Jeffrey R. Brook’s research while affiliated with University of Toronto and other places

Free view-only link: https://rdcu.be/d0syc

Objectives Long-term exposure to air pollution has been associated with higher risk of cardiovascular mortality. Less is known about the association of air pollution with initial development of cardiovascular disease. Herein, the association between low-level exposure to air pollutants and subclinical carotid atherosclerosis in adults without known clinical cardiovascular disease was investigated. Design Cross-sectional analysis within a prospective cohort study. Setting The Canadian Alliance for Healthy Hearts and Minds Cohort Study; a pan-Canadian cohort of cohorts. Participants Canadian adults (n = 6645) recruited between 2014–2018 from the provinces of British Columbia, Alberta, Ontario, Quebec, and Nova Scotia, were studied, for whom averages of exposures to nitrogen dioxide (NO2), ozone (O3), and fine particulate matter (PM2.5) were estimated for the years 2008–2012. Main outcome measure Carotid vessel wall volume (CWV) measured by magnetic resonance imaging (MRI). Results In adjusted linear mixed models, PM2.5 was not consistently associated with CWV (per 5 μg/m³ PM2.5; adjusted estimate = -8.4 mm³; 95% Confidence Intervals (CI) -23.3 to 6.48; p = 0.27). A 5 ppb higher NO2 concentration was associated with 11.8 mm³ lower CWV (95% CI -16.2 to -7.31; p<0.0001). A 3 ppb increase in O3 was associated with 9.34 mm³ higher CWV (95% CI 4.75 to 13.92; p<0.0001). However, the coarse/insufficient O3 resolution (10 km) is a limitation. Conclusions In a cohort of healthy Canadian adults there was no consistent association between PM2.5 or NO2 and increased CWV as a measure of subclinical atherosclerosis by MRI. The reasons for these inconsistent associations warrant further study.

Background Fine particulate matter (PM 2.5 ) occurs within a mixture of other pollutant gases that interact and impact its composition and toxicity. To characterize the local toxicity of PM 2.5 , it is useful to have an index that accounts for the whole pollutant mix, including gaseous pollutants. We consider a recently proposed pollutant mixture complexity index (PMCI) to evaluate to which extent it relates to PM 2.5 toxicity. Methods The PMCI is constructed as an index spanning seven different pollutants, relative to the PM 2.5 levels. We consider a standard two-stage analysis using data from 264 cities in the Northern Hemisphere. The first stage estimates the city-specific relative risks between daily PM 2.5 and all-cause mortality, which are then pooled into a second-stage meta-regression model with which we estimate the effect modification from the PMCI. Results We estimate a relative excess risk of 1.0042 (95% confidence interval: 1.0023, 1.0061) for an interquartile range increase (from 1.09 to 1.95) of the PMCI. The PMCI predicts a substantial part of within-country relative risk heterogeneity with much less between-country heterogeneity explained. The Akaike information criterion and Bayesian information criterion of the main model are lower than those of alternative meta-regression models considering the oxidative capacity of PM 2.5 or its composition. Conclusions The PMCI represents an efficient and simple predictor of local PM 2.5 -related mortality, providing evidence that PM 2.5 toxicity depends on the surrounding gaseous pollutant mix. With the advent of remote sensing for pollutants, the PMCI can provide a useful index to track air quality.

Maternal stress and depression during pregnancy and the first year of the infant’s life affect a large percentage of mothers. Maternal stress and depression have been associated with adverse fetal and childhood outcomes as well as differential child DNA methylation (DNAm). However, the biological mechanisms connecting maternal stress and depression to poor health outcomes in children are still largely unknown. Here we aim to determine whether prenatal stress and depression are associated with differences in cord blood mononuclear cell DNAm (CBMC-DNAm) in newborns (n = 119) and whether postnatal stress and depression are associated with differences in peripheral blood mononuclear cell DNAm (PBMC-DNAm) in children of 12 months of age (n = 113) from the Canadian Healthy Infant Longitudinal Development (CHILD) cohort. Stress was measured using the 10-item Perceived Stress Scale (PSS) and depression was measured using the 20-item Center for Epidemiologic Studies Depression Questionnaire (CESD). Both stress and depression were measured longitudinally at 18 weeks and 36 weeks of pregnancy and six months and 12 months postpartum. We conducted epigenome-wide association studies (EWAS) using robust linear regression followed by a sensitivity analysis in which we bias-adjusted for inflation and unmeasured confounding using the bacon and cate methods. To quantify the cumulative effect of maternal stress and depression, we created composite prenatal and postnatal adversity scores. We identified a significant association between prenatal stress and differential CBMC-DNAm at 8 CpG sites and between prenatal depression and differential CBMC-DNAm at 2 CpG sites. Additionally, we identified a significant association between postnatal stress and differential PBMC-DNAm at 8 CpG sites and between postnatal depression and differential PBMC-DNAm at 11 CpG sites. Using our composite scores, we further identified 2 CpG sites significantly associated with prenatal adversity and 7 CpG sites significantly associated with postnatal adversity. Several of the associated genes, including PLAGL1, HYMAI, BRD2, and ERC2 have been implicated in adverse fetal outcomes and neuropsychiatric disorders. These data further support the finding that differential DNAm may play a role in the relationship between maternal mental health and child health.

The CHILD Cohort Study is an active multi-center longitudinal, prospective, population pregnancy cohort study following Canadian infants from fetal life until adulthood. We hypothesized that early life physical and psychosocial environments interact with biological factors (e.g. immunologic, genetic, physiologic, and metabolic) influencing burdensome non-communicable disease outcomes, including asthma and allergic disorders, growth and development, cardio-metabolic health, and neurodevelopmental outcomes that manifest during the life-course. Detailed clinical and physiologic phenotyping at strategic intervals was complemented by environmental sampling, actigraphy and global positioning system measures, biological sampling including gut, breastmilk and nasal microbiome, nutritional studies, genetics, and epigenetic profiling. Of 3,454 families recruited from 2008 to 2012, study retention was 96.0% at 1-year, 93.2% at 5-years and 90.7% at 8-years. Data collection during the SARS-2 COVID-19 pandemic was partially completed via virtual visits. A sub-cohort was implemented, capturing detailed information on the prevalence and predictors of SARS-CoV-2 infection and the health and psychosocial impact of the pandemic on Canadian families. The 13-year clinical assessment launched in 2022 will be completed in 2025. Ultimately, the CHILD Cohort Study provides a data science platform designed to enable a deep understanding of early life factors associated with the development of chronic non-communicable diseases and multimorbidity.

Background Ambient polycyclic aromatic hydrocarbons (PAHs) are a class of toxicologically important and understudied air pollutants. Epidemiologic evidence suggests that chronic exposure to PAHs increases breast cancer risk; however, there are few studies in nonoccupational settings that focus on early-onset diagnoses. Methods The relationship between residentially-based ambient PAH concentrations and female breast cancer, among those 18–45 years of age, was characterized in the Ontario Environment and Health Study (OEHS). The OEHS was a population-based case–control study undertaken in Ontario, Canada between 2013 and 2015. Primary incident breast cancers were identified within 3 months of diagnosis, and a population-based series of controls were recruited. Concentrations of ambient PAHs, using fluoranthene as a surrogate, were derived using a chemical transport model at a 2.5 km spatial resolution. These estimates were assigned to participants’ residences at the time of the interview and 5 years prior. Logistic regression was used to estimate odds ratios (ORs) and their 95% confidence intervals (CIs) based on a quartile categorization of fluoranthene exposure while adjusting for a series of individual- and area-level risk factors. The shape of the exposure–response trend was evaluated using cubic splines. Results Median fluoranthene exposure for cases and controls was 0.0017 µg/m ³ and 0.0014 µg/m ³ , respectively. In models adjusted for a parsimonious set of risk factors, the highest quartile of exposure was associated with an increased risk of breast cancer (OR = 2.16; 95% CI = 1.22, 3.84). Restricted spline analyses revealed nonlinear dose–response patterns. Conclusions These findings support the hypothesis that ambient PAH exposures increases the risk of early-onset breast cancer.

Prospective longitudinal cohort studies and national health datasets play a key role in understanding the mechanisms of cancer and chronic disease. The Canadian Partnership for Tomorrow’s Health (CanPath) is a prospective longitudinal cohort study that supports evidence-based health decision-making using genomic, clinical and behavioural data that was provided by participant volunteers across Canada. The Canadian Urban Environmental Health Research Consortium (CANUE) is a geospatial database of environmental exposure metrics characterizing various aspects of urban form to help conceptualize the associations between urban environments and health. CanPath participants have been linked to CANUE environmental datasets using residential postal codes. This chapter provides an overview of the CanPath and CANUE data platforms. Two case studies from the BC Generations Project, a regional CanPath cohort, are included as exemplars of research utilizing these linked data. The first case study examines the relationship between the built environment and two preventable cancer risk factors: physical activity and body composition (body mass index). The second case study investigates the effects of greenness, walkability, and nitrogen dioxide air pollution on bladder cancer risk. The studies utilizing the successful linkage of geospatial environmental data from CANUE with CanPath participant data highlight the value of integrating these databases for geospatial cancer prevention research.

Data stand as the foundation for studying, evaluating, and addressing the multifaceted challenges within environmental health research. This chapter highlights the contributions of the Canadian Urban Environmental Health Research Consortium (CANUE) in generating and democratizing access to environmental exposure data across Canada. Through a consortium-driven approach, CANUE standardizes a variety of datasets – including air quality, greenness, neighborhood characteristics, and weather and climatic factors – into a centralized, analysis-ready, postal code-indexed database. CANUE’s mandate extends beyond data integration, encompassing the design and development of environmental health-related web applications, facilitating the linkage of data to a wide range of health databases and sociodemographic data, and providing educational training and events such as webinars, summits, and workshops. The operational and technical aspects of CANUE are explored in this chapter, detailing its human resources, data sources, computational infrastructure, and data management practices. These efforts collectively enhance research capabilities and public awareness, fostering strategic collaboration and generating actionable insights that promote physical and mental health and well-being.