Department of Environmental Medicine and Public Health, Icahn School of Medicine, New York, NY, USA.
Department of Environmental Medicine and Public Health, Icahn School of Medicine, New York, NY, USA.
Environ Res. 2020 Apr;183:109148. doi: 10.1016/j.envres.2020.109148. Epub 2020 Jan 20.
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.
传统的暴露评估依赖于测量个体生物介质（即血液、尿液等）中化学浓度的生物标志物。然而，化学物质在不同的生物介质中分布不均匀；因此，每种介质提供的关于身体负担的信息都不完整。我们提出，机器学习和统计方法可以从多个生物标志物矩阵中创建综合的暴露估计值，这些估计值更好地代表了整体身体负担，我们称之为多介质生物标志物（MMB）。我们从 251 名年龄在 11-14 岁的意大利青少年的血液、尿液、头发和指甲中测量了铅（Pb），这些青少年均来自公共卫生影响金属暴露（PHIME）队列。我们从四个生物标志物中得出了综合的 MMB，然后测试了它们与韦氏儿童智力量表（WISC）智商分数的关联。我们使用了三种方法来推导 Pb MMB：一种监督学习技术，加权分位数总和回归（WQS），以及两种无监督学习技术，独立成分分析（ICA）和非负矩阵分解（NMF）。总的来说，使用 WQS 推导的 Pb MMB 与智商得分最一致，并且是唯一一种对言语智商、表现智商和总智商具有统计学意义的方法。WQS MMB 增加一个标准差与言语智商降低相关（β[95%CI]=-2.2 点[-3.7，-0.6]），表现智商（-1.9 点[-3.5，-0.4]）和总智商（-2.1 点[-3.8，-0.5]）。血液中的 Pb 仅与言语智商呈负相关，血液中的 Pb 增加一个标准差与言语智商降低 1.7 点（95%CI：[-3.3，-0.1]）相关。ICA MMB 增加一个标准差与言语智商降低（-1.7 点[-3.3，-0.1]）和总智商降低（-1.7 点[-3.3，-0.1]）相关。同样，NMF MMB 增加一个标准差与言语智商降低（-1.8 点[-3.4，-0.2]）和总智商降低（-1.8 点[-3.4，-0.2]）相关。突出每个介质对 MMB 贡献的权重表明，血液中的 Pb 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 推导的 MMB 的 30%至 54%不等。我们的研究结果表明，MMB 比单个生物标志物更好地反映了化学物质对靶器官的总体身体负担。估计 MMB 提高了我们估计 Pb 对智商的全部影响的能力。与包括血液在内的单个 Pb 生物标志物相比，使用 WQS 推导的 Pb MMB 与智商得分的相关性更强。当暴露介质的选择不明确或样本量较小时，MMB 可能会增加统计能力。未来的工作将需要在其他队列和其他化学物质中验证这些方法。
