Using blood test parameters to define biological age among older adults: association with morbidity and mortality independent of chronological age validated in two separate birth cohorts.
Aging
BASE-II
Bioage
Biological age
Biomarkers
Mortality
Journal
GeroScience
ISSN: 2509-2723
Titre abrégé: Geroscience
Pays: Switzerland
ID NLM: 101686284
Informations de publication
Date de publication:
12 2022
12 2022
Historique:
received:
07
04
2022
accepted:
12
09
2022
pubmed:
24
9
2022
medline:
23
12
2022
entrez:
23
9
2022
Statut:
ppublish
Résumé
Biomarkers defining biological age are typically laborious or expensive to assess. Instead, in the current study, we identified parameters based on standard laboratory blood tests across metabolic, cardiovascular, inflammatory, and kidney functioning that had been assessed in the Berlin Aging Study (BASE) (n = 384) and Berlin Aging Study II (BASE-II) (n = 1517). We calculated biological age using those 12 parameters that individually predicted mortality hazards over 26 years in BASE. In BASE, older biological age was associated with more physician-observed morbidity and higher mortality hazards, over and above the effects of chronological age, sex, and education. Similarly, in BASE-II, biological age was associated with physician-observed morbidity and subjective health, over and above the effects of chronological age, sex, and education as well as alternative biomarkers including telomere length, DNA methylation age, skin age, and subjective age but not PhenoAge. We discuss the importance of biological age as one indicator of aging.
Identifiants
pubmed: 36151431
doi: 10.1007/s11357-022-00662-9
pii: 10.1007/s11357-022-00662-9
pmc: PMC9768057
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2685-2699Informations de copyright
© 2022. The Author(s).
Références
Baltes PB, Lindenberger U, Staudinger UM. Life span theory in developmental psychology. In: Handbook of Child Psychology. John Wiley & Sons, Inc. 2006. https://doi.org/10.1002/9780470147658.chpsy0111
Anne Nelson E, Dannefer D. Aged heterogeneity: fact or fiction? The fate of diversity in gerontological research. Gerontologist. 1992;32(1):17–23. https://doi.org/10.1093/geront/32.1.17 .
doi: 10.1093/geront/32.1.17
Jylhävä J, Pedersen NL, Hägg S. Biological age predictors. EBioMedicine. 2017;21:29–36. https://doi.org/10.1016/j.ebiom.2017.03.046 .
doi: 10.1016/j.ebiom.2017.03.046
Butler RN, Sprott R, Warner H, et al. Biomarkers of aging: from primitive organisms to humans. Published online. 2004. https://academic.oup.com/biomedgerontology/article/59/6/B560/662122 . Accessed 7 Jan 2022.
Johnson TE. Recent results: biomarkers of aging. Exp Gerontol. 2006;41(12):1243–6. https://doi.org/10.1016/J.EXGER.2006.09.006 .
doi: 10.1016/J.EXGER.2006.09.006
Butler RN, Sprott R, Warner H, et al. Biomarkers of aging: from primitive organisms to humans. J Gerontol A Biol Sci Med Sci. 2004;59(6):560–7. https://doi.org/10.1093/GERONA/59.6.B560 .
doi: 10.1093/GERONA/59.6.B560
Bekaert S, de Meyer T, van Oostveldt P. Telomere attrition as ageing biomarker. Anticancer Res. 2005;25:3011–22.
Rode L, Bojesen SE, Weischer M, Vestbo J, Nordestgaard BG. Short telomere length, lung function and chronic obstructive pulmonary disease in 46 396 individuals. Thorax. 2013;68(5):429–35. https://doi.org/10.1136/THORAXJNL-2012-202544 .
doi: 10.1136/THORAXJNL-2012-202544
Weischer M, Bojesen SE, Cawthon RM, Freiberg JJ, Tybjrg-Hansen A, Nordestgaard BG. Short telomere length, myocardial infarction, ischemic heart disease, and early death. Arterioscler Thromb Vasc Biol. 2012;32(3):822–9. https://doi.org/10.1161/ATVBAHA.111.237271 .
doi: 10.1161/ATVBAHA.111.237271
Zee RYL, Castonguay AJ, Barton NS, Germer S, Martin M. Mean leukocyte telomere length shortening and type 2 diabetes mellitus: a case-control study. Transl Res. 2010;155(4):166–9. https://doi.org/10.1016/J.TRSL.2009.09.012 .
doi: 10.1016/J.TRSL.2009.09.012
Saßenroth D, Meyer A, Salewsky B, et al. Sports and exercise at different ages and leukocyte telomere length in later life–data from the Berlin Aging Study II (BASE-II). PLoS One. 2015;10(12):e0142131. https://doi.org/10.1371/JOURNAL.PONE.0142131 .
doi: 10.1371/JOURNAL.PONE.0142131
Meyer A, Salewsky B, Buchmann N, Steinhagen-Thiessen E, Demuth I. Relative leukocyte telomere length, hematological parameters and anemia - data from the Berlin Aging Study II (BASE-II). Gerontology. 2016;62(3):330–6. https://doi.org/10.1159/000430950 .
doi: 10.1159/000430950
Meyer A, Salewsky B, Spira D, Steinhagen-Thiessen E, Kristina N, Demuth I. Leukocyte telomere length is related to appendicular lean mass: cross-sectional data from the Berlin Aging Study II (BASE-II). Am J Clin Nutr. 2016;103(1):178–83. https://doi.org/10.3945/AJCN.115.116806 .
doi: 10.3945/AJCN.115.116806
Hannum G, Guinney J, Zhao L, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49(2):359. https://doi.org/10.1016/J.MOLCEL.2012.10.016 .
doi: 10.1016/J.MOLCEL.2012.10.016
Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):1–20. https://doi.org/10.1186/GB-2013-14-10-R115/COMMENTS .
doi: 10.1186/GB-2013-14-10-R115/COMMENTS
Vetter VM, Meyer A, Karbasiyan M, Steinhagen-Thiessen E, Hopfenmüller W, Demuth I. Epigenetic clock and relative telomere length represent largely different aspects of aging in the Berlin Aging Study II (BASE-II). J Gerontol A Biol Sci Med Sci. 2019;74(1):27–32. https://doi.org/10.1093/GERONA/GLY184 .
doi: 10.1093/GERONA/GLY184
Vidal-Bralo L, Lopez-Golan Y, Gonzalez A. Simplified assay for epigenetic age estimation in whole blood of adults. Front Genet. 2016;7(JUL). https://doi.org/10.3389/FGENE.2016.00126
Banszerus VL, Vetter VM, Salewsky B, König M, Demuth I. Exploring the relationship of relative telomere length and the epigenetic clock in the LipidCardio cohort. Int J Mol Sci. 2019;20(12):3032. https://doi.org/10.3390/IJMS20123032 .
doi: 10.3390/IJMS20123032
Belsky DW, Moffitt TE, Cohen AA, 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 .
doi: 10.1093/AJE/KWX346
Breitling LP, Saum KU, Perna L, Schöttker B, Holleczek B, Brenner H. Frailty is associated with the epigenetic clock but not with telomere length in a German cohort. Clin Epigenetics. 2016;8(1):1–8. https://doi.org/10.1186/S13148-016-0186-5 .
doi: 10.1186/S13148-016-0186-5
Chen BH, Carty CL, Kimura M, et al. Leukocyte telomere length, T cell composition and DNA methylation age. Aging (Albany NY). 2017;9(9):1983. https://doi.org/10.18632/AGING.101293 .
doi: 10.18632/AGING.101293
Marioni RE, Harris SE, Shah S, et al. The epigenetic clock and telomere length are independently associated with chronological age and mortality. Int J Epidemiol. 2018;45(2):424–32. https://doi.org/10.1093/IJE/DYW041 .
doi: 10.1093/IJE/DYW041
Kwon D, Belsky DW. A toolkit for quantification of biological age from blood chemistry and organ function test data: BioAge. Geroscience. 2021;43(6):2795. https://doi.org/10.1007/S11357-021-00480-5 .
doi: 10.1007/S11357-021-00480-5
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 .
doi: 10.1093/GERONA/GLS233
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 .
doi: 10.1016/J.MAD.2005.10.004
Belsky DW, Caspi A, Houts R, et al. Quantification of biological aging in young adults. Proc Natl Acad Sci U S A. 2015;112(30):E4104–10. https://doi.org/10.1073/PNAS.1506264112/-/DCSUPPLEMENTAL .
doi: 10.1073/PNAS.1506264112/-/DCSUPPLEMENTAL
Sebastiani P, Thyagarajan B, Sun F, et al. Biomarker signatures of aging. Aging Cell. 2017;16(2):329–38. https://doi.org/10.1111/ACEL.12557 .
doi: 10.1111/ACEL.12557
Zhang Q, Vallerga CL, Walker RM, et al. Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing. Genome Medicine. 2019;11(1). https://doi.org/10.1186/s13073-019-0667-1
Alpert A, Pickman Y, Leipold M, et al. A clinically meaningful metric of immune age derived from high-dimensional longitudinal monitoring. Nat Med. 2019;25(3):487–95. https://doi.org/10.1038/s41591-019-0381-y .
doi: 10.1038/s41591-019-0381-y
Mayer KU, Maas I, Wagner M. Socioeconomic conditions and social inequalities in old age. In: Baltes PB, Mayer KU, eds. The Berlin Aging Study: Aging from 70 to 100. Cambridge University Press. 1999:227–255. https://psycnet.apa.org/record/1999-08020-006 . Accessed 7 Jan 2022.
Bertram L, Böckenhoff A, Demuth I, et al. Cohort profile: the Berlin Aging Study II (BASE-II). Int J Epidemiol. 2014;43(3):703–12. https://doi.org/10.1093/IJE/DYT018 .
doi: 10.1093/IJE/DYT018
Gerstorf D, Ram N, Lindenberger U, Smith J. Age and time-to-death trajectories of change in indicators of cognitive, sensory, physical, health, social, and self-related functions. Dev Psychol. 2013;49(10):1805–21. https://doi.org/10.1037/A0031340 .
doi: 10.1037/A0031340
Drewelies J, Eibich P, Düzel S, et al. Location, location, location: The role of objective neighborhood characteristics for perceptions of control. Gerontology. Published online 2021:1–10. https://doi.org/10.1159/000515634
Steinhagen-Thiessen E, Borchelt M. Morbidity, medication, and functional limitations in very old age. In: Baltes PB, Mayer KU, eds. The Berlin Aging Study: Aging from 70 to 100. Cambridge University Press. 1999. 131–166 https://psycnet.apa.org/record/1999-08020-003 . Accessed 7 Jan 2022.
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83. https://doi.org/10.1016/0021-9681(87)90171-8 .
doi: 10.1016/0021-9681(87)90171-8
Hill K, Goldstein RS, Guyatt GH, et al. Prevalence and underdiagnosis of chronic obstructive pulmonary disease among patients at risk in primary care. CMAJ. 2010;182(7):673–8. https://doi.org/10.1503/CMAJ.091784 .
doi: 10.1503/CMAJ.091784
Levine ME, Lu AT, Quach A, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging. 2018;10(4):573–91. https://doi.org/10.18632/AGING.101414 .
doi: 10.18632/AGING.101414
R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria; 2017. Retrieved from https://www.R-project.org/ .
Notthoff N, Drewelies J, Kazanecka P, et al. Feeling older, walking slower—but only if someone’s watching. Subjective age is associated with walking speed in the laboratory, but not in real life. Eur J Ageing. 2018;15(4):425–33. https://doi.org/10.1007/s10433-017-0450-3 .
doi: 10.1007/s10433-017-0450-3
Stephan Y, Sutin AR, Terracciano A. Subjective age and personality development: a 10-year study. J Pers. 2015;83(2):142–54. https://doi.org/10.1111/JOPY.12090 .
doi: 10.1111/JOPY.12090
Rubin DC, Berntsen D. People over forty feel 20% younger than their age: subjective age across the lifespan. Psychon Bull Rev. 2006;13(5):776–80. https://doi.org/10.3758/BF03193996 .
doi: 10.3758/BF03193996
Weiss D, Lang FR. “They” are old but “I” feel younger: age-group dissociation as a self-protective strategy in old age. Psychol Aging. 2012;27(1):153–63. https://doi.org/10.1037/A0024887 .
doi: 10.1037/A0024887
Cox DR. Regression models and life-tables. J Roy Stat Soc: Ser B (Methodol). 1972;34(2):187–202. https://doi.org/10.1111/J.2517-6161.1972.TB00899.X .
doi: 10.1111/J.2517-6161.1972.TB00899.X
Kuo CL, Pilling LC, Liu Z, Atkins JL, Levine ME. Genetic associations for two biological age measures point to distinct aging phenotypes. Aging Cell. 2021;20(6). https://doi.org/10.1111/ACEL.13376
Gerstorf D, Hülür G, Drewelies J, et al. Secular changes in late-life cognition and well-being: towards a long bright future with a short brisk ending? Psychol Aging. 2015;30(2):301–10. https://doi.org/10.1037/pag0000016 .
doi: 10.1037/pag0000016
Hülür G, Drewelies J, Eibich P, et al. Cohort differences in psychosocial function over 20 years: current older adults feel less lonely and less dependent on external circumstances. Gerontology. 2016;62(3):354–61. https://doi.org/10.1159/000438991 .
doi: 10.1159/000438991
König M, Drewelies J, Norman K, et al. Historical trends in modifiable indicators of cardiovascular health and self-rated health among older adults: cohort differences over 20 years between the Berlin Aging Study (BASE) and the Berlin Aging Study II (BASE-II). PLoS One. 2018;13(1):e0191699. https://doi.org/10.1371/JOURNAL.PONE.0191699 .
doi: 10.1371/JOURNAL.PONE.0191699
Deelen J, Kettunen J, Fischer K, et al. A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals. Nat Commun. 2019;10(1):1–8. https://doi.org/10.1038/s41467-019-11311-9 .
doi: 10.1038/s41467-019-11311-9
Ashiqur Rahman S, Giacobbi P, Pyles L, Mullett C, Doretto G, Adjeroh DA. Deep learning for biological age estimation. Brief Bioinform. 2021;22(2):1767–81. https://doi.org/10.1093/BIB/BBAA021 .
doi: 10.1093/BIB/BBAA021
Schrempft S, Belsky DW, Draganski B, et al. Associations between life course socioeconomic conditions and the pace of aging. J Gerontol Ser A, Biol Sci Med Sci. 2021. (Advanced online publication). https://doi.org/10.1093/gerona/glab383/6482783
Vidal-Pineiro D, Wang Y, Krogsrud SK, et al. Individual variations in ‘brain age’ relate to early-life factors more than to longitudinal brain change. Elife. 2021;10. https://doi.org/10.7554/eLife.69995