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Health and disease phenotyping in old age using a cluster network analysis

Human ageing is a complex trait that involves the synergistic action of numerous biological processes that interact to form a complex network. Here we performed a network analysis to examine the interrelationships between physiological and psychological functions, disease, disability, quality of lif...

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Autores principales: Valenzuela, Jesus Felix, Monterola, Christopher, Tong, Victor Joo Chuan, Ng, Tze Pin, Larbi, Anis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5688160/
https://www.ncbi.nlm.nih.gov/pubmed/29142224
http://dx.doi.org/10.1038/s41598-017-15753-3
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author Valenzuela, Jesus Felix
Monterola, Christopher
Tong, Victor Joo Chuan
Ng, Tze Pin
Larbi, Anis
author_facet Valenzuela, Jesus Felix
Monterola, Christopher
Tong, Victor Joo Chuan
Ng, Tze Pin
Larbi, Anis
author_sort Valenzuela, Jesus Felix
collection PubMed
description Human ageing is a complex trait that involves the synergistic action of numerous biological processes that interact to form a complex network. Here we performed a network analysis to examine the interrelationships between physiological and psychological functions, disease, disability, quality of life, lifestyle and behavioural risk factors for ageing in a cohort of 3,270 subjects aged ≥55 years. We considered associations between numerical and categorical descriptors using effect-size measures for each variable pair and identified clusters of variables from the resulting pairwise effect-size network and minimum spanning tree. We show, by way of a correspondence analysis between the two sets of clusters, that they correspond to coarse-grained and fine-grained structure of the network relationships. The clusters obtained from the minimum spanning tree mapped to various conceptual domains and corresponded to physiological and syndromic states. Hierarchical ordering of these clusters identified six common themes based on interactions with physiological systems and common underlying substrates of age-associated morbidity and disease chronicity, functional disability, and quality of life. These findings provide a starting point for indepth analyses of ageing that incorporate immunologic, metabolomic and proteomic biomarkers, and ultimately offer low-level-based typologies of healthy and unhealthy ageing.
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spelling pubmed-56881602017-11-29 Health and disease phenotyping in old age using a cluster network analysis Valenzuela, Jesus Felix Monterola, Christopher Tong, Victor Joo Chuan Ng, Tze Pin Larbi, Anis Sci Rep Article Human ageing is a complex trait that involves the synergistic action of numerous biological processes that interact to form a complex network. Here we performed a network analysis to examine the interrelationships between physiological and psychological functions, disease, disability, quality of life, lifestyle and behavioural risk factors for ageing in a cohort of 3,270 subjects aged ≥55 years. We considered associations between numerical and categorical descriptors using effect-size measures for each variable pair and identified clusters of variables from the resulting pairwise effect-size network and minimum spanning tree. We show, by way of a correspondence analysis between the two sets of clusters, that they correspond to coarse-grained and fine-grained structure of the network relationships. The clusters obtained from the minimum spanning tree mapped to various conceptual domains and corresponded to physiological and syndromic states. Hierarchical ordering of these clusters identified six common themes based on interactions with physiological systems and common underlying substrates of age-associated morbidity and disease chronicity, functional disability, and quality of life. These findings provide a starting point for indepth analyses of ageing that incorporate immunologic, metabolomic and proteomic biomarkers, and ultimately offer low-level-based typologies of healthy and unhealthy ageing. Nature Publishing Group UK 2017-11-15 /pmc/articles/PMC5688160/ /pubmed/29142224 http://dx.doi.org/10.1038/s41598-017-15753-3 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Valenzuela, Jesus Felix
Monterola, Christopher
Tong, Victor Joo Chuan
Ng, Tze Pin
Larbi, Anis
Health and disease phenotyping in old age using a cluster network analysis
title Health and disease phenotyping in old age using a cluster network analysis
title_full Health and disease phenotyping in old age using a cluster network analysis
title_fullStr Health and disease phenotyping in old age using a cluster network analysis
title_full_unstemmed Health and disease phenotyping in old age using a cluster network analysis
title_short Health and disease phenotyping in old age using a cluster network analysis
title_sort health and disease phenotyping in old age using a cluster network analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5688160/
https://www.ncbi.nlm.nih.gov/pubmed/29142224
http://dx.doi.org/10.1038/s41598-017-15753-3
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