Skip to main content
Erschienen in: European Journal of Epidemiology 8/2019

13.04.2019 | AGEING

Quantification of biological age as a determinant of age-related diseases in the Rotterdam Study: a structural equation modeling approach

verfasst von: Reem Waziry, Luuk Gras, Sanaz Sedaghat, Henning Tiemeier, Gerrit J. Weverling, Mohsen Ghanbari, Jaco Klap, Frank de Wolf, Albert Hofman, M. Arfan Ikram, Jaap Goudsmit

Erschienen in: European Journal of Epidemiology | Ausgabe 8/2019

Einloggen, um Zugang zu erhalten

Abstract

Chronological age alone is not a sufficient measure of the true physiological state of the body. The aims of the present study were to: (1) quantify biological age based on a physiological biomarker composite model; (2) and evaluate its association with death and age-related disease onset in the setting of an elderly population. Using structural equation modeling we computed biological age for 1699 individuals recruited from the first and second waves of the Rotterdam study. The algorithm included nine physiological parameters (c-reactive protein, creatinine, albumin, total cholesterol, cytomegalovirus optical density, urea nitrogen, alkaline phosphatase, forced expiratory volume and systolic blood pressure). We assessed the association between biological age, all-cause mortality, all-cause morbidity and specific age-related diseases over a median follow-up of 11 years. Biological age, compared to chronological age or the traditional biomarkers of age-related diseases, showed a stronger association with all-cause mortality (HR 1.15 vs. 1.13 and 1.10), all-cause morbidity (HR 1.06 vs. 1.05 and 1.03), stroke (HR 1.17 vs. 1.08 and 1.04), cancer (HR 1.07 vs. 1.04 and 1.02) and diabetes mellitus (HR 1.12 vs. 1.01 and 0.98). Individuals who were biologically younger exhibited a healthier life-style as reflected in their lower BMI (P < 0.001) and lower incidence of stroke (P < 0.001), cancer (P < 0.01) and diabetes mellitus (P = 0.02). Collectively, our findings suggest that biological age based on the biomarker composite model of nine physiological parameters is a useful construct to assess individuals 65 years and older at increased risk for specific age-related diseases.
Anhänge
Nur mit Berechtigung zugänglich
Literatur
5.
Zurück zum Zitat Comfort A. Test-battery to measure ageing-rate in man. Lancet. 1969;294(7635):1411–5.CrossRef Comfort A. Test-battery to measure ageing-rate in man. Lancet. 1969;294(7635):1411–5.CrossRef
6.
Zurück zum Zitat Klemera P, Doubal S. A new approach to the concept and computation of biological age. Mech Ageing Dev. 2006;127(3):240–8.CrossRefPubMed Klemera P, Doubal S. A new approach to the concept and computation of biological age. Mech Ageing Dev. 2006;127(3):240–8.CrossRefPubMed
7.
Zurück zum Zitat Levine ME. Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age? J Gerontol Ser A: Biomed Sci Med Sci. 2012;68(6):667–74.CrossRef Levine ME. Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age? J Gerontol Ser A: Biomed Sci Med Sci. 2012;68(6):667–74.CrossRef
8.
Zurück zum Zitat Ikram MA, Brusselle GGO, Murad SD, et al. The Rotterdam Study: 2018 update on objectives, design and main results. Eur J Epidemiol. 2017;32:807–50.CrossRefPubMedPubMedCentral Ikram MA, Brusselle GGO, Murad SD, et al. The Rotterdam Study: 2018 update on objectives, design and main results. Eur J Epidemiol. 2017;32:807–50.CrossRefPubMedPubMedCentral
10.
Zurück zum Zitat Levine ME. Response to Dr. Mitnitski’s and Dr. Rockwood’s letter to the editor: biological age revisited. J Gerontol Ser A: Biomed Sci Med Sci. 2013;69(3):297–8.CrossRef Levine ME. Response to Dr. Mitnitski’s and Dr. Rockwood’s letter to the editor: biological age revisited. J Gerontol Ser A: Biomed Sci Med Sci. 2013;69(3):297–8.CrossRef
12.
Zurück zum Zitat Yoo J, Kim Y, Cho ER, Jee SH. Biological age as a useful index to predict seventeen-year survival and mortality in Koreans. BMC Geriatr. 2017;17(1):7.CrossRefPubMedPubMedCentral Yoo J, Kim Y, Cho ER, Jee SH. Biological age as a useful index to predict seventeen-year survival and mortality in Koreans. BMC Geriatr. 2017;17(1):7.CrossRefPubMedPubMedCentral
13.
14.
Zurück zum Zitat Hollingsworth JW, Hashizume A, Jablon S. Correlations between tests of aging in Hiroshima subjects—an attempt to define “physiologic age”. Yale J Biol Med. 1965;38(1):11–26.PubMedPubMedCentral Hollingsworth JW, Hashizume A, Jablon S. Correlations between tests of aging in Hiroshima subjects—an attempt to define “physiologic age”. Yale J Biol Med. 1965;38(1):11–26.PubMedPubMedCentral
15.
Zurück zum Zitat Takeda H, Inada H, Inoue M, Yoshikawa H, Abe H. Evaluation of biological age and physical age by multiple regression analysis. Med Inform = Medecine et informatique. 1982;7(3):221–7.CrossRefPubMed Takeda H, Inada H, Inoue M, Yoshikawa H, Abe H. Evaluation of biological age and physical age by multiple regression analysis. Med Inform = Medecine et informatique. 1982;7(3):221–7.CrossRefPubMed
16.
Zurück zum Zitat Kroll J, Saxtrup O. On the use of regression analysis for the estimation of human biological age. Biogerontology. 2000;1(4):363–8.CrossRefPubMed Kroll J, Saxtrup O. On the use of regression analysis for the estimation of human biological age. Biogerontology. 2000;1(4):363–8.CrossRefPubMed
18.
Zurück zum Zitat Hofecker G, Skalicky M, Kment A, Niedermuller H. Models of the biological age of the rat. I. A factor model of age parameters. Mech Ageing Dev. 1980;14(3–4):345–59.CrossRefPubMed Hofecker G, Skalicky M, Kment A, Niedermuller H. Models of the biological age of the rat. I. A factor model of age parameters. Mech Ageing Dev. 1980;14(3–4):345–59.CrossRefPubMed
19.
Zurück zum Zitat Skalicky M, Hofecker G, Kment A, Niedermuller H. Models of the biological age of the rat. II. Multiple regression models in the study on influencing aging. Mech Ageing Dev. 1980;14(3–4):361–77.CrossRefPubMed Skalicky M, Hofecker G, Kment A, Niedermuller H. Models of the biological age of the rat. II. Multiple regression models in the study on influencing aging. Mech Ageing Dev. 1980;14(3–4):361–77.CrossRefPubMed
20.
Zurück zum Zitat Nakamura E, Miyao K, Ozeki T. Assessment of biological age by principal component analysis. Mech Ageing Dev. 1988;46(1–3):1–18.CrossRefPubMed Nakamura E, Miyao K, Ozeki T. Assessment of biological age by principal component analysis. Mech Ageing Dev. 1988;46(1–3):1–18.CrossRefPubMed
21.
Zurück zum Zitat Nakamura E, Miyao K. A method for identifying biomarkers of aging and constructing an index of biological age in humans. J Gerontol Ser A, Biol Sci Med Sci. 2007;62(10):1096–105.CrossRef Nakamura E, Miyao K. A method for identifying biomarkers of aging and constructing an index of biological age in humans. J Gerontol Ser A, Biol Sci Med Sci. 2007;62(10):1096–105.CrossRef
25.
Zurück zum Zitat Barker DJ, Eriksson JG, Forsen T, Osmond C. Fetal origins of adult disease: strength of effects and biological basis. Int J Epidemiol. 2002;31(6):1235–9.CrossRefPubMed Barker DJ, Eriksson JG, Forsen T, Osmond C. Fetal origins of adult disease: strength of effects and biological basis. Int J Epidemiol. 2002;31(6):1235–9.CrossRefPubMed
26.
Zurück zum Zitat 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. 2017;187(6):1220–30.PubMedCentral 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. 2017;187(6):1220–30.PubMedCentral
32.
Zurück zum Zitat Kirkwood TB, Austad SN. Why do we age? Nature. 2000;408(6809):233–8.CrossRef Kirkwood TB, Austad SN. Why do we age? Nature. 2000;408(6809):233–8.CrossRef
Metadaten
Titel
Quantification of biological age as a determinant of age-related diseases in the Rotterdam Study: a structural equation modeling approach
verfasst von
Reem Waziry
Luuk Gras
Sanaz Sedaghat
Henning Tiemeier
Gerrit J. Weverling
Mohsen Ghanbari
Jaco Klap
Frank de Wolf
Albert Hofman
M. Arfan Ikram
Jaap Goudsmit
Publikationsdatum
13.04.2019
Verlag
Springer Netherlands
Erschienen in
European Journal of Epidemiology / Ausgabe 8/2019
Print ISSN: 0393-2990
Elektronische ISSN: 1573-7284
DOI
https://doi.org/10.1007/s10654-019-00497-3

Weitere Artikel der Ausgabe 8/2019

European Journal of Epidemiology 8/2019 Zur Ausgabe