There is an active debate in the literature on censored data about the relative performance of model based maximum likelihood estimators, IPCW-estimators, and a variety of double robust semiparametric efficient estimators. Kang and Schafer (2007) demonstrate the fragility of double robust and IPCW-estimators in a simulation study with positivity violations. They focus on a simple missing data problem with covariates where one desires to estimate the mean of an outcome that is subject to missingness. Responses by Robins, et al. (2007), Tsiatis and Davidian (2007), Tan (2007) and Ridgeway and McCaffrey (2007) further explore the challenges faced by double robust estimators and offer suggestions for improving their stability. In this article, we join the debate by presenting targeted maximum likelihood estimators (TMLEs). We demonstrate that TMLEs that guarantee that the parametric submodel employed by the TMLE procedure respects the global bounds on the continuous outcomes, are especially suitable for dealing with positivity violations because in addition to being double robust and semiparametric efficient, they are substitution estimators. We demonstrate the practical performance of TMLEs relative to other estimators in the simulations designed by Kang and Schafer (2007) and in modified simulations with even greater estimation challenges.

Variation in individual susceptibility to arsenic-induced disease may be partially explained by genetic differences in arsenic metabolism. Mounting epidemiological evidence and in vitro studies suggest that methylated arsenic metabolites, particularly monomethylarsonic (MMA3), are more acutely toxic than inorganic arsenic; thus, MMA3 may be the primary toxic arsenic species. To test the role of genetic variation in arsenic metabolism, polymorphisms in genes involved in one-carbon metabolism [methylenetetrahydrofolate reductase (MTHFR), methionine synthase (MTR), cystathionine-β-synthase (CBS), thymidylate synthase (TYMS), dihydrofolate reductase (DHFR), serine hydroxymethyltransferase 1 (SHMT1] and glutathione biosynthesis [glutathione S-transferase omega 1 (GSTO1)] were examined in an arsenic exposed population to determine their influence in urinary arsenic metabolite patterns. In 142 subjects in Cordoba Province, Argentina, variant genotypes for CBS rs234709 and rs4920037 SNPs compared with wild-type homozygotes were associated with 24% and 26% increases, respectively, in the mean proportion of arsenic excreted as monomethylarsonic acid (%MMA). This difference is within the range of differences in %MMA seen between people with arsenic-related disease and those without such disease in other studies. Small inverse associations with CBS rs234709 and rs4920037 variants were also found for the mean levels of the proportion of arsenic excreted as dimethylarsinous acid (%DMA). No other genetic associations were found. These findings are the first to suggest that CBS polymorphisms may influence arsenic metabolism in humans and susceptibility to arsenic-related disease.

Background

Benzene, an established cause of acute myeloid leukemia (AML), may also cause one or more lymphoid malignancies in humans. Previously, we identified genes and pathways associated with exposure to high (> 10 ppm) levels of benzene through transcriptomic analyses of blood cells from a small number of occupationally exposed workers.

Objectives

The goals of this study were to identify potential biomarkers of benzene exposure and/or early effects and to elucidate mechanisms relevant to risk of hematotoxicity, leukemia, and lymphoid malignancy in occupationally exposed individuals, many of whom were exposed to benzene levels < 1 ppm, the current U.S. occupational standard.

Methods

We analyzed global gene expression in the peripheral blood mononuclear cells of 125 workers exposed to benzene levels ranging from < 1 ppm to > 10 ppm. Study design and analysis with a mixed-effects model minimized potential confounding and experimental variability.

Results

We observed highly significant widespread perturbation of gene expression at all exposure levels. The AML pathway was among the pathways most significantly associated with benzene exposure. Immune response pathways were associated with most exposure levels, potentially providing biological plausibility for an association between lymphoma and benzene exposure. We identified a 16-gene expression signature associated with all levels of benzene exposure.

Conclusions

Our findings suggest that chronic benzene exposure, even at levels below the current U.S. occupational standard, perturbs many genes, biological processes, and pathways. These findings expand our understanding of the mechanisms by which benzene may induce hematotoxicity, leukemia, and lymphoma and reveal relevant potential biomarkers associated with a range of exposures.