Measuring biological age using omics data

Abstract

Age is the key risk factor for diseases and disabilities of the elderly. Efforts to tackle age-related diseases and increase healthspan have suggested targeting the ageing process itself to 'rejuvenate' physiological functioning. However, achieving this aim requires measures of biological age and rates of ageing at the molecular level. Spurred by recent advances in high-throughput omics technologies, a new generation of tools to measure biological ageing now enables the quantitative characterization of ageing at molecular resolution. Epigenomic, transcriptomic, proteomic and metabolomic data can be harnessed with machine learning to build 'ageing clocks' with demonstrated capacity to identify new biomarkers of biological ageing.

Fig. 1 |. Classes of ageing biomarkers.

Obvious features of ageing (top), such as muscular frailty and greying hair, have been used since ancient times to assess an individual’s biological age. However, with the advent of modern biomedicine, the diagnosis of health versus disease using physical and molecular readouts of organ function (second from top), such as blood pressure, inflammatory markers and metabolic markers, became the primary focus. Only recently have we turned our attention to assessing biological age by leveraging advances in cellular and molecular biology. Hallmarks of ageing (third from top), such as telomere shortening and cellular senescence, became the modern scientific framework for understanding ageing that has guided investigation of ageing at the molecular level. This has led, in part, to the development of omics-based ageing clock biomarkers of ageing (bottom), which attempt to integrate the entire breadth of molecular changes that occur with ageing into composite measures of biological age.

Fig. 2 |. Select machine learning concepts important in the field of omics ageing clocks.

a | Basic concept of an ageing clock illustrated for a simple linear regression model. Population sampling is used to learn a relationship between a molecular feature (such as the expression level of a protein) and a dependent variable (age) that minimizes a cost function (graph). The learned relationship is then used to predict age, and the residual between chronological and predicted age is used as a measure of biological age (output model). b | The curse of dimensionality is a challenge for omics machine learning. The number of samples required to sample a distribution at a given density increases exponentially with the number of features measured in each sample. It is effectively impossible to densely sample high-dimensional omics distributions, which motivates the use of additional methods to reduce the feature space. c | The general architecture of a simple deep neural network. Features are taken as inputs and passed to a set of nodes (hidden layer 1), which transform the inputs with a mathematical function (typically a linear combination with a set of learned weights) and then pass the values to the next layer. The model gains additional expressive power by chaining together many simple functions with learnable weights over multiple hidden layers. The weights for each node can be jointly optimized by minimizing a cost function similar to the linear regression case.

Fig. 3 |. Timeline of major advances and studies in ageing clocks research between 2008 and 2021.

Timeline of enabling technologies for omics ageing clocks and select studies that have moved the field forward between 2008 and 2021. Notably, this is not a complete list of important studies or technologies. Timeline refers to publication dates according to PubMed^{,,–,,,,,,,,,,–}. GH, growth hormone; DHEA, dehydroepiandrosterone; DNAm, DNA methylation; RNA-seq, RNA sequencing.

Fig. 4 |. Associations between clock age gaps and ageing phenotypes.

The age gap (top) is the primary measure of biological ageing for most ageing clocks. Age gaps for different ageing clocks have shown different sensitivities to various ageing phenotypes, suggesting they may measure different aspects of ageing biology to various extents. Methylation clocks are quite sensitive to mortality, whereas transcriptomic, proteomic and metabolomic clocks have shown increased sensitivity to disease-of-ageing phenotypes.

Fig. 5 |. Measuring ageing across the body.

Biological ageing varies between individuals (left) and across the body within a single individual (middle). Different tissues age at different rates and through different mechanisms. The heart, brain, immune or metabolic tissues may experience ageing to a greater or lesser degree in different individuals, who may then develop diseases of ageing afflicting these tissues in particular but have otherwise better function elsewhere in the body. Even within a single tissue, different cells age at different rates (right). Senescent cells are one example of a cellular ageing phenotype, which has an impact on different organs and cell types, such as macrophages, endothelia and glia, to varying extents and rates^,–. Aged cells may accelerate ageing of other cells through secretion of pro-ageing factors^,, and certain cell types may be more susceptible to pro-ageing factors in their environment^,,.