Literature DB >> 7757832

Genetic and environmental influences on functional age: a twin study.

D Finkel1, K Whitfield, M McGue.   

Abstract

Twin analyses were conducted to determine the relative influence of genetic and environmental factors on functional aging. As part of the ongoing Minnesota Twin Study of Adult Development and Aging (MTSADA), measures of 30 demographic, cognitive, physiological, personality, and behavioral variables were available from 140 monozygotic twin pairs and 97 dizygotic twin pairs ranging in age from 27 to 88 years. Functional age was based on a general linear regression model with chronological age as the dependent variable. Stepwise regression determined the subset of variables by MTSADA providing the best prediction of chronological age. Factor analysis of these 12 variables resulted in three factors: physiological measures, cognitive abilities, and processing speed. When entered into, a regression equation, the three factors accounted for 66% of the variance in chronological age. Analysis of twin similarity for components of functional age suggested the relative influence of genetic and environmental factors varies greatly for different components of functional aging. In addition, the genetic and shared environmental influences on the three components were common to all three, while the nonshared environmental influences were specific to each component.

Mesh:

Year:  1995        PMID: 7757832     DOI: 10.1093/geronb/50b.2.p104

Source DB:  PubMed          Journal:  J Gerontol B Psychol Sci Soc Sci        ISSN: 1079-5014            Impact factor:   4.077


  15 in total

Review 1.  Disentangling the genetic determinants of human aging: biological age as an alternative to the use of survival measures.

Authors:  David Karasik; Serkalem Demissie; L Adrienne Cupples; Douglas P Kiel
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2005-05       Impact factor: 6.053

2.  Gene-Environment Interplay in Physical, Psychological, and Cognitive Domains in Mid to Late Adulthood: Is APOE a Variability Gene?

Authors:  Chandra A Reynolds; Margaret Gatz; Kaare Christensen; Lene Christiansen; Anna K Dahl Aslan; Jaakko Kaprio; Tellervo Korhonen; William S Kremen; Robert Krueger; Matt McGue; Jenae M Neiderhiser; Nancy L Pedersen
Journal:  Behav Genet       Date:  2015-11-04       Impact factor: 2.805

3.  A model for estimating body shape biological age based on clinical parameters associated with body composition.

Authors:  Chul-Young Bae; Young Gon Kang; Young-Sung Suh; Jee Hye Han; Sung-Soo Kim; Kyung Won Shim
Journal:  Clin Interv Aging       Date:  2012-12-28       Impact factor: 4.458

4.  Shorter telomeres in peripheral blood mononuclear cells from older persons with sarcopenia: results from an exploratory study.

Authors:  Emanuele Marzetti; Maria Lorenzi; Manuela Antocicco; Stefano Bonassi; Michela Celi; Simona Mastropaolo; Silvana Settanni; Vanessa Valdiglesias; Francesco Landi; Roberto Bernabei; Graziano Onder
Journal:  Front Aging Neurosci       Date:  2014-08-28       Impact factor: 5.750

5.  Immune function parameters as markers of biological age and predictors of longevity.

Authors:  Irene Martínez de Toda; Ianire Maté; Carmen Vida; Julia Cruces; Mónica De la Fuente
Journal:  Aging (Albany NY)       Date:  2016-11-28       Impact factor: 5.682

6.  Models for estimating the metabolic syndrome biological age as the new index for evaluation and management of metabolic syndrome.

Authors:  Young Gon Kang; Eunkyung Suh; Hyejin Chun; Sun-Hyun Kim; Deog Ki Kim; Chul-Young Bae
Journal:  Clin Interv Aging       Date:  2017-02-01       Impact factor: 4.458

7.  Biological age as a health index for mortality and major age-related disease incidence in Koreans: National Health Insurance Service - Health screening 11-year follow-up study.

Authors:  Young Gon Kang; Eunkyung Suh; Jae-Woo Lee; Dong Wook Kim; Kyung Hee Cho; Chul-Young Bae
Journal:  Clin Interv Aging       Date:  2018-03-20       Impact factor: 4.458

8.  Measurement of biological age may help to assess the risk of colorectal adenoma in screening colonoscopy.

Authors:  Sang-Jung Kim; Beom Jin Kim; Hyun Kang
Journal:  World J Gastroenterol       Date:  2017-10-07       Impact factor: 5.742

9.  Dietary Polyphenol Intake Is Associated with Biological Aging, a Novel Predictor of Cardiovascular Disease: Cross-Sectional Findings from the Moli-Sani Study.

Authors:  Simona Esposito; Alessandro Gialluisi; Simona Costanzo; Augusto Di Castelnuovo; Emilia Ruggiero; Amalia De Curtis; Mariarosaria Persichillo; Chiara Cerletti; Maria Benedetta Donati; Giovanni de Gaetano; Licia Iacoviello; Marialaura Bonaccio
Journal:  Nutrients       Date:  2021-05-17       Impact factor: 5.717

10.  Heritability of a skeletal biomarker of biological aging.

Authors:  Ida Malkin; Leonid Kalichman; Eugene Kobyliansky
Journal:  Biogerontology       Date:  2007-06-23       Impact factor: 4.277

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