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Their analysis showed the strongest signals of caries clustering in very young children \u2014 who also displayed patterns of iron and vitamin D deficiency \u2014 and in older adults. (iStock)<\/p>\nTheir analysis showed the strongest signals of caries clustering in very young children \u2014 who also displayed patterns of iron and vitamin D deficiency \u2014 and in older adults. (iStock)<\/p>\n
Is there a link between pollutant exposure and dental caries risk? Using artificial intelligence to analyze large U.S. health datasets, researchers at the University of Pennsylvania say they have uncovered previously unrecognized patterns that broaden how dental caries risk may be understood.<\/p>\n
Researchers at Penn Dental Medicine analyzed data from the National Health and Nutrition Examination Survey using machine-learning methods and found novel associations linking dental caries with environmental exposures, nutritional indicators and systemic health markers.<\/p>\n
The December 2025 study, published in the Journal of Dental Research<\/a>, identified associations between caries status and factors such as lead and other pollutant exposure, specific laboratory markers, food types and sleep patterns \u2014 particularly among children under five and adults aged 65 and older.<\/p>\n The research was led by orthodontics professor Hyun (Michel) Koo and biostatistics and epidemiology professor Jason Moore.<\/p>\n \u201cThis kind of machine-learning pipeline can turn complex national health data into clearer hypotheses and better predictive models \u2014 starting with oral health, and potentially extending to other areas of medicine,\u201d Koo said in a Penn Dental Medicine news release.<\/a> He is co-founding director of the university\u2019s Center for Innovation & Precision Dentistry.<\/p>\n Related: Why most dental practices are still on the fence about AI<\/a><\/p>\n Cleaning complex national data<\/p>\n NHANES, overseen by the Centers for Disease Control and Prevention, has collected nationally representative health and nutrition data in two-year cycles since 1999 through interviews, physical examinations and laboratory testing. While the survey is considered one of the most comprehensive population-level health datasets in the United States, its size and non-uniform structure present challenges for advanced analysis.<\/p>\n To address this, the researchers developed a data-cleaning pipeline that incorporates a novel outlier-detection algorithm and unsupervised machine learning. The approach allowed them to identify distinct caries-related subtypes and age-driven heterogeneity within the NHANES dental data.<\/p>\n Their analysis showed the strongest signals of caries clustering in very young children \u2014 who also displayed patterns of iron and vitamin D deficiency \u2014 and in older adults.<\/p>\n \u201cOur results point to the importance of age-targeted prevention and prediction \u2014 especially for young children and older adults \u2014 guided by real-world diet patterns, laboratory signals and environmental risk context,\u201d Koo said.<\/p>\n Related: Harvard dental researchers explore AI for earlier head and neck cancer detection<\/a><\/p>\n High lead levels<\/p>\n The study also found that while individuals with caries had higher blood lead levels \u2014 consistent with earlier research \u2014 they also showed elevated levels of other markers, including cadmium and cotinine, suggesting that lead exposure may reflect broader environmental risk rather than a single causal pathway.<\/p>\n