Active ingredients in residential and agricultural insecticides have changed over time, due in part to regulatory restrictions. Few studies have evaluated how changes in active ingredients have impacted insecticide levels measured in homes. We measured concentrations of insecticides in one carpet-dust sample from each of 434 homes in California from 2001 to 2006. Analytes included four insecticides sold for indoor home use during our study period (carbaryl, cypermethrin, permethrin, and propoxur) and four that are no longer sold for indoor use including dichlorodiphenyltrichloroethylene (DDT, removed from the market in 1972), chlordane (1988), chlorpyrifos (2001), and diazinon (2004). We considered other potential determinants of concentrations of insecticides in carpet dust, such as home and garden use, occupational exposure, and nearby agricultural applications. We calculated the percentage change in the concentration of each insecticide per year, adjusting for significant determinants. In adjusted models, concentrations of insecticides in carpet dust decreased for three of four insecticides no longer sold for residential use: chlordane (-15% per year), chlorpyrifos (-31%), diazinon (-48%), and propoxur (-34%), which is currently sold for residential use but with increased restrictions since 1997. Concentrations of other insecticides sold for indoor use (carbaryl, cypermethrin, and permethrin) and DDT did not change over time in our study population.

We evaluated the relationship between the risk of childhood acute lymphoblastic leukemia (ALL) and the levels of metals in carpet dust. A dust sample was collected from the homes of 142 ALL cases and 187 controls participating in the California Childhood Leukemia Study using a high volume small surface sampler (2001-2006). Samples were analyzed using microwave-assisted acid digestion in combination with inductively coupled plasma mass spectrometry for arsenic, cadmium, chromium, copper, lead, nickel, tin, tungsten, and zinc. Eight metals were detected in at least 85% of the case and control homes; tungsten was detected in <15% of homes. Relationships between dust metal loadings (μg metal per m(2) carpet) and ALL risk were modeled using multivariable logistic regression, adjusting for the child's age, sex, and race/ethnicity and confounders, including household annual income. A doubling of dust metal loadings was not associated with significant changes in ALL risk (odds ratio (95% confidence interval): arsenic: 0.96 (0.86, 1.07), cadmium: 0.92 (0.81, 1.05), chromium: 1.01 (0.90, 1.14), copper: 0.97 (0.91, 1.03), lead: 1.01 (0.93, 1.10), nickel: 0.95 (0.82, 1.09), tin: 0.96 (0.86, 1.08), and zinc: 0.94 (0.84, 1.05)). Our findings do not support the hypothesis that metals in carpet dust are risk factors for childhood ALL.

Background

Recent meta-analyses demonstrate an association between self-reported residential pesticide use and childhood leukemia risk. Self-reports may suffer from recall bias and provide information only on broad pesticide categories. We compared parental self-reported home and garden pest treatments to pesticides measured in carpet dust.

Methods

Parents of 277 children with leukemia and 306 controls in Northern and Central California (2001-2007) were asked about insect and weed treatments during the previous year. Carpet dust samples were analyzed for 47 pesticides. We present results for the 7 insecticides (carbaryl, propoxur, chlorpyrifos, diazinon, cyfluthrin, cypermethrin, permethrin), 5 herbicides (2,4-dichlorophenoxyacetic acid [2,4-D], chlorthal, dicamba, mecoprop, simazine), and 1 synergist (piperonyl butoxide) that were present in home and garden products during the study period and were detected in ≥25% of carpet dust samples. We constructed linear regression models for the relative change in pesticide concentrations associated with self-reported treatment of pest types in cases and controls separately and combined, adjusting for demographics, housing characteristics, and nearby agricultural pesticide applications.

Results

Several self-reported treatments were associated with pesticide concentrations in dust. For example, households with flea/tick treatments had 2.3 (95% Confidence Interval [CI]: 1.4, 3.7) times higher permethrin concentrations than households not reporting this treatment. Households reporting treatment for ants/cockroaches had 2.5 (95% CI: 1.5, 4.2) times higher cypermethrin levels than households not reporting this treatment. Weed treatment by a household member was associated with 1.9 (1.4, 2.6), 2.2 (1.6, 3.1), and 2.8 (2.1, 3.7) times higher dust concentrations of dicamba, mecoprop, and 2,4-D, respectively. Weed treatments by professional applicators were null/inversely associated with herbicide concentrations in dust. Associations were generally similar between cases and controls and were consistent with pesticide active ingredients in these products during the study time period.

Conclusions

Consistency between self-reported pest treatments, concentrations in dust, and pesticides in products lends credibility to the exposure assessment methods and suggests that differential recall by case-control status is minimal.

Background

House dust is a major source of exposure to polybrominated diphenyl ethers (PBDEs), which are found at high levels in U.S. homes.

Methods

We studied 167 acute lymphoblastic leukemia (ALL) cases 0-7 years of age and 214 birth certificate controls matched on date of birth, sex, and race/ethnicity from the Northern California Childhood Leukemia Study. In 2001-2007, we sampled carpets in the room where the child spent the most time while awake; we used a high-volume small-surface sampler or we took dust from the home vacuum. We measured concentrations of 14 PBDE congeners including penta (28, 47, 99, 100, 153, 154), octa (183, 196, 197, 203), and decaBDEs (206-209). Odds ratios (ORs) were calculated using logistic regression, adjusting for demographics, income, year of dust collection, and sampling method.

Results

BDE-47, BDE-99, and BDE-209 were found at the highest concentrations (medians, 1,173, 1,579, and 938 ng/g, respectively). Comparing the highest to lowest quartile, we found no association with ALL for summed pentaBDEs (OR = 0.7; 95% CI: 0.4, 1.3), octaBDEs (OR = 1.3; 95% CI: 0.7, 2.3), or decaBDEs (OR = 1.0; 95% CI: 0.6, 1.8). Comparing homes in the highest concentration (nanograms per gram) tertile to those with no detections, we observed significantly increased ALL risk for BDE-196 (OR = 2.1; 95% CI: 1.1, 3.8), BDE-203 (OR = 2.0; 95% CI: 1.1, 3.6), BDE-206 (OR = 2.1; 95% CI: 1.1, 3.9), and BDE-207 (OR = 2.0; 95% CI: 1.03, 3.8).

Conclusion

We found no association with ALL for common PBDEs, but we observed positive associations for specific octa and nonaBDEs. Additional studies with repeated sampling and biological measures would be informative.

Background

Studies of related individuals have consistently demonstrated notable familial aggregation of cancer. We aim to estimate the heritability and genetic correlation attributable to the additive effects of common single-nucleotide polymorphisms (SNPs) for cancer at 13 anatomical sites.

Methods

Between 2007 and 2014, the US National Cancer Institute has generated data from genome-wide association studies (GWAS) for 49 492 cancer case patients and 34 131 control patients. We apply novel mixed model methodology (GCTA) to this GWAS data to estimate the heritability of individual cancers, as well as the proportion of heritability attributable to cigarette smoking in smoking-related cancers, and the genetic correlation between pairs of cancers.

Results

GWAS heritability was statistically significant at nearly all sites, with the estimates of array-based heritability, hl (2), on the liability threshold (LT) scale ranging from 0.05 to 0.38. Estimating the combined heritability of multiple smoking characteristics, we calculate that at least 24% (95% confidence interval [CI] = 14% to 37%) and 7% (95% CI = 4% to 11%) of the heritability for lung and bladder cancer, respectively, can be attributed to genetic determinants of smoking. Most pairs of cancers studied did not show evidence of strong genetic correlation. We found only four pairs of cancers with marginally statistically significant correlations, specifically kidney and testes (ρ = 0.73, SE = 0.28), diffuse large B-cell lymphoma (DLBCL) and pediatric osteosarcoma (ρ = 0.53, SE = 0.21), DLBCL and chronic lymphocytic leukemia (CLL) (ρ = 0.51, SE =0.18), and bladder and lung (ρ = 0.35, SE = 0.14). Correlation analysis also indicates that the genetic architecture of lung cancer differs between a smoking population of European ancestry and a nonsmoking Asian population, allowing for the possibility that the genetic etiology for the same disease can vary by population and environmental exposures.

Conclusion

Our results provide important insights into the genetic architecture of cancers and suggest new avenues for investigation.

Genome-wide association studies (GWAS) have mapped risk alleles for at least 10 distinct cancers to a small region of 63 000 bp on chromosome 5p15.33. This region harbors the TERT and CLPTM1L genes; the former encodes the catalytic subunit of telomerase reverse transcriptase and the latter may play a role in apoptosis. To investigate further the genetic architecture of common susceptibility alleles in this region, we conducted an agnostic subset-based meta-analysis (association analysis based on subsets) across six distinct cancers in 34 248 cases and 45 036 controls. Based on sequential conditional analysis, we identified as many as six independent risk loci marked by common single-nucleotide polymorphisms: five in the TERT gene (Region 1: rs7726159, P = 2.10 × 10(-39); Region 3: rs2853677, P = 3.30 × 10(-36) and PConditional = 2.36 × 10(-8); Region 4: rs2736098, P = 3.87 × 10(-12) and PConditional = 5.19 × 10(-6), Region 5: rs13172201, P = 0.041 and PConditional = 2.04 × 10(-6); and Region 6: rs10069690, P = 7.49 × 10(-15) and PConditional = 5.35 × 10(-7)) and one in the neighboring CLPTM1L gene (Region 2: rs451360; P = 1.90 × 10(-18) and PConditional = 7.06 × 10(-16)). Between three and five cancers mapped to each independent locus with both risk-enhancing and protective effects. Allele-specific effects on DNA methylation were seen for a subset of risk loci, indicating that methylation and subsequent effects on gene expression may contribute to the biology of risk variants on 5p15.33. Our results provide strong support for extensive pleiotropy across this region of 5p15.33, to an extent not previously observed in other cancer susceptibility loci.