This paper provides an introduction to the several different methodological approaches that can be used to assemble a measure of biological age from data sets that exhibit changes with age. In recent years, many varied aging clocks have been produced and tested. Where such clocks are derived from epigenetic, transcriptomic, proteomic, and similar data, it remains unclear as to which processes of aging they reflect, and to what level of sensitivity. Clocks that use very few data points can produce good measures in a naturally aging population, but are unlikely to be useful when assessing the outcome of a potential rejuvenation therapy that targets only one or a few specific mechanisms of aging.
Aging is accompanied by a progressive decline in physiological functions and an accumulation of damage to the body, leading to an increased risk of morbidity and mortality. Based on birth date, chronological age (CA) is the traditional criterion for assessing aging. However, the degree of aging may vary significantly between individuals with the same CA. Therefore, CA is not the best indicator for evaluating the degree of aging in human individuals.
To seek a better index to assess the degree of aging of individuals, biological age (BA) are used as alternatives to CA to estimate aging status. BA is the most popularly used model. Aging markers are the basis for constructing biological age, and in this article we summarize the markers used in constructing biological age.
There are many ways to classify markers of aging, e.g., the aging markers can classify into two categories: histology-based data (DNA methylation, metabolomics, proteomics, etc.), and clinical biomarkers obtained from blood chemistry, hematology, anthropometry, and organ function test measurements. The "aging clock" developed from omics data is another form of biological age, multiple omics data can be combined to build the clock.
Until now, omics data have rarely been used in the construction of BA because of the high cost of its application in large-scale populations. Previously built BA models commonly choose aging biomarkers in multiple organs/systems, such as blood biomarkers, genetic indicators, and physical activity data. Biomarkers from diverse organs are more reflective of the overall body state. To build the BA model, these biomarkers can be applied to different model building methods like multiple linear regression (MLR), principal component analysis (PCA), Klemera and Doubal's method (KDM), deep learning, and other methods.