Multi-omics approaches to human biological age estimation
Introduction
We live in an unprecedented period of history. According to the United Nations Prognosis, the number of people in the world aged 60 years or over will be 2.1 billion by 2050 (United Nations, 2017). Together with elevating proportion of elderly people number of chronic aging-related disease cases increases. This will significantly elevate the burden on the healthcare system and economy. Researchers are demanded to develop approaches to improve the performance and quality of life of the elderly.
Aging-related preventive interventions are not possible without personal aging speed measurement. Biomarkers of aging are molecular, cellular or physiological parameters of the body that demonstrate reproducible quantitative or qualitative changes with age. Ideally, interventions should reverse these biomarkers to a younger state or slow down the changes with age (Zhavoronkov et al., 2014). The problem of biomarkers’ identification in the field was postulated in classic works on gerontology (Adelman, 1987; Dean, 1988; Ingram, 1988), but the basis of an approach was built by V. M. Dilman in his elevation hypothesis (1968) and in further neuroendocrine theory of aging, where the hormones played a key role of indicators in homeostasis disorganization during aging (Dil’man and Dean, 1992). Nowadays, hormones (insulin, cortisol, growth hormone, etc.) and other small molecules (glucose, urea, lipids, etc.), associated with aging-related signaling cascades, are the most common biomarkers for a physician, below we discuss the criteria which are essential to sort out an aging biomarker.
The following main criteria for aging biomarker were proposed by Butler et al. (2004):
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Must change with age;
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Have to predict mortality better than chronological age;
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Allow foreseeing the early stages of a specific age-related disease;
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To be minimally invasive - do not require serious intervention or painful procedure.
We extended the list by additional criteria, which could increase the translational potential:
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To be sensitive to early signs of aging (as opposed to frailty and mortality, which are too late for prevention and geroprotection);
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Have predictability with collecting in the foreseeable time range;
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Have low analytical variability (robustness and reproducibility).
While there is no single definitive biomarker for aging, that concise all necessary criteria, a range of different measures have been proposed. The most accessible online database of human aging biomarkers is Digital Aging Atlas (Craig et al., 2015), but this resource have not been updated since 2014, in 2019 the most comprehensive source shedding light on biomarkers in gerontology is a book “Biomarkers of Human Aging” (Moskalev, 2019). In different organs and systems, aging processes occur at different times and at different speeds. Thus, aging biomarker should be multimodal, based on different molecular and physiological parameters.
There are three different experimental approaches to develop new aging biomarkers, but it is critical to mention deep transcriptomic clocks separately in the framework of the “Omics-based” measurements (Table 1):
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Empirical. Search for significant correlations with age among a variety of physiological, psychological, biochemical and other clinical parameters. The advantage of the approach is that the methods have been already used in clinical practice. This approach has maximum translational potential and minimal price, high personal variability and low predictive power.
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Aging-mechanisms-oriented. Search for predictors of aging among changes associated with known aging mechanisms. Since the approach is based on one of the hypotheses about the causes of aging, it is difficult to confuse the cause with the effect or to base on the false correlation between parameter and age as in the previous one. However, there is always a chance that this is not the main reason of aging. In this case, the variability of the index will be great, and the predictive power will be minimal.
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Omics. Analysis of age-related correlations among the big data obtained from the analysis of various “omics”: genome, epigenome, transcriptome, metabolome, proteome, microbiome. The main advantage of this approach is that we can assume that we know nothing about the causes of aging at the moment and analyze all possible data of the single person. Deep learning approach. The most up to date and complex way to identify biomarkers of human aging is based on the utilization of deep neural networks which may be trained on any type of appropriate (usually omics) biological data to predict the subject’s age.
Currently, various national projects for biobanking of samples obtained from many people at different ages for subsequent omics analysis exist. The most approximate to aging research omics projects are RNASeq of different tissues of twins (EUROBATS, http://eurobats.eu/), different omics of 3200 subjects (MARK-AGE, (Bürkle et al., 2015)), genomic data of 75,244 participants (UKBiobank, (Pilling et al., 2016)) and multi-omics biobank BBMRI.nl. A multi-omics approach is the most promising due to fast development of the world biobank network, a collection of big data sets, the improvement of bioinformatics methods and artificial intelligence in data analysis (Zhavoronkov et al., 2019). It will allow in the near future to significantly deepen our understanding of aging, and to translate into the clinic the most robust methods for assessing biological age.
Multi-omics approach becomes a golden standard in different fields of bioscience. It gives a deep view of multilayer functional molecular landscape, and it deciphers the complex plexuses of pathways and opens the holistic view of studied processes. Aging as a multifactorial process consequently needs complex approach to be formulated which provides emergence of hidden associations and pathways which may be critical for both geriatric patient and specialist or gerontologist. Multi-omics approach augments the number of obtained markers for biological age measurement and of targets for anti-aging interventions. In the current work we review studies which may impact omics methods common for geriatric practice and fundamental gerontology.
Section snippets
Biological vs. chronological age
The usage of chronological age is a common practice in aging studies. But the substrate of aging makes the chronological age an uninformative indicator of aging rate. The existence of premature aging phenomena and tough connection of several chronic diseases with natural manifestations of aging creates a gap between predictive quality of chronological and biological ages. Taking into account the heterochronous origin of aging, the measurement of biological age becomes a complex task based on
Methylation aging clocks
The term “DNA methylation aging clock” was suggested by Horvath (2013). Nowadays the DNA methylation-based method of biological age estimation has three widely accepted interpretations: Horvath’s, Hannum’s and Levine’s clock (Hannum et al., 2013; Levine et al., 2018). All that methodologies are accurately discussed in Horvath and Raj (2018). Epigenetic clock is a group of “age estimators”, modified CpG islands that are collected all together in one algorithm to estimate biological (epigenetic)
Conclusion
Recently original multi-omics studies begin to appear regularly. In the present work we reviewed metabolome-transcriptome studies, methylome-transcriptome research and numerous traditional omics studies which were dedicated to biological age prediction. For translational gerontology new informative biomarkers found in revisions of existing datasets as well as new approaches of biological age measurement are of great value. Possibly, the most popular and rational approach to biological age
Acknowledgements
The study was carried out within the framework of the state task on theme "Molecular-genetic mechanisms of aging, lifespan, and stress resistance of Drosophila melanogaster", state registration № АААА-А18-118011120004-5, “Development of gerorotective and radioprotective drugs”, state registration № АААА-А19-119021590022-2, and complex UrB RAS Programme № 18-7-4-23 "A combination of factors of different nature (low temperature, lack of lighting, restrictive diet, and geroprotector) to maximize
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