The prevalence of autism spectrum disorder has risen in recent decades. Given the growing evidence that prenatal fluoride exposure may be neurotoxic, we examined associations between prenatal fluoride exposure and parent-reported autistic behaviors in preschool-aged children. We studied 453 mother-child pairs using data from the Maternal-Infant Research on Environmental Chemicals (MIREC) study, a prospective Canadian birth cohort. Autistic behaviors were assessed in children at 3 to 4 years using the Social Responsiveness Scale-Second Edition (SRS-2) Preschool Form, where a higher score indicates more autistic behaviors. We estimated prenatal fluoride exposure using three methods: (i) maternal urinary fluoride adjusted for specific gravity (MUFSG), from spot urine samples collected at each trimester and the mean calculated across samples, (ii) daily maternal fluoride intake, based on self-reported consumption of tap water, coffee, and tea during the first and third trimesters, and (iii) water fluoride concentration in tap water. We used multivariable linear regression models to estimate associations between the SRS-2 scale T-scores and each fluoride exposure separately. We used multivariable logistic regression to estimate the association between each prenatal fluoride exposure and an elevated SRS-2 total T-score (i.e., 90th percentile or higher). Potential effect modification of MUFSG was examined by child sex, daily folic acid supplementation, and plasma total folate in pregnancy. The mean SRS-2 total T-score for children aged 3 to 4 years was 45.3 (SD = 6.1, range = 34 to 85). The median MUFSG concentration was 0.43 mg/L (interquartile range = 0.33 mg/L). MUFSG was not significantly associated with the SRS-2 total T-score in multivariable linear regression (β = -0.16; 95

The direct and indirect effects of socioeconomic status (SES) on colorectal cancer (CRC) were examined using structural equation modeling in a case-control study with 488 CRCs and 651 controls. SES was measured by education, income and resident region. SES (odds ratio (OR)=0.89), age (OR = 1.03), processed meat intake (OR = 1.08), lack of CRC screening (OR = 2.67), smoking (OR = 1.85) and family history (OR = 1.06) were significantly associated with CRC risk. SES had a direct effect on CRC risk (β = -0.05). An indirect effect of SES on CRC also existed which was mediated by processed meat intake (β = -0.02), vegetable intake (β = -0.01), CRC screening uptake (β = -0.02), and smoking (β = -0.02).

BackgroundWorldwide, lung cancer is the second leading cause of cancer death in women. The present study explored associations between occupational exposures that are prevalent among women, and lung cancer.MethodsData from 10 case-control studies of lung cancer from Europe, Canada, and New Zealand conducted between 1988 and 2008 were combined. Lifetime occupational history and information on nonoccupational factors including smoking were available for 3040 incident lung cancer cases and 4187 controls. We linked each reported job to the Canadian Job-Exposure Matrix (CANJEM), which provided estimates of probability, intensity, and frequency of exposure to each selected agent in each job. For this analysis, we selected 15 agents (cleaning agents, biocides, cotton dust, synthetic fibers, formaldehyde, cooking fumes, organic solvents, cellulose, polycyclic aromatic hydrocarbons from petroleum, ammonia, metallic dust, alkanes C18+, iron compounds, isopropanol, and calcium carbonate) that had lifetime exposure prevalence of at least 5% in the combined study population. For each agent, we estimated lung cancer risk in each study center for ever-exposure, by duration of exposure, and by cumulative exposure, using separate logistic regression models adjusted for smoking and other covariates. We then estimated the meta-odds ratios using random-effects meta-analysis.Results and ConclusionsNone of the agents assessed showed consistent and compelling associations with lung cancer among women. The following agents showed elevated odds ratio in some analyses: metallic dust, iron compounds, isopropanol, and organic solvents. Future research into occupational lung cancer risk factors among women should prioritize these agents.

Various birth characteristics may influence healthy childhood development, including the risk of developing childhood brain tumors (CBTs). In this study, we aimed to investigate the association between delivery methods, obstetric history, and birth anthropometrics with the risk of CBTs. This study used data from the Childhood Brain Tumour Epidemiology Study of Ontario (CBREO) which included children 0-15 years of age and newly diagnosed with CBTs from 1997 to 2003. Multivariable logistic regressions were performed to explore the association between delivery methods, obstetric history, and birth anthropometric variables, with subsequent CBT development. Models were adjusted for maternal and index child characteristics, and stratified by histology where sample size permitted. The use of assistive instruments (forceps or suction) during childbirth was significantly associated with overall CBTs (OR 1.84, 95% CI 1.30-2.61) and non-glial tumors (OR 2.57, 95% CI 1.60-4.13). Compared to first-born children, those second-born or greater had a lower risk of overall CBT development (OR 0.74, 95% CI 0.55-0.98), and glial histological subtype. All other birth characteristic variables explored were not associated with CBTs. The use of assistive devices such as forceps or suction during vaginal delivery carries potential risks, including increased risk of CBT development. There is an inverse association between birth order and CBTs, and future studies examining early childhood common infection may be warranted.

BACKGROUND:While much research has been done to identify individual workplace lung carcinogens, little is known about joint effects on risk when workers are exposed to multiple agents. OBJECTIVES:We investigated the pairwise joint effects of occupational exposures to asbestos, respirable crystalline silica, metals (i.e., nickel, chromium-VI), and polycyclic aromatic hydrocarbons (PAH) on lung cancer risk, overall and by major histologic subtype, while accounting for cigarette smoking. METHODS:In the international 14-center SYNERGY project, occupational exposures were assigned to 16,901 lung cancer cases and 20,965 control subjects using a quantitative job-exposure matrix (SYN-JEM). Odds ratios (ORs) and 95% confidence intervals (CIs) were computed for ever vs. never exposure using logistic regression models stratified by sex and adjusted for study center, age, and smoking habits. Joint effects among pairs of agents were assessed on multiplicative and additive scales, the latter by calculating the relative excess risk due to interaction (RERI). RESULTS:All pairwise joint effects of lung carcinogens in men were associated with an increased risk of lung cancer. However, asbestos/metals and metals/PAH resulted in less than additive effects; while the chromium-VI/silica pair showed marginally synergistic effect in relation to adenocarcinoma (RERI: 0.24; CI: 0.02, 0.46; p = 0.05). In women, several pairwise joint effects were observed for small cell lung cancer including exposure to PAH/silica (OR = 5.12; CI: 1.77, 8.48), and to asbestos/silica (OR = 4.32; CI: 1.35, 7.29), where exposure to PAH/silica resulted in a synergistic effect (RERI: 3.45; CI: 0.10, 6.8). DISCUSSION:Small or no deviation from additive or multiplicative effects was observed, but co-exposure to the selected lung carcinogens resulted generally in higher risk than exposure to individual agents, highlighting the importance to reduce and control exposure to carcinogens in workplaces and the general environment. https://doi.org/10.1289/EHP13380.

RATIONALE Benzene has been classified as carcinogenic to humans, but there is limited evidence linking benzene exposure to lung cancer. OBJECTIVES We aimed to examine the relationship between occupational benzene exposure and lung cancer. METHODS Subjects from 14 case-control studies across Europe and Canada were pooled. We used a quantitative job-exposure matrix to estimate benzene exposure. Logistic regression models assessed lung cancer risk across different exposure indices. We adjusted for smoking and five main occupational lung carcinogens and stratified analyses by smoking status and lung cancer subtypes. MEASUREMENTS AND MAIN RESULTS Analyses included 28048 subjects (12329 cases, 15719 controls). Lung cancer odds ratios ranged from 1.12 (95% CI: 1.03-1.22) to 1.32 (95% CI: 1.18-1.48) (Ptrend=0.002) for groups with the lowest and highest cumulative occupational exposure, respectively, compared to unexposed subjects. We observed an increasing trend of lung cancer with longer duration of exposure (Ptrend<0.001) and decreasing trend with longer time since last exposure (Ptrend=0.02). These effects were seen for all lung cancer subtypes, regardless of smoking status, and were not influenced by specific occupational groups, exposures, or studies. CONCLUSION We found consistent and robust associations between different dimensions of occupational benzene exposure and lung cancer after adjusting for smoking and main occupational lung carcinogens. These associations were observed across different subgroups, including non-smokers. Our findings support the hypothesis that occupational benzene exposure increases the risk of developing lung cancer. Consequently, there is a need to revisit published epidemiological and molecular data on the pulmonary carcinogenicity of benzene.

Objectives The quantitative job -exposure matrix SYN-JEM consists of various dimensions: job -specific estimates, region -specific estimates, and prior expert ratings of jobs by the semi -quantitative DOM-JEM. We analyzed the effect of different JEM dimensions on the exposure-response relationships between occupational silica exposure and lung cancer risk to investigate how these variations influence estimates of exposure by a quantitative JEM and associated health endpoints. Methods Using SYN-JEM, and alternative SYN-JEM specifications with varying dimensions included, cumulative silica exposure estimates were assigned to 16 901 lung cancer cases and 20 965 controls pooled from 14 international community -based case -control studies. Exposure-response relationships based on SYN-JEM and alternative SYN-JEM specifications were analyzed using regression analyses (by quartiles and log -transformed continuous silica exposure) and generalized additive models (GAM), adjusted for age, sex, study, cigarette pack -years, time since quitting smoking, and ever employment in occupations with established lung cancer risk. Results SYN-JEM and alternative specifications generated overall elevated and similar lung cancer odds ratios ranging from 1.13 (1st quartile) to 1.50 (4th quartile). In the categorical and log -linear analyses SYN-JEM with all dimensions included yielded the best model fit, and exclusion of job -specific estimates from SYN-JEM yielded the poorest model fit. Additionally, GAM showed the poorest model fit when excluding job -specific estimates. Conclusion The established exposure-response relationship between occupational silica exposure and lung cancer was marginally influenced by varying the dimensions of SYN-JEM. Optimized modelling of exposure-response relationships will be obtained when incorporating all relevant dimensions, namely prior rating, job, time, and region. Quantitative job -specific estimates appeared to be the most prominent dimension for this general population JEM.

To identify credible causal risk variants (CCVs) associated with different histotypes of epithelial ovarian cancer (EOC), we performed genome-wide association analysis for 470,825 genotyped and 10,163,797 imputed SNPs in 25,981 EOC cases and 105,724 controls of European origin. We identified five histotype-specific EOC risk regions (p value <5 × 10-8) and confirmed previously reported associations for 27 risk regions. Conditional analyses identified an additional 11 signals independent of the primary signal at six risk regions (p value <10-5). Fine mapping identified 4,008 CCVs in these regions, of which 1,452 CCVs were located in ovarian cancer-related chromatin marks with significant enrichment in active enhancers, active promoters, and active regions for CCVs from each EOC histotype. Transcriptome-wide association and colocalization analyses across histotypes using tissue-specific and cross-tissue datasets identified 86 candidate susceptibility genes in known EOC risk regions and 32 genes in 23 additional genomic regions that may represent novel EOC risk loci (false discovery rate <0.05). Finally, by integrating genome-wide HiChIP interactome analysis with transcriptome-wide association study (TWAS), variant effect predictor, transcription factor ChIP-seq, and motifbreakR data, we identified candidate gene-CCV interactions at each locus. This included risk loci where TWAS identified one or more candidate susceptibility genes (e.g., HOXD-AS2, HOXD8, and HOXD3 at 2q31) and other loci where no candidate gene was identified (e.g., MYC and PVT1 at 8q24) by TWAS. In summary, this study describes a functional framework and provides a greater understanding of the biological significance of risk alleles and candidate gene targets at EOC susceptibility loci identified by a genome-wide association study.

Tumor- and treatment-related factors are established predictors of ovarian cancer survival. New studies suggest a differential impact of exposures on ovarian cancer survival trajectories (i.e., rapidly fatal to long-term disease). This study examined the impact of pre-diagnostic risk factors on short- and long-term ovarian cancer survival trajectories in the Canadian context. This population-based longitudinal observational study included women diagnosed with invasive epithelial ovarian cancer from 1995 to 2004 in Ontario. Data were obtained from medical records, interviews, and the provincial cancer registry. Extended Cox proportional hazard models estimated the association between risk factors and all-cause and ovarian cancer-specific mortality by survival time intervals (<3 years (i.e., short-term survival), 3 to <6 years, 6 to <10 years, and ≥10 years (i.e., long-term survival)). Among 1421 women, histology, stage, and residual disease were the most important predictors of all-cause mortality in all survival trajectories, particularly for short-term survival. Reproductive and lifestyle factors did not strongly impact short-term overall survival but were associated with long-term overall survival. As such, among long-term survivors, history of breastfeeding significantly decreased the risk of all-cause mortality (HR 0.65; 95% CI 0.46, 0.93; p < 0.05), whereas smoking history (HR 1.75; 95% CI 1.27, 2.40; p < 0.05) and obesity (HR 1.81; 95% CI 1.24, 2.65; p < 0.05) significantly increased the risk of all-cause mortality. The findings were consistent with ovarian cancer-specific mortality. These findings suggest that pre-diagnostic exposures differentially influence survival time following a diagnosis of ovarian cancer.

Background:Increased lung cancer risks for low socioeconomic status (SES) groups are only partially attributable to smoking habits. Little effort has been made to investigate the persistent risks related to low SES by quantification of potential biases.Methods:Based on 12 case-control studies, including 18 centers of the international SYNERGY project (16,550 cases, 20,147 controls), we estimated controlled direct effects (CDE) of SES on lung cancer via multiple logistic regression, adjusted for age, study center, and smoking habits and stratified by sex. We conducted mediation analysis by inverse odds ratio weighting to estimate natural direct effects and natural indirect effects via smoking habits. We considered misclassification of smoking status, selection bias, and unmeasured mediator-outcome confounding by genetic risk, both separately and by multiple quantitative bias analyses, using bootstrap to create 95% simulation intervals (SI).Results:Mediation analysis of lung cancer risks for SES estimated mean proportions of 43% in men and 33% in women attributable to smoking. Bias analyses decreased the direct effects of SES on lung cancer, with selection bias showing the strongest reduction in lung cancer risk in the multiple bias analysis. Lung cancer risks remained increased for lower SES groups, with higher risks in men (fourth vs. first [highest] SES quartile: CDE, 1.50 [SI, 1.32, 1.69]) than women (CDE: 1.20 [SI: 1.01, 1.45]). Natural direct effects were similar to CDE, particularly in men.Conclusions:Bias adjustment lowered direct lung cancer risk estimates of lower SES groups. However, risks for low SES remained elevated, likely attributable to occupational hazards or other environmental exposures.

Journal Article Cohort Profile: The Ontario Health Study (OHS) Get access Victoria A Kirsh, Victoria A Kirsh Ontario Institute for Cancer Research, Toronto, ON, CanadaDalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada (formerly for N.K.) https://orcid.org/0000-0003-2422-2225 Search for other works by this author on: Oxford Academic PubMed Google Scholar Kimberly Skead, Kimberly Skead Ontario Institute for Cancer Research, Toronto, ON, CanadaDepartment of Molecular Genetics, University of Toronto, Toronto, ON, Canada Search for other works by this author on: Oxford Academic PubMed Google Scholar Kelly McDonald, Kelly McDonald Ontario Institute for Cancer Research, Toronto, ON, Canada Search for other works by this author on: Oxford Academic PubMed Google Scholar Nancy Kreiger, Nancy Kreiger Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada (formerly for N.K.)Prevention and Cancer Control, Ontario Health, Cancer Care Ontario, Toronto, ON, Canada Search for other works by this author on: Oxford Academic PubMed Google Scholar Julian Little, Julian Little Faculty of Medicine, School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada Search for other works by this author on: Oxford Academic PubMed Google Scholar Karen Menard, Karen Menard Office of Institutional Research and Planning, University of Guelph, Guelph, ON, Canada Search for other works by this author on: Oxford Academic PubMed Google Scholar John McLaughlin, John McLaughlin Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada (formerly for N.K.) Search for other works by this author on: Oxford Academic PubMed Google Scholar Sutapa Mukherjee, Sutapa Mukherjee Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia https://orcid.org/0000-0001-5021-1648 Search for other works by this author on: Oxford Academic PubMed Google Scholar Lyle J Palmer, Lyle J Palmer School of Public Health, University of Adelaide, Adelaide, South Australia, Australia Search for other works by this author on: Oxford Academic PubMed Google Scholar Vivek Goel, Vivek Goel Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada (formerly for N.K.)Office of the President, University of Waterloo, Waterloo, ON, Canada Search for other works by this author on: Oxford Academic PubMed Google Scholar ... Show more Mark P Purdue, Mark P Purdue Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA Search for other works by this author on: Oxford Academic PubMed Google Scholar Philip Awadalla Philip Awadalla Ontario Institute for Cancer Research, Toronto, ON, CanadaDalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada (formerly for N.K.)Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada Corresponding author. Ontario Institute for Cancer Research, 661 University Ave, Suite 510, Toronto, ON, M5G 0A3, Canada. E-mail: philip.awadalla@oicr.on.ca https://orcid.org/0000-0001-9946-6393 Search for other works by this author on: Oxford Academic PubMed Google Scholar International Journal of Epidemiology, Volume 52, Issue 2, April 2023, Pages e137–e151, https://doi.org/10.1093/ije/dyac156 Published: 13 August 2022 Article history Received: 22 December 2021 Editorial decision: 01 July 2022 Accepted: 20 July 2022 Published: 13 August 2022

List of 251 globally significant associations (p < 5E-8) for CYP2A6 in the MEC Cohort

Background The role of ovulation in epithelial ovarian cancer (EOC) is supported by the consistent protective effects of parity and oral contraceptive use. Whether these factors protect through anovulation alone remains unclear. We explored the association between lifetime ovulatory years (LOY) and EOC. Methods LOY was calculated using 12 algorithms. Odds ratios (ORs) and 95% confidence intervals (CIs) estimated the association between LOY or LOY components and EOC among 26 204 control participants and 21 267 case patients from 25 studies. To assess whether LOY components act through ovulation suppression alone, we compared beta coefficients obtained from regression models with expected estimates assuming 1 year of ovulation suppression has the same effect regardless of source. Results LOY was associated with increased EOC risk (OR per year increase = 1.014, 95% CI = 1.009 to 1.020 to OR per year increase = 1.044, 95% CI = 1.041 to 1.048). Individual LOY components, except age at menarche, also associated with EOC. The estimated model coefficient for oral contraceptive use and pregnancies were 4.45 times and 12- to 15-fold greater than expected, respectively. LOY was associated with high-grade serous, low-grade serous, endometrioid, and clear cell histotypes (ORs per year increase = 1.054, 1.040, 1.065, and 1.098, respectively) but not mucinous tumors. Estimated coefficients of LOY components were close to expected estimates for high-grade serous but larger than expected for low-grade serous, endometrioid, and clear cell histotypes. Conclusions LOY is positively associated with nonmucinous EOC. Differences between estimated and expected model coefficients for LOY components suggest factors beyond ovulation underlie the associations between LOY components and EOC in general and for non-HGSOC.

Top independently associated SNPs from the TRICL lung cancer GWAS meta-analysis.

Objective: Leptin (LEP) is an obesity-associated adipokine associated with tumor cell growth. We examined the relevance of genetic variants of LEP and leptin receptor (LEPR) to colorectal cancer (CRC) survival by using data from the Newfoundland Familial Colorectal Cancer Study.Methods: A total of 532 patients newly diagnosed with CRC between 1997 and 2003 were followed up until April 2010. Data on their demographics and lifestyles were collected via questionnaires. Genotyping of blood samples was performed with the Illumina Human Omni-Quad Bead chip. Multivariable Cox models were used to assess the relationships of 35 tag single-nucleotide polymorphisms (SNPs) in LEP and LEPR with overall survival (OS), disease-free survival (DFS), and CRC-specific survival.Results: At the gene level, LEP was associated with DFS (P = 0.017), and LEPR was associated with both DFS (P = 0.021) and CRC specific survival (P = 0.013) in patients with CRC. In single-SNP analysis, LEP rs11763517, LEPR rs9436301, and LEPR rs7602 were associated with DFS after adjustment for multiple testing. The LEPR haplotypes G-C-T (rs7534511-rs9436301-rs1887285) and A-A-G (rs7602-rs970467-rs9436748) were associated with prolonged OS among patients with CRC overall (G-C-T: HR, 0.63; 95% CI, 0.43-0.93; A-A-G: HR, 0.59; 95% CI, 0.38-0.91) and those diagnosed with colon cancer (G-C-T: HR, 0.54; 95% CI, 0.34-0.86; A-A-G: HR, 0.49; 95% CI, 0.29-0.83). Similar results were observed for DFS. Moreover, significant interactions were found among LEPR rs7602 (A vs. G), LEPR rs1171278 (T vs. C), red meat intake, and BMI status: the associations between these variants and prolonged DFS were limited to patients with below-median red meat consumption and body mass index (BMI) < 25 kg/m2.Conclusions: Polymorphic variations in the LEP and LEPR genes were associated with survival of patients after CRC diagnosis. The LEP/LEPR-CRC survival association was modified by participants' red meat intake and BMI.

Whether genetic testing in autism can help understand longitudinal health outcomes and health service needs is unclear. The objective of this study was to determine whether carrying an autism-associated rare genetic variant is associated with differences in health system utilization by autistic children and youth. This retrospective cohort study examined 415 autistic children/youth who underwent genome sequencing and data collection through a translational neuroscience program (Province of Ontario Neurodevelopmental Disorders Network). Participant data were linked to provincial health administrative databases to identify historical health service utilization, health care costs, and complex chronic medical conditions during a 3-year period. Health administrative data were compared between participants with and without a rare genetic variant in at least 1 of 74 genes associated with autism. Participants with a rare variant impacting an autism-associated gene (n = 83, 20%) were less likely to have received psychiatric care (at least one psychiatrist visit: 19.3% vs. 34.3%, p = 0.01; outpatient mental health visit: 66% vs. 77%, p = 0.04). Health care costs were similar between groups (median: $5589 vs. $4938, p = 0.4) and genetic status was not associated with odds of being a high-cost participant (top 20%) in this cohort. There were no differences in the proportion with complex chronic medical conditions between those with and without an autism-associated genetic variant. Our study highlights the feasibility and potential value of genomic and health system data linkage to understand health service needs, disparities, and health trajectories in individuals with neurodevelopmental conditions.

Descriptive Characteristics of TRICL participants

List of 226 overlapping associations for CYP2A6 in the TRICL & MEC Studies

Supplementary Tables S1-6, Figures S1-2. Supplementary Table S1: Summary of serous EOC GWAS data sets; Supplementary Table S2: Genes in the six significant co-expression networks; Supplementary Table S3: GSEA results after LD-based clumping of SNPs; Supplementary Table S4: GSEA results for the replication data set (COGS); Supplementary Table S5: GSEA results for all networks with > 10 genes; Supplementary Table S6: Number of intragenic SNPs and genes covered by SNPs in the different analyses; Supplementary Figure S1: Q-Q plots of the minimum P-value among all SNPs in each gene with and without the modified Sidak correction; Supplementary Figure S2: Disease Association Protein-Protein Link Evaluator (DAPPLE) protein-protein interaction network