Literature DB >> 23213031

Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age?

Morgan E Levine1.   

Abstract

Biological age (BA) is useful for examining differences in aging rates. Nevertheless, little consensus exists regarding optimal methods for calculating BA. The aim of this study is to compare the predictive ability of five BA algorithms. The sample included 9,389 persons, aged 30-75 years, from National Health and Nutrition Examination Survey III. During the 18-year follow-up, 1,843 deaths were counted. Each BA algorithm was compared with chronological age on the basis of predictive sensitivity and strength of association with mortality. Results found that the Klemera and Doubal method was the most reliable predictor of mortality and performed significantly better than chronological age. Furthermore, when included with chronological age in a model, Klemera and Doubal method had more robust predictive ability and caused chronological age to no longer be significantly associated with mortality. Given the potential of BA to highlight heterogeneity, the Klemera and Doubal method algorithm may be useful for studying a number of questions regarding the biology of aging.

Entities:  

Keywords:  Biological age; Biomarkers; Mortality.

Mesh:

Substances:

Year:  2012        PMID: 23213031      PMCID: PMC3660119          DOI: 10.1093/gerona/gls233

Source DB:  PubMed          Journal:  J Gerontol A Biol Sci Med Sci        ISSN: 1079-5006            Impact factor:   6.053


  34 in total

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Authors:  Eileen Crimmins; Sarinnapha Vasunilashorn; Jung Ki Kim; Dawn Alley
Journal:  Adv Clin Chem       Date:  2008       Impact factor: 5.394

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Authors:  E Nakamura; K Miyao; T Ozeki
Journal:  Mech Ageing Dev       Date:  1988-12       Impact factor: 5.432

5.  Mortality and aging in a heterogeneous population: a stochastic process model with observed and unobserved variables.

Authors:  A I Yashin; K G Manton; J W Vaupel
Journal:  Theor Popul Biol       Date:  1985-04       Impact factor: 1.570

6.  An empirical comparative study on biological age estimation algorithms with an application of Work Ability Index (WAI).

Authors:  Il Haeng Cho; Kyung S Park; Chang Joo Lim
Journal:  Mech Ageing Dev       Date:  2009-12-11       Impact factor: 5.432

7.  A method for identifying biomarkers of aging and constructing an index of biological age in humans.

Authors:  Eitaro Nakamura; Kenji Miyao
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2007-10       Impact factor: 6.053

Review 8.  Sex, stress and the hippocampus: allostasis, allostatic load and the aging process.

Authors:  Bruce S McEwen
Journal:  Neurobiol Aging       Date:  2002 Sep-Oct       Impact factor: 4.673

9.  Correlations between tests of aging in Hiroshima subjects--an attempt to define "physiologic age".

Authors:  J W Hollingsworth; A Hashizume; S Jablon
Journal:  Yale J Biol Med       Date:  1965-08

10.  Frailty, fitness and late-life mortality in relation to chronological and biological age.

Authors:  Arnold B Mitnitski; Janice E Graham; Alexander J Mogilner; Kenneth Rockwood
Journal:  BMC Geriatr       Date:  2002-02-27       Impact factor: 3.921

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  171 in total

1.  The Longitudinal Study of Aging in Human Young Adults: Knowledge Gaps and Research Agenda.

Authors:  Terrie E Moffitt; Daniel W Belsky; Andrea Danese; Richie Poulton; Avshalom Caspi
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2016-10-07       Impact factor: 6.053

2.  Response to Dr. Mitnitski's and Dr. Rockwood's letter to the editor: Biological age revisited.

Authors:  Morgan E Levine
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2013-10-10       Impact factor: 6.053

3.  Model Construction for Biological Age Based on a Cross-Sectional Study of a Healthy Chinese Han population.

Authors:  W Zhang; L Jia; G Cai; F Shao; H Lin; Z Liu; F Liu; D Zhao; Z Li; X Bai; Z Feng; X Sun; X Chen
Journal:  J Nutr Health Aging       Date:  2017       Impact factor: 4.075

4.  Biological Age, Not Chronological Age, Is Associated with Late-Life Depression.

Authors:  Patrick J Brown; Melanie M Wall; Chen Chen; Morgan E Levine; Kristine Yaffe; Steven P Roose; Bret R Rutherford
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2018-09-11       Impact factor: 6.053

5.  Disability Trajectories at the End of Life: A "Countdown" Model.

Authors:  Douglas A Wolf; Vicki A Freedman; Jan I Ondrich; Christopher L Seplaki; Brenda C Spillman
Journal:  J Gerontol B Psychol Sci Soc Sci       Date:  2015-03-03       Impact factor: 4.077

6.  Shift work, DNA methylation and epigenetic age.

Authors:  Alexandra J White; Jacob K Kresovich; Zongli Xu; Dale P Sandler; Jack A Taylor
Journal:  Int J Epidemiol       Date:  2019-10-01       Impact factor: 7.196

7.  Comparing Biological Age Estimates Using Domain-Specific Measures From the Canadian Longitudinal Study on Aging.

Authors:  Chris P Verschoor; Daniel W Belsky; Jinhui Ma; Alan A Cohen; Lauren E Griffith; Parminder Raina
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2021-01-18       Impact factor: 6.053

Review 8.  Metabolomics Signatures of Aging: Recent Advances.

Authors:  Sunil S Adav; Yulan Wang
Journal:  Aging Dis       Date:  2021-04-01       Impact factor: 6.745

Review 9.  The risks of biomarker-based epidemiology: Associations of circulating calcium levels with age, mortality, and frailty vary substantially across populations.

Authors:  Alan A Cohen; Véronique Legault; Georg Fuellen; Tamàs Fülöp; Linda P Fried; Luigi Ferrucci
Journal:  Exp Gerontol       Date:  2017-07-16       Impact factor: 4.032

10.  Quantification of biological aging in young adults.

Authors:  Daniel W Belsky; Avshalom Caspi; Renate Houts; Harvey J Cohen; David L Corcoran; Andrea Danese; HonaLee Harrington; Salomon Israel; Morgan E Levine; Jonathan D Schaefer; Karen Sugden; Ben Williams; Anatoli I Yashin; Richie Poulton; Terrie E Moffitt
Journal:  Proc Natl Acad Sci U S A       Date:  2015-07-06       Impact factor: 11.205

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