Calculating Biological Age Using Omics Data

Studies have proven that biological age is a more accurate indicator of ageing compared to chronological age. Multiple Linear Regression (MLR), Principal Component Analysis (PCA), Klemera and Doubal's Method (KDM), and, in recent years, deep learning methods have been used to calculate and get an approximation of biological age. Age related physiological changes can be studied based on aging markers which can be broadly categorised into 2 types: histology-based biomarkers like metabolomics, proteomics, etc and clinical biomarkers obtained from blood chemistry, hematology, anthropometry, and organ function test measurements. Recent technological advances have made it possible to calculate biological age using omics data. Metabolomics is the global profiling of small molecules (generally <1 kDa) or metabolites that are present in biological samples and is increasingly applied in population based epidemiological studies. Blood metabolites can provide a better estimation of biological age because blood represents the final products of metabolism. Research has suggested the potential of metabolomics in prediction of biological age. Similarly, proteomics is the profiling of proteins present in the biological samples. The cumulative profiling of all these molecules can be termed as omics data profiling which has proven to be an important tool for calculation of biological age. Ageing clock i.e., chronological age predictors are a set of framework to interpret omics data in context of ageing, by learning patterns of molecules in a large number of samples in order to predict the biological age. The difference between this predicted biological age and the actual chronological age is termed to be age gap. It has been shown that the individuals with a positive age gap i.e, age acceleration are at a higher risk of mortality and age related diseases like heart diseases, metabolic diseases etc .The science of studying omics data has added more insights into the biomarkers related to ageing and has raised hopes regarding the success if anti-ageing interventions. With the future advances in the field of machine learning, prediction of biological age with the help of omics data seems to be extremely promising.