Aging is a complex biological process linked to the progressive decline in physiological functions and an increased risk of age-related diseases, multimorbidity, and mortality. Existing research has demonstrated that proteomic aging clocks, such as the one developed in the UK Biobank study!, can effectively predict biological age and associated disease risks using plasma proteins. While these clocks achieve a high level of accuracy, integrating additional layers of omics data, including genomic and telomere data information exploiting machine / deep learning algorithms could potentially improve the prediction of biological age and age-related diseases. This research aims to incorporate genomic and telomere data with proteomic information to enhance the accuracy of biological aging predictions.
