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By developing predictive models, the research seeks to improve early detection, enhance diagnostic accuracy, and facilitate personalized interventions for individuals at risk of sarcopenia.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>We analysed multimodal data from 484 older adults. Two scenarios: Set-a (including SARC\u2013CalF, excluding SARC-F) and Set-b (including SARC-F, excluding SARC\u2013CalF) were applied in a three-stage modeling process with progressively reduced features and optimized predictive performance using machine learning models. Key predictors were ranked using SHAP values, and model performance was evaluated using AUC, accuracy, sensitivity, and specificity. Internal validation and DeLong\u2019s test were applied to assess robustness and statistical differences.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>The most predictive risk factors included functional measures (chair stand, gait speed), nutritional indicators (protein, folate, copper, vitamin B7), clinical conditions (diabetes, comorbidities, low-density lipoprotein (LDL)), and anthropometric markers (body mass index (BMI), calf circumference). Genetic features also contributed to risk stratification. The best-performing model Set-b (with screening test SARC-F) achieved an AUC of 0.951 and an accuracy of 93.62%. While SARC\u2013CalF showed higher individual feature importance, the model achieved an AUC of 0.945 and accuracy of 92.2%.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>This study highlights that traditional sarcopenia screening can be enhanced by capturing complex interplay of functional, nutritional, clinical, and genetic factors, offering clinicians a more accurate and tailored tool for early detection and risk stratification. Future research should focus on validating these models in larger, independent, and longitudinal cohorts to assess their predictive utility across diverse populations and over time.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s41999-025-01245-5","type":"journal-article","created":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T03:23:40Z","timestamp":1749093820000},"page":"1777-1788","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Identifying risk factors for sarcopenia using machine learning: insights from multimodal data"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8401-823X","authenticated-orcid":false,"given":"Felicita","family":"Urzi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Domen","family":"\u0160oberl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ornella","family":"Caputo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Narici","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,5]]},"reference":[{"key":"1245_CR1","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1093\/ageing\/afy169","volume":"48","author":"AJ Cruz-Jentoft","year":"2019","unstructured":"Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruy\u00e8re O, Cederholm T et al (2019) Sarcopenia: revised European consensus on definition and diagnosis. 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