In a research paper published in The Journal of Gerontology, the authors present a novel deep-learning based hematological human aging clock, a biomarker that predicts the biological age of individual patients. The developed model predicts the age better than models tailored to the specific populations highlighting the differences of subregion-specific patterns of aging. In addition, the developed clocks were shown to be a better predictor of all-cause mortality than chronological age.

The study used a large dataset of fully anonymized Canadian, South Korean and Eastern European blood test records to train an aging clock. Researchers worked on multiple biomarkers using deep learning and incorporating blood biochemistry, transcriptomics, and even imaging data to be able to track the effectiveness of the various interventions.

The pursuit of biological aging clocks is a major focus point of the aging field and is a key step in the development of interventions in human aging. The paper represents the evolution of the first easily adaptable clock that can be applied at a population level regardless of population biases. The clock is very cost-effective, without the requirement of next-generation sequencing or other specialized equipment.

Development of effective biomarkers of age is one of the most pressing goals in geroscience as it lays the foundation for efficient preclinical and clinical evaluation of potential healthspan-extending interventions. By developing accurate biomarkers of aging, the efficacy of potential healthspan-extending interventions could be tested according to changes in study participants' biomarkers of age.

While significant attention is paid to the development of highly accurate biomarkers of aging, less attention is paid to developing actionable biomarkers of aging that can be tested inexpensively using the tools at hand to the majority of researchers and clinicians. The team developed the deep-learning based, blood biochemistry aging clock in the hopes of making progress toward the goal of more actionable biomarkers of aging.

Age is one of the features possessed by every living creature. Previously when the team trained the deep neural networks to predict the age of the person, the DNNs captured the most biologically-relevant features and could be re-trained on diseases and could be used to integrate the multiple data types and also extract the most important features within each data type and across the data types.

In the paper they showed one of the proofs of concept on a very simple and abundant data type that can assess the population-specificity of the predictors, the importance of ethnicity and population group in age prediction and the differences in the most important features contributing to the accuracy of these predictors. The work might improve clinical trial enrollment practices, assess the population specificity of a variety of the biomarkers and pave the way for the development of more complex multi-modal biomarkers of aging and disease.