Association of selenoprotein and selenium pathway gnotypes with risk of colorectal cancer and interaction with selenium status

Selenoprotein genetic variations and suboptimal selenium (Se) levels may contribute to the risk of colorectal cancer (CRC) development. We examined the association between CRC risk and genotype for single nucleotide polymorphisms (SNPs) in selenoprotein and Se metabolic pathway genes. Illumina Goldengate assays were designed and resulted in the genotyping of 1040 variants in 154 genes from 1420 cases and 1421 controls within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Multivariable logistic regression revealed an association of 144 individual SNPs from 63 Se pathway genes with CRC risk. However, regarding the selenoprotein genes, only TXNRD1 rs11111979 retained borderline statistical significance after adjustment for correlated tests (PACT = 0.10; PACT significance threshold was P < 0.1). SNPs in Wingless/Integrated (Wnt) and Transforming growth factor (TGF) beta-signaling genes (FRZB, SMAD3, SMAD7) from pathways affected by Se intake were also associated with CRC risk after multiple testing adjustments. Interactions with Se status (using existing serum Se and Selenoprotein P data) were tested at the SNP, gene, and pathway levels. Pathway analyses using the modified Adaptive Rank Truncated Product method suggested that genes and gene x Se status interactions in antioxidant, apoptosis, and TGF-beta signaling pathways may be associated with CRC risk. This study suggests that SNPs in the Se pathway alone or in combination with suboptimal Se status may contribute to CRC development

Genome-wide Association Study Identifies Five Susceptibility Loci for Follicular Lymphoma outside the HLA Region

Genome-wide association studies (GWASs) of follicular lymphoma (FL) have previously identified human leukocyte antigen (HLA) gene variants. To identify additional FL susceptibility loci, we conducted a large-scale two-stage GWAS in 4,523 case subjects and 13,344 control subjects of European ancestry. Five non-HLA loci were associated with FL risk: 11q23.3 (rs4938573, p = 5.79 × 10−20) near CXCR5; 11q24.3 (rs4937362, p = 6.76 × 10−11) near ETS1; 3q28 (rs6444305, p = 1.10 × 10−10) in LPP; 18q21.33 (rs17749561, p = 8.28 × 10−10) near BCL2; and 8q24.21 (rs13254990, p = 1.06 × 10−8) near PVT1. In an analysis of the HLA region, we identified four linked HLA-DRβ1 multiallelic amino acids at positions 11, 13, 28, and 30 that were associated with FL risk (pomnibus = 4.20 × 10−67 to 2.67 × 10−70). Additional independent signals included rs17203612 in HLA class II (odds ratio [ORper-allele] = 1.44; p = 4.59 × 10−16) and rs3130437 in HLA class I (ORper-allele = 1.23; p = 8.23 × 10−9). Our findings further expand the number of loci associated with FL and provide evidence that multiple common variants outside the HLA region make a significant contribution to FL risk

Genetically predicted longer telomere length is associated with increased risk of B-cell lymphoma subtypes

Evidence from a small number of studies suggests that longer telomere length measured in peripheral leukocytes is associated with an increased risk of non-Hodgkin lymphoma (NHL). However, these studies may be biased by reverse causation, confounded by unmeasured environmental exposures and might miss time points for which prospective telomere measurement would best reveal a relationship between telomere length and NHL risk. We performed an analysis of genetically inferred telomere length and NHL risk in a study of 10 102 NHL cases of the four most common B-cell histologic types and 9562 controls using a genetic risk score (GRS) comprising nine telomere length-associated single-nucleotide polymorphisms. This approach uses existing genotype data and estimates telomere length by weighing the number of telomere length-associated variant alleles an individual carries with the published change in kb of telomere length. The analysis of the telomere length GRS resulted in an association between longer telomere length and increased NHL risk [four B-cell histologic types combined; odds ratio (OR) = 1.49, 95% CI 1.22–1.82, P-value = 8.5 × 10−5]. Subtype-specific analyses indicated that chronic lymphocytic leukemia or small lymphocytic lymphoma (CLL/SLL) was the principal NHL subtype contributing to this association (OR = 2.60, 95% CI 1.93–3.51, P-value = 4.0 × 10−10). Significant interactions were observed across strata of sex for CLL/SLL and marginal zone lymphoma subtypes as well as age for the follicular lymphoma subtype. Our results indicate that a genetic background that favors longer telomere length may increase NHL risk, particularly risk of CLL/SLL, and are consistent with earlier studies relating longer telomere length with increased NHL risk

Performance of Prediction Algorithms for Modeling Outdoor Air Pollution Spatial Surfaces

Land use regression (LUR) models for air pollutants are often developed using multiple linear regression techniques. However, in the past decade linear (stepwise) regression methods have been criticized for their lack of flexibility, their ignorance of potential interaction between predictors, and their limited ability to incorporate highly correlated predictors. We used two training sets of ultrafine particles (UFP) data (mobile measurements (8200 segments, 25 s monitoring per segment), and short-term stationary measurements (368 sites, 3 × 30 min per site)) to evaluate different modeling approaches to estimate long-term UFP concentrations by estimating precision and bias based on an independent external data set (42 sites, average of three 24-h measurements). Higher training data R2 did not equate to higher test R2 for the external long-term average exposure estimates, making the argument that external validation data are critical to compare model performance. Machine learning algorithms trained on mobi..

Performance of Prediction Algorithms for Modeling Outdoor Air Pollution Spatial Surfaces

Land use regression (LUR) models for air pollutants are often developed using multiple linear regression techniques. However, in the past decade linear (stepwise) regression methods have been criticized for their lack of flexibility, their ignorance of potential interaction between predictors, and their limited ability to incorporate highly correlated predictors. We used two training sets of ultrafine particles (UFP) data (mobile measurements (8200 segments, 25 s monitoring per segment), and short-term stationary measurements (368 sites, 3 × 30 min per site)) to evaluate different modeling approaches to estimate long-term UFP concentrations by estimating precision and bias based on an independent external data set (42 sites, average of three 24-h measurements). Higher training data R2 did not equate to higher test R2 for the external long-term average exposure estimates, making the argument that external validation data are critical to compare model performance. Machine learning algorithms trained on mobi..

Longitudinal associations between risk appraisal of base stations for mobile phones, radio or television and non-specific symptoms

Introduction: Studies found that higher risk appraisal of radiofrequency electromagnetic fields is associated with reporting more non-specific symptoms such as headache and back pain. There is limited data available on the longitudinal nature of such associations and what aspects of risk appraisal and characteristics of subjects are relevant. Objective: To examine cross-sectional and longitudinal associations between risk appraisal measures and non-specific symptoms, and assess the role of subject characteristics (sex, age, education, trait negative affect) in a general population cohort. Methods: This study was nested in the Dutch general population AMIGO cohort that was established in 2011/2012, when participants were 31–65 years old. We studied a sample of participants (n = 1720) who filled in two follow-up questionnaires in 2013 and 2014, including questions about perceived exposure, perceived risk, and health concerns as indicators of risk appraisal of base stations, and non-specific symptoms. Results: Perceived exposure, perceived risk, and health concerns, respectively, were associated with higher symptom scores in cross-sectional and longitudinal analyses. Only health concerns (not perceived exposure and perceived risk) temporally preceded high symptom scores and vice versa. Female sex, younger age, higher education, and higher trait negative affect were associated with higher risk appraisal of mobile phone base stations. Discussion: The findings in this study strengthen the evidence base for cross-sectional and longitudinal associations between higher risk appraisal and non-specific symptoms in the general population. However, the directionality of potential causal relations in non-sensitive general population samples should be examined further in future studies, providing information to the benefit of risk communication strategies

Modeled and perceived RF-EMF, noise and air pollution and symptoms in a population cohort. Is perception key in predicting symptoms?

Background: Psychosocial research has shown that perceived exposure can influence symptom reporting, regardless of actual exposure. The impact of this phenomenon on the interpretation of results from epidemiological research on environmental determinants of symptoms is unclear. Objective: Our aim was to compare associations between modeled exposures, the perceived level of these exposures and reported symptoms (non-specific symptoms, sleep disturbances, and respiratory symptoms) for three different environmental exposures (radiofrequency electromagnetic fields (RF-EMF), noise, and air pollution). These environmental exposures vary in the degree to which they can be sensorially observed. Methods: Participant characteristics, perceived exposures, and self-reported health were assessed with a baseline (n = 14,829, 2011/2012) and follow-up (n = 7905, 2015) questionnaire in the Dutch population-based Occupational and Environmental Health Cohort (AMIGO). Environmental exposures were estimated at the home address using spatial models. Cross-sectional and longitudinal regression models were used to examine the associations between modeled and perceived exposures, and reported symptoms. Results: The extent to which exposure sources could be observed by participants likely influenced correlations between modeled and perceived exposure as correlations were moderate for air pollution (rSp = 0.34) and noise (rSp = 0.40), but less so for RF-EMF (rSp = 0.11). Perceived exposures were consistently associated with increased symptom scores (respiratory, sleep, non-specific). Modeled exposures, except RF-EMF, were associated with increased symptom scores, but these associations disappeared or strongly diminished when accounted for perceived exposure in the analyses. Discussion: Perceived exposure has an important role in symptom reporting. When environmental determinants of symptoms are studied without acknowledging the potential role of both modeled and perceived exposures, there is a risk of bias in health risk assessment. However, the etiological role of exposure perceptions in relation to symptom reporting requires further research

Spatio-temporal variation of outdoor and indoor pesticide air concentrations in homes near agricultural fields

Background: Previous research has shown that many current-use pesticides can be detected in air around application areas. Environmental exposure to pesticides may cause adverse health effects, necessitating accurate assessment of outdoor and indoor air concentrations for people living close to spraying sites. We evaluated outdoor and indoor air concentrations of different pesticides, as well as factors influencing spatial and temporal variations. Methods: We collected outdoor air samples at 58 homes located within 250 m of bulb fields and 15 control homes located further than 500 m from any agricultural field. Outdoor air sampling following a pesticide spray event was performed 24-h a day for 7 consecutive days. Two full day samples were collected at the same locations during a non-use period. In homes located within 50 m from agricultural fields (N = 18), indoor air was also sampled for the first 24 h after field spraying. Samples were analysed for a total of 46 pesticides and degradation products. From these, 11 were actively used on nearby fields, 3 were used in bulb disinfection and 6 were degradation products. Results: Compared to non-use periods, pesticides concentrations were 5–10 times higher in outdoor air during application periods. Similar concentration differences were observed between exposed homes and controls both during pesticide use and non-use period. For 14 pesticides, there were moderate correlations (spearman > 0.4–0.7) between outdoor and indoor air concentrations. Wind direction, evapotranspiration and agricultural area surrounding a home were the most important determinants of air concentration of the applied pesticides. Conclusions: This study provides strong evidence suggesting that environmental exposure to pesticides via air is not limited to the day of application and may occur year-round. The concentrations appeared higher during the use period. Factors influencing the local fate of pesticides in air may differ significantly between compounds

Multicentre, population-based, case-control study of particulates, combustion products and amyotrophic lateral sclerosis risk

Objective To investigate whether exposure to particulates and combustion products may explain the association between certain occupations and amyotrophic lateral sclerosis (ALS) risk in a large, multicentre, population-based, case-control study, based on full job histories, using job-exposure matrices, with detailed information on possible confounders. Methods Population-based patients with ALS and controls were recruited from five registries in the Netherlands, Ireland and Italy. Demographics and data regarding educational level, smoking, alcohol habits and lifetime occupational history were obtained using a validated questionnaire. Using job-exposure matrices, we assessed occupational exposure to silica, asbestos, organic dust, contact with animals or fresh animal products, endotoxins, polycyclic aromatic hydrocarbons and diesel motor exhaust. Multivariate logistic regression models adjusting for confounding factors were used to determine the association between these exposures and ALS risk. Results We included 1557 patients and 2922 controls. Associations were positive for all seven occupational exposures (ORs ranging from 1.13 to 1.73 for high vs never exposed), and significant on the continuous scale for silica, organic dust and diesel motor exhaust (p values for trend ≤0.03). Additional analyses, adding an exposure (one at a time) to the model in the single exposure analysis, revealed a stable OR for silica. We found similar results when patients with a C9orf72 mutation were excluded. Conclusion In a large, multicentre study, using harmonised methodology to objectively quantify occupational exposure to particulates and combustion products, we found an association between ALS risk and exposure to silica, independent of the other occupational exposures studied

Spatio-temporal variation of outdoor and indoor pesticide air concentrations in homes near agricultural fields

Background: Previous research has shown that many current-use pesticides can be detected in air around application areas. Environmental exposure to pesticides may cause adverse health effects, necessitating accurate assessment of outdoor and indoor air concentrations for people living close to spraying sites. We evaluated outdoor and indoor air concentrations of different pesticides, as well as factors influencing spatial and temporal variations. Methods: We collected outdoor air samples at 58 homes located within 250 m of bulb fields and 15 control homes located further than 500 m from any agricultural field. Outdoor air sampling following a pesticide spray event was performed 24-h a day for 7 consecutive days. Two full day samples were collected at the same locations during a non-use period. In homes located within 50 m from agricultural fields (N = 18), indoor air was also sampled for the first 24 h after field spraying. Samples were analysed for a total of 46 pesticides and degradation products. From these, 11 were actively used on nearby fields, 3 were used in bulb disinfection and 6 were degradation products. Results: Compared to non-use periods, pesticides concentrations were 5–10 times higher in outdoor air during application periods. Similar concentration differences were observed between exposed homes and controls both during pesticide use and non-use period. For 14 pesticides, there were moderate correlations (spearman > 0.4–0.7) between outdoor and indoor air concentrations. Wind direction, evapotranspiration and agricultural area surrounding a home were the most important determinants of air concentration of the applied pesticides. Conclusions: This study provides strong evidence suggesting that environmental exposure to pesticides via air is not limited to the day of application and may occur year-round. The concentrations appeared higher during the use period. Factors influencing the local fate of pesticides in air may differ significantly between compounds