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A toolkit for quantification of biological age from blood chemistry and organ function test data: BioAge

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Abstract

Methods to quantify biological aging are emerging as new measurement tools for epidemiology and population science and have been proposed as surrogate measures for healthy lifespan extension in geroscience clinical trials. Publicly available software packages to compute biological aging measurements from DNA methylation data have accelerated dissemination of these measures and generated rapid gains in knowledge about how different measures perform in a range of datasets. Biological age measures derived from blood chemistry data were introduced at the same time as the DNA methylation measures and, in multiple studies, demonstrate superior performance to these measures in prediction of healthy lifespan. However, their dissemination has been slow by comparison, resulting in a significant gap in knowledge. We developed a software package to help address this knowledge gap. The BioAge R package, available for download at GitHub (http://github.com/dayoonkwon/BioAge), implements three published methods to quantify biological aging based on analysis of chronological age and mortality risk: Klemera-Doubal biological age, PhenoAge, and homeostatic dysregulation. The package allows users to parametrize measurement algorithms using custom sets of biomarkers, to compare the resulting measurements to published versions of the Klemera-Doubal method and PhenoAge algorithms, and to score the measurements in new datasets. We applied BioAge to safety lab data from the CALERIE™ randomized controlled trial, the first-ever human trial of long-term calorie restriction in healthy, non-obese adults, to test effects of intervention on biological aging. Results contribute evidence that CALERIE intervention slowed biological aging. BioAge is a toolkit to facilitate measurement of biological age for geroscience.

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Data availability

All data used in this manuscript are publicly available. Data from the US National Health and Nutrition Examinations Surveys are available from the US Centers for Disease Control and Prevention at https://wwwn.cdc.gov/nchs/nhanes/Default.aspx. Data from the CALERIE trial are available from the CALERIE Research Network at https://calerie.duke.edu/samples-data-access-and-analysis.

Code availability

All the code used in these analyses is covered by GPL-3.0 License and is available from GitHub: https://github.com/dayoonkwon/BioAge.

References

  1. Kirkwood TB. Understanding the odd science of aging. Cell. 2005;120(4):437–47. https://doi.org/10.1016/j.cell.2005.01.027.

    Article  CAS  PubMed  Google Scholar 

  2. Kennedy BK, Berger SL, Brunet A, Campisi J, Cuervo AM, Epel ES, et al. Geroscience: linking aging to chronic disease. Cell. 2014;159(4):709–13. https://doi.org/10.1016/j.cell.2014.10.039.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153(6):1194–217. https://doi.org/10.1016/j.cell.2013.05.039.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Campisi J, Kapahi P, Lithgow GJ, Melov S, Newman JC, Verdin E. From discoveries in ageing research to therapeutics for healthy ageing. Nature. 2019;571(7764):183–92. https://doi.org/10.1038/s41586-019-1365-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Kaeberlein M, Rabinovitch PS, Martin GM. Healthy aging: the ultimate preventative medicine. Science. 2015;350(6265):1191–3. https://doi.org/10.1126/science.aad3267.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Barzilai N, Cuervo AM, Austad S. Aging as a biological target for prevention and therapy. Jama-J Am Med Assoc. 2018;320(13):1321–2. https://doi.org/10.1001/jama.2018.9562.

    Article  Google Scholar 

  7. Sierra F. Special issue: moving geroscience into uncharted waters: Guest Editorial Moving Geroscience Into Uncharted Waters. J Gerontol a-Biol. 2016;71(11):1385–7. https://doi.org/10.1093/gerona/glw087.

    Article  Google Scholar 

  8. Ferrucci L, Gonzalez-Freire M, Fabbri E, Simonsick E, Tanaka T, Moore Z, et al. Measuring biological aging in humans: a quest. Aging Cell. 2020;19(2): e13080. https://doi.org/10.1111/acel.13080.

    Article  CAS  PubMed  Google Scholar 

  9. Justice J, Miller JD, Newman JC, Hashmi SK, Halter J, Austad SN, et al. Frameworks for proof-of-concept clinical trials of interventions that target fundamental aging processes. J Gerontol a-Biol. 2016;71(11):1415–23. https://doi.org/10.1093/gerona/glw126.

    Article  Google Scholar 

  10. Moffitt TE, Belsky DW, Danese A, Poulton R, Caspi A. The longitudinal study of aging in human young adults: knowledge gaps and research agenda. J Gerontol A Biol Sci Med Sci. 2017;72(2):210–5. https://doi.org/10.1093/gerona/glw191.

    Article  PubMed  Google Scholar 

  11. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49(2):359–67. https://doi.org/10.1016/j.molcel.2012.10.016.

    Article  CAS  PubMed  Google Scholar 

  12. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115. https://doi.org/10.1186/gb-2013-14-10-r115.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY). 2018;10(4):573–91. https://doi.org/10.18632/aging.101414.

  14. Lu AT, Quach A, Wilson JG, Reiner AP, Aviv A, Raj K, et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging-Us. 2019;11(2):303–27. https://doi.org/10.18632/aging.101684.

  15. Shireby GL, Davies JP, Francis PT, Burrage J, Walker EM, Neilson GWA, et al. Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex. Brain. 2020;143:3763–75. https://doi.org/10.1093/brain/awaa334.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Weidner CI, Lin Q, Koch CM, Eisele L, Beier F, Ziegler P, et al. Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biology. 2014;15(2). https://doi.org/10.1186/gb-2014-15-2-r24

  17. Youn A, Wang S. The MiAge calculator: a DNA methylation-based mitotic age calculator of human tissue types. Epigenetics-Us. 2018;13(2):192–206. https://doi.org/10.1080/15592294.2017.1389361.

    Article  Google Scholar 

  18. Zhang Q, Vallerga CL, Walker RM, Lin T, Henders AK, Montgomery GW, et al. Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing. Genome Med. 2019;11(1). https://doi.org/10.1186/s13073-019-0667-1.

  19. Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet. 2018;19(6):371–84. https://doi.org/10.1038/s41576-018-0004-3.

    Article  CAS  PubMed  Google Scholar 

  20. Belsky DW, Moffitt TE, Cohen AA, Corcoran DL, Levine ME, Prinz JA, et al. Eleven telomere, epigenetic clock, and biomarker-composite quantifications of biological aging: do they measure the same thing? Am J Epidemiol. 2018;187(6):1220–30. https://doi.org/10.1093/aje/kwx346.

    Article  PubMed  Google Scholar 

  21. Li X, Ploner A, Wang Y, Magnusson PK, Reynolds C, Finkel D, et al. Longitudinal trajectories, correlations and mortality associations of nine biological ages across 20-years follow-up. Elife. 2020;9. https://doi.org/10.7554/eLife.51507.

  22. Murabito JM, Zhao Q, Larson MG, Rong J, Lin H, Benjamin EJ, et al. Measures of biologic age in a community sample predict mortality and age-related disease: the Framingham Offspring Study. J Gerontol A Biol Sci Med Sci. 2018;73(6):757–62. https://doi.org/10.1093/gerona/glx144.

    Article  PubMed  Google Scholar 

  23. Belsky DW, Caspi A, Cohen HJ, Kraus WE, Ramrakha S, Poulton R, et al. Impact of early personal-history characteristics on the pace of aging: implications for clinical trials of therapies to slow aging and extend healthspan. Aging Cell. 2017;16(4):644–51. https://doi.org/10.1111/acel.12591.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Hastings WJ, Shalev I, Belsky DW. Comparability of biological aging measures in the National Health and Nutrition Examination Study, 1999–2002. Psychoneuroendocrinology. 2019;106:171–8. https://doi.org/10.1016/j.psyneuen.2019.03.012.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Levine ME, Crimmins EM. Evidence of accelerated aging among African Americans and its implications for mortality. Soc Sci Med. 2014;118:27–32. https://doi.org/10.1016/j.socscimed.2014.07.022.

    Article  CAS  PubMed  Google Scholar 

  26. Shirazi TN, Hastings WJ, Rosinger AY, Ryan CP. Parity predicts biological age acceleration in post-menopausal, but not pre-menopausal, women. Sci Rep-Uk. 2020;10(1). https://doi.org/10.1038/s41598-020-77082-2.

  27. Klemera P, Doubal S. A new approach to the concept and computation of biological age. Mech Ageing Dev. 2006;127(3):240–8. https://doi.org/10.1016/j.mad.2005.10.004.

    Article  PubMed  Google Scholar 

  28. Cohen AA, Milot E, Yong J, Seplaki CL, Fulop T, Bandeen-Roche K, et al. A novel statistical approach shows evidence for multi-system physiological dysregulation during aging. Mech Ageing Dev. 2013;134(3–4):110–7. https://doi.org/10.1016/j.mad.2013.01.004.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Levine ME. Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age? J Gerontol a-Biol. 2013;68(6):667–74. https://doi.org/10.1093/gerona/gls233.

    Article  Google Scholar 

  30. Belsky DW, Caspi A, Houts R, Cohen HJ, Corcoran DL, Danese A, et al. Quantification of biological aging in young adults. Proc Natl Acad Sci USA. 2015;112(30):E4104–10. https://doi.org/10.1073/pnas.1506264112.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Belsky DW, Huffman KM, Pieper CF, Shalev I, Kraus WE. Change in the rate of biological aging in response to caloric restriction: CALERIE Biobank Analysis. J Gerontol A Biol Sci Med Sci. 2017;73(1):4–10. https://doi.org/10.1093/gerona/glx096.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Levine ME, Crimmins EM. Is 60 the new 50? Examining changes in biological age over the past two decades. Demography. 2018;55(2):387–402. https://doi.org/10.1007/s13524-017-0644-5.

    Article  PubMed  Google Scholar 

  33. Li Q, Wang S, Milot E, Bergeron P, Ferrucci L, Fried LP, et al. Homeostatic dysregulation proceeds in parallel in multiple physiological systems. Aging Cell. 2015;14(6):1103–12. https://doi.org/10.1111/acel.12402.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Liu Z, Kuo PL, Horvath S, Crimmins E, Ferrucci L, Levine M. A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: a cohort study. PLoS Med. 2018;15(12): e1002718. https://doi.org/10.1371/journal.pmed.1002718.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Cohen AA. Complex systems dynamics in aging: new evidence, continuing questions. Biogerontology. 2016;17(1):205–20. https://doi.org/10.1007/s10522-015-9584-x.

    Article  CAS  PubMed  Google Scholar 

  36. Mahalanobis PC. Mahalanobis distance. Proceedings of the National Academy of Sciences, India. 1936;73:1370–6.

    Google Scholar 

  37. Parker DC, Bartlett BN, Cohen HJ, Fillenbaum G, Huebner JL, Kraus VB, et al. Association of blood chemistry quantifications of biological aging with disability and mortality in older adults. J Gerontol A Biol Sci Med Sci. 2019. https://doi.org/10.1093/gerona/glz219.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Levine ME. Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age? J Gerontol A Biol Sci Med Sci. 2013;68(6):667–74. https://doi.org/10.1093/gerona/gls233.

    Article  PubMed  Google Scholar 

  39. Ingram DD, Lochner KA, Cox CS. Mortality experience of the 1986–2000 National Health Interview Survey Linked Mortality Files participants. Vital Health Stat 2. 2008, (147):1–37.

  40. Ravussin E, Redman LM, Rochon J, Das SK, Fontana L, Kraus WE, et al. A 2-year randomized controlled trial of human caloric restriction: feasibility and effects on predictors of health span and longevity. J Gerontol A Biol Sci Med Sci. 2015;70(9):1097–104. https://doi.org/10.1093/gerona/glv057.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kraus WE, Bhapkar M, Huffman KM, Pieper CF, Krupa Das S, Redman LM, et al. 2 years of calorie restriction and cardiometabolic risk (CALERIE): exploratory outcomes of a multicentre, phase 2, randomised controlled trial. Lancet Diabetes Endocrinol. 2019;7(9):673–83. https://doi.org/10.1016/S2213-8587(19)30151-2.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Gladyshev VN. The ground zero of organismal life and aging. Trends Mol Med. 2021;27(1):11–9. https://doi.org/10.1016/j.molmed.2020.08.012.

    Article  CAS  PubMed  Google Scholar 

  43. Graf G, Crowe C, Kothari M, Kwon D, Manly J, Turney I, et al. Testing DNA-methylation and blood-chemistry measures of biological aging in models of Black-White disparities in healthspan characteristics. medRxiv. 2021:2021.03.02.21252685. https://doi.org/10.1101/2021.03.02.21252685.

  44. Liu Z, Chen X, Gill TM, Ma C, Crimmins EM, Levine ME. Associations of genetics, behaviors, and life course circumstances with a novel aging and healthspan measure: evidence from the Health and Retirement Study. PLoS Med. 2019;16(6): e1002827. https://doi.org/10.1371/journal.pmed.1002827.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Parker DC, Bartlett BN, Cohen HJ, Fillenbaum G, Huebner JL, Kraus VB, et al. Association of blood chemistry quantifications of biological aging with disability and mortality in older adults. J Gerontol A Biol Sci Med Sci. 2020;75(9):1671–9. https://doi.org/10.1093/gerona/glz219.

    Article  PubMed  Google Scholar 

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Funding

This work was supported by the National Institute on Aging grants R01AG061378 and R01AG066887 and Russel Sage Foundation BioSS grant 1810–08987. DWB is a fellow of the Canadian Institute for Advanced Research Child Brain Development Network.

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Authors and Affiliations

  1. Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, 90095, USA

    Dayoon Kwon

  2. Robert N. Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA

    Dayoon Kwon & Daniel W. Belsky

  3. Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA

    Daniel W. Belsky

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  1. Dayoon Kwon
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Contributions

DWB and DK conceived the research and designed the software and analysis. DK wrote the software, conducted the analysis, and produced the figures. DWB and DK wrote the manuscript. Both authors had access to the data.

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Correspondence to Daniel W. Belsky.

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Kwon, D., Belsky, D.W. A toolkit for quantification of biological age from blood chemistry and organ function test data: BioAge. GeroScience 43, 2795–2808 (2021). https://doi.org/10.1007/s11357-021-00480-5

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