Abstract
Exposure assessment traditionally relies on biomarkers that measure chemical concentrations in individual biological media (i.e., blood, urine, etc.). However, chemicals distribute unevenly among different biological media; thus, each medium provides incomplete information about body burden. We propose that machine learning and statistical approaches can create integrated exposure estimates from multiple biomarker matrices that better represent the overall body burden, which we term multi-media biomarkers (MMBs). We measured lead (Pb) in blood, urine, hair and nails from 251 Italian adolescents aged 11-14 years from the Public Health Impact of Metals Exposure (PHIME) cohort. We derived aggregated MMBs from the four biomarkers and then tested their association with Wechsler Intelligence Scale for Children (WISC) IQ scores. We used three approaches to derive the Pb MMB: one supervised learning technique, weighted quantile sum regression (WQS), and two unsupervised learning techniques, independent component analysis (ICA) and non-negative matrix factorization (NMF). Overall, the Pb MMB derived using WQS was most consistently associated with IQ scores and was the only method to be statistically significant for Verbal IQ, Performance IQ and Total IQ. A one standard deviation increase in the WQS MMB was associated with lower Verbal IQ (β [95% CI] = -2.2 points [-3.7, -0.6]), Performance IQ (-1.9 points [-3.5, -0.4]) and Total IQ (-2.1 points [-3.8, -0.5]). Blood Pb was negatively associated with only Verbal IQ, with a one standard deviation increase in blood Pb being associated with a -1.7 point (95% CI: [-3.3, -0.1]) decrease in Verbal IQ. Increases of one standard deviation in the ICA MMB were associated with lower Verbal IQ (-1.7 points [-3.3, -0.1]) and lower Total IQ (-1.7 points [-3.3, -0.1]). Similarly, an increase of one standard deviation in the NMF MMB was associated with lower Verbal IQ (-1.8 points [-3.4, -0.2]) and lower Total IQ (-1.8 points [-3.4, -0.2]). Weights highlighting the contributions of each medium to the MMB revealed that blood Pb was the largest contributor to most MMBs, although the weights varied from more than 80% for the ICA and NMF MMBs to between 30% and 54% for the WQS-derived MMBs. Our results suggest that MMBs better reflect the total body burden of a chemical that may be acting on target organs than individual biomarkers. Estimating MMBs improved our ability to estimate the full impact of Pb on IQ. Compared with individual Pb biomarkers, including blood, a Pb MMB derived using WQS was more strongly associated with IQ scores. MMBs may increase statistical power when the choice of exposure medium is unclear or when the sample size is small. Future work will need to validate these methods in other cohorts and for other chemicals.