e-112) showed these clocks’ promise. Response to help varied warmly, 68.6% for Group A, 67.4% for Group B, and 62.5% for Group C, hinting at our diverse human hues. The pre- and post-intervention shifts and adherence shaping age, while Table 1 unpacks a logistic model where chronological age (coefficient 0.0941, p < 0.001) steals the spotlight. Though our synthetic world softens life’s rough edges, these clocks offer a tender start for personalized care. They whisper hope for tailoring treatments and watching progress, though real-life validation will bring the full story to light.