{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T05:11:40Z","timestamp":1793164300634,"version":"4.5.0"},"reference-count":50,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T00:00:00Z","timestamp":1744761600000},"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>Exposure to heavy metals has been implicated in adverse auditory health outcomes, yet the precise relationships between heavy metal biomarkers and hearing status remain underexplored. This study leverages a machine learning framework to investigate these associations, offering a novel approach to understanding the interplay between environmental exposures and hearing loss.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We conducted a retrospective cross-sectional analysis using data from the 2012\u20132018 National Health and Nutrition Examination Survey (NHANES), encompassing 2,772 participants after applying exclusion criteria. Demographic, clinical, and heavy metal biomarker data (e.g., blood lead and cadmium levels) were analyzed as features, with hearing loss status\u2014defined as a pure-tone average threshold exceeding 25\u202fdB HL across 500, 1,000, 2000, and 4,000\u202fHz in the better ear\u2014serving as the binary outcome. Multiple machine learning algorithms, including Random Forest, XGBoost, Gradient Boosting, Logistic Regression, CatBoost, and MLP, were optimized and evaluated. Model performance was assessed using accuracy, area under the curve (AUC), sensitivity, and specificity, while SHAP (SHapley Additive exPlanations) elucidated feature contributions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The CatBoost model demonstrated the strongest performance, achieving an accuracy of 74.9% and an AUC of 0.792 on test data. Age, education level, gender, and blood levels of lead and cadmium emerged as the most significant features associated with hearing loss, as determined by SHAP analysis. These findings highlight key correlates of hearing impairment within the study population.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study underscores the utility of a machine learning framework in identifying associations between heavy metal biomarkers and hearing loss in a nationally representative sample. While not designed to forecast hearing loss over time, our findings suggest potential clinical relevance for identifying individuals with elevated heavy metal exposure who may warrant further audiometric evaluation. This work lays a foundation for future longitudinal studies to explore these relationships more comprehensively.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fpubh.2025.1471490","type":"journal-article","created":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T05:28:32Z","timestamp":1744781312000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis"],"prefix":"10.3389","volume":"13","author":[{"given":"Ali","family":"Nabavi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Kashkooli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara Sadat","family":"Nabavizadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farimah","family":"Safari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,4,16]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1001\/jamaoto.2016.3527","article-title":"Declining prevalence of hearing loss in US adults aged 20 to 69 years","volume":"143","author":"Hoffman","year":"2017","journal-title":"JAMA Otolaryngol Head Neck Surg"},{"key":"ref2","first-page":"308","volume-title":"Prevalence and factors associated with hearing loss and hearing aid use in Korean eldersIran J Public Health","author":"Kim","year":"2015"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"996","DOI":"10.1016\/S0140-6736(21)00516-X","article-title":"Hearing loss prevalence and years lived with disability, 1990\u20132019: findings from the global burden of disease study 2019","volume":"397","author":"Haile","year":"2021","journal-title":"Lancet"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.3390\/ijerph13121223","article-title":"The adverse effects of heavy metals with and without noise exposure on the human peripheral and central auditory system: a literature review","volume":"13","author":"Castellanos","year":"2016","journal-title":"Int J Environ Res Public Health"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"1230","DOI":"10.1002\/lio2.1230","article-title":"Population-based study of environmental heavy metal exposure and hearing loss: a systematic review and meta-analysis. 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