Blood metal levels predict digestive tract cancer risk using machine learning in a U.S. cohort
Abstract Background: Environmental metal exposure has been implicated in the development of digestive tract cancers, although the specific associations remain poorly defined. This study aimed to investigate the relationship between blood metal levels and the risk of digestive tract cancers among U.S...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85659-y |
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| author | Chenyuan Shi Hanfeng Jiang Fangzhou Zhao Yigang Zhang Haoming Chen |
| author_facet | Chenyuan Shi Hanfeng Jiang Fangzhou Zhao Yigang Zhang Haoming Chen |
| author_sort | Chenyuan Shi |
| collection | DOAJ |
| description | Abstract Background: Environmental metal exposure has been implicated in the development of digestive tract cancers, although the specific associations remain poorly defined. This study aimed to investigate the relationship between blood metal levels and the risk of digestive tract cancers among U.S. adults. Methods: Data from the National Health and Nutrition Examination Survey (NHANES) 2011–2018, including 13,467 participants aged 20 years and older, were analyzed. Nine blood metals were measured. Multivariable logistic regression, restricted cubic spline models, and subgroup analyses were employed to assess the associations between metal levels and cancer risk. Additionally, a Random Forest (RF) model was used for cancer risk prediction. Results: Among the participants, 9 had esophagus cancer (EC), 11 had gastric cancer (GC), and 83 had colorectal cancer (CRC). Compared to healthy controls, EC patients exhibited significantly higher blood levels of potassium (K, 4.40 vs. 4.00 mmol/L), cadmium (Cd, 12.46 vs. 2.49 µg/L), and lead (Pb, 0.09 vs. 0.05 µg/L). GC patients had elevated Pb levels (0.08 vs. 0.05 µg/L), while CRC patients showed higher concentrations of Cd (3.11 vs. 2.49 µg/L) and Pb (0.06 vs. 0.04 µg/L). Logistic regression analysis revealed significant associations between higher K (odds ratio [OR] = 7.58, 95% CI: 3.48–16.48, P < 0.0001), Cd (OR = 1.06, 95% CI: 1.04–1.08, P < 0.0001), and Pb (OR = 7.60, 95% CI: 3.26–17.72, P < 0.0001) levels and EC risk. Pb was also significantly associated with GC (OR = 5.26, 95% CI: 2.11–13.10, P < 0.001). The RF model showed an accuracy of 76% in predicting cancer risk, with SHapley Additive exPlanations (SHAP) analysis highlighting Cd and iron (Fe) as key contributors. Conclusions: The study reveals a positive association between certain blood metals and digestive tract cancer risk, suggesting that limiting exposure to these metals may serve as a potential preventive measure. |
| format | Article |
| id | doaj-art-b60d47d880f04b3b8375e7e46ce7563c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-b60d47d880f04b3b8375e7e46ce7563c2025-01-12T12:23:46ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-025-85659-yBlood metal levels predict digestive tract cancer risk using machine learning in a U.S. cohortChenyuan Shi0Hanfeng Jiang1Fangzhou Zhao2Yigang Zhang3Haoming Chen4Department of Emergency Medicine, The First Affiliated Hospital of Nanjing Medical UniversitySchool of Environmental and Biological Engineering, Nanjing University of Science and TechnologySchool of Environmental and Biological Engineering, Nanjing University of Science and TechnologyDepartment of General Surgery, The First Affiliated Hospital of Nanjing Medical UniversitySchool of Environmental and Biological Engineering, Nanjing University of Science and TechnologyAbstract Background: Environmental metal exposure has been implicated in the development of digestive tract cancers, although the specific associations remain poorly defined. This study aimed to investigate the relationship between blood metal levels and the risk of digestive tract cancers among U.S. adults. Methods: Data from the National Health and Nutrition Examination Survey (NHANES) 2011–2018, including 13,467 participants aged 20 years and older, were analyzed. Nine blood metals were measured. Multivariable logistic regression, restricted cubic spline models, and subgroup analyses were employed to assess the associations between metal levels and cancer risk. Additionally, a Random Forest (RF) model was used for cancer risk prediction. Results: Among the participants, 9 had esophagus cancer (EC), 11 had gastric cancer (GC), and 83 had colorectal cancer (CRC). Compared to healthy controls, EC patients exhibited significantly higher blood levels of potassium (K, 4.40 vs. 4.00 mmol/L), cadmium (Cd, 12.46 vs. 2.49 µg/L), and lead (Pb, 0.09 vs. 0.05 µg/L). GC patients had elevated Pb levels (0.08 vs. 0.05 µg/L), while CRC patients showed higher concentrations of Cd (3.11 vs. 2.49 µg/L) and Pb (0.06 vs. 0.04 µg/L). Logistic regression analysis revealed significant associations between higher K (odds ratio [OR] = 7.58, 95% CI: 3.48–16.48, P < 0.0001), Cd (OR = 1.06, 95% CI: 1.04–1.08, P < 0.0001), and Pb (OR = 7.60, 95% CI: 3.26–17.72, P < 0.0001) levels and EC risk. Pb was also significantly associated with GC (OR = 5.26, 95% CI: 2.11–13.10, P < 0.001). The RF model showed an accuracy of 76% in predicting cancer risk, with SHapley Additive exPlanations (SHAP) analysis highlighting Cd and iron (Fe) as key contributors. Conclusions: The study reveals a positive association between certain blood metals and digestive tract cancer risk, suggesting that limiting exposure to these metals may serve as a potential preventive measure.https://doi.org/10.1038/s41598-025-85659-yEsophagus cancerGastric cancerColorectal CancerBlood metalMachine learningRandom Forest |
| spellingShingle | Chenyuan Shi Hanfeng Jiang Fangzhou Zhao Yigang Zhang Haoming Chen Blood metal levels predict digestive tract cancer risk using machine learning in a U.S. cohort Scientific Reports Esophagus cancer Gastric cancer Colorectal Cancer Blood metal Machine learning Random Forest |
| title | Blood metal levels predict digestive tract cancer risk using machine learning in a U.S. cohort |
| title_full | Blood metal levels predict digestive tract cancer risk using machine learning in a U.S. cohort |
| title_fullStr | Blood metal levels predict digestive tract cancer risk using machine learning in a U.S. cohort |
| title_full_unstemmed | Blood metal levels predict digestive tract cancer risk using machine learning in a U.S. cohort |
| title_short | Blood metal levels predict digestive tract cancer risk using machine learning in a U.S. cohort |
| title_sort | blood metal levels predict digestive tract cancer risk using machine learning in a u s cohort |
| topic | Esophagus cancer Gastric cancer Colorectal Cancer Blood metal Machine learning Random Forest |
| url | https://doi.org/10.1038/s41598-025-85659-y |
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