Leveraging machine learning in precision medicine to unveil organochlorine pesticides as predictive biomarkers for thyroid dysfunction

Abstract

Exposure to organochlorine pesticides (OCPs) poses significant health risks, including cancer, endocrine dysregulation, neurological disorders, and reproductive disruption. This study investigates the association between OCP exposure and thyroid disturbances using machine learning (ML) models. Blood samples were analyzed for the concentration of 16 OCPs and thyroid hormones (T3, T4, TSH) using traditional methods such as Logistic Regression and least absolute shrinkage and selection operator (LASSO) and more advanced ML models such as Random Forest (RF), Support Vector Machine (SVM), XGBoost, and Gradient Boosting Machine (GBM). High frequencies of OCPs, including Heptachlor, Heptachlor epoxide, γ-HCH, Aldrin, Endrin aldehyde, α-endosulfan, and Methoxychlor, were detected in over 70% of serum samples. The RF and GBM models achieved the highest accuracy at 90.91%, while XGBoost demonstrated a high ROC-AUC score of 94.02%. The SVM model also showed robust performance, whereas Logistic Regression exhibited weaker results. Our findings highlighted specific OCPs, such as Methoxychlor, p,p-DDT, Heptachlor, Endrin, and various HCH isomers, could impact thyroid function. The study supports a strong correlation between OCP exposure and thyroid dysfunction, demonstrating high accuracy in classifying thyroid status using ML models. Significant OCPs identified include p, p-DDT, Methoxychlor, Endrin, β-endosulfan, and Heptachlor, which are associated with thyroid dysfunction.