{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T18:49:45Z","timestamp":1783277385107,"version":"3.54.6"},"reference-count":115,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T00:00:00Z","timestamp":1735603200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019927","name":"Universidad de Deusto","doi-asserted-by":"publisher","award":["FPI UD_2022_10"],"award-info":[{"award-number":["FPI UD_2022_10"]}],"id":[{"id":"10.13039\/501100019927","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019927","name":"Universidad de Deusto","doi-asserted-by":"crossref","award":["ACM 2019-07"],"award-info":[{"award-number":["ACM 2019-07"]}],"id":[{"id":"10.13039\/501100019927","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Background<\/jats:title>\n<jats:p>Sarcopenia and reduced muscle quality index have garnered special attention due to their prevalence among older individuals and the adverse effects they generate. Early detection of these geriatric pathologies holds significant potential, enabling the implementation of interventions that may slow or reverse their progression, thereby improving the individual\u2019s overall health and quality of life. In this context, artificial intelligence opens up new opportunities to identify the key identifying factors of these pathologies, thus facilitating earlier intervention and personalized treatment approaches.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Objectives<\/jats:title>\n<jats:p>investigate anthropomorphic, functional, and socioeconomic factors associated with muscle quality and sarcopenia using machine learning approaches and identify key determinant factors for their potential future integration into clinical practice.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Methods<\/jats:title>\n<jats:p>A total of 1253 older adults (89.5% women) with a mean age of 78.13 \u00b1 5.78 voluntarily participated in this descriptive cross-sectional study, which examines determining factors in sarcopenia and MQI using machine learning techniques. Feature selection was completed using a variety of techniques and feature datasets were constructed according to feature selection. Three machine learning classification algorithms classified sarcopenia and MQI in each dataset, and the performance of classification models was compared.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Results<\/jats:title>\n<jats:p>The predictive models used in this study exhibited AUC scores of 0.7671 for MQI and 0.7649 for sarcopenia, with the most successful algorithms being SVM and MLP. Key factors in predicting both conditions have been shown to be relative power, age, weight, and the 5STS. No single factor is sufficient to predict either condition, and by comprehensively considering all selected features, the study underscores the importance of a holistic approach in understanding and addressing sarcopenia and MQI among older adults.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec005\">\n<jats:title>Conclusions<\/jats:title>\n<jats:p>Exploring the factors that affect sarcopenia and MQI in older adults, this study highlights that relative power, age, weight, and the 5STS are significant determinants. While considering these clinical markers and using a holistic approach, this can provide crucial information for designing personalized and effective interventions to promote healthy aging.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0316174","type":"journal-article","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T19:05:17Z","timestamp":1735671917000},"page":"e0316174","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["Exploring determinant factors influencing muscle quality and sarcopenia in Bilbao\u2019s older adult population through machine learning: A comprehensive analysis approach"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2850-8717","authenticated-orcid":true,"given":"Naiara","family":"Virto","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4054-0157","authenticated-orcid":true,"given":"Danielle Marie","family":"Dequin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9303-619X","authenticated-orcid":true,"given":"Xabier","family":"R\u00edo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amaia","family":"M\u00e9ndez-Zorrilla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bego\u00f1a","family":"Garc\u00eda-Zapirain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,12,31]]},"reference":[{"issue":"6","key":"pone.0316174.ref001","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1016\/j.cell.2013.05.039","article-title":"The hallmarks of aging","volume":"153","author":"C L\u00f3pez-Ot\u00edn","year":"2013","journal-title":"Cell"},{"issue":"1","key":"pone.0316174.ref002","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.cmet.2022.11.001","article-title":"Meta-hallmarks of aging and cancer","volume":"35","author":"C L\u00f3pez-Ot\u00edn","year":"2023","journal-title":"Cell Metab"},{"issue":"6","key":"pone.0316174.ref003","doi-asserted-by":"crossref","first-page":"e16678","DOI":"10.2196\/16678","article-title":"Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches.","volume":"8","author":"A Tarekegn","year":"2020","journal-title":"JMIR Medical Informatics."},{"key":"pone.0316174.ref004","doi-asserted-by":"crossref","first-page":"23","DOI":"10.2147\/RMHP.S168750","article-title":"Frailty syndrome: implications and challenges for health care policy.","volume":"12","author":"G Kojima","year":"2019","journal-title":"Risk Manag Healthc Policy"},{"issue":"10","key":"pone.0316174.ref005","doi-asserted-by":"crossref","first-page":"afac220","DOI":"10.1093\/ageing\/afac220","article-title":"Sarcopenia definition, diagnosis and treatment: consensus is growing.","volume":"51","author":"AA Sayer","year":"2022","journal-title":"Age Ageing"},{"issue":"2","key":"pone.0316174.ref006","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1298\/ptr.R0009","article-title":"Age-Related Change in Muscle Characteristics and Resistance Training for Older Adults","volume":"23","author":"T. 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