AUTHOR=Xia Fang , Li Qingwen , Luo Xin , Wu Jinyi 

TITLE=Identification for heavy metals exposure on osteoarthritis among aging people and Machine learning for prediction: A study based on NHANES 2011-2020

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.906774

DOI=10.3389/fpubh.2022.906774

ISSN=2296-2565

ABSTRACT=Heavy metals widely existed on environmental pollutants, and have cumulative effects on human body of water or food, which would lead to many diseases , osteoarthritis (OA ) is one of them . We extracted 18 variables including age , gender , race , education level , marital status , smoking , BMI , physical activity , PLI , diabetes mellitus , hypertension and poverty level index , Lead (Pb ) , cadmium (Cd ) , mercury (Hg ) , selenium (Se ) and manganese (Mn ) and OA status from Nutrition Examination Survey (NHANES ) 2011-2020 datasets . In the baseline data comparison , t and Chi-square test were conducted . For heavy metal , quantile description and limit to detection were adopted . To analyzed the association with / for heavy metal and OA among the elderly, multivariable logistic regression was conducted and subgroup analysis of gender logistic was also carried out . Furthremore , to make the prediction based on heavy metal for OA , we compared 8 machines learning algorithms , and XGboost (AUC of 0.8 , accuracy value of 0.773 and kappa value of 0.358 ) was the best machine learning model for the prediction . For interactive prediction , a shiny application was made (https://alanwu.shinyapps.io/NHANES-OA/) In the descriptive analysis , all indicators had the difference between OA and no OA groups . The overall and gender subgroup logistic regression all showed that Pb and Cd promoted the prevalence of OA while Mn could be a protective factor of OA prevalence among the U.S. adult population . In short , we confirmed the association with Pb , Cd and Mn with OA . Xgboost model was the best for OA prediction.