Blood metal levels predict digestive tract cancer risk using machine learning in a U.S. cohort

• Published in: Scientific reports Vol. 15; no. 1; pp. 1285 - 14
• Main Authors: Shi, Chenyuan, Jiang, Hanfeng, Zhao, Fangzhou, Zhang, Yigang, Chen, Haoming
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 08.01.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 692/4028/67/1504/1477; 692/4028/67/1504/1829; 692/4028/67/1504/1885; 692/4028/67/2324; 704/172; Adult; Aged; Blood; Blood levels; Blood metal; Cadmium; Cadmium - blood; Cancer; Cohort Studies; Colorectal Cancer; Colorectal carcinoma; Environmental Exposure - adverse effects; Esophageal Neoplasms - blood; Esophageal Neoplasms - epidemiology; Esophagus cancer; Female; Gastric cancer; Gastrointestinal tract; Health risks; Heavy metals; Humanities and Social Sciences; Humans; Lead; Lead - blood; Machine Learning; Male; Metal concentrations; Metals; Metals - blood; Middle Aged; multidisciplinary; Nutrition; Nutrition Surveys; Random Forest; Regression analysis; Risk Factors; Science; Science (multidisciplinary); Stomach Neoplasms - blood; Stomach Neoplasms - epidemiology; United States - epidemiology; United States; United States > US; Colorectal Cancer; Blood metal; Gastric cancer; Esophagus cancer; Machine learning; Random Forest

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....