Literature DB >> 17921421

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

Eitaro Nakamura1, Kenji Miyao.   

Abstract

This study was conducted to identify biomarkers of aging and to construct an index of biological age in humans. Healthy adult men (n = 86) who had received an annual health examination from 1992 through 1998 were studied. From 29 physiological variables, five variables (forced expiratory volume in 1 second, systolic blood pressure, hematocrit, albumin, blood urea nitrogen) were selected as candidate biomarkers of aging. Five candidate biomarkers expressed substantial covariance along one principal component. The first principal component obtained from a principal component analysis was used to calculate biological age scores (BAS). Individual BAS showed high longitudinal stability of age-related changes. Age-related changes of BAS are characterized by three components: age, peak functional capacity, and aging rate. A logistic regression analysis suggested that aging rate was influenced by environmental factors, but peak functional capacity was almost independent of environmental factors.

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Year:  2007        PMID: 17921421     DOI: 10.1093/gerona/62.10.1096

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


  30 in total

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Review 2.  How pleiotropic genetics of the musculoskeletal system can inform genomics and phenomics of aging.

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3.  Select aging biomarkers based on telomere length and chronological age to build a biological age equation.

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Journal:  Age (Dordr)       Date:  2014-06

4.  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

5.  Linking biological and cognitive aging: toward improving characterizations of developmental time.

Authors:  Stuart W S MacDonald; Correne A DeCarlo; Roger A Dixon
Journal:  J Gerontol B Psychol Sci Soc Sci       Date:  2011-07       Impact factor: 4.077

6.  Caloric Restriction and Healthy Life Span: Frail Phenotype of Nonhuman Primates in the Wisconsin National Primate Research Center Caloric Restriction Study.

Authors:  Yosuke Yamada; Joseph W Kemnitz; Richard Weindruch; Rozalyn M Anderson; Dale A Schoeller; Ricki J Colman
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2018-03-02       Impact factor: 6.053

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

Authors:  Morgan E Levine
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2012-12-03       Impact factor: 6.053

Review 8.  BioAge: toward a multi-determined, mechanistic account of cognitive aging.

Authors:  Correne A DeCarlo; Holly A Tuokko; Dorothy Williams; Roger A Dixon; Stuart W S MacDonald
Journal:  Ageing Res Rev       Date:  2014-09-30       Impact factor: 10.895

9.  Dynamic determinants of longevity and exceptional health.

Authors:  Anatoli I Yashin; Konstantin G Arbeev; Igor Akushevich; Liubov Arbeeva; Julia Kravchenko; Dora Il'yasova; Alexander Kulminski; Lucy Akushevich; Irina Culminskaya; Deqing Wu; Svetlana V Ukraintseva
Journal:  Curr Gerontol Geriatr Res       Date:  2010-09-30

10.  How long will my mouse live? Machine learning approaches for prediction of mouse life span.

Authors:  William R Swindell; James M Harper; Richard A Miller
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2008-09       Impact factor: 6.053

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