Determinants of Serum PCBs in Adolescents and Adults: Regression Tree Analysis and Linear Regression Analysis

Regression tree analysis, a non-parametric method, was undertaken to identify predictors of the serum concentration of polychlorinated biphenyls (sum of marker PCB ¹ ABBREVIATIONS: BMI: body-mass index, CV: cross validation, ln: natural logarithm, ns: not significant, PCAHs: polychlorinated aromatic hydrocarbons, PCBs: polychlorinated biphenyls, R² _a: adjusted coefficient of determination, VIF: variance inflation factor. View all notes 138, 153, and 180) in humans. This method was applied on biomonitoring data of the Flemish Environment and Health study (2002–2006) and included 1679 adolescents and 1583 adults. Potential predictor variables were collected via a self-administered questionnaire, assessing information on lifestyle, food intake, use of tobacco and alcohol, residence history, health, education, hobbies, and occupation. Relevant predictors of human PCB exposure were identified with regression tree analysis using ln-transformed sum of PCBs, separately in adolescents and adults. The obtained results were compared with those from a standard linear regression approach. The results of the non-parametric analysis confirm the selection of the covariates in the multiple regression models. In both analyses, blood fat, gender, age, body-mass index (BMI) or change in bodyweight, former breast-feeding, and a number of nutritional factors were identified as statistically significant predictors in the serum PCB concentration, either in adolescents, in adults or in both. Regression trees can be used as an explorative analysis in combination with multiple linear regression models, where relationships between the determinants and the biomarkers can be quantified.