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dc.rights.licensehttp://creativecommons.org/licenses/by/4.0es_MX
dc.creatorMaría del Carmen García Peñaes_MX
dc.creatorRICARDO RAMIREZ ALDANAes_MX
dc.creatorLORENA PARRA RODRIGUEZes_MX
dc.creatorJUAN CARLOS GOMEZ VERJANes_MX
dc.creatorMARIO ULISES PEREZ ZEPEDAes_MX
dc.creatorLUIS MIGUEL FRANCISCO GUTIERREZ ROBLEDOes_MX
dc.date2019-
dc.date.accessioned2021-08-24T18:19:28Z-
dc.date.available2021-08-24T18:19:28Z-
dc.identifier.urihttp://repositorio.inger.gob.mx/jspui/handle/20.500.12100/17227-
dc.descriptionBackground Frailty remains a challenge in the aging research area with a number of gaps in knowledge still to be filled. Frailty seems to behave as a network, and in silico evidence is available on this matter. Having in vivo evidence that frailty behaves as a complex network was the main purpose of our study. Methods Data from the Mexican Health and Aging Study (main data 2012, mortality 2015) was used. Frailty was operationalized with a 35-deficit frailty index (FI). Analyzed nodes were the deficits plus death. The edges, linking those nodes were obtained through structural learning, and an undirected graph associated with a discrete probabilistic graphical model (Markov network) was derived. Two algorithms, hill-climbing (hc) and Peter and Clark (PC), were used to derive the graph structure. Analyses were performed for the whole population and tertiles of the total FI score. Results From the total sample of 10,983 adults aged 50 or older, 43.8% were women, and the mean age was 64.6 years (SD = 9.3). The number of connections increased according to the tertile level of the FI score. As the FI score raised, groups of interconnected deficits increased and how the nodes are connected changed. Conclusions Frailty phenomenon can be modeled using a Bayesian network. Using the full sample, the most central nodes were self-report of health (most connected node) and difficulty walking a block, and all deficits related to mobility were very interconnected. When frailty levels are considered, the most connected nodes differ, but are related with vitality, mainly at lower frailty levels. We derived that not all deficits are equally related since clusters of very related deficits and non-connected deficits were obtained, which might be considered in the construction of the FI score. Further research should aim to identify the nature of all observed interactions, which might allow the development of specific interventions to mitigate the consequences of frailty in older adults.es_MX
dc.formatAdobe PDFes_MX
dc.languageenges_MX
dc.publisherElsevieres_MX
dc.relationhttps://www.sciencedirect.com/science/article/abs/pii/S053155651930004X?via%3Dihubes_MX
dc.relation.requiresSies_MX
dc.rightsAcceso Abiertoes_MX
dc.sourceExperimental Gerontology (0531-5565) Vol. 128 (2019)es_MX
dc.subjectMEDICINA Y CIENCIAS DE LA SALUDes_MX
dc.subjectCiencias médicases_MX
dc.subjectCiencias clínicases_MX
dc.subjectGeriatríaes_MX
dc.subjectFenómenos fisiológicoses_MX
dc.subjectCrecimiento y desarrolloes_MX
dc.subjectEnvejecimientoes_MX
dc.subjectEnvejecimiento biológicoes_MX
dc.subjectPersonas mayoreses_MX
dc.subjectProcesos patológicoses_MX
dc.subjectFragilidades_MX
dc.subjectEpidemiología geriátricaes_MX
dc.subjectGeriatricses_MX
dc.subjectPhysiological phenomenaes_MX
dc.subjectGrowth and developmentes_MX
dc.subjectAginges_MX
dc.subjectBiological aginges_MX
dc.subjectOlder adultses_MX
dc.subjectPathologic processeses_MX
dc.subjectFrailtyes_MX
dc.subjectGeriatric epidemiologyes_MX
dc.titleNetwork analysis of frailty and aging: Empirical data from the MexicanHealth and Aging Studyes_MX
dc.typeArtículoes_MX
dc.audienceResearcherses_MX
dc.creator.idGAPC560716MDFRXR00es_MX
dc.creator.idRAAR780730HDFMLC00es_MX
dc.creator.idPARL860513MDFRDR04es_MX
dc.creator.idGOVJ850305HDFMRN07es_MX
dc.creator.idPEZM760111HDFRPR07es_MX
dc.creator.idGURL571005HDFTBS14es_MX
dc.creator.nameIdentifiercurpes_MX
dc.creator.nameIdentifiercurpes_MX
dc.creator.nameIdentifiercurpes_MX
dc.creator.nameIdentifiercurpes_MX
dc.creator.nameIdentifiercurpes_MX
dc.creator.nameIdentifiercurpes_MX
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