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Identification of predictors of sarcopenia in older adults using machine learning: English Longitudinal Study of Ageing

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dc.contributor.authorPavón Pulido, Nieves
dc.contributor.authorDomínguez, Ligia
dc.contributor.authorBlasco García, Jesús Damián
dc.contributor.authorVeronese, Nicola
dc.contributor.authorLucas Ochoa, Ana María
dc.contributor.authorFernández Villalba, Emiliano
dc.contributor.authorGonzález Cuello, Ana María
dc.contributor.authorBarbagallo, Mario
dc.contributor.authorHerrero Ezquerro, María Trinidad
dc.contributor.authorGOING FWD Investigators
dc.contributor.departmentMedicina Interna
dc.contributor.otherFacultades de la UMU::Facultad de Medicina
dc.date.accessioned2026-01-21T10:04:42Z
dc.date.available2026-01-21T10:04:42Z
dc.date.copyright© 2024 by the authors
dc.date.issued2024-11-12
dc.description.abstractBackground: After its introduction in the ICD-10-CM in 2016, sarcopenia is a condition widely considered to be a medical disease with important consequences for the elderly. Considering its high prevalence in older adults and its detrimental effects on health, it is essential to identify its risk factors to inform targeted interventions. Methods: Taking data from wave 2 of the ELSA, using ML-based methods, this study investigates which factors are significantly associated with sarcopenia. The Minimum Redundancy Maximum Relevance algorithm has been used to allow for an optimal set of features that could predict the dependent variable. Such a feature is the input of a ML-based prediction model, trained and validated to predict the risk of developing or not developing a disease. Results: The presented methods are suitable to identify the risk of acquired sarcopenia. Age and other relevant features related with dementia and musculoskeletal conditions agree with previous knowledge about sarcopenia. The present classifier has an excellent performance since the “true positive rate” is 0.81 and the low “false positive rate” is 0.26. Conclusions: There is a high prevalence of sarcopenia in elderly people, with age and the presence of dementia and musculoskeletal conditions being strong predictors. The new proposed approach paves the path to test the prediction of the incidence of sarcopenia in older adults.202
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dc.format.extent11
dc.identifier.citation Journal of Clinical Medicine. 2024 Nov 12;13(22):6794. doi: 10.3390/jcm13226794. PMID: 39597937; PMCID: PMC11594410.
dc.identifier.doihttps://doi.org/10.3390/jcm13226794
dc.identifier.eissn2077-0383
dc.identifier.urihttp://hdl.handle.net/10201/189989
dc.languageeng
dc.publisherMDPI
dc.relationThis study was supported by the grants from: the “Ministerio de Ciencia y Tecnología. Agencia Estatal e Investigación”, Spain, (Project Ref. Number: TED2021-130942B-C21//MICIU/AEI/10.13039/501100011033 and TED2021-130942A-C22); the “Fundación Primafrío” with the code number 39747; the COST Participatory Approaches with Older Adults (PAAR-net), with the code number CA22167, and La Caixa Foundation (ID 100010434, with code LCF/PR/DE18/52010001) as part of the GOING-FWD Consortium funded by the GENDER-NET Plus ERA-NET Initiative (Project Ref. Number: GNP-78).
dc.relation.publisherversionhttps://www.mdpi.com/2077-0383/13/22/6794
dc.rightsAttribution 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectMachine learning
dc.subjectDecision tree
dc.subjectSarcopenia
dc.subjectEnglish longitudinal study of ageing
dc.subjectOlder adults
dc.subjectEpidemiology
dc.subjectAging
dc.subjectCohort
dc.subjectProspective
dc.subjectArtificial intelligence
dc.subject.odsObjetivo 3: Salud
dc.titleIdentification of predictors of sarcopenia in older adults using machine learning: English Longitudinal Study of Ageing
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublicationes
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