A novel use of neural network model to determine the effects of multibiomarker on early health damage among Chinese steel workers.

Coke-oven workers are exposed to many kinds of pollutants that can cause health damage even lead to carcinogenesis. Therefore, it is critical to identify biomarkers that predict early health damage in these exposed individuals in molecular epidemiological studies. We applied an artificial neural network (ANN) model to the identification of such predictors in a study of coke-oven workers. The study included 330 steel-factory workers who were exposed to different levels of polycyclic aromatic hydrocarbons (PAHs) in the workplace and their levels of early health damage were determined by cytokinesis-block micronuclei (CMN), heat shock protein 70 (Hsp70) expression, benzo(a)pyrene diolepoxide-albumin adduct (BPDE-AA), and olive tail moment (OTM). The ANN model was built to predict the early health damage index, and the receiver operating characteristic (ROC) curve was used to illustrate the judged criteria and the ANN model. Trend Chi-square modeling was also performed. We found that there were 55 subjects with early health damage among 330 workers based on the multibiomarker criteria using the 95 percentile of the control group as the cut-off value, while there were 22-35 positive subjects if screening by any single biomarker. The Cochran-Armitage trend test for these findings were statistically significant (Z = 3.21, P = 0.0013). Six variables were selected to simulate the ANN model. The area under ROC (AUROC) was 0.726 ± 0.037 (P < 0.001), and the predictors included workplace, cholesterol, waistline, and others. Therefore, collective using CMN frequency, Hsp70 level, BPDE-AA level, and OTM with equal weights to make an initial screening test for early health damage in coke-oven workers is feasible and superior to any single biomarker. The determinants of the effects of multibiomarker on early health damage screening can be identified by the ANN model and ROC curve method.