Literature DB >> 28255823

Aging, frailty and complex networks.

A B Mitnitski1,2, A D Rutenberg3, S Farrell3, K Rockwood4,5.   

Abstract

When people age their mortality rate increases exponentially, following Gompertz's law. Even so, individuals do not die from old age. Instead, they accumulate age-related illnesses and conditions and so become increasingly vulnerable to death from various external and internal stressors. As a measure of such vulnerability, frailty can be quantified using the frailty index (FI). Larger values of the FI are strongly associated with mortality and other adverse health outcomes. This association, and the insensitivity of the FI to the particular health variables that are included in its construction, makes it a powerful, convenient, and increasingly popular integrative health measure. Still, little is known about why the FI works so well. Our group has recently developed a theoretical network model of health deficits to better understand how changes in health are captured by the FI. In our model, health-related variables are represented by the nodes of a complex network. The network has a scale-free shape or "topology": a few nodes have many connections with other nodes, whereas most nodes have few connections. These nodes can be in two states, either damaged or undamaged. Transitions between damaged and non-damaged states are governed by the stochastic environment of individual nodes. Changes in the degree of damage of connected nodes change the local environment and make further damage more likely. Our model shows how age-dependent acceleration of the FI and of mortality emerges, even without specifying an age-damage relationship or any other time-dependent parameter. We have also used our model to assess how informative individual deficits are with respect to mortality. We find that the information is larger for nodes that are well connected than for nodes that are not. The model supports the idea that aging occurs as an emergent phenomenon, and not as a result of age-specific programming. Instead, aging reflects how damage propagates through a complex network of interconnected elements.

Entities:  

Keywords:  Aging; Complex networks; Frailty; Frailty index; Frailty maximum; Information theory; Mathematical modeling; Mortality

Mesh:

Year:  2017        PMID: 28255823     DOI: 10.1007/s10522-017-9684-x

Source DB:  PubMed          Journal:  Biogerontology        ISSN: 1389-5729            Impact factor:   4.277


  33 in total

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6.  Association of the Systemic Lupus International Collaborating Clinics Frailty Index and Damage Accrual in Long Standing Systemic Lupus Erythematosus.

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7.  The Burden of Frailty Among U.S. Veterans and Its Association With Mortality, 2002-2012.

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Journal:  J Gerontol A Biol Sci Med Sci       Date:  2019-07-12       Impact factor: 6.053

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9.  Evidence from two cohorts for the frailty syndrome as an emergent state of parallel dysregulation in multiple physiological systems.

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Journal:  Biogerontology       Date:  2020-10-16       Impact factor: 4.277

10.  Epigenetic age is associated with baseline and 3-year change in frailty in the Canadian Longitudinal Study on Aging.

Authors:  Chris P Verschoor; David T S Lin; Michael S Kobor; Oxana Mian; Jinhui Ma; Guillaume Pare; Gustavo Ybazeta
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