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.

中文翻译：

---

多媒体生物标志物：整合信息以改善铅暴露评估。

传统上，接触评估是依靠生物标记来测量单个生物介质（例如血液，尿液等）中的化学浓度。但是，化学物质在不同的生物介质之间分布不均。因此，每种介质都无法提供有关身体负担的不完整信息。我们建议，机器学习和统计方法可以从多个生物标记矩阵创建综合的暴露估计，以更好地代表整体身体负担，我们称其为多媒体生物标记（MMB）。我们测量了251名11-14岁的意大利青少年的血液，尿液，头发和指甲中的铅（Pb），这些人群来自金属公共卫生影响（PHIME）队列。我们从这四个生物标记中得出了汇总的MMB，然后将其与韦氏儿童智力量表（WISC）的IQ分数进行了关联。我们使用三种方法来导出Pb MMB：一种监督学习技术，加权分位数和回归（WQS）和两种不受监督的学习技术，独立成分分析（ICA）和非负矩阵分解（NMF）。总体而言，使用WQS得出的Pb MMB与智商得分最一致，并且是唯一对口头智商，绩效智商和总智商具有统计学意义的方法。WQS MMB中的一个标准差增加与较低的口头智商（β[95％CI] = -2.2点[-3.7，-0.6]），绩效智商（-1.9点[-3.5，-0.4]）和总智商（-2.1分[-3.8，-0.5]）。血铅仅与口头智商呈负相关，血铅的一个标准差增加与口头智商降低-1.7点（95％CI：[-3.3，-0.1]）有关。ICA MMB中一个标准差的增加与较低的语言智商（-1.7分[-3.3，-0.1]）和较低的总智商（-1.7分[-3.3，-0.1]）相关。同样，NMF MMB中一个标准差的增加与较低的语言智商（-1.8分[-3.4，-0.2]）和较低的总智商（-1.8分[-3.4，-0.2]）相关。权重强调每种介质对MMB的贡献表明，血铅是大多数MMB的最大贡献者，尽管权重从ICA和NMF MMB的80％以上到WQS衍生MMB的30％至54％不等。 。我们的结果表明，MMB比单个生物标记物更好地反映了可能作用于目标器官的化学物质的总负荷。估计MMB可以提高我们估计铅对智商的全部影响的能力。与单个铅生物标志物相比，包括血液在内，使用WQS得出的Pb MMB与IQ得分的关联更强。当暴露介质的选择不清楚或样本量较小时，MMB可能会提高统计功效。未来的工作将需要在其他队列和其他化学品中验证这些方法。