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

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

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

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

Early life manganese exposure and reported attention-related behaviors in Italian adolescents

Background: Manganese (Mn) is an essential nutrient and neurotoxicant, and the neurodevelopmental effects of Mn may depend on exposure timing. Less research has quantitatively compared the impact of Mn exposure on neurodevelopment across exposure periods. Methods: We used data from 125 Italian adolescents (10-14 years) from the Public Health Impact of Metals Exposure Study to estimate prospective associations of Mn in three early life exposure periods with adolescent attention-related behaviors. Mn was quantified in deciduous teeth using laser ablation-inductively coupled plasma-mass spectrometry to represent prenatal (2nd trimester-birth), postnatal (birth ∼1.5 years), and childhood (∼1.5-6 years) exposure. Attention-related behavior was evaluated using the Conners Behavior Rating Scales in adolescence. We used multivariable linear regression models to quantify associations between Mn in each exposure period, and multiple informant models to compare associations across exposure periods. Results: Median tooth Mn levels (normalized to calcium) were 0.4 area under the curve (AUC) 55Mn:43Ca × 104, 0.1 AUC 55Mn:43Ca × 104, and 0.0006 55Mn:43Ca for the prenatal, postnatal, and childhood periods. A doubling in prenatal tooth Mn levels was associated with 5.3% (95% confidence intervals [CI] = -10.3%, 0.0%) lower (i.e., better) teacher-reported inattention scores, whereas a doubling in postnatal tooth Mn levels was associated with 4.5% (95% CI = -9.3%, 0.6%) and 4.6% (95% CI = -9.5%, 0.6%) lower parent-reported inattention and attention deficit/hyperactivity disorder index scores, respectively. Childhood Mn was not beneficially associated with reported attention-related behaviors. Conclusion: Protective associations in the prenatal and postnatal periods suggest Mn is beneficial for attention-related behavior, but not in the childhood period

Manganese in teeth and neurobehavior: Sex-specific windows of susceptibility

Manganese (Mn) is an essential element required for growth and development, but higher body burdens have been associated with neurobehavioral decrements in children