Biological Age is a Universal Marker of Aging, Stress, and Frailty

  • Timothy V. PyrkovEmail author
  • Peter O. Fedichev
Part of the Healthy Ageing and Longevity book series (HAL, volume 10)


We carried out a systematic investigation of supervised learning techniques for biological age modeling. The biological aging acceleration is associated with the remaining health- and life-span. Artificial Deep Neural Networks (DNN) could be used to reduce the error of chronological age predictors, though often at the expense of the ability to distinguish health conditions. Mortality and morbidity hazards models based on survival follow-up data showed the best performance. Alternatively, logistic regression trained to identify chronic diseases was shown to be a good approximation of hazards models when data on survival follow-up times were unavailable. In all models, the biological aging acceleration was associated with disease burden in persons with diagnosed chronic age-related conditions. For healthy individuals, the same quantity was associated with molecular markers of inflammation (such as C-reactive protein), smoking, current physical, and mental health (including sleeping troubles, feeling tired or little interest in doing things). The biological age thus emerged as a universal biomarker of age, frailty and stress for applications involving large scale studies of the effects of longevity drugs on risks of diseases and quality of life.


Biological age Frailty index Blood sample Questionnaire Self-reported NHANES Inflammation Physical health Mental health 



The authors would like to thank Konstantin Avchaciov from Gero team for proof reading and thoughtful comments. The work was supported by Gero LLC.


  1. Abbott RD (1985) Logistic regression in survival analysis. Am J Epidemiol 121(3):465–471CrossRefGoogle Scholar
  2. Baird GS, Nelson SK, Keeney TR, Stewart A, Williams S, Kraemer S, Peskind ER, Montine TJ (2012) Age-dependent changes in the cerebrospinal fluid proteome by slow off-rate modified aptamer array. Am J Pathol 180(2):446–56. Scholar
  3. Barzilai N, Rennert G (2012) The rationale for delaying aging and the prevention of age-related diseases. Rambam Maimonides Med J 3(4)CrossRefGoogle Scholar
  4. Bender R, Augustin T, Blettner M (2005) Generating survival times to simulate cox proportional hazards models. Stat Med 24(11):1713–1723CrossRefGoogle Scholar
  5. Bobrov E, Georgievskaya A, Kiselev K, Sevastopolsky A, Zhavoronkov A, Gurov S, Rudakov K, Tobar MdPB, Jaspers S, Clemann S (2018) Photoageclock: deep learning algorithms for development of non-invasive visual biomarkers of aging. Aging (Albany NY) 10(11):3249CrossRefGoogle Scholar
  6. Christiansen L, Lenart A, Tan Q, Vaupel JW, Aviv A, McGue M, Christensen K (2016) DNA methylation age is associated with mortality in a longitudinal Danish twin study. Aging Cell 15(1):149–54. Scholar
  7. Cox DR (1992) Regression models and life-tables. In: Breakthroughs in statistics. Springer, pp 527–541Google Scholar
  8. Doll R, Peto R, Boreham J, Sutherland I (2004) Mortality in relation to smoking: 50 years’ observations on male british doctors. BMJ 328(7455):1519CrossRefGoogle Scholar
  9. Enroth S, Enroth SB, Johansson A, Gyllensten U (2015) Protein profiling reveals consequences of lifestyle choices on predicted biological aging. Sci Rep 5Google Scholar
  10. Fedichev PO (2018) Hacking aging: a strategy to use big data from medical studies to extend human life. Front Gen 9:483Google Scholar
  11. Gao X, Zhang Y, Saum KU, Schöttker B, Breitling LP, Brenner H (2016) Tobacco smoking and smoking-related DNA methylation are associated with the development of frailty among older adults. Epigenetics (just-accepted)Google Scholar
  12. Gompertz B (1820) A sketch of an analysis and notation applicable to the value of life contingencies. Philos Trans R Soc 110:214–294CrossRefGoogle Scholar
  13. Green MS, Symons MJ (1983) A comparison of the logistic risk function and the proportional hazards model in prospective epidemiologic studies. J Chronic Dis 36(10):715–723CrossRefGoogle Scholar
  14. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, Bibikova M, Fan JB, Gao Y et al (2013) Genome-wide methylation profiles reveal quantitative views of human aging rates. Molecular Cell 49(2):359–367CrossRefGoogle Scholar
  15. Heikkilä K, Ebrahim S, Lawlor DA (2007) A systematic review of the association between circulating concentrations of C reactive protein and cancer. J Epidemiol Commun Health 61(9):824–833CrossRefGoogle Scholar
  16. Horvath S (2013) Dna methylation age of human tissues and cell types. Genome Biol 14(10):3156CrossRefGoogle Scholar
  17. Horvath S, Levine AJ (2015) HIV-1 infection accelerates age according to the epigenetic clock. J Infect Dis 212(10):1563–73. Scholar
  18. Horvath S, Erhart W, Brosch M, Ammerpohl O, von Schönfels W, Ahrens M, Heits N, Bell JT, Tsai PC, Spector TD et al (2014a) Obesity accelerates epigenetic aging of human liver. Proc Natl Acad Sci 111(43):15538–15543CrossRefGoogle Scholar
  19. Horvath S, Erhart W, Brosch M, Ammerpohl O, von Schönfels W, Ahrens M, Heits N, Bell JT, Tsai PC, Spector TD, Deloukas P, Siebert R, Sipos B, Becker T, Röucken C, Schafmayer C, Hampe J, (2014b) Obesity accelerates epigenetic aging of human liver. Proc Natl Acad Sci USA 111(43):15538–15543. Scholar
  20. Horvath S, Garagnani P, Bacalini MG, Pirazzini C, Salvioli S, Gentilini D, Blasio AMD, Giuliani C, Tung S, Vinters HV, Franceschi C (2015a) Accelerated epigenetic aging in Down syndrome. Aging Cell 14(3):491–5. Scholar
  21. Horvath S, Pirazzini C, Bacalini MG, Gentilini D, Blasio AMD, Delledonne M, Mari D, Arosio B, Monti D, Passarino G, Rango FD, D’Aquila P, Giuliani C, Marasco E, Collino S, Descombes P, Garagnani P, Franceschi C (2015b) Decreased epigenetic age of PBMCs from Italian semi-supercentenarians and their offspring. Aging (Albany NY) 7(12):1159–1170 Scholar
  22. Jia L, Zhang W, Jia R, Zhang H, Chen X (2016) Construction formula of biological age using the principal component analysis. BioMed Research intGoogle Scholar
  23. Kristic J, Vuckovic F, Menni C, Klaric L, Keser T, Beceheli I, Pucic-Bakovic M, Novokmet M, Mangino M, Thaqi K, Rudan P, Novokmet N, Sarac J, Missoni S, Kolcic I, Polasek O, Rudan I, Campbell H, Hayward C, Aulchenko Y, Valdes A, Wilson JF, Gornik O, Primorac D, Zoldos V, Spector T, Lauc G (2014) Glycans are a novel biomarker of chronological and biological ages. J Gerontol A Biol Sci Med Sci 69(7):779–89. Scholar
  24. Levine ME (2013) Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age? J Gerontol A Biol Sci Med Sci 68(6):667–674CrossRefGoogle Scholar
  25. Levine ME, Crimmins EM (2014) A comparison of methods for assessing mortality risk. Am J Hum Biol 26(6):768–776CrossRefGoogle Scholar
  26. Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, Hou L, Baccarelli AA, Stewart JD, Li Y et al (2018) An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY) 10(4):573CrossRefGoogle Scholar
  27. Liu Z, Kuo PL, Horvath S, Crimmins E, Ferrucci L, Levine M (2018) Phenotypic age: a novel signature of mortality and morbidity risk. p 363291Google Scholar
  28. Makeham WM (1860) On the law of mortality and construction of annuity tables. Assur Mag J Inst Actuaries 8(06):301–310CrossRefGoogle Scholar
  29. Marioni RE, Shah S, McRae AF, Chen BH, Colicino E, Harris SE, Gibson J, Henders AK, Redmond P, Cox SR et al (2015) Dna methylation age of blood predicts all-cause mortality in later life. Genome Biol 16(1):1CrossRefGoogle Scholar
  30. Mitnitski A, Rockwood K (2016) The rate of aging: the rate of deficit accumulation does not change over the adult life span. Biogerontology 17(1):199–204CrossRefGoogle Scholar
  31. Nakamura E, Miyao K (2007) A method for identifying biomarkers of aging and constructing an index of biological age in humans. J Gerontol Ser A: Biol Sci Med Sci 62(10):1096–1105CrossRefGoogle Scholar
  32. Nakamura E, Miyao K, Ozeki T (1988) Assessment of biological age by principal component analysis. Mech Ageing Dev 46(1–3):1–18CrossRefGoogle Scholar
  33. Niccoli T, Partridge L (2012) Ageing as a risk factor for disease. Current Biol 22(17):R741–R752CrossRefGoogle Scholar
  34. Odamaki T, Kato K, Sugahara H, Hashikura N, Takahashi S, Xiao JZ, Abe F, Osawa R (2016) Age-related changes in gut microbiota composition from newborn to centenarian: a cross-sectional study. BMC Microb 16:90CrossRefGoogle Scholar
  35. Park J, Cho B, Kwon H, Lee C (2009) Developing a biological age assessment equation using principal component analysis and clinical biomarkers of aging in Korean men. Arch Gerontol Geriatr 49(1):7–12CrossRefGoogle Scholar
  36. Peters MJ, Joehanes R, Pilling LC, Schurmann C, Conneely KN, Powell J, Reinmaa E, Sutphin GL, Zhernakova A, Schramm K, Wilson YA, Kobes S, Tukiainen T, Ramos YF, Goring HH, Fornage M, Liu Y, Gharib SA, Stranger BE, De Jager PL, Aviv A, Levy D, Murabito JM, Munson PJ, Huan T, Hofman A, Uitterlinden AG, Rivadeneira F, van Rooij J, Stolk L, Broer L, Verbiest MM, Jhamai M, Arp P, Metspalu A, Tserel L, Milani L, Samani NJ, Peterson P, Kasela S, Codd V, Peters A, Ward-Caviness CK, Herder C, Waldenberger M, Roden M, Singmann P, Zeilinger S, Illig T, Homuth G, Grabe HJ, Volzke H, Steil L, Kocher T, Murray A, Melzer D, Yaghootkar H, Bandinelli S, Moses EK, Kent JW, Curran JE, Johnson MP, Williams-Blangero S, Westra HJ, McRae AF, Smith JA, Kardia SL, Hovatta I, Perola M, Ripatti S, Salomaa V, Henders AK, Martin NG, Smith AK, Mehta D, Binder EB, Nylocks KM, Kennedy EM, Klengel T, Ding J, Suchy-Dicey AM, Enquobahrie DA, Brody J, Rotter JI, Chen YD, Houwing-Duistermaat J, Kloppenburg M, Slagboom PE, Helmer Q, den Hollander W, Bean S, Raj T, Bakhshi N, Wang QP, Oyston LJ, Psaty BM, Tracy RP, Montgomery GW, Turner ST, Blangero J, Meulenbelt I, Ressler KJ, Yang J, Franke L, Kettunen J, Visscher PM, Neely GG, Korstanje R, Hanson RL, Prokisch H, Ferrucci L, Esko T, Teumer A, van Meurs JB, Johnson AD, Nalls MA, Hernandez DG, Cookson MR, Gibbs RJ, Hardy J, Ramasamy A, Zonderman AB, Dillman A, Traynor B, Smith C, Longo DL, Trabzuni D, Troncoso J, van der Brug M, Weale ME, OBrien R, Johnson R, Walker R, Zielke RH, Arepalli S, Ryten M, Singleton AB (2015) The transcriptional landscape of age in human peripheral blood. Nat Commun 6:8570Google Scholar
  37. Podolskiy D, Molodtcov I, Zenin A, Kogan V, Menshikov L, Gladyshev V, Reis RJS, Fedichev P (2015) Critical dynamics of gene networks is a mechanism behind ageing and Gompertz law. arXiv preprint arXiv:150204307
  38. Podolskiy DI, Lobanov AV, Kryukov GV, Gladyshev VN (2016) Analysis of cancer genomes reveals basic features of human aging and its role in cancer development. Nat Commun 7:12157CrossRefGoogle Scholar
  39. Putin E, Mamoshina P, Aliper A, Korzinkin M, Moskalev A, Kolosov A, Ostrovskiy A, Cantor C, Vijg J, Zhavoronkov A (2016) Deep biomarkers of human aging: application of deep neural networks to biomarker development. Aging (Albany NY) 8(5):1021CrossRefGoogle Scholar
  40. Pyrkov TV, Getmantsev E, Zhurov B, Avchaciov K, Pyatnitskiy M, Menshikov L, Khodova K, Gudkov AV, Fedichev PO (2018a) Quantitative characterization of biological age and frailty based on locomotor activity records. Aging 10(10):2973–2990.,
  41. Pyrkov TV, Slipensky K, Barg M, Kondrashin A, Zhurov B, Zenin A, Pyatnitskiy M, Menshikov L, Markov S, Fedichev PO (2018b) Extracting biological age from biomedical data via deep learning: too much of a good thing? Sci Rep 8(1):5210Google Scholar
  42. Ringnér M (2008) What is principal component analysis? Nat Biotechnol 26(3):303CrossRefGoogle Scholar
  43. Tarkhov AE, Menshikov LI, Fedichev PO (2017) Strehler-mildvan correlation is a degenerate manifold of Gompertz fit. J Theor Biol 416:180–189CrossRefGoogle Scholar
  44. WHO (2016) World health statistics 2016: monitoring health for the SDGs sustainable development goals. World Health OrganizationGoogle Scholar
  45. Yu R, Wu WC, Leung J, Hu SC, Woo J (2017) Frailty and its contributory factors in older adults: a comparison of two asian regions (Hong Kong and Taiwan). Int J Environ Res Public Health 14(10):1096CrossRefGoogle Scholar
  46. Zenin A, Tsepilov Y, Sharapov S, Getmantsev E, Menshikov L, Fedichev PO, Aulchenko Y (2019) Identification of 12 genetic loci associated with human healthspan. Commun Biol 2(1):41CrossRefGoogle Scholar
  47. Zhang X, Justice AC, Hu Y, Wang Z, Zhao H, Wang G, Johnson EO, Emu B, Sutton RE, Krystal JH et al (2016) Epigenome-wide differential dna methylation between HIV-infected and uninfected individuals. Epigenetics 11(10):750–760CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Gero LLCMoscowRussia
  2. 2.Moscow Institute of Physics and TechnologyDolgoprudnyRussia

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