{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T01:14:12Z","timestamp":1785806052230,"version":"3.56.0"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T00:00:00Z","timestamp":1751587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Sarcopenia is a condition that adversely affects individuals\u2019 quality of life and physical health. Exposure to heavy metals poses a significant risk to human health; however, the impact of heavy metal exposure on sarcopenia remains unclear. Therefore, this study expects to construct a risk prediction machine model of heavy metal exposure on sarcopenia and to interpret and analyze it.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Model construction was based on data from the NHANES database, covering the years 2011 to 2018. The predictor variables included BA, CD, CO, CS, MN, MO, PB, SB, SN, TL, and W. Additionally, demographic characteristics and health factors were included in the study as confounders. After identifying the core variables, optimal machine learning models were constructed, and SHAP analyses were performed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We found that the LGBM model exhibited the best predictive performance. SHAP analysis revealed that TL, SN, and CS negatively influenced the prediction of sarcopenia, while CD positively contributed to it. Additionally, le8 BMI was the covariate that had the most significant positive impact on the prediction of sarcopenia in our model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>For the first time, we have developed a machine learning (ML) model to predict sarcopenia based on indicators of heavy metal exposure. This model has accurately identified a series of key factors that are strongly associated with sarcopenia induced by heavy metal exposure. We can now identify individuals at an early stage who are suffering from sarcopenia due to heavy metal exposure, thereby reducing the physical and economic burden on public health.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fpubh.2025.1588041","type":"journal-article","created":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T13:28:07Z","timestamp":1751635687000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Associations between exposure to heavy metal and sarcopenia prevalence: a cross-sectional study using NHANES data"],"prefix":"10.3389","volume":"13","author":[{"given":"Yingying","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianbing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangfei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,7,4]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1002\/jcsm.12783","article-title":"Global prevalence of sarcopenia and severe sarcopenia: a systematic review and meta-analysis","volume":"13","author":"Petermann-Rocha","year":"2022","journal-title":"J Cachexia Sarcopenia Muscle"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1002\/jcsm.12411","article-title":"Sarcopenia and its association with falls and fractures in older adults: a systematic review and meta-analysis","volume":"10","author":"Yeung","year":"2019","journal-title":"J Cachexia Sarcopenia Muscle"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"951","DOI":"10.1111\/hepr.12774","article-title":"Japan Society of Hepatology guidelines for sarcopenia in liver disease (1st edition): recommendation from the working group for creation of sarcopenia assessment criteria","volume":"46","author":"Nishikawa","year":"2016","journal-title":"Hepatol Res"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"101264","DOI":"10.1016\/j.eclinm.2021.101264","article-title":"Association between sarcopenia and cardiovascular disease among middle-aged and older adults: findings from the China health and retirement longitudinal study","volume":"44","author":"Gao","year":"2022","journal-title":"eClinicalMedicine"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1017\/S002966511500419X","article-title":"Cancer-associated malnutrition, cachexia and sarcopenia: the skeleton in the hospital closet 40 years later","volume":"75","author":"Ryan","year":"2016","journal-title":"Proc Nutr Soc"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"766","DOI":"10.1007\/s12603-018-1058-9","article-title":"Muscle, health and costs: a glance at their relationship","volume":"22","author":"Mijnarends","year":"2018","journal-title":"J Nutr Health Aging"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1093\/ageing\/afy169","article-title":"Sarcopenia: revised European consensus on definition and diagnosis","volume":"48","author":"Cruz-Jentoft","year":"2019","journal-title":"Age Ageing"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"956","DOI":"10.1002\/jcsm.12483","article-title":"Sarcopenia: a time for action. 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