Please use this identifier to cite or link to this item: https://doi.org/10.1038/s41598-017-15753-3
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dc.titleHealth and disease phenotyping in old age using a cluster network analysis
dc.contributor.authorValenzuela, J.F
dc.contributor.authorMonterola, C
dc.contributor.authorTong, V.J.C
dc.contributor.authorNg, T.P
dc.contributor.authorLarbi, A
dc.date.accessioned2020-09-04T03:30:25Z
dc.date.available2020-09-04T03:30:25Z
dc.date.issued2017
dc.identifier.citationValenzuela, 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
dc.identifier.issn2045-2322
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/174384
dc.description.abstractHuman 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).
dc.publisherNature Publishing Group
dc.sourceUnpaywall 20200831
dc.subjectaging
dc.subjectcluster analysis
dc.subjectdisabled person
dc.subjectgenetics
dc.subjecthuman
dc.subjectlifestyle
dc.subjectmiddle aged
dc.subjectpathology
dc.subjectphenotype
dc.subjectphysiology
dc.subjectproteomics
dc.subjectpsychology
dc.subjectquality of life
dc.subjectrisk factor
dc.subjectAging
dc.subjectCluster Analysis
dc.subjectDisabled Persons
dc.subjectHumans
dc.subjectLife Style
dc.subjectMiddle Aged
dc.subjectPhenotype
dc.subjectProteomics
dc.subjectQuality of Life
dc.subjectRisk Factors
dc.typeArticle
dc.contributor.departmentDEPT OF BIOCHEMISTRY
dc.contributor.departmentDEPT OF PSYCHOLOGICAL MEDICINE
dc.contributor.departmentMICROBIOLOGY AND IMMUNOLOGY
dc.description.doi10.1038/s41598-017-15753-3
dc.description.sourcetitleScientific Reports
dc.description.volume7
dc.description.issue1
dc.description.page15608
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