Mortality: The challenges of estimating biological age

A comparison of nine different approaches over a period of 20 years reveals the most promising indicators for biological age.
  1. Alexey Moskalev  Is a corresponding author
  1. Ural Branch of Russian Academy of Sciences, Russian Federation
  2. Syktyvkar State University, Russian Federation
  3. Russian Academy of Sciences, Russian Federation

To see if treatments to ward off aging work, first we need a way to measure biological age reliably (Moskalev, 2019). Biological age is a complex parameter involving the calendar age of a person, their health as relating to their age, and medical signs of when they might die of old age. Historically, the first estimates of biological age were based on markers that could be measured in the clinic (such as inflammation, glucose resistance, and endocrine markers) and on functional tests (such as cognitive function and cardiorespiratory fitness; reviewed in Jia et al., 2017). Such markers have a direct clinical interpretation, but even if they predict mortality better than passport age, it is unclear to what extent they measure biological aging itself, rather than health deterioration for other reasons. Additionally, these markers often only work well as averaged indicators in very large samples, and vary a lot between individuals. However, it may be possible to overcome these limitations by using artificial intelligence to generate models using several aging biomarkers (Zhavoronkov et al., 2019).

Other approaches, based on a deeper understanding of the molecular and cellular causes of aging, include measuring the levels of p16 (a marker for cellular senescence or when a cell stops dividing) and measuring the telomere length in leukocytes (biological age increases as telomere length decreases; Waaijer et al., 2012; Epel et al., 2009). Theoretically, these markers should be more sensitive to early signs of aging (as opposed to mortality and frailty) but, similar to clinical markers for individual patients, they lack robustness and reproducibility. This is because aging is a multi-level process, so markers of individual mechanisms cannot cover all its aspects.

A third approach is to use ‘omics’ (that is, to analyze the transcriptome, methylome, proteome and metabolome). Changes in the ‘omes’ are the result of changes in the organism at different levels, making them a useful way to approach the complexity of the aging process. Using this approach, there is no single biological age, but rather a metabolic, proteomic or methylome age. Multi-omics approaches have also been used to assess the rate of aging (Solovev et al., 2020).

Within omics, analyses of DNA methylation or epigenetic clocks are the most robust indicator of age-related changes and have become a booming area of research (Bell et al., 2019). But questions still remain. To what extent are epigenetic clocks a function of age, and to what extent part of biological aging? How does the epigenome change with age? How closely are epigenetic clocks associated with mortality? Is it possible to reverse the epigenetic age, for example through lifestyle changes or interventions? Diet, exercise, education and lifestyle factors seem to be able to influence the rate of aging according to the epigenetic clock (Quach et al., 2017; Gensous et al., 2019; Sae-Lee et al., 2018). Certain drugs can slow down the epigenetic clock in cells cultured in the lab (Horvath et al., 2019) and certain treatments have also proved to be effective in vivo (Chen et al., 2019; Fahy et al., 2019).

Now, in eLife, Sara Hägg from the Karolinska Institute and colleagues from the University of California Riverside, Indiana University Southeast and Jönköping University – with Xia Li as first author – study how nine different methods to estimate biological age change over time in a cohort of 845 middle-aged and older individuals from Sweden who were studied over a period of 20 years (Li et al., 2020). Three of the biological ages measured were functional (cognitive function, functional aging index, and frailty index) and four were based on the levels of DNA methylation (called Horvath, Hannum, PhenoAge and GrimAge). The other two were telomere length (measured by qPCR) and physiological age (calculated as a composite score of clinical measurements such as body-mass index or waist circumference, and blood biomarkers such as hemoglobin or cholesterol).

This study is unique because it compares several approaches at once and evaluates how the measurements change over time: functional data and biological samples were collected nine times between 1986 and 2014. The profiles for the three functional measurements indicated that accelerated aging started around the age of 70, whereas the other biological ages showed linear growth with time.

The authors found sex differences in the mean levels of the different biological ages. Women exhibited longer telomere length and lower DNA methylation age compared to men, but also averaged higher in two of the three functional estimates. Telomere length showed the weakest correlations with both chronological age and with the other measurements. The highest correlations were between two of the DNA methylation ages (Horvath and Hannum), and between the functional aging index and the other two functional biological ages. Regarding the ability of biological ages to predict age-related mortality, one of the functional estimates (frailty index) and one of the methylation clocks (GrimAge) were the best predictors, while telomere length was the worst.

These results indicate that methylation age and frailty index are the most promising approaches to estimating biological age, and underline the value of assessing these estimates overtime in the same population.

References

  1. Book
    1. Moskalev A
    (2019) Introduction
    In: Moskalev A, editors. Biomarkers of Human Aging. Cham: Springer International Publishing. pp. 1–4.
    https://doi.org/10.1007/978-3-030-24970-0_1

Article and author information

Author details

  1. Alexey Moskalev

    Alexey Moskalev is in the Institute of Biology, Komi Science Center, Ural Branch of the Russian Academy of Sciences, and Syktyvkar State University, both in Syktyvkar, Russia, and the Engelhard Institute for Molecular Biology, Russian Academy of Sciences, Moscow, Russia

    For correspondence
    amoskalev@list.ru
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3248-1633

Publication history

  1. Version of Record published: February 11, 2020 (version 1)

Copyright

© 2020, Moskalev

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Alexey Moskalev
(2020)
Mortality: The challenges of estimating biological age
eLife 9:e54969.
https://doi.org/10.7554/eLife.54969

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    Whilst timely clinical characterisation of infections caused by novel SARS-CoV-2 variants is necessary for evidence-based policy response, individual-level data on infecting variants are typically only available for a minority of patients and settings.

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    Here, we propose an innovative approach to study changes in COVID-19 hospital presentation and outcomes after the Omicron variant emergence using publicly available population-level data on variant relative frequency to infer SARS-CoV-2 variants likely responsible for clinical cases. We apply this method to data collected by a large international clinical consortium before and after the emergence of the Omicron variant in different countries.

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    Our analysis, that includes more than 100,000 patients from 28 countries, suggests that in many settings patients hospitalised with Omicron variant infection less often presented with commonly reported symptoms compared to patients infected with pre-Omicron variants. Patients with COVID-19 admitted to hospital after Omicron variant emergence had lower mortality compared to patients admitted during the period when Omicron variant was responsible for only a minority of infections (odds ratio in a mixed-effects logistic regression adjusted for likely confounders, 0.67 [95% confidence interval 0.61–0.75]). Qualitatively similar findings were observed in sensitivity analyses with different assumptions on population-level Omicron variant relative frequencies, and in analyses using available individual-level data on infecting variant for a subset of the study population.

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    Bronner P. Gonçalves, Peter Horby, Gail Carson, Piero L. Olliaro, Valeria Balan, Barbara Wanjiru Citarella, and research costs were supported by the UK Foreign, Commonwealth and Development Office (FCDO) and Wellcome [215091/Z/18/Z, 222410/Z/21/Z, 225288/Z/22/Z]; and Janice Caoili and Madiha Hashmi were supported by the UK FCDO and Wellcome [222048/Z/20/Z]. Peter Horby, Gail Carson, Piero L. Olliaro, Kalynn Kennon and Joaquin Baruch were supported by the Bill & Melinda Gates Foundation [OPP1209135]; Laura Merson was supported by University of Oxford’s COVID-19 Research Response Fund - with thanks to its donors for their philanthropic support. Matthew Hall was supported by a Li Ka Shing Foundation award to Christophe Fraser. Moritz U.G. Kraemer was supported by the Branco Weiss Fellowship, Google.org, the Oxford Martin School, the Rockefeller Foundation, and the European Union Horizon 2020 project MOOD (#874850). The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission. Contributions from Srinivas Murthy, Asgar Rishu, Rob Fowler, James Joshua Douglas, François Martin Carrier were supported by CIHR Coronavirus Rapid Research Funding Opportunity OV2170359 and coordinated out of Sunnybrook Research Institute. Contributions from Evert-Jan Wils and David S.Y. Ong were supported by a grant from foundation Bevordering Onderzoek Franciscus; and Andrea Angheben by the Italian Ministry of Health “Fondi Ricerca corrente–L1P6” to IRCCS Ospedale Sacro Cuore–Don Calabria. The data contributions of J.Kenneth Baillie, Malcolm G. Semple, and Ewen M. Harrison were supported by grants from the National Institute for Health Research (NIHR; award CO-CIN-01), the Medical Research Council (MRC; grant MC_PC_19059), and by the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE) (award 200907), NIHR HPRU in Respiratory Infections at Imperial College London with PHE (award 200927), Liverpool Experimental Cancer Medicine Centre (grant C18616/A25153), NIHR Biomedical Research Centre at Imperial College London (award IS-BRC-1215-20013), and NIHR Clinical Research Network providing infrastructure support. All funders of the ISARIC Clinical Characterisation Group are listed in the appendix.

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