ABSTRACT
Aging represents the greatest risk factor for chronic diseases and mortality, but to understand it we need the ability to measure biological age. In recent years, many machine learning algorithms based on omics data, termed aging clocks, have been developed that can accurately predict the age of biological samples. However, there is currently no resource for systematic profiling of biological age. Here, we describe ClockBase, a platform that features biological age estimates based on multiple aging clock models applied to more than 2,000 DNA methylation datasets and nearly 200,000 samples. We further provide an online interface for statistical analyses and visualization of the data. To show how this resource could facilitate the discovery of biological age-modifying factors, we describe a novel anti-aging drug candidate, zebularine, which reduces the biological age estimates based on all aging clock models tested. We also show that pulmonary fibrosis accelerates epigenetic age. Together, ClockBase provides a resource for the scientific community to quantify and explore biological ages of samples, thus facilitating discovery of new longevity interventions and age-accelerating conditions.
Competing Interest Statement
The authors have declared no competing interest.
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