54 research outputs found
Integrated measures of lead and manganese exposure improve estimation of their joint effects on cognition in Italian school-age children
Every day humans are exposed to mixtures of chemicals, such as lead (Pb) and manganese (Mn). An underappreciated aspect of studying the health effects of mixtures is the role that the exposure biomarker media (blood, hair, etc.) may play in estimating the effects of the mixture. Different biomarker media represent different aspects of each chemical's toxicokinetics, thus no single medium can fully capture the toxicokinetic profile for all the chemicals in a mixture. A potential solution to this problem is to combine exposure data across different media to derive integrated estimates of each chemical's internal concentration. This concept, formalized as a multi-media biomarker (MMB) has proven effective for estimating the health impacts of Pb exposure, but may also be useful to estimate mixture effects, such as the joint effects of metals like Pb and Mn, while factoring in how the association changes based upon the biomarker media. Levels of Pb and Mn were quantified in five media: blood, hair, nails, urine, and saliva in the Public Health Impact of Metals Exposure (PHIME) project, a study of Italian adolescents aged 10–14 years. MMBs were derived for both metals using weighted quantile sum (WQS) regression across the five media. Age-adjusted Wechsler Intelligence Scale for Children (WISC) IQ scores, measured at the same time as the exposure measures, were the primary outcome and models were adjusted for sex and socioeconomic status. The levels Pb and Mn were relatively low, with median blood Pb of 1.27 (IQR: 0.84) μg/dL and median blood Mn of 1.09 (IQR: 0.45) μg/dL. Quartile increases in a Pb-Mn combination predicted decreased Full Scale IQ of 1.9 points (95% CI: 0.3, 3.5) when Pb and Mn exposure levels were estimated using MMBs, while individual regressions for each metal were not associated with Full Scale IQ. Additionally, a quartile increase in the WQS index of Pb and Mn, measured using MMBs, were associated with reductions in Verbal IQ by 2.8 points (1.0, 4.5). Weights that determine the contributions of the metals to the joint effect highlighted that the contribution of the Pb-Mn was 72–28% for Full Scale IQ and 42–58% for Verbal IQ. We found that the joint effects of Pb and Mn are strongly affected by the medium used to measure exposure and that the joint effects of the Pb and Mn MMBs on cognition were the stronger than any individual biomarker. Thus, increase power and accuracy for measuring mixture effects compared to individual biomarkers. As the number of chemicals in mixtures increases, appropriate biomarker selection will become increasingly important and MMBs are a natural way to reduce bias in such analyses
The Optical Afterglow Light Curve of GRB 980519
We present V -, R-, and I-band observations made at the US Naval Observatory, Flagstaff Station, of the afterglow of GRB 980519 on UT 1998 May 20 and 22. These observations are combined with extensive data from the literature, and all are placed on a uniform magnitude system. The resultant R- and I-band light curves are fit by simple power laws with no breaks and indices of αR = 2.30 ± 0.12 and αI = 2.05 ± 0.07. This makes the afterglow of GRB 980519 one of the two steepest afterglows yet observed. The combined B-, V -, R-, and I-band observations are used to estimate the spectral power-law index, β = 1.4 ± 0.3, after correction for reddening. Unfortunately, GRB 980519 occurred at a relatively low Galactic latitude (b ≈ +15) where the Galactic reddening is poorly known and, hence, the actual value of β is poorly constrained. The observed α and range of likely β-values are, however, found to be consistent with simple relativistic blast-wave models. This afterglow and that of GRB 980326 displayed much steeper declines than the other seven well-observed afterglows, which cluster near α ≈ 1.2. GRB 980519 and GRB 980326 did not display burst characteristics in common that might distinguish them from the gamma-ray bursts with more typical light curves
An overview of methods to address distinct research questions on environmental mixtures: an application to persistent organic pollutants and leukocyte telomere length
Background Numerous methods exist to analyze complex environmental mixtures in health studies. As an illustration of the different uses of mixture methods, we employed methods geared toward distinct research questions concerning persistent organic chemicals (POPs) as a mixture and leukocyte telomere length (LTL) as an outcome. Methods With information on 18 POPs and LTL among 1,003 U.S. adults (NHANES, 2001-2002), we used unsupervised methods including clustering to identify profiles of similarly exposed participants, and Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) to identify common exposure patterns. We also employed supervised learning techniques, including penalized, weighted quantile sum (WQS), and Bayesian kernel machine (BKMR) regressions, to identify potentially toxic agents, and characterize nonlinear associations, interactions, and the overall mixture effect. Results Clustering separated participants into high, medium, and low POP exposure groups; longer log-LTL was found among those with high exposure. The first PCA component represented overall POP exposure and was positively associated with log-LTL. Two EFA factors, one representing furans and the other PCBs 126 and 118, were positively associated with log-LTL. Penalized regression methods selected three congeners in common (PCB 126, PCB 118, and furan 2,3,4,7,8-pncdf) as potentially toxic agents. WQS found a positive overall effect of the POP mixture and identified six POPs as potentially toxic agents (furans 1,2,3,4,6,7,8-hxcdf, 2,3,4,7,8-pncdf, and 1,2,3,6,7,8-hxcdf, and PCBs 99, 126, 169). BKMR found a positive linear association with furan 2,3,4,7,8-pncdf, suggestive evidence of linear associations with PCBs 126 and 169, and a positive overall effect of the mixture, but no interactions among congeners. Conclusions Using different methods, we identified patterns of POP exposure, potentially toxic agents, the absence of interaction, and estimated the overall mixture effect. These applications and results may serve as a guide for mixture method selection based on specific research questions
The endpoints project: Novel testing strategies for endocrine disruptors linked to developmental neurotoxicity
Copyright © 2020 by the authors. Ubiquitous exposure to endocrine-disrupting chemicals (EDCs) has caused serious concerns about the ability of these chemicals to affect neurodevelopment, among others. Since endocrine disruption (ED)-induced developmental neurotoxicity (DNT) is hardly covered by the chemical testing tools that are currently in regulatory use, the Horizon 2020 research and innovation action ENDpoiNTs has been launched to fill the scientific and methodological gaps related to the assessment of this type of chemical toxicity. The ENDpoiNTs project will generate new knowledge about ED-induced DNT and aims to develop and improve in vitro, in vivo, and in silico models pertaining to ED-linked DNT outcomes for chemical testing. This will be achieved by establishing correlative and causal links between known and novel neurodevelopmental endpoints and endocrine pathways through integration of molecular, cellular, and organismal data from in vitro and in vivo models. Based on this knowledge, the project aims to provide adverse outcome pathways (AOPs) for ED-induced DNT and to develop and integrate new testing tools with high relevance for human health into European and international regulatory frameworks.European Union’s Horizon 2020 Research and Innovation Programme, under Grant Agreement number: 825759 (The ENDpoiNTs project)
Multi-media biomarkers: Integrating information to improve lead exposure assessment
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
Modeling the health effects of time-varying complex environmental mixtures: Mean field variational Bayes for lagged kernel machine regression
Copyright © 2018 John Wiley & Sons, Ltd. There is substantial interest in assessing how exposure to environmental mixtures, such as chemical mixtures, affects child health. Researchers are also interested in identifying critical time windows of susceptibility to these complex mixtures. A recently developed method, called lagged kernel machine regression (LKMR), simultaneously accounts for these research questions by estimating the effects of time-varying mixture exposures and by identifying their critical exposure windows. However, LKMR inference using Markov chain Monte Carlo (MCMC) methods (MCMC-LKMR) is computationally burdensome and time intensive for large data sets, limiting its applicability. Therefore, we develop a mean field variational approximation method for Bayesian inference (MFVB) procedure for LKMR (MFVB-LKMR). The procedure achieves computational efficiency and reasonable accuracy as compared with the corresponding MCMC estimation method. Updating parameters using MFVB may only take minutes, whereas the equivalent MCMC method may take many hours or several days. We apply MFVB-LKMR to Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS), a prospective cohort study in Mexico City. Results from a subset of PROGRESS using MFVB-LKMR provide evidence of significant and positive association between second trimester cobalt levels and z-scored birth weight. This positive association is heightened by cesium exposure. MFVB-LKMR is a promising approach for computationally efficient analysis of environmental health data sets, to identify critical windows of exposure to complex mixtures
Effect of electric field on light scattering by aqueous colloids of diamond and graphite
Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods
Humans are exposed to a diverse mixture of chemical and non-chemical exposures across their lifetimes. Well-designed epidemiology studies as well as sophisticated exposure science and related technologies enable the investigation of the health impacts of mixtures. While existing statistical methods can address the most basic questions related to the association between environmental mixtures and health endpoints, there were gaps in our ability to learn from mixtures data in several common epidemiologic scenarios, including high correlation among health and exposure measures in space and/or time, the presence of missing observations, the violation of important modeling assumptions, and the presence of computational challenges incurred by current implementations. To address these and other challenges, NIEHS initiated the Powering Research through Innovative methods for Mixtures in Epidemiology (PRIME) program, to support work on the development and expansion of statistical methods for mixtures. Six independent projects supported by PRIME have been highly productive but their methods have not yet been described collectively in a way that would inform application. We review 37 new methods from PRIME projects and summarize the work across previously published research questions, to inform methods selection and increase awareness of these new methods. We highlight important statistical advancements considering data science strategies, exposure-response estimation, timing of exposures, epidemiological methods, the incorporation of toxicity/chemical information, spatiotemporal data, risk assessment, and model performance, efficiency, and interpretation. Importantly, we link to software to encourage application and testing on other datasets. This review can enable more informed analyses of environmental mixtures. We stress training for early career scientists as well as innovation in statistical methodology as an ongoing need. Ultimately, we direct efforts to the common goal of reducing harmful exposures to improve public health
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