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

Dayoon Kwon, Daniel W Belsky, Dayoon Kwon, Daniel W Belsky

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.

Keywords: Aging; Biological age; Biomarkers; CALERIE; Geroscience; Healthspan.

Conflict of interest statement

The authors declare no competing interests.

© 2021. The Author(s), under exclusive licence to American Aging Association.

Figures

Fig. 1
Fig. 1
Correlations of published versions of Klemera-Doubal method (KDM) biological age and PhenoAge with versions of KDM biological age, PhenoAge, and a measure computed using the homeostatic dysregulation method based on a modified set of biomarkers. The figure plots data from NHANES IV generated with the _nhanes commands within the BioAge package. All measures were developed from analysis of NHANES III and computed using data from NHANES IV. KDM biological age and PhenoAge values were differenced from chronological age for analysis. The “Levine Original” KDM algorithm was composed from chronological age and 10 biomarkers: albumin, alkaline phosphatase, blood urea nitrogen, creatinine, C-reactive protein, cytomegalovirus optical density, HbA1C, total cholesterol, systolic blood pressure, and forced expiratory volume in 1 s (FEV1). The “Levine Original” PhenoAge was composed from chronological age and 9 biomarkers: albumin, alkaline phosphatase, creatinine, C-reactive protein, fasting glucose, white blood cell count, lymphocyte percentage, mean cell volume, and red cell distribution width. The “V2” versions of the KDM, PhenoAge, and HD algorithms included chronological age and 12 biomarkers: albumin, alkaline phosphatase, blood urea nitrogen, creatinine, C-reactive protein, HbA1C, total cholesterol, uric acid, white blood cell count, lymphocyte percentage, mean cell volume, and red cell distribution width
Fig. 2
Fig. 2
Associations of Klemera-Doubal method (KDM) biological age, PhenoAge, and homeostatic dysregulation (HD) measures of biological age with chronological age among participants in the CALERIE trial at pre-intervention baseline. The figure plots pre-intervention baseline values of the three biological aging measures against chronological age for men (blue) and women (pink) participating in the CALERIE trial (n = 207)
Fig. 3
Fig. 3
Change in Klemera-Doubal method (KDM) biological age, PhenoAge, and homeostatic dysregulation (HD) from baseline to 12- and 24-month follow-ups in the ad libitum (dark blue dots) and caloric restriction (light blue triangles) groups of the CALERIE trial. The figure plots predicted values and 95% confidence intervals estimated from mixed-effects growth models for participants in the ad libitum control group (dark blue circles, solid line) and caloric restriction intervention group (light blue triangles, dashed line). Values of KDM biological age and PhenoAge are denominated in years. Values of HD are denominated in log Mahalanobis distance units

References

    1. Kirkwood TB. Understanding the odd science of aging. Cell. 2005;120(4):437–447. doi: 10.1016/j.cell.2005.01.027.
    1. Kennedy BK, Berger SL, Brunet A, Campisi J, Cuervo AM, Epel ES, et al. Geroscience: linking aging to chronic disease. Cell. 2014;159(4):709–713. doi: 10.1016/j.cell.2014.10.039.
    1. Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153(6):1194–1217. doi: 10.1016/j.cell.2013.05.039.
    1. 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):183–192. doi: 10.1038/s41586-019-1365-2.
    1. Kaeberlein M, Rabinovitch PS, Martin GM. Healthy aging: the ultimate preventative medicine. Science. 2015;350(6265):1191–1193. doi: 10.1126/science.aad3267.
    1. Barzilai N, Cuervo AM, Austad S. Aging as a biological target for prevention and therapy. Jama-J Am Med Assoc. 2018;320(13):1321–1322. doi: 10.1001/jama.2018.9562.
    1. Sierra F. Special issue: moving geroscience into uncharted waters: Guest Editorial Moving Geroscience Into Uncharted Waters. J Gerontol a-Biol. 2016;71(11):1385–1387. doi: 10.1093/gerona/glw087.
    1. 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. doi: 10.1111/acel.13080.
    1. 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):1415–1423. doi: 10.1093/gerona/glw126.
    1. 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):210–215. doi: 10.1093/gerona/glw191.
    1. 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):359–367. doi: 10.1016/j.molcel.2012.10.016.
    1. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115. doi: 10.1186/gb-2013-14-10-r115.
    1. 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. 10.18632/aging.101414.
    1. 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. 10.18632/aging.101684.
    1. 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;143:3763–3775. doi: 10.1093/brain/awaa334.
    1. 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). 10.1186/gb-2014-15-2-r24
    1. 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. doi: 10.1080/15592294.2017.1389361.
    1. 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). 10.1186/s13073-019-0667-1.
    1. Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet. 2018;19(6):371–384. doi: 10.1038/s41576-018-0004-3.
    1. 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):1220–1230. doi: 10.1093/aje/kwx346.
    1. 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. 10.7554/eLife.51507.
    1. 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):757–762. doi: 10.1093/gerona/glx144.
    1. 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):644–651. doi: 10.1111/acel.12591.
    1. Hastings WJ, Shalev I, Belsky DW. Comparability of biological aging measures in the National Health and Nutrition Examination Study, 1999–2002. Psychoneuroendocrinology. 2019;106:171–178. doi: 10.1016/j.psyneuen.2019.03.012.
    1. Levine ME, Crimmins EM. Evidence of accelerated aging among African Americans and its implications for mortality. Soc Sci Med. 2014;118:27–32. doi: 10.1016/j.socscimed.2014.07.022.
    1. 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). 10.1038/s41598-020-77082-2.
    1. Klemera P, Doubal S. A new approach to the concept and computation of biological age. Mech Ageing Dev. 2006;127(3):240–248. doi: 10.1016/j.mad.2005.10.004.
    1. 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):110–117. doi: 10.1016/j.mad.2013.01.004.
    1. 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):667–674. doi: 10.1093/gerona/gls233.
    1. 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–E4110. doi: 10.1073/pnas.1506264112.
    1. 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. doi: 10.1093/gerona/glx096.
    1. 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. doi: 10.1007/s13524-017-0644-5.
    1. 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):1103–1112. doi: 10.1111/acel.12402.
    1. 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. doi: 10.1371/journal.pmed.1002718.
    1. Cohen AA. Complex systems dynamics in aging: new evidence, continuing questions. Biogerontology. 2016;17(1):205–220. doi: 10.1007/s10522-015-9584-x.
    1. Mahalanobis PC. Mahalanobis distance. Proceedings of the National Academy of Sciences, India. 1936;73:1370–1376.
    1. 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 doi: 10.1093/gerona/glz219.
    1. 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):667–674. doi: 10.1093/gerona/gls233.
    1. 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.
    1. 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):1097–1104. doi: 10.1093/gerona/glv057.
    1. 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):673–683. doi: 10.1016/S2213-8587(19)30151-2.
    1. Gladyshev VN. The ground zero of organismal life and aging. Trends Mol Med. 2021;27(1):11–19. doi: 10.1016/j.molmed.2020.08.012.
    1. 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. 10.1101/2021.03.02.21252685.
    1. 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. doi: 10.1371/journal.pmed.1002827.
    1. 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):1671–1679. doi: 10.1093/gerona/glz219.

Source: PubMed

3
Suscribir