Low Blood Lead Levels Do Not Appear to Be Further Reduced by Dietary Supplements

OBJECTIVE: Our objective was to evaluate the association of dietary intakes of selected micronutrients and blood lead (PbB) concentrations in female adults and in children. DESIGN: With longitudinal monitoring, we measured daily intakes of the micronutrients calcium, magnesium, sodium, potassium, barium, strontium, phosphorus, zinc, iron (limited data), and copper from 6-day duplicate diets (2–13 collections per individual) and PbB concentrations. Participants were three groups of females of child-bearing age (one cohort consisting of 21 pregnant subjects and 15 nonpregnant controls, a second cohort of nine pregnant migrants), and one group of 10 children 6–11 years of age. RESULTS: Mean PbB concentrations were < 5 μg/dL. A mixed linear model that included only group and time accounted for 5.9% of the variance of the PbB measurements; neither the effect of time nor the effect of group was significant. The model containing all of the micronutrients (except iron, for which there was a great deal of missing data), along with time and group, accounted for approximately 9.2% of the variance of PbB; this increase was not statistically significant. There was, however, a significant association of PbB with phosphorus, magnesium, and copper when all micronutrients were included in the statistical analysis, perhaps reflecting a synergistic effect. CONCLUSIONS: In contrast to most previous studies, we found no statistically significant relationships between the PbB concentrations and micronutrient intake. In adults and older children with low PbB concentrations and minimal exposure to Pb, micronutrient supplementation is probably unnecessary

Lead Increases Lipopolysaccharide-Induced Liver Injury through Tumor Necrosis Factor-α Overexpression by Monocytes/Macrophages: Role of Protein Kinase C and p42/44 Mitogen-Activated Protein Kinase

Although lead and lipopolysaccharide (LPS), both important environmental pollutants, activate cells through different receptors and participate in distinct upstream signaling pathways, Pb increases the amount of LPS-induced tumor necrosis factor-α (TNF-α). We examined the cells responsible for the excess production of Pb-increased LPS-induced TNF-α and liver injury, and the roles of protein kinase C (PKC) and p42/44 mitogen-activated protein kinase (MAPK) in the induction of TNF-α. Peritoneal injection of Pb alone (100 μmol/kg) or a low dose of LPS (5 mg/kg) did not affect serum TNF-α or liver functions in A/J mice. In contrast, coexposure to these noneffective doses of Pb plus LPS (Pb+LPS) strongly induced TNF-α expression and resulted in profound liver injury. Direct inhibition of TNF-α or functional inactivation of monocytes/macrophages significantly decreased the level of Pb+LPS-induced serum TNF-α and concurrently ameliorated liver injury. Pb+LPS coexposure stimulated the phosphorylation of p42/44 MAPK and the expression of TNF-α in CD14(+) cells of cultured mouse whole blood, peritoneal macrophages, and RAW264.7 cells. Moreover, blocking PKC or MAPK effectively reduced Pb+LPS-induced TNF-α expression and liver injury. In summary, monocytes/macrophages were the cells primarily responsible for producing, through the PKC/MAPK pathway, the excess Pb-increased/LPS-induced TNF-α that caused liver injury

Evaluating Statistical Methods Using Plasmode Data Sets in the Age of Massive Public Databases: An Illustration Using False Discovery Rates

Plasmode is a term coined several years ago to describe data sets that are derived from real data but for which some truth is known. Omic techniques, most especially microarray and genomewide association studies, have catalyzed a new zeitgeist of data sharing that is making data and data sets publicly available on an unprecedented scale. Coupling such data resources with a science of plasmode use would allow statistical methodologists to vet proposed techniques empirically (as opposed to only theoretically) and with data that are by definition realistic and representative. We illustrate the technique of empirical statistics by consideration of a common task when analyzing high dimensional data: the simultaneous testing of hundreds or thousands of hypotheses to determine which, if any, show statistical significance warranting follow-on research. The now-common practice of multiple testing in high dimensional experiment (HDE) settings has generated new methods for detecting statistically significant results. Although such methods have heretofore been subject to comparative performance analysis using simulated data, simulating data that realistically reflect data from an actual HDE remains a challenge. We describe a simulation procedure using actual data from an HDE where some truth regarding parameters of interest is known. We use the procedure to compare estimates for the proportion of true null hypotheses, the false discovery rate (FDR), and a local version of FDR obtained from 15 different statistical methods

Prospective Study of Blood and Tibia Lead in Women Undergoing Surgical Menopause

Despite the dramatic decline in environmental lead exposure in the United States during the past couple of decades, concern has been expressed regarding mobilization during menopause of existing lead stored in bone. To investigate whether bone lead concentrations decrease and blood lead levels increase, we conducted a prospective study of 91 women who were scheduled to undergo a bilateral oophorectomy for a benign condition at Mount Sinai Hospital in New York City during October 1994 through April 1999. We excluded women who were younger than 30 years of age or who were postmenopausal at the time of the surgery. We observed a small but significant increase in median blood lead levels between the baseline visit and the 6-month visit (0.4 μg/dL, p < 0.0001), particularly for women who were not on estrogen replacement therapy (0.7 μg/dL, p = 0.008). No significant change was observed in blood lead values between 6 and 18 months postsurgery, nor was there evidence of significant changes in tibia lead concentrations during the follow-up period. These findings do not point to substantial mobilization of lead from cortical bone during menopause

Re-sampling strategy to improve the estimation of number of null hypotheses in FDR control under strong correlation structures

<p>Abstract</p> <p>Background</p> <p>When conducting multiple hypothesis tests, it is important to control the number of false positives, or the False Discovery Rate (FDR). However, there is a tradeoff between controlling FDR and maximizing power. Several methods have been proposed, such as the q-value method, to estimate the proportion of true null hypothesis among the tested hypotheses, and use this estimation in the control of FDR. These methods usually depend on the assumption that the test statistics are independent (or only weakly correlated). However, many types of data, for example microarray data, often contain large scale correlation structures. Our objective was to develop methods to control the FDR while maintaining a greater level of power in highly correlated datasets by improving the estimation of the proportion of null hypotheses.</p> <p>Results</p> <p>We showed that when strong correlation exists among the data, which is common in microarray datasets, the estimation of the proportion of null hypotheses could be highly variable resulting in a high level of variation in the FDR. Therefore, we developed a re-sampling strategy to reduce the variation by breaking the correlations between gene expression values, then using a conservative strategy of selecting the upper quartile of the re-sampling estimations to obtain a strong control of FDR.</p> <p>Conclusion</p> <p>With simulation studies and perturbations on actual microarray datasets, our method, compared to competing methods such as q-value, generated slightly biased estimates on the proportion of null hypotheses but with lower mean square errors. When selecting genes with controlling the same FDR level, our methods have on average a significantly lower false discovery rate in exchange for a minor reduction in the power.</p

External costs of atmospheric Pb emissions: valuation of neurotoxic impacts due to inhalation

<p>Abstract</p> <p>Background</p> <p>The Impact Pathway Approach (IPA) is an innovative methodology to establish links between emissions, related impacts and monetary estimates. Only few attempts have so far been presented regarding emissions of metals; in this study the external costs of airborne lead (Pb) emissions are assessed using the IPA. Exposure to Pb is known to provoke impacts especially on children's cognition. As cognitive abilities (measured as IQ, intelligence quotient) are known to have implications for lifetime income, a pathway can be established leading from figures for Pb emissions to the implied loss in earnings, and on this basis damage costs per unit of Pb emission can be assessed.</p> <p>Methods</p> <p>Different types of models are here linked. It is relatively straightforward to establish the relationship between Pb emissions and consequent increase in air-Pb concentration, by means of a Gaussian plume dispersion model (OML). The exposed population can then be modelled by linking the OML-output to population data nested in geo-referenced grid cells. Less straightforward is to establish the relationship between exposure to air-Pb concentrations and the resulting blood-Pb concentration. Here an Age-Dependent Biokinetic Model (ADBM) for Pb is applied. On basis of previous research which established links between increases in blood-Pb concentrations during childhood and resulting IQ-loss we arrive at our results.</p> <p>Results</p> <p>External costs of Pb airborne emissions, even at low doses, in our site are in the range of 41-83 €/kg emitted Pb, depending on the considered meteorological year. This estimate applies only to the initial effects of air-Pb, as our study does not address the effects due to the Pb environmental-accumulation and to the subsequent Pb re-exposure. These are likely to be between one and two orders of magnitude higher.</p> <p>Conclusions</p> <p>Biokinetic modelling is a novel tool not previously included when applying the IPA to explore impacts of Pb emissions and related external costs; it allows for more fine-tuned, age-dependent figures for the external costs from low-dose exposure. Valuation of additional health effects and impacts e.g. due to exposure via ingestion appear to be feasible when extending the insights from the present pilot study.</p

Lead exposure and periodontitis in US adults

Lead is known to have significant effects on bone metabolism and the immune system. This study tested the hypothesis that lead exposure affects periodontitis in adults. Material and Methods:  This study used the data from the Third National Health and Nutrition Examination Survey (NHANES III, 1988–94). It analyzed data from 2500 men and 2399 women, 20–56 yr old, who received complete periodontal examination. Periodontitis was defined as the presence of > 20% of mesial sites with ≥ 4 mm of attachment loss. Lead exposure was grouped into three categories:  7 μg/dL. Covariates were cotinine levels, poverty ratio, race/ethnicity, education, bone mineral density, diabetes, calcium intake, dental visit, and menopause (for women). All analyses were performed separately for men and women and considering the effect design. Univariate, bivariate, and stratified analysis was followed by multivariable analysis by estimating prevalence ratios through poisson regression. Results:  After adjustment for confounders, the prevalence ratios, comparing those with a lead blood level of > 7 μg/dL to those with a lead blood level of < 3 μg/dL was 1.70 (95% confidence interval (CI): 1.02, 2.85) for men and 3.80 (95% CI: 1.66, 8.73) for women. Conclusion:  The lead blood level was positively and statistically associated with periodontitis for both men and women. Considering the public health importance of periodontitis and lead exposure, further studies are necessary to confirm this association.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65253/1/j.1600-0765.2006.00913.x.pd

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Validation of differential gene expression algorithms: Application comparing fold-change estimation to hypothesis testing

<p>Abstract</p> <p>Background</p> <p>Sustained research on the problem of determining which genes are differentially expressed on the basis of microarray data has yielded a plethora of statistical algorithms, each justified by theory, simulation, or ad hoc validation and yet differing in practical results from equally justified algorithms. Recently, a concordance method that measures agreement among gene lists have been introduced to assess various aspects of differential gene expression detection. This method has the advantage of basing its assessment solely on the results of real data analyses, but as it requires examining gene lists of given sizes, it may be unstable.</p> <p>Results</p> <p>Two methodologies for assessing predictive error are described: a cross-validation method and a posterior predictive method. As a nonparametric method of estimating prediction error from observed expression levels, cross validation provides an empirical approach to assessing algorithms for detecting differential gene expression that is fully justified for large numbers of biological replicates. Because it leverages the knowledge that only a small portion of genes are differentially expressed, the posterior predictive method is expected to provide more reliable estimates of algorithm performance, allaying concerns about limited biological replication. In practice, the posterior predictive method can assess when its approximations are valid and when they are inaccurate. Under conditions in which its approximations are valid, it corroborates the results of cross validation. Both comparison methodologies are applicable to both single-channel and dual-channel microarrays. For the data sets considered, estimating prediction error by cross validation demonstrates that empirical Bayes methods based on hierarchical models tend to outperform algorithms based on selecting genes by their fold changes or by non-hierarchical model-selection criteria. (The latter two approaches have comparable performance.) The posterior predictive assessment corroborates these findings.</p> <p>Conclusions</p> <p>Algorithms for detecting differential gene expression may be compared by estimating each algorithm's error in predicting expression ratios, whether such ratios are defined across microarray channels or between two independent groups.</p> <p>According to two distinct estimators of prediction error, algorithms using hierarchical models outperform the other algorithms of the study. The fact that fold-change shrinkage performed as well as conventional model selection criteria calls for investigating algorithms that combine the strengths of significance testing and fold-change estimation.</p