{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T16:01:18Z","timestamp":1784563278731,"version":"3.55.0"},"reference-count":25,"publisher":"SAGE Publications","issue":"8","license":[{"start":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T00:00:00Z","timestamp":1765238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T00:00:00Z","timestamp":1765238400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100000092","name":"U.S. National Library of Medicine","doi-asserted-by":"publisher","award":["LM010098"],"award-info":[{"award-number":["LM010098"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000072","name":"National Institute of Dental and Craniofacial Research","doi-asserted-by":"publisher","award":["R90DE031532"],"award-info":[{"award-number":["R90DE031532"]}],"id":[{"id":"10.13039\/100000072","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Dent Res"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:p>National Health and Nutrition Examination Survey (NHANES), one of the largest curated repositories of population-level health indicators including physical examinations, blood\/urine biochemistry, self-reported surveys, and dietary intake, offers rich resources for oral health research but presents challenges for machine learning analysis due to heterogeneity, missing data, and complexity. Dental caries, the most prevalent chronic disease worldwide, is a multifactorial disease and exhibits variability in clinical manifestation, calling for advanced analytical approaches for deeper understanding. Here, we develop an integrated data-cleaning and subtype discovery pipeline using unsupervised machine learning for comprehensive analysis and visualization of data patterns in the NHANES database. Our multidimensional pipeline declutters and optimizes the NHANES dataset by addressing missingness and outliers to streamline data integration and create a machine learning\u2013ready version. Applying this pipeline reveals data patterns that led to the discovery of previously unrecognized subtypes and variables associated with the clinical heterogeneity of dental caries. We observed diverging patterns of similarity across age groups and variable subsets, identifying distinct clusters particularly in children (&lt;5\u2009y) and senior adults (&gt;65\u2009y). We also discovered unexpected associations involving lead exposure and specific laboratory markers and, importantly, identified novel dietary signatures by linking food type and co-occurring consumption patterns to caries. Altogether, we report a comprehensive data-processing and data-analysis approach that reveals significant dental caries heterogeneity in NHANES data and can support the development of more precise and robust machine learning models for dental caries and other health conditions.<\/jats:p>","DOI":"10.1177\/00220345251398027","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T16:00:15Z","timestamp":1765296015000},"page":"1031-1040","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Uncovering Dental Caries Heterogeneity in NHANES Using Machine Learning"],"prefix":"10.1177","volume":"105","author":[{"given":"A.","family":"Orlenko","sequence":"first","affiliation":[{"name":"Department of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J.D.","family":"Mure","sequence":"additional","affiliation":[{"name":"School of Dental Medicine, University of Pennsylvania, Philadelphia, PA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J.I.","family":"Gluch","sequence":"additional","affiliation":[{"name":"Department of Preventive and Restorative Sciences, University of Pennsylvania School of Dental Medicine, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J.","family":"Gregg","sequence":"additional","affiliation":[{"name":"Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C.W.","family":"Compher","sequence":"additional","affiliation":[{"name":"Biobehavioral Health Science Department, School of Nursing, University of Pennsylvania, Philadelphia, PA, USA"},{"name":"Clinical Nutrition Support Services, Hospital of the University of Pennsylvania, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3553-1958","authenticated-orcid":false,"given":"Z.","family":"Ren","sequence":"additional","affiliation":[{"name":"Center for Innovation &amp; Precision Dentistry, School of Dental Medicine and School of Engineering &amp; Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9143-2076","authenticated-orcid":false,"given":"H.","family":"Koo","sequence":"additional","affiliation":[{"name":"Center for Innovation &amp; Precision Dentistry, School of Dental Medicine and School of Engineering &amp; Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J.H.","family":"Moore","sequence":"additional","affiliation":[{"name":"Department of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,12,9]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"crossref","unstructured":"Akinkugbe AA Moreno O Brickhouse TH. 2019. Serum cotinine vitamin D exposure levels and dental caries experience in U.S. adolescents. Community Dent Oral Epidemiol. 47(2):185\u2013192. https:\/\/doi.org\/10.1111\/cdoe.12442","DOI":"10.1111\/cdoe.12442"},{"key":"e_1_3_2_3_1","doi-asserted-by":"publisher","DOI":"10.1542\/peds.113.S3.1120"},{"key":"#cr-split#-e_1_3_2_4_1.1","unstructured":"Centers for Disease Control and Prevention. 2018. National Health and Nutrition Examination Survey data 2017-2018. US Department of Health and Human Services Centers for Disease Control and Prevention"},{"key":"#cr-split#-e_1_3_2_4_1.2","unstructured":"[accessed 2025 Jun 22]. https:\/\/wwwn.cdc.gov\/nchs\/nhanes\/continuousnhanes\/default.aspx?BeginYear=2017"},{"issue":"1","key":"e_1_3_2_5_1","first-page":"73","article-title":"Dental caries are associated with anemia in pediatric patients: a systematic literature review","volume":"50","author":"Delimont NM","year":"2021","unstructured":"Delimont NM, Carlson BN, Nickel S. 2021. 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Caries Res. 58(3):141\u2013152. https:\/\/doi.org\/10.1159\/000537826","DOI":"10.1159\/000537826"},{"key":"e_1_3_2_12_1","doi-asserted-by":"crossref","unstructured":"Marcenes W et al. 2013. Global burden of oral conditions in 1990-2010: a systematic analysis. J Dent Res. 92(7):592\u2013597. https:\/\/doi.org\/10.1177\/0022034513490168","DOI":"10.1177\/0022034513490168"},{"key":"e_1_3_2_13_1","doi-asserted-by":"crossref","unstructured":"Pfeiffer CM Lacher DA Schleicher RL Johnson CL Yetley EA. 2017. Challenges and lessons learned in generating and interpreting NHANES nutritional biomarker data. Adv Nutr. 8(2):290\u2013307. https:\/\/doi.org\/10.3945\/an.116.014076","DOI":"10.3945\/an.116.014076"},{"key":"e_1_3_2_14_1","doi-asserted-by":"crossref","unstructured":"Pitts NB et al 2017. Dental caries. 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