Biological Clocks - What are they?
The world population is ageing. According to the WHO, this is “the result of the continued decline in fertility rates and increased life expectancy” [1]. The current world population of 7.6 billion is expected to reach 8.6 billion by 2030, at which point an estimated 1 in 6 people will be aged 60 years and over [2]. This is expected to cause the UK GDP on health to grow from 7.3 to 8.3% and on long-term care from 1.1 to 2.2% between 2020 & 2065 [3]. This demographic shift is pushing ageing research to the forefront, with a potentially huge impact on the lives of millions of people around the world.
Chronological versus biological age
Chronological age measures the number of years an individual has been alive and is a key factor in determining the individual’s probability of having reduced cognitive and physical function, suffering chronic diseases, and ultimately dying. However, as we all know, chronological age does not tell the whole story. People with the same chronological age might have very different functional capacity and have strikingly different health statuses. There is significant interest in better understanding these discrepancies not only at the population scale but also at the individual level, in order to assess the true degree of frailty and loss of function of our bodies as we age. Additionally, it can help us better understand the underlying mechanisms behind the ageing process, and (why not) determine whether it is possible to decelerate - or even revert - this process.
Biological age can be thought of as an adjusted age taking into account how age-associated physiological changes are reflected in our system. In order to quantify biological age, researchers have considered several molecular and phenotypic indicators, often referred to as biomarkers of ageing, and have developed predictive tools known as biological clocks to better assess the risk of disease and mortality accordingly.
Phenotypic basis of biological clocks
Biological age predictors can be classified according to the type of information or biomarkers they rely on: telomere length, DNA methylation, transcriptomic, proteomics, metabolomics, or a mix of different measurement types (as in the case of allostatic load scores) [4].
Telomeres are sections of DNA at the ends of a chromosome which protect them from becoming frayed or tangled. While telomeres are maintained at constant length in unicellular eukaryotes, in most multicellular eukaryotes telomere shortening (or telomere attrition) occurs coupled to cell division owing to the incapacity of normal DNA polymerases to copy the very ends of chromosomes [15]. Telomere attrition is thus considered one of the hallmarks of ageing [16] and has been extensively studied in connection with ageing and various disease processes. However, results available in the literature, especially regarding its connection with mortality are sometimes contradictory and hence require further investigation [6].
The term epigenetic clock is typically used to refer to predictors based on DNA methylation, an epigenetic mechanism involving the transfer of a methyl group onto the C5 position of the cytosine to form 5-methylcytosine. DNA methylation regulates gene expression by recruiting proteins involved in gene repression or by inhibiting the binding of transcription factor(s) to DNA [8]. Although they are often referred to as the “most accurate” age estimators, many epigenetic clocks exist (e.g., [9-14]) and their performance can differ significantly, as they were often designed with slightly different goals in mind and since they analyse methylation levels at specific Cytosine-Phosphate-Guanine (CpG) sites.
Recent advances in high-throughput omics technologies have made possible the development of other omics predictors including transcriptomics clocks, i.e., biological age predictors based on RNA gene expression levels (such as in [17-19]), proteomics clocks, highlighting the specific role of proteins as functional products in age-related mechanisms (as in [20-21]), and metabolomics predictors (e.g., [23,24]), where age predictions are made based on the global profiling of metabolites present in biological samples. Finally, combining multiple biomarkers of different nature into a composite biological age predictor is also possible and many possibilities exist (e.g., using biomarkers reflecting systemic function of different levels/organs [25], multi-omics approaches as in [26], or even allostatic load models [27], just to name a few).
Challenges and future perspectives
Although one may think that all these clocks measure the same feature, i.e., biological age, predictions can vary widely as it is well established that different systems/tissues/organs within the same individual age at different rates. There can be in fact considerable inter-individual as well as intra-individual variability and it is thus important to tailor biological clocks to the particular aspect (or aspects) of interest. It is also important to mention that clocks based on different types of information have been explored to different degrees and it is unclear whether there is a definite winner among all the options available. Finally, all biological age predictors are essentially measuring correlations with the ageing process and “to move beyond correlation, the field needs to make further progress in experimentally testing the molecular mechanisms underlying ageing clocks” [7].
In light of the ageing society and the unmatched progress in extending healthspan (the period of life free from disease), the need to better understand the ageing process and identify factors determining the incidence of age-related diseases, which place an enormous burden on social and healthcare resources, is becoming paramount. While a wide variety of biological age predictors that can reveal information about mortality and morbidity is currently being investigated, accessibility of robust biomarker measures and the possibility to act in order to decelerate ageing and reduce age-related risks are fundamental aspects and will be key differentiating factors when deciding on which are the best alternatives available on the market.
References
[1] WHO, Ageing: Global population. Accessed on October 17, 2022
https://www.who.int/news-room/questions-and-answers/item/population-ageing
[2] WHO, Ageing and Health. Accessed on October 17, 2022
https://www.who.int/news-room/fact-sheets/detail/ageing-and-health
[3] Government Office for Science. (2016). Future of an Ageing Population. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/816458/future-of-an-ageing-population.pdf
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[15] M.A. Blasco. Telomere length, stem cells and ageing. Nature Chemical Biology (2007) 3, 640-649
[16] C. López-Otín et al. The hallmarks of aging. Cell (2013) 153(6), 1194-1217
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[18] J.G. Fleischer et al. Predicting age from the transcriptome of human dermal fibroblasts. Genome Biology (2018) 19:221
[19] X. Ren & P.F. Kuan. RNAAgeCalc: A multi-tissue transcriptional age calculator. Plos One (2020), 15(8):e0237006
[20] T. Tanaka et al. Plasma proteomic biomarker signature of age predicts health and life span. eLife (2020) 9:e61073
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[22] A.A. Johnson et al. Systematic review and analysis of human proteomics aging studies unveils a novel proteomic aging clock and identifies key processes that change with age. Ageing Research Reviews (2020) 60:101070
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[25] M.E. Levine. Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age? The Journals of Gerontology Series A: Biological Sciences and Medical Sciences (2013) 68, 667–674.
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