Please use this identifier to cite or link to this item: https://doi.org/10.1038/s41598-017-15753-3
Title: Health and disease phenotyping in old age using a cluster network analysis
Authors: Valenzuela, J.F
Monterola, C
Tong, V.J.C 
Ng, T.P 
Larbi, A 
Keywords: aging
cluster analysis
disabled person
genetics
human
lifestyle
middle aged
pathology
phenotype
physiology
proteomics
psychology
quality of life
risk factor
Aging
Cluster Analysis
Disabled Persons
Humans
Life Style
Middle Aged
Phenotype
Proteomics
Quality of Life
Risk Factors
Issue Date: 2017
Publisher: Nature Publishing Group
Citation: Valenzuela, J.F, Monterola, C, Tong, V.J.C, Ng, T.P, Larbi, A (2017). Health and disease phenotyping in old age using a cluster network analysis. Scientific Reports 7 (1) : 15608. ScholarBank@NUS Repository. https://doi.org/10.1038/s41598-017-15753-3
Abstract: 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. © 2017 The Author(s).
Source Title: Scientific Reports
URI: https://scholarbank.nus.edu.sg/handle/10635/174384
ISSN: 2045-2322
DOI: 10.1038/s41598-017-15753-3
Appears in Collections:Elements
Staff Publications

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_1038_s41598-017-15753-3.pdf4.19 MBAdobe PDF

OPEN

NoneView/Download

SCOPUSTM   
Citations

7
checked on Oct 14, 2021

Page view(s)

89
checked on Oct 14, 2021

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.