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

Integrated Assessment of Shallow-Aquifer Vulnerability to Multiple Contaminants and Drinking-Water Exposure Pathways in Holliston, Massachusetts

Half of U.S. drinking water comes from aquifers, and very shallow ones (table) are especially vulnerable to anthropogenic contamination. We present the case of Holliston, a Boston, Massachusetts suburb that draws its drinking water from very shallow aquifers, and where metals and solvents have been reported in groundwater. Community concerns focus on water discolored by naturally occurring manganese (Mn), despite reports stating regulatory aesthetic compliance. Epidemiologic studies suggest Mn is a potentially toxic element (PTE) for children exposed by the drinking-water pathway at levels near the regulatory aesthetic level. We designed an integrated, community-based project: five sites were profiled for contaminant releases; service areas for wells were modeled; and the capture zone for one vulnerable well was estimated. Manganese, mercury, and trichloroethylene are among 20 contaminants of interest. Findings show that past and/or current exposures to multiple contaminants in drinking water are plausible, satisfying the criteria for complete exposure pathways. This case questions the adequacy of aquifer protection and monitoring regulations, and highlights the need for integrated assessment of multiple contaminants, associated exposures and health risks. It posits that community-researcher partnerships are essential for understanding and solving complex problems

Associations of Early Childhood Manganese and Lead Coexposure with Neurodevelopment

Background: Most toxicologic studies focus on a single agent, although this does not reflect real-world scenarios in which humans are exposed to multiple chemicals

D=3 N=6 superconformal symmetry of AdS_4 x CP^3 superstring

Invariance of the AdS_4 x CP^3 superstring under D=3 N=6 superconformal symmetry is discussed in the sector described by the OSp(4|6)/(SO(1,3) x U(3)) supercoset sigma-model action presented in the conformal basis for the osp(4|6)/(so(1,3) x u(3)) Cartan forms. Transformation rules under D=3 N=6 superconformal symmetry for the (10|24)-dimensional 'reduced' AdS_4 x CP^3 superspace coordinates are obtained and used to derive corresponding world-sheet currents.Comment: LaTeX, 23 pages; v2: presentation refined, typos corrected, references adde

Maternal and Cord Blood Manganese Concentrations and Early Childhood Neurodevelopment among Residents near a Mining-Impacted Superfund Site

Background: Environmental manganese exposure has been associated with adverse neurodevelopmental outcomes among school-aged children; yet, few studies have evaluated prenatal exposure. Objectives: Our study examines associations between prenatal manganese concentrations and placental transfer of manganese with neurodevelopment in 224 2-y-old children residing near the Tar Creek Superfund Site. Methods: We collected maternal and cord blood at delivery, measured manganese using inductively coupled plasma mass spectrometry, and assessed neurodevelopment using the Bayley Scales of Infant Development-II. Associations between manganese and mental (MDI) and psychomotor (PDI) development indices were estimated in multivariable models. Placental transfer, approximated by cord/maternal manganese ratio, cord/total manganese ratio (total=maternal+cord), and by joint classification according to high or low (above or below median) maternal and cord manganese, was evaluated as a predictor of neurodevelopment. Results: Median levels [interquartile ranges (IQR)] of manganese in maternal and cord blood, respectively, were 24.0 (19.5–29.7) and 43.1 (33.5–52.1) μg/L. Adjusting for lead, arsenic, and other potential confounders, an IQR increase in maternal manganese was associated with −3.0 (95% CI: −5.3, −0.7) points on MDI and −2.3 (95% CI: −4.1, −0.4) points on PDI. Cord manganese concentrations were not associated with neurodevelopment scores. Cord/maternal and cord/total manganese ratios were positively associated with MDI [cord/maternal: β=2.6 (95% Cl: −0.04, 5.3); cord/total: β=22.0 (95% Cl: 3.2, 40.7)] and PDI (cord/maternal: β=1.7 (95% Cl: −0.5, 3.9); cord/total: β=15.6 (95% Cl: 0.3, 20.9)). Compared to mother–child pairs with low maternal and cord manganese, associations with neurodevelopment scores were negative for pairs with either high maternal, high cord, or high maternal and cord manganese. Conclusions: Maternal blood manganese concentrations were negatively associated with early childhood neurodevelopment scores in our study. Findings highlight the importance of understanding maternal exposures during pregnancy and factors influencing placental transfer. https://doi.org/10.1289/EHP92

Associations of iron metabolism genes with blood manganese levels: a population-based study with validation data from animal models

<p>Abstract</p> <p>Background</p> <p>Given mounting evidence for adverse effects from excess manganese exposure, it is critical to understand host factors, such as genetics, that affect manganese metabolism.</p> <p>Methods</p> <p>Archived blood samples, collected from 332 Mexican women at delivery, were analyzed for manganese. We evaluated associations of manganese with functional variants in three candidate iron metabolism genes: <it>HFE </it>[hemochromatosis], <it>TF </it>[transferrin], and <it>ALAD </it>[δ-aminolevulinic acid dehydratase]. We used a knockout mouse model to parallel our significant results as a novel method of validating the observed associations between genotype and blood manganese in our epidemiologic data.</p> <p>Results</p> <p>Percentage of participants carrying at least one copy of <it>HFE C282Y</it>, <it>HFE H63D</it>, <it>TF P570S</it>, and <it>ALAD K59N </it>variant alleles was 2.4%, 17.7%, 20.1%, and 6.4%, respectively. Percentage carrying at least one copy of either <it>C282Y </it>or <it>H63D </it>allele in <it>HFE </it>gene was 19.6%. Geometric mean (geometric standard deviation) manganese concentrations were 17.0 (1.5) μg/l. Women with any <it>HFE </it>variant allele had 12% lower blood manganese concentrations than women with no variant alleles (β = -0.12 [95% CI = -0.23 to -0.01]). <it>TF </it>and <it>ALAD </it>variants were not significant predictors of blood manganese. In animal models, <it>Hfe</it>^-/-mice displayed a significant reduction in blood manganese compared with <it>Hfe</it>^+/+mice, replicating the altered manganese metabolism found in our human research.</p> <p>Conclusions</p> <p>Our study suggests that genetic variants in iron metabolism genes may contribute to variability in manganese exposure by affecting manganese absorption, distribution, or excretion. Genetic background may be critical to consider in studies that rely on environmental manganese measurements.</p

Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression

Abstract Background Estimating the health effects of multi-pollutant mixtures is of increasing interest in environmental epidemiology. Recently, a new approach for estimating the health effects of mixtures, Bayesian kernel machine regression (BKMR), has been developed. This method estimates the multivariable exposure-response function in a flexible and parsimonious way, conducts variable selection on the (potentially high-dimensional) vector of exposures, and allows for a grouped variable selection approach that can accommodate highly correlated exposures. However, the application of this novel method has been limited by a lack of available software, the need to derive interpretable output in a computationally efficient manner, and the inability to apply the method to non-continuous outcome variables. Methods This paper addresses these limitations by (i) introducing an open-source software package in the R programming language, the bkmr R package, (ii) demonstrating methods for visualizing high-dimensional exposure-response functions, and for estimating scientifically relevant summaries, (iii) illustrating a probit regression implementation of BKMR for binary outcomes, and (iv) describing a fast version of BKMR that utilizes a Gaussian predictive process approach. All of the methods are illustrated using fully reproducible examples with the provided R code. Results Applying the methods to a continuous outcome example illustrated the ability of the BKMR implementation to estimate the health effects of multi-pollutant mixtures in the context of a highly nonlinear, biologically-based dose-response function, and to estimate overall, single-exposure, and interactive health effects. The Gaussian predictive process method led to a substantial reduction in the runtime, without a major decrease in accuracy. In the setting of a larger number of exposures and a dichotomous outcome, the probit BKMR implementation was able to correctly identify the variables included in the exposure-response function and yielded interpretable quantities on the scale of a latent continuous outcome or on the scale of the outcome probability. Conclusions This newly developed software, integrated suite of tools, and extended methodology makes BKMR accessible for use across a broad range of epidemiological applications in which multiple risk factors have complex effects on health